diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/CMakeLists.txt b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..17dc38b8a933d0b8fd84e5579426f7933ca45c99 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/CMakeLists.txt @@ -0,0 +1,41 @@ +# Copyright (c) Huawei Technologies Co., Ltd. 2019. All rights reserved. +# CMake lowest version requirement +cmake_minimum_required(VERSION 3.5.1) +# project information +project(ascendcl) +# Compile options +add_compile_options(-std=c++11) +# Specify target generation path +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "../outputs") +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "../outputs") +set(CMAKE_INSTALL_PREFIX "../../../") +set(CMAKE_OUTPUT_DIR "out") +set(CMAKE_CXX_FLAGS_RELEASE "-fPIC -O2 -g -Wall") + +ADD_DEFINITIONS("-DENABLE_DVPP_INTERFACE -D_GLIBCXX_USE_CXX11_ABI=0") + +# Header path +include_directories( +inc +#/usr/include/gflags +$ENV{install_path}/acllib/include +$ENV{install_path}/driver/kernel/libc_sec/include +/usr/include +) + +# add host lib path +link_directories($ENV{install_path}/acllib/lib64/stub) +#link_directories(/usr/local/Ascend/driver/lib64) +#link_directories(/usr/local/Ascend/atc/lib64) +#link_directories(/usr/local/lib) +link_directories(../thirdpart_lib) + +# 设置需要编译的源文件 +add_executable(benchmark main.cpp util.cpp post_process.cpp infer_engine.cpp) + +# 设置共享库 RC为待扩展的offline模型 +#target_link_libraries(benchmark acl_dvpp ascendcl pthread protobuf cryptopp) +target_link_libraries(benchmark acl_dvpp ascendcl pthread) + +install(TARGETS benchmark DESTINATION ${CMAKE_OUTPUT_DIR}) + diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/defines.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/defines.h new file mode 100644 index 0000000000000000000000000000000000000000..f0be3dcb485269718125445537ce3616c3078d34 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/defines.h @@ -0,0 +1,48 @@ +/* Generated from defines.h.in during build configuration using CMake. */ + +// Note: This header file is only used internally. It is not part of public interface! +// Any cmakedefine is defined using the -D flag instead when Bazel is used. +// For Bazel, this file is thus not used to avoid a private file in $(GENDIR). + +#ifndef GFLAGS_DEFINES_H_ +#define GFLAGS_DEFINES_H_ + + +// Define if you build this library for a MS Windows OS. +/* #undef OS_WINDOWS */ + +// Define if you have the header file. +#define HAVE_STDINT_H + +// Define if you have the header file. +#define HAVE_SYS_TYPES_H + +// Define if you have the header file. +#define HAVE_INTTYPES_H + +// Define if you have the header file. +#define HAVE_SYS_STAT_H + +// Define if you have the header file. +#define HAVE_UNISTD_H + +// Define if you have the header file. +#define HAVE_FNMATCH_H + +// Define if you have the header file (Windows 2000/XP). +/* #undef HAVE_SHLWAPI_H */ + +// Define if you have the strtoll function. +#define HAVE_STRTOLL + +// Define if you have the strtoq function. +/* #undef HAVE_STRTOQ */ + +// Define if you have the header file. +#define HAVE_PTHREAD + +// Define if your pthread library defines the type pthread_rwlock_t +#define HAVE_RWLOCK + + +#endif // GFLAGS_DEFINES_H_ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags.h new file mode 100644 index 0000000000000000000000000000000000000000..4f3168a03d878a16ac0e05d1d240396b9d422c63 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags.h @@ -0,0 +1,624 @@ +// Copyright (c) 2006, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// --- +// Revamped and reorganized by Craig Silverstein +// +// This is the file that should be included by any file which declares +// or defines a command line flag or wants to parse command line flags +// or print a program usage message (which will include information about +// flags). Executive summary, in the form of an example foo.cc file: +// +// #include "foo.h" // foo.h has a line "DECLARE_int32(start);" +// #include "validators.h" // hypothetical file defining ValidateIsFile() +// +// DEFINE_int32(end, 1000, "The last record to read"); +// +// DEFINE_string(filename, "my_file.txt", "The file to read"); +// // Crash if the specified file does not exist. +// static bool dummy = RegisterFlagValidator(&FLAGS_filename, +// &ValidateIsFile); +// +// DECLARE_bool(verbose); // some other file has a DEFINE_bool(verbose, ...) +// +// void MyFunc() { +// if (FLAGS_verbose) printf("Records %d-%d\n", FLAGS_start, FLAGS_end); +// } +// +// Then, at the command-line: +// ./foo --noverbose --start=5 --end=100 +// +// For more details, see +// doc/gflags.html +// +// --- A note about thread-safety: +// +// We describe many functions in this routine as being thread-hostile, +// thread-compatible, or thread-safe. Here are the meanings we use: +// +// thread-safe: it is safe for multiple threads to call this routine +// (or, when referring to a class, methods of this class) +// concurrently. +// thread-hostile: it is not safe for multiple threads to call this +// routine (or methods of this class) concurrently. In gflags, +// most thread-hostile routines are intended to be called early in, +// or even before, main() -- that is, before threads are spawned. +// thread-compatible: it is safe for multiple threads to read from +// this variable (when applied to variables), or to call const +// methods of this class (when applied to classes), as long as no +// other thread is writing to the variable or calling non-const +// methods of this class. + +#ifndef GFLAGS_GFLAGS_H_ +#define GFLAGS_GFLAGS_H_ + +#include +#include + +#include "gflags/gflags_declare.h" // IWYU pragma: export + + +// We always want to export variables defined in user code +#ifndef GFLAGS_DLL_DEFINE_FLAG +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DEFINE_FLAG __declspec(dllexport) +# else +# define GFLAGS_DLL_DEFINE_FLAG +# endif +#endif + + +namespace GFLAGS_NAMESPACE { + + +// -------------------------------------------------------------------- +// To actually define a flag in a file, use DEFINE_bool, +// DEFINE_string, etc. at the bottom of this file. You may also find +// it useful to register a validator with the flag. This ensures that +// when the flag is parsed from the commandline, or is later set via +// SetCommandLineOption, we call the validation function. It is _not_ +// called when you assign the value to the flag directly using the = operator. +// +// The validation function should return true if the flag value is valid, and +// false otherwise. If the function returns false for the new setting of the +// flag, the flag will retain its current value. If it returns false for the +// default value, ParseCommandLineFlags() will die. +// +// This function is safe to call at global construct time (as in the +// example below). +// +// Example use: +// static bool ValidatePort(const char* flagname, int32 value) { +// if (value > 0 && value < 32768) // value is ok +// return true; +// printf("Invalid value for --%s: %d\n", flagname, (int)value); +// return false; +// } +// DEFINE_int32(port, 0, "What port to listen on"); +// static bool dummy = RegisterFlagValidator(&FLAGS_port, &ValidatePort); + +// Returns true if successfully registered, false if not (because the +// first argument doesn't point to a command-line flag, or because a +// validator is already registered for this flag). +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const bool* flag, bool (*validate_fn)(const char*, bool)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int32* flag, bool (*validate_fn)(const char*, int32)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint32* flag, bool (*validate_fn)(const char*, uint32)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int64* flag, bool (*validate_fn)(const char*, int64)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint64* flag, bool (*validate_fn)(const char*, uint64)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const double* flag, bool (*validate_fn)(const char*, double)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const std::string* flag, bool (*validate_fn)(const char*, const std::string&)); + +// Convenience macro for the registration of a flag validator +#define DEFINE_validator(name, validator) \ + static const bool name##_validator_registered = \ + GFLAGS_NAMESPACE::RegisterFlagValidator(&FLAGS_##name, validator) + + +// -------------------------------------------------------------------- +// These methods are the best way to get access to info about the +// list of commandline flags. Note that these routines are pretty slow. +// GetAllFlags: mostly-complete info about the list, sorted by file. +// ShowUsageWithFlags: pretty-prints the list to stdout (what --help does) +// ShowUsageWithFlagsRestrict: limit to filenames with restrict as a substr +// +// In addition to accessing flags, you can also access argv[0] (the program +// name) and argv (the entire commandline), which we sock away a copy of. +// These variables are static, so you should only set them once. +// +// No need to export this data only structure from DLL, avoiding VS warning 4251. +struct CommandLineFlagInfo { + std::string name; // the name of the flag + std::string type; // the type of the flag: int32, etc + std::string description; // the "help text" associated with the flag + std::string current_value; // the current value, as a string + std::string default_value; // the default value, as a string + std::string filename; // 'cleaned' version of filename holding the flag + bool has_validator_fn; // true if RegisterFlagValidator called on this flag + bool is_default; // true if the flag has the default value and + // has not been set explicitly from the cmdline + // or via SetCommandLineOption + const void* flag_ptr; // pointer to the flag's current value (i.e. FLAGS_foo) +}; + +// Using this inside of a validator is a recipe for a deadlock. +// TODO(user) Fix locking when validators are running, to make it safe to +// call validators during ParseAllFlags. +// Also make sure then to uncomment the corresponding unit test in +// gflags_unittest.sh +extern GFLAGS_DLL_DECL void GetAllFlags(std::vector* OUTPUT); +// These two are actually defined in gflags_reporting.cc. +extern GFLAGS_DLL_DECL void ShowUsageWithFlags(const char *argv0); // what --help does +extern GFLAGS_DLL_DECL void ShowUsageWithFlagsRestrict(const char *argv0, const char *restrict); + +// Create a descriptive string for a flag. +// Goes to some trouble to make pretty line breaks. +extern GFLAGS_DLL_DECL std::string DescribeOneFlag(const CommandLineFlagInfo& flag); + +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetArgv(int argc, const char** argv); + +// The following functions are thread-safe as long as SetArgv() is +// only called before any threads start. +extern GFLAGS_DLL_DECL const std::vector& GetArgvs(); +extern GFLAGS_DLL_DECL const char* GetArgv(); // all of argv as a string +extern GFLAGS_DLL_DECL const char* GetArgv0(); // only argv0 +extern GFLAGS_DLL_DECL uint32 GetArgvSum(); // simple checksum of argv +extern GFLAGS_DLL_DECL const char* ProgramInvocationName(); // argv0, or "UNKNOWN" if not set +extern GFLAGS_DLL_DECL const char* ProgramInvocationShortName(); // basename(argv0) + +// ProgramUsage() is thread-safe as long as SetUsageMessage() is only +// called before any threads start. +extern GFLAGS_DLL_DECL const char* ProgramUsage(); // string set by SetUsageMessage() + +// VersionString() is thread-safe as long as SetVersionString() is only +// called before any threads start. +extern GFLAGS_DLL_DECL const char* VersionString(); // string set by SetVersionString() + + + +// -------------------------------------------------------------------- +// Normally you access commandline flags by just saying "if (FLAGS_foo)" +// or whatever, and set them by calling "FLAGS_foo = bar" (or, more +// commonly, via the DEFINE_foo macro). But if you need a bit more +// control, we have programmatic ways to get/set the flags as well. +// These programmatic ways to access flags are thread-safe, but direct +// access is only thread-compatible. + +// Return true iff the flagname was found. +// OUTPUT is set to the flag's value, or unchanged if we return false. +extern GFLAGS_DLL_DECL bool GetCommandLineOption(const char* name, std::string* OUTPUT); + +// Return true iff the flagname was found. OUTPUT is set to the flag's +// CommandLineFlagInfo or unchanged if we return false. +extern GFLAGS_DLL_DECL bool GetCommandLineFlagInfo(const char* name, CommandLineFlagInfo* OUTPUT); + +// Return the CommandLineFlagInfo of the flagname. exit() if name not found. +// Example usage, to check if a flag's value is currently the default value: +// if (GetCommandLineFlagInfoOrDie("foo").is_default) ... +extern GFLAGS_DLL_DECL CommandLineFlagInfo GetCommandLineFlagInfoOrDie(const char* name); + +enum GFLAGS_DLL_DECL FlagSettingMode { + // update the flag's value (can call this multiple times). + SET_FLAGS_VALUE, + // update the flag's value, but *only if* it has not yet been updated + // with SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef". + SET_FLAG_IF_DEFAULT, + // set the flag's default value to this. If the flag has not yet updated + // yet (via SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef") + // change the flag's current value to the new default value as well. + SET_FLAGS_DEFAULT +}; + +// Set a particular flag ("command line option"). Returns a string +// describing the new value that the option has been set to. The +// return value API is not well-specified, so basically just depend on +// it to be empty if the setting failed for some reason -- the name is +// not a valid flag name, or the value is not a valid value -- and +// non-empty else. + +// SetCommandLineOption uses set_mode == SET_FLAGS_VALUE (the common case) +extern GFLAGS_DLL_DECL std::string SetCommandLineOption (const char* name, const char* value); +extern GFLAGS_DLL_DECL std::string SetCommandLineOptionWithMode(const char* name, const char* value, FlagSettingMode set_mode); + + +// -------------------------------------------------------------------- +// Saves the states (value, default value, whether the user has set +// the flag, registered validators, etc) of all flags, and restores +// them when the FlagSaver is destroyed. This is very useful in +// tests, say, when you want to let your tests change the flags, but +// make sure that they get reverted to the original states when your +// test is complete. +// +// Example usage: +// void TestFoo() { +// FlagSaver s1; +// FLAG_foo = false; +// FLAG_bar = "some value"; +// +// // test happens here. You can return at any time +// // without worrying about restoring the FLAG values. +// } +// +// Note: This class is marked with GFLAGS_ATTRIBUTE_UNUSED because all +// the work is done in the constructor and destructor, so in the standard +// usage example above, the compiler would complain that it's an +// unused variable. +// +// This class is thread-safe. However, its destructor writes to +// exactly the set of flags that have changed value during its +// lifetime, so concurrent _direct_ access to those flags +// (i.e. FLAGS_foo instead of {Get,Set}CommandLineOption()) is unsafe. + +class GFLAGS_DLL_DECL FlagSaver { + public: + FlagSaver(); + ~FlagSaver(); + + private: + class FlagSaverImpl* impl_; // we use pimpl here to keep API steady + + FlagSaver(const FlagSaver&); // no copying! + void operator=(const FlagSaver&); +}__attribute((unused)); + +// -------------------------------------------------------------------- +// Some deprecated or hopefully-soon-to-be-deprecated functions. + +// This is often used for logging. TODO(csilvers): figure out a better way +extern GFLAGS_DLL_DECL std::string CommandlineFlagsIntoString(); +// Usually where this is used, a FlagSaver should be used instead. +extern GFLAGS_DLL_DECL +bool ReadFlagsFromString(const std::string& flagfilecontents, + const char* prog_name, + bool errors_are_fatal); // uses SET_FLAGS_VALUE + +// These let you manually implement --flagfile functionality. +// DEPRECATED. +extern GFLAGS_DLL_DECL bool AppendFlagsIntoFile(const std::string& filename, const char* prog_name); +extern GFLAGS_DLL_DECL bool ReadFromFlagsFile(const std::string& filename, const char* prog_name, bool errors_are_fatal); // uses SET_FLAGS_VALUE + + +// -------------------------------------------------------------------- +// Useful routines for initializing flags from the environment. +// In each case, if 'varname' does not exist in the environment +// return defval. If 'varname' does exist but is not valid +// (e.g., not a number for an int32 flag), abort with an error. +// Otherwise, return the value. NOTE: for booleans, for true use +// 't' or 'T' or 'true' or '1', for false 'f' or 'F' or 'false' or '0'. + +extern GFLAGS_DLL_DECL bool BoolFromEnv(const char *varname, bool defval); +extern GFLAGS_DLL_DECL int32 Int32FromEnv(const char *varname, int32 defval); +extern GFLAGS_DLL_DECL uint32 Uint32FromEnv(const char *varname, uint32 defval); +extern GFLAGS_DLL_DECL int64 Int64FromEnv(const char *varname, int64 defval); +extern GFLAGS_DLL_DECL uint64 Uint64FromEnv(const char *varname, uint64 defval); +extern GFLAGS_DLL_DECL double DoubleFromEnv(const char *varname, double defval); +extern GFLAGS_DLL_DECL const char *StringFromEnv(const char *varname, const char *defval); + + +// -------------------------------------------------------------------- +// The next two functions parse gflags from main(): + +// Set the "usage" message for this program. For example: +// string usage("This program does nothing. Sample usage:\n"); +// usage += argv[0] + " "; +// SetUsageMessage(usage); +// Do not include commandline flags in the usage: we do that for you! +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetUsageMessage(const std::string& usage); + +// Sets the version string, which is emitted with --version. +// For instance: SetVersionString("1.3"); +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetVersionString(const std::string& version); + + +// Looks for flags in argv and parses them. Rearranges argv to put +// flags first, or removes them entirely if remove_flags is true. +// If a flag is defined more than once in the command line or flag +// file, the last definition is used. Returns the index (into argv) +// of the first non-flag argument. +// See top-of-file for more details on this function. +#ifndef SWIG // In swig, use ParseCommandLineFlagsScript() instead. +extern GFLAGS_DLL_DECL uint32 ParseCommandLineFlags(int *argc, char*** argv, bool remove_flags); +#endif + + +// Calls to ParseCommandLineNonHelpFlags and then to +// HandleCommandLineHelpFlags can be used instead of a call to +// ParseCommandLineFlags during initialization, in order to allow for +// changing default values for some FLAGS (via +// e.g. SetCommandLineOptionWithMode calls) between the time of +// command line parsing and the time of dumping help information for +// the flags as a result of command line parsing. If a flag is +// defined more than once in the command line or flag file, the last +// definition is used. Returns the index (into argv) of the first +// non-flag argument. (If remove_flags is true, will always return 1.) +extern GFLAGS_DLL_DECL uint32 ParseCommandLineNonHelpFlags(int *argc, char*** argv, bool remove_flags); + +// This is actually defined in gflags_reporting.cc. +// This function is misnamed (it also handles --version, etc.), but +// it's too late to change that now. :-( +extern GFLAGS_DLL_DECL void HandleCommandLineHelpFlags(); // in gflags_reporting.cc + +// Allow command line reparsing. Disables the error normally +// generated when an unknown flag is found, since it may be found in a +// later parse. Thread-hostile; meant to be called before any threads +// are spawned. +extern GFLAGS_DLL_DECL void AllowCommandLineReparsing(); + +// Reparse the flags that have not yet been recognized. Only flags +// registered since the last parse will be recognized. Any flag value +// must be provided as part of the argument using "=", not as a +// separate command line argument that follows the flag argument. +// Intended for handling flags from dynamically loaded libraries, +// since their flags are not registered until they are loaded. +extern GFLAGS_DLL_DECL void ReparseCommandLineNonHelpFlags(); + +// Clean up memory allocated by flags. This is only needed to reduce +// the quantity of "potentially leaked" reports emitted by memory +// debugging tools such as valgrind. It is not required for normal +// operation, or for the google perftools heap-checker. It must only +// be called when the process is about to exit, and all threads that +// might access flags are quiescent. Referencing flags after this is +// called will have unexpected consequences. This is not safe to run +// when multiple threads might be running: the function is +// thread-hostile. +extern GFLAGS_DLL_DECL void ShutDownCommandLineFlags(); + + +// -------------------------------------------------------------------- +// Now come the command line flag declaration/definition macros that +// will actually be used. They're kind of hairy. A major reason +// for this is initialization: we want people to be able to access +// variables in global constructors and have that not crash, even if +// their global constructor runs before the global constructor here. +// (Obviously, we can't guarantee the flags will have the correct +// default value in that case, but at least accessing them is safe.) +// The only way to do that is have flags point to a static buffer. +// So we make one, using a union to ensure proper alignment, and +// then use placement-new to actually set up the flag with the +// correct default value. In the same vein, we have to worry about +// flag access in global destructors, so FlagRegisterer has to be +// careful never to destroy the flag-values it constructs. +// +// Note that when we define a flag variable FLAGS_, we also +// preemptively define a junk variable, FLAGS_no. This is to +// cause a link-time error if someone tries to define 2 flags with +// names like "logging" and "nologging". We do this because a bool +// flag FLAG can be set from the command line to true with a "-FLAG" +// argument, and to false with a "-noFLAG" argument, and so this can +// potentially avert confusion. +// +// We also put flags into their own namespace. It is purposefully +// named in an opaque way that people should have trouble typing +// directly. The idea is that DEFINE puts the flag in the weird +// namespace, and DECLARE imports the flag from there into the current +// namespace. The net result is to force people to use DECLARE to get +// access to a flag, rather than saying "extern GFLAGS_DLL_DECL bool FLAGS_whatever;" +// or some such instead. We want this so we can put extra +// functionality (like sanity-checking) in DECLARE if we want, and +// make sure it is picked up everywhere. +// +// We also put the type of the variable in the namespace, so that +// people can't DECLARE_int32 something that they DEFINE_bool'd +// elsewhere. + +class GFLAGS_DLL_DECL FlagRegisterer { + public: + // We instantiate this template ctor for all supported types, + // so it is possible to place implementation of the FlagRegisterer ctor in + // .cc file. + // Calling this constructor with unsupported type will produce linker error. + template + FlagRegisterer(const char* name, + const char* help, const char* filename, + FlagType* current_storage, FlagType* defvalue_storage); +}; + +// Force compiler to not generate code for the given template specialization. +#if defined(_MSC_VER) && _MSC_VER < 1800 // Visual Studio 2013 version 12.0 + #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type) +#else + #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type) \ + extern template GFLAGS_DLL_DECL FlagRegisterer::FlagRegisterer( \ + const char* name, const char* help, const char* filename, \ + type* current_storage, type* defvalue_storage) +#endif + +// Do this for all supported flag types. +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(bool); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int32); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint32); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int64); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint64); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(double); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(std::string); + +#undef GFLAGS_DECLARE_FLAG_REGISTERER_CTOR + +// If your application #defines STRIP_FLAG_HELP to a non-zero value +// before #including this file, we remove the help message from the +// binary file. This can reduce the size of the resulting binary +// somewhat, and may also be useful for security reasons. + +extern GFLAGS_DLL_DECL const char kStrippedFlagHelp[]; + + +} // namespace GFLAGS_NAMESPACE + + +#ifndef SWIG // In swig, ignore the main flag declarations + +#if defined(STRIP_FLAG_HELP) && STRIP_FLAG_HELP > 0 +// Need this construct to avoid the 'defined but not used' warning. +#define MAYBE_STRIPPED_HELP(txt) \ + (false ? (txt) : GFLAGS_NAMESPACE::kStrippedFlagHelp) +#else +#define MAYBE_STRIPPED_HELP(txt) txt +#endif + +// Each command-line flag has two variables associated with it: one +// with the current value, and one with the default value. However, +// we have a third variable, which is where value is assigned; it's a +// constant. This guarantees that FLAG_##value is initialized at +// static initialization time (e.g. before program-start) rather than +// than global construction time (which is after program-start but +// before main), at least when 'value' is a compile-time constant. We +// use a small trick for the "default value" variable, and call it +// FLAGS_no. This serves the second purpose of assuring a +// compile error if someone tries to define a flag named no +// which is illegal (--foo and --nofoo both affect the "foo" flag). +#define DEFINE_VARIABLE(type, shorttype, name, value, help) \ + namespace fL##shorttype { \ + static const type FLAGS_nono##name = value; \ + /* We always want to export defined variables, dll or no */ \ + GFLAGS_DLL_DEFINE_FLAG type FLAGS_##name = FLAGS_nono##name; \ + static type FLAGS_no##name = FLAGS_nono##name; \ + static GFLAGS_NAMESPACE::FlagRegisterer o_##name( \ + #name, MAYBE_STRIPPED_HELP(help), __FILE__, \ + &FLAGS_##name, &FLAGS_no##name); \ + } \ + using fL##shorttype::FLAGS_##name + +// For DEFINE_bool, we want to do the extra check that the passed-in +// value is actually a bool, and not a string or something that can be +// coerced to a bool. These declarations (no definition needed!) will +// help us do that, and never evaluate From, which is important. +// We'll use 'sizeof(IsBool(val))' to distinguish. This code requires +// that the compiler have different sizes for bool & double. Since +// this is not guaranteed by the standard, we check it with a +// COMPILE_ASSERT. +namespace fLB { +struct CompileAssert {}; +typedef CompileAssert expected_sizeof_double_neq_sizeof_bool[ + (sizeof(double) != sizeof(bool)) ? 1 : -1]; +template double GFLAGS_DLL_DECL IsBoolFlag(const From& from); +GFLAGS_DLL_DECL bool IsBoolFlag(bool from); +} // namespace fLB + +// Here are the actual DEFINE_*-macros. The respective DECLARE_*-macros +// are in a separate include, gflags_declare.h, for reducing +// the physical transitive size for DECLARE use. +#define DEFINE_bool(name, val, txt) \ + namespace fLB { \ + typedef ::fLB::CompileAssert FLAG_##name##_value_is_not_a_bool[ \ + (sizeof(::fLB::IsBoolFlag(val)) != sizeof(double))? 1: -1]; \ + } \ + DEFINE_VARIABLE(bool, B, name, val, txt) + +#define DEFINE_int32(name, val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::int32, I, \ + name, val, txt) + +#define DEFINE_uint32(name,val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint32, U, \ + name, val, txt) + +#define DEFINE_int64(name, val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::int64, I64, \ + name, val, txt) + +#define DEFINE_uint64(name,val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint64, U64, \ + name, val, txt) + +#define DEFINE_double(name, val, txt) \ + DEFINE_VARIABLE(double, D, name, val, txt) + +// Strings are trickier, because they're not a POD, so we can't +// construct them at static-initialization time (instead they get +// constructed at global-constructor time, which is much later). To +// try to avoid crashes in that case, we use a char buffer to store +// the string, which we can static-initialize, and then placement-new +// into it later. It's not perfect, but the best we can do. + +namespace fLS { + +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + const char *value) { + return new(stringspot) clstring(value); +} +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + const clstring &value) { + return new(stringspot) clstring(value); +} +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + int value); + +// Auxiliary class used to explicitly call destructor of string objects +// allocated using placement new during static program deinitialization. +// The destructor MUST be an inline function such that the explicit +// destruction occurs in the same compilation unit as the placement new. +class StringFlagDestructor { + void *current_storage_; + void *defvalue_storage_; + +public: + + StringFlagDestructor(void *current, void *defvalue) + : current_storage_(current), defvalue_storage_(defvalue) {} + + ~StringFlagDestructor() { + reinterpret_cast(current_storage_ )->~clstring(); + reinterpret_cast(defvalue_storage_)->~clstring(); + } +}; + +} // namespace fLS + +// We need to define a var named FLAGS_no##name so people don't define +// --string and --nostring. And we need a temporary place to put val +// so we don't have to evaluate it twice. Two great needs that go +// great together! +// The weird 'using' + 'extern' inside the fLS namespace is to work around +// an unknown compiler bug/issue with the gcc 4.2.1 on SUSE 10. See +// http://code.google.com/p/google-gflags/issues/detail?id=20 +#define DEFINE_string(name, val, txt) \ + namespace fLS { \ + using ::fLS::clstring; \ + using ::fLS::StringFlagDestructor; \ + static union { void* align; char s[sizeof(clstring)]; } s_##name[2]; \ + clstring* const FLAGS_no##name = ::fLS:: \ + dont_pass0toDEFINE_string(s_##name[0].s, \ + val); \ + static GFLAGS_NAMESPACE::FlagRegisterer o_##name( \ + #name, MAYBE_STRIPPED_HELP(txt), __FILE__, \ + FLAGS_no##name, new (s_##name[1].s) clstring(*FLAGS_no##name)); \ + static StringFlagDestructor d_##name(s_##name[0].s, s_##name[1].s); \ + extern GFLAGS_DLL_DEFINE_FLAG clstring& FLAGS_##name; \ + using fLS::FLAGS_##name; \ + clstring& FLAGS_##name = *FLAGS_no##name; \ + } \ + using fLS::FLAGS_##name + +#endif // SWIG + +// Import gflags library symbols into alternative/deprecated namespace(s) +#include "gflags_gflags.h" +#endif // GFLAGS_GFLAGS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_completions.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_completions.h new file mode 100644 index 0000000000000000000000000000000000000000..15637eb3de853a79eccb9e7ba71771c607767a2e --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_completions.h @@ -0,0 +1,119 @@ +// Copyright (c) 2008, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +// +// --- + +// +// Implement helpful bash-style command line flag completions +// +// ** Functional API: +// HandleCommandLineCompletions() should be called early during +// program startup, but after command line flag code has been +// initialized, such as the beginning of HandleCommandLineHelpFlags(). +// It checks the value of the flag --tab_completion_word. If this +// flag is empty, nothing happens here. If it contains a string, +// however, then HandleCommandLineCompletions() will hijack the +// process, attempting to identify the intention behind this +// completion. Regardless of the outcome of this deduction, the +// process will be terminated, similar to --helpshort flag +// handling. +// +// ** Overview of Bash completions: +// Bash can be told to programatically determine completions for the +// current 'cursor word'. It does this by (in this case) invoking a +// command with some additional arguments identifying the command +// being executed, the word being completed, and the previous word +// (if any). Bash then expects a sequence of output lines to be +// printed to stdout. If these lines all contain a common prefix +// longer than the cursor word, bash will replace the cursor word +// with that common prefix, and display nothing. If there isn't such +// a common prefix, bash will display the lines in pages using 'more'. +// +// ** Strategy taken for command line completions: +// If we can deduce either the exact flag intended, or a common flag +// prefix, we'll output exactly that. Otherwise, if information +// must be displayed to the user, we'll take the opportunity to add +// some helpful information beyond just the flag name (specifically, +// we'll include the default flag value and as much of the flag's +// description as can fit on a single terminal line width, as specified +// by the flag --tab_completion_columns). Furthermore, we'll try to +// make bash order the output such that the most useful or relevent +// flags are the most likely to be shown at the top. +// +// ** Additional features: +// To assist in finding that one really useful flag, substring matching +// was implemented. Before pressing a to get completion for the +// current word, you can append one or more '?' to the flag to do +// substring matching. Here's the semantics: +// --foo Show me all flags with names prefixed by 'foo' +// --foo? Show me all flags with 'foo' somewhere in the name +// --foo?? Same as prior case, but also search in module +// definition path for 'foo' +// --foo??? Same as prior case, but also search in flag +// descriptions for 'foo' +// Finally, we'll trim the output to a relatively small number of +// flags to keep bash quiet about the verbosity of output. If one +// really wanted to see all possible matches, appending a '+' to the +// search word will force the exhaustive list of matches to be printed. +// +// ** How to have bash accept completions from a binary: +// Bash requires that it be informed about each command that programmatic +// completion should be enabled for. Example addition to a .bashrc +// file would be (your path to gflags_completions.sh file may differ): + +/* +$ complete -o bashdefault -o default -o nospace -C \ + '/home/build/eng/bash/bash_completions.sh --tab_completion_columns $COLUMNS' \ + time env binary_name another_binary [...] +*/ + +// This would allow the following to work: +// $ /path/to/binary_name --vmodule +// Or: +// $ ./bin/path/another_binary --gfs_u +// (etc) +// +// Sadly, it appears that bash gives no easy way to force this behavior for +// all commands. That's where the "time" in the above example comes in. +// If you haven't specifically added a command to the list of completion +// supported commands, you can still get completions by prefixing the +// entire command with "env". +// $ env /some/brand/new/binary --vmod +// Assuming that "binary" is a newly compiled binary, this should still +// produce the expected completion output. + + +#ifndef GFLAGS_COMPLETIONS_H_ +#define GFLAGS_COMPLETIONS_H_ + +namespace google { +extern void HandleCommandLineCompletions(void); +} + +#endif // GFLAGS_COMPLETIONS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_declare.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_declare.h new file mode 100644 index 0000000000000000000000000000000000000000..a9c6759707846f63ab97a66c13cb446975364448 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_declare.h @@ -0,0 +1,155 @@ +// Copyright (c) 1999, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// --- +// +// Revamped and reorganized by Craig Silverstein +// +// This is the file that should be included by any file which declares +// command line flag. + +#ifndef GFLAGS_DECLARE_H_ +#define GFLAGS_DECLARE_H_ + + +// --------------------------------------------------------------------------- +// Namespace of gflags library symbols. +#define GFLAGS_NAMESPACE google + +// --------------------------------------------------------------------------- +// Windows DLL import/export. + +// Whether gflags library is a DLL. +// +// Set to 1 by default when the shared gflags library was built on Windows. +// Must be overwritten when this header file is used with the optionally also +// built static library instead; set by CMake's INTERFACE_COMPILE_DEFINITIONS. +#ifndef GFLAGS_IS_A_DLL +# define GFLAGS_IS_A_DLL 1 +#endif + +// We always want to import the symbols of the gflags library. +#ifndef GFLAGS_DLL_DECL +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DECL __declspec(dllimport) +# elif defined(__GNUC__) && __GNUC__ >= 4 +# define GFLAGS_DLL_DECL __attribute__((visibility("default"))) +# else +# define GFLAGS_DLL_DECL +# endif +#endif + +// We always want to import variables declared in user code. +#ifndef GFLAGS_DLL_DECLARE_FLAG +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DECLARE_FLAG __declspec(dllimport) +# elif defined(__GNUC__) && __GNUC__ >= 4 +# define GFLAGS_DLL_DECLARE_FLAG __attribute__((visibility("default"))) +# else +# define GFLAGS_DLL_DECLARE_FLAG +# endif +#endif + +// --------------------------------------------------------------------------- +// Flag types +#include +#if 1 +# include // the normal place uint32_t is defined +#elif 1 +# include // the normal place u_int32_t is defined +#elif 1 +# include // a third place for uint32_t or u_int32_t +#endif + +namespace GFLAGS_NAMESPACE { + +#if 1 // C99 +typedef int32_t int32; +typedef uint32_t uint32; +typedef int64_t int64; +typedef uint64_t uint64; +#elif 0 // BSD +typedef int32_t int32; +typedef u_int32_t uint32; +typedef int64_t int64; +typedef u_int64_t uint64; +#elif 0 // Windows +typedef __int32 int32; +typedef unsigned __int32 uint32; +typedef __int64 int64; +typedef unsigned __int64 uint64; +#else +# error Do not know how to define a 32-bit integer quantity on your system +#endif + +} // namespace GFLAGS_NAMESPACE + + +namespace fLS { + +// The meaning of "string" might be different between now and when the +// macros below get invoked (e.g., if someone is experimenting with +// other string implementations that get defined after this file is +// included). Save the current meaning now and use it in the macros. +typedef std::string clstring; + +} // namespace fLS + + +#define DECLARE_VARIABLE(type, shorttype, name) \ + /* We always want to import declared variables, dll or no */ \ + namespace fL##shorttype { extern GFLAGS_DLL_DECLARE_FLAG type FLAGS_##name; } \ + using fL##shorttype::FLAGS_##name + +#define DECLARE_bool(name) \ + DECLARE_VARIABLE(bool, B, name) + +#define DECLARE_int32(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int32, I, name) + +#define DECLARE_uint32(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint32, U, name) + +#define DECLARE_int64(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int64, I64, name) + +#define DECLARE_uint64(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint64, U64, name) + +#define DECLARE_double(name) \ + DECLARE_VARIABLE(double, D, name) + +#define DECLARE_string(name) \ + /* We always want to import declared variables, dll or no */ \ + namespace fLS { \ + extern GFLAGS_DLL_DECLARE_FLAG ::fLS::clstring& FLAGS_##name; \ + } \ + using fLS::FLAGS_##name + +#endif // GFLAGS_DECLARE_H_ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_gflags.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_gflags.h new file mode 100644 index 0000000000000000000000000000000000000000..3780704e1caa005e3102c189291b73a1972fa080 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/gflags/gflags_gflags.h @@ -0,0 +1,99 @@ +// Copyright (c) 2014, Andreas Schuh +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// ----------------------------------------------------------------------------- +// Imports the gflags library symbols into an alternative/deprecated namespace. + +#ifndef GFLAGS_GFLAGS_H_ +# error The internal header gflags_gflags.h may only be included by gflags.h +#endif + +#ifndef GFLAGS_NS_GFLAGS_H_ +#define GFLAGS_NS_GFLAGS_H_ + + +namespace gflags { + + +using GFLAGS_NAMESPACE::int32; +using GFLAGS_NAMESPACE::uint32; +using GFLAGS_NAMESPACE::int64; +using GFLAGS_NAMESPACE::uint64; + +using GFLAGS_NAMESPACE::RegisterFlagValidator; +using GFLAGS_NAMESPACE::CommandLineFlagInfo; +using GFLAGS_NAMESPACE::GetAllFlags; +using GFLAGS_NAMESPACE::ShowUsageWithFlags; +using GFLAGS_NAMESPACE::ShowUsageWithFlagsRestrict; +using GFLAGS_NAMESPACE::DescribeOneFlag; +using GFLAGS_NAMESPACE::SetArgv; +using GFLAGS_NAMESPACE::GetArgvs; +using GFLAGS_NAMESPACE::GetArgv; +using GFLAGS_NAMESPACE::GetArgv0; +using GFLAGS_NAMESPACE::GetArgvSum; +using GFLAGS_NAMESPACE::ProgramInvocationName; +using GFLAGS_NAMESPACE::ProgramInvocationShortName; +using GFLAGS_NAMESPACE::ProgramUsage; +using GFLAGS_NAMESPACE::VersionString; +using GFLAGS_NAMESPACE::GetCommandLineOption; +using GFLAGS_NAMESPACE::GetCommandLineFlagInfo; +using GFLAGS_NAMESPACE::GetCommandLineFlagInfoOrDie; +using GFLAGS_NAMESPACE::FlagSettingMode; +using GFLAGS_NAMESPACE::SET_FLAGS_VALUE; +using GFLAGS_NAMESPACE::SET_FLAG_IF_DEFAULT; +using GFLAGS_NAMESPACE::SET_FLAGS_DEFAULT; +using GFLAGS_NAMESPACE::SetCommandLineOption; +using GFLAGS_NAMESPACE::SetCommandLineOptionWithMode; +using GFLAGS_NAMESPACE::FlagSaver; +using GFLAGS_NAMESPACE::CommandlineFlagsIntoString; +using GFLAGS_NAMESPACE::ReadFlagsFromString; +using GFLAGS_NAMESPACE::AppendFlagsIntoFile; +using GFLAGS_NAMESPACE::ReadFromFlagsFile; +using GFLAGS_NAMESPACE::BoolFromEnv; +using GFLAGS_NAMESPACE::Int32FromEnv; +using GFLAGS_NAMESPACE::Uint32FromEnv; +using GFLAGS_NAMESPACE::Int64FromEnv; +using GFLAGS_NAMESPACE::Uint64FromEnv; +using GFLAGS_NAMESPACE::DoubleFromEnv; +using GFLAGS_NAMESPACE::StringFromEnv; +using GFLAGS_NAMESPACE::SetUsageMessage; +using GFLAGS_NAMESPACE::SetVersionString; +using GFLAGS_NAMESPACE::ParseCommandLineNonHelpFlags; +using GFLAGS_NAMESPACE::HandleCommandLineHelpFlags; +using GFLAGS_NAMESPACE::AllowCommandLineReparsing; +using GFLAGS_NAMESPACE::ReparseCommandLineNonHelpFlags; +using GFLAGS_NAMESPACE::ShutDownCommandLineFlags; +using GFLAGS_NAMESPACE::FlagRegisterer; + +#ifndef SWIG +using GFLAGS_NAMESPACE::ParseCommandLineFlags; +#endif + +} // namespace gflags +#endif // GFLAGS_NS_GFLAGS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/infer_engine.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/infer_engine.h new file mode 100644 index 0000000000000000000000000000000000000000..cc7c5e0aa74b720099c01808be36fd4586ceec12 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/infer_engine.h @@ -0,0 +1,45 @@ +#ifndef BENCHMARK_INFER_ENGINE_H +#define BENCHMARK_INFER_ENGINE_H +#include "util.h" +#include "acl/acl_base.h" +#include "post_process.h" +#include +#include +#include +#include +#include +#include "acl/acl_mdl.h" +#include +#include + +aclError InitContext(const char* configPath = ""); +aclError UnInitContext(); +aclError LoadModel(); +aclError InitInput(std::vector files); +aclError Inference(); +aclError PostProcess(); +aclError DvppSetup(); +aclError DvppInitInput(std::vector files); +aclError UnloadModel(); +void getImgResizeShape(); +acldvppRoiConfig* InitCropRoiConfig(uint32_t width, uint32_t height); + +/* + * @brief : 初始化中心抠图配置信息。 + * @param [in] uint32_t newInputWidth : 输入图像的宽（等比例缩放后的宽度） + * @param [in] uint32_t newInputHeight : 输入图像的高（等比例缩放后的高） + * @param [in] uint32_t modelInputWidth : 中心抠图后输入给模型的宽 + * @param [in] uint32_t modelInputHeight : 中心抠图后输入给模型的高 + * @return : acldvppRoiConfig：中心抠图配置信息 + */ +acldvppRoiConfig* InitCropCenterRoiConfig(uint32_t newInputWidth, uint32_t newInputHeight,uint32_t modelInputWidth, uint32_t modelInputHeight); + +/* + * @brief : 宽高较短的边缩放至RESIZE_MIN(256)，较长的边做等比例缩放。 + * @param [in] uint32_t width : 输入图片宽 + * @param [in] uint32_t height : 输入图片高 + * @param [in] uint32_t &newInputWidth : 等比例缩放后的宽 + * @param [in] uint32_t &newInputHeight : 等比例缩放后的高 + */ +void SmallSizeAtLeast(uint32_t width, uint32_t height, uint32_t& newInputWidth, uint32_t& newInputHeigh); +#endif diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/post_process.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/post_process.h new file mode 100644 index 0000000000000000000000000000000000000000..d413c66567109cb8647cf7b385ad66478f217af8 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/post_process.h @@ -0,0 +1,5 @@ +#ifndef BENCHMARK_POST_PROCESS_H +#define BENCHMARK_POST_PROCESS_H +#include "util.h" +aclError SaveBinPostprocess(); +#endif diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/util.h b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/util.h new file mode 100644 index 0000000000000000000000000000000000000000..734db74083d316e3c6fdc52c3f5caf0548789684 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/inc/util.h @@ -0,0 +1,157 @@ +#ifndef BENCHMARK_UTIL_H +#define BENCHMARK_UTIL_H +#include +#include +#include +#include "acl/acl_base.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include "acl/acl_rt.h" +#include "acl/ops/acl_dvpp.h" +#include +#include +#include +#include +#include +#include + +// self defined problem code. +const int ACL_ERROR_PATH_INVALID = 101; +const int ACL_ERROR_CREATE_DATASET_FAILED = 102; +const int ACL_ERROR_PARSE_PARAM_FAILED = 103; +const int ACL_ERROR_DVPP_ERROR = 104; +const int ACL_ERROR_OTHERS = 255; +#define MODEL_INPUT_NUM_MAX (4) +#define MODEL_INPUT_OUTPUT_NUM_MAX (16) + +#define LOG(fmt, args...) \ + do { \ + printf(fmt, ##args); \ + } while(0) + + +#define START_PROC \ + struct timeval start, end; \ + long long time_use; \ + do { \ + gettimeofday(&start, NULL); \ + } while (0); + + +#define END_PROC \ + do { \ + gettimeofday(&end, NULL); \ + time_use = (end.tv_sec-start.tv_sec)*1000000+(end.tv_usec-start.tv_usec); \ + LOG("time use: %lld us\n", time_use); \ + } while (0); + + +#define CHECK_ACL_RET(msg, ret) \ + if (ret != ACL_ERROR_NONE) { \ + std::cout << msg << ", ret "<< ret << std::endl; \ + return ret; \ + } + + +#define CHECK_WITH_RET(condition, ret, msg) \ + if(!(condition)) { \ + std::cout << msg << ", ret "<< ret << std::endl; \ + return ret; \ + } + + +#define CHECK_RET(ret) \ + if(ret != ACL_ERROR_NONE) { \ + return ret; \ + } + +bool FolderExists(std::string foldname); + +bool FileExists(std::string filename); + +char* ReadBinFile(std::string fileName, uint32_t& fileSize); + +aclError GetFiles(std::string path, std::vector& files); + +aclError FreeDevMemory(aclmdlDataset* dataset); + +aclError DestroyDatasetResurce(aclmdlDataset* dataset, uint32_t flag); + +void* ReadFile(std::string fileLocation, uint64_t &fileSize); + +struct DvppConfig { + uint32_t resizedWidth; + uint32_t resizedHeight; + std::unordered_map> imgSizes; +}; + +struct ModelInfo +{ + aclFormat Format; + const char* Name; + size_t size; + size_t dimCount; + int64_t dims[ACL_MAX_DIM_CNT]; + aclDataType Type; +}; + +struct Config { + std::string om; + std::string dataDir; + std::string outDir; + DvppConfig dvppConfig; + bool useDvpp; + size_t batchSize; + ModelInfo inputInfo[MODEL_INPUT_OUTPUT_NUM_MAX]; + ModelInfo outputInfo[MODEL_INPUT_OUTPUT_NUM_MAX]; + size_t inputNum; + size_t outputNum; + aclmdlDesc* modelDesc; + uint32_t modelId; + aclrtContext context; + char* modelData_ptr; + void* devMem_ptr; + void* weightMem_ptr; + std::string imgType; + std::string modelType; + uint32_t deviceId; + uint32_t loopNum; + std::string framework; + int64_t curOutputSize[MODEL_INPUT_OUTPUT_NUM_MAX]; + Config() + { + om = ""; + dataDir = ""; + batchSize = 0; + useDvpp = 0; + inputNum = 0; + outputNum = 0; + modelDesc = nullptr; + modelId = 0; + context = nullptr; + imgType = ""; + modelType = ""; + deviceId = 0; + loopNum = 1; + framework = "caffe"; + outDir = "../../results"; + modelData_ptr = nullptr; + devMem_ptr = nullptr; + weightMem_ptr = nullptr; + } +}; + +struct Resnet50Result { + int top1; + int top5; + int total; + std::unordered_map cmp; + Resnet50Result(): top1(0), top5(0), total(0) {}; +}; + +struct DataFrame { + std::vector fileNames; + aclmdlDataset* dataset; +}; + +#endif diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/infer_engine.cpp b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/infer_engine.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7f2b8507a510844689239d71443d8b4b4480be3d --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/infer_engine.cpp @@ -0,0 +1,728 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "acl/acl.h" +#include "infer_engine.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include "acl/ops/acl_dvpp.h" +#include +#include +using namespace std; + +std::unordered_map dvppTime; +extern Resnet50Result resnet50Res; +extern Config cfg; +extern aclError ret; +extern int processedCnt; +extern long long inferTime; +aclrtContext context; +uint32_t modelId; +aclmdlDesc *modelDesc; +std::vector files; +DataFrame inputDataframe; +DataFrame outputDataframe; +aclDataBuffer *yoloImgInfo; +aclrtStream stream = nullptr; +acldvppChannelDesc *dvpp_channel_desc = nullptr; +std::unordered_map> imgSizes; + +#define RESIZE_MIN 256 +#define NUM_2 2 +#define NUM_3 3 +#define NUM_16 16 +#define NUM_128 128 + +uint32_t resizedWidth; +uint32_t resizedHeight; +uint32_t resizedWidthAligned; +uint32_t resizedHeightAligned; +uint32_t resizedOutputBufferSize; + +void getImgResizeShape() +{ + if (ACL_FORMAT_NCHW == cfg.inputInfo[0].Format) { + resizedHeight = cfg.inputInfo[0].dims[NUM_2]; + resizedWidth = cfg.inputInfo[0].dims[NUM_3]; + } else if (ACL_FORMAT_NHWC == cfg.inputInfo[0].Format) { + resizedHeight = cfg.inputInfo[0].dims[1]; + resizedWidth = cfg.inputInfo[0].dims[NUM_2]; + } + return; +} + +aclError InitContext(const char *configPath) +{ + LOG("context init start\n"); + ret = aclInit(configPath); + CHECK_ACL_RET("acl init failed", ret); + + ret = aclrtSetDevice(cfg.deviceId); + CHECK_ACL_RET("open device failed ret", ret); + + ret = aclrtCreateContext(&context, cfg.deviceId); + CHECK_ACL_RET("create context failed", ret); + + cfg.context = context; + LOG("context init done\n"); + return ACL_ERROR_NONE; +} + +aclError UnInitContext() +{ + ret = aclrtDestroyContext(context); + CHECK_ACL_RET("destory context failed", ret); + LOG("destory context done\n"); + + ret = aclrtResetDevice(cfg.deviceId); + CHECK_ACL_RET("reset device failed", ret); + + ret = aclFinalize(); + CHECK_ACL_RET("finalize failed", ret); + LOG("reset device done\n"); + + return ACL_ERROR_NONE; +} + +aclError LoadModel() +{ + LOG("load model start\n"); + size_t memSize; + size_t weightsize; + uint32_t modelSize = 0; + std::string modelPath = cfg.om; + + cfg.modelData_ptr = ReadBinFile(modelPath, modelSize); + CHECK_WITH_RET(cfg.modelData_ptr != nullptr, ACL_ERROR_READ_MODEL_FAILURE, "can't read model"); + + aclError ret = aclmdlQuerySizeFromMem(cfg.modelData_ptr, modelSize, &memSize, &weightsize); + CHECK_ACL_RET("query memory size failed", ret); + + ret = aclrtMalloc(&(cfg.devMem_ptr), memSize, ACL_MEM_MALLOC_HUGE_ONLY); + CHECK_ACL_RET("alloc dev_ptr failed", ret); + ret = aclrtMalloc(&(cfg.weightMem_ptr), weightsize, ACL_MEM_MALLOC_HUGE_ONLY); + CHECK_ACL_RET("alloc weight_ptr failed", ret); + + ret = aclmdlLoadFromMemWithMem(cfg.modelData_ptr, modelSize, &modelId, cfg.devMem_ptr, memSize, cfg.weightMem_ptr, + weightsize); + CHECK_ACL_RET("load model from memory failed", ret); + LOG("Load model success. memSize: %lu, weightSize: %lu.\n", memSize, weightsize); + + modelDesc = aclmdlCreateDesc(); + CHECK_WITH_RET(modelDesc != nullptr, ACL_ERROR_READ_MODEL_FAILURE, "create model desc failed"); + ret = aclmdlGetDesc(modelDesc, modelId); + CHECK_ACL_RET("get model desc failed", ret); + + cfg.modelDesc = modelDesc; + cfg.modelId = modelId; + + LOG("load model done\n"); + return ACL_ERROR_NONE; +} + +aclError DvppSetup() +{ + ret = aclrtSetCurrentContext(context); + if (ret != ACL_ERROR_NONE) { + LOG("Set context failed\n"); + return ret; + } + + ret = aclrtCreateStream(&stream); + if (ret != ACL_ERROR_NONE) { + LOG("create dvpp stream failed\n"); + return ret; + } + + dvpp_channel_desc = acldvppCreateChannelDesc(); + if (dvpp_channel_desc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create dvpp channel desc failed\n"); + return ret; + } + + ret = acldvppCreateChannel(dvpp_channel_desc); + if (ret != ACL_ERROR_NONE) { + LOG("create dvpp channel failed\n"); + return ret; + } + + imgSizes = cfg.dvppConfig.imgSizes; + + resizedWidthAligned = (resizedWidth + 15) / NUM_16 * NUM_16; + resizedHeightAligned = (resizedHeight + 1) / NUM_2 * NUM_2; + + resizedOutputBufferSize = resizedWidthAligned * resizedHeightAligned * NUM_3 / NUM_2; + LOG("resizedWidth %d resizedHeight %d resizedWidthAligned %d resizedHeightAligned %d resizedOutputBufferSize %d\n", + resizedWidth, resizedHeight, resizedWidthAligned, resizedHeightAligned, resizedOutputBufferSize); + + return ACL_ERROR_NONE; +} + +/* + * @brief : 生成dvpp图像描述信息 + * @param [in] void *dataDev : 码流buffer信息. + * @param [in] acldvppPixelFormat format: 图像格式 + * @param [in] uint32_t width : 宽度 + * @param [in] uint32_t height: 高度 + * @param [in] uint32_t widthStride : 宽度对齐. + * @param [in] uint32_t heightStride: 高度对齐. + * @param [in] uint32_t size: 码流大小. + * @return : acldvppPicDesc：图像描述信息 + */ +acldvppPicDesc *createDvppPicDesc(void *dataDev, acldvppPixelFormat format, uint32_t width, uint32_t height, + uint32_t widthStride, uint32_t heightStride, uint32_t size) +{ + acldvppPicDesc *picDesc = acldvppCreatePicDesc(); + if (picDesc == nullptr) { + LOG("failed to create pic desc\n"); + return nullptr; + } + + ret = acldvppSetPicDescData(picDesc, dataDev); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc data\n"); + return nullptr; + } + ret = acldvppSetPicDescSize(picDesc, size); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc size\n"); + return nullptr; + } + + ret = acldvppSetPicDescFormat(picDesc, format); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc format\n"); + return nullptr; + } + + ret = acldvppSetPicDescWidth(picDesc, width); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc width\n"); + return nullptr; + } + + ret = acldvppSetPicDescHeight(picDesc, height); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc height\n"); + return nullptr; + } + + ret = acldvppSetPicDescWidthStride(picDesc, widthStride); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc widthStride\n"); + return nullptr; + } + + ret = acldvppSetPicDescHeightStride(picDesc, heightStride); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc heightStride\n"); + return nullptr; + } + return picDesc; +} + +aclError InitInput(std::vector files) +{ + LOG("init input batch %d start\n", processedCnt); + ret = aclrtSetCurrentContext(context); + if (ret != ACL_ERROR_NONE) { + LOG("Set context failed, ret[%d]\n", ret); + return ret; + } + + size_t modelInputSize = cfg.inputInfo[0].size; + size_t imgSize = modelInputSize / cfg.batchSize; + + void *dst; + ret = aclrtMalloc(&dst, modelInputSize, ACL_MEM_MALLOC_NORMAL_ONLY); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc device failed, ret[%d]\n", ret); + return ret; + } + LOG("dst = %p, size = %ld\n", dst, modelInputSize); + + char *ptr = (char *)dst; + inputDataframe.fileNames.clear(); + for (int i = 0; i < files.size(); i++) { + + std::string fileLocation = cfg.dataDir + "/" + files[i]; + FILE *pFile = fopen(fileLocation.c_str(), "r"); + + if (pFile == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("open file %s failed\n", fileLocation.c_str()); + return ret; + } + + fseek(pFile, 0, SEEK_END); + size_t fileSize = ftell(pFile); + + if (fileSize > imgSize) { + ret = ACL_ERROR_OTHERS; + LOG("%s fileSize %lu * batch %lu don't match with model inputSize %lu\n", fileLocation.c_str(), + fileSize, cfg.batchSize, modelInputSize); + return ret; + } + + void *buff = nullptr; + ret = aclrtMallocHost(&buff, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host buff failed[%d]\n", ret); + return ret; + } + + rewind(pFile); + fread(buff, sizeof(char), fileSize, pFile); + fclose(pFile); + + void *dstTmp = (void *)ptr; + ret = aclrtMemcpy(dstTmp, fileSize, buff, fileSize, ACL_MEMCPY_HOST_TO_DEVICE); + ptr += fileSize; + LOG("input file: %s, memory addr: %p, file size: %ld\n",files[i].c_str(), dstTmp, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("init input %d, Copy host to device failed, ret[%d]\n", i, ret); + LOG("input addr %p, len %ld\n", dstTmp, fileSize); + aclrtFreeHost(buff); + return ret; + } + + aclrtFreeHost(buff); + inputDataframe.fileNames.push_back(files[i]); + } + + aclDataBuffer *inputData = aclCreateDataBuffer((void *)dst, modelInputSize); + if (inputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("aclCreateDataBuffer failed\n"); + return ret; + } + + aclmdlDataset *input = aclmdlCreateDataset(); + ret = aclmdlAddDatasetBuffer(input, inputData); + if (ret != ACL_ERROR_NONE) { + LOG("ACL_ModelInputDataAdd failed, ret[%d]\n", ret); + aclmdlDestroyDataset(input); + return ret; + } + + inputDataframe.dataset = input; + LOG("init input batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} + +/* + * @brief : 获取图像宽高 + * @param [in] void* buff : 输入码流地址. + * @param [in] uint32_t fileSize : 输入码流长度 + * @param [in] std::string fileLocation : 输入文件路径. + * @param [in] uint32_t &W : 输入码流宽度. + * @param [in] uint32_t &H : 输入码流高度. + */ +void GetImageHW(void* buff, uint32_t fileSize, std::string fileLocation, uint32_t &W, uint32_t &H) +{ + int32_t components = 0; + ret = acldvppJpegGetImageInfo((void *)buff, fileSize, &W, &H, &components); + if (ret != ACL_ERROR_NONE) { + cout << "acldvppJpegGetImageInfo failed, ret " << ret << "filename: " << fileLocation.c_str() << endl; + } +} + +/* + * @brief : dvpp在推理中的预处理流程 + * @param [in] string fileLocation : 输入文件路径. + * @param [in] char *&ptr : 输出buffer指针. + * @return : ACL_ERROR_NONE：预处理成功， 其他：预处理失败 + */ +aclError DVPP_Resnet50(std::string fileLocation, char *&ptr) +{ + // 1 获取输入码流 + uint32_t W, H, W_Aligned, H_Aligned, outputBuffSize; + void *decodeInput = nullptr; + void *decodeOutput = nullptr; + acldvppPicDesc *decodeOutputDesc = nullptr; + uint64_t fileSize; + void *buff = ReadFile(fileLocation, fileSize); + if( buff == nullptr) { + LOG("read pic failed\n"); + return 1; + } + + ret = acldvppMalloc(&decodeInput, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc dvpp in buff failed[%d]\n", ret); + return ret; + } + ret = aclrtMemcpy(decodeInput, fileSize, buff, fileSize, ACL_MEMCPY_HOST_TO_DEVICE); + if (ret != ACL_ERROR_NONE) { + LOG("copy host to device failed[%d]\n", ret); + return ret; + } + + // 2 获取解码输出描述信息 + GetImageHW(buff, fileSize, fileLocation, W, H); + W_Aligned = (W + 127) / NUM_128 * NUM_128; + H_Aligned = (H + 15) / NUM_16 * NUM_16; + outputBuffSize = W_Aligned * H_Aligned * NUM_3 / NUM_2; + ret = acldvppMalloc(&decodeOutput, outputBuffSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc decodeOutput buff failed[%d]\n", ret); + return ret; + } + decodeOutputDesc = createDvppPicDesc(decodeOutput, PIXEL_FORMAT_YUV_SEMIPLANAR_420, W, H, W_Aligned, H_Aligned, + outputBuffSize); + if (decodeOutputDesc == nullptr) { + LOG("create jpeg_output_desc failed\n"); + return 1; + } + LOG("file[%s] jpeg picDesc info: W=%d, H=%d, W_Aligned=%d, H_Aligned=%d, outBufSize=%d, format=%d\n", \ + fileLocation.c_str(),W, H, W_Aligned, H_Aligned, outputBuffSize, PIXEL_FORMAT_YUV_SEMIPLANAR_420); + + // 3 使用jpegd图像解码 + ret = acldvppJpegDecodeAsync(dvpp_channel_desc, decodeInput, fileSize, decodeOutputDesc, stream); + if (ret != ACL_ERROR_NONE) { + LOG(" dvppJpegDecodeAsync failed\n"); + return ret; + } + aclrtFreeHost(buff); + aclrtSynchronizeStream(stream); + + // 4 对jpegd解码的图片进行原分辨率抠图及短边256等比例缩放 + acldvppRoiConfig *cropConfig = nullptr; + acldvppPicDesc *cropOutputDesc = nullptr; + // 设置对解码后的图片进行原图裁剪，目的是为了减少因jpegd解码后对齐的无效数据对图像精度的影响 + cropConfig = InitCropRoiConfig(W, H); + + uint32_t newInputWidth = 0; + uint32_t newInputHeight = 0; + void *cropOutBufferDev = nullptr; + // 宽和高较短的一条边缩放至256，较长边做等比例缩放。对齐至256目的是为了给224x224中心抠图做准备,短边256对齐，获得对齐后的宽高 + SmallSizeAtLeast(W, H, newInputWidth, newInputHeight); + uint32_t cropOutputWidthStride = (newInputWidth + (NUM_16 - 1)) / NUM_16 * NUM_16; + uint32_t cropOutputHeightStride = (newInputHeight + (NUM_2 - 1)) / NUM_2 * NUM_2; + uint32_t cropOutBufferSize = cropOutputWidthStride * cropOutputHeightStride * NUM_3 / NUM_2; + ret = acldvppMalloc(&cropOutBufferDev, cropOutBufferSize); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] AcldvppMalloc cropOutBufferDev_ failed, ret = " << ret << " cropOutBufferSize_ = " + << cropOutBufferSize << endl; + return ret; + } + cropOutputDesc = createDvppPicDesc(cropOutBufferDev, PIXEL_FORMAT_YUV_SEMIPLANAR_420, newInputWidth, newInputHeight, + cropOutputWidthStride, cropOutputHeightStride, cropOutBufferSize); + if (cropOutputDesc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create cropOutputDesc failed\n"); + return ret; + } + + ret = acldvppVpcCropAsync(dvpp_channel_desc, decodeOutputDesc, cropOutputDesc, cropConfig, stream); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] acldvppVpcCropAsync failed, ret = " << ret << std::endl; + return ret; + } + aclrtSynchronizeStream(stream); + + // 5 对等比例缩放后的图片做224x224中心抠图，中心抠图后的数据会发送给aipp进行YUV转RGB格式转换。需要注意：中心抠图后的输出格式和aipp + // 的输入格式需要保持一致。 + acldvppRoiConfig *centerCropConfig = nullptr; + acldvppPicDesc *centerCropOutputDesc = nullptr; // resize output desc + centerCropConfig = InitCropCenterRoiConfig(newInputWidth, newInputHeight, resizedWidth, resizedHeight); + void *vpcOutBufferDev = nullptr; + uint32_t vpcOutBufferSize = resizedWidthAligned * resizedHeightAligned * NUM_3 / NUM_2; + + vpcOutBufferDev = (void *)ptr; + centerCropOutputDesc = createDvppPicDesc(vpcOutBufferDev, PIXEL_FORMAT_YUV_SEMIPLANAR_420, resizedWidth, + resizedHeight, resizedWidthAligned, resizedHeightAligned, + vpcOutBufferSize); + if (centerCropOutputDesc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create centerCropOutputDesc failed\n"); + return ret; + } + + ret = acldvppVpcCropAsync(dvpp_channel_desc, cropOutputDesc, centerCropOutputDesc, centerCropConfig, stream); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] acldvppVpcCropAsync failed, ret = " << ret << "fileName: " << fileLocation.c_str() << std::endl; + return ret; + } + + ptr += vpcOutBufferSize; + aclrtSynchronizeStream(stream); + + // 6 释放资源 + acldvppFree(decodeInput); + acldvppFree(decodeOutput); + acldvppFree(cropOutBufferDev); + acldvppDestroyPicDesc(decodeOutputDesc); + acldvppDestroyPicDesc(cropOutputDesc); + acldvppDestroyPicDesc(centerCropOutputDesc); + acldvppDestroyRoiConfig(cropConfig); + acldvppDestroyRoiConfig(centerCropConfig); + return ret; +} + +aclError DvppInitInput(std::vector files) +{ + struct timeval process_start; + struct timeval process_end; + std::string funcName; + long long costTime; + funcName = "DvppTotalProcess"; + gettimeofday(&process_start, NULL); + + void *dst; + ret = acldvppMalloc(&dst, cfg.inputInfo[0].size); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc device failed, ret[%d]\n", ret); + return ret; + } + + char *ptr = (char *)dst; + inputDataframe.fileNames.clear(); + + for (int i = 0; i < files.size(); i++) { + std::string fileLocation = cfg.dataDir + "/" + files[i]; + ret = DVPP_Resnet50(fileLocation, ptr); + if(ret != ACL_ERROR_NONE) { + LOG("dvpp config failed"); + return ret; + } + inputDataframe.fileNames.push_back(files[i]); + } + + funcName = "DvppTotalProcess"; + gettimeofday(&process_end, NULL); + costTime = (process_end.tv_sec - process_start.tv_sec) * 1000000 + (process_end.tv_usec - process_start.tv_usec); + dvppTime[funcName] += costTime; + + aclmdlDataset *input = aclmdlCreateDataset(); + aclDataBuffer *inputData = aclCreateDataBuffer((void *)dst, cfg.inputInfo[0].size); + + if (inputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("aclCreateDataBuffer failed\n"); + return ret; + } + + ret = aclmdlAddDatasetBuffer(input, inputData); + + if (ret != ACL_ERROR_NONE) { + LOG("ACL_ModelInputDataAdd failed, ret[%d]\n", ret); + aclmdlDestroyDataset(input); + return ret; + } + + inputDataframe.dataset = input; + return ACL_ERROR_NONE; +} + +acldvppRoiConfig *InitCropRoiConfig(uint32_t width, uint32_t height) +{ + uint32_t right = 0; + uint32_t bottom = 0; + acldvppRoiConfig *cropConfig; + + if (width % NUM_2 == 0) { + right = width - 1; + } else { + right = width; + } + + if (height % NUM_2 == 0) { + bottom = height - 1; + } else { + bottom = height; + } + + cropConfig = acldvppCreateRoiConfig(0, right, 0, bottom); + if (cropConfig == nullptr) { + std::cout << "[ERROR][Vision] acldvppCreateRoiConfig failed " << std::endl; + return nullptr; + } + + return cropConfig; +} + +acldvppRoiConfig *InitCropCenterRoiConfig(uint32_t newInputWidth, uint32_t newInputHeight, uint32_t modelInputWidth, + uint32_t modelInputHeight) +{ + uint32_t left = 0; + uint32_t right = 0; + uint32_t top = 0; + uint32_t bottom = 0; + uint32_t amount_to_be_cropped_w = 0; + uint32_t amount_to_be_cropped_h = 0; + uint32_t left_half = 0; + uint32_t top_half = 0; + acldvppRoiConfig *centerCropConfig = nullptr; + + // 计算中心抠图起始点的坐标距离码流左边界和上边界的距离 + amount_to_be_cropped_w = newInputWidth - modelInputWidth; + left_half = amount_to_be_cropped_w / NUM_2; + amount_to_be_cropped_h = newInputHeight - modelInputHeight; + top_half = amount_to_be_cropped_h / NUM_2; + + // 保证起始点坐标为偶数 + left = (left_half % NUM_2 == 0) ? (amount_to_be_cropped_w / NUM_2) : (amount_to_be_cropped_w / NUM_2 + 1); + top = (top_half % NUM_2 == 0) ? (amount_to_be_cropped_h / NUM_2) : (amount_to_be_cropped_h / NUM_2 + 1); + + // 结束点为奇数 + right = left + modelInputWidth - 1; + bottom = top + modelInputHeight - 1; + + centerCropConfig = acldvppCreateRoiConfig(left, right, top, bottom); + if (centerCropConfig == nullptr) { + std::cout << "[ERROR][Vision] acldvppCreateRoiConfig failed " << std::endl; + return nullptr; + } + return centerCropConfig; +} + +void SmallSizeAtLeast(uint32_t width, uint32_t height, uint32_t &newInputWidth, uint32_t &newInputHeight) +{ + float scaleRatio = 0.0; + float inputWidth = 0.0; + float inputHeight = 0.0; + float resizeMin = 0.0; + bool minWidthFlag = false; + + inputWidth = (float)width; + inputHeight = (float)height; + resizeMin = (float)(RESIZE_MIN); + minWidthFlag = (width <= height) ? true : false; + + // 短边缩放为256，长边等比例缩放 + if (minWidthFlag == true) { + newInputWidth = resizeMin; + newInputHeight = (resizeMin / width) * inputHeight; + std::cout << "[INFO]scaleRatio: " << resizeMin / width << " inputWidth_: " << width << " newInputWidth: " << + newInputWidth << " inputHeight_: " << inputHeight << " newInputHeight_:" << newInputHeight << std::endl; + } else { + newInputWidth = (resizeMin / height) * width; + newInputHeight = resizeMin; + std::cout << "[INFO]scaleRatio: " << resizeMin / height << " width: " << width << " newInputWidth: " << + newInputWidth << " height: " << height << " newInputHeight:" << newInputHeight << std::endl; + } +} + +aclError Inference() +{ + LOG("inference batch %d start\n", processedCnt); + ret = aclrtSetCurrentContext(context); + + if (ret != ACL_ERROR_NONE) { + LOG("Set infer context failed\n"); + return ret; + } + + struct timeval startTmp, endTmp; + long long timeUse; + + if (inputDataframe.fileNames.size() == 0) { + ret = ACL_ERROR_OTHERS; + LOG("No file found\n"); + return ret; + } + + aclmdlDataset *output = aclmdlCreateDataset(); + if (output == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("Create Output Dataset failed\n"); + return ret; + } + + std::vector outputDevPtrs; + + for (size_t i = 0; i < cfg.outputNum; ++i) { + size_t buffer_size = cfg.outputInfo[i].size; + void *outputBuffer = nullptr; + ret = aclrtMalloc(&outputBuffer, (size_t)buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY); + + if (ret != ACL_ERROR_NONE) { + LOG("Malloc output host failed, ret[%d]\n", ret); + return ret; + } + + outputDevPtrs.push_back(outputBuffer); + aclDataBuffer *outputData = aclCreateDataBuffer(outputBuffer, buffer_size); + + if (outputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("Create output data buffer failed\n"); + return ret; + } + + ret = aclmdlAddDatasetBuffer(output, outputData); + + if (ret != ACL_ERROR_NONE) { + LOG("Add output model dataset failed, ret[%d]\n", ret); + return ret; + } + } + + gettimeofday(&startTmp, NULL); + ret = aclmdlExecute(modelId, inputDataframe.dataset, output); + gettimeofday(&endTmp, NULL); + timeUse = (endTmp.tv_sec - startTmp.tv_sec) * 1000000 + (endTmp.tv_usec - startTmp.tv_usec); + LOG("inference time cost: %lld us\n", timeUse); + inferTime += timeUse; + + if (ret != ACL_ERROR_NONE) { + LOG("%s inference failed.\n", inputDataframe.fileNames[0].c_str()); + FreeDevMemory(inputDataframe.dataset); + aclmdlDestroyDataset(inputDataframe.dataset); + return ret; + } + + outputDataframe.fileNames = inputDataframe.fileNames; + outputDataframe.dataset = output; + + uint32_t dvppFlag = (cfg.useDvpp) ? 1 : 0; + ret = DestroyDatasetResurce(inputDataframe.dataset, dvppFlag); + if (ret != ACL_ERROR_NONE) { + LOG("DestroyDatasetResurce failed\n"); + return ret; + } + + LOG("inference batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} + +aclError UnloadModel() +{ + LOG("unload model start\n"); + ret = aclmdlUnload(modelId); + CHECK_ACL_RET("unload model failed", ret); + LOG("unload model done\n"); + + aclmdlDestroyDesc(cfg.modelDesc); + + if (cfg.devMem_ptr != nullptr) { + aclrtFree(cfg.devMem_ptr); + cfg.devMem_ptr = nullptr; + } + + if (cfg.weightMem_ptr != nullptr) { + aclrtFree(cfg.weightMem_ptr); + cfg.weightMem_ptr = nullptr; + } + + if (cfg.modelData_ptr != nullptr) { + delete[] cfg.modelData_ptr; + cfg.modelData_ptr = nullptr; + } + return ACL_ERROR_NONE; +} diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/main.cpp b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/main.cpp new file mode 100644 index 0000000000000000000000000000000000000000..99a8d46ab89f9c616048a09f0f995e68502c4330 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/main.cpp @@ -0,0 +1,493 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "util.h" +#include "infer_engine.h" +#include "acl/acl_base.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include "acl/acl.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace std; +using std::cout; +using std::endl; +Resnet50Result resnet50Res; +Config cfg; +aclError ret; +int processedCnt; +long long dataProcTime = 0; +long long inferTime = 0; +float avgTime = 0; +float avgPreTime = 0; + +extern std::unordered_map dvppTime; +extern DataFrame outputDataframe; + +void getCommandLineParam(int argc, char** argv, Config& config) +{ + while (1) { + int option_index = 0; + struct option long_options[] = + { + {"om", 1, 0, 'a'}, + {"dataDir", 1, 0, 'b'}, + {"outDir", 1, 0, 'c'}, + {"batchSize", 1, 0, 'd'}, + {"deviceId", 1, 0, 'e'}, + {"loopNum", 1, 0, 'f'}, + {"modelType", 1, 0, 'g'}, + {"imgType", 1, 0, 'h'}, + {"framework", 1, 0, 'i'}, + {"useDvpp", 1 , 0 , 'j'}, + {0, 0, 0, 0} + }; + + int c; + c = getopt_long(argc, argv, "a:b:c:e:f:j:k:l:m:n:u:t:", long_options, &option_index); + if (c == -1) { + break; + } + + switch (c) { + case 'a': + config.om = std::string(optarg); + printf("[INFO]om = %s\n", config.om.c_str()); + break; + case 'b': + config.dataDir = std::string(optarg); + printf("[INFO]dataDir = %s\n", config.dataDir.c_str()); + break; + case 'c': + config.outDir = std::string(optarg); + printf("[INFO]outDir = %s\n", config.outDir.c_str()); + break; + case 'd': + config.batchSize = atoi(optarg); + printf("[INFO]batchSize = %d\n", config.batchSize); + break; + case 'e': + config.deviceId = atoi(optarg); + printf("[INFO]deviceId = %d\n", config.deviceId); + break; + case 'f': + config.loopNum = atoi(optarg); + printf("[INFO]loopNum = %d\n", config.loopNum); + break; + case 'g': + config.modelType = std::string(optarg); + printf("[INFO]modelType = %s\n", config.modelType.c_str()); + break; + case 'h': + config.imgType = std::string(optarg); + printf("[INFO]imgType = %s\n", config.imgType.c_str()); + break; + case 'i': + config.framework = std::string(optarg); + printf("[INFO]framework = %s\n", config.framework.c_str()); + break; + case 'j': + config.useDvpp = atoi(optarg); + printf("[INFO]useDvpp = %d\n", config.useDvpp); + break; + default: + break; + } + } +} + +// 只校验必须的参数 +aclError ParseParams(int argc, char** argv, Config& config, std::string& errorMsg) +{ + getCommandLineParam(argc, argv, config); + + LOG("parase params start\n"); + + if (config.om.empty() || !FileExists(config.om)) { + LOG("om is empty\n"); + errorMsg = "om path is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + + if (config.dataDir.empty() || !FolderExists(config.dataDir)) { + errorMsg = "data Dir is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("dataDir %s \n", config.dataDir.c_str()); + + if (!config.outDir.empty() && !FolderExists(config.outDir)) { + LOG("output dir %s not exists, try to make dir.\n", config.outDir.c_str()); + mkdir(config.outDir.c_str(), 0755); + LOG("outDir %s \n", config.outDir.c_str()); + } + + if(config.batchSize <= 0){ + errorMsg = "batch Size should be > 0"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("batchSize %zd \n", config.batchSize); + + if (config.modelType.empty()) + { + LOG("FLAGS_modelType is empty\n"); + errorMsg = "modelType is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("modelType %s \n", config.modelType.c_str()); + + if (config.imgType.empty()) + { + LOG("imgType is empty\n"); + errorMsg = "imgType is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("imgType %s \n", config.imgType.c_str()); + LOG("useDvpp is %d \n", config.useDvpp); + LOG("parase params done\n"); + return ACL_ERROR_NONE; +} + +aclError Process() +{ + std::vector fileNames; + ret = GetFiles(cfg.dataDir, fileNames); + CHECK_RET(ret); + size_t fileNum = fileNames.size(); + LOG("fileNum:%zd\n",fileNames.size()); + struct timeval startTmp, endTmp; + + // 获取模型输入所需要的宽高 + getImgResizeShape(); + + if(cfg.useDvpp) { + ret = DvppSetup(); + CHECK_RET(ret); + } + + size_t inferCnt = 0; + size_t loopCnt = 0; + while(loopCnt < cfg.loopNum) { + LOG("loopCnt %d, loopNum %d\n", loopCnt, cfg.loopNum); + for(size_t i = 0; i < fileNum / cfg.batchSize; i++) { + gettimeofday(&startTmp, NULL); + std::vector batchFileNames; + for (int j = 0; j < cfg.batchSize; j++) { + batchFileNames.push_back(fileNames[i*cfg.batchSize+j]); + } + processedCnt++; + + if(cfg.useDvpp) { + ret = DvppInitInput(batchFileNames); + } else { + ret = InitInput(batchFileNames); + } + gettimeofday(&endTmp, NULL); + dataProcTime += (endTmp.tv_sec-startTmp.tv_sec)*1000000+(endTmp.tv_usec-startTmp.tv_usec); + CHECK_RET(ret); + + ret = Inference(); + CHECK_RET(ret); + + ret = SaveBinPostprocess(); + CHECK_RET(ret); + } + + if (fileNum % cfg.batchSize != 0) { + std::vector batchFileNames; + for(size_t i = (fileNum - fileNum % cfg.batchSize); i < fileNum; i++) { + batchFileNames.push_back(fileNames[i]); + } + + gettimeofday(&startTmp, NULL); + processedCnt++; + + if(cfg.useDvpp) { + ret = DvppInitInput(batchFileNames); + } else { + ret = InitInput(batchFileNames); + } + gettimeofday(&endTmp, NULL); + dataProcTime += (endTmp.tv_sec-startTmp.tv_sec) * 1000000 + (endTmp.tv_usec - startTmp.tv_usec); + CHECK_RET(ret); + + ret = Inference(); + CHECK_RET(ret); + + ret = SaveBinPostprocess(); + CHECK_RET(ret); + } + loopCnt++; + } + return ACL_ERROR_NONE; +} + +void SaveResult() +{ + ofstream outfile("test_perform_static.txt"); +#if 0 + std::string model_name; + int dex = (cfg.om).find_last_of("/"); + model_name = cfg.om.substr(dex+1); + + std:: string title = "model_name total batch top1 top5 pre_avg/ms pre_imgs/s infer_avg/ms infer_imgs/s mAP"; + outfile << title << endl; + + outfile << model_name << " "; + outfile << processedCnt*cfg.batchSize << " "; + outfile << cfg.batchSize << " "; + if (cfg.postprocessType == "resnet") { + outfile << 1.0*resnet50Res.top1/resnet50Res.total << " " << 1.0*resnet50Res.top5/resnet50Res.total << " "; + } else { + outfile << "NA" << " " << "NA" << " "; + } + + outfile << avgPreTime << " " << 1.0*1000/avgPreTime << " "; + outfile << avgTime << " " << 1.0*1000/avgTime << " "; + outfile << endl; +#endif + char tmpCh[256]; + memset(tmpCh, 0, sizeof(tmpCh)); + snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms %4.3f fps/s\n", + avgTime, (1.0 * 1000 / avgTime)); + outfile << tmpCh; + outfile.close(); +} + +aclError GetModelInputOutputInfo(Config& cfg) +{ + aclError ret; + std::ofstream outFile("modelInputOutputInfo", std::ios::trunc); + char tmpChr[256] = {0}; + + // 获取模型输入信息 + size_t inputNum = aclmdlGetNumInputs(cfg.modelDesc); + LOG("model input num %zd\n", inputNum); + snprintf(tmpChr, sizeof(tmpChr), "model input num %zd\n", inputNum); + outFile << tmpChr; + + cfg.inputNum = inputNum; + for (size_t i = 0; i < inputNum && i < MODEL_INPUT_OUTPUT_NUM_MAX; i++) { + size_t size = aclmdlGetInputSizeByIndex(cfg.modelDesc, i); + cfg.inputInfo[i].size = size; + LOG("model input[%zd] size %zd\n", i, cfg.inputInfo[i].size); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] size %zd\n", i, cfg.inputInfo[i].size); + outFile << tmpChr; + + aclmdlIODims dims; + ret = aclmdlGetInputDims(cfg.modelDesc, i, &dims); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetInputDims fail ret %d\n", ret); + return 1; + } + + cfg.inputInfo[i].dimCount = dims.dimCount; + ret = aclrtMemcpy(cfg.inputInfo[i].dims , cfg.inputInfo[i].dimCount * sizeof(int64_t), dims.dims, + cfg.inputInfo[i].dimCount * sizeof(int64_t), ACL_MEMCPY_HOST_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtMemcpy fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + LOG("model input[%zd] dimCount %zd\n", i, cfg.inputInfo[i].dimCount); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] dimCount %zd\n", i, cfg.inputInfo[i].dimCount); + outFile << tmpChr; + for (size_t dimIdx = 0; dimIdx < cfg.inputInfo[i].dimCount; dimIdx++) { + LOG("model input[%zd] dim[%zd] info %ld\n", i, dimIdx, cfg.inputInfo[i].dims[dimIdx]); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] dim[%zd] info %ld\n", + i, dimIdx, cfg.inputInfo[i].dims[dimIdx]); + outFile << tmpChr; + } + + cfg.inputInfo[i].Format = aclmdlGetInputFormat(cfg.modelDesc, i); + cfg.inputInfo[i].Type = aclmdlGetInputDataType(cfg.modelDesc, i); + + LOG("model input[%zd] format %d inputType %d\n", i, cfg.inputInfo[i].Format, cfg.inputInfo[i].Type); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] format %d inputType %d\n", i, cfg.inputInfo[i].Format, + cfg.inputInfo[i].Type); + outFile << tmpChr; + + cfg.inputInfo[i].Name = aclmdlGetInputNameByIndex(cfg.modelDesc, i); + LOG("model input[%zd] name %s\n", i, cfg.inputInfo[i].Name); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] name %s\n", i, cfg.inputInfo[i].Name); + outFile << tmpChr; + + size_t index; + ret = aclmdlGetInputIndexByName(cfg.modelDesc, cfg.inputInfo[i].Name, &index); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetInputIndexByName fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + if (i != index) { + LOG("aclmdlGetInputNameByIndex not equal aclmdlGetInputIndexByName\n"); + return 1; + } else { + LOG("model input name %s is belone to input %zd\n", cfg.inputInfo[i].Name, index); + } + } + + // 获取模型输出信息 + size_t outputNum = aclmdlGetNumOutputs(cfg.modelDesc); + LOG("model output num %zd\n", outputNum); + snprintf(tmpChr, sizeof(tmpChr), "model output num %zd\n", outputNum); + outFile << tmpChr; + + cfg.outputNum = outputNum; + for (size_t i = 0; i < outputNum && i < MODEL_INPUT_OUTPUT_NUM_MAX; i++) { + size_t size = aclmdlGetOutputSizeByIndex(cfg.modelDesc, i); + cfg.outputInfo[i].size = size; + LOG("model output[%zd] size %zd\n", i, cfg.outputInfo[i].size); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] size %zd\n", i, cfg.outputInfo[i].size); + outFile << tmpChr; + + aclmdlIODims dims; + ret = aclmdlGetOutputDims(cfg.modelDesc, i, &dims); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetOutputDims fail ret %d\n", ret); + return 1; + } + + cfg.outputInfo[i].dimCount = dims.dimCount; + ret = aclrtMemcpy(cfg.outputInfo[i].dims, cfg.outputInfo[i].dimCount * sizeof(int64_t), dims.dims, + cfg.outputInfo[i].dimCount * sizeof(int64_t), ACL_MEMCPY_HOST_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtMemcpy fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + LOG("model output[%zd] dimCount %zd\n", i, cfg.outputInfo[i].dimCount); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] dimCount %zd\n", i, cfg.outputInfo[i].dimCount); + outFile << tmpChr; + + for (size_t dimIdx = 0; dimIdx < cfg.outputInfo[i].dimCount; dimIdx++) { + LOG("model output[%zd] dim[%zd] info %ld\n", i, dimIdx, cfg.outputInfo[i].dims[dimIdx]); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] dim[%zd] info %ld\n", + i, dimIdx, cfg.outputInfo[i].dims[dimIdx]); + outFile << tmpChr; + } + + cfg.outputInfo[i].Format = aclmdlGetOutputFormat(cfg.modelDesc, i); + cfg.outputInfo[i].Type = aclmdlGetOutputDataType(cfg.modelDesc, i); + LOG("model output[%zd] format %d outputType %d\n", i, cfg.outputInfo[i].Format, cfg.outputInfo[i].Type); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] format %d outputType %d\n", i, cfg.outputInfo[i].Format, + cfg.outputInfo[i].Type); + outFile << tmpChr; + + cfg.outputInfo[i].Name = aclmdlGetOutputNameByIndex(cfg.modelDesc, i); + LOG("model output[%zd] name %s\n", i, cfg.outputInfo[i].Name); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] name %s\n", i, cfg.outputInfo[i].Name); + outFile << tmpChr; + + size_t index; + ret = aclmdlGetOutputIndexByName(cfg.modelDesc, cfg.outputInfo[i].Name, &index); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetOutputIndexByName fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + if (i != index) { + LOG("aclmdlGetOutputNameByIndex not equal aclmdlGetOutputIndexByName\n"); + return 1; + } else { + LOG("model output name %s is belone to output %d\n", cfg.outputInfo[i].Name, index); + } + } + + outFile.close(); + return ACL_ERROR_NONE; +} + +int main(int argc, char** argv) +{ + processedCnt = 0; + inferTime = 0; + + std::string errorMsg; + ret = ParseParams(argc, argv, cfg, errorMsg); + CHECK_ACL_RET(errorMsg, ret); + + ret = InitContext(); + CHECK_RET(ret); + + ret = LoadModel(); + CHECK_RET(ret); + + ret = GetModelInputOutputInfo(cfg); + CHECK_RET(ret); + + ret = Process(); + CHECK_RET(ret); + + ret = UnloadModel(); + CHECK_RET(ret); + + ret = UnInitContext(); + CHECK_RET(ret); + LOG("\n"); + + avgTime = 1.0 * inferTime / processedCnt /cfg.batchSize / 1000; + avgPreTime = 1.0 * dataProcTime / processedCnt / cfg.batchSize / 1000; + + if (cfg.useDvpp) { + LOG("\n"); + LOG("DVPP performance details:\n"); + LOG("#############################################\n"); + std::unordered_map::iterator iter; + for (iter = dvppTime.begin(); iter != dvppTime.end(); iter++) { + LOG("%s using avg time %0.2f ms\n",iter->first.c_str(),1.0*iter->second/processedCnt/cfg.batchSize/1000); + } + LOG("\n"); + } + + LOG("performance summary:\n"); + LOG("#############################################\n"); + LOG("total %ld imgs processed and batch size %ld\n", processedCnt*cfg.batchSize, cfg.batchSize); +#if 0 + if(cfg.postprocessType == "resnet") { + LOG("top1 ratio %0.3f top5 ratio %0.3f\n", + 1.0*resnet50Res.top1/resnet50Res.total, 1.0*resnet50Res.top5/resnet50Res.total); + } +#endif + + LOG("avg preprocess time %0.2f ms, %0.2f imgs/s\n", avgPreTime, 1.0 * 1000 / avgPreTime); + LOG("avg inference time %0.2f ms, %0.2f imgs/s\n", avgTime, 1.0 * 1000 / avgTime); + + SaveResult(); +} diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/post_process.cpp b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/post_process.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c66491757b8bb1388a8ac9210619b55b5a2dcb9e --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/post_process.cpp @@ -0,0 +1,127 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "post_process.h" +#include "util.h" +#include +#include +#include +#include +#include "stdio.h" +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +extern int processedCnt; + +extern Config cfg; +extern DataFrame outputDataframe; +extern aclError ret; +int topNum = 5; + +extern int processedCnt; + +aclError SaveBinPostprocess() +{ + aclError retVal; + + LOG("save batch %d start\n", processedCnt); + DataFrame dataframe = outputDataframe; + std::vector& inferFile_vec = outputDataframe.fileNames; + aclmdlDataset* output = dataframe.dataset; + + std::string resultFolder = cfg.outDir + "/" + cfg.modelType; + DIR* op = opendir(resultFolder.c_str()); + if (NULL == op) { + mkdir(resultFolder.c_str(), 00775); + } else { + closedir(op); + } + + for (size_t i = 0; i < cfg.outputNum; ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output, i); + void* data = aclGetDataBufferAddr(dataBuffer); + uint32_t len; + len = cfg.outputInfo[i].size; + + void* outHostData = NULL; + ret = aclrtMallocHost(&outHostData, len); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host failed.\n"); + return 1; + } + + ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("Copy device to host failed.\n"); + aclrtFreeHost(outHostData); + return 1; + } + + uint32_t eachSize = len / cfg.batchSize; + for (size_t j = 0; j < inferFile_vec.size(); j++) { + FILE* outputFile; + std::string framename = inferFile_vec[j]; + std::size_t dex = (framename).find_first_of("."); + std::string inputFileName = (framename).erase(dex); + + if (cfg.modelType.compare(0, 6, "resnet") == 0) { + outputFile = fopen((resultFolder + "/" + "davinci_" + inputFileName + "_" + "output" + ".bin").c_str(), "wb"); + } else { + outputFile = fopen((resultFolder + "/" + "davinci_" + inputFileName + "_" + "output" + std::to_string(i) + ".bin").c_str(), "wb"); + } + + if (outputFile == nullptr) { + aclrtFreeHost(outHostData); + return 1; + } + + fwrite((uint8_t *)outHostData + (j * eachSize), eachSize, sizeof(char), outputFile); + fclose(outputFile); + } + + ret = aclrtFreeHost(outHostData); + if (ret != ACL_ERROR_NONE) { + LOG("Free output host failed.\n"); + } + } + + (void)DestroyDatasetResurce(outputDataframe.dataset, 0); + + LOG("save batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/util.cpp b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ec437321c75c4c883ca35d56a0c5ab218f16efa7 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/Benchmark/util.cpp @@ -0,0 +1,230 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "util.h" +#include +#include +#include +#if 0 +static std::unordered_map errorMap = { + {ACL_ERROR_NONE, "success"}, + {ACL_ERROR_INVALID_PARAM, "params may not valid"}, + {ACL_ERROR_BAD_ALLOC, "alloc memory failed"}, + {ACL_ERROR_RT_FAILURE, "runtime failure"}, + {ACL_ERROR_GE_FAILURE, "GE failure"}, + {ACL_ERROR_OP_NOT_FOUND, "OP not find"}, + {ACL_ERROR_OP_LOAD_FAILED, "OP loads failed"}, + {ACL_ERROR_READ_MODEL_FAILURE, "load model failed"}, + {ACL_ERROR_PARSE_MODEL, "parse model failed"}, + {ACL_ERROR_MODEL_MISSING_ATTR, "model misssing attr"}, + {ACL_ERROR_DESERIALIZE_MODEL, "deserilize model failed"}, + // {ACL_ERROR_MULTIPLE_MODEL_MATCHED, "multiple model matched"}, + //{ACL_ERROR_EVENT_NOT_READY, "event not ready"}, + //{ACL_ERROR_EVENT_COMPLETE, "event not complete"}, + {ACL_ERROR_UNSUPPORTED_DATA_TYPE, "unsupport datatype"}, + {ACL_ERROR_REPEAT_INITIALIZE, "initial repeated"}, + //{ACL_ERROR_COMPILER_NOT_REGISTERED, "compilter not registered"}, + {ACL_ERROR_PATH_INVALID, "path invalid"}, + {ACL_ERROR_PARSE_PARAM_FAILED, "parse params failed"}, + {ACL_ERROR_DVPP_ERROR, "dvpp errors"} +}; + + +std::string CausedBy(aclError error) +{ + return errorMap[error]; +} +#endif + +bool FolderExists(std::string foldname) +{ + DIR* dir; + if ((dir = opendir(foldname.c_str())) == NULL) { + return false; + } + closedir(dir); + return true; +} + +void* ReadFile(std::string fileLocation, uint64_t &fileSize) +{ + aclError ret; + FILE *pFile = fopen(fileLocation.c_str(), "r"); + if (pFile == nullptr) { + LOG("open file %s failed\n", fileLocation.c_str()); + return nullptr; + } + + fseek(pFile, 0, SEEK_END); + fileSize = ftell(pFile); + + void *buff = nullptr; + ret = aclrtMallocHost(&buff, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host buff failed[%d]\n", ret); + return nullptr; + } + + rewind(pFile); + fread(buff, sizeof(char), fileSize, pFile); + fclose(pFile); + return buff; +} + +bool FileExists(std::string filename) +{ + std::fstream file; + file.open(filename, std::ios::in); + if (!file) { + return false; + } + + file.close(); + return true; +} + +char* ReadBinFile(std::string fileName, uint32_t& fileSize) +{ + std::ifstream binFile(fileName, std::ifstream::binary); + + if (binFile.is_open() == false) { + LOG("open file[%s] failed\n", fileName.c_str()); + return nullptr; + } + + binFile.seekg(0, binFile.end); + uint32_t binFileBufferLen = binFile.tellg(); + + if (binFileBufferLen == 0) { + LOG("binfile is empty, filename: %s", fileName.c_str()); + binFile.close(); + return nullptr; + } + + binFile.seekg(0, binFile.beg); + char* binFileBufferData = new(std::nothrow) char[binFileBufferLen]; + LOG("binFileBufferData:%p\n", binFileBufferData); + + if (binFileBufferData == nullptr) { + LOG("malloc binFileBufferData failed\n"); + binFile.close(); + return nullptr; + } + + binFile.read(binFileBufferData, binFileBufferLen); + binFile.close(); + fileSize = binFileBufferLen; + return binFileBufferData; +} + +aclError GetFiles(std::string path, std::vector& files) +{ + DIR* dir; + struct dirent* ptr; + char base[1000]; + + if ((dir = opendir(path.c_str())) == NULL) { + LOG("Open dir %s error.\n", path.c_str()); + return ACL_ERROR_PATH_INVALID; + } + + while ((ptr = readdir(dir)) != NULL) { + if (strcmp(ptr->d_name, ".") == 0 || strcmp(ptr->d_name, "..") == 0) { + //current dir OR parrent dir + continue; + } else if (ptr->d_type == 8) { + //file + files.push_back(ptr->d_name); + } else if (ptr->d_type == 10) { + //link file + continue; + } else if (ptr->d_type == 4) { + //dir + continue; + } + } + + closedir(dir); + std::sort(files.begin(), files.end()); + return ACL_ERROR_NONE; +} + +aclError FreeDevMemory(aclmdlDataset* dataset) +{ + aclError ret; + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i); + void* data = aclGetDataBufferAddr(dataBuffer); + aclrtFree(data); + aclDestroyDataBuffer(dataBuffer); + } + + return ACL_ERROR_NONE; +} + +aclError DestroyDatasetResurce(aclmdlDataset* dataset, uint32_t flag) +{ + aclError ret = ACL_ERROR_NONE; + + if (nullptr == dataset) { + LOG("dataset == null\n"); + return 1; + } + + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i); + if (nullptr == dataBuffer) { + LOG("dataBuffer == null\n"); + continue; + } + + void* data = aclGetDataBufferAddr(dataBuffer); + if (nullptr != data) { + if (1 == flag) { + if (i > 0) { + ret = aclrtFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree data failed, ret %d\n", ret); + } + } else { + ret = acldvppFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("acldvppFree data failed, ret %d\n", ret); + } + } + } else { + ret = aclrtFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree data failed, ret %d\n", ret); + } + } + } + + ret = aclDestroyDataBuffer(dataBuffer); + if (ret != ACL_ERROR_NONE) { + LOG("Destroy dataBuffer failed, ret %d\n", ret); + } + } + + ret = aclmdlDestroyDataset(dataset); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree dataset failed, ret %d\n", ret); + } + + return ret; +} + + diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/LICENSE b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..57bc88a15a0ee8266c259b2667e64608d3f7e292 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/LICENSE @@ -0,0 +1,202 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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If they do not match, may leading in failure. + +| Conditions | Need | +| --- | --- | +| CANN Version | >=5.0.3 | +| Chip Platform| Ascend310/Ascend710 | +| 3rd Party Requirements| Please follow the 'requirements.txt' | + +## Quick Start Guide + +### 1. Clone the respository + +```shell +git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git +cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL +``` + +### 2. Download and preprocess the dataset + +1. Download the ImageNet2012 Validation dataset by yourself. You can get the validation pictures(50000 JPEGS and a ILSVRC2012val-label-index.txt) + +2. Put JPEGS to **'scripts/ILSVRC2012val'** and label text to **'scripts/'** + +3. Images Preprocess: +``` +cd scripts +mkdir input_bins +python3 inception_preprocessing.py ./ILSVRC2012val/ ./input_bins/ +``` +The jpegs pictures will be preprocessed to bin fils. + +### 3. Offline Inference + +**Convert pb to om.** + +- configure the env + + ``` + export install_path=/usr/local/Ascend + export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH + export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH + export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH + export ASCEND_OPP_PATH=${install_path}/opp + ``` + +- convert pb to om + + [pb download link](https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/modelzoo/Official/cv/Inceptionv2_for_ACL/inceptionv2_tf.pb) + + ``` + atc --model=inceptionv2_tf.pb --framework=3 --output=inceptionv3_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="input:1,224,224,3" --insert_op_conf=inceptionv2_aipp.cfg --enable_small_channel=1 --log=info + ``` + +- Build the program + + ``` + bash build.sh + ``` + +- Run the program: + + ``` + cd scripts + bash benchmark_tf.sh + ``` + +## Performance + +### Result + +Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide. + +#### Inference accuracy results + +| model | **data** | Top1/Top5 | +| :---------------: | :-------: | :-------------: | +| offline Inference | 50000 images | 74.0 %/ 91.8% | diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/build.sh b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..dae86211d2691b82ecfd8c2637d1276092e476ed --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/build.sh @@ -0,0 +1,9 @@ +rm -rf ./Benchmark/build + +mkdir -p Benchmark/build/intermediates/host +cd Benchmark/build/intermediates/host +cmake ../../../../Benchmark/ -DCAMKE_CXX_COMPILER=g++ +make clean +make install +cd - +cd Benchmark/out/ diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/inceptionv2_aipp.cfg b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/inceptionv2_aipp.cfg new file mode 100644 index 0000000000000000000000000000000000000000..484f80d3d1da6bc802e3f0aced5f914fd53b6e25 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/inceptionv2_aipp.cfg @@ -0,0 +1,13 @@ +aipp_op { + aipp_mode: static + input_format : RGB888_U8 + src_image_size_w : 224 + src_image_size_h : 224 + mean_chn_0 : 128 + mean_chn_1 : 128 + mean_chn_2 : 128 + var_reci_chn_0 : 0.00781 + var_reci_chn_1 : 0.00781 + var_reci_chn_2 : 0.00781 +} + diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..0934af7691acabd7981d82342b3a2310fe606d3d --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/modelzoo_level.txt @@ -0,0 +1,6 @@ +ModelCovert:OK +QuantStatus:OK +FuncStatus:OK +PrecisionStatus:OK +AutoTune:OK +PerfStatus:OK diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/requirements.txt b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f66bb9f75c74849c47871a646493af6c2eb83d3 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/requirements.txt @@ -0,0 +1,3 @@ +tensorflow==1.15 +numpy==1.16 +Pillow==7.1.2 diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/benchmark_tf.sh b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/benchmark_tf.sh new file mode 100644 index 0000000000000000000000000000000000000000..e269edf1c0a43a2d1bef3a9495aa66a3c04f8eeb --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/benchmark_tf.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#set -x +cur_dir=`pwd` +benchmark_dir=$cur_dir/../Benchmark/out +om_name=$cur_dir/../inceptionv2_tf_1batch.om +batchsize=1 +model_name=inceptionv2 +output_dir='results' +rm -rf $cur_dir/$output_dir/* + +#start offline inference +$benchmark_dir/benchmark --om $om_name --dataDir $cur_dir/input_bins/ --modelType $model_name --outDir $cur_dir/$output_dir --batchSize $batchsize --imgType bin --useDvpp 0 + +#post process +python3 $cur_dir/imagenet_accuarcy_cal.py --infer_result $cur_dir/$output_dir/$model_name --label $cur_dir/ILSVRC2012val-label-index.txt --offset 1 diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/imagenet_accuarcy_cal.py b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/imagenet_accuarcy_cal.py new file mode 100644 index 0000000000000000000000000000000000000000..38f0d91170e48644f8bafffa9e0a098881c52b48 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/imagenet_accuarcy_cal.py @@ -0,0 +1,75 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +import time +import argparse + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--infer_result", type=str, default="../../result_Files") + parser.add_argument("--label", type=str, default="../data/input_50000.csv") + parser.add_argument("--output_index", type=int, default=0) + parser.add_argument("--offset", type=int, default=0) + parser.add_argument("--dtype", type=str, default='float32') #datatype of bin files + args = parser.parse_args() + + image_cnt = 0 + top1_cnt = 0 + top5_cnt = 0 + ground_truth={} + if args.label.endswith(".csv"): + with open(args.label, 'r') as cs: + rs_list = cs.readlines() + for line in rs_list: + image_name = line.split(',')[0].split('.JPEG')[0] + label = int(line.split(',')[1]) + label += args.offset + ground_truth[image_name]=label + elif args.label.endswith(".txt"): + with open(args.label, 'r') as cs: + rs_list = cs.readlines() + for line in rs_list: + image_name = line.split(' ')[0].split('.JPEG')[0] + label = int(line.split(' ')[1].replace("\n","")) + label += args.offset + ground_truth[image_name]=label + + for i in sorted(ground_truth): + try: + image_name = i + label = ground_truth[i] + #查看输出的文件 + if os.path.exists(os.path.join(args.infer_result,'davinci_{}_output{}.bin'.format(image_name,args.output_index))): + bin_path = os.path.join(args.infer_result,'davinci_{}_output{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + elif os.path.exists(os.path.join(args.infer_result,'davinci_{}.JPEG_output{}.bin'.format(image_name, args.output_index))): + bin_path = os.path.join(args.infer_result,'davinci_{}.JPEG_output{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + elif os.path.exists(os.path.join(args.infer_result,'{}_output_{}.bin'.format(image_name,args.output_index))): + bin_path = os.path.join(args.infer_result,'{}_output_{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + else: + continue + top1=np.argmax(pred) + if label == top1: + top1_cnt += 1 + if label in np.argsort(-pred)[0:5]: + top5_cnt += 1 + image_cnt+=1 + print("{}, gt label:{: >4}, predict results:{}".format(image_name,label,str(np.argsort(-pred)[0:5]))) + except Exception as e: + print("Can't find " + bin_path) + print('imag_count %d, top1_accuracy %.3f top5_accuracy %.3f'%(image_cnt,top1_cnt/image_cnt,top5_cnt/image_cnt)) \ No newline at end of file diff --git a/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/inception_preprocessing.py b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/inception_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..0a2b3d578a4f401a4b5ebdd222cb2488d2ae3c66 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Inceptionv2_for_ACL/scripts/inception_preprocessing.py @@ -0,0 +1,405 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Provides utilities to preprocess images for the Inception networks.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow.compat.v1 as tf +import os +import sys +import shutil +import numpy as np +from PIL import Image +from tensorflow.python.ops import control_flow_ops + + +def apply_with_random_selector(x, func, num_cases): + """Computes func(x, sel), with sel sampled from [0...num_cases-1]. + + Args: + x: input Tensor. + func: Python function to apply. + num_cases: Python int32, number of cases to sample sel from. + + Returns: + The result of func(x, sel), where func receives the value of the + selector as a python integer, but sel is sampled dynamically. + """ + sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32) + # Pass the real x only to one of the func calls. + return control_flow_ops.merge([ + func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case) + for case in range(num_cases)])[0] + + +def distort_color(image, color_ordering=0, fast_mode=True, scope=None): + """Distort the color of a Tensor image. + + Each color distortion is non-commutative and thus ordering of the color ops + matters. Ideally we would randomly permute the ordering of the color ops. + Rather then adding that level of complication, we select a distinct ordering + of color ops for each preprocessing thread. + + Args: + image: 3-D Tensor containing single image in [0, 1]. + color_ordering: Python int, a type of distortion (valid values: 0-3). + fast_mode: Avoids slower ops (random_hue and random_contrast) + scope: Optional scope for name_scope. + Returns: + 3-D Tensor color-distorted image on range [0, 1] + Raises: + ValueError: if color_ordering not in [0, 3] + """ + with tf.name_scope(scope, 'distort_color', [image]): + if fast_mode: + if color_ordering == 0: + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + else: + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_brightness(image, max_delta=32. / 255.) + else: + if color_ordering == 0: + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_hue(image, max_delta=0.2) + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + elif color_ordering == 1: + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + image = tf.image.random_hue(image, max_delta=0.2) + elif color_ordering == 2: + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + image = tf.image.random_hue(image, max_delta=0.2) + image = tf.image.random_brightness(image, max_delta=32. / 255.) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + elif color_ordering == 3: + image = tf.image.random_hue(image, max_delta=0.2) + image = tf.image.random_saturation(image, lower=0.5, upper=1.5) + image = tf.image.random_contrast(image, lower=0.5, upper=1.5) + image = tf.image.random_brightness(image, max_delta=32. / 255.) + else: + raise ValueError('color_ordering must be in [0, 3]') + + # The random_* ops do not necessarily clamp. + return tf.clip_by_value(image, 0.0, 1.0) + + +def distorted_bounding_box_crop(image, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(0.75, 1.33), + area_range=(0.05, 1.0), + max_attempts=100, + scope=None): + """Generates cropped_image using a one of the bboxes randomly distorted. + + See `tf.image.sample_distorted_bounding_box` for more documentation. + + Args: + image: 3-D Tensor of image (it will be converted to floats in [0, 1]). + bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords] + where each coordinate is [0, 1) and the coordinates are arranged + as [ymin, xmin, ymax, xmax]. If num_boxes is 0 then it would use the whole + image. + min_object_covered: An optional `float`. Defaults to `0.1`. The cropped + area of the image must contain at least this fraction of any bounding box + supplied. + aspect_ratio_range: An optional list of `floats`. The cropped area of the + image must have an aspect ratio = width / height within this range. + area_range: An optional list of `floats`. The cropped area of the image + must contain a fraction of the supplied image within in this range. + max_attempts: An optional `int`. Number of attempts at generating a cropped + region of the image of the specified constraints. After `max_attempts` + failures, return the entire image. + scope: Optional scope for name_scope. + Returns: + A tuple, a 3-D Tensor cropped_image and the distorted bbox + """ + with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]): + # Each bounding box has shape [1, num_boxes, box coords] and + # the coordinates are ordered [ymin, xmin, ymax, xmax]. + + # A large fraction of image datasets contain a human-annotated bounding + # box delineating the region of the image containing the object of interest. + # We choose to create a new bounding box for the object which is a randomly + # distorted version of the human-annotated bounding box that obeys an + # allowed range of aspect ratios, sizes and overlap with the human-annotated + # bounding box. If no box is supplied, then we assume the bounding box is + # the entire image. + sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box( + tf.shape(image), + bounding_boxes=bbox, + min_object_covered=min_object_covered, + aspect_ratio_range=aspect_ratio_range, + area_range=area_range, + max_attempts=max_attempts, + use_image_if_no_bounding_boxes=True) + bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box + + # Crop the image to the specified bounding box. + cropped_image = tf.slice(image, bbox_begin, bbox_size) + return cropped_image, distort_bbox + + +def preprocess_for_train(image, + height, + width, + bbox, + fast_mode=True, + scope=None, + add_image_summaries=True, + random_crop=True, + use_grayscale=False): + """Distort one image for training a network. + + Distorting images provides a useful technique for augmenting the data + set during training in order to make the network invariant to aspects + of the image that do not effect the label. + + Additionally it would create image_summaries to display the different + transformations applied to the image. + + Args: + image: 3-D Tensor of image. If dtype is tf.float32 then the range should be + [0, 1], otherwise it would converted to tf.float32 assuming that the range + is [0, MAX], where MAX is largest positive representable number for + int(8/16/32) data type (see `tf.image.convert_image_dtype` for details). + height: integer + width: integer + bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords] + where each coordinate is [0, 1) and the coordinates are arranged + as [ymin, xmin, ymax, xmax]. + fast_mode: Optional boolean, if True avoids slower transformations (i.e. + bi-cubic resizing, random_hue or random_contrast). + scope: Optional scope for name_scope. + add_image_summaries: Enable image summaries. + random_crop: Enable random cropping of images during preprocessing for + training. + use_grayscale: Whether to convert the image from RGB to grayscale. + Returns: + 3-D float Tensor of distorted image used for training with range [-1, 1]. + """ + with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]): + if bbox is None: + bbox = tf.constant([0.0, 0.0, 1.0, 1.0], + dtype=tf.float32, + shape=[1, 1, 4]) + if image.dtype != tf.float32: + image = tf.image.convert_image_dtype(image, dtype=tf.float32) + # Each bounding box has shape [1, num_boxes, box coords] and + # the coordinates are ordered [ymin, xmin, ymax, xmax]. + image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), + bbox) + if add_image_summaries: + tf.summary.image('image_with_bounding_boxes', image_with_box) + + if not random_crop: + distorted_image = image + else: + distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox) + # Restore the shape since the dynamic slice based upon the bbox_size loses + # the third dimension. + distorted_image.set_shape([None, None, 3]) + image_with_distorted_box = tf.image.draw_bounding_boxes( + tf.expand_dims(image, 0), distorted_bbox) + if add_image_summaries: + tf.summary.image('images_with_distorted_bounding_box', + image_with_distorted_box) + + # This resizing operation may distort the images because the aspect + # ratio is not respected. We select a resize method in a round robin + # fashion based on the thread number. + # Note that ResizeMethod contains 4 enumerated resizing methods. + + # We select only 1 case for fast_mode bilinear. + num_resize_cases = 1 if fast_mode else 4 + distorted_image = apply_with_random_selector( + distorted_image, + lambda x, method: tf.image.resize_images(x, [height, width], method), + num_cases=num_resize_cases) + + if add_image_summaries: + tf.summary.image(('cropped_' if random_crop else '') + 'resized_image', + tf.expand_dims(distorted_image, 0)) + + # Randomly flip the image horizontally. + distorted_image = tf.image.random_flip_left_right(distorted_image) + + # Randomly distort the colors. There are 1 or 4 ways to do it. + num_distort_cases = 1 if fast_mode else 4 + distorted_image = apply_with_random_selector( + distorted_image, + lambda x, ordering: distort_color(x, ordering, fast_mode), + num_cases=num_distort_cases) + + if use_grayscale: + distorted_image = tf.image.rgb_to_grayscale(distorted_image) + + if add_image_summaries: + tf.summary.image('final_distorted_image', + tf.expand_dims(distorted_image, 0)) + distorted_image = tf.subtract(distorted_image, 0.5) + distorted_image = tf.multiply(distorted_image, 2.0) + return distorted_image + + +def preprocess_for_eval(image, + height, + width, + central_fraction=0.875, + scope=None, + central_crop=True, + use_grayscale=False): + """Prepare one image for evaluation. + + If height and width are specified it would output an image with that size by + applying resize_bilinear. + + If central_fraction is specified it would crop the central fraction of the + input image. + + Args: + image: 3-D Tensor of image. If dtype is tf.float32 then the range should be + [0, 1], otherwise it would converted to tf.float32 assuming that the range + is [0, MAX], where MAX is largest positive representable number for + int(8/16/32) data type (see `tf.image.convert_image_dtype` for details). + height: integer + width: integer + central_fraction: Optional Float, fraction of the image to crop. + scope: Optional scope for name_scope. + central_crop: Enable central cropping of images during preprocessing for + evaluation. + use_grayscale: Whether to convert the image from RGB to grayscale. + Returns: + 3-D float Tensor of prepared image. + """ + with tf.name_scope(scope, 'eval_image', [image, height, width]): + if image.dtype != tf.float32: + image = tf.image.convert_image_dtype(image, dtype=tf.float32) + if use_grayscale: + image = tf.image.rgb_to_grayscale(image) + # Crop the central region of the image with an area containing 87.5% of + # the original image. + if central_crop and central_fraction: + image = tf.image.central_crop(image, central_fraction=central_fraction) + + if height and width: + # Resize the image to the specified height and width. + image = tf.expand_dims(image, 0) + image = tf.image.resize_bilinear(image, [height, width], + align_corners=False) + image = tf.squeeze(image, [0]) + image = tf.subtract(image, 0.5) + image = tf.multiply(image, 2.0) + return image + + +def preprocess_image(image, + height, + width, + is_training=False, + bbox=None, + fast_mode=True, + add_image_summaries=True, + crop_image=True, + use_grayscale=False): + """Pre-process one image for training or evaluation. + + Args: + image: 3-D Tensor [height, width, channels] with the image. If dtype is + tf.float32 then the range should be [0, 1], otherwise it would converted + to tf.float32 assuming that the range is [0, MAX], where MAX is largest + positive representable number for int(8/16/32) data type (see + `tf.image.convert_image_dtype` for details). + height: integer, image expected height. + width: integer, image expected width. + is_training: Boolean. If true it would transform an image for train, + otherwise it would transform it for evaluation. + bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords] + where each coordinate is [0, 1) and the coordinates are arranged as + [ymin, xmin, ymax, xmax]. + fast_mode: Optional boolean, if True avoids slower transformations. + add_image_summaries: Enable image summaries. + crop_image: Whether to enable cropping of images during preprocessing for + both training and evaluation. + use_grayscale: Whether to convert the image from RGB to grayscale. + + Returns: + 3-D float Tensor containing an appropriately scaled image + + Raises: + ValueError: if user does not provide bounding box + """ + if is_training: + return preprocess_for_train( + image, + height, + width, + bbox, + fast_mode, + add_image_summaries=add_image_summaries, + random_crop=crop_image, + use_grayscale=use_grayscale) + else: + return preprocess_for_eval( + image, + height, + width, + central_crop=crop_image, + use_grayscale=use_grayscale) + +def convert_RGB(img_name): + image = Image.open(img_name).convert('RGB') + return image + +def preprocess(src_path, save_path): + in_files = os.listdir(src_path) + in_files.sort() + resize_shape = [224, 224, 3] + sqz_mean = np.array([127.5, 127.5, 127.5], np.float32) + img_std = np.array([[0.5*255, 0.5*255, 0.5*255]], np.float32) + if os.path.isdir(save_path): + shutil.rmtree(save_path) + os.makedirs(save_path) + for file in in_files: + with tf.Session().as_default(): + if not os.path.isdir(file): + print(file) + img = convert_RGB(os.path.join(src_path, file)) + img = np.array(img) + img = tf.convert_to_tensor(img) + img = preprocess_image(img, + 224, + 224, + is_training=False, + use_grayscale=False) + img = img.eval() + img = img * img_std + img = img + sqz_mean + img = img.astype(np.uint8, copy=False) + img.tofile(os.path.join(save_path, file.split('.')[0]+".bin")) + tf.reset_default_graph() + +if __name__ == "__main__": + if len(sys.argv) < 3: + raise Exception("usage: python3 xxx.py [src_path] [save_path]") + + src_path = sys.argv[1] + save_path = sys.argv[2] + preprocess(src_path, save_path) diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/CMakeLists.txt b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..17dc38b8a933d0b8fd84e5579426f7933ca45c99 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/CMakeLists.txt @@ -0,0 +1,41 @@ +# Copyright (c) Huawei Technologies Co., Ltd. 2019. All rights reserved. +# CMake lowest version requirement +cmake_minimum_required(VERSION 3.5.1) +# project information +project(ascendcl) +# Compile options +add_compile_options(-std=c++11) +# Specify target generation path +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "../outputs") +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "../outputs") +set(CMAKE_INSTALL_PREFIX "../../../") +set(CMAKE_OUTPUT_DIR "out") +set(CMAKE_CXX_FLAGS_RELEASE "-fPIC -O2 -g -Wall") + +ADD_DEFINITIONS("-DENABLE_DVPP_INTERFACE -D_GLIBCXX_USE_CXX11_ABI=0") + +# Header path +include_directories( +inc +#/usr/include/gflags +$ENV{install_path}/acllib/include +$ENV{install_path}/driver/kernel/libc_sec/include +/usr/include +) + +# add host lib path +link_directories($ENV{install_path}/acllib/lib64/stub) +#link_directories(/usr/local/Ascend/driver/lib64) +#link_directories(/usr/local/Ascend/atc/lib64) +#link_directories(/usr/local/lib) +link_directories(../thirdpart_lib) + +# 设置需要编译的源文件 +add_executable(benchmark main.cpp util.cpp post_process.cpp infer_engine.cpp) + +# 设置共享库 RC为待扩展的offline模型 +#target_link_libraries(benchmark acl_dvpp ascendcl pthread protobuf cryptopp) +target_link_libraries(benchmark acl_dvpp ascendcl pthread) + +install(TARGETS benchmark DESTINATION ${CMAKE_OUTPUT_DIR}) + diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/defines.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/defines.h new file mode 100644 index 0000000000000000000000000000000000000000..f0be3dcb485269718125445537ce3616c3078d34 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/defines.h @@ -0,0 +1,48 @@ +/* Generated from defines.h.in during build configuration using CMake. */ + +// Note: This header file is only used internally. It is not part of public interface! +// Any cmakedefine is defined using the -D flag instead when Bazel is used. +// For Bazel, this file is thus not used to avoid a private file in $(GENDIR). + +#ifndef GFLAGS_DEFINES_H_ +#define GFLAGS_DEFINES_H_ + + +// Define if you build this library for a MS Windows OS. +/* #undef OS_WINDOWS */ + +// Define if you have the header file. +#define HAVE_STDINT_H + +// Define if you have the header file. +#define HAVE_SYS_TYPES_H + +// Define if you have the header file. +#define HAVE_INTTYPES_H + +// Define if you have the header file. +#define HAVE_SYS_STAT_H + +// Define if you have the header file. +#define HAVE_UNISTD_H + +// Define if you have the header file. +#define HAVE_FNMATCH_H + +// Define if you have the header file (Windows 2000/XP). +/* #undef HAVE_SHLWAPI_H */ + +// Define if you have the strtoll function. +#define HAVE_STRTOLL + +// Define if you have the strtoq function. +/* #undef HAVE_STRTOQ */ + +// Define if you have the header file. +#define HAVE_PTHREAD + +// Define if your pthread library defines the type pthread_rwlock_t +#define HAVE_RWLOCK + + +#endif // GFLAGS_DEFINES_H_ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags.h new file mode 100644 index 0000000000000000000000000000000000000000..4f3168a03d878a16ac0e05d1d240396b9d422c63 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags.h @@ -0,0 +1,624 @@ +// Copyright (c) 2006, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// --- +// Revamped and reorganized by Craig Silverstein +// +// This is the file that should be included by any file which declares +// or defines a command line flag or wants to parse command line flags +// or print a program usage message (which will include information about +// flags). Executive summary, in the form of an example foo.cc file: +// +// #include "foo.h" // foo.h has a line "DECLARE_int32(start);" +// #include "validators.h" // hypothetical file defining ValidateIsFile() +// +// DEFINE_int32(end, 1000, "The last record to read"); +// +// DEFINE_string(filename, "my_file.txt", "The file to read"); +// // Crash if the specified file does not exist. +// static bool dummy = RegisterFlagValidator(&FLAGS_filename, +// &ValidateIsFile); +// +// DECLARE_bool(verbose); // some other file has a DEFINE_bool(verbose, ...) +// +// void MyFunc() { +// if (FLAGS_verbose) printf("Records %d-%d\n", FLAGS_start, FLAGS_end); +// } +// +// Then, at the command-line: +// ./foo --noverbose --start=5 --end=100 +// +// For more details, see +// doc/gflags.html +// +// --- A note about thread-safety: +// +// We describe many functions in this routine as being thread-hostile, +// thread-compatible, or thread-safe. Here are the meanings we use: +// +// thread-safe: it is safe for multiple threads to call this routine +// (or, when referring to a class, methods of this class) +// concurrently. +// thread-hostile: it is not safe for multiple threads to call this +// routine (or methods of this class) concurrently. In gflags, +// most thread-hostile routines are intended to be called early in, +// or even before, main() -- that is, before threads are spawned. +// thread-compatible: it is safe for multiple threads to read from +// this variable (when applied to variables), or to call const +// methods of this class (when applied to classes), as long as no +// other thread is writing to the variable or calling non-const +// methods of this class. + +#ifndef GFLAGS_GFLAGS_H_ +#define GFLAGS_GFLAGS_H_ + +#include +#include + +#include "gflags/gflags_declare.h" // IWYU pragma: export + + +// We always want to export variables defined in user code +#ifndef GFLAGS_DLL_DEFINE_FLAG +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DEFINE_FLAG __declspec(dllexport) +# else +# define GFLAGS_DLL_DEFINE_FLAG +# endif +#endif + + +namespace GFLAGS_NAMESPACE { + + +// -------------------------------------------------------------------- +// To actually define a flag in a file, use DEFINE_bool, +// DEFINE_string, etc. at the bottom of this file. You may also find +// it useful to register a validator with the flag. This ensures that +// when the flag is parsed from the commandline, or is later set via +// SetCommandLineOption, we call the validation function. It is _not_ +// called when you assign the value to the flag directly using the = operator. +// +// The validation function should return true if the flag value is valid, and +// false otherwise. If the function returns false for the new setting of the +// flag, the flag will retain its current value. If it returns false for the +// default value, ParseCommandLineFlags() will die. +// +// This function is safe to call at global construct time (as in the +// example below). +// +// Example use: +// static bool ValidatePort(const char* flagname, int32 value) { +// if (value > 0 && value < 32768) // value is ok +// return true; +// printf("Invalid value for --%s: %d\n", flagname, (int)value); +// return false; +// } +// DEFINE_int32(port, 0, "What port to listen on"); +// static bool dummy = RegisterFlagValidator(&FLAGS_port, &ValidatePort); + +// Returns true if successfully registered, false if not (because the +// first argument doesn't point to a command-line flag, or because a +// validator is already registered for this flag). +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const bool* flag, bool (*validate_fn)(const char*, bool)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int32* flag, bool (*validate_fn)(const char*, int32)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint32* flag, bool (*validate_fn)(const char*, uint32)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const int64* flag, bool (*validate_fn)(const char*, int64)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const uint64* flag, bool (*validate_fn)(const char*, uint64)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const double* flag, bool (*validate_fn)(const char*, double)); +extern GFLAGS_DLL_DECL bool RegisterFlagValidator(const std::string* flag, bool (*validate_fn)(const char*, const std::string&)); + +// Convenience macro for the registration of a flag validator +#define DEFINE_validator(name, validator) \ + static const bool name##_validator_registered = \ + GFLAGS_NAMESPACE::RegisterFlagValidator(&FLAGS_##name, validator) + + +// -------------------------------------------------------------------- +// These methods are the best way to get access to info about the +// list of commandline flags. Note that these routines are pretty slow. +// GetAllFlags: mostly-complete info about the list, sorted by file. +// ShowUsageWithFlags: pretty-prints the list to stdout (what --help does) +// ShowUsageWithFlagsRestrict: limit to filenames with restrict as a substr +// +// In addition to accessing flags, you can also access argv[0] (the program +// name) and argv (the entire commandline), which we sock away a copy of. +// These variables are static, so you should only set them once. +// +// No need to export this data only structure from DLL, avoiding VS warning 4251. +struct CommandLineFlagInfo { + std::string name; // the name of the flag + std::string type; // the type of the flag: int32, etc + std::string description; // the "help text" associated with the flag + std::string current_value; // the current value, as a string + std::string default_value; // the default value, as a string + std::string filename; // 'cleaned' version of filename holding the flag + bool has_validator_fn; // true if RegisterFlagValidator called on this flag + bool is_default; // true if the flag has the default value and + // has not been set explicitly from the cmdline + // or via SetCommandLineOption + const void* flag_ptr; // pointer to the flag's current value (i.e. FLAGS_foo) +}; + +// Using this inside of a validator is a recipe for a deadlock. +// TODO(user) Fix locking when validators are running, to make it safe to +// call validators during ParseAllFlags. +// Also make sure then to uncomment the corresponding unit test in +// gflags_unittest.sh +extern GFLAGS_DLL_DECL void GetAllFlags(std::vector* OUTPUT); +// These two are actually defined in gflags_reporting.cc. +extern GFLAGS_DLL_DECL void ShowUsageWithFlags(const char *argv0); // what --help does +extern GFLAGS_DLL_DECL void ShowUsageWithFlagsRestrict(const char *argv0, const char *restrict); + +// Create a descriptive string for a flag. +// Goes to some trouble to make pretty line breaks. +extern GFLAGS_DLL_DECL std::string DescribeOneFlag(const CommandLineFlagInfo& flag); + +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetArgv(int argc, const char** argv); + +// The following functions are thread-safe as long as SetArgv() is +// only called before any threads start. +extern GFLAGS_DLL_DECL const std::vector& GetArgvs(); +extern GFLAGS_DLL_DECL const char* GetArgv(); // all of argv as a string +extern GFLAGS_DLL_DECL const char* GetArgv0(); // only argv0 +extern GFLAGS_DLL_DECL uint32 GetArgvSum(); // simple checksum of argv +extern GFLAGS_DLL_DECL const char* ProgramInvocationName(); // argv0, or "UNKNOWN" if not set +extern GFLAGS_DLL_DECL const char* ProgramInvocationShortName(); // basename(argv0) + +// ProgramUsage() is thread-safe as long as SetUsageMessage() is only +// called before any threads start. +extern GFLAGS_DLL_DECL const char* ProgramUsage(); // string set by SetUsageMessage() + +// VersionString() is thread-safe as long as SetVersionString() is only +// called before any threads start. +extern GFLAGS_DLL_DECL const char* VersionString(); // string set by SetVersionString() + + + +// -------------------------------------------------------------------- +// Normally you access commandline flags by just saying "if (FLAGS_foo)" +// or whatever, and set them by calling "FLAGS_foo = bar" (or, more +// commonly, via the DEFINE_foo macro). But if you need a bit more +// control, we have programmatic ways to get/set the flags as well. +// These programmatic ways to access flags are thread-safe, but direct +// access is only thread-compatible. + +// Return true iff the flagname was found. +// OUTPUT is set to the flag's value, or unchanged if we return false. +extern GFLAGS_DLL_DECL bool GetCommandLineOption(const char* name, std::string* OUTPUT); + +// Return true iff the flagname was found. OUTPUT is set to the flag's +// CommandLineFlagInfo or unchanged if we return false. +extern GFLAGS_DLL_DECL bool GetCommandLineFlagInfo(const char* name, CommandLineFlagInfo* OUTPUT); + +// Return the CommandLineFlagInfo of the flagname. exit() if name not found. +// Example usage, to check if a flag's value is currently the default value: +// if (GetCommandLineFlagInfoOrDie("foo").is_default) ... +extern GFLAGS_DLL_DECL CommandLineFlagInfo GetCommandLineFlagInfoOrDie(const char* name); + +enum GFLAGS_DLL_DECL FlagSettingMode { + // update the flag's value (can call this multiple times). + SET_FLAGS_VALUE, + // update the flag's value, but *only if* it has not yet been updated + // with SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef". + SET_FLAG_IF_DEFAULT, + // set the flag's default value to this. If the flag has not yet updated + // yet (via SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef") + // change the flag's current value to the new default value as well. + SET_FLAGS_DEFAULT +}; + +// Set a particular flag ("command line option"). Returns a string +// describing the new value that the option has been set to. The +// return value API is not well-specified, so basically just depend on +// it to be empty if the setting failed for some reason -- the name is +// not a valid flag name, or the value is not a valid value -- and +// non-empty else. + +// SetCommandLineOption uses set_mode == SET_FLAGS_VALUE (the common case) +extern GFLAGS_DLL_DECL std::string SetCommandLineOption (const char* name, const char* value); +extern GFLAGS_DLL_DECL std::string SetCommandLineOptionWithMode(const char* name, const char* value, FlagSettingMode set_mode); + + +// -------------------------------------------------------------------- +// Saves the states (value, default value, whether the user has set +// the flag, registered validators, etc) of all flags, and restores +// them when the FlagSaver is destroyed. This is very useful in +// tests, say, when you want to let your tests change the flags, but +// make sure that they get reverted to the original states when your +// test is complete. +// +// Example usage: +// void TestFoo() { +// FlagSaver s1; +// FLAG_foo = false; +// FLAG_bar = "some value"; +// +// // test happens here. You can return at any time +// // without worrying about restoring the FLAG values. +// } +// +// Note: This class is marked with GFLAGS_ATTRIBUTE_UNUSED because all +// the work is done in the constructor and destructor, so in the standard +// usage example above, the compiler would complain that it's an +// unused variable. +// +// This class is thread-safe. However, its destructor writes to +// exactly the set of flags that have changed value during its +// lifetime, so concurrent _direct_ access to those flags +// (i.e. FLAGS_foo instead of {Get,Set}CommandLineOption()) is unsafe. + +class GFLAGS_DLL_DECL FlagSaver { + public: + FlagSaver(); + ~FlagSaver(); + + private: + class FlagSaverImpl* impl_; // we use pimpl here to keep API steady + + FlagSaver(const FlagSaver&); // no copying! + void operator=(const FlagSaver&); +}__attribute((unused)); + +// -------------------------------------------------------------------- +// Some deprecated or hopefully-soon-to-be-deprecated functions. + +// This is often used for logging. TODO(csilvers): figure out a better way +extern GFLAGS_DLL_DECL std::string CommandlineFlagsIntoString(); +// Usually where this is used, a FlagSaver should be used instead. +extern GFLAGS_DLL_DECL +bool ReadFlagsFromString(const std::string& flagfilecontents, + const char* prog_name, + bool errors_are_fatal); // uses SET_FLAGS_VALUE + +// These let you manually implement --flagfile functionality. +// DEPRECATED. +extern GFLAGS_DLL_DECL bool AppendFlagsIntoFile(const std::string& filename, const char* prog_name); +extern GFLAGS_DLL_DECL bool ReadFromFlagsFile(const std::string& filename, const char* prog_name, bool errors_are_fatal); // uses SET_FLAGS_VALUE + + +// -------------------------------------------------------------------- +// Useful routines for initializing flags from the environment. +// In each case, if 'varname' does not exist in the environment +// return defval. If 'varname' does exist but is not valid +// (e.g., not a number for an int32 flag), abort with an error. +// Otherwise, return the value. NOTE: for booleans, for true use +// 't' or 'T' or 'true' or '1', for false 'f' or 'F' or 'false' or '0'. + +extern GFLAGS_DLL_DECL bool BoolFromEnv(const char *varname, bool defval); +extern GFLAGS_DLL_DECL int32 Int32FromEnv(const char *varname, int32 defval); +extern GFLAGS_DLL_DECL uint32 Uint32FromEnv(const char *varname, uint32 defval); +extern GFLAGS_DLL_DECL int64 Int64FromEnv(const char *varname, int64 defval); +extern GFLAGS_DLL_DECL uint64 Uint64FromEnv(const char *varname, uint64 defval); +extern GFLAGS_DLL_DECL double DoubleFromEnv(const char *varname, double defval); +extern GFLAGS_DLL_DECL const char *StringFromEnv(const char *varname, const char *defval); + + +// -------------------------------------------------------------------- +// The next two functions parse gflags from main(): + +// Set the "usage" message for this program. For example: +// string usage("This program does nothing. Sample usage:\n"); +// usage += argv[0] + " "; +// SetUsageMessage(usage); +// Do not include commandline flags in the usage: we do that for you! +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetUsageMessage(const std::string& usage); + +// Sets the version string, which is emitted with --version. +// For instance: SetVersionString("1.3"); +// Thread-hostile; meant to be called before any threads are spawned. +extern GFLAGS_DLL_DECL void SetVersionString(const std::string& version); + + +// Looks for flags in argv and parses them. Rearranges argv to put +// flags first, or removes them entirely if remove_flags is true. +// If a flag is defined more than once in the command line or flag +// file, the last definition is used. Returns the index (into argv) +// of the first non-flag argument. +// See top-of-file for more details on this function. +#ifndef SWIG // In swig, use ParseCommandLineFlagsScript() instead. +extern GFLAGS_DLL_DECL uint32 ParseCommandLineFlags(int *argc, char*** argv, bool remove_flags); +#endif + + +// Calls to ParseCommandLineNonHelpFlags and then to +// HandleCommandLineHelpFlags can be used instead of a call to +// ParseCommandLineFlags during initialization, in order to allow for +// changing default values for some FLAGS (via +// e.g. SetCommandLineOptionWithMode calls) between the time of +// command line parsing and the time of dumping help information for +// the flags as a result of command line parsing. If a flag is +// defined more than once in the command line or flag file, the last +// definition is used. Returns the index (into argv) of the first +// non-flag argument. (If remove_flags is true, will always return 1.) +extern GFLAGS_DLL_DECL uint32 ParseCommandLineNonHelpFlags(int *argc, char*** argv, bool remove_flags); + +// This is actually defined in gflags_reporting.cc. +// This function is misnamed (it also handles --version, etc.), but +// it's too late to change that now. :-( +extern GFLAGS_DLL_DECL void HandleCommandLineHelpFlags(); // in gflags_reporting.cc + +// Allow command line reparsing. Disables the error normally +// generated when an unknown flag is found, since it may be found in a +// later parse. Thread-hostile; meant to be called before any threads +// are spawned. +extern GFLAGS_DLL_DECL void AllowCommandLineReparsing(); + +// Reparse the flags that have not yet been recognized. Only flags +// registered since the last parse will be recognized. Any flag value +// must be provided as part of the argument using "=", not as a +// separate command line argument that follows the flag argument. +// Intended for handling flags from dynamically loaded libraries, +// since their flags are not registered until they are loaded. +extern GFLAGS_DLL_DECL void ReparseCommandLineNonHelpFlags(); + +// Clean up memory allocated by flags. This is only needed to reduce +// the quantity of "potentially leaked" reports emitted by memory +// debugging tools such as valgrind. It is not required for normal +// operation, or for the google perftools heap-checker. It must only +// be called when the process is about to exit, and all threads that +// might access flags are quiescent. Referencing flags after this is +// called will have unexpected consequences. This is not safe to run +// when multiple threads might be running: the function is +// thread-hostile. +extern GFLAGS_DLL_DECL void ShutDownCommandLineFlags(); + + +// -------------------------------------------------------------------- +// Now come the command line flag declaration/definition macros that +// will actually be used. They're kind of hairy. A major reason +// for this is initialization: we want people to be able to access +// variables in global constructors and have that not crash, even if +// their global constructor runs before the global constructor here. +// (Obviously, we can't guarantee the flags will have the correct +// default value in that case, but at least accessing them is safe.) +// The only way to do that is have flags point to a static buffer. +// So we make one, using a union to ensure proper alignment, and +// then use placement-new to actually set up the flag with the +// correct default value. In the same vein, we have to worry about +// flag access in global destructors, so FlagRegisterer has to be +// careful never to destroy the flag-values it constructs. +// +// Note that when we define a flag variable FLAGS_, we also +// preemptively define a junk variable, FLAGS_no. This is to +// cause a link-time error if someone tries to define 2 flags with +// names like "logging" and "nologging". We do this because a bool +// flag FLAG can be set from the command line to true with a "-FLAG" +// argument, and to false with a "-noFLAG" argument, and so this can +// potentially avert confusion. +// +// We also put flags into their own namespace. It is purposefully +// named in an opaque way that people should have trouble typing +// directly. The idea is that DEFINE puts the flag in the weird +// namespace, and DECLARE imports the flag from there into the current +// namespace. The net result is to force people to use DECLARE to get +// access to a flag, rather than saying "extern GFLAGS_DLL_DECL bool FLAGS_whatever;" +// or some such instead. We want this so we can put extra +// functionality (like sanity-checking) in DECLARE if we want, and +// make sure it is picked up everywhere. +// +// We also put the type of the variable in the namespace, so that +// people can't DECLARE_int32 something that they DEFINE_bool'd +// elsewhere. + +class GFLAGS_DLL_DECL FlagRegisterer { + public: + // We instantiate this template ctor for all supported types, + // so it is possible to place implementation of the FlagRegisterer ctor in + // .cc file. + // Calling this constructor with unsupported type will produce linker error. + template + FlagRegisterer(const char* name, + const char* help, const char* filename, + FlagType* current_storage, FlagType* defvalue_storage); +}; + +// Force compiler to not generate code for the given template specialization. +#if defined(_MSC_VER) && _MSC_VER < 1800 // Visual Studio 2013 version 12.0 + #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type) +#else + #define GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(type) \ + extern template GFLAGS_DLL_DECL FlagRegisterer::FlagRegisterer( \ + const char* name, const char* help, const char* filename, \ + type* current_storage, type* defvalue_storage) +#endif + +// Do this for all supported flag types. +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(bool); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int32); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint32); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(int64); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(uint64); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(double); +GFLAGS_DECLARE_FLAG_REGISTERER_CTOR(std::string); + +#undef GFLAGS_DECLARE_FLAG_REGISTERER_CTOR + +// If your application #defines STRIP_FLAG_HELP to a non-zero value +// before #including this file, we remove the help message from the +// binary file. This can reduce the size of the resulting binary +// somewhat, and may also be useful for security reasons. + +extern GFLAGS_DLL_DECL const char kStrippedFlagHelp[]; + + +} // namespace GFLAGS_NAMESPACE + + +#ifndef SWIG // In swig, ignore the main flag declarations + +#if defined(STRIP_FLAG_HELP) && STRIP_FLAG_HELP > 0 +// Need this construct to avoid the 'defined but not used' warning. +#define MAYBE_STRIPPED_HELP(txt) \ + (false ? (txt) : GFLAGS_NAMESPACE::kStrippedFlagHelp) +#else +#define MAYBE_STRIPPED_HELP(txt) txt +#endif + +// Each command-line flag has two variables associated with it: one +// with the current value, and one with the default value. However, +// we have a third variable, which is where value is assigned; it's a +// constant. This guarantees that FLAG_##value is initialized at +// static initialization time (e.g. before program-start) rather than +// than global construction time (which is after program-start but +// before main), at least when 'value' is a compile-time constant. We +// use a small trick for the "default value" variable, and call it +// FLAGS_no. This serves the second purpose of assuring a +// compile error if someone tries to define a flag named no +// which is illegal (--foo and --nofoo both affect the "foo" flag). +#define DEFINE_VARIABLE(type, shorttype, name, value, help) \ + namespace fL##shorttype { \ + static const type FLAGS_nono##name = value; \ + /* We always want to export defined variables, dll or no */ \ + GFLAGS_DLL_DEFINE_FLAG type FLAGS_##name = FLAGS_nono##name; \ + static type FLAGS_no##name = FLAGS_nono##name; \ + static GFLAGS_NAMESPACE::FlagRegisterer o_##name( \ + #name, MAYBE_STRIPPED_HELP(help), __FILE__, \ + &FLAGS_##name, &FLAGS_no##name); \ + } \ + using fL##shorttype::FLAGS_##name + +// For DEFINE_bool, we want to do the extra check that the passed-in +// value is actually a bool, and not a string or something that can be +// coerced to a bool. These declarations (no definition needed!) will +// help us do that, and never evaluate From, which is important. +// We'll use 'sizeof(IsBool(val))' to distinguish. This code requires +// that the compiler have different sizes for bool & double. Since +// this is not guaranteed by the standard, we check it with a +// COMPILE_ASSERT. +namespace fLB { +struct CompileAssert {}; +typedef CompileAssert expected_sizeof_double_neq_sizeof_bool[ + (sizeof(double) != sizeof(bool)) ? 1 : -1]; +template double GFLAGS_DLL_DECL IsBoolFlag(const From& from); +GFLAGS_DLL_DECL bool IsBoolFlag(bool from); +} // namespace fLB + +// Here are the actual DEFINE_*-macros. The respective DECLARE_*-macros +// are in a separate include, gflags_declare.h, for reducing +// the physical transitive size for DECLARE use. +#define DEFINE_bool(name, val, txt) \ + namespace fLB { \ + typedef ::fLB::CompileAssert FLAG_##name##_value_is_not_a_bool[ \ + (sizeof(::fLB::IsBoolFlag(val)) != sizeof(double))? 1: -1]; \ + } \ + DEFINE_VARIABLE(bool, B, name, val, txt) + +#define DEFINE_int32(name, val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::int32, I, \ + name, val, txt) + +#define DEFINE_uint32(name,val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint32, U, \ + name, val, txt) + +#define DEFINE_int64(name, val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::int64, I64, \ + name, val, txt) + +#define DEFINE_uint64(name,val, txt) \ + DEFINE_VARIABLE(GFLAGS_NAMESPACE::uint64, U64, \ + name, val, txt) + +#define DEFINE_double(name, val, txt) \ + DEFINE_VARIABLE(double, D, name, val, txt) + +// Strings are trickier, because they're not a POD, so we can't +// construct them at static-initialization time (instead they get +// constructed at global-constructor time, which is much later). To +// try to avoid crashes in that case, we use a char buffer to store +// the string, which we can static-initialize, and then placement-new +// into it later. It's not perfect, but the best we can do. + +namespace fLS { + +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + const char *value) { + return new(stringspot) clstring(value); +} +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + const clstring &value) { + return new(stringspot) clstring(value); +} +inline clstring* dont_pass0toDEFINE_string(char *stringspot, + int value); + +// Auxiliary class used to explicitly call destructor of string objects +// allocated using placement new during static program deinitialization. +// The destructor MUST be an inline function such that the explicit +// destruction occurs in the same compilation unit as the placement new. +class StringFlagDestructor { + void *current_storage_; + void *defvalue_storage_; + +public: + + StringFlagDestructor(void *current, void *defvalue) + : current_storage_(current), defvalue_storage_(defvalue) {} + + ~StringFlagDestructor() { + reinterpret_cast(current_storage_ )->~clstring(); + reinterpret_cast(defvalue_storage_)->~clstring(); + } +}; + +} // namespace fLS + +// We need to define a var named FLAGS_no##name so people don't define +// --string and --nostring. And we need a temporary place to put val +// so we don't have to evaluate it twice. Two great needs that go +// great together! +// The weird 'using' + 'extern' inside the fLS namespace is to work around +// an unknown compiler bug/issue with the gcc 4.2.1 on SUSE 10. See +// http://code.google.com/p/google-gflags/issues/detail?id=20 +#define DEFINE_string(name, val, txt) \ + namespace fLS { \ + using ::fLS::clstring; \ + using ::fLS::StringFlagDestructor; \ + static union { void* align; char s[sizeof(clstring)]; } s_##name[2]; \ + clstring* const FLAGS_no##name = ::fLS:: \ + dont_pass0toDEFINE_string(s_##name[0].s, \ + val); \ + static GFLAGS_NAMESPACE::FlagRegisterer o_##name( \ + #name, MAYBE_STRIPPED_HELP(txt), __FILE__, \ + FLAGS_no##name, new (s_##name[1].s) clstring(*FLAGS_no##name)); \ + static StringFlagDestructor d_##name(s_##name[0].s, s_##name[1].s); \ + extern GFLAGS_DLL_DEFINE_FLAG clstring& FLAGS_##name; \ + using fLS::FLAGS_##name; \ + clstring& FLAGS_##name = *FLAGS_no##name; \ + } \ + using fLS::FLAGS_##name + +#endif // SWIG + +// Import gflags library symbols into alternative/deprecated namespace(s) +#include "gflags_gflags.h" +#endif // GFLAGS_GFLAGS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_completions.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_completions.h new file mode 100644 index 0000000000000000000000000000000000000000..15637eb3de853a79eccb9e7ba71771c607767a2e --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_completions.h @@ -0,0 +1,119 @@ +// Copyright (c) 2008, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +// +// --- + +// +// Implement helpful bash-style command line flag completions +// +// ** Functional API: +// HandleCommandLineCompletions() should be called early during +// program startup, but after command line flag code has been +// initialized, such as the beginning of HandleCommandLineHelpFlags(). +// It checks the value of the flag --tab_completion_word. If this +// flag is empty, nothing happens here. If it contains a string, +// however, then HandleCommandLineCompletions() will hijack the +// process, attempting to identify the intention behind this +// completion. Regardless of the outcome of this deduction, the +// process will be terminated, similar to --helpshort flag +// handling. +// +// ** Overview of Bash completions: +// Bash can be told to programatically determine completions for the +// current 'cursor word'. It does this by (in this case) invoking a +// command with some additional arguments identifying the command +// being executed, the word being completed, and the previous word +// (if any). Bash then expects a sequence of output lines to be +// printed to stdout. If these lines all contain a common prefix +// longer than the cursor word, bash will replace the cursor word +// with that common prefix, and display nothing. If there isn't such +// a common prefix, bash will display the lines in pages using 'more'. +// +// ** Strategy taken for command line completions: +// If we can deduce either the exact flag intended, or a common flag +// prefix, we'll output exactly that. Otherwise, if information +// must be displayed to the user, we'll take the opportunity to add +// some helpful information beyond just the flag name (specifically, +// we'll include the default flag value and as much of the flag's +// description as can fit on a single terminal line width, as specified +// by the flag --tab_completion_columns). Furthermore, we'll try to +// make bash order the output such that the most useful or relevent +// flags are the most likely to be shown at the top. +// +// ** Additional features: +// To assist in finding that one really useful flag, substring matching +// was implemented. Before pressing a to get completion for the +// current word, you can append one or more '?' to the flag to do +// substring matching. Here's the semantics: +// --foo Show me all flags with names prefixed by 'foo' +// --foo? Show me all flags with 'foo' somewhere in the name +// --foo?? Same as prior case, but also search in module +// definition path for 'foo' +// --foo??? Same as prior case, but also search in flag +// descriptions for 'foo' +// Finally, we'll trim the output to a relatively small number of +// flags to keep bash quiet about the verbosity of output. If one +// really wanted to see all possible matches, appending a '+' to the +// search word will force the exhaustive list of matches to be printed. +// +// ** How to have bash accept completions from a binary: +// Bash requires that it be informed about each command that programmatic +// completion should be enabled for. Example addition to a .bashrc +// file would be (your path to gflags_completions.sh file may differ): + +/* +$ complete -o bashdefault -o default -o nospace -C \ + '/home/build/eng/bash/bash_completions.sh --tab_completion_columns $COLUMNS' \ + time env binary_name another_binary [...] +*/ + +// This would allow the following to work: +// $ /path/to/binary_name --vmodule +// Or: +// $ ./bin/path/another_binary --gfs_u +// (etc) +// +// Sadly, it appears that bash gives no easy way to force this behavior for +// all commands. That's where the "time" in the above example comes in. +// If you haven't specifically added a command to the list of completion +// supported commands, you can still get completions by prefixing the +// entire command with "env". +// $ env /some/brand/new/binary --vmod +// Assuming that "binary" is a newly compiled binary, this should still +// produce the expected completion output. + + +#ifndef GFLAGS_COMPLETIONS_H_ +#define GFLAGS_COMPLETIONS_H_ + +namespace google { +extern void HandleCommandLineCompletions(void); +} + +#endif // GFLAGS_COMPLETIONS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_declare.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_declare.h new file mode 100644 index 0000000000000000000000000000000000000000..a9c6759707846f63ab97a66c13cb446975364448 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_declare.h @@ -0,0 +1,155 @@ +// Copyright (c) 1999, Google Inc. +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// --- +// +// Revamped and reorganized by Craig Silverstein +// +// This is the file that should be included by any file which declares +// command line flag. + +#ifndef GFLAGS_DECLARE_H_ +#define GFLAGS_DECLARE_H_ + + +// --------------------------------------------------------------------------- +// Namespace of gflags library symbols. +#define GFLAGS_NAMESPACE google + +// --------------------------------------------------------------------------- +// Windows DLL import/export. + +// Whether gflags library is a DLL. +// +// Set to 1 by default when the shared gflags library was built on Windows. +// Must be overwritten when this header file is used with the optionally also +// built static library instead; set by CMake's INTERFACE_COMPILE_DEFINITIONS. +#ifndef GFLAGS_IS_A_DLL +# define GFLAGS_IS_A_DLL 1 +#endif + +// We always want to import the symbols of the gflags library. +#ifndef GFLAGS_DLL_DECL +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DECL __declspec(dllimport) +# elif defined(__GNUC__) && __GNUC__ >= 4 +# define GFLAGS_DLL_DECL __attribute__((visibility("default"))) +# else +# define GFLAGS_DLL_DECL +# endif +#endif + +// We always want to import variables declared in user code. +#ifndef GFLAGS_DLL_DECLARE_FLAG +# if GFLAGS_IS_A_DLL && defined(_MSC_VER) +# define GFLAGS_DLL_DECLARE_FLAG __declspec(dllimport) +# elif defined(__GNUC__) && __GNUC__ >= 4 +# define GFLAGS_DLL_DECLARE_FLAG __attribute__((visibility("default"))) +# else +# define GFLAGS_DLL_DECLARE_FLAG +# endif +#endif + +// --------------------------------------------------------------------------- +// Flag types +#include +#if 1 +# include // the normal place uint32_t is defined +#elif 1 +# include // the normal place u_int32_t is defined +#elif 1 +# include // a third place for uint32_t or u_int32_t +#endif + +namespace GFLAGS_NAMESPACE { + +#if 1 // C99 +typedef int32_t int32; +typedef uint32_t uint32; +typedef int64_t int64; +typedef uint64_t uint64; +#elif 0 // BSD +typedef int32_t int32; +typedef u_int32_t uint32; +typedef int64_t int64; +typedef u_int64_t uint64; +#elif 0 // Windows +typedef __int32 int32; +typedef unsigned __int32 uint32; +typedef __int64 int64; +typedef unsigned __int64 uint64; +#else +# error Do not know how to define a 32-bit integer quantity on your system +#endif + +} // namespace GFLAGS_NAMESPACE + + +namespace fLS { + +// The meaning of "string" might be different between now and when the +// macros below get invoked (e.g., if someone is experimenting with +// other string implementations that get defined after this file is +// included). Save the current meaning now and use it in the macros. +typedef std::string clstring; + +} // namespace fLS + + +#define DECLARE_VARIABLE(type, shorttype, name) \ + /* We always want to import declared variables, dll or no */ \ + namespace fL##shorttype { extern GFLAGS_DLL_DECLARE_FLAG type FLAGS_##name; } \ + using fL##shorttype::FLAGS_##name + +#define DECLARE_bool(name) \ + DECLARE_VARIABLE(bool, B, name) + +#define DECLARE_int32(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int32, I, name) + +#define DECLARE_uint32(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint32, U, name) + +#define DECLARE_int64(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::int64, I64, name) + +#define DECLARE_uint64(name) \ + DECLARE_VARIABLE(::GFLAGS_NAMESPACE::uint64, U64, name) + +#define DECLARE_double(name) \ + DECLARE_VARIABLE(double, D, name) + +#define DECLARE_string(name) \ + /* We always want to import declared variables, dll or no */ \ + namespace fLS { \ + extern GFLAGS_DLL_DECLARE_FLAG ::fLS::clstring& FLAGS_##name; \ + } \ + using fLS::FLAGS_##name + +#endif // GFLAGS_DECLARE_H_ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_gflags.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_gflags.h new file mode 100644 index 0000000000000000000000000000000000000000..3780704e1caa005e3102c189291b73a1972fa080 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/gflags/gflags_gflags.h @@ -0,0 +1,99 @@ +// Copyright (c) 2014, Andreas Schuh +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are +// met: +// +// * Redistributions of source code must retain the above copyright +// notice, this list of conditions and the following disclaimer. +// * Redistributions in binary form must reproduce the above +// copyright notice, this list of conditions and the following disclaimer +// in the documentation and/or other materials provided with the +// distribution. +// * Neither the name of Google Inc. nor the names of its +// contributors may be used to endorse or promote products derived from +// this software without specific prior written permission. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +// ----------------------------------------------------------------------------- +// Imports the gflags library symbols into an alternative/deprecated namespace. + +#ifndef GFLAGS_GFLAGS_H_ +# error The internal header gflags_gflags.h may only be included by gflags.h +#endif + +#ifndef GFLAGS_NS_GFLAGS_H_ +#define GFLAGS_NS_GFLAGS_H_ + + +namespace gflags { + + +using GFLAGS_NAMESPACE::int32; +using GFLAGS_NAMESPACE::uint32; +using GFLAGS_NAMESPACE::int64; +using GFLAGS_NAMESPACE::uint64; + +using GFLAGS_NAMESPACE::RegisterFlagValidator; +using GFLAGS_NAMESPACE::CommandLineFlagInfo; +using GFLAGS_NAMESPACE::GetAllFlags; +using GFLAGS_NAMESPACE::ShowUsageWithFlags; +using GFLAGS_NAMESPACE::ShowUsageWithFlagsRestrict; +using GFLAGS_NAMESPACE::DescribeOneFlag; +using GFLAGS_NAMESPACE::SetArgv; +using GFLAGS_NAMESPACE::GetArgvs; +using GFLAGS_NAMESPACE::GetArgv; +using GFLAGS_NAMESPACE::GetArgv0; +using GFLAGS_NAMESPACE::GetArgvSum; +using GFLAGS_NAMESPACE::ProgramInvocationName; +using GFLAGS_NAMESPACE::ProgramInvocationShortName; +using GFLAGS_NAMESPACE::ProgramUsage; +using GFLAGS_NAMESPACE::VersionString; +using GFLAGS_NAMESPACE::GetCommandLineOption; +using GFLAGS_NAMESPACE::GetCommandLineFlagInfo; +using GFLAGS_NAMESPACE::GetCommandLineFlagInfoOrDie; +using GFLAGS_NAMESPACE::FlagSettingMode; +using GFLAGS_NAMESPACE::SET_FLAGS_VALUE; +using GFLAGS_NAMESPACE::SET_FLAG_IF_DEFAULT; +using GFLAGS_NAMESPACE::SET_FLAGS_DEFAULT; +using GFLAGS_NAMESPACE::SetCommandLineOption; +using GFLAGS_NAMESPACE::SetCommandLineOptionWithMode; +using GFLAGS_NAMESPACE::FlagSaver; +using GFLAGS_NAMESPACE::CommandlineFlagsIntoString; +using GFLAGS_NAMESPACE::ReadFlagsFromString; +using GFLAGS_NAMESPACE::AppendFlagsIntoFile; +using GFLAGS_NAMESPACE::ReadFromFlagsFile; +using GFLAGS_NAMESPACE::BoolFromEnv; +using GFLAGS_NAMESPACE::Int32FromEnv; +using GFLAGS_NAMESPACE::Uint32FromEnv; +using GFLAGS_NAMESPACE::Int64FromEnv; +using GFLAGS_NAMESPACE::Uint64FromEnv; +using GFLAGS_NAMESPACE::DoubleFromEnv; +using GFLAGS_NAMESPACE::StringFromEnv; +using GFLAGS_NAMESPACE::SetUsageMessage; +using GFLAGS_NAMESPACE::SetVersionString; +using GFLAGS_NAMESPACE::ParseCommandLineNonHelpFlags; +using GFLAGS_NAMESPACE::HandleCommandLineHelpFlags; +using GFLAGS_NAMESPACE::AllowCommandLineReparsing; +using GFLAGS_NAMESPACE::ReparseCommandLineNonHelpFlags; +using GFLAGS_NAMESPACE::ShutDownCommandLineFlags; +using GFLAGS_NAMESPACE::FlagRegisterer; + +#ifndef SWIG +using GFLAGS_NAMESPACE::ParseCommandLineFlags; +#endif + +} // namespace gflags +#endif // GFLAGS_NS_GFLAGS_H_ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/infer_engine.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/infer_engine.h new file mode 100644 index 0000000000000000000000000000000000000000..cc7c5e0aa74b720099c01808be36fd4586ceec12 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/infer_engine.h @@ -0,0 +1,45 @@ +#ifndef BENCHMARK_INFER_ENGINE_H +#define BENCHMARK_INFER_ENGINE_H +#include "util.h" +#include "acl/acl_base.h" +#include "post_process.h" +#include +#include +#include +#include +#include +#include "acl/acl_mdl.h" +#include +#include + +aclError InitContext(const char* configPath = ""); +aclError UnInitContext(); +aclError LoadModel(); +aclError InitInput(std::vector files); +aclError Inference(); +aclError PostProcess(); +aclError DvppSetup(); +aclError DvppInitInput(std::vector files); +aclError UnloadModel(); +void getImgResizeShape(); +acldvppRoiConfig* InitCropRoiConfig(uint32_t width, uint32_t height); + +/* + * @brief : 初始化中心抠图配置信息。 + * @param [in] uint32_t newInputWidth : 输入图像的宽（等比例缩放后的宽度） + * @param [in] uint32_t newInputHeight : 输入图像的高（等比例缩放后的高） + * @param [in] uint32_t modelInputWidth : 中心抠图后输入给模型的宽 + * @param [in] uint32_t modelInputHeight : 中心抠图后输入给模型的高 + * @return : acldvppRoiConfig：中心抠图配置信息 + */ +acldvppRoiConfig* InitCropCenterRoiConfig(uint32_t newInputWidth, uint32_t newInputHeight,uint32_t modelInputWidth, uint32_t modelInputHeight); + +/* + * @brief : 宽高较短的边缩放至RESIZE_MIN(256)，较长的边做等比例缩放。 + * @param [in] uint32_t width : 输入图片宽 + * @param [in] uint32_t height : 输入图片高 + * @param [in] uint32_t &newInputWidth : 等比例缩放后的宽 + * @param [in] uint32_t &newInputHeight : 等比例缩放后的高 + */ +void SmallSizeAtLeast(uint32_t width, uint32_t height, uint32_t& newInputWidth, uint32_t& newInputHeigh); +#endif diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/post_process.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/post_process.h new file mode 100644 index 0000000000000000000000000000000000000000..d413c66567109cb8647cf7b385ad66478f217af8 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/post_process.h @@ -0,0 +1,5 @@ +#ifndef BENCHMARK_POST_PROCESS_H +#define BENCHMARK_POST_PROCESS_H +#include "util.h" +aclError SaveBinPostprocess(); +#endif diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/util.h b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/util.h new file mode 100644 index 0000000000000000000000000000000000000000..734db74083d316e3c6fdc52c3f5caf0548789684 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/inc/util.h @@ -0,0 +1,157 @@ +#ifndef BENCHMARK_UTIL_H +#define BENCHMARK_UTIL_H +#include +#include +#include +#include "acl/acl_base.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include "acl/acl_rt.h" +#include "acl/ops/acl_dvpp.h" +#include +#include +#include +#include +#include +#include + +// self defined problem code. +const int ACL_ERROR_PATH_INVALID = 101; +const int ACL_ERROR_CREATE_DATASET_FAILED = 102; +const int ACL_ERROR_PARSE_PARAM_FAILED = 103; +const int ACL_ERROR_DVPP_ERROR = 104; +const int ACL_ERROR_OTHERS = 255; +#define MODEL_INPUT_NUM_MAX (4) +#define MODEL_INPUT_OUTPUT_NUM_MAX (16) + +#define LOG(fmt, args...) \ + do { \ + printf(fmt, ##args); \ + } while(0) + + +#define START_PROC \ + struct timeval start, end; \ + long long time_use; \ + do { \ + gettimeofday(&start, NULL); \ + } while (0); + + +#define END_PROC \ + do { \ + gettimeofday(&end, NULL); \ + time_use = (end.tv_sec-start.tv_sec)*1000000+(end.tv_usec-start.tv_usec); \ + LOG("time use: %lld us\n", time_use); \ + } while (0); + + +#define CHECK_ACL_RET(msg, ret) \ + if (ret != ACL_ERROR_NONE) { \ + std::cout << msg << ", ret "<< ret << std::endl; \ + return ret; \ + } + + +#define CHECK_WITH_RET(condition, ret, msg) \ + if(!(condition)) { \ + std::cout << msg << ", ret "<< ret << std::endl; \ + return ret; \ + } + + +#define CHECK_RET(ret) \ + if(ret != ACL_ERROR_NONE) { \ + return ret; \ + } + +bool FolderExists(std::string foldname); + +bool FileExists(std::string filename); + +char* ReadBinFile(std::string fileName, uint32_t& fileSize); + +aclError GetFiles(std::string path, std::vector& files); + +aclError FreeDevMemory(aclmdlDataset* dataset); + +aclError DestroyDatasetResurce(aclmdlDataset* dataset, uint32_t flag); + +void* ReadFile(std::string fileLocation, uint64_t &fileSize); + +struct DvppConfig { + uint32_t resizedWidth; + uint32_t resizedHeight; + std::unordered_map> imgSizes; +}; + +struct ModelInfo +{ + aclFormat Format; + const char* Name; + size_t size; + size_t dimCount; + int64_t dims[ACL_MAX_DIM_CNT]; + aclDataType Type; +}; + +struct Config { + std::string om; + std::string dataDir; + std::string outDir; + DvppConfig dvppConfig; + bool useDvpp; + size_t batchSize; + ModelInfo inputInfo[MODEL_INPUT_OUTPUT_NUM_MAX]; + ModelInfo outputInfo[MODEL_INPUT_OUTPUT_NUM_MAX]; + size_t inputNum; + size_t outputNum; + aclmdlDesc* modelDesc; + uint32_t modelId; + aclrtContext context; + char* modelData_ptr; + void* devMem_ptr; + void* weightMem_ptr; + std::string imgType; + std::string modelType; + uint32_t deviceId; + uint32_t loopNum; + std::string framework; + int64_t curOutputSize[MODEL_INPUT_OUTPUT_NUM_MAX]; + Config() + { + om = ""; + dataDir = ""; + batchSize = 0; + useDvpp = 0; + inputNum = 0; + outputNum = 0; + modelDesc = nullptr; + modelId = 0; + context = nullptr; + imgType = ""; + modelType = ""; + deviceId = 0; + loopNum = 1; + framework = "caffe"; + outDir = "../../results"; + modelData_ptr = nullptr; + devMem_ptr = nullptr; + weightMem_ptr = nullptr; + } +}; + +struct Resnet50Result { + int top1; + int top5; + int total; + std::unordered_map cmp; + Resnet50Result(): top1(0), top5(0), total(0) {}; +}; + +struct DataFrame { + std::vector fileNames; + aclmdlDataset* dataset; +}; + +#endif diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/infer_engine.cpp b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/infer_engine.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7f2b8507a510844689239d71443d8b4b4480be3d --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/infer_engine.cpp @@ -0,0 +1,728 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "acl/acl.h" +#include "infer_engine.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include "acl/ops/acl_dvpp.h" +#include +#include +using namespace std; + +std::unordered_map dvppTime; +extern Resnet50Result resnet50Res; +extern Config cfg; +extern aclError ret; +extern int processedCnt; +extern long long inferTime; +aclrtContext context; +uint32_t modelId; +aclmdlDesc *modelDesc; +std::vector files; +DataFrame inputDataframe; +DataFrame outputDataframe; +aclDataBuffer *yoloImgInfo; +aclrtStream stream = nullptr; +acldvppChannelDesc *dvpp_channel_desc = nullptr; +std::unordered_map> imgSizes; + +#define RESIZE_MIN 256 +#define NUM_2 2 +#define NUM_3 3 +#define NUM_16 16 +#define NUM_128 128 + +uint32_t resizedWidth; +uint32_t resizedHeight; +uint32_t resizedWidthAligned; +uint32_t resizedHeightAligned; +uint32_t resizedOutputBufferSize; + +void getImgResizeShape() +{ + if (ACL_FORMAT_NCHW == cfg.inputInfo[0].Format) { + resizedHeight = cfg.inputInfo[0].dims[NUM_2]; + resizedWidth = cfg.inputInfo[0].dims[NUM_3]; + } else if (ACL_FORMAT_NHWC == cfg.inputInfo[0].Format) { + resizedHeight = cfg.inputInfo[0].dims[1]; + resizedWidth = cfg.inputInfo[0].dims[NUM_2]; + } + return; +} + +aclError InitContext(const char *configPath) +{ + LOG("context init start\n"); + ret = aclInit(configPath); + CHECK_ACL_RET("acl init failed", ret); + + ret = aclrtSetDevice(cfg.deviceId); + CHECK_ACL_RET("open device failed ret", ret); + + ret = aclrtCreateContext(&context, cfg.deviceId); + CHECK_ACL_RET("create context failed", ret); + + cfg.context = context; + LOG("context init done\n"); + return ACL_ERROR_NONE; +} + +aclError UnInitContext() +{ + ret = aclrtDestroyContext(context); + CHECK_ACL_RET("destory context failed", ret); + LOG("destory context done\n"); + + ret = aclrtResetDevice(cfg.deviceId); + CHECK_ACL_RET("reset device failed", ret); + + ret = aclFinalize(); + CHECK_ACL_RET("finalize failed", ret); + LOG("reset device done\n"); + + return ACL_ERROR_NONE; +} + +aclError LoadModel() +{ + LOG("load model start\n"); + size_t memSize; + size_t weightsize; + uint32_t modelSize = 0; + std::string modelPath = cfg.om; + + cfg.modelData_ptr = ReadBinFile(modelPath, modelSize); + CHECK_WITH_RET(cfg.modelData_ptr != nullptr, ACL_ERROR_READ_MODEL_FAILURE, "can't read model"); + + aclError ret = aclmdlQuerySizeFromMem(cfg.modelData_ptr, modelSize, &memSize, &weightsize); + CHECK_ACL_RET("query memory size failed", ret); + + ret = aclrtMalloc(&(cfg.devMem_ptr), memSize, ACL_MEM_MALLOC_HUGE_ONLY); + CHECK_ACL_RET("alloc dev_ptr failed", ret); + ret = aclrtMalloc(&(cfg.weightMem_ptr), weightsize, ACL_MEM_MALLOC_HUGE_ONLY); + CHECK_ACL_RET("alloc weight_ptr failed", ret); + + ret = aclmdlLoadFromMemWithMem(cfg.modelData_ptr, modelSize, &modelId, cfg.devMem_ptr, memSize, cfg.weightMem_ptr, + weightsize); + CHECK_ACL_RET("load model from memory failed", ret); + LOG("Load model success. memSize: %lu, weightSize: %lu.\n", memSize, weightsize); + + modelDesc = aclmdlCreateDesc(); + CHECK_WITH_RET(modelDesc != nullptr, ACL_ERROR_READ_MODEL_FAILURE, "create model desc failed"); + ret = aclmdlGetDesc(modelDesc, modelId); + CHECK_ACL_RET("get model desc failed", ret); + + cfg.modelDesc = modelDesc; + cfg.modelId = modelId; + + LOG("load model done\n"); + return ACL_ERROR_NONE; +} + +aclError DvppSetup() +{ + ret = aclrtSetCurrentContext(context); + if (ret != ACL_ERROR_NONE) { + LOG("Set context failed\n"); + return ret; + } + + ret = aclrtCreateStream(&stream); + if (ret != ACL_ERROR_NONE) { + LOG("create dvpp stream failed\n"); + return ret; + } + + dvpp_channel_desc = acldvppCreateChannelDesc(); + if (dvpp_channel_desc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create dvpp channel desc failed\n"); + return ret; + } + + ret = acldvppCreateChannel(dvpp_channel_desc); + if (ret != ACL_ERROR_NONE) { + LOG("create dvpp channel failed\n"); + return ret; + } + + imgSizes = cfg.dvppConfig.imgSizes; + + resizedWidthAligned = (resizedWidth + 15) / NUM_16 * NUM_16; + resizedHeightAligned = (resizedHeight + 1) / NUM_2 * NUM_2; + + resizedOutputBufferSize = resizedWidthAligned * resizedHeightAligned * NUM_3 / NUM_2; + LOG("resizedWidth %d resizedHeight %d resizedWidthAligned %d resizedHeightAligned %d resizedOutputBufferSize %d\n", + resizedWidth, resizedHeight, resizedWidthAligned, resizedHeightAligned, resizedOutputBufferSize); + + return ACL_ERROR_NONE; +} + +/* + * @brief : 生成dvpp图像描述信息 + * @param [in] void *dataDev : 码流buffer信息. + * @param [in] acldvppPixelFormat format: 图像格式 + * @param [in] uint32_t width : 宽度 + * @param [in] uint32_t height: 高度 + * @param [in] uint32_t widthStride : 宽度对齐. + * @param [in] uint32_t heightStride: 高度对齐. + * @param [in] uint32_t size: 码流大小. + * @return : acldvppPicDesc：图像描述信息 + */ +acldvppPicDesc *createDvppPicDesc(void *dataDev, acldvppPixelFormat format, uint32_t width, uint32_t height, + uint32_t widthStride, uint32_t heightStride, uint32_t size) +{ + acldvppPicDesc *picDesc = acldvppCreatePicDesc(); + if (picDesc == nullptr) { + LOG("failed to create pic desc\n"); + return nullptr; + } + + ret = acldvppSetPicDescData(picDesc, dataDev); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc data\n"); + return nullptr; + } + ret = acldvppSetPicDescSize(picDesc, size); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc size\n"); + return nullptr; + } + + ret = acldvppSetPicDescFormat(picDesc, format); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc format\n"); + return nullptr; + } + + ret = acldvppSetPicDescWidth(picDesc, width); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc width\n"); + return nullptr; + } + + ret = acldvppSetPicDescHeight(picDesc, height); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc height\n"); + return nullptr; + } + + ret = acldvppSetPicDescWidthStride(picDesc, widthStride); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc widthStride\n"); + return nullptr; + } + + ret = acldvppSetPicDescHeightStride(picDesc, heightStride); + if (ret != ACL_ERROR_NONE) { + LOG("failed to set desc heightStride\n"); + return nullptr; + } + return picDesc; +} + +aclError InitInput(std::vector files) +{ + LOG("init input batch %d start\n", processedCnt); + ret = aclrtSetCurrentContext(context); + if (ret != ACL_ERROR_NONE) { + LOG("Set context failed, ret[%d]\n", ret); + return ret; + } + + size_t modelInputSize = cfg.inputInfo[0].size; + size_t imgSize = modelInputSize / cfg.batchSize; + + void *dst; + ret = aclrtMalloc(&dst, modelInputSize, ACL_MEM_MALLOC_NORMAL_ONLY); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc device failed, ret[%d]\n", ret); + return ret; + } + LOG("dst = %p, size = %ld\n", dst, modelInputSize); + + char *ptr = (char *)dst; + inputDataframe.fileNames.clear(); + for (int i = 0; i < files.size(); i++) { + + std::string fileLocation = cfg.dataDir + "/" + files[i]; + FILE *pFile = fopen(fileLocation.c_str(), "r"); + + if (pFile == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("open file %s failed\n", fileLocation.c_str()); + return ret; + } + + fseek(pFile, 0, SEEK_END); + size_t fileSize = ftell(pFile); + + if (fileSize > imgSize) { + ret = ACL_ERROR_OTHERS; + LOG("%s fileSize %lu * batch %lu don't match with model inputSize %lu\n", fileLocation.c_str(), + fileSize, cfg.batchSize, modelInputSize); + return ret; + } + + void *buff = nullptr; + ret = aclrtMallocHost(&buff, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host buff failed[%d]\n", ret); + return ret; + } + + rewind(pFile); + fread(buff, sizeof(char), fileSize, pFile); + fclose(pFile); + + void *dstTmp = (void *)ptr; + ret = aclrtMemcpy(dstTmp, fileSize, buff, fileSize, ACL_MEMCPY_HOST_TO_DEVICE); + ptr += fileSize; + LOG("input file: %s, memory addr: %p, file size: %ld\n",files[i].c_str(), dstTmp, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("init input %d, Copy host to device failed, ret[%d]\n", i, ret); + LOG("input addr %p, len %ld\n", dstTmp, fileSize); + aclrtFreeHost(buff); + return ret; + } + + aclrtFreeHost(buff); + inputDataframe.fileNames.push_back(files[i]); + } + + aclDataBuffer *inputData = aclCreateDataBuffer((void *)dst, modelInputSize); + if (inputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("aclCreateDataBuffer failed\n"); + return ret; + } + + aclmdlDataset *input = aclmdlCreateDataset(); + ret = aclmdlAddDatasetBuffer(input, inputData); + if (ret != ACL_ERROR_NONE) { + LOG("ACL_ModelInputDataAdd failed, ret[%d]\n", ret); + aclmdlDestroyDataset(input); + return ret; + } + + inputDataframe.dataset = input; + LOG("init input batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} + +/* + * @brief : 获取图像宽高 + * @param [in] void* buff : 输入码流地址. + * @param [in] uint32_t fileSize : 输入码流长度 + * @param [in] std::string fileLocation : 输入文件路径. + * @param [in] uint32_t &W : 输入码流宽度. + * @param [in] uint32_t &H : 输入码流高度. + */ +void GetImageHW(void* buff, uint32_t fileSize, std::string fileLocation, uint32_t &W, uint32_t &H) +{ + int32_t components = 0; + ret = acldvppJpegGetImageInfo((void *)buff, fileSize, &W, &H, &components); + if (ret != ACL_ERROR_NONE) { + cout << "acldvppJpegGetImageInfo failed, ret " << ret << "filename: " << fileLocation.c_str() << endl; + } +} + +/* + * @brief : dvpp在推理中的预处理流程 + * @param [in] string fileLocation : 输入文件路径. + * @param [in] char *&ptr : 输出buffer指针. + * @return : ACL_ERROR_NONE：预处理成功， 其他：预处理失败 + */ +aclError DVPP_Resnet50(std::string fileLocation, char *&ptr) +{ + // 1 获取输入码流 + uint32_t W, H, W_Aligned, H_Aligned, outputBuffSize; + void *decodeInput = nullptr; + void *decodeOutput = nullptr; + acldvppPicDesc *decodeOutputDesc = nullptr; + uint64_t fileSize; + void *buff = ReadFile(fileLocation, fileSize); + if( buff == nullptr) { + LOG("read pic failed\n"); + return 1; + } + + ret = acldvppMalloc(&decodeInput, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc dvpp in buff failed[%d]\n", ret); + return ret; + } + ret = aclrtMemcpy(decodeInput, fileSize, buff, fileSize, ACL_MEMCPY_HOST_TO_DEVICE); + if (ret != ACL_ERROR_NONE) { + LOG("copy host to device failed[%d]\n", ret); + return ret; + } + + // 2 获取解码输出描述信息 + GetImageHW(buff, fileSize, fileLocation, W, H); + W_Aligned = (W + 127) / NUM_128 * NUM_128; + H_Aligned = (H + 15) / NUM_16 * NUM_16; + outputBuffSize = W_Aligned * H_Aligned * NUM_3 / NUM_2; + ret = acldvppMalloc(&decodeOutput, outputBuffSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc decodeOutput buff failed[%d]\n", ret); + return ret; + } + decodeOutputDesc = createDvppPicDesc(decodeOutput, PIXEL_FORMAT_YUV_SEMIPLANAR_420, W, H, W_Aligned, H_Aligned, + outputBuffSize); + if (decodeOutputDesc == nullptr) { + LOG("create jpeg_output_desc failed\n"); + return 1; + } + LOG("file[%s] jpeg picDesc info: W=%d, H=%d, W_Aligned=%d, H_Aligned=%d, outBufSize=%d, format=%d\n", \ + fileLocation.c_str(),W, H, W_Aligned, H_Aligned, outputBuffSize, PIXEL_FORMAT_YUV_SEMIPLANAR_420); + + // 3 使用jpegd图像解码 + ret = acldvppJpegDecodeAsync(dvpp_channel_desc, decodeInput, fileSize, decodeOutputDesc, stream); + if (ret != ACL_ERROR_NONE) { + LOG(" dvppJpegDecodeAsync failed\n"); + return ret; + } + aclrtFreeHost(buff); + aclrtSynchronizeStream(stream); + + // 4 对jpegd解码的图片进行原分辨率抠图及短边256等比例缩放 + acldvppRoiConfig *cropConfig = nullptr; + acldvppPicDesc *cropOutputDesc = nullptr; + // 设置对解码后的图片进行原图裁剪，目的是为了减少因jpegd解码后对齐的无效数据对图像精度的影响 + cropConfig = InitCropRoiConfig(W, H); + + uint32_t newInputWidth = 0; + uint32_t newInputHeight = 0; + void *cropOutBufferDev = nullptr; + // 宽和高较短的一条边缩放至256，较长边做等比例缩放。对齐至256目的是为了给224x224中心抠图做准备,短边256对齐，获得对齐后的宽高 + SmallSizeAtLeast(W, H, newInputWidth, newInputHeight); + uint32_t cropOutputWidthStride = (newInputWidth + (NUM_16 - 1)) / NUM_16 * NUM_16; + uint32_t cropOutputHeightStride = (newInputHeight + (NUM_2 - 1)) / NUM_2 * NUM_2; + uint32_t cropOutBufferSize = cropOutputWidthStride * cropOutputHeightStride * NUM_3 / NUM_2; + ret = acldvppMalloc(&cropOutBufferDev, cropOutBufferSize); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] AcldvppMalloc cropOutBufferDev_ failed, ret = " << ret << " cropOutBufferSize_ = " + << cropOutBufferSize << endl; + return ret; + } + cropOutputDesc = createDvppPicDesc(cropOutBufferDev, PIXEL_FORMAT_YUV_SEMIPLANAR_420, newInputWidth, newInputHeight, + cropOutputWidthStride, cropOutputHeightStride, cropOutBufferSize); + if (cropOutputDesc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create cropOutputDesc failed\n"); + return ret; + } + + ret = acldvppVpcCropAsync(dvpp_channel_desc, decodeOutputDesc, cropOutputDesc, cropConfig, stream); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] acldvppVpcCropAsync failed, ret = " << ret << std::endl; + return ret; + } + aclrtSynchronizeStream(stream); + + // 5 对等比例缩放后的图片做224x224中心抠图，中心抠图后的数据会发送给aipp进行YUV转RGB格式转换。需要注意：中心抠图后的输出格式和aipp + // 的输入格式需要保持一致。 + acldvppRoiConfig *centerCropConfig = nullptr; + acldvppPicDesc *centerCropOutputDesc = nullptr; // resize output desc + centerCropConfig = InitCropCenterRoiConfig(newInputWidth, newInputHeight, resizedWidth, resizedHeight); + void *vpcOutBufferDev = nullptr; + uint32_t vpcOutBufferSize = resizedWidthAligned * resizedHeightAligned * NUM_3 / NUM_2; + + vpcOutBufferDev = (void *)ptr; + centerCropOutputDesc = createDvppPicDesc(vpcOutBufferDev, PIXEL_FORMAT_YUV_SEMIPLANAR_420, resizedWidth, + resizedHeight, resizedWidthAligned, resizedHeightAligned, + vpcOutBufferSize); + if (centerCropOutputDesc == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("create centerCropOutputDesc failed\n"); + return ret; + } + + ret = acldvppVpcCropAsync(dvpp_channel_desc, cropOutputDesc, centerCropOutputDesc, centerCropConfig, stream); + if (ret != ACL_ERROR_NONE) { + std::cout << "[ERROR][Vision] acldvppVpcCropAsync failed, ret = " << ret << "fileName: " << fileLocation.c_str() << std::endl; + return ret; + } + + ptr += vpcOutBufferSize; + aclrtSynchronizeStream(stream); + + // 6 释放资源 + acldvppFree(decodeInput); + acldvppFree(decodeOutput); + acldvppFree(cropOutBufferDev); + acldvppDestroyPicDesc(decodeOutputDesc); + acldvppDestroyPicDesc(cropOutputDesc); + acldvppDestroyPicDesc(centerCropOutputDesc); + acldvppDestroyRoiConfig(cropConfig); + acldvppDestroyRoiConfig(centerCropConfig); + return ret; +} + +aclError DvppInitInput(std::vector files) +{ + struct timeval process_start; + struct timeval process_end; + std::string funcName; + long long costTime; + funcName = "DvppTotalProcess"; + gettimeofday(&process_start, NULL); + + void *dst; + ret = acldvppMalloc(&dst, cfg.inputInfo[0].size); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc device failed, ret[%d]\n", ret); + return ret; + } + + char *ptr = (char *)dst; + inputDataframe.fileNames.clear(); + + for (int i = 0; i < files.size(); i++) { + std::string fileLocation = cfg.dataDir + "/" + files[i]; + ret = DVPP_Resnet50(fileLocation, ptr); + if(ret != ACL_ERROR_NONE) { + LOG("dvpp config failed"); + return ret; + } + inputDataframe.fileNames.push_back(files[i]); + } + + funcName = "DvppTotalProcess"; + gettimeofday(&process_end, NULL); + costTime = (process_end.tv_sec - process_start.tv_sec) * 1000000 + (process_end.tv_usec - process_start.tv_usec); + dvppTime[funcName] += costTime; + + aclmdlDataset *input = aclmdlCreateDataset(); + aclDataBuffer *inputData = aclCreateDataBuffer((void *)dst, cfg.inputInfo[0].size); + + if (inputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("aclCreateDataBuffer failed\n"); + return ret; + } + + ret = aclmdlAddDatasetBuffer(input, inputData); + + if (ret != ACL_ERROR_NONE) { + LOG("ACL_ModelInputDataAdd failed, ret[%d]\n", ret); + aclmdlDestroyDataset(input); + return ret; + } + + inputDataframe.dataset = input; + return ACL_ERROR_NONE; +} + +acldvppRoiConfig *InitCropRoiConfig(uint32_t width, uint32_t height) +{ + uint32_t right = 0; + uint32_t bottom = 0; + acldvppRoiConfig *cropConfig; + + if (width % NUM_2 == 0) { + right = width - 1; + } else { + right = width; + } + + if (height % NUM_2 == 0) { + bottom = height - 1; + } else { + bottom = height; + } + + cropConfig = acldvppCreateRoiConfig(0, right, 0, bottom); + if (cropConfig == nullptr) { + std::cout << "[ERROR][Vision] acldvppCreateRoiConfig failed " << std::endl; + return nullptr; + } + + return cropConfig; +} + +acldvppRoiConfig *InitCropCenterRoiConfig(uint32_t newInputWidth, uint32_t newInputHeight, uint32_t modelInputWidth, + uint32_t modelInputHeight) +{ + uint32_t left = 0; + uint32_t right = 0; + uint32_t top = 0; + uint32_t bottom = 0; + uint32_t amount_to_be_cropped_w = 0; + uint32_t amount_to_be_cropped_h = 0; + uint32_t left_half = 0; + uint32_t top_half = 0; + acldvppRoiConfig *centerCropConfig = nullptr; + + // 计算中心抠图起始点的坐标距离码流左边界和上边界的距离 + amount_to_be_cropped_w = newInputWidth - modelInputWidth; + left_half = amount_to_be_cropped_w / NUM_2; + amount_to_be_cropped_h = newInputHeight - modelInputHeight; + top_half = amount_to_be_cropped_h / NUM_2; + + // 保证起始点坐标为偶数 + left = (left_half % NUM_2 == 0) ? (amount_to_be_cropped_w / NUM_2) : (amount_to_be_cropped_w / NUM_2 + 1); + top = (top_half % NUM_2 == 0) ? (amount_to_be_cropped_h / NUM_2) : (amount_to_be_cropped_h / NUM_2 + 1); + + // 结束点为奇数 + right = left + modelInputWidth - 1; + bottom = top + modelInputHeight - 1; + + centerCropConfig = acldvppCreateRoiConfig(left, right, top, bottom); + if (centerCropConfig == nullptr) { + std::cout << "[ERROR][Vision] acldvppCreateRoiConfig failed " << std::endl; + return nullptr; + } + return centerCropConfig; +} + +void SmallSizeAtLeast(uint32_t width, uint32_t height, uint32_t &newInputWidth, uint32_t &newInputHeight) +{ + float scaleRatio = 0.0; + float inputWidth = 0.0; + float inputHeight = 0.0; + float resizeMin = 0.0; + bool minWidthFlag = false; + + inputWidth = (float)width; + inputHeight = (float)height; + resizeMin = (float)(RESIZE_MIN); + minWidthFlag = (width <= height) ? true : false; + + // 短边缩放为256，长边等比例缩放 + if (minWidthFlag == true) { + newInputWidth = resizeMin; + newInputHeight = (resizeMin / width) * inputHeight; + std::cout << "[INFO]scaleRatio: " << resizeMin / width << " inputWidth_: " << width << " newInputWidth: " << + newInputWidth << " inputHeight_: " << inputHeight << " newInputHeight_:" << newInputHeight << std::endl; + } else { + newInputWidth = (resizeMin / height) * width; + newInputHeight = resizeMin; + std::cout << "[INFO]scaleRatio: " << resizeMin / height << " width: " << width << " newInputWidth: " << + newInputWidth << " height: " << height << " newInputHeight:" << newInputHeight << std::endl; + } +} + +aclError Inference() +{ + LOG("inference batch %d start\n", processedCnt); + ret = aclrtSetCurrentContext(context); + + if (ret != ACL_ERROR_NONE) { + LOG("Set infer context failed\n"); + return ret; + } + + struct timeval startTmp, endTmp; + long long timeUse; + + if (inputDataframe.fileNames.size() == 0) { + ret = ACL_ERROR_OTHERS; + LOG("No file found\n"); + return ret; + } + + aclmdlDataset *output = aclmdlCreateDataset(); + if (output == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("Create Output Dataset failed\n"); + return ret; + } + + std::vector outputDevPtrs; + + for (size_t i = 0; i < cfg.outputNum; ++i) { + size_t buffer_size = cfg.outputInfo[i].size; + void *outputBuffer = nullptr; + ret = aclrtMalloc(&outputBuffer, (size_t)buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY); + + if (ret != ACL_ERROR_NONE) { + LOG("Malloc output host failed, ret[%d]\n", ret); + return ret; + } + + outputDevPtrs.push_back(outputBuffer); + aclDataBuffer *outputData = aclCreateDataBuffer(outputBuffer, buffer_size); + + if (outputData == nullptr) { + ret = ACL_ERROR_OTHERS; + LOG("Create output data buffer failed\n"); + return ret; + } + + ret = aclmdlAddDatasetBuffer(output, outputData); + + if (ret != ACL_ERROR_NONE) { + LOG("Add output model dataset failed, ret[%d]\n", ret); + return ret; + } + } + + gettimeofday(&startTmp, NULL); + ret = aclmdlExecute(modelId, inputDataframe.dataset, output); + gettimeofday(&endTmp, NULL); + timeUse = (endTmp.tv_sec - startTmp.tv_sec) * 1000000 + (endTmp.tv_usec - startTmp.tv_usec); + LOG("inference time cost: %lld us\n", timeUse); + inferTime += timeUse; + + if (ret != ACL_ERROR_NONE) { + LOG("%s inference failed.\n", inputDataframe.fileNames[0].c_str()); + FreeDevMemory(inputDataframe.dataset); + aclmdlDestroyDataset(inputDataframe.dataset); + return ret; + } + + outputDataframe.fileNames = inputDataframe.fileNames; + outputDataframe.dataset = output; + + uint32_t dvppFlag = (cfg.useDvpp) ? 1 : 0; + ret = DestroyDatasetResurce(inputDataframe.dataset, dvppFlag); + if (ret != ACL_ERROR_NONE) { + LOG("DestroyDatasetResurce failed\n"); + return ret; + } + + LOG("inference batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} + +aclError UnloadModel() +{ + LOG("unload model start\n"); + ret = aclmdlUnload(modelId); + CHECK_ACL_RET("unload model failed", ret); + LOG("unload model done\n"); + + aclmdlDestroyDesc(cfg.modelDesc); + + if (cfg.devMem_ptr != nullptr) { + aclrtFree(cfg.devMem_ptr); + cfg.devMem_ptr = nullptr; + } + + if (cfg.weightMem_ptr != nullptr) { + aclrtFree(cfg.weightMem_ptr); + cfg.weightMem_ptr = nullptr; + } + + if (cfg.modelData_ptr != nullptr) { + delete[] cfg.modelData_ptr; + cfg.modelData_ptr = nullptr; + } + return ACL_ERROR_NONE; +} diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/main.cpp b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/main.cpp new file mode 100644 index 0000000000000000000000000000000000000000..99a8d46ab89f9c616048a09f0f995e68502c4330 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/main.cpp @@ -0,0 +1,493 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "util.h" +#include "infer_engine.h" +#include "acl/acl_base.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include "acl/acl.h" +#include "acl/acl_mdl.h" +#include "acl/acl_rt.h" +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace std; +using std::cout; +using std::endl; +Resnet50Result resnet50Res; +Config cfg; +aclError ret; +int processedCnt; +long long dataProcTime = 0; +long long inferTime = 0; +float avgTime = 0; +float avgPreTime = 0; + +extern std::unordered_map dvppTime; +extern DataFrame outputDataframe; + +void getCommandLineParam(int argc, char** argv, Config& config) +{ + while (1) { + int option_index = 0; + struct option long_options[] = + { + {"om", 1, 0, 'a'}, + {"dataDir", 1, 0, 'b'}, + {"outDir", 1, 0, 'c'}, + {"batchSize", 1, 0, 'd'}, + {"deviceId", 1, 0, 'e'}, + {"loopNum", 1, 0, 'f'}, + {"modelType", 1, 0, 'g'}, + {"imgType", 1, 0, 'h'}, + {"framework", 1, 0, 'i'}, + {"useDvpp", 1 , 0 , 'j'}, + {0, 0, 0, 0} + }; + + int c; + c = getopt_long(argc, argv, "a:b:c:e:f:j:k:l:m:n:u:t:", long_options, &option_index); + if (c == -1) { + break; + } + + switch (c) { + case 'a': + config.om = std::string(optarg); + printf("[INFO]om = %s\n", config.om.c_str()); + break; + case 'b': + config.dataDir = std::string(optarg); + printf("[INFO]dataDir = %s\n", config.dataDir.c_str()); + break; + case 'c': + config.outDir = std::string(optarg); + printf("[INFO]outDir = %s\n", config.outDir.c_str()); + break; + case 'd': + config.batchSize = atoi(optarg); + printf("[INFO]batchSize = %d\n", config.batchSize); + break; + case 'e': + config.deviceId = atoi(optarg); + printf("[INFO]deviceId = %d\n", config.deviceId); + break; + case 'f': + config.loopNum = atoi(optarg); + printf("[INFO]loopNum = %d\n", config.loopNum); + break; + case 'g': + config.modelType = std::string(optarg); + printf("[INFO]modelType = %s\n", config.modelType.c_str()); + break; + case 'h': + config.imgType = std::string(optarg); + printf("[INFO]imgType = %s\n", config.imgType.c_str()); + break; + case 'i': + config.framework = std::string(optarg); + printf("[INFO]framework = %s\n", config.framework.c_str()); + break; + case 'j': + config.useDvpp = atoi(optarg); + printf("[INFO]useDvpp = %d\n", config.useDvpp); + break; + default: + break; + } + } +} + +// 只校验必须的参数 +aclError ParseParams(int argc, char** argv, Config& config, std::string& errorMsg) +{ + getCommandLineParam(argc, argv, config); + + LOG("parase params start\n"); + + if (config.om.empty() || !FileExists(config.om)) { + LOG("om is empty\n"); + errorMsg = "om path is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + + if (config.dataDir.empty() || !FolderExists(config.dataDir)) { + errorMsg = "data Dir is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("dataDir %s \n", config.dataDir.c_str()); + + if (!config.outDir.empty() && !FolderExists(config.outDir)) { + LOG("output dir %s not exists, try to make dir.\n", config.outDir.c_str()); + mkdir(config.outDir.c_str(), 0755); + LOG("outDir %s \n", config.outDir.c_str()); + } + + if(config.batchSize <= 0){ + errorMsg = "batch Size should be > 0"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("batchSize %zd \n", config.batchSize); + + if (config.modelType.empty()) + { + LOG("FLAGS_modelType is empty\n"); + errorMsg = "modelType is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("modelType %s \n", config.modelType.c_str()); + + if (config.imgType.empty()) + { + LOG("imgType is empty\n"); + errorMsg = "imgType is invalid"; + return ACL_ERROR_PARSE_PARAM_FAILED; + } + LOG("imgType %s \n", config.imgType.c_str()); + LOG("useDvpp is %d \n", config.useDvpp); + LOG("parase params done\n"); + return ACL_ERROR_NONE; +} + +aclError Process() +{ + std::vector fileNames; + ret = GetFiles(cfg.dataDir, fileNames); + CHECK_RET(ret); + size_t fileNum = fileNames.size(); + LOG("fileNum:%zd\n",fileNames.size()); + struct timeval startTmp, endTmp; + + // 获取模型输入所需要的宽高 + getImgResizeShape(); + + if(cfg.useDvpp) { + ret = DvppSetup(); + CHECK_RET(ret); + } + + size_t inferCnt = 0; + size_t loopCnt = 0; + while(loopCnt < cfg.loopNum) { + LOG("loopCnt %d, loopNum %d\n", loopCnt, cfg.loopNum); + for(size_t i = 0; i < fileNum / cfg.batchSize; i++) { + gettimeofday(&startTmp, NULL); + std::vector batchFileNames; + for (int j = 0; j < cfg.batchSize; j++) { + batchFileNames.push_back(fileNames[i*cfg.batchSize+j]); + } + processedCnt++; + + if(cfg.useDvpp) { + ret = DvppInitInput(batchFileNames); + } else { + ret = InitInput(batchFileNames); + } + gettimeofday(&endTmp, NULL); + dataProcTime += (endTmp.tv_sec-startTmp.tv_sec)*1000000+(endTmp.tv_usec-startTmp.tv_usec); + CHECK_RET(ret); + + ret = Inference(); + CHECK_RET(ret); + + ret = SaveBinPostprocess(); + CHECK_RET(ret); + } + + if (fileNum % cfg.batchSize != 0) { + std::vector batchFileNames; + for(size_t i = (fileNum - fileNum % cfg.batchSize); i < fileNum; i++) { + batchFileNames.push_back(fileNames[i]); + } + + gettimeofday(&startTmp, NULL); + processedCnt++; + + if(cfg.useDvpp) { + ret = DvppInitInput(batchFileNames); + } else { + ret = InitInput(batchFileNames); + } + gettimeofday(&endTmp, NULL); + dataProcTime += (endTmp.tv_sec-startTmp.tv_sec) * 1000000 + (endTmp.tv_usec - startTmp.tv_usec); + CHECK_RET(ret); + + ret = Inference(); + CHECK_RET(ret); + + ret = SaveBinPostprocess(); + CHECK_RET(ret); + } + loopCnt++; + } + return ACL_ERROR_NONE; +} + +void SaveResult() +{ + ofstream outfile("test_perform_static.txt"); +#if 0 + std::string model_name; + int dex = (cfg.om).find_last_of("/"); + model_name = cfg.om.substr(dex+1); + + std:: string title = "model_name total batch top1 top5 pre_avg/ms pre_imgs/s infer_avg/ms infer_imgs/s mAP"; + outfile << title << endl; + + outfile << model_name << " "; + outfile << processedCnt*cfg.batchSize << " "; + outfile << cfg.batchSize << " "; + if (cfg.postprocessType == "resnet") { + outfile << 1.0*resnet50Res.top1/resnet50Res.total << " " << 1.0*resnet50Res.top5/resnet50Res.total << " "; + } else { + outfile << "NA" << " " << "NA" << " "; + } + + outfile << avgPreTime << " " << 1.0*1000/avgPreTime << " "; + outfile << avgTime << " " << 1.0*1000/avgTime << " "; + outfile << endl; +#endif + char tmpCh[256]; + memset(tmpCh, 0, sizeof(tmpCh)); + snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms %4.3f fps/s\n", + avgTime, (1.0 * 1000 / avgTime)); + outfile << tmpCh; + outfile.close(); +} + +aclError GetModelInputOutputInfo(Config& cfg) +{ + aclError ret; + std::ofstream outFile("modelInputOutputInfo", std::ios::trunc); + char tmpChr[256] = {0}; + + // 获取模型输入信息 + size_t inputNum = aclmdlGetNumInputs(cfg.modelDesc); + LOG("model input num %zd\n", inputNum); + snprintf(tmpChr, sizeof(tmpChr), "model input num %zd\n", inputNum); + outFile << tmpChr; + + cfg.inputNum = inputNum; + for (size_t i = 0; i < inputNum && i < MODEL_INPUT_OUTPUT_NUM_MAX; i++) { + size_t size = aclmdlGetInputSizeByIndex(cfg.modelDesc, i); + cfg.inputInfo[i].size = size; + LOG("model input[%zd] size %zd\n", i, cfg.inputInfo[i].size); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] size %zd\n", i, cfg.inputInfo[i].size); + outFile << tmpChr; + + aclmdlIODims dims; + ret = aclmdlGetInputDims(cfg.modelDesc, i, &dims); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetInputDims fail ret %d\n", ret); + return 1; + } + + cfg.inputInfo[i].dimCount = dims.dimCount; + ret = aclrtMemcpy(cfg.inputInfo[i].dims , cfg.inputInfo[i].dimCount * sizeof(int64_t), dims.dims, + cfg.inputInfo[i].dimCount * sizeof(int64_t), ACL_MEMCPY_HOST_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtMemcpy fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + LOG("model input[%zd] dimCount %zd\n", i, cfg.inputInfo[i].dimCount); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] dimCount %zd\n", i, cfg.inputInfo[i].dimCount); + outFile << tmpChr; + for (size_t dimIdx = 0; dimIdx < cfg.inputInfo[i].dimCount; dimIdx++) { + LOG("model input[%zd] dim[%zd] info %ld\n", i, dimIdx, cfg.inputInfo[i].dims[dimIdx]); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] dim[%zd] info %ld\n", + i, dimIdx, cfg.inputInfo[i].dims[dimIdx]); + outFile << tmpChr; + } + + cfg.inputInfo[i].Format = aclmdlGetInputFormat(cfg.modelDesc, i); + cfg.inputInfo[i].Type = aclmdlGetInputDataType(cfg.modelDesc, i); + + LOG("model input[%zd] format %d inputType %d\n", i, cfg.inputInfo[i].Format, cfg.inputInfo[i].Type); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] format %d inputType %d\n", i, cfg.inputInfo[i].Format, + cfg.inputInfo[i].Type); + outFile << tmpChr; + + cfg.inputInfo[i].Name = aclmdlGetInputNameByIndex(cfg.modelDesc, i); + LOG("model input[%zd] name %s\n", i, cfg.inputInfo[i].Name); + snprintf(tmpChr, sizeof(tmpChr), "model input[%zd] name %s\n", i, cfg.inputInfo[i].Name); + outFile << tmpChr; + + size_t index; + ret = aclmdlGetInputIndexByName(cfg.modelDesc, cfg.inputInfo[i].Name, &index); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetInputIndexByName fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + if (i != index) { + LOG("aclmdlGetInputNameByIndex not equal aclmdlGetInputIndexByName\n"); + return 1; + } else { + LOG("model input name %s is belone to input %zd\n", cfg.inputInfo[i].Name, index); + } + } + + // 获取模型输出信息 + size_t outputNum = aclmdlGetNumOutputs(cfg.modelDesc); + LOG("model output num %zd\n", outputNum); + snprintf(tmpChr, sizeof(tmpChr), "model output num %zd\n", outputNum); + outFile << tmpChr; + + cfg.outputNum = outputNum; + for (size_t i = 0; i < outputNum && i < MODEL_INPUT_OUTPUT_NUM_MAX; i++) { + size_t size = aclmdlGetOutputSizeByIndex(cfg.modelDesc, i); + cfg.outputInfo[i].size = size; + LOG("model output[%zd] size %zd\n", i, cfg.outputInfo[i].size); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] size %zd\n", i, cfg.outputInfo[i].size); + outFile << tmpChr; + + aclmdlIODims dims; + ret = aclmdlGetOutputDims(cfg.modelDesc, i, &dims); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetOutputDims fail ret %d\n", ret); + return 1; + } + + cfg.outputInfo[i].dimCount = dims.dimCount; + ret = aclrtMemcpy(cfg.outputInfo[i].dims, cfg.outputInfo[i].dimCount * sizeof(int64_t), dims.dims, + cfg.outputInfo[i].dimCount * sizeof(int64_t), ACL_MEMCPY_HOST_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtMemcpy fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + LOG("model output[%zd] dimCount %zd\n", i, cfg.outputInfo[i].dimCount); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] dimCount %zd\n", i, cfg.outputInfo[i].dimCount); + outFile << tmpChr; + + for (size_t dimIdx = 0; dimIdx < cfg.outputInfo[i].dimCount; dimIdx++) { + LOG("model output[%zd] dim[%zd] info %ld\n", i, dimIdx, cfg.outputInfo[i].dims[dimIdx]); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] dim[%zd] info %ld\n", + i, dimIdx, cfg.outputInfo[i].dims[dimIdx]); + outFile << tmpChr; + } + + cfg.outputInfo[i].Format = aclmdlGetOutputFormat(cfg.modelDesc, i); + cfg.outputInfo[i].Type = aclmdlGetOutputDataType(cfg.modelDesc, i); + LOG("model output[%zd] format %d outputType %d\n", i, cfg.outputInfo[i].Format, cfg.outputInfo[i].Type); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] format %d outputType %d\n", i, cfg.outputInfo[i].Format, + cfg.outputInfo[i].Type); + outFile << tmpChr; + + cfg.outputInfo[i].Name = aclmdlGetOutputNameByIndex(cfg.modelDesc, i); + LOG("model output[%zd] name %s\n", i, cfg.outputInfo[i].Name); + snprintf(tmpChr, sizeof(tmpChr), "model output[%zd] name %s\n", i, cfg.outputInfo[i].Name); + outFile << tmpChr; + + size_t index; + ret = aclmdlGetOutputIndexByName(cfg.modelDesc, cfg.outputInfo[i].Name, &index); + if (ret != ACL_ERROR_NONE) { + LOG("aclmdlGetOutputIndexByName fail ret %d line %d\n", ret, __LINE__); + return 1; + } + + if (i != index) { + LOG("aclmdlGetOutputNameByIndex not equal aclmdlGetOutputIndexByName\n"); + return 1; + } else { + LOG("model output name %s is belone to output %d\n", cfg.outputInfo[i].Name, index); + } + } + + outFile.close(); + return ACL_ERROR_NONE; +} + +int main(int argc, char** argv) +{ + processedCnt = 0; + inferTime = 0; + + std::string errorMsg; + ret = ParseParams(argc, argv, cfg, errorMsg); + CHECK_ACL_RET(errorMsg, ret); + + ret = InitContext(); + CHECK_RET(ret); + + ret = LoadModel(); + CHECK_RET(ret); + + ret = GetModelInputOutputInfo(cfg); + CHECK_RET(ret); + + ret = Process(); + CHECK_RET(ret); + + ret = UnloadModel(); + CHECK_RET(ret); + + ret = UnInitContext(); + CHECK_RET(ret); + LOG("\n"); + + avgTime = 1.0 * inferTime / processedCnt /cfg.batchSize / 1000; + avgPreTime = 1.0 * dataProcTime / processedCnt / cfg.batchSize / 1000; + + if (cfg.useDvpp) { + LOG("\n"); + LOG("DVPP performance details:\n"); + LOG("#############################################\n"); + std::unordered_map::iterator iter; + for (iter = dvppTime.begin(); iter != dvppTime.end(); iter++) { + LOG("%s using avg time %0.2f ms\n",iter->first.c_str(),1.0*iter->second/processedCnt/cfg.batchSize/1000); + } + LOG("\n"); + } + + LOG("performance summary:\n"); + LOG("#############################################\n"); + LOG("total %ld imgs processed and batch size %ld\n", processedCnt*cfg.batchSize, cfg.batchSize); +#if 0 + if(cfg.postprocessType == "resnet") { + LOG("top1 ratio %0.3f top5 ratio %0.3f\n", + 1.0*resnet50Res.top1/resnet50Res.total, 1.0*resnet50Res.top5/resnet50Res.total); + } +#endif + + LOG("avg preprocess time %0.2f ms, %0.2f imgs/s\n", avgPreTime, 1.0 * 1000 / avgPreTime); + LOG("avg inference time %0.2f ms, %0.2f imgs/s\n", avgTime, 1.0 * 1000 / avgTime); + + SaveResult(); +} diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/post_process.cpp b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/post_process.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f2dc4997af21b868ac1944276fe98354e76c93f4 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/post_process.cpp @@ -0,0 +1,123 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "post_process.h" +#include "util.h" +#include +#include +#include +#include +#include "stdio.h" +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +extern int processedCnt; + +extern Config cfg; +extern DataFrame outputDataframe; +extern aclError ret; +int topNum = 5; + +extern int processedCnt; + +aclError SaveBinPostprocess() +{ + aclError retVal; + + LOG("save batch %d start\n", processedCnt); + DataFrame dataframe = outputDataframe; + std::vector& inferFile_vec = outputDataframe.fileNames; + aclmdlDataset* output = dataframe.dataset; + + std::string resultFolder = cfg.outDir + "/" + cfg.modelType; + DIR* op = opendir(resultFolder.c_str()); + if (NULL == op) { + mkdir(resultFolder.c_str(), 00775); + } else { + closedir(op); + } + + for (size_t i = 0; i < cfg.outputNum; ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output, i); + void* data = aclGetDataBufferAddr(dataBuffer); + uint32_t len; + len = cfg.outputInfo[i].size; + + void* outHostData = NULL; + ret = aclrtMallocHost(&outHostData, len); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host failed.\n"); + return 1; + } + + ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST); + if (ret != ACL_ERROR_NONE) { + LOG("Copy device to host failed.\n"); + aclrtFreeHost(outHostData); + return 1; + } + + uint32_t eachSize = len / cfg.batchSize; + for (size_t j = 0; j < inferFile_vec.size(); j++) { + FILE* outputFile; + std::string framename = inferFile_vec[j]; + std::size_t dex = (framename).find_first_of("."); + std::string inputFileName = (framename).erase(dex); + + outputFile = fopen((resultFolder + "/" + "davinci_" + inputFileName + "_" + "output" + std::to_string(i) + ".bin").c_str(), "wb"); + + if (outputFile == nullptr) { + aclrtFreeHost(outHostData); + return 1; + } + + fwrite((uint8_t *)outHostData + (j * eachSize), eachSize, sizeof(char), outputFile); + fclose(outputFile); + } + + ret = aclrtFreeHost(outHostData); + if (ret != ACL_ERROR_NONE) { + LOG("Free output host failed.\n"); + } + } + + (void)DestroyDatasetResurce(outputDataframe.dataset, 0); + + LOG("save batch %d done\n", processedCnt); + return ACL_ERROR_NONE; +} diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/util.cpp b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ec437321c75c4c883ca35d56a0c5ab218f16efa7 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/Benchmark/util.cpp @@ -0,0 +1,230 @@ +/* * +* Copyright 2020 Huawei Technologies Co., Ltd +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* */ + +#include "util.h" +#include +#include +#include +#if 0 +static std::unordered_map errorMap = { + {ACL_ERROR_NONE, "success"}, + {ACL_ERROR_INVALID_PARAM, "params may not valid"}, + {ACL_ERROR_BAD_ALLOC, "alloc memory failed"}, + {ACL_ERROR_RT_FAILURE, "runtime failure"}, + {ACL_ERROR_GE_FAILURE, "GE failure"}, + {ACL_ERROR_OP_NOT_FOUND, "OP not find"}, + {ACL_ERROR_OP_LOAD_FAILED, "OP loads failed"}, + {ACL_ERROR_READ_MODEL_FAILURE, "load model failed"}, + {ACL_ERROR_PARSE_MODEL, "parse model failed"}, + {ACL_ERROR_MODEL_MISSING_ATTR, "model misssing attr"}, + {ACL_ERROR_DESERIALIZE_MODEL, "deserilize model failed"}, + // {ACL_ERROR_MULTIPLE_MODEL_MATCHED, "multiple model matched"}, + //{ACL_ERROR_EVENT_NOT_READY, "event not ready"}, + //{ACL_ERROR_EVENT_COMPLETE, "event not complete"}, + {ACL_ERROR_UNSUPPORTED_DATA_TYPE, "unsupport datatype"}, + {ACL_ERROR_REPEAT_INITIALIZE, "initial repeated"}, + //{ACL_ERROR_COMPILER_NOT_REGISTERED, "compilter not registered"}, + {ACL_ERROR_PATH_INVALID, "path invalid"}, + {ACL_ERROR_PARSE_PARAM_FAILED, "parse params failed"}, + {ACL_ERROR_DVPP_ERROR, "dvpp errors"} +}; + + +std::string CausedBy(aclError error) +{ + return errorMap[error]; +} +#endif + +bool FolderExists(std::string foldname) +{ + DIR* dir; + if ((dir = opendir(foldname.c_str())) == NULL) { + return false; + } + closedir(dir); + return true; +} + +void* ReadFile(std::string fileLocation, uint64_t &fileSize) +{ + aclError ret; + FILE *pFile = fopen(fileLocation.c_str(), "r"); + if (pFile == nullptr) { + LOG("open file %s failed\n", fileLocation.c_str()); + return nullptr; + } + + fseek(pFile, 0, SEEK_END); + fileSize = ftell(pFile); + + void *buff = nullptr; + ret = aclrtMallocHost(&buff, fileSize); + if (ret != ACL_ERROR_NONE) { + LOG("Malloc host buff failed[%d]\n", ret); + return nullptr; + } + + rewind(pFile); + fread(buff, sizeof(char), fileSize, pFile); + fclose(pFile); + return buff; +} + +bool FileExists(std::string filename) +{ + std::fstream file; + file.open(filename, std::ios::in); + if (!file) { + return false; + } + + file.close(); + return true; +} + +char* ReadBinFile(std::string fileName, uint32_t& fileSize) +{ + std::ifstream binFile(fileName, std::ifstream::binary); + + if (binFile.is_open() == false) { + LOG("open file[%s] failed\n", fileName.c_str()); + return nullptr; + } + + binFile.seekg(0, binFile.end); + uint32_t binFileBufferLen = binFile.tellg(); + + if (binFileBufferLen == 0) { + LOG("binfile is empty, filename: %s", fileName.c_str()); + binFile.close(); + return nullptr; + } + + binFile.seekg(0, binFile.beg); + char* binFileBufferData = new(std::nothrow) char[binFileBufferLen]; + LOG("binFileBufferData:%p\n", binFileBufferData); + + if (binFileBufferData == nullptr) { + LOG("malloc binFileBufferData failed\n"); + binFile.close(); + return nullptr; + } + + binFile.read(binFileBufferData, binFileBufferLen); + binFile.close(); + fileSize = binFileBufferLen; + return binFileBufferData; +} + +aclError GetFiles(std::string path, std::vector& files) +{ + DIR* dir; + struct dirent* ptr; + char base[1000]; + + if ((dir = opendir(path.c_str())) == NULL) { + LOG("Open dir %s error.\n", path.c_str()); + return ACL_ERROR_PATH_INVALID; + } + + while ((ptr = readdir(dir)) != NULL) { + if (strcmp(ptr->d_name, ".") == 0 || strcmp(ptr->d_name, "..") == 0) { + //current dir OR parrent dir + continue; + } else if (ptr->d_type == 8) { + //file + files.push_back(ptr->d_name); + } else if (ptr->d_type == 10) { + //link file + continue; + } else if (ptr->d_type == 4) { + //dir + continue; + } + } + + closedir(dir); + std::sort(files.begin(), files.end()); + return ACL_ERROR_NONE; +} + +aclError FreeDevMemory(aclmdlDataset* dataset) +{ + aclError ret; + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i); + void* data = aclGetDataBufferAddr(dataBuffer); + aclrtFree(data); + aclDestroyDataBuffer(dataBuffer); + } + + return ACL_ERROR_NONE; +} + +aclError DestroyDatasetResurce(aclmdlDataset* dataset, uint32_t flag) +{ + aclError ret = ACL_ERROR_NONE; + + if (nullptr == dataset) { + LOG("dataset == null\n"); + return 1; + } + + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(dataset); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(dataset, i); + if (nullptr == dataBuffer) { + LOG("dataBuffer == null\n"); + continue; + } + + void* data = aclGetDataBufferAddr(dataBuffer); + if (nullptr != data) { + if (1 == flag) { + if (i > 0) { + ret = aclrtFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree data failed, ret %d\n", ret); + } + } else { + ret = acldvppFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("acldvppFree data failed, ret %d\n", ret); + } + } + } else { + ret = aclrtFree(data); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree data failed, ret %d\n", ret); + } + } + } + + ret = aclDestroyDataBuffer(dataBuffer); + if (ret != ACL_ERROR_NONE) { + LOG("Destroy dataBuffer failed, ret %d\n", ret); + } + } + + ret = aclmdlDestroyDataset(dataset); + if (ret != ACL_ERROR_NONE) { + LOG("aclrtFree dataset failed, ret %d\n", ret); + } + + return ret; +} + + diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/LICENSE b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..57bc88a15a0ee8266c259b2667e64608d3f7e292 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/LICENSE @@ -0,0 +1,202 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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If they do not match, may leading in failure. + +| Conditions | Need | +| --- | --- | +| CANN Version | >=5.0.3 | +| Chip Platform| Ascend310/Ascend710 | +| 3rd Party Requirements| Please follow the 'requirements.txt' | + +## Quick Start Guide + +### 1. Clone the respository + +```shell +git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git +cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL +``` + +### 2. Download and preprocess the dataset + +1. Download the ImageNet2012 Validation dataset by yourself. You can get the validation pictures(50000 JPEGS and a ILSVRC2012val-label-index.txt) + +2. Put JPEGS to **'scripts/ILSVRC2012val'** and label text to **'scripts/'** + +3. Images Preprocess: +``` +cd scripts +mkdir input_bins +python3 resnet50v15_preprocessing.py ./ILSVRC2012val/ ./input_bins/ +``` +The jpegs pictures will be preprocessed to bin fils. + +### 3. Offline Inference + +**Convert pb to om.** + +- configure the env + + ``` + export install_path=/usr/local/Ascend + export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH + export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH + export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH + export ASCEND_OPP_PATH=${install_path}/opp + ``` + +- convert pb to om + + [pb download link](https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/modelzoo/Official/cv/Resnet50v1.5_for_ACL/resnet50v15_tf.pb) + + ``` + atc --model=resnet50v15_tf.pb --framework=3 --output=resnet50v15_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="input_tensor:1,224,224,3" --insert_op_conf=resnet50v15_aipp.cfg --enable_small_channel=1 --log=info + ``` + +- Build the program + + ``` + bash build.sh + ``` + +- Run the program: + + ``` + cd scripts + bash benchmark_tf.sh + ``` + +## Performance + +### Result + +Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide. + +#### Inference accuracy results + +| model | **data** | Top1/Top5 | +| :---------------: | :-------: | :-------------: | +| offline Inference | 50000 images | 76.5 %/ 93.1% | + +## Reference + +[1] https://github.com/IntelAI/models diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/build.sh b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..dae86211d2691b82ecfd8c2637d1276092e476ed --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/build.sh @@ -0,0 +1,9 @@ +rm -rf ./Benchmark/build + +mkdir -p Benchmark/build/intermediates/host +cd Benchmark/build/intermediates/host +cmake ../../../../Benchmark/ -DCAMKE_CXX_COMPILER=g++ +make clean +make install +cd - +cd Benchmark/out/ diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..0934af7691acabd7981d82342b3a2310fe606d3d --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/modelzoo_level.txt @@ -0,0 +1,6 @@ +ModelCovert:OK +QuantStatus:OK +FuncStatus:OK +PrecisionStatus:OK +AutoTune:OK +PerfStatus:OK diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/requirements.txt b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f66bb9f75c74849c47871a646493af6c2eb83d3 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/requirements.txt @@ -0,0 +1,3 @@ +tensorflow==1.15 +numpy==1.16 +Pillow==7.1.2 diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/resnet50v15_aipp.cfg b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/resnet50v15_aipp.cfg new file mode 100644 index 0000000000000000000000000000000000000000..ee696ee994f923f97017fa1aadba1318a37e6c30 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/resnet50v15_aipp.cfg @@ -0,0 +1,13 @@ +aipp_op { + aipp_mode: static + input_format : RGB888_U8 + src_image_size_w : 224 + src_image_size_h : 224 + mean_chn_0 : 124 + mean_chn_1 : 117 + mean_chn_2 : 104 + var_reci_chn_0 : 1 + var_reci_chn_1 : 1 + var_reci_chn_2 : 1 +} + diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/benchmark_tf.sh b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/benchmark_tf.sh new file mode 100644 index 0000000000000000000000000000000000000000..4d8ffccae6d4f1d1ae0e25a8951d21f0380f9c7d --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/benchmark_tf.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#set -x +cur_dir=`pwd` +benchmark_dir=$cur_dir/../Benchmark/out +om_name=$cur_dir/../resnet50v15_tf_1batch.om +batchsize=1 +model_name=resnet50v15 +output_dir='results' +rm -rf $cur_dir/$output_dir/* + +#start offline inference +$benchmark_dir/benchmark --om $om_name --dataDir $cur_dir/input_bins/ --modelType $model_name --outDir $cur_dir/$output_dir --batchSize $batchsize --imgType bin --useDvpp 0 + +#post process +python3 $cur_dir/imagenet_accuarcy_cal.py --infer_result $cur_dir/$output_dir/$model_name --label $cur_dir/ILSVRC2012val-label-index.txt --offset 1 diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/imagenet_accuarcy_cal.py b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/imagenet_accuarcy_cal.py new file mode 100644 index 0000000000000000000000000000000000000000..38f0d91170e48644f8bafffa9e0a098881c52b48 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/imagenet_accuarcy_cal.py @@ -0,0 +1,75 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +import time +import argparse + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--infer_result", type=str, default="../../result_Files") + parser.add_argument("--label", type=str, default="../data/input_50000.csv") + parser.add_argument("--output_index", type=int, default=0) + parser.add_argument("--offset", type=int, default=0) + parser.add_argument("--dtype", type=str, default='float32') #datatype of bin files + args = parser.parse_args() + + image_cnt = 0 + top1_cnt = 0 + top5_cnt = 0 + ground_truth={} + if args.label.endswith(".csv"): + with open(args.label, 'r') as cs: + rs_list = cs.readlines() + for line in rs_list: + image_name = line.split(',')[0].split('.JPEG')[0] + label = int(line.split(',')[1]) + label += args.offset + ground_truth[image_name]=label + elif args.label.endswith(".txt"): + with open(args.label, 'r') as cs: + rs_list = cs.readlines() + for line in rs_list: + image_name = line.split(' ')[0].split('.JPEG')[0] + label = int(line.split(' ')[1].replace("\n","")) + label += args.offset + ground_truth[image_name]=label + + for i in sorted(ground_truth): + try: + image_name = i + label = ground_truth[i] + #查看输出的文件 + if os.path.exists(os.path.join(args.infer_result,'davinci_{}_output{}.bin'.format(image_name,args.output_index))): + bin_path = os.path.join(args.infer_result,'davinci_{}_output{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + elif os.path.exists(os.path.join(args.infer_result,'davinci_{}.JPEG_output{}.bin'.format(image_name, args.output_index))): + bin_path = os.path.join(args.infer_result,'davinci_{}.JPEG_output{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + elif os.path.exists(os.path.join(args.infer_result,'{}_output_{}.bin'.format(image_name,args.output_index))): + bin_path = os.path.join(args.infer_result,'{}_output_{}.bin'.format(image_name, args.output_index)) + pred = np.fromfile(bin_path, dtype=args.dtype) + else: + continue + top1=np.argmax(pred) + if label == top1: + top1_cnt += 1 + if label in np.argsort(-pred)[0:5]: + top5_cnt += 1 + image_cnt+=1 + print("{}, gt label:{: >4}, predict results:{}".format(image_name,label,str(np.argsort(-pred)[0:5]))) + except Exception as e: + print("Can't find " + bin_path) + print('imag_count %d, top1_accuracy %.3f top5_accuracy %.3f'%(image_cnt,top1_cnt/image_cnt,top5_cnt/image_cnt)) \ No newline at end of file diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/resnet50v15_preprocessing.py b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/resnet50v15_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..44d63f9676ee2bdc2c206ee6e932d38ddd163ec7 --- /dev/null +++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/scripts/resnet50v15_preprocessing.py @@ -0,0 +1,100 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Provides utilities to preprocess images for the Inception networks.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow.compat.v1 as tf +import os +import sys +import shutil +import numpy as np +from PIL import Image +from tensorflow.python.ops import control_flow_ops + +def eval_image(image, height, width, resize_method, + central_fraction=0.875, scope=None): + + with tf.name_scope('eval_image'): + if resize_method == 'crop': + shape = tf.shape(input=image) + image = tf.cond(pred=tf.less(shape[0], shape[1]), + true_fn=lambda: tf.image.resize(image, + tf.convert_to_tensor(value=[256, 256 * shape[1] / shape[0]], + dtype=tf.int32)), + false_fn=lambda: tf.image.resize(image, + tf.convert_to_tensor(value=[256 * shape[0] / shape[1], 256], + dtype=tf.int32))) + + shape = tf.shape(input=image) + y0 = (shape[0] - height) // 2 + x0 = (shape[1] - width) // 2 + distorted_image = tf.image.crop_to_bounding_box(image, y0, x0, height, width) + distorted_image.set_shape([height, width, 3]) + means = tf.broadcast_to([123.68, 116.78, 103.94], tf.shape(input=distorted_image)) + return distorted_image - means + else: # bilinear + if image.dtype != tf.float32: + image = tf.image.convert_image_dtype(image, dtype=tf.float32) + # Crop the central region of the image with an area containing 87.5% of + # the original image. + if central_fraction: + image = tf.image.central_crop(image, central_fraction=central_fraction) + + if height and width: + # Resize the image to the specified height and width. + image = tf.expand_dims(image, 0) + image = tf.image.resize(image, [height, width], + method=tf.image.ResizeMethod.BILINEAR) + image = tf.squeeze(image, [0]) + image = tf.subtract(image, 0.5) + image = tf.multiply(image, 2.0) + return image + +def preprocess(src_path, save_path): + in_files = os.listdir(src_path) + in_files.sort() + resize_shape = [224, 224, 3] + sqz_mean = np.array([123.68, 116.78, 103.94], np.float32) + img_std = np.array([[0.5*255, 0.5*255, 0.5*255]], np.float32) + if os.path.isdir(save_path): + shutil.rmtree(save_path) + os.makedirs(save_path) + for file in in_files: + with tf.Session().as_default(): + if not os.path.isdir(file): + print(file) + img_buffer = tf.io.gfile.GFile(os.path.join(src_path, file), 'rb').read() + img = tf.image.decode_jpeg(img_buffer,channels=3,fancy_upscaling=False,dct_method='INTEGER_FAST') + img = eval_image( img, + 224, + 224, + 'crop') + img = img.eval() + #img = img * img_std + img = img + sqz_mean + img = img.astype(np.uint8, copy=False) + img.tofile(os.path.join(save_path, file.split('.')[0]+".bin")) + tf.reset_default_graph() + +if __name__ == "__main__": + if len(sys.argv) < 3: + raise Exception("usage: python3 xxx.py [src_path] [save_path]") + + src_path = sys.argv[1] + save_path = sys.argv[2] + preprocess(src_path, save_path) diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..8389e23f5575d034f02543f0b7613cff48ae7bbc --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE @@ -0,0 +1,284 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT +HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED +WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE +GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER +IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN +IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c07aedecfdf53cb439f04796e4a0b2ce1a501c34 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md @@ -0,0 +1,70 @@ + +## 推理过程 +环境 +- Tensorflow 1.15 +- python 3.7 + +1. ckpt文件 + +- ckpt文件下载地址: + + https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/checkpoint65.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698491&Signature=Ltfv5%2B5VbaFSklW3pI6W6oTh73A%3D + + 通过freeze_graph.py转换成pb文件bliznet_tf_310.pb + +- pb文件下载地址: + + https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.pb?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656586&Signature=JhBRfk5dpeDFE%2BPy1jQg6Q4mvHY%3D + +2. om模型 + +- om模型下载地址: + + https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.om?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656644&Signature=Z7DyzKRGPd27pYipfD2Ke/KSGAo%3D + + 使用ATC模型转换工具进行模型转换时可以参考如下指令: + +``` +atc --model=/home/HwHiAiUser/atc/bliznet_tf_310.pb --framework=3 --output=/home/HwHiAiUser/atc/bliznet_tf_310 --soc_version=Ascend310 \ + --input_shape="input:1,300,300,3" \ + --log=info \ + --out_nodes="concat_1:0;concat_2:0;ssd_2/Conv_7/BiasAdd:0" +``` + +3. 使用msame工具推理 + + 参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + + 获取到msame可执行文件之后，将待检测om文件放在model文件夹，然后进行性能测试。 + + msame推理可以参考如下指令: +``` +./msame --model "/home/HwHiAiUser/msame/bliznet_tf_310.om" --input "/home/HwHiAiUser/msame/data" --output "/home/HwHiAiUser/msame/out/" --outfmt TXT +``` +- 将测试集数据转为bin文件: +``` + imageToBin.py +``` + +- 测试数据bin文件下载地址: + + https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/img.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698452&Signature=f3aLaUdPnodF8PKtCaI5Ox4wb6c%3D + + +4. 性能测试 + + 使用testBliznetPb_OM_Data.py对推理完成后获得的txt文件进行测试 + +

精度测试

+ +训练集：VOC12 train-seg-aug + +测试集：VOC12 val + +| | mIoU | mAP | +| ---------- | -------- | -------- | +| 论文精度 | 72.8 | 80.0 | +| GPU精度32 | 72.8 | 80.0 | +| GPU精度16 | 72.0 | 78.3 | +| NPU精度 | 70.1 | 77.6 | +| 推理精度 | 70.1 | 77.6 | \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..775d0e09f523b9fd28f7e965d696ff8d98d6fc62 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py @@ -0,0 +1,90 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from npu_bridge.npu_init import * +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +import os +from Train.config import args +from help_modelarts import modelarts_result2obs + +from Train.resnet import ResNet +from Train.config import config as net_config + +INIT_CKPT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'checkpoint65') +ckpt_path = os.path.join(INIT_CKPT_DIR, 'model.ckpt-65000') + +def main(): + print("start ckpt To pb") + print("ckpt_path") + tf.reset_default_graph() + img_ph = tf.placeholder(tf.float32, shape=[1, 300, 300, 3], name="input") + dataset_num_classes = 21 + + net = ResNet + depth = 50 + net = net(config=net_config, depth=depth, training=False) + + net.create_trunk(img_ph) + + if args.detect: + net.create_multibox_head(dataset_num_classes) + confidence = net.outputs['confidence'] + location = net.outputs['location'] + else: + location, confidence = None, None + + if args.segment: + net.create_segmentation_head(dataset_num_classes) + seg_logits = net.outputs['segmentation'] + else: + seg_logits = None + + print("confidence = ", confidence) + print("location = ", location) + print("seg_logits = ", seg_logits) + + with tf.Session() as sess: + tf.train.write_graph(sess.graph_def, args.result_dir, 'model.pb') + modelarts_result2obs(args) + freeze_graph.freeze_graph( + input_graph=os.path.join(args.result_dir, 'model.pb'), + input_saver='', + input_binary=False, + input_checkpoint=ckpt_path, + output_node_names="concat_1, concat_2, ssd_2/Conv_7/BiasAdd", # graph outputs node + restore_op_name='save/restore_all', + filename_tensor_name='save/Const:0', + output_graph=os.path.join(args.result_dir, 'bliznet_tf_310.pb'), # graph outputs name + clear_devices=False, + initializer_nodes="") + print("done") + + modelarts_result2obs(args) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py new file mode 100644 index 0000000000000000000000000000000000000000..23635dd95f92c9a96ee56505a4f017200762d98c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py @@ -0,0 +1,70 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from config import args +from getData.voc_loader import VOCLoader + +import progressbar +import logging +log = logging.getLogger() +import numpy as np + +def main(argv=None): + if args.dataset == 'voc07' or args.dataset == 'voc07+12': + loader = VOCLoader('07', 'test') + if args.dataset == 'voc12-val': + loader = VOCLoader('12', 'val', segmentation=args.segment) + + filenames = loader.get_filenames() + image_list = [] + + inputs = tf.placeholder(tf.float32, shape=[None, None, 3], name="input") + img_ph = tf.image.resize_bilinear(tf.expand_dims(inputs, 0), (300, 300))# 增加一维，并reshape + + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess0: + bar = progressbar.ProgressBar()# 显示进度条 + for i in bar(range(len(filenames))): + name = filenames[i] + img = loader.load_image(name) # 获取图片 + image = sess0.run(img_ph, feed_dict={inputs: img}) + + image_list.append(image) + gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息 + image.tofile("./binFile/img/{0:05d}.bin".format(i)) + # im = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32) + # print(im) + gt_bboxes.tofile("./binFile/gt_bboxes/{0:05d}.bin".format(i)) + seg_gt.tofile("./binFile/seg_gt/{0:05d}.bin".format(i)) + gt_cats.tofile("./binFile/gt_cats/{0:05d}.bin".format(i)) + # w.tofile("./binFile/w/{0:05d}.bin".format(i)) + # h.tofile("./binFile/h/{0:05d}.bin".format(i)) + difficulty.tofile("./binFile/difficulty/{0:05d}.bin".format(i)) + + +if __name__ == '__main__': + tf.app.run() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..1badb843c0738a566804f03237972aee0ba2299e --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt @@ -0,0 +1,6 @@ +FuncStatus:OK +PrecisionStatus:POK +AutoTune:POK +PerfStatus:POK +ModelConvert:OK +QuantStatus:OK \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/maps/.keep b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/requirements.txt similarity index 100% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/maps/.keep rename to ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/requirements.txt diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9564af69a2a3d5cb8a83dc74fe53a79d99a885 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py @@ -0,0 +1,233 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from config import args +from getData.voc_loader import VOCLoader + +from tensorflow.python.ops.metrics_impl import mean_iou as streaming_mean_iou +from utils import decode_bboxes +from getData.boxer import PriorBoxGrid +from config import config as net_config +from detector import Detector +from tabulate import tabulate +import progressbar +import numpy as np +import logging +log = logging.getLogger() + +def eval_category(gt, dets, cid): + """Computes average precision for one category""" + cgt = gt[cid] + cdets = np.array(dets[cid]) + if (cdets.shape == (0, )): + return None, None + scores = cdets[:, 1] + sorted_inds = np.argsort(-scores) + image_ids = cdets[sorted_inds, 0].astype(int) + BB = cdets[sorted_inds] + + npos = 0 + for img_gt in cgt.values(): + img_gt['ignored'] = np.array(img_gt['difficult']) + img_gt['det'] = np.zeros(len(img_gt['difficult']), dtype=np.bool) + npos += np.sum(~img_gt['ignored']) + + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + ovmax = -np.inf + if image_ids[d] in cgt: + R = cgt[image_ids[d]] + bb = BB[d, 2:].astype(float) + + BBGT = R['bbox'].astype(float) + + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 0] + BBGT[:, 2], bb[0] + bb[2]) + iymax = np.minimum(BBGT[:, 1] + BBGT[:, 3], bb[1] + bb[3]) + iw = np.maximum(ixmax - ixmin, 0.) + ih = np.maximum(iymax - iymin, 0.) + inters = iw * ih + + # union + uni = (bb[2] * bb[3] + BBGT[:, 2] * BBGT[:, 3] - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > args.voc_iou_thresh: + if not R['ignored'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = True + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + N = float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = rec * N / np.maximum(rec * N + fp, np.finfo(np.float32).eps) + return rec, prec + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + p = 0 if np.sum(rec >= t) == 0 else np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + +def compute_ap(gt, dets, loader): + """computes average precision for all categories""" + aps = {} + for cid in range(1, loader.num_classes): + cat_name = loader.ids_to_cats[cid] + rec, prec = eval_category(gt, dets, cid) + ap = voc_ap(rec, prec, loader.year == '07') + aps[loader.ids_to_cats[cid]] = ap + return aps + +def make_detection_table(gt, dets, loader): + """creates a table with AP per category and mean AP""" + aps = compute_ap(gt, dets, loader) + print("ap = ", aps) + eval_cache = [aps] + + table = [] + for cid in range(1, loader.num_classes): + cat_name = loader.ids_to_cats[cid] + table.append((cat_name, ) + tuple(aps.get(cat_name, 'N/A') for aps in eval_cache)) + mean_ap = np.mean([a for a in list(aps.values()) if a >= 0]) + table.append(("AVERAGE", ) + tuple(np.mean(list(aps.values())) for aps in eval_cache)) + x = tabulate(table, headers=(["Category", "mAP (all)"]), + tablefmt='orgtbl', floatfmt=".3f") + log.info("Eval results:\n%s", x) + return table + +def compute_mean_iou(detector): + iou = detector.get_mean_iou() + print(iou) + log.info("\n Mean IoU is %f", iou) + return iou + +def main(argv=None): + if args.dataset == 'voc07' or args.dataset == 'voc07+12': + loader = VOCLoader('07', 'test') + if args.dataset == 'voc12-val': + loader = VOCLoader('12', 'val', segmentation=args.segment) + + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, + log_device_placement=False)) as sess: + detector = Detector(sess, loader, net_config, no_gt=args.no_seg_gt) + + filenames = loader.get_filenames() + gt = {cid: {} for cid in range(1, loader.num_classes)} + dets = {cid: [] for cid in range(1, loader.num_classes)} + + bar = progressbar.ProgressBar()# 显示进度条 + # print("filenames = ", filenames) + + init_op = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer()) + sess.run(init_op) + for i in bar(range(len(filenames))): + name = filenames[i] + # print("name = ", name) + img_id = i + img = loader.load_image(name) # 获取图片 + # img = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32) + # img.shape = 1, 300, 300, 3 + gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息 + + confidence = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_0.txt".format(i)) + location = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_1.txt".format(i)) + seg_logits = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_2.txt".format(i)) + confidence.shape = 1, 45390, 21 + location.shape = 1, 45390, 4 + seg_logits.shape = 1, 75, 75, 21 + + for cid in np.unique(gt_cats): + mask = (gt_cats == cid) + bbox = gt_bboxes[mask] + diff = difficulty[mask] + det = np.zeros(len(diff), dtype=np.bool) + gt[cid][img_id] = {'bbox': bbox, 'difficult': diff, 'det': det} + + confidence1 = confidence + location1 = location + seg_logits1 = seg_logits + output = detector.feed_forward(img, seg_gt, confidence1, location1, seg_logits1, + w, h, name, gt_bboxes, gt_cats) # result + + if args.detect: + det_bboxes, det_probs, det_cats = output[:3] + for i in range(len(det_cats)): + dets[det_cats[i]].append((img_id, det_probs[i]) + tuple(det_bboxes[i])) + + # print("gt = ", gt) + # print("dets = ", dets) + print("table result:") + table = make_detection_table(gt, dets, loader) if args.detect else None + print("iou result:") + iou = compute_mean_iou(detector) if args.segment else None + + +if __name__ == '__main__': + tf.app.run() \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/.keep b/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/.keep similarity index 100% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/.keep rename to ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/README.md new file mode 100644 index 0000000000000000000000000000000000000000..50b9a505a73ef6a3bc6fd09d5ad20950b2afa52c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/README.md @@ -0,0 +1,96 @@ +## 模型功能 + +目标跟踪 + +## 原始模型 + +参考： + + +原实现模型： + +https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/DeepSort_ID0505_for_TensorFlow + +pb文件下载地址 : + +链接：https://pan.baidu.com/s/1v7Fe_YYT-hZUGCf2u7TI5g +提取码：v41g + +## om模型 + +pb模型转om模型参考freeze_pb.py + + +om模型下载地址： + +链接：https://pan.baidu.com/s/1GCuGdEUiniYZlTdFn2sknw +提取码：keil + +使用ATC模型转换工具进行模型转换时可以参考如下指令: + +``` +atc --model=/root/deepsort/deep_sort.pb --framework=3 --output=/root/deepsort/deep_sort --soc_version=Ascend310 --input_shape="input:1,128,64,3" +``` + +## 数据集准备 + +market1501测试集中的图像数据转换为bin数据集 地址： + + +链接：https://pan.baidu.com/s/1h9bWDVEW7-voFHFi7TaTuw 提取码：nwyj + + +## 使用msame工具推理 + + +参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + + +获取到msame可执行文件之后，进行性能测试。 + +./msame --model "/root/deepsort/deep_sort.om" --input "/root/osnet/query" --output "/root/deepsort/out/" --outfmt TXT + + +## 性能测试 + +使用msame推理工具，参考如下命令，发起推理性能测试： + +``` + +``` + +``` +... +Inference average time : 10.66 ms +Inference average time without first time: 10.66 ms +[INFO] unload model success, model Id is 1 +[INFO] Execute sample success +[INFO] end to destroy stream +[INFO] end to destroy context +[INFO] end to reset device is 0 +[INFO] end to finalize acl + +... +``` + +平均推理性能为 10.66ms + +## 精度测试 + + +``` + +``` + +最终精度：(暂无) + +``` +Ascend310推理结果： + gpu结果: + npu结果: +``` + + + + + diff --git a/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/freeze_pb.py b/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/freeze_pb.py new file mode 100644 index 0000000000000000000000000000000000000000..a8f8cc678115f9fba953826715467bd34d9ad6d6 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/DeepSort_ID0505_for_ACL/freeze_pb.py @@ -0,0 +1,66 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +from tensorflow.python.framework import graph_util +import os, sys +import argparse + +base_path=os.path.split(os.path.realpath(__file__))[0] +sys.path.append(base_path + "/../") + +from nets.deep_sort.network_definition import create_network + + +def main(): + + tf.reset_default_graph() + + # set inputs node + inputs = tf.placeholder(tf.float32, shape=[1, 128, 64, 3], name="input") + + features, logits = create_network(inputs, \ + num_classes=1502, \ + add_logits=True, \ + reuse=None, \ + create_summaries=False, \ + weight_decay=1e-8) + + prediction = tf.argmax(input=logits, axis=-1, output_type=tf.dtypes.int32, name="output") + + graph = tf.get_default_graph() + input_graph_def = graph.as_graph_def() + + output_graph="deep_sort.pb" + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + + saver = tf.train.Saver() + saver.restore(sess, "/home/HwHiAiUser/deep/lognckpt_bak/KOOKKJ/model.ckpt-98077") + + output_graph_def = graph_util.convert_variables_to_constants( + sess=sess, + input_graph_def=input_graph_def, + output_node_names=["output"]) + + with tf.gfile.GFile(output_graph, "wb") as f: + f.write(output_graph_def.SerializeToString()) + + print("done") + +if __name__ == '__main__': + main() + diff --git a/ACL_TensorFlow/contrib/cv/MT-NET_ID1283_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/MT-NET_ID1283_for_ACL/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation." *2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020.] +(https://arxiv.org/pdf/1908.07433.pdf) + +- 参考实现： + + [Pix2Pose](https://github.com/kirumang/Pix2Pose) + + + +### hdf5转pb + +本模型基于keras框架实现，训练模型时以HDF5格式保存模型训练的权重，使用hdf52pb.py将模型和权重转化为pb，我们提供转换好的[hdf5模型文件](obs://pix2pose/tless_inference/pix2pose_weights/)。 + +hdf52pb.py主要代码如下： +``` +def h5_to_pb(h5_weight_path, output_dir, out_prefix="output_", log_tensorboard=True): + if not os.path.exists(output_dir): + os.mkdir(output_dir) + + h5_model = build_model() + h5_model.load_weights(h5_weight_path) + + out_nodes = [] + for i in range(len(h5_model.outputs)): + out_nodes.append(out_prefix + str(i + 1)) + tf.identity(h5_model.output[i], out_prefix + str(i + 1)) + + model_name = os.path.splitext(os.path.split(h5_weight_path)[-1])[0] + index + '.pb' + + sess = K.get_session() + init_graph = sess.graph.as_graph_def() + main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes) + graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False) + if log_tensorboard: + from tensorflow.python.tools import import_pb_to_tensorboard + import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir) + +def build_model(): + h5_model = load_model(inference_model_hdf5) + return h5_model + +if __name__ == '__main__': + output_dir = os.path.join(output_path) + h5_weight_path=os.path.join(inference_weight_hdf5) + h5_to_pb(h5_weight_path, output_dir) + print('finished') +``` +我们提供转换好的[pb模型文件](obs://pix2pose/tless_inference/pb/)。 + +### pb转om +使用ATC模型转换工具进行模型转换时可以参考如下指令: +``` +atc --model=/home/HwHiAiUser/AscendProjects/path_to_file/file.pb --framework=3 --output=/home/HwHiAiUser/AscendProjects/path_to_file/filename_OM --soc_version=Ascend310 --input_shape="input_1:1,128,128,3" --log=info --out_nodes="output_1:0;output_2:0" +``` + +我们提供转换好的[om模型文件](obs://pix2pose/tless_inference/OM/)。 + +![输出结果](picture/pb2om.png) + +### msame工具 +我们采用msame工具进行离线推理，参考[msame简介](https://gitee.com/ascend/tools/tree/master/msame), 获取msame推理工具及使用方法。 + +获取到msame可执行文件之后，进行推理测试。 + +### 数据集转bin +该过程原训练代码3_train_pix2pose.py中generator_train.predict（）函数后加入以下代码，直接获取预处理好的图片，并以bin格式存储： +``` + if not (os.path.exists(weight_dir + "/pb_input/")): + os.makedirs(weight_dir + "/pb_input/") + + for i in range(n): + img_org = X_src[i] + inference = weight_dir + "/pb_input/" + str(i) + ".bin" + img_org.tofile(inference) +``` + +为了测试，我们提供了测试数据集,这是我们转换好的[bin文件](obs://pix2pose/tless_inference/bin_input/)。 + +### 推理测试 +使用msame推理工具，参考如下命令，发起推理测试： +``` +./msame --model "/home/HwHiAiUser/AscendProjects/path_to_file/filename_OM.om" --input "/home/HwHiAiUser/AscendProjects/path_to_file/bin_name.bin" --output "/home/HwHiAiUser/AscendProjects/Pix2pose/out " --outfmt TXT --loop 1 +``` +![输出结果](picture/om_output.png) + + + +最后，可视化后可以得到类似如下结果： +![可视化结果](picture/output_viz.png) + + + + diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/hdf52pb.py b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/hdf52pb.py new file mode 100644 index 0000000000000000000000000000000000000000..f8ed2e0d55625fe1d70b7e99a2f44cc10ac215e2 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/hdf52pb.py @@ -0,0 +1,75 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# =========================== +# Author : Ma Shiyuan +# Time : 2022/4 +# Language : Python +# =========================== +import os,sys +os.system("pip install keras==2.2.4") +print("-----------------------------------------") + +from keras.models import load_model +import tensorflow as tf +from keras import backend as K +from tensorflow.python.framework import graph_util, graph_io + +def h5_to_pb(h5_weight_path, output_dir, out_prefix="output_", log_tensorboard=True): + if not os.path.exists(output_dir): + os.mkdir(output_dir) + + h5_model = build_model() + h5_model.load_weights(h5_weight_path) + + out_nodes = [] + for i in range(len(h5_model.outputs)): + out_nodes.append(out_prefix + str(i + 1)) + tf.identity(h5_model.output[i], out_prefix + str(i + 1)) + + model_name = os.path.splitext(os.path.split(h5_weight_path)[-1])[0] + '.pb' + + sess = K.get_session() + init_graph = sess.graph.as_graph_def() + main_graph = graph_util.convert_variables_to_constants(sess, init_graph, out_nodes) + graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False) + if log_tensorboard: + from tensorflow.python.tools import import_pb_to_tensorboard + import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir) + + +def build_model(): + inference_model_hdf5='' + h5_model = load_model(inference_model_hdf5) + return h5_model + + +if __name__ == '__main__': + output_dir = '' + h5_weight_path='' + h5_to_pb(h5_weight_path, output_dir) + print('finished') \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..4f4eeb699cb9bfd53ef6361efe5807c90ff799c8 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/modelzoo_level.txt @@ -0,0 +1,6 @@ +ModelConvert:OK +QuantStatus:NOK +FuncStatus:OK +PrecisionStatus:OK +AutoTune:NOK +PerfStatus:NOK diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/om_output.png b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/om_output.png new file mode 100644 index 0000000000000000000000000000000000000000..a9f9785d98b344ed545d6990d02c44c41423cf75 Binary files /dev/null and b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/om_output.png differ diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/output_viz.png b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/output_viz.png new file mode 100644 index 0000000000000000000000000000000000000000..0e7cf5acc5cf0e2b49d3490ffd11153f79baaa48 Binary files /dev/null and b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/output_viz.png differ diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/pb2om.png b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/pb2om.png new file mode 100644 index 0000000000000000000000000000000000000000..dafc79aa354030f56c78496a9a53a87b7c04970e Binary files /dev/null and b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/picture/pb2om.png differ diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/requirements.txt b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ad87d4400008c9821a834ea4b30b0683c0f253ab --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/requirements.txt @@ -0,0 +1,6 @@ +python==3.7.5 +numpy==1.16.4 +tensorflow-gpu==1.15 +tensorboard==1.14.0 +matplotlib==2.2.3 +keras==2.2.4 diff --git a/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/txt2png.py b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/txt2png.py new file mode 100644 index 0000000000000000000000000000000000000000..79303da2505336f0ed87407e84cd3e57cc812158 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/Pix2pose_ID1164_for_TensorFlow_ACL/txt2png.py @@ -0,0 +1,61 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +from PIL import Image +import argparse +import os +from glob import glob +import matplotlib.pyplot as plt + + +def main(): + parser = argparse.ArgumentParser(description='') + parser.add_argument('--atc_dir', dest='atc_dir', default='', help='directory for atc result') + parser.add_argument('--width', dest='width', type=int, default=128) + parser.add_argument('--height', dest='height', type=int, default=128) + + args = parser.parse_args() + + result = np.loadtxt(args.atc_dir, dtype=np.float) + print(result.shape) + result_1 = result.reshape(args.width, args.height, 3) + print(result_1) + # import pdb + # pdb.set_trace() + # im = Image.fromarray(np.clip(result_1 * 255.0, , 255.0).astype('uint8')) + # im = Image.fromarray(np.uint8((result_1+1)/2 *255)) + # image=np.uint8((result_1 + 1) / 2 * 255) + image = result_1 + plt.figure() + plt.imshow(image) + plt.show() + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8fa513bf5ffc81ab7fd82922a19231a62ccfbd13 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/README.md @@ -0,0 +1,63 @@ +# SYNET +## 1.模型概述 +Symnet结合属性-对象转换的对称性原理和群论公理，由耦合网络和解耦网络两个模块组成，提出了基于Relative Moving Distance(RMD)的识别方法，利用属性的变化而非属性本身去分类属性。在Attribute-Object Composition零样本学习任务上取得了重大改进。 + +- 参考论文： + + [Li, Yong-Lu, et al. "Symmetry and group in attribute-object compositions." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.](https://arxiv.org/abs/2004.00587) + +- 官方实现： + + [SymNet](https://github.com/DirtyHarryLYL/SymNet) +## 2.环境 +基于昇腾310推理Ai1S环境，参考[快速创建离线推理Ai1S环境](https://gitee.com/ascend/modelzoo/wikis/%E7%A6%BB%E7%BA%BF%E6%8E%A8%E7%90%86%E6%A1%88%E4%BE%8B/%E5%BF%AB%E9%80%9F%E5%88%9B%E5%BB%BA%E7%A6%BB%E7%BA%BF%E6%8E%A8%E7%90%86Ai1S%E7%8E%AF%E5%A2%83) +配置环境。 +## 3.数据准备 +用户可运行download_data.sh下载数据集并进行预处理。预处理后的数据集已上传至obs中，包括原始数据，ckpt，pb，om，bin文件。 +obs路径：obs://cann-id1292-symnet/data/data.tar.gz。可以下载解压到项目根目录(SYMNET_ID1292_for_ACL)，目录结构： +``` +SYMNET_ID1292_for_ACL +├── data # 数据集 +├── data_bin.py # 制作bin文件 +├── evaluate_acc.py # 精度评估 +├── freeze_graph.py # ckpt转pb +├── inference.sh # msame推理 +├── modelarts_entry.py # modelarts训练拉起 +├── train_full_1p.sh # 执行freeze_graph.py +├── LICENSE +├── modelzoo_level.txt +├── pb_om.sh # pb转om +├── README.md +├── requirement.txt # 环境依赖 +└── utils +``` +## 4.CKPT转PB +在ModelArts平台，通过`modelarts_entry.py`拉起训练，执行`train_full_1p.sh`，运行`freeze_graph.py`将ckpt转pb。 +pb模型已上传至obs，路径 obs://cann-id1292-symnet/data/pb/ +## 5.PB转OM +执行`pb_om.sh`，使用atc转换pb模型为om模型，atc模型转换参考[ATC模型转换](https://support.huaweicloud.com/atctool-cann51RC1alpha2/atlasatc_16_0005.html) +om模型已上传obs，路径 obs://cann-id1292-symnet/data/om/ +``` +atc --model=./data/pb/symnet_new.pb --framework=3 --output=./data/om/symnet --soc_version=Ascend310 --input_shape="Placeholder_2:1,512;test_attr_id:116;test_obj_id:116;Placeholder_6:1,12" --out_nodes="Mul_18:0;Softmax_3:0;Placeholder_6:0" +``` +## 6.bin文件制作 +运行`data_bin.py`，将测试集数据制作为bin文件。 +bin文件已上传至obs，路径 obs://cann-id1292-symnet/data/bin_file/ +``` +python3 data_bin.py --data_url=./data --obj_pred=UT_obj_lr1e-3_test_ep260.pkl --bin_file=./data/bin_file/ +``` +## 7.msame离线推理 +参考 [msame](https://gitee.com/ascend/tools/tree/master/msame) 配置msame工具。 +可将msame生成工具（tools/msame/out下)复制至本项目文件夹下，或修改`inference.sh`中msame路径为工具路径。 +自定义bin文件输入路径，推理结果输出路径，执行`inference.sh`，进行推理。 +## 8.精度计算 +执行`evaluate_acc.py`，评估推理结果精度。 +``` +python3 evaluate_acc.py --input=/path/to/msame/output/ --data_url=./data --obj_pred=UT_obj_lr1e-3_test_ep260.pkl +``` +| | 数据集 | EPOCH| 精度 | +|-------|------|------|------| +| 原文 | UT | <700 | T1:52.1   T2:67.8   T3:76.0 | +| GPU | UT | 574 | T1:0.5116   T2:0.6719   T3:0.7616 | +| NPU | UT | 636 | T1:0.5007   T2:0.6684   T3:0.7571 | +| NPU离线推理 | UT | 636 | T1:0.4991   T2:0.6696   T3:0.7561| diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/data_bin.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/data_bin.py new file mode 100644 index 0000000000000000000000000000000000000000..fbb4b533eeeb42bd1d95a6e57f76880da4e17471 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/data_bin.py @@ -0,0 +1,76 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +from utils import dataset +import argparse, os + + +def make_parser(): + parser = argparse.ArgumentParser() + + parser.add_argument("--data_url", type=str, default="./data", help="Path to dataset") + parser.add_argument("--data", type=str, default='UT',choices=['MIT', 'UT', 'MITg', 'UTg'],help="Dataset name") + parser.add_argument("--test_bz", type=int, default=1024, help="Test batch size") + parser.add_argument("--obj_pred", type=str, default=None, help="Object prediction from pretrained model") + parser.add_argument("--bin_path", type=str, default='./data/bin_file/') + return parser + + +def main(): + parser = make_parser() + args = parser.parse_args() + + test_dataloader = dataset.get_dataloader(args.data_url, args.data, 'test', batchsize=args.test_bz, + obj_pred=args.obj_pred) + input_node = ["Placeholder_2", "test_att_id", "test_obj_id", "Placeholder_6"] + if not os.path.exists(args.bin_path): + os.mkdir(args.bin_path) + + for node in input_node: + if not os.path.exists(args.bin_path + node): + os.mkdir(args.bin_path + node + "/") + + dset = test_dataloader.dataset + test_att_id = np.array([dset.attr2idx[attr] for attr, _ in dset.pairs], dtype=np.int32) + test_obj_id = np.array([dset.obj2idx[obj] for _, obj in dset.pairs], dtype=np.int32) + + count = 0 + for image_ind, batch in enumerate(test_dataloader): + placeholder_2 = np.array(batch[4]) + placeholder_6 = np.array(batch[-1]) + + for i in range(0, len(placeholder_2)): + placeholder_2[i, :].tofile(args.bin_path + input_node[0] + "/{0:05d}.bin".format(count)) + test_att_id.tofile(args.bin_path + input_node[1] + "/{0:05d}.bin".format(count)) + test_obj_id.tofile(args.bin_path + input_node[2] + "/{0:05d}.bin".format(count)) + placeholder_6[i, :].tofile(args.bin_path + input_node[3] + "/{0:05d}.bin".format(count)) + count += 1 + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/download_data.sh b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..57bc362acd52039bd3ac34567b68f58b3c4433fd --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/download_data.sh @@ -0,0 +1,73 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +mkdir tmp + +# Download everything +mwget --show-progress -O tmp/attr-ops-data.tar.gz https://www.cs.utexas.edu/~tushar/attribute-ops/attr-ops-data.tar.gz +mwget --show-progress -O tmp/mitstates.zip http://wednesday.csail.mit.edu/joseph_result/state_and_transformation/release_dataset.zip +mwget --show-progress -O tmp/utzap.zip http://vision.cs.utexas.edu/projects/finegrained/utzap50k/ut-zap50k-images.zip +mwget --show-progress -O tmp/natural.tar.gz http://www.cs.cmu.edu/~spurushw/publication/compositional/compositional_split_natural.tar.gz +echo "Data downloaded. Extracting files..." + + +# Dataset metadata and features +tar -zxvf tmp/attr-ops-data.tar.gz --strip 1 +mv data/mit-states mit-states-original +mv data/ut-zap50k ut-zap50k-original +rm -r cv tensor-completion data + + + +# MIT-States +unzip tmp/mitstates.zip 'release_dataset/images/*' -d mit-states-original/ +mv mit-states-original/release_dataset/images mit-states-original/images/ +rm -r mit-states-original/release_dataset +rename "s/ /_/g" mit-states-original/images/* + +# UT-Zappos50k +unzip tmp/utzap.zip -d ut-zap50k-original/ +mv ut-zap50k-original/ut-zap50k-images ut-zap50k-original/_images/ +python reorganize_utzap.py +rm -r ut-zap50k-original/_images + + +# Natural split +tar -zxvf tmp/natural.tar.gz +mv mit-states/metadata_compositional-split-natural.t7 mit-states/metadata.t7 +mv ut-zap50k/metadata_compositional-split-natural.t7 ut-zap50k/metadata.t7 +mv mit-states/compositional-split-natural mit-states/compositional-split +mv ut-zap50k/compositional-split-natural ut-zap50k/compositional-split +mv mit-states mit-states-natural +mv ut-zap50k ut-zap50k-natural +ln -s ../mit-states-original/images mit-states-natural/images +ln -s ../ut-zap50k-original/images ut-zap50k-natural/images + + +# remove all zip files and temporary files +rm -r tmp diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/evaluate_acc.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/evaluate_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..03c354e64228064eaca05e27bd2a0ce895eed592 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/evaluate_acc.py @@ -0,0 +1,129 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import os, argparse +import torch +from utils import dataset +from collections import defaultdict +from utils.evaluator import CZSL_Evaluator + + +def make_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--input", type=str, required=True, help="Path to msame inference result") + parser.add_argument("--data_url", type=str, default="./data", help="Path to dataset") + parser.add_argument("--data", type=str, default='UT', choices=['MIT', 'UT', 'MITg', 'UTg'], help="Dataset name") + parser.add_argument("--test_bz", type=int, default=1, help="Test batch size") + parser.add_argument("--obj_pred", type=str, default=None, help="Object prediction from pretrained model") + return parser + + +def load_txt(path, type): + with open(path, 'r')as f: + line = f.readline().strip().split(" ") + + predict = np.asarray(line, dtype=type).reshape(1, -1) + return predict + + +def formated_czsl_result(report): + fstr = 'rA:{real_attr_acc:.4f}|rO:{real_obj_acc:.4f}|Cl/T1:{top1_acc:.4f}|T2:{top2_acc:.4f}|T3:{top3_acc:.4f}' + return fstr.format(**report) + + +def main(): + parser = make_parser() + args = parser.parse_args() + test_dataloader = dataset.get_dataloader(args.data_url, args.data, 'test', batchsize=args.test_bz, + obj_pred=args.obj_pred) + dset = test_dataloader.dataset + test_att_id = np.array([dset.attr2idx[attr] for attr, _ in dset.pairs]) + test_obj_id = np.array([dset.obj2idx[obj] for _, obj in dset.pairs]) + + evaluator = CZSL_Evaluator(test_dataloader.dataset, None) + + accuracies_pair = defaultdict(list) + accuracies_attr = defaultdict(list) + accuracies_obj = defaultdict(list) + + for idx, batch in enumerate(test_dataloader): + id = "{0:05d}_output_".format(idx) + prob_P_rmd = load_txt(os.path.join(args.input, id + "0.txt")) + prob_A_attr = load_txt(os.path.join(args.input, id + "1.txt")) + prob_O = load_txt(os.path.join(args.input, id + "2.txt")) + score = dict([ + ("score_rmd", [prob_P_rmd, prob_A_attr, prob_O]), # Mul_18, Softmax_3, Placeholder_6 + ]) + + for key in score.keys(): + score[key][0] = { + (a, o): torch.from_numpy(score[key][0][:, j]) + for j, (a, o) in enumerate(zip(test_att_id, test_obj_id)) + } + + prediction = score + attr_truth, obj_truth = batch[1], batch[2] + attr_truth, obj_truth = torch.from_numpy(attr_truth), torch.from_numpy(obj_truth) + + for key in prediction.keys(): + p_pair, p_a, p_o = prediction[key] + pair_results = evaluator.score_model(p_pair, obj_truth) + match_stats = evaluator.evaluate_predictions(pair_results, attr_truth, obj_truth) + accuracies_pair[key].append(match_stats) # 0/1 sequence of t/f + + a_match, o_match = evaluator.evaluate_only_attr_obj(p_a, attr_truth, p_o, obj_truth) + + accuracies_attr[key].append(a_match) + accuracies_obj[key].append(o_match) + + for name in accuracies_pair.keys(): + accuracies = accuracies_pair[name] + accuracies = zip(*accuracies) + accuracies = map(torch.mean, map(torch.cat, accuracies)) + attr_acc, obj_acc, closed_1_acc, closed_2_acc, closed_3_acc, _, objoracle_acc = map(lambda x: x.item(), + accuracies) + + real_attr_acc = torch.mean(torch.cat(accuracies_attr[name])).item() + real_obj_acc = torch.mean(torch.cat(accuracies_obj[name])).item() + + report_dict = { + 'real_attr_acc': real_attr_acc, + 'real_obj_acc': real_obj_acc, + 'top1_acc': closed_1_acc, + 'top2_acc': closed_2_acc, + 'top3_acc': closed_3_acc, + } + + print(name + ": " + formated_czsl_result(report_dict)) + + pass + + +if __name__ == '__main__': + main() + diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/freeze_graph.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/freeze_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..a6a231df1787241a89678b82bc25f8a443dbf91a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/freeze_graph.py @@ -0,0 +1,193 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from tensorflow.python.framework import graph_util +from utils import dataset, utils +import argparse +import os, tqdm +import numpy as np +import torch +from utils.evaluator import CZSL_Evaluator +from collections import defaultdict +from npu_bridge.npu_init import * + + +def make_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--train_url", type=str, default="./output", required=True, + help="output path") + parser.add_argument("--data_url", type=str, default="./data", required=True, + help="input path") + parser.add_argument("--ckpt", type=str, required=True) + parser.add_argument("--data", type=str, default='UT', choices=['MIT', 'UT', 'MITg', 'UTg'], help="Dataset name") + parser.add_argument("--test_bz", type=int, default=1024, help="Test batch size") + parser.add_argument("--obj_pred", type=str, default=None, help="Object prediction from pretrained model") + return parser + + +def freeze_graph(input_checkpoint, output_graph): + """ + ckpt转pb + """ + # 输出节点 + output_node_names = "Mul_18,Softmax_3,Placeholder_6" + saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True) + graph = tf.get_default_graph() + input_graph_def = graph.as_graph_def() + + with utils.create_session() as sess: + saver.restore(sess, input_checkpoint) + output_graph_def = graph_util.convert_variables_to_constants( + sess=sess, + input_graph_def=input_graph_def, + output_node_names=output_node_names.split(",")) # 多个输出节点，以逗号隔开 + + with tf.gfile.GFile(output_graph, "wb") as f: # 保存模型 + f.write(output_graph_def.SerializeToString()) # 序列化输出 + print("%d ops in the final graph." % len(output_graph_def.node)) + + +def test_pb(args, pb_path): + """ + 在线推理，测试pb模型 + """ + print("Loading test dataset") + test_dataloader = dataset.get_dataloader(args.data_url, args.data, 'test', batchsize=args.test_bz, + obj_pred=args.obj_pred) + + network = 1 + evaluator = CZSL_Evaluator(test_dataloader.dataset, network) + with tf.Graph().as_default(): + output_graph_def = tf.GraphDef() + with open(pb_path, "rb") as f: + output_graph_def.ParseFromString(f.read()) + tf.import_graph_def(output_graph_def, name="") + + with utils.create_session() as sess: + sess.run(tf.global_variables_initializer()) + + print("get input tensor") + # 输入张量 + pos_image_feat = sess.graph.get_tensor_by_name("Placeholder_2:0") + test_attr_id = sess.graph.get_tensor_by_name("test_attr_id:0") + test_obj_id = sess.graph.get_tensor_by_name("test_obj_id:0") + pos_obj_prediction = sess.graph.get_tensor_by_name("Placeholder_6:0") + + print("get output tensor") + # 输出张量 + prob_P_rmd = sess.graph.get_tensor_by_name("Mul_18:0") + prob_A_attr = sess.graph.get_tensor_by_name("Softmax_3:0") + prob_O = sess.graph.get_tensor_by_name("Placeholder_6:0") + score_op = dict([ + ("score_rmd", [prob_P_rmd, prob_A_attr, prob_O]), # Mul_18, Softmax_3, Placeholder_6 + ]) + + accuracies_pair = defaultdict(list) + accuracies_attr = defaultdict(list) + accuracies_obj = defaultdict(list) + + for image_ind, batch in tqdm.tqdm(enumerate(test_dataloader), total=len(test_dataloader), postfix='test'): + dset = test_dataloader.dataset + test_att = np.array([dset.attr2idx[attr] for attr, _ in dset.pairs]) + test_obj = np.array([dset.obj2idx[obj] for _, obj in dset.pairs]) + + feed_dict = { + pos_image_feat: batch[4], # Placeholder_2 + test_attr_id: test_att, # test_attr_id + test_obj_id: test_obj, # test_obj_id + pos_obj_prediction: batch[-1], # Placeholder_6 + } + score = sess.run(score_op, feed_dict=feed_dict) + for key in score_op.keys(): + score[key][0] = { + (a, o): torch.from_numpy(score[key][0][:, i]) + for i, (a, o) in enumerate(zip(test_att, test_obj)) + } + + attr_truth, obj_truth = batch[1], batch[2] + attr_truth, obj_truth = torch.from_numpy(attr_truth), torch.from_numpy(obj_truth) + + match_stats = [] + for key in score_op.keys(): + p_pair, p_a, p_o = score[key] + pair_results = evaluator.score_model(p_pair, obj_truth) + match_stats = evaluator.evaluate_predictions(pair_results, attr_truth, obj_truth) + accuracies_pair[key].append(match_stats) # 0/1 sequence of t/f + + a_match, o_match = evaluator.evaluate_only_attr_obj(p_a, attr_truth, p_o, obj_truth) + + accuracies_attr[key].append(a_match) + accuracies_obj[key].append(o_match) + + for name in accuracies_pair.keys(): + accuracies = accuracies_pair[name] + accuracies = zip(*accuracies) + accuracies = map(torch.mean, map(torch.cat, accuracies)) + attr_acc, obj_acc, closed_1_acc, closed_2_acc, closed_3_acc, _, objoracle_acc = map(lambda x: x.item(), + accuracies) + + real_attr_acc = torch.mean(torch.cat(accuracies_attr[name])).item() + real_obj_acc = torch.mean(torch.cat(accuracies_obj[name])).item() + + report_dict = { + 'real_attr_acc': real_attr_acc, + 'real_obj_acc': real_obj_acc, + 'top1_acc': closed_1_acc, + 'top2_acc': closed_2_acc, + 'top3_acc': closed_3_acc, + 'name': "symnet", + 'epoch': 636, + } + + print(name + ": " + utils.formated_czsl_result(report_dict)) + + pass + + +def main(): + parser = make_parser() + args = parser.parse_args() + + weight_dir = os.path.join(args.data_url, './weights') + ckpt_path = os.path.join(weight_dir, args.ckpt) + print("ckpt path => ", ckpt_path) + + pb_path = os.path.join(args.train_url, './pb/') + print("pb path => ", pb_path) + if not os.path.exists(pb_path): + os.mkdir(pb_path) + + saved_pb_path = os.path.join(args.data_url, './pb/symnet.pb') + print("saved pb path => ", saved_pb_path) + + freeze_graph(ckpt_path, pb_path + "symnet.pb") + # test_pb(args, pb_path=pb_path+"symnet.pb") + + +if __name__ == '__main__': + main() diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/inference.sh b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..24ee9a5aaff4bb7f7168fc11ea6d76a97a4d7758 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/inference.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +Placeholder_2=./data/bin_file/Placeholder_2 +test_att_id=./data/bin_file/test_att_id +test_obj_id=./data/bin_file/test_obj_id +Placeholder_6=./data/bin_file/Placeholder_6 + +om_path="/home/HwHiAiUser/AscendProjects/SYMNET_ID1292_for_ACL/data/om/symnet.om" +output_path=./data/output +ulimit -c 0 +./msame --model ${om_path} --input ${Placeholder_2},${test_att_id},${test_obj_id},${Placeholder_6} --output ${output_path} --outfmt TXT diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelarts_entry.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelarts_entry.py new file mode 100644 index 0000000000000000000000000000000000000000..28515c5d102a251ac55d7d9a0c0bd2d1512e4906 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelarts_entry.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..2a39f2221b8103c0ae90337cb4b6bd67c69f2d11 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/modelzoo_level.txt @@ -0,0 +1,2 @@ +FuncStatus:OK +PrecisionStatus:OK \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/pb_om.sh b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/pb_om.sh new file mode 100644 index 0000000000000000000000000000000000000000..8540599f94fcea2381368f9df6ec526a8364cad9 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/pb_om.sh @@ -0,0 +1,3 @@ +#!/bin/bash +atc --model=./data/pb/symnet_new.pb --framework=3 --output=./data/om/symnet --soc_version=Ascend310 --input_shape="Placeholder_2:1,512;test_attr_id:116;test_obj_id:116;Placeholder_6:1,12" --out_nodes="Mul_18:0;Softmax_3:0;Placeholder_6:0" + diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/requirement.txt b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/requirement.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a7a6d70cba558a4ae408f9f1a5180a12e275ddd --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/requirement.txt @@ -0,0 +1,4 @@ +tensorflow==1.15.0 +torch==1.10.0 +torchvision +numpy==1.21.2 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train_full_1p.sh b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/train_full_1p.sh similarity index 88% rename from TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train_full_1p.sh rename to ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/train_full_1p.sh index 27cfdbd7a5c7316e9f82ea6d5e88abf661858bbb..d712ad6ee86d9cab42bb8b4ac25e2f4cc41155bd 100644 --- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train_full_1p.sh +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/train_full_1p.sh @@ -1,187 +1,185 @@ -#!/bin/bash - -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## -# shell脚本所在路径 -cur_path=`echo $(cd $(dirname $0);pwd)` - -# 判断当前shell是否是performance -perf_flag=`echo $0 | grep performance | wc -l` - -# 当前执行网络的名称 -Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` - -export RANK_SIZE=1 -export RANK_ID=0 -export JOB_ID=10087 - -# 路径参数初始化 -data_path='' -output_path='' - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_performance_1P.sh " - echo " " - echo "parameter explain: - --data_path # dataset of training - --output_path # output of training - --train_steps # max_step for training - --train_epochs # max_epoch for training - --batch_size # batch size - -h/--help show help message - " - exit 1 -fi - -# 参数校验，不需要修改 -for para in $* -do - if [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - elif [[ $para == --output_path* ]];then - output_path=`echo ${para#*=}` - elif [[ $para == --train_steps* ]];then - train_steps=`echo ${para#*=}` - elif [[ $para == --train_epochs* ]];then - train_epochs=`echo ${para#*=}` - elif [[ $para == --batch_size* ]];then - batch_size=`echo ${para#*=}` - fi -done - -# 校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be config" - exit 1 -fi - -# 校验是否传入output_path,不需要修改 -if [[ $output_path == "" ]];then - output_path="./test/output/${ASCEND_DEVICE_ID}" -fi - -# 设置打屏日志文件名，请保留，文件名为${print_log} -print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" -modelarts_flag=${MODELARTS_MODEL_PATH} -if [ x"${modelarts_flag}" != x ]; -then - echo "running without etp..." - print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` - print_log="/home/ma-user/modelarts/log/${print_log_name}" -fi -echo "### get your log here : ${print_log}" - -CaseName="" -function get_casename() -{ - if [ x"${perf_flag}" = x1 ]; - then - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' - else - CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' - fi -} - -# 跳转到code目录 -cd ${cur_path}/../ -rm -rf ./test/output/${ASCEND_DEVICE_ID} -mkdir -p ./test/output/${ASCEND_DEVICE_ID} - -# 训练开始时间记录，不需要修改 -start_time=$(date +%s) -########################################################## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -#########第3行 至 100行，请一定不要、不要、不要修改########## -########################################################## - -#========================================================= -#========================================================= -#========训练执行命令，需要根据您的网络进行修改============== -#========================================================= -#========================================================= -# 基础参数，需要模型审视修改 -# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 -# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 -# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 -batch_size=128 - - -if [ x"${modelarts_flag}" != x ]; -then - python3.7 ./train.py - python3.7 ./get_map.py -else - python3.7 ./train.py 1>>${print_log} 2>&1 - python3.7 ./get_map.py 1>>${print_log} 2>&1 -fi - -# 性能相关数据计算 -StepTime=`grep "ms/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}*1000'}'` - -# 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` -# 提取所有loss打印信息 -grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - - -########################################################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -#########后面的所有内容请不要修改########################### -########################################################### - -# 判断本次执行是否正确使用Ascend NPU -use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` -if [ x"${use_npu_flag}" == x0 ]; -then - echo "------------------ ERROR NOTICE START ------------------" - echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." - echo "------------------ ERROR NOTICE END------------------" -else - echo "------------------ INFO NOTICE START------------------" - echo "INFO, your task have used Ascend NPU, please check your result." - echo "------------------ INFO NOTICE END------------------" -fi - -# 获取最终的casename，请保留，case文件名为${CaseName} -get_casename - -# 重命名loss文件 -if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; -then - mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt -fi - -# 训练端到端耗时 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -echo "------------------ Final result ------------------" -# 输出性能FPS/单step耗时/端到端耗时 -echo "Final Performance images/sec : $FPS" -echo "Final Performance ms/step : $StepTime" -echo "E2E Training Duration sec : $e2e_time" - -# 输出训练精度 -echo "Final Train Accuracy : ${train_accuracy}" - -# 最后一个迭代loss值，不需要修改 -ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=256 +epochs=636 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./freeze_graph.py --data_url=${data_path} --train_url=${output_path} --ckpt snapshot_epoch_636.ckpt --data UT --obj_pred UT_obj_lr1e-3_test_ep260.pkl +else + python3.7 ./freeze_graph.py --data_url=${data_path} --train_url=${output_path} --ckpt snapshot_epoch_636.ckpt --data UT --obj_pred UT_obj_lr1e-3_test_ep260.pkl +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 20 | awk -F ':' '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Best score_rmd" ${print_log} | tail -n 1 | awk -F / '{print $NF}'` +# 提取所有loss打印信息 +grep "Current score_rmd" ${print_log} | awk -F "/" '{print NF}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/__init__.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_attrs.json b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_attrs.json new file mode 100644 index 0000000000000000000000000000000000000000..8b45cb23eb771598ffe18a75268dec69026450d4 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_attrs.json @@ -0,0 +1 @@ +{"crumpled": "crumpled", "upright": "upright", "bright": "bright", "rough": "rough", "shattered": "shattered", "cut": "cut", "torn": "torn", "folded": "folded", "young": "young", "wet": "wet", "cluttered": "cluttered", "verdant": "verdant", "sunny": "sunny", "runny": "runny", "thawed": "thawed", "dark": "dark", "windblown": "windblown", "burnt": "burnt", "molten": "molten", "eroded": "eroded", "frayed": "frayed", "blunt": "blunt", "cloudy": "cloudy", "large": "large", "whipped": "whipped", "small": "small", "engraved": "engraved", "heavy": "heavy", "old": "old", "thin": "thin", "diced": "diced", "rusty": "rusty", "inflated": "inflated", "ruffled": "ruffled", "steaming": "steaming", "unpainted": "unpainted", "moldy": "moldy", "closed": "closed", "new": "new", "filled": "filled", "dirty": "dirty", "ripped": "ripped", "full": "full", "squished": "squished", "peeled": "peeled", "broken": "broken", "mashed": "mashed", "pureed": "pureed", "dry": "dry", "chipped": "chipped", "spilled": "spilled", "coiled": "coiled", "wrinkled": "wrinkled", "narrow": "narrow", "fallen": "fallen", "muddy": "muddy", "sliced": "sliced", "sharp": "sharp", "unripe": "unripe", "thick": "thick", "open": "open", "standing": "standing", "ancient": "ancient", "toppled": "toppled", "weathered": "weathered", "murky": "murky", "damp": "damp", "tiny": "tiny", "grimy": "grimy", "viscous": "viscous", "empty": "empty", "scratched": "scratched", "painted": "painted", "pierced": "pierced", "draped": "draped", "loose": "loose", "browned": "browned", "foggy": "foggy", "brushed": "brushed", "dull": "dull", "wide": "wide", "winding": "winding", "frozen": "frozen", "straight": "straight", "smooth": "smooth", "worn": "worn", "melted": "melted", "pressed": "pressed", "cracked": "cracked", "bent": "bent", "ripe": "ripe", "mossy": "mossy", "modern": "modern", "raw": "raw", "lightweight": "lightweight", "creased": "creased", "curved": "curved", "huge": "huge", "tight": "tight", "crinkled": "crinkled", "wilted": "wilted", "dented": "dented", "crushed": "crushed", "tall": "tall", "short": "short", "shiny": "shiny", "clear": "clear", "splintered": "splintered", "cored": "cored", "cooked": "cooked", "clean": "clean", "deflated": "deflated", "barren": "barren", "fresh": "fresh", "caramelized": "caramelized"} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_gamma.json b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_gamma.json new file mode 100644 index 0000000000000000000000000000000000000000..2801ae1e2654ea04e35420ab3e2248e978e44a13 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_gamma.json @@ -0,0 +1 @@ +{"attr_b": [0.8, 0.8, 0.8, 1.0, 1.2, 1.0, 1.0, 1.0, 1.0, 1.2, 0.8, 1.2, 0.8, 0.8, 1.0, 1.2, 1.0, 1.2, 0.8, 1.0, 0.8, 1.0, 0.8, 1.0, 1.2, 0.8, 0.8, 1.0, 0.8, 1.0, 1.0, 0.8, 1.0, 0.8, 1.0, 0.8, 1.2, 1.2, 1.0, 1.0, 1.2, 0.8, 0.8, 1.2, 0.8, 0.8, 0.8, 0.8, 1.2, 1.2, 0.8, 1.2, 0.8, 1.0, 0.8, 1.2, 1.2, 1.0, 0.8, 1.0, 1.2, 0.8, 1.0, 1.2, 0.8, 0.8, 1.2, 0.8, 0.8, 0.8, 1.2, 1.0, 0.8, 0.8, 1.2, 0.8, 1.0, 0.8, 0.8, 1.0, 1.0, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8, 0.8, 1.2, 0.8, 1.2, 0.8, 0.8, 1.0, 0.8, 1.0, 0.8, 0.8, 1.0, 1.2, 0.8, 1.0, 0.8, 0.8, 1.2, 1.2, 0.8, 0.8, 0.8], "attr_a": [0.8, 0.6000000000000001, 0.6000000000000001, 1.0, 1.2, 1.0, 1.0, 1.0, 1.0, 1.2, 0.6000000000000001, 1.2, 0.6000000000000001, 0.6000000000000001, 1.0, 1.2, 1.0, 1.2, 0.6000000000000001, 1.0, 0.6000000000000001, 1.0, 0.8, 1.0, 1.2, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 1.0, 1.0, 0.8, 1.0, 0.6000000000000001, 1.0, 0.8, 1.2, 1.2, 1.0, 1.0, 1.2, 0.8, 0.6000000000000001, 1.2, 0.8, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.2, 1.2, 0.6000000000000001, 1.2, 0.6000000000000001, 1.0, 0.8, 1.0, 1.2, 1.0, 0.8, 1.0, 1.2, 0.6000000000000001, 1.0, 1.0, 0.6000000000000001, 0.8, 1.2, 0.8, 0.6000000000000001, 0.6000000000000001, 1.0, 1.0, 0.6000000000000001, 0.6000000000000001, 1.2, 0.6000000000000001, 1.0, 0.8, 0.6000000000000001, 1.0, 1.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 1.0, 1.0, 1.0, 1.0, 0.6000000000000001, 0.8, 1.2, 0.8, 1.2, 0.6000000000000001, 0.6000000000000001, 1.0, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 1.2, 0.6000000000000001, 1.0, 0.6000000000000001, 0.8, 1.2, 1.2, 0.6000000000000001, 0.8, 0.6000000000000001], "comp_b": [0.74, 0.14, 0.54, 1, 0.78, 0.84, 0.8, 1.06, 1.18, 1.28, 0.02, 0.02, 1.0, 0.0, 1.0, 1.62, 1.0, 0.58, 0.08, 1, 1.0, 1.26, 1.02, 1, 1.1400000000000001, 1, 0.0, 0.72, 0.74, 0.66, 1.04, 0.56, 0.56, 0.64, 1.34, 1.04, 1.08, 0.84, 1.0, 0.78, 1.0, 0.66, 0.02, 0.54, 0.7000000000000001, 0.56, 1, 0.6, 0.66, 0.9, 0.84, 1.82, 0.9400000000000001, 1.46, 0.46, 0.62, 1.1, 1.7, 0.64, 0.6, 0.52, 0.38, 0.76, 1.0, 1.0, 0.64, 0.72, 0.28, 0.28, 0.48, 0.46, 0.52, 1.0, 1.0, 1.0, 1.0, 0.68, 1.18, 1.0, 0.52, 0.12, 1.0, 1.0, 0.84, 1.0, 0.0, 0.34, 0.08, 1.0, 1.06, 0.46, 1.36, 1.0, 1.1400000000000001, 1.0, 0.56, 0.88, 1.0, 1.0, 1.06, 0.04, 0.78, 1.0, 1.02, 0.0, 1.0, 0.08, 0.56, 0.56, 0.36, 0.24, 0.32, 0.06, 1.02, 0.14], "comp_a": [0.7800000000000002, 0.22, 0.6200000000000003, 1, 0.8800000000000003, 0.8600000000000001, 0.7800000000000002, 1.0600000000000003, 1.1400000000000001, 1.1600000000000001, 0.02, 0.06, 1.0, 0.12, 1.0, 1.4600000000000002, 0.9400000000000002, 0.6800000000000002, 0.08, 1, 1.0, 1.1600000000000001, 1.0000000000000002, 1, 1.1200000000000003, 1, 0.0, 0.8200000000000003, 0.7800000000000002, 0.7000000000000002, 1.0400000000000003, 0.6400000000000003, 0.7200000000000004, 0.7200000000000002, 1.4800000000000004, 1.0600000000000003, 1.0200000000000002, 0.8800000000000001, 1.0, 0.8200000000000003, 1.0, 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0.4199999999999998, 0.22, 1.0200000000000002, 0.2]} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_objs.json b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_objs.json new file mode 100644 index 0000000000000000000000000000000000000000..c25a453353dec1fd265e136ebd46adcf259716e6 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_objs.json @@ -0,0 +1 @@ +{"lightbulb": "lightbulb", "shoes": "shoes", "deck": "deck", "laptop": "laptop", "ceramic": "ceramic", "paper": "paper", "vacuum": "vacuum", "keyboard": "keyboard", "chair": "chair", "milk": "milk", "roots": "roots", "carpet": "carpet", "sauce": "sauce", "tire": "tire", "mud": "mud", "sky": "sky", "lake": "lake", "sugar": "sugar", "bush": "bush", "bike": "bike", "fig": "fig", "garage": "garage", "orange": "orange", "furniture": "furniture", "hat": "hat", "persimmon": "persimmon", "boulder": "boulder", "plate": "plate", "coffee": "coffee", "handle": "handle", "branch": "branch", "wire": "wire", "bear": "bear", "coast": "coast", "vegetable": "vegetable", "bean": "bean", "tulip": "tulip", "fan": "fan", "snake": "snake", "desk": "desk", "silk": "silk", "bread": "bread", "aluminum": "aluminum", "cable": "cable", "gemstone": "gemstone", "bracelet": "bracelet", "candy": "candy", "bay": "bay", "bag": "bag", "sand": "sand", "steps": "steps", "knife": "knife", "clay": "clay", "iguana": "iguana", "tower": "tower", "river": "river", "garden": "garden", "clothes": "clothes", "copper": "copper", "creek": "creek", "fence": "fence", "house": "house", "fish": "fish", "library": "library", "chocolate": "chocolate", "computer": "computer", "palm": "palm", "roof": "roof", "sea": "sea", "mirror": "mirror", "candle": "candle", "lightning": "lightning", "chicken": "chicken", "ribbon": "ribbon", "redwood": "redwood", "shower": "shower", "flower": "flower", "leaf": "leaf", "jewelry": "jewelry", "lead": "lead", "clock": "clock", "armor": "armor", "tube": "tube", "ice": "ice", "granite": "granite", "sword": "sword", "cloud": "cloud", "ground": "ground", "chains": "chains", "nest": "nest", "highway": "highway", "pants": "pants", "cord": "cord", "hose": "hose", "dirt": "dirt", "salmon": "salmon", "rock": "rock", "horse": "horse", "water": "water", "newspaper": "newspaper", "cookie": "cookie", "key": "key", "pasta": "pasta", "paste": "paste", "trail": "trail", "card": "card", "kitchen": "kitchen", "box": "box", "stone": "stone", "drum": "drum", "thread": "thread", "column": "column", "island": "island", "tie": "tie", "berry": "berry", "smoke": "smoke", "castle": "castle", "glasses": "glasses", "road": "road", "cheese": "cheese", "apple": "apple", "wall": "wall", "pot": "pot", "canyon": "canyon", "tomato": "tomato", "frame": "frame", "church": "church", "table": "table", "ring": "ring", "brass": "brass", "boat": "boat", "belt": "belt", "city": "city", "bathroom": "bathroom", "toy": "toy", "fabric": "fabric", "beef": "beef", "window": "window", "tree": "tree", "plastic": "plastic", "paint": "paint", "camera": "camera", "bronze": "bronze", "tea": "tea", "valley": "valley", "bubble": "bubble", "banana": "banana", "building": "building", "ceiling": "ceiling", "diamond": "diamond", "door": "door", "gear": "gear", "shorts": "shorts", "fire": "fire", "bus": "bus", "wax": "wax", "envelope": "envelope", "oil": "oil", "cabinet": "cabinet", "tiger": "tiger", "glass": "glass", "nut": "nut", "potato": "potato", "steel": "steel", "wood": "wood", "wool": "wool", "room": "room", "salad": "salad", "car": "car", "blade": "blade", "bucket": "bucket", "bed": "bed", "cat": "cat", "rope": "rope", "soup": "soup", "street": "street", "flame": "flame", "cake": "cake", "bridge": "bridge", "stream": "stream", "well": "well", "penny": "penny", "pie": "pie", "shell": "shell", "pond": "pond", "ocean": "ocean", "dress": "dress", "cotton": "cotton", "mountain": "mountain", "sandwich": "sandwich", "lemon": "lemon", "shirt": "shirt", "concrete": "concrete", "town": "town", "balloon": "balloon", "cave": "cave", "bowl": "bowl", "snow": "snow", "rubber": "rubber", "field": "field", "book": "book", "forest": "forest", "animal": "animal", "elephant": "elephant", "tile": "tile", "gate": "gate", "beach": "beach", "pizza": "pizza", "wheel": "wheel", "wave": "wave", "plant": "plant", "ball": "ball", "mat": "mat", "screw": "screw", "farm": "farm", "eggs": "eggs", "foam": "foam", "pear": "pear", "necklace": "necklace", "fruit": "fruit", "garlic": "garlic", "log": "log", "moss": "moss", "dust": "dust", "velvet": "velvet", "basement": "basement", "coin": "coin", "desert": "desert", "pool": "pool", "cliff": "cliff", "butter": "butter", "phone": "phone", "coat": "coat", "seafood": "seafood", "floor": "floor", "metal": "metal", "dog": "dog", "meat": "meat", "jacket": "jacket", "coal": "coal", "shore": "shore", "truck": "truck", "jungle": "jungle", "bottle": "bottle", "basket": "basket"} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_weight.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_weight.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa77c11445fbbc99fd3bbedc43d90221fc8d06e --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/MIT_weight.py @@ -0,0 +1,30 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +attr_weight = [4.561632133281056, 4.325826700719732, 5.5088676690046325, 7.118305581438733, 4.974046539682426, 3.6046302163498876, 4.8447080253179395, 4.474159182316003, 3.8754342510143167, 4.419824831352674, 4.471624326712815, 5.098651958137871, 4.88216558785284, 6.260855349587511, 7.192413553592455, 5.638325736241519, 3.8465662670134644, 5.560160963392184, 5.537855205877885, 4.041981300554439, 5.137304112572149, 4.417423869815136, 4.382084503369827, 4.048592894886752, 4.794518280794085, 4.454058002994916, 5.4070849746946905, 5.487665461354029, 5.606323005155346, 5.070612738073478, 4.474159182316003, 4.937081345448955, 4.85952311110308, 4.129741797363355, 7.231634266745736, 4.266361200676454, 3.769655412823722, 4.542427153445006, 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--- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_attrs.json @@ -0,0 +1 @@ +{"Synthetic": "synthetic", "Canvas": "canvas", "Sheepskin": "sheepskin", "Patent.Leather": "leather", "Wool": "wool", "Leather": "leather", "Satin": "satin", "Hair.Calf": "hair", "Full.grain.leather": "leather", "Rubber": "rubber", "Faux.Leather": "leather", "Suede": "suede", "Nylon": "nylon", "Nubuck": "leather", "Faux.Fur": "fur", "Cotton": "cotton"} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_gamma.json b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_gamma.json new file mode 100644 index 0000000000000000000000000000000000000000..e7630ac336f349ede2925aeb3a7dd4fd772c661b --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_gamma.json @@ -0,0 +1 @@ +{"attr_b": [1.0, 1.0, 0.8, 1.0, 1.2, 1.0, 1.0, 1.0, 1.2, 0.8, 1.0, 0.8, 1.2, 1.0, 0.8, 1.0], "attr_a": [1.0, 1.0, 0.8, 0.8, 1.2, 1.0, 0.8, 1.0, 1.0, 0.8, 0.8, 0.8, 1.2, 1.0, 0.8, 0.8], "comp_b": [0.84, 0.1, 1.0, 0.46, 0.5, 0.74, 0.62, 2.3000000000000003, 0.55, 0.56, 0.36, 0.6000000000000001, 0.68, 0.78, 0.54, 0.5], "comp_a": [0.8400000000000001, 0.0, 1.0, 0.44000000000000017, 0.48000000000000015, 0.7800000000000002, 0.6000000000000001, 2.3000000000000003, 0.45, 0.5600000000000003, 0.3199999999999999, 0.6000000000000001, 0.6599999999999997, 0.7800000000000002, 0.5400000000000003, 0.48000000000000015]} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_objs.json b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_objs.json new file mode 100644 index 0000000000000000000000000000000000000000..13ff1ede97754a862c1a95e629bca8386ba08e1a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_objs.json @@ -0,0 +1 @@ +{"Shoes.Clogs.and.Mules": "clogs", "Shoes.Heels": "heels", "Boots.Mid-Calf": "midcalf", "Shoes.Flats": "flats", "Boots.Knee.High": "knee-high", "Shoes.Sneakers.and.Athletic.Shoes": "sneakers", "Shoes.Boat.Shoes": "shoes", "Shoes.Oxfords": "oxfords", "Boots.Ankle": "boots", "Sandals": "sandals", "Slippers": "slippers", "Shoes.Loafers": "loafers"} \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_weight.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_weight.py new file mode 100644 index 0000000000000000000000000000000000000000..d3315e704f21f9af935dc4cf260fb388db34902a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/UT_weight.py @@ -0,0 +1,30 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +attr_weight = [3.3346977756321516, 6.045005314036012, 6.315880268171411, 4.128581330635162, 3.4429435720973482, 5.932888015915306, 0.5385720013774027, 4.785004678240413, 4.751904729814068, 3.435434150075217, 3.7892631298020407, 4.174507768761085, 5.468582407784208, 2.0151239462217565, 2.3481072476387737, 5.180900335332427] +obj_weight = [2.0469601212700113, 2.7404183922042185, 2.442367117505141, 2.230464336693612, 5.948155488046094, 3.5078171577379704, 2.256203834895188, 1.7291003739616688, 2.5755686404252045, 2.947818448105355, 1.840059754211169, 4.629481314601183] +pair_weight = [6.190717125217406, 7.637636108153731, 6.7903382477665275, -0.0, 7.078020320218308, -0.0, -0.0, 7.483485428326473, 3.4879094000800452, 7.414492556839521, -0.0, -0.0, 6.9444889275937856, 7.819957664947686, 7.819957664947686, 7.557593400480195, -0.0, 6.656806855142005, 6.01166889376842, -0.0, -0.0, 5.29422902063943, 6.539023819485621, 5.468582407784208, 7.232171000045566, 6.097191067206582, -0.0, 5.765833931252139, 4.8861007951117825, 5.449713923479825, 7.557593400480195, -0.0, 5.859862880900415, 6.152250844389609, -0.0, 5.173782867563563, 5.145809015521157, 7.289329413885515, 7.637636108153731, 7.078020320218308, -0.0, 7.34995403570195, 2.445142327426924, 3.2258484263190192, 2.9401906460564686, 2.412785893487567, -0.0, 3.8254334380077957, 2.78041102735701, 2.6231194673495026, 2.928105906841397, 3.1359762985353044, 2.7908277882152652, -0.0, 6.230722459831105, 7.724647485143361, -0.0, -0.0, 7.557593400480195, 6.864446219920249, 6.721345376279576, 7.232171000045566, -0.0, 6.01166889376842, -0.0, 7.232171000045566, 5.111907463845475, 6.987048542012581, 6.9444889275937856, -0.0, 7.178103778775291, 7.178103778775291, -0.0, 6.33835312402347, -0.0, 3.678411501241291, 6.045005314036012, 6.656806855142005, -0.0, 5.679891501451415, 4.16411806491195, -0.0, 7.34995403570195, 5.805054644405421, -0.0, 7.414492556839521, 4.2144598197728005, -0.0, 5.917850138550765, 6.484956598215345, 3.382023398335508, 4.5695831730201135, -0.0, 5.105262921126807, 6.171299039360304, -0.0, 4.258911582343634, 3.4181284029776244, 4.270340278167257, -0.0, 3.915966831216804, -0.0, -0.0, 4.935156952100976, -0.0, -0.0, 5.404043886646637, 3.855342209400369, 3.7492229683647187, 5.579247975671727, 6.511624845297507, 3.440434160491922, 6.293901361452637, 7.557593400480195, -0.0, 5.27835567148314] diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/__init__.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc82d5136ef0402074d79102c4ee033e0e2a7b09 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/__init__.py @@ -0,0 +1,58 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib + +def load_loss_weight(dataset_name): + """Loss weight to balance the categories + weight = -log(frequency)""" + + if dataset_name[-1]=='g': + dataset_name = dataset_name[:-1] + + try: + Weight = importlib.import_module('utils.aux_data.%s_weight'%dataset_name) + + if 'pair_weight' in Weight.__dict__: + return Weight.attr_weight, Weight.obj_weight, Weight.pair_weight + else: + return Weight.attr_weight, Weight.obj_weight, None + + except ImportError: + raise NotImplementedError("Loss weight for %s is not implemented yet"%dataset_name) + + +def load_wordvec_dict(dataset_name, vec_type): + if dataset_name[-1]=='g': + dataset_name = dataset_name[:-1] + + try: + Wordvec = importlib.import_module('utils.aux_data.%s_%s'%(vec_type, dataset_name)) + return Wordvec.attrs_dict, Wordvec.objs_dict + + except ImportError: + raise NotImplementedError("%s vector for %s is not ready yet"%(vec_type, dataset_name)) diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/glove_MIT.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/glove_MIT.py new file mode 100644 index 0000000000000000000000000000000000000000..b7835eb22f93959f8ac23611745032e9f38b26e6 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/aux_data/glove_MIT.py @@ -0,0 +1,29 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +attrs_dict = {u'crumpled': [-0.34428998827934265, -0.041088998317718506, -0.2932800054550171, -0.4592599868774414, 0.11011999845504761, -0.04722899943590164, -0.19939999282360077, -0.6640099883079529, -0.5397099852561951, 0.3364099860191345, -0.33511999249458313, 0.2157599925994873, -0.007037200033664703, -0.18472999334335327, -0.07743799686431885, 0.45440998673439026, -0.1596899926662445, 0.13877999782562256, 0.07464399933815002, -0.42572999000549316, 0.5098099708557129, 0.1652500033378601, 0.19673000276088715, -0.053564999252557755, -0.6513400077819824, -0.5163699984550476, -0.027967000380158424, 0.33294999599456787, 0.39096999168395996, -0.5551900267601013, -0.3602299988269806, -0.08196599781513214, 0.24785000085830688, 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +attrs_dict = {u'Synthetic': [-0.04577599838376045, 0.3800700008869171, 0.06601399928331375, 0.30629000067710876, -0.3903000056743622, -0.4559600055217743, -0.24815000593662262, -0.15424999594688416, 0.251910001039505, -1.542199969291687, 0.296889990568161, 0.04115299880504608, -0.10824000090360641, -0.5511000156402588, 0.15072999894618988, -0.6427199840545654, -0.6002699732780457, 0.6632300019264221, 0.01593099907040596, 0.25126999616622925, -0.7840200066566467, -0.06940600275993347, -0.7046599984169006, 1.0583000183105469, -0.7482500076293945, -0.047265999019145966, 0.12145999819040298, -0.19754000008106232, -0.39076000452041626, 0.8264899849891663, -0.23531000316143036, 0.12178999930620193, -0.4373599886894226, 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-0.4771699905395508, 0.20774999260902405, 0.4093799889087677, -0.10367000102996826, -0.33474001288414, -0.8932700157165527, -0.18310999870300293, 0.6604300141334534, -0.2998200058937073, 0.9802299737930298, -0.6742600202560425, -0.4010699987411499, -0.046254999935626984, -0.12870000302791595, 0.3562000095844269, 0.13203999400138855]} diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/config.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..d3211f4d14fe71cac8397081692f5b8d4a685bc3 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/config.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging, os + +logging.basicConfig(format='[%(asctime)s] %(name)s: %(message)s', level=logging.INFO) + + +RANDOM_SEED = None + +SUMMARY_INTERVAL = 'auto' # int(of iter) or 'auto' +IMAGE_SUMMARY_INTERVAL = 'auto' # int(of iter) or 'auto' + + +ROOT_DIR = "." # change this to the project folder + + +WEIGHT_ROOT_DIR = ROOT_DIR+"/SymNet_NPU/weights/" +LOG_ROOT_DIR = ROOT_DIR+"/SymNet_NPU/output_log/" +DATA_ROOT_DIR = ROOT_DIR+"/data" + + +CZSL_DS_ROOT = { + 'MIT': DATA_ROOT_DIR+'/mit-states-original', + 'UT': 'ut-zap50k-original', +} + +GCZSL_DS_ROOT = { + 'MIT': DATA_ROOT_DIR+'/mit-states-natural', + 'UT': DATA_ROOT_DIR+'/ut-zap50k-natural', +} + +GRADIENT_CLIPPING = 5 + + +# if not os.path.exists(WEIGHT_ROOT_DIR): +# os.makedirs(WEIGHT_ROOT_DIR) +# if not os.path.exists(LOG_ROOT_DIR): +# os.makedirs(LOG_ROOT_DIR) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/CZSL_dataset.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/CZSL_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..904ac1fd0e071f46d0374f44ba5b53f900620839 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/CZSL_dataset.py @@ -0,0 +1,248 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Modified from attributes-as-operators""" +import numpy as np +import torch, torchvision +import os, pickle, json +import tqdm + + +try: + from . import data_utils + from .. import config as cfg +except (ValueError, ImportError): + import data_utils + + +class CompositionDatasetActivations(torch.utils.data.Dataset): + + def __init__(self, train_url, name, root, phase, feat_file, split='compositional-split', with_image=False, obj_pred=None, transform_type='normal'): + # root: /home/ma-user/modelarts/inputs/data_url_0/ut-zap50k-original + self.train_url = train_url + self.root = root + self.phase = phase + self.split = split + self.with_image = with_image + + self.feat_dim = None + self.transform = data_utils.imagenet_transform(phase, transform_type) + self.loader = data_utils.ImageLoader(self.root+'/images/') + + feat_file = os.path.join(self.root, feat_file) + activation_data = torch.load(feat_file) + + self.activation_dict = dict(zip(activation_data['files'], activation_data['features'])) + self.feat_dim = activation_data['features'].size(1) + # pair = (attr, obj) + self.attrs, self.objs, self.pairs, self.train_pairs, self.test_pairs = self.parse_split() + self.attr2idx = {attr: idx for idx, attr in enumerate(self.attrs)} + self.obj2idx = {obj: idx for idx, obj in enumerate(self.objs)} + self.pair2idx = {pair: idx for idx, pair in enumerate(self.pairs)} + + + self.train_data, self.test_data = self.get_split_info() + + self.data = self.train_data if self.phase=='train' else self.test_data # list of [img_name, attr, obj, attr_id, obj_id, feat] + + # return {object: all attrs that occur with obj} + self.obj_affordance_mask = [] + for _obj in self.objs: + candidates = [attr for (_,attr,obj,_,_,_) in self.train_data+self.test_data if obj==_obj] + affordance = set(candidates) + mask = [1 if x in affordance else 0 for x in self.attrs] + self.obj_affordance_mask.append(mask) + + + # negative image pool + samples_grouped_by_obj = [[] for _ in range(len(self.objs))] + for i,x in enumerate(self.train_data): + samples_grouped_by_obj[x[4]].append(i) + + self.neg_pool = [] # [obj_id][attr_id] => list of sample id + for obj_id in range(len(self.objs)): + self.neg_pool.append([]) + for attr_id in range(len(self.attrs)): + self.neg_pool[obj_id].append( + [i for i in samples_grouped_by_obj[obj_id] if + self.train_data[i][3] != attr_id ] + ) + aux_data_root = './utils/aux_data' + print("aux_data_root",aux_data_root) + gamma = json.load(open(aux_data_root + "/%s_gamma.json"%name)) + gamma = {k:np.array(v, dtype=np.float32) for k,v in gamma.items()} + self.comp_gamma = {'a':gamma['comp_a'], 'b':gamma['comp_b']} + self.attr_gamma = {'a':gamma['attr_a'], 'b':gamma['attr_b']} + + + if obj_pred is None: + self.obj_pred = None + else: + obj_pred_path = os.path.join(self.train_url, 'obj_scores', obj_pred) + print("Loading object prediction from %s"%obj_pred_path) + with open(obj_pred_path, 'rb') as fp: + self.obj_pred = np.array(pickle.load(fp), dtype=np.float32) + + + def get_split_info(self): + data = torch.load(self.root+'/metadata.t7') + train_pair_set = set(self.train_pairs) + test_pair_set = set(self.test_pairs) + train_data, test_data = [], [] + + for instance in data: + + image, attr, obj = instance['image'], instance['attr'], instance['obj'] + + if attr=='NA' or (attr, obj) not in self.pairs: + # ignore instances with unlabeled attributes + # ignore instances that are not in current split + continue + + data_i = [image, attr, obj, self.attr2idx[attr], self.obj2idx[obj], self.activation_dict[image]] + if (attr, obj) in train_pair_set: + train_data.append(data_i) + else: + test_data.append(data_i) + + return train_data, test_data + + def parse_split(self): + + def parse_pairs(pair_list): + with open(pair_list,'r') as f: + pairs = f.read().strip().split('\n') + pairs = [t.split() for t in pairs] + pairs = list(map(tuple, pairs)) + attrs, objs = zip(*pairs) + return attrs, objs, pairs + + tr_attrs, tr_objs, tr_pairs = parse_pairs('%s/%s/train_pairs.txt'%(self.root, self.split)) + ts_attrs, ts_objs, ts_pairs = parse_pairs('%s/%s/test_pairs.txt'%(self.root, self.split)) + + all_attrs, all_objs = sorted(list(set(tr_attrs+ts_attrs))), sorted(list(set(tr_objs+ts_objs))) + all_pairs = sorted(list(set(tr_pairs + ts_pairs))) + + return all_attrs, all_objs, all_pairs, tr_pairs, ts_pairs + + + def sample_negative(self, attr_id, obj_id): + return np.random.choice(self.neg_pool[obj_id][attr_id]) + + + def __getitem__(self, index): + def get_sample(i): + image, attr, obj, attr_id, obj_id, feat = self.data[i] + if self.with_image: + img = self.loader(image) + img = self.transform(img) + else: + img = None + + return [img, attr_id, obj_id, self.pair2idx[(attr, obj)], feat] + + pos = get_sample(index) + + mask = np.array(self.obj_affordance_mask[pos[2]], dtype=np.float32) + + + if self.phase=='train': + negid = self.sample_negative(pos[1], pos[2]) # negative example + neg = get_sample(negid) + + data = pos + neg + [mask] + else: + data = pos + [mask] + + # train [img, attr_id, obj_id, pair_id, img_feature, img, attr_id, obj_id, pair_id, img_feature, aff_mask] + # test [img, attr_id, obj_id, pair_id, img_feature, aff_mask] + + if self.obj_pred is not None: + data.append(self.obj_pred[index,:]) + + return data + + def __len__(self): + return len(self.data) + + + + + + + +class CompositionDatasetActivationsGenerator(CompositionDatasetActivations): + + def __init__(self, root, feat_file, split='compositional-split', feat_extractor=None, transform_type='normal'): + super(CompositionDatasetActivationsGenerator, self).__init__(root, 'train', feat_file, split, transform_type=transform_type) + + assert os.path.exists(root) + with torch.no_grad(): + self.generate_features(feat_file, feat_extractor, transform_type) + print('Features generated.') + + + def generate_features(self, out_file, feat_extractor, transform_type): + + data = self.train_data+self.test_data + transform = data_utils.imagenet_transform('test', transform_type) + + if feat_extractor is None: + feat_extractor = torchvision.models.resnet18(pretrained=True) + feat_extractor.fc = torch.nn.Sequential() + feat_extractor.eval().cuda() + + image_feats = [] + image_files = [] + for chunk in tqdm.tqdm(data_utils.chunks(data, 512), total=len(data)//512): + files, attrs, objs = zip(*chunk) + imgs = list(map(self.loader, files)) + imgs = list(map(transform, imgs)) + feats = feat_extractor(torch.stack(imgs, 0).cuda()) + image_feats.append(feats.data.cpu()) + image_files += files + image_feats = torch.cat(image_feats, 0) + print ('features for %d images generated'%(len(image_files))) + + torch.save({'features': image_feats, 'files': image_files}, out_file) + + + + + +if __name__=='__main__': + """example code for generating new features for MIT states and UT Zappos + CompositionDatasetActivationsGenerator( + root = 'data-dir', + feat_file = 'filename-to-save', + feat_extractor = torchvision.models.resnet18(pretrained=True), + ) + """ + CompositionDatasetActivationsGenerator( + root = 'data/attributes-as-operators/data/mit-states', + feat_file = 'data/attributes-as-operators/data/mit-states/features.t7', + ) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/__init__.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..82c27872446334b07185cbf8795dc194f085d915 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/__init__.py @@ -0,0 +1,57 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .. import config as cfg +from utils.dataset import CZSL_dataset +from torch.utils.data import DataLoader +import numpy as np +import os.path as osp +import os +cwd = os.getcwd() +def get_dataloader(train_url, dataset_name, phase, feature_file="features.t7", batchsize=1, num_workers=1, shuffle=None, **kwargs): + dt_path = osp.join(train_url, cfg.CZSL_DS_ROOT[dataset_name]) + + dataset = CZSL_dataset.CompositionDatasetActivations( + train_url = train_url, + name = dataset_name, + root = dt_path, #data/mit-states-original/features.t7 + phase = phase, + feat_file = feature_file, + **kwargs) + + + if shuffle is None: + shuffle = (phase=='train') + print(shuffle) + + return DataLoader(dataset, batchsize, shuffle, num_workers=num_workers, + collate_fn = lambda data: [np.stack(d, axis=0) for d in zip(*data)] + ) + + + + diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/data_utils.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd29df853e288b4eedadda71c82907ad20e7115 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/dataset/data_utils.py @@ -0,0 +1,90 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torchvision.transforms as transforms +from PIL import Image + + + +class ImageLoader: + def __init__(self, root): + self.img_dir = root + + def __call__(self, img): + str_types = [str] + try: + str_types.append(unicode) + except NameError: + pass + + if type(img) in str_types: + f = '%s/%s'%(self.img_dir, img) + img = Image.open(f).convert('RGB') + elif type(img) in [list, tuple]: + f = '%s/%s'%(self.img_dir, img[0]) + x,y,w,h = img[1:] # bbox + img = Image.open(f).convert('RGB') + img = img.crop((x, y, x+w, y+h)) + else: + raise NotImplementedError(str(type(img))) + return img + + +def imagenet_transform(phase, transform_type): + mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] + + if transform_type == 'normal': + if phase=='train': + transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + elif phase in ['test', 'val']: + transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + elif transform_type == 'fixed': + transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + else: + raise NotImplementedError("transform_type %s"%transform_type) + + return transform + + +def chunks(l, n): + """Yield successive n-sized chunks from l.""" + for i in range(0, len(l), n): + yield l[i:i + n] \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/evaluator.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..eff09727b55ae5b4bc6f248dd47cc3019af2e01a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/evaluator.py @@ -0,0 +1,307 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as tmodels +import numpy as np +from . import utils +import itertools +import math +import collections +import logging + + +class CZSL_Evaluator: + """modified from AttrOperator""" + + def __init__(self, dset, model): + + self.dset = dset + + # convert text pairs to idx tensors: [('sliced', 'apple'), ('ripe', 'apple'), ...] --> torch.LongTensor([[0,1],[1,1], ...]) + pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.pairs] + self.pairs = torch.LongTensor(pairs) + + # mask over pairs that occur in closed world + test_pair_set = set(dset.test_pairs) + mask = [1 if pair in test_pair_set else 0 for pair in dset.pairs] + self.closed_mask = torch.ByteTensor(mask) + + # object specific mask over which pairs occur in the object oracle setting + oracle_obj_mask = [] + for _obj in dset.objs: + mask = [1 if _obj==obj else 0 for attr, obj in dset.pairs] + oracle_obj_mask.append(torch.ByteTensor(mask)) + self.oracle_obj_mask = torch.stack(oracle_obj_mask, 0) + + + # generate masks for each setting, mask scores, and get prediction labels + def generate_predictions(self, scores, obj_truth): # (B, #pairs) + + def get_pred_from_scores(_scores): + _, pair_pred = _scores.max(1) + attr_pred, obj_pred = self.pairs[pair_pred][:,0], self.pairs[pair_pred][:,1] + return (attr_pred, obj_pred) # attr/obj word id (not name) + + def get_pred_from_scores_and_mask_best(_scores): + _, pair_pred = _scores.max(1) + attr_pred, obj_pred = self.pairs[pair_pred][:,0], self.pairs[pair_pred][:,1] + _scores[range(pair_pred.shape[0]),pair_pred] = -1e10 + return _scores, (attr_pred, obj_pred) # attr/obj word id (not name) + + results = {} + + # open world setting -- no mask + results.update({'open': get_pred_from_scores(scores)}) + + + # closed world setting - set the score for all NON test-pairs to -1e10 + mask = self.closed_mask.repeat(scores.shape[0], 1) + closed_scores = scores.clone() + if hasattr(mask, 'bool'): + closed_scores[(1-mask).bool()] = -1e10 + else: + closed_scores[(1-mask).byte()] = -1e10 + closed_scores, closed1 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed1': closed1}) + closed_scores, closed2 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed2': closed2}) + closed_scores, closed3 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed3': closed3}) + + + # object_oracle setting - set the score to -1e10 for all pairs where the true object does NOT participate + mask = self.oracle_obj_mask[obj_truth] + oracle_obj_scores = scores.clone() + if hasattr(mask, 'bool'): + oracle_obj_scores[(1-mask).bool()] = -1e10 + else: + oracle_obj_scores[(1-mask).byte()] = -1e10 + + results.update({'object_oracle': get_pred_from_scores(oracle_obj_scores)}) + + return results + + def score_model(self, scores, obj_truth): + + # put everything on CPU + #scores = {k:v.cpu() for k,v in scores.items()} + #obj_truth = obj_truth.cpu() + + # gather scores for all relevant (a,o) pairs + scores = torch.stack([ + scores[(self.dset.attr2idx[attr], self.dset.obj2idx[obj])] + for attr, obj in self.dset.pairs + ], 1) # (B, #pairs) + results = self.generate_predictions(scores, obj_truth) + return results + + def evaluate_predictions(self, predictions, attr_truth, obj_truth, histogram=False, synonym_mode=False): + assert not histogram + + # put everything on cpu + #attr_truth, obj_truth = attr_truth.cpu(), obj_truth.cpu() + + # top 1 pair accuracy + # open world: attribute, object and pair + attr_match = (attr_truth==predictions['open'][0]).float() + obj_match = (obj_truth==predictions['open'][1]).float() + open_match = attr_match*obj_match + + # closed world, obj_oracle: pair + closed_1_match = (attr_truth==predictions['closed1'][0]).float() * (obj_truth==predictions['closed1'][1]).float() + closed_2_match = (attr_truth==predictions['closed2'][0]).float() * (obj_truth==predictions['closed2'][1]).float() + closed_1_match + closed_3_match = (attr_truth==predictions['closed3'][0]).float() * (obj_truth==predictions['closed3'][1]).float() + closed_2_match + + if synonym_mode: + closed_2_match[closed_2_match>1] = 1 + closed_3_match[closed_3_match>1] = 1 + + assert torch.max(closed_1_match).item()<=1, torch.max(closed_1_match).item() + assert torch.max(closed_2_match).item()<=1, torch.max(closed_2_match).item() + assert torch.max(closed_3_match).item()<=1, torch.max(closed_3_match).item() + + + obj_oracle_match = (attr_truth==predictions['object_oracle'][0]).float() * (obj_truth==predictions['object_oracle'][1]).float() + + return attr_match, obj_match, closed_1_match, closed_2_match, closed_3_match, open_match, obj_oracle_match + + + def evaluate_only_attr_obj(self, prob_a, gt_a, prob_o, gt_o): + prob_a, prob_o = torch.from_numpy(prob_a), torch.from_numpy(prob_o) + _, pred_a = prob_a.max(1) + _, pred_o = prob_o.max(1) + + attr_match = (pred_a == gt_a).float() + obj_match = (pred_o == gt_o).float() + + return attr_match, obj_match + + + + + + + +class GCZSL_Evaluator: + """modified from TMN""" + + def __init__(self, dset): + + self.dset = dset + + # convert text pairs to idx tensors: [('sliced', 'apple'), ('ripe', 'apple'), ...] --> torch.LongTensor([[0,1],[1,1], ...]) + pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.pairs] + self.train_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.train_pairs] + self.pairs = torch.LongTensor(pairs) + + # mask over pairs that occur in closed world + if dset.phase == 'train': + print('Evaluating with train pairs') + test_pair_set = set(dset.train_pairs) + elif dset.phase == 'val': + print('Evaluating with val pairs') + test_pair_set = set(dset.val_pairs + dset.train_pairs) + else: + print('Evaluating with test pairs') + test_pair_set = set(dset.test_pairs + dset.train_pairs) + self.test_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in list(test_pair_set)] + mask = [1 if pair in test_pair_set else 0 for pair in dset.pairs] + self.closed_mask = torch.ByteTensor(mask) + + seen_pair_set = set(dset.train_pairs) + mask = [1 if pair in seen_pair_set else 0 for pair in dset.pairs] + self.seen_mask = torch.ByteTensor(mask) + + # object specific mask over which pairs occur in the object oracle setting + oracle_obj_mask = [] + for _obj in dset.objs: + mask = [1 if _obj == obj else 0 for attr, obj in dset.pairs] + oracle_obj_mask.append(torch.ByteTensor(mask)) + self.oracle_obj_mask = torch.stack(oracle_obj_mask, 0) + + + # generate masks for each setting, mask scores, and get prediction labels + def generate_predictions(self, scores, obj_truth): # (B, #pairs) + def get_pred_from_scores(_scores): + _, pair_pred = _scores.topk(10, dim=1) #sort(1, descending=True) + pair_pred = pair_pred[:, :10].contiguous().view(-1) + attr_pred, obj_pred = self.pairs[pair_pred][:, 0].view( + -1, 10), self.pairs[pair_pred][:, 1].view(-1, 10) + return (attr_pred, obj_pred) + + results = {} + + # open world setting -- no mask + mask = self.closed_mask.repeat(scores.shape[0], 1) + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + closed_scores = scores.clone() + closed_scores[mask] = -1e10 + results.update({'open': get_pred_from_scores(closed_scores)}) + + # closed world setting - set the score for all NON test-pairs to -1e10 + #results.update({'closed': get_pred_from_scores(closed_scores)}) + results.update({'closed': results['open']}) + + # object_oracle setting - set the score to -1e10 for all pairs where the true object does NOT participate + mask = self.oracle_obj_mask[obj_truth] + oracle_obj_scores = scores.clone() + + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + oracle_obj_scores[mask] = -1e10 + + results.update({ + 'object_oracle': get_pred_from_scores(oracle_obj_scores) + }) + + return results + + + def score_model(self, scores, obj_truth, bias=0.0): + # put everything on CPU + scores = {k: v.cpu() for k, v in scores.items()} + obj_truth = obj_truth.cpu() + # gather scores for all relevant (a,o) pairs + scores = torch.stack( + [scores[(self.dset.attr2idx[attr], self.dset.obj2idx[obj])] for attr, obj in self.dset.pairs], + 1) # (B, #pairs) + orig_scores = scores.clone() + mask = self.seen_mask.repeat(scores.shape[0], 1) + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + scores[mask] += bias + results = self.generate_predictions(scores, obj_truth) + results['biased_scores'] = scores + results['scores'] = orig_scores + return results + + def evaluate_predictions(self, predictions, attr_truth, obj_truth, topk=1): + + # put everything on cpu + attr_truth, obj_truth = attr_truth.cpu(), obj_truth.cpu() + pairs = list( + zip(list(attr_truth.cpu().numpy()), list(obj_truth.cpu().numpy()))) + seen_ind = torch.LongTensor([ + i for i in range(len(attr_truth)) if pairs[i] in self.train_pairs + ]) + unseen_ind = torch.LongTensor([ + i for i in range(len(attr_truth)) + if pairs[i] not in self.train_pairs + ]) + + # top 1 pair accuracy + # open world: attribute, object and pair + attr_match = (attr_truth.unsqueeze(1).repeat( + 1, topk) == predictions['open'][0][:, :topk]) + obj_match = (obj_truth.unsqueeze(1).repeat( + 1, topk) == predictions['open'][1][:, :topk]) + open_match = (attr_match * obj_match).any(1).float() + attr_match = attr_match.any(1).float() + obj_match = obj_match.any(1).float() + open_seen_match = open_match[seen_ind] + open_unseen_match = open_match[unseen_ind] + + # closed world, obj_oracle: pair + closed_match = (attr_truth == predictions['closed'][0][:, 0]).float( + ) * (obj_truth == predictions['closed'][1][:, 0]).float() + + obj_oracle_match = ( + attr_truth == predictions['object_oracle'][0][:, 0]).float() * ( + obj_truth == predictions['object_oracle'][1][:, 0]).float() + + return attr_match, obj_match, closed_match, open_match, obj_oracle_match, open_seen_match, open_unseen_match \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/utils.py b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ea680f47be437c91e9514127ffe4bbf1527e3e09 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SYMNET_ID1292_for_ACL/utils/utils.py @@ -0,0 +1,193 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +import os.path as osp +import numpy as np + +from . import config as cfg +from . import aux_data +from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig + +################################################################################ +# tools for solvers # +################################################################################ + +def create_session(): + """create tensorflow session""" + ####################### add ######################## + configs = tf.ConfigProto() + custom_op = configs.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["use_off_line"].b = True + + custom_op.parameter_map["dynamic_input"].b = True + custom_op.parameter_map["dynamic_graph_execute_mode"].s = tf.compat.as_bytes("lazy_recompile") + + # mix add white + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") + # custom_op.parameter_map["modify_mixlist"].s = tf.compat.as_bytes("/home/test/ops_info.json") + + custom_op.parameter_map["profiling_mode"].b = True + custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes( + '{"output":"/cache/profiling","task_trace":"on", "aicpu":"on"}') + # custom_op.parameter_map["auto_tune_mode"].s = tf.compat.as_bytes("RL,GA") + + # close + configs.graph_options.rewrite_options.remapping = RewriterConfig.OFF + # close + configs.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + ####################### add ######################## + return tf.Session(config=configs) + + + +def display_args(args, logger, verbose=False): + """print some essential arguments""" + if verbose: + ignore = [] + for k,v in args.__dict__.items(): + if not callable(v) and not k.startswith('__') and k not in ignore: + logger.info("{:30s}{}".format(k,v)) + else: + logger.info('Name: %s'%args.name) + logger.info('Network: %s'%args.network) + logger.info('Data: %s'%args.data) + logger.info('FC layers: At {fc_att}, Cm {fc_compress}, Cls {fc_cls}'.format( + **args.__dict__)) + + + +def duplication_check(args): + if args.force: + return + elif args.trained_weight is None or args.trained_weight.split('/')[0] != args.name: + assert not osp.exists(osp.join(cfg.WEIGHT_ROOT_DIR, args.name)), \ + "weight dir with same name exists (%s)"%(args.name) + assert not osp.exists(osp.join(cfg.LOG_ROOT_DIR, args.name)), \ + "log dir with same name exists (%s)"%(args.name) + + +def formated_czsl_result(report): + fstr = '[{name}/{epoch}] rA:{real_attr_acc:.4f}|rO:{real_obj_acc:.4f}|Cl/T1:{top1_acc:.4f}|T2:{top2_acc:.4f}|T3:{top3_acc:.4f}' + + return fstr.format(**report) + + +################################################################################ +# glove embedder # +################################################################################ + +class Embedder: + """word embedder (for various vector type) + __init__(self) + """ + + def __init__(self, vec_type, vocab, data): + self.vec_type = vec_type + + if vec_type != 'onehot': + self.embeds = self.load_word_embeddings(vec_type, vocab, data) + self.emb_dim = self.embeds.shape[1] + else: + self.emb_dim = len(vocab) + + def get_embedding(self, i): + """actually implements __getitem__() function""" + if self.vec_type == 'onehot': + return tf.one_hot(i, depth=self.emb_dim, axis=1) + else: + i_onehot = tf.one_hot(i, depth=self.embeds.shape[0], axis=1) + return tf.matmul(i_onehot, self.embeds) + + + def load_word_embeddings(self, vec_type, vocab, data): + tmp = aux_data.load_wordvec_dict(data, vec_type) + if type(tmp) == tuple: + attr_dict, obj_dict = tmp + attr_dict.update(obj_dict) + embeds = attr_dict + else: + embeds = tmp + + embeds_list = [] + for k in vocab: + if k in embeds: + embeds_list.append(embeds[k]) + else: + raise NotImplementedError('some vocabs are not in dictionary: %s'%k) + + embeds = np.array(embeds_list, dtype=np.float32) + + print ('Embeddings shape = %s'%str(embeds.shape)) + return embeds + + + + + +################################################################################ +# network utils # +################################################################################ + + +def repeat_tensor(tensor, axis, multiple): + """e.g. (1,2,3)x3 = (1,1,1,2,2,2,3,3,3)""" + + result_shape = tensor.shape.as_list() + for i,v in enumerate(result_shape): + if v is None: + result_shape[i] = tf.shape(tensor)[i] + result_shape[axis] *= multiple + + tensor = tf.expand_dims(tensor, axis+1) + mul = [1]*len(tensor.shape) + mul[axis+1] = multiple + tensor = tf.tile(tensor, mul) + tensor = tf.reshape(tensor, result_shape) + + return tensor + + +def tile_tensor(tensor, axis, multiple): + """e.g. (1,2,3)x3 = (1,2,3,1,2,3,1,2,3)""" + mul = [1]*len(tensor.shape) + mul[axis] = multiple + + return tf.tile(tensor, mul) + + +def activation_func(name): + if name == "none": + return (lambda x:x) + elif name == "sigmoid": + return tf.sigmoid + elif name == "relu": + return tf.nn.relu + else: + raise NotImplementedError("activation function %s not implemented"%name) + diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e00e198bb23fda3eea7b1dd48f8f8d5001a69368 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/README.md @@ -0,0 +1,97 @@ +## 模型功能 + +行人重识别（REID) + +## 原始模型 + +参考： + +https://github.com/ultralytics/yolov5 + +原实现模型： + +https://gitee.com/dw8023/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow + +pb文件下载地址 : + +链接：https://pan.baidu.com/s/1lgZmbp8SlZGSkLluzyM5mg +提取码：ofwm + +## om模型 + +om模型下载地址： + +链接：https://pan.baidu.com/s/1SXq5KX8qZEEQi_JTDji-XQ +提取码：214y + +使用ATC模型转换工具进行模型转换时可以参考如下指令: + +``` +atc --model=/root/yolov5/model/yolov5.pb --framework=3 --output=/root/yolov5/yolov5 --soc_version=Ascend310 --input_shape="input:1,640,640,3" +``` + +## 数据集准备 + +VOC原始验证集中的图像数据转换为bin文件参见img2bin.py文件： + + +bin格式数据集地址：(bin.zip) + +obs://yolov5-id0378/dataset/ + + + +## 使用msame工具推理 + +参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + +获取到msame可执行文件之后，进行性能测试。 + + + +## 性能测试 + +使用msame推理工具，参考如下命令，发起推理性能测试： + +``` +msame --model /root/yolov5/yolov5.om --input /root/yolov5/bin --output /root/yolov5/output/ --outfmt TXT +``` + +``` +... +[INFO] get max dynamic batch size success +[INFO] output data success +[INFO] destroy model input success +Inference average time : 89.70 ms +Inference average time without first time: 89.70 ms +[INFO] unload model success, model Id is 1 +[INFO] Execute sample success +[INFO] end to destroy stream +[INFO] end to destroy context +[INFO] end to reset device is 0 +[INFO] end to finalize acl +... +``` + +平均推理性能为 89.70ms + +## 精度测试 + +执行精度对比文件： + +``` +python3 compare.py +``` + +最终精度：(暂未达标) + +``` +Ascend310推理结果： + gpu结果: + npu结果: +``` + + + + + diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/eval.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..4d660469168e09375f279c330a208689d2d61ba9 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/eval.py @@ -0,0 +1,333 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tensorflow as tf +import numpy as np +from tensorflow.keras import backend as K +from tqdm import tqdm +from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input, + resize_image) +import xml.etree.ElementTree as ET +from utils.utils_bbox import DecodeBox +from utils.utils_map import get_map +from PIL import Image + +MINOVERLAP = 0.5 +classes_path = 'model_data/voc_classes.txt' +output_path = 'out/2022410_0_32_31_672278' +VOCdevkit_path = 'VOCdevkit' +map_out_path = 'map_out_om' +anchors_path = 'model_data/yolo_anchors.txt' +anchors, num_anchors = get_anchors(anchors_path) +class_names, num_classes = get_classes(classes_path) + + +def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image): + #-----------------------------------------------------------------# + # 把y轴放前面是因为方便预测框和图像的宽高进行相乘 + #-----------------------------------------------------------------# + box_yx = box_xy[..., ::-1] + box_hw = box_wh[..., ::-1] + input_shape = K.cast(input_shape, K.dtype(box_yx)) + image_shape = K.cast(image_shape, K.dtype(box_yx)) + + if letterbox_image: + #-----------------------------------------------------------------# + # 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况 + # new_shape指的是宽高缩放情况 + #-----------------------------------------------------------------# + new_shape = K.round(image_shape * K.min(input_shape/image_shape)) + offset = (input_shape - new_shape)/2./input_shape + scale = input_shape/new_shape + + box_yx = (box_yx - offset) * scale + box_hw *= scale + + box_mins = box_yx - (box_hw / 2.) + box_maxes = box_yx + (box_hw / 2.) + boxes = K.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]]) + boxes *= K.concatenate([image_shape, image_shape]) + return boxes + + +def get_anchors_and_decode(feats, anchors, num_classes, input_shape, calc_loss=False): + num_anchors = len(anchors) + # ------------------------------------------# + # grid_shape指的是特征层的高和宽 + # ------------------------------------------# + grid_shape = K.shape(feats)[1:3] + # --------------------------------------------------------------------# + # 获得各个特征点的坐标信息。生成的shape为(20, 20, num_anchors, 2) + # --------------------------------------------------------------------# + grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1]) + grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1]) + grid = K.cast(K.concatenate([grid_x, grid_y]), K.dtype(feats)) + # ---------------------------------------------------------------# + # 将先验框进行拓展，生成的shape为(20, 20, num_anchors, 2) + # ---------------------------------------------------------------# + anchors_tensor = K.reshape(K.constant(anchors), [1, 1, num_anchors, 2]) + anchors_tensor = K.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1]) + + # ---------------------------------------------------# + # 将预测结果调整成(batch_size, 20, 20, 3, 85) + # 85可拆分成4 + 1 + 80 + # 4代表的是中心宽高的调整参数 + # 1代表的是框的置信度 + # 80代表的是种类的置信度 + # ---------------------------------------------------# + feats = K.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5]) + # ------------------------------------------# + # 对先验框进行解码，并进行归一化 + # ------------------------------------------# + box_xy = (K.sigmoid(feats[..., :2]) * 2 - 0.5 + grid) / K.cast(grid_shape[::-1], K.dtype(feats)) + box_wh = (K.sigmoid(feats[..., 2:4]) * 2) ** 2 * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats)) + # ------------------------------------------# + # 获得预测框的置信度 + # ------------------------------------------# + box_confidence = K.sigmoid(feats[..., 4:5]) + box_class_probs = K.sigmoid(feats[..., 5:]) + + # ---------------------------------------------------------------------# + # 在计算loss的时候返回grid, feats, box_xy, box_wh + # 在预测的时候返回box_xy, box_wh, box_confidence, box_class_probs + # ---------------------------------------------------------------------# + if calc_loss == True: + return grid, feats, box_xy, box_wh + return box_xy, box_wh, box_confidence, box_class_probs + + +def Decodebox(outputs, + anchors, + num_classes, + image_shape, + input_shape, + #-----------------------------------------------------------# + # 13x13的特征层对应的anchor是[116,90],[156,198],[373,326] + # 26x26的特征层对应的anchor是[30,61],[62,45],[59,119] + # 52x52的特征层对应的anchor是[10,13],[16,30],[33,23] + #-----------------------------------------------------------# + anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]], + max_boxes = 100, + confidence = 0.5, + nms_iou = 0.3, + letterbox_image = True): + + box_xy = [] + box_wh = [] + box_confidence = [] + box_class_probs = [] + for i in range(len(outputs)): + sub_box_xy, sub_box_wh, sub_box_confidence, sub_box_class_probs = \ + get_anchors_and_decode(outputs[i], anchors[anchor_mask[i]], num_classes, input_shape) + box_xy.append(K.reshape(sub_box_xy, [-1, 2])) + box_wh.append(K.reshape(sub_box_wh, [-1, 2])) + box_confidence.append(K.reshape(sub_box_confidence, [-1, 1])) + box_class_probs.append(K.reshape(sub_box_class_probs, [-1, num_classes])) + box_xy = K.concatenate(box_xy, axis = 0) + box_wh = K.concatenate(box_wh, axis = 0) + box_confidence = K.concatenate(box_confidence, axis = 0) + box_class_probs = K.concatenate(box_class_probs, axis = 0) + + #------------------------------------------------------------------------------------------------------------# + # 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条，因此生成的box_xy, box_wh是相对于有灰条的图像的 + # 我们需要对其进行修改，去除灰条的部分。 将box_xy、和box_wh调节成y_min,y_max,xmin,xmax + # 如果没有使用letterbox_image也需要将归一化后的box_xy, box_wh调整成相对于原图大小的 + #------------------------------------------------------------------------------------------------------------# + boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image) + box_scores = box_confidence * box_class_probs + + #-----------------------------------------------------------# + # 判断得分是否大于score_threshold + #-----------------------------------------------------------# + mask = box_scores >= confidence + max_boxes_tensor = K.constant(max_boxes, dtype='int32') + boxes_out = [] + scores_out = [] + classes_out = [] + + for c in range(num_classes): + #-----------------------------------------------------------# + # 取出所有box_scores >= score_threshold的框，和成绩 + #-----------------------------------------------------------# + class_boxes = tf.boolean_mask(boxes, mask[:, c]) + class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c]) + + #-----------------------------------------------------------# + # 非极大抑制 + # 保留一定区域内得分最大的框 + #-----------------------------------------------------------# + nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=nms_iou) + + #-----------------------------------------------------------# + # 获取非极大抑制后的结果 + # 下列三个分别是：框的位置，得分与种类 + #-----------------------------------------------------------# + class_boxes = K.gather(class_boxes, nms_index) + class_box_scores = K.gather(class_box_scores, nms_index) + classes = K.ones_like(class_box_scores, 'int32') * c + + boxes_out.append(class_boxes) + scores_out.append(class_box_scores) + classes_out.append(classes) + boxes_out = K.concatenate(boxes_out, axis=0) + scores_out = K.concatenate(scores_out, axis=0) + classes_out = K.concatenate(classes_out, axis=0) + + return boxes_out, scores_out, classes_out + + +# def generate(model_path, anchors_mask, num_classes, phi, output): +# model_path = os.path.expanduser(model_path) +# assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.' +# # +# # yolo_model = yolo_body([None, None, 3], anchors_mask, num_classes, phi) +# # yolo_model.load_weights(model_path) +# # print('{} model, anchors, and classes loaded.'.format(model_path)) +# # +# # # anchors, num_anchors = get_anchors(anchors_path) +# # # class_names, num_classes = get_classes(classes_path) +# # # ---------------------------------------------------------# +# # # 在yolo_eval函数中，我们会对预测结果进行后处理 +# # # 后处理的内容包括，解码、非极大抑制、门限筛选等 +# # # ---------------------------------------------------------# +# boxes, scores, classes = Decodebox( +# outputs=output, +# anchors=anchors, +# num_classes=num_classes, +# image_shape=K.placeholder(shape=(2, )), +# input_shape=[640, 640], +# anchor_mask=anchors_mask, +# max_boxes=100, +# confidence=0.5, +# nms_iou=0.3, +# letterbox_image=True +# ) +# return boxes, scores, classes + +def main(): + if not os.path.exists(map_out_path): + os.makedirs(map_out_path) + if not os.path.exists(os.path.join(map_out_path, 'ground-truth')): + os.makedirs(os.path.join(map_out_path, 'ground-truth')) + if not os.path.exists(os.path.join(map_out_path, 'detection-results')): + os.makedirs(os.path.join(map_out_path, 'detection-results')) + if not os.path.exists(os.path.join(map_out_path, 'images-optional')): + os.makedirs(os.path.join(map_out_path, 'images-optional')) + + + + image_ids = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Main/test.txt")).read().strip().split() + for image_id in tqdm(image_ids): + image_path = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/" + image_id + ".jpg") + image = Image.open(image_path) + image = cvtColor(image) + f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w", encoding='utf-8') + feats = [] + feats_path0 = os.path.join(output_path, "image_" + image_id + "_output_0.txt") + feats_path1 = os.path.join(output_path, "image_" + image_id + "_output_1.txt") + feats_path2 = os.path.join(output_path, "image_" + image_id + "_output_2.txt") + feats0 = np.loadtxt(feats_path0) + feats0 = np.reshape(feats0, newshape=(1, 80, 80, 75)) + feats0 = feats0.astype("float32") + feats0 = tf.convert_to_tensor(feats0) + feats.append(feats0) + + feats1 = np.loadtxt(feats_path1) + feats1 = np.reshape(feats1, newshape=(1, 40, 40, 75)) + feats1 = feats1.astype("float32") + feats1 = tf.convert_to_tensor(feats1) + feats.append(feats1) + + feats2 = np.loadtxt(feats_path2) + feats2 = np.reshape(feats2, newshape=(1, 20, 20, 75)) + feats2 = feats2.astype("float32") + feats2 = tf.convert_to_tensor(feats2) + feats.append(feats2) + + out_boxes, out_scores, out_classes = Decodebox(outputs=feats, + anchors=anchors, + num_classes=num_classes, + image_shape=[image.size[1], image.size[0]], + input_shape=[640, 640], + # anchor_mask=anchors_mask, + # max_boxes=100, + # confidence=0.5, + # nms_iou=0.3, + # letterbox_image=True + ) + out_boxes = K.eval(out_boxes) + out_scores = K.eval(out_scores) + out_classes = K.eval(out_classes) + + # with tf.Session() as sess: + # out_boxes = out_boxes.eval(session=sess, feed_dict={out_boxes: zero_array1}) + # out_scores = out_scores.eval(session=sess, feed_dict={out_scores: zero_array2}) + # out_classes = out_classes.eval(session=sess, feed_dict={out_classes: zero_array3}) + + for i, c in enumerate(out_classes): + predicted_class = class_names[int(c)] + score = str(out_scores[i]) + top, left, bottom, right = out_boxes[i] + if predicted_class not in class_names: + continue + f.write("%s %s %s %s %s %s\n" % ( + predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom)))) + f.close() + print("Get ground truth result.") + + for image_id in tqdm(image_ids): + with open(os.path.join(map_out_path, "ground-truth/" + image_id + ".txt"), "w") as new_f: + root = ET.parse(os.path.join(VOCdevkit_path, "VOC2007/Annotations/" + image_id + ".xml")).getroot() + for obj in root.findall('object'): + difficult_flag = False + if obj.find('difficult') != None: + difficult = obj.find('difficult').text + if int(difficult) == 1: + difficult_flag = True + obj_name = obj.find('name').text + if obj_name not in class_names: + continue + bndbox = obj.find('bndbox') + left = bndbox.find('xmin').text + top = bndbox.find('ymin').text + right = bndbox.find('xmax').text + bottom = bndbox.find('ymax').text + + if difficult_flag: + new_f.write("%s %s %s %s %s difficult\n" % (obj_name, left, top, right, bottom)) + else: + new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom)) + print("Get ground truth result done.") + + print("Get map.") + get_map(MINOVERLAP, True, path=map_out_path) + print("Get map done.") + +if __name__=="__main__": + main() \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/img2bin.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/img2bin.py new file mode 100644 index 0000000000000000000000000000000000000000..df12663c2f4ae8909b189b98ee61a33a7ac1cd49 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/img2bin.py @@ -0,0 +1,44 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import os +from tqdm import tqdm +from PIL import Image +from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input, resize_image) +from utils.utils_bbox import DecodeBox + +VOCdevkit_path = 'VOCdevkit' +image_ids = open(os.path.join(VOCdevkit_path, "VOC2007/ImageSets/Main/test.txt")).read().strip().split() + +# for image_id in tqdm(image_ids): +image_path = os.path.join(VOCdevkit_path, "VOC2007/JPEGImages/"+"000001"+".jpg") +image = Image.open(image_path) +image = cvtColor(image) +image_data = resize_image(image, (640, 640), True) +image_data = np.expand_dims(preprocess_input(np.array(image_data, dtype='float32')), 0) +image_data.tofile('bin/image.bin') \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/.keep b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/__init__.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9ef5ce451c77930159fb61d7447db731e4bbcc47 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..690d8505fa63395ac4db9ff3f4f36e025d709da8 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils.py @@ -0,0 +1,91 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from functools import reduce + +import numpy as np +from PIL import Image + + +def compose(*funcs): + if funcs: + return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs) + else: + raise ValueError('Composition of empty sequence not supported.') + +#---------------------------------------------------------# +# 将图像转换成RGB图像，防止灰度图在预测时报错。 +# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB +#---------------------------------------------------------# +def cvtColor(image): + if len(np.shape(image)) == 3 and np.shape(image)[2] == 3: + return image + else: + image = image.convert('RGB') + return image + +#---------------------------------------------------# +# 对输入图像进行resize +#---------------------------------------------------# +def resize_image(image, size, letterbox_image): + iw, ih = image.size + w, h = size + if letterbox_image: + scale = min(w/iw, h/ih) + nw = int(iw*scale) + nh = int(ih*scale) + + image = image.resize((nw,nh), Image.BICUBIC) + new_image = Image.new('RGB', size, (128,128,128)) + new_image.paste(image, ((w-nw)//2, (h-nh)//2)) + else: + new_image = image.resize((w, h), Image.BICUBIC) + return new_image + +#---------------------------------------------------# +# 获得类 +#---------------------------------------------------# +def get_classes(classes_path): + with open(classes_path, encoding='utf-8') as f: + class_names = f.readlines() + class_names = [c.strip() for c in class_names] + return class_names, len(class_names) + +#---------------------------------------------------# +# 获得先验框 +#---------------------------------------------------# +def get_anchors(anchors_path): + '''loads the anchors from a file''' + with open(anchors_path, encoding='utf-8') as f: + anchors = f.readline() + anchors = [float(x) for x in anchors.split(',')] + anchors = np.array(anchors).reshape(-1, 2) + return anchors, len(anchors) + +def preprocess_input(image): + image /= 255.0 + return image diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_bbox.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_bbox.py new file mode 100644 index 0000000000000000000000000000000000000000..16cfadcb1db8a42e16290bb6ab134a10a22bbe51 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_bbox.py @@ -0,0 +1,298 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from tensorflow.keras import backend as K + + +#---------------------------------------------------# +# 对box进行调整，使其符合真实图片的样子 +#---------------------------------------------------# +def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image): + #-----------------------------------------------------------------# + # 把y轴放前面是因为方便预测框和图像的宽高进行相乘 + #-----------------------------------------------------------------# + box_yx = box_xy[..., ::-1] + box_hw = box_wh[..., ::-1] + input_shape = K.cast(input_shape, K.dtype(box_yx)) + image_shape = K.cast(image_shape, K.dtype(box_yx)) + + if letterbox_image: + #-----------------------------------------------------------------# + # 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况 + # new_shape指的是宽高缩放情况 + #-----------------------------------------------------------------# + new_shape = K.round(image_shape * K.min(input_shape/image_shape)) + offset = (input_shape - new_shape)/2./input_shape + scale = input_shape/new_shape + + box_yx = (box_yx - offset) * scale + box_hw *= scale + + box_mins = box_yx - (box_hw / 2.) + box_maxes = box_yx + (box_hw / 2.) + boxes = K.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]]) + boxes *= K.concatenate([image_shape, image_shape]) + return boxes + +#---------------------------------------------------# +# 将预测值的每个特征层调成真实值 +#---------------------------------------------------# +def get_anchors_and_decode(feats, anchors, num_classes, input_shape, calc_loss=False): + num_anchors = len(anchors) + #------------------------------------------# + # grid_shape指的是特征层的高和宽 + #------------------------------------------# + grid_shape = K.shape(feats)[1:3] + #--------------------------------------------------------------------# + # 获得各个特征点的坐标信息。生成的shape为(20, 20, num_anchors, 2) + #--------------------------------------------------------------------# + grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1]) + grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1]) + grid = K.cast(K.concatenate([grid_x, grid_y]), K.dtype(feats)) + #---------------------------------------------------------------# + # 将先验框进行拓展，生成的shape为(20, 20, num_anchors, 2) + #---------------------------------------------------------------# + anchors_tensor = K.reshape(K.constant(anchors), [1, 1, num_anchors, 2]) + anchors_tensor = K.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1]) + + #---------------------------------------------------# + # 将预测结果调整成(batch_size, 20, 20, 3, 85) + # 85可拆分成4 + 1 + 80 + # 4代表的是中心宽高的调整参数 + # 1代表的是框的置信度 + # 80代表的是种类的置信度 + #---------------------------------------------------# + feats = K.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5]) + #------------------------------------------# + # 对先验框进行解码，并进行归一化 + #------------------------------------------# + box_xy = (K.sigmoid(feats[..., :2]) * 2 - 0.5 + grid) / K.cast(grid_shape[::-1], K.dtype(feats)) + box_wh = (K.sigmoid(feats[..., 2:4]) * 2) ** 2 * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats)) + #------------------------------------------# + # 获得预测框的置信度 + #------------------------------------------# + box_confidence = K.sigmoid(feats[..., 4:5]) + box_class_probs = K.sigmoid(feats[..., 5:]) + + #---------------------------------------------------------------------# + # 在计算loss的时候返回grid, feats, box_xy, box_wh + # 在预测的时候返回box_xy, box_wh, box_confidence, box_class_probs + #---------------------------------------------------------------------# + if calc_loss == True: + return grid, feats, box_xy, box_wh + return box_xy, box_wh, box_confidence, box_class_probs + +#---------------------------------------------------# +# 图片预测 +#---------------------------------------------------# +def DecodeBox(outputs, + anchors, + num_classes, + image_shape, + input_shape, + #-----------------------------------------------------------# + # 13x13的特征层对应的anchor是[116,90],[156,198],[373,326] + # 26x26的特征层对应的anchor是[30,61],[62,45],[59,119] + # 52x52的特征层对应的anchor是[10,13],[16,30],[33,23] + #-----------------------------------------------------------# + anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]], + max_boxes = 100, + confidence = 0.5, + nms_iou = 0.3, + letterbox_image = True): + + box_xy = [] + box_wh = [] + box_confidence = [] + box_class_probs = [] + for i in range(len(outputs)): + # test = K.eval(outputs[i]) + sub_box_xy, sub_box_wh, sub_box_confidence, sub_box_class_probs = \ + get_anchors_and_decode(outputs[i], anchors[anchor_mask[i]], num_classes, input_shape) + box_xy.append(K.reshape(sub_box_xy, [-1, 2])) + box_wh.append(K.reshape(sub_box_wh, [-1, 2])) + box_confidence.append(K.reshape(sub_box_confidence, [-1, 1])) + box_class_probs.append(K.reshape(sub_box_class_probs, [-1, num_classes])) + box_xy = K.concatenate(box_xy, axis = 0) + box_wh = K.concatenate(box_wh, axis = 0) + box_confidence = K.concatenate(box_confidence, axis = 0) + box_class_probs = K.concatenate(box_class_probs, axis = 0) + + #------------------------------------------------------------------------------------------------------------# + # 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条，因此生成的box_xy, box_wh是相对于有灰条的图像的 + # 我们需要对其进行修改，去除灰条的部分。 将box_xy、和box_wh调节成y_min,y_max,xmin,xmax + # 如果没有使用letterbox_image也需要将归一化后的box_xy, box_wh调整成相对于原图大小的 + #------------------------------------------------------------------------------------------------------------# + boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image) + box_scores = box_confidence * box_class_probs + + #-----------------------------------------------------------# + # 判断得分是否大于score_threshold + #-----------------------------------------------------------# + mask = box_scores >= confidence + max_boxes_tensor = K.constant(max_boxes, dtype='int32') + boxes_out = [] + scores_out = [] + classes_out = [] + for c in range(num_classes): + #-----------------------------------------------------------# + # 取出所有box_scores >= score_threshold的框，和成绩 + #-----------------------------------------------------------# + class_boxes = tf.boolean_mask(boxes, mask[:, c]) + class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c]) + + #-----------------------------------------------------------# + # 非极大抑制 + # 保留一定区域内得分最大的框 + #-----------------------------------------------------------# + nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=nms_iou) + + #-----------------------------------------------------------# + # 获取非极大抑制后的结果 + # 下列三个分别是：框的位置，得分与种类 + #-----------------------------------------------------------# + class_boxes = K.gather(class_boxes, nms_index) + class_box_scores = K.gather(class_box_scores, nms_index) + classes = K.ones_like(class_box_scores, 'int32') * c + + boxes_out.append(class_boxes) + scores_out.append(class_box_scores) + classes_out.append(classes) + boxes_out = K.concatenate(boxes_out, axis=0) + scores_out = K.concatenate(scores_out, axis=0) + classes_out = K.concatenate(classes_out, axis=0) + + return boxes_out, scores_out, classes_out + + +if __name__ == "__main__": + import matplotlib.pyplot as plt + import numpy as np + + def sigmoid(x): + s = 1 / (1 + np.exp(-x)) + return s + #---------------------------------------------------# + # 将预测值的每个特征层调成真实值 + #---------------------------------------------------# + def get_anchors_and_decode(feats, anchors, num_classes): + # feats [batch_size, 20, 20, 3 * (5 + num_classes)] + # anchors [3, 2] + # num_classes + # 3 + num_anchors = len(anchors) + #------------------------------------------# + # grid_shape指的是特征层的高和宽 + # grid_shape [20, 20] + #------------------------------------------# + grid_shape = np.shape(feats)[1:3] + #--------------------------------------------------------------------# + # 获得各个特征点的坐标信息。生成的shape为(20, 20, num_anchors, 2) + # grid_x [20, 20, 3, 1] + # grid_y [20, 20, 3, 1] + # grid [20, 20, 3, 2] + #--------------------------------------------------------------------# + grid_x = np.tile(np.reshape(np.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1]) + grid_y = np.tile(np.reshape(np.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1]) + grid = np.concatenate([grid_x, grid_y], -1) + #---------------------------------------------------------------# + # 将先验框进行拓展，生成的shape为(20, 20, num_anchors, 2) + # [1, 1, 3, 2] + # [20, 20, 3, 2] + #---------------------------------------------------------------# + anchors_tensor = np.reshape(anchors, [1, 1, num_anchors, 2]) + anchors_tensor = np.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1]) + + #---------------------------------------------------# + # 将预测结果调整成(batch_size, 20, 20, 3, 85) + # 85可拆分成4 + 1 + 80 + # 4代表的是中心宽高的调整参数 + # 1代表的是框的置信度 + # 80代表的是种类的置信度 + # [batch_size, 20, 20, 3 * (5 + num_classes)] + # [batch_size, 20, 20, 3, 5 + num_classes] + #---------------------------------------------------# + feats = np.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5]) + + #------------------------------------------# + # 对先验框进行解码，并进行归一化 + #------------------------------------------# + box_xy = (sigmoid(feats[..., :2]) * 2 - 0.5 + grid) + box_wh = (sigmoid(feats[..., 2:4]) * 2) ** 2 * anchors_tensor + #------------------------------------------# + # 获得预测框的置信度 + #------------------------------------------# + box_confidence = sigmoid(feats[..., 4:5]) + box_class_probs = sigmoid(feats[..., 5:]) + + box_wh = box_wh / 32 + anchors_tensor = anchors_tensor / 32 + fig = plt.figure() + ax = fig.add_subplot(121) + plt.ylim(-2, 22) + plt.xlim(-2, 22) + plt.scatter(grid_x,grid_y) + plt.scatter(5, 5, c='black') + plt.gca().invert_yaxis() + + anchor_left = grid_x - anchors_tensor/2 + anchor_top = grid_y - anchors_tensor/2 + print(np.shape(anchors_tensor)) + print(np.shape(box_xy)) + rect1 = plt.Rectangle([anchor_left[5,5,0,0],anchor_top[5,5,0,1]],anchors_tensor[0,0,0,0],anchors_tensor[0,0,0,1],color="r",fill=False) + rect2 = plt.Rectangle([anchor_left[5,5,1,0],anchor_top[5,5,1,1]],anchors_tensor[0,0,1,0],anchors_tensor[0,0,1,1],color="r",fill=False) + rect3 = plt.Rectangle([anchor_left[5,5,2,0],anchor_top[5,5,2,1]],anchors_tensor[0,0,2,0],anchors_tensor[0,0,2,1],color="r",fill=False) + + ax.add_patch(rect1) + ax.add_patch(rect2) + ax.add_patch(rect3) + + ax = fig.add_subplot(122) + plt.ylim(-2, 22) + plt.xlim(-2, 22) + plt.scatter(grid_x,grid_y) + plt.scatter(5, 5, c='black') + plt.scatter(box_xy[0, 5, 5, :, 0],box_xy[0, 5, 5, :, 1],c='r') + plt.gca().invert_yaxis() + + pre_left = box_xy[...,0] - box_wh[...,0] / 2 + pre_top = box_xy[...,1] - box_wh[...,1] / 2 + + rect1 = plt.Rectangle([pre_left[0,5,5,0],pre_top[0,5,5,0]],box_wh[0,5,5,0,0],box_wh[0,5,5,0,1],color="r",fill=False) + rect2 = plt.Rectangle([pre_left[0,5,5,1],pre_top[0,5,5,1]],box_wh[0,5,5,1,0],box_wh[0,5,5,1,1],color="r",fill=False) + rect3 = plt.Rectangle([pre_left[0,5,5,2],pre_top[0,5,5,2]],box_wh[0,5,5,2,0],box_wh[0,5,5,2,1],color="r",fill=False) + + ax.add_patch(rect1) + ax.add_patch(rect2) + ax.add_patch(rect3) + + plt.show() + # + feat = np.random.normal(-0.5,0.5, [4, 20, 20, 75]) + anchors = [[116, 90], [156, 198], [373, 326]] + get_anchors_and_decode(feat, anchors, 20) diff --git a/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_map.py b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_map.py new file mode 100644 index 0000000000000000000000000000000000000000..24dc903c86787c89f065b5e84bf2e74ae0688a64 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/YOLOV5_ID0378_for_ACL/utils/utils_map.py @@ -0,0 +1,928 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import glob +import json +import math +import operator +import os +import shutil +import sys + +import cv2 +import matplotlib.pyplot as plt +import numpy as np + +''' + 0,0 ------> x (width) + | + | (Left,Top) + | *_________ + | | | + | | + y |_________| + (height) * + (Right,Bottom) +''' + +def log_average_miss_rate(precision, fp_cumsum, num_images): + """ + log-average miss rate: + Calculated by averaging miss rates at 9 evenly spaced FPPI points + between 10e-2 and 10e0, in log-space. + + output: + lamr | log-average miss rate + mr | miss rate + fppi | false positives per image + + references: + [1] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the + State of the Art." Pattern Analysis and Machine Intelligence, IEEE + Transactions on 34.4 (2012): 743 - 761. + """ + + if precision.size == 0: + lamr = 0 + mr = 1 + fppi = 0 + return lamr, mr, fppi + + fppi = fp_cumsum / float(num_images) + mr = (1 - precision) + + fppi_tmp = np.insert(fppi, 0, -1.0) + mr_tmp = np.insert(mr, 0, 1.0) + + ref = np.logspace(-2.0, 0.0, num = 9) + for i, ref_i in enumerate(ref): + j = np.where(fppi_tmp <= ref_i)[-1][-1] + ref[i] = mr_tmp[j] + + lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref)))) + + return lamr, mr, fppi + +""" + throw error and exit +""" +def error(msg): + print(msg) + sys.exit(0) + +""" + check if the number is a float between 0.0 and 1.0 +""" +def is_float_between_0_and_1(value): + try: + val = float(value) + if val > 0.0 and val < 1.0: + return True + else: + return False + except ValueError: + return False + +""" + Calculate the AP given the recall and precision array + 1st) We compute a version of the measured precision/recall curve with + precision monotonically decreasing + 2nd) We compute the AP as the area under this curve by numerical integration. +""" +def voc_ap(rec, prec): + """ + --- Official matlab code VOC2012--- + mrec=[0 ; rec ; 1]; + mpre=[0 ; prec ; 0]; + for i=numel(mpre)-1:-1:1 + mpre(i)=max(mpre(i),mpre(i+1)); + end + i=find(mrec(2:end)~=mrec(1:end-1))+1; + ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); + """ + rec.insert(0, 0.0) # insert 0.0 at begining of list + rec.append(1.0) # insert 1.0 at end of list + mrec = rec[:] + prec.insert(0, 0.0) # insert 0.0 at begining of list + prec.append(0.0) # insert 0.0 at end of list + mpre = prec[:] + """ + This part makes the precision monotonically decreasing + (goes from the end to the beginning) + matlab: for i=numel(mpre)-1:-1:1 + mpre(i)=max(mpre(i),mpre(i+1)); + """ + for i in range(len(mpre)-2, -1, -1): + mpre[i] = max(mpre[i], mpre[i+1]) + """ + This part creates a list of indexes where the recall changes + matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1; + """ + i_list = [] + for i in range(1, len(mrec)): + if mrec[i] != mrec[i-1]: + i_list.append(i) # if it was matlab would be i + 1 + """ + The Average Precision (AP) is the area under the curve + (numerical integration) + matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i)); + """ + ap = 0.0 + for i in i_list: + ap += ((mrec[i]-mrec[i-1])*mpre[i]) + return ap, mrec, mpre + + +""" + Convert the lines of a file to a list +""" +def file_lines_to_list(path): + # open txt file lines to a list + with open(path) as f: + content = f.readlines() + # remove whitespace characters like `\n` at the end of each line + content = [x.strip() for x in content] + return content + +""" + Draws text in image +""" +def draw_text_in_image(img, text, pos, color, line_width): + font = cv2.FONT_HERSHEY_PLAIN + fontScale = 1 + lineType = 1 + bottomLeftCornerOfText = pos + cv2.putText(img, text, + bottomLeftCornerOfText, + font, + fontScale, + color, + lineType) + text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0] + return img, (line_width + text_width) + +""" + Plot - adjust axes +""" +def adjust_axes(r, t, fig, axes): + # get text width for re-scaling + bb = t.get_window_extent(renderer=r) + text_width_inches = bb.width / fig.dpi + # get axis width in inches + current_fig_width = fig.get_figwidth() + new_fig_width = current_fig_width + text_width_inches + propotion = new_fig_width / current_fig_width + # get axis limit + x_lim = axes.get_xlim() + axes.set_xlim([x_lim[0], x_lim[1]*propotion]) + +""" + Draw plot using Matplotlib +""" +def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar): + # sort the dictionary by decreasing value, into a list of tuples + sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1)) + # unpacking the list of tuples into two lists + sorted_keys, sorted_values = zip(*sorted_dic_by_value) + # + if true_p_bar != "": + """ + Special case to draw in: + - green -> TP: True Positives (object detected and matches ground-truth) + - red -> FP: False Positives (object detected but does not match ground-truth) + - orange -> FN: False Negatives (object not detected but present in the ground-truth) + """ + fp_sorted = [] + tp_sorted = [] + for key in sorted_keys: + fp_sorted.append(dictionary[key] - true_p_bar[key]) + tp_sorted.append(true_p_bar[key]) + plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive') + plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted) + # add legend + plt.legend(loc='lower right') + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + fp_val = fp_sorted[i] + tp_val = tp_sorted[i] + fp_str_val = " " + str(fp_val) + tp_str_val = fp_str_val + " " + str(tp_val) + # trick to paint multicolor with offset: + # first paint everything and then repaint the first number + t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold') + plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold') + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + else: + plt.barh(range(n_classes), sorted_values, color=plot_color) + """ + Write number on side of bar + """ + fig = plt.gcf() # gcf - get current figure + axes = plt.gca() + r = fig.canvas.get_renderer() + for i, val in enumerate(sorted_values): + str_val = " " + str(val) # add a space before + if val < 1.0: + str_val = " {0:.2f}".format(val) + t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold') + # re-set axes to show number inside the figure + if i == (len(sorted_values)-1): # largest bar + adjust_axes(r, t, fig, axes) + # set window title + fig.canvas.set_window_title(window_title) + # write classes in y axis + tick_font_size = 12 + plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size) + """ + Re-scale height accordingly + """ + init_height = fig.get_figheight() + # comput the matrix height in points and inches + dpi = fig.dpi + height_pt = n_classes * (tick_font_size * 1.4) # 1.4 (some spacing) + height_in = height_pt / dpi + # compute the required figure height + top_margin = 0.15 # in percentage of the figure height + bottom_margin = 0.05 # in percentage of the figure height + figure_height = height_in / (1 - top_margin - bottom_margin) + # set new height + if figure_height > init_height: + fig.set_figheight(figure_height) + + # set plot title + plt.title(plot_title, fontsize=14) + # set axis titles + # plt.xlabel('classes') + plt.xlabel(x_label, fontsize='large') + # adjust size of window + fig.tight_layout() + # save the plot + fig.savefig(output_path) + # show image + if to_show: + plt.show() + # close the plot + plt.close() + +def get_map(MINOVERLAP, draw_plot, path = './map_out'): + GT_PATH = os.path.join(path, 'ground-truth') + DR_PATH = os.path.join(path, 'detection-results') + IMG_PATH = os.path.join(path, 'images-optional') + TEMP_FILES_PATH = os.path.join(path, '.temp_files') + RESULTS_FILES_PATH = os.path.join(path, 'results') + + show_animation = True + if os.path.exists(IMG_PATH): + for dirpath, dirnames, files in os.walk(IMG_PATH): + if not files: + show_animation = False + else: + show_animation = False + + if not os.path.exists(TEMP_FILES_PATH): + os.makedirs(TEMP_FILES_PATH) + + if os.path.exists(RESULTS_FILES_PATH): + shutil.rmtree(RESULTS_FILES_PATH) + if draw_plot: + os.makedirs(os.path.join(RESULTS_FILES_PATH, "AP")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "F1")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "Recall")) + os.makedirs(os.path.join(RESULTS_FILES_PATH, "Precision")) + if show_animation: + os.makedirs(os.path.join(RESULTS_FILES_PATH, "images", "detections_one_by_one")) + + ground_truth_files_list = glob.glob(GT_PATH + '/*.txt') + if len(ground_truth_files_list) == 0: + error("Error: No ground-truth files found!") + ground_truth_files_list.sort() + gt_counter_per_class = {} + counter_images_per_class = {} + + for txt_file in ground_truth_files_list: + file_id = txt_file.split(".txt", 1)[0] + file_id = os.path.basename(os.path.normpath(file_id)) + temp_path = os.path.join(DR_PATH, (file_id + ".txt")) + if not os.path.exists(temp_path): + error_msg = "Error. File not found: {}\n".format(temp_path) + error(error_msg) + lines_list = file_lines_to_list(txt_file) + bounding_boxes = [] + is_difficult = False + already_seen_classes = [] + for line in lines_list: + try: + if "difficult" in line: + class_name, left, top, right, bottom, _difficult = line.split() + is_difficult = True + else: + class_name, left, top, right, bottom = line.split() + except: + if "difficult" in line: + line_split = line.split() + _difficult = line_split[-1] + bottom = line_split[-2] + right = line_split[-3] + top = line_split[-4] + left = line_split[-5] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + is_difficult = True + else: + line_split = line.split() + bottom = line_split[-1] + right = line_split[-2] + top = line_split[-3] + left = line_split[-4] + class_name = "" + for name in line_split[:-4]: + class_name += name + " " + class_name = class_name[:-1] + + bbox = left + " " + top + " " + right + " " + bottom + if is_difficult: + bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True}) + is_difficult = False + else: + bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False}) + if class_name in gt_counter_per_class: + gt_counter_per_class[class_name] += 1 + else: + gt_counter_per_class[class_name] = 1 + + if class_name not in already_seen_classes: + if class_name in counter_images_per_class: + counter_images_per_class[class_name] += 1 + else: + counter_images_per_class[class_name] = 1 + already_seen_classes.append(class_name) + + with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile: + json.dump(bounding_boxes, outfile) + + gt_classes = list(gt_counter_per_class.keys()) + gt_classes = sorted(gt_classes) + n_classes = len(gt_classes) + + dr_files_list = glob.glob(DR_PATH + '/*.txt') + dr_files_list.sort() + for class_index, class_name in enumerate(gt_classes): + bounding_boxes = [] + for txt_file in dr_files_list: + file_id = txt_file.split(".txt",1)[0] + file_id = os.path.basename(os.path.normpath(file_id)) + temp_path = os.path.join(GT_PATH, (file_id + ".txt")) + if class_index == 0: + if not os.path.exists(temp_path): + error_msg = "Error. File not found: {}\n".format(temp_path) + error(error_msg) + lines = file_lines_to_list(txt_file) + for line in lines: + try: + tmp_class_name, confidence, left, top, right, bottom = line.split() + except: + line_split = line.split() + bottom = line_split[-1] + right = line_split[-2] + top = line_split[-3] + left = line_split[-4] + confidence = line_split[-5] + tmp_class_name = "" + for name in line_split[:-5]: + tmp_class_name += name + " " + tmp_class_name = tmp_class_name[:-1] + + if tmp_class_name == class_name: + bbox = left + " " + top + " " + right + " " +bottom + bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox}) + + bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True) + with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile: + json.dump(bounding_boxes, outfile) + + sum_AP = 0.0 + ap_dictionary = {} + lamr_dictionary = {} + with open(RESULTS_FILES_PATH + "/results.txt", 'w') as results_file: + results_file.write("# AP and precision/recall per class\n") + count_true_positives = {} + + for class_index, class_name in enumerate(gt_classes): + count_true_positives[class_name] = 0 + dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json" + dr_data = json.load(open(dr_file)) + + nd = len(dr_data) + tp = [0] * nd + fp = [0] * nd + score = [0] * nd + score05_idx = 0 + for idx, detection in enumerate(dr_data): + file_id = detection["file_id"] + score[idx] = float(detection["confidence"]) + if score[idx] > 0.5: + score05_idx = idx + + if show_animation: + ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*") + if len(ground_truth_img) == 0: + error("Error. Image not found with id: " + file_id) + elif len(ground_truth_img) > 1: + error("Error. Multiple image with id: " + file_id) + else: + img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0]) + img_cumulative_path = RESULTS_FILES_PATH + "/images/" + ground_truth_img[0] + if os.path.isfile(img_cumulative_path): + img_cumulative = cv2.imread(img_cumulative_path) + else: + img_cumulative = img.copy() + bottom_border = 60 + BLACK = [0, 0, 0] + img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK) + + gt_file = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" + ground_truth_data = json.load(open(gt_file)) + ovmax = -1 + gt_match = -1 + bb = [float(x) for x in detection["bbox"].split()] + for obj in ground_truth_data: + if obj["class_name"] == class_name: + bbgt = [ float(x) for x in obj["bbox"].split() ] + bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])] + iw = bi[2] - bi[0] + 1 + ih = bi[3] - bi[1] + 1 + if iw > 0 and ih > 0: + ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0] + + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih + ov = iw * ih / ua + if ov > ovmax: + ovmax = ov + gt_match = obj + + if show_animation: + status = "NO MATCH FOUND!" + + min_overlap = MINOVERLAP + if ovmax >= min_overlap: + if "difficult" not in gt_match: + if not bool(gt_match["used"]): + tp[idx] = 1 + gt_match["used"] = True + count_true_positives[class_name] += 1 + with open(gt_file, 'w') as f: + f.write(json.dumps(ground_truth_data)) + if show_animation: + status = "MATCH!" + else: + fp[idx] = 1 + if show_animation: + status = "REPEATED MATCH!" + else: + fp[idx] = 1 + if ovmax > 0: + status = "INSUFFICIENT OVERLAP" + + """ + Draw image to show animation + """ + if show_animation: + height, widht = img.shape[:2] + white = (255,255,255) + light_blue = (255,200,100) + green = (0,255,0) + light_red = (30,30,255) + margin = 10 + # 1nd line + v_pos = int(height - margin - (bottom_border / 2.0)) + text = "Image: " + ground_truth_img[0] + " " + img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) + text = "Class [" + str(class_index) + "/" + str(n_classes) + "]: " + class_name + " " + img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), light_blue, line_width) + if ovmax != -1: + color = light_red + if status == "INSUFFICIENT OVERLAP": + text = "IoU: {0:.2f}% ".format(ovmax*100) + "< {0:.2f}% ".format(min_overlap*100) + else: + text = "IoU: {0:.2f}% ".format(ovmax*100) + ">= {0:.2f}% ".format(min_overlap*100) + color = green + img, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) + # 2nd line + v_pos += int(bottom_border / 2.0) + rank_pos = str(idx+1) + text = "Detection #rank: " + rank_pos + " confidence: {0:.2f}% ".format(float(detection["confidence"])*100) + img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0) + color = light_red + if status == "MATCH!": + color = green + text = "Result: " + status + " " + img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width) + + font = cv2.FONT_HERSHEY_SIMPLEX + if ovmax > 0: + bbgt = [ int(round(float(x))) for x in gt_match["bbox"].split() ] + cv2.rectangle(img,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) + cv2.rectangle(img_cumulative,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2) + cv2.putText(img_cumulative, class_name, (bbgt[0],bbgt[1] - 5), font, 0.6, light_blue, 1, cv2.LINE_AA) + bb = [int(i) for i in bb] + cv2.rectangle(img,(bb[0],bb[1]),(bb[2],bb[3]),color,2) + cv2.rectangle(img_cumulative,(bb[0],bb[1]),(bb[2],bb[3]),color,2) + cv2.putText(img_cumulative, class_name, (bb[0],bb[1] - 5), font, 0.6, color, 1, cv2.LINE_AA) + + cv2.imshow("Animation", img) + cv2.waitKey(20) + output_img_path = RESULTS_FILES_PATH + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg" + cv2.imwrite(output_img_path, img) + cv2.imwrite(img_cumulative_path, img_cumulative) + + cumsum = 0 + for idx, val in enumerate(fp): + fp[idx] += cumsum + cumsum += val + + cumsum = 0 + for idx, val in enumerate(tp): + tp[idx] += cumsum + cumsum += val + + rec = tp[:] + for idx, val in enumerate(tp): + rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1) + + prec = tp[:] + for idx, val in enumerate(tp): + prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1) + + ap, mrec, mprec = voc_ap(rec[:], prec[:]) + F1 = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec))) + + sum_AP += ap + text = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP " #class_name + " AP = {0:.2f}%".format(ap*100) + + if len(prec)>0: + F1_text = "{0:.2f}".format(F1[score05_idx]) + " = " + class_name + " F1 " + Recall_text = "{0:.2f}%".format(rec[score05_idx]*100) + " = " + class_name + " Recall " + Precision_text = "{0:.2f}%".format(prec[score05_idx]*100) + " = " + class_name + " Precision " + else: + F1_text = "0.00" + " = " + class_name + " F1 " + Recall_text = "0.00%" + " = " + class_name + " Recall " + Precision_text = "0.00%" + " = " + class_name + " Precision " + + rounded_prec = [ '%.2f' % elem for elem in prec ] + rounded_rec = [ '%.2f' % elem for elem in rec ] + results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n") + if len(prec)>0: + print(text + "\t||\tscore_threhold=0.5 : " + "F1=" + "{0:.2f}".format(F1[score05_idx])\ + + " ; Recall=" + "{0:.2f}%".format(rec[score05_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score05_idx]*100)) + else: + print(text + "\t||\tscore_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%") + ap_dictionary[class_name] = ap + + n_images = counter_images_per_class[class_name] + lamr, mr, fppi = log_average_miss_rate(np.array(rec), np.array(fp), n_images) + lamr_dictionary[class_name] = lamr + + if draw_plot: + plt.plot(rec, prec, '-o') + area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]] + area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]] + plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r') + + fig = plt.gcf() + fig.canvas.set_window_title('AP ' + class_name) + + plt.title('class: ' + text) + plt.xlabel('Recall') + plt.ylabel('Precision') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/AP/" + class_name + ".png") + plt.cla() + + plt.plot(score, F1, "-", color='orangered') + plt.title('class: ' + F1_text + "\nscore_threhold=0.5") + plt.xlabel('Score_Threhold') + plt.ylabel('F1') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/F1/" + class_name + ".png") + plt.cla() + + plt.plot(score, rec, "-H", color='gold') + plt.title('class: ' + Recall_text + "\nscore_threhold=0.5") + plt.xlabel('Score_Threhold') + plt.ylabel('Recall') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/Recall/" + class_name + ".png") + plt.cla() + + plt.plot(score, prec, "-s", color='palevioletred') + plt.title('class: ' + Precision_text + "\nscore_threhold=0.5") + plt.xlabel('Score_Threhold') + plt.ylabel('Precision') + axes = plt.gca() + axes.set_xlim([0.0,1.0]) + axes.set_ylim([0.0,1.05]) + fig.savefig(RESULTS_FILES_PATH + "/Precision/" + class_name + ".png") + plt.cla() + + if show_animation: + cv2.destroyAllWindows() + + results_file.write("\n# mAP of all classes\n") + mAP = sum_AP / n_classes + text = "mAP = {0:.2f}%".format(mAP*100) + results_file.write(text + "\n") + print(text) + + shutil.rmtree(TEMP_FILES_PATH) + + """ + Count total of detection-results + """ + det_counter_per_class = {} + for txt_file in dr_files_list: + lines_list = file_lines_to_list(txt_file) + for line in lines_list: + class_name = line.split()[0] + if class_name in det_counter_per_class: + det_counter_per_class[class_name] += 1 + else: + det_counter_per_class[class_name] = 1 + dr_classes = list(det_counter_per_class.keys()) + + """ + Write number of ground-truth objects per class to results.txt + """ + with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: + results_file.write("\n# Number of ground-truth objects per class\n") + for class_name in sorted(gt_counter_per_class): + results_file.write(class_name + ": " + str(gt_counter_per_class[class_name]) + "\n") + + """ + Finish counting true positives + """ + for class_name in dr_classes: + if class_name not in gt_classes: + count_true_positives[class_name] = 0 + + """ + Write number of detected objects per class to results.txt + """ + with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file: + results_file.write("\n# Number of detected objects per class\n") + for class_name in sorted(dr_classes): + n_det = det_counter_per_class[class_name] + text = class_name + ": " + str(n_det) + text += " (tp:" + str(count_true_positives[class_name]) + "" + text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n" + results_file.write(text) + + """ + Plot the total number of occurences of each class in the ground-truth + """ + if draw_plot: + window_title = "ground-truth-info" + plot_title = "ground-truth\n" + plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)" + x_label = "Number of objects per class" + output_path = RESULTS_FILES_PATH + "/ground-truth-info.png" + to_show = False + plot_color = 'forestgreen' + draw_plot_func( + gt_counter_per_class, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + '', + ) + + # """ + # Plot the total number of occurences of each class in the "detection-results" folder + # """ + # if draw_plot: + # window_title = "detection-results-info" + # # Plot title + # plot_title = "detection-results\n" + # plot_title += "(" + str(len(dr_files_list)) + " files and " + # count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values())) + # plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)" + # # end Plot title + # x_label = "Number of objects per class" + # output_path = RESULTS_FILES_PATH + "/detection-results-info.png" + # to_show = False + # plot_color = 'forestgreen' + # true_p_bar = count_true_positives + # draw_plot_func( + # det_counter_per_class, + # len(det_counter_per_class), + # window_title, + # plot_title, + # x_label, + # output_path, + # to_show, + # plot_color, + # true_p_bar + # ) + + """ + Draw log-average miss rate plot (Show lamr of all classes in decreasing order) + """ + if draw_plot: + window_title = "lamr" + plot_title = "log-average miss rate" + x_label = "log-average miss rate" + output_path = RESULTS_FILES_PATH + "/lamr.png" + to_show = False + plot_color = 'royalblue' + draw_plot_func( + lamr_dictionary, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + "" + ) + + """ + Draw mAP plot (Show AP's of all classes in decreasing order) + """ + if draw_plot: + window_title = "mAP" + plot_title = "mAP = {0:.2f}%".format(mAP*100) + x_label = "Average Precision" + output_path = RESULTS_FILES_PATH + "/mAP.png" + to_show = True + plot_color = 'royalblue' + draw_plot_func( + ap_dictionary, + n_classes, + window_title, + plot_title, + x_label, + output_path, + to_show, + plot_color, + "" + ) + +def preprocess_gt(gt_path, class_names): + image_ids = os.listdir(gt_path) + results = {} + + images = [] + bboxes = [] + for i, image_id in enumerate(image_ids): + lines_list = file_lines_to_list(os.path.join(gt_path, image_id)) + boxes_per_image = [] + image = {} + image_id = os.path.splitext(image_id)[0] + image['file_name'] = image_id + '.jpg' + image['width'] = 1 + image['height'] = 1 + #-----------------------------------------------------------------# + # 感谢 多学学英语吧 的提醒 + # 解决了'Results do not correspond to current coco set'问题 + #-----------------------------------------------------------------# + image['id'] = str(image_id) + + for line in lines_list: + difficult = 0 + if "difficult" in line: + line_split = line.split() + left, top, right, bottom, _difficult = line_split[-5:] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + difficult = 1 + else: + line_split = line.split() + left, top, right, bottom = line_split[-4:] + class_name = "" + for name in line_split[:-4]: + class_name += name + " " + class_name = class_name[:-1] + + left, top, right, bottom = float(left), float(top), float(right), float(bottom) + cls_id = class_names.index(class_name) + 1 + bbox = [left, top, right - left, bottom - top, difficult, str(image_id), cls_id, (right - left) * (bottom - top) - 10.0] + boxes_per_image.append(bbox) + images.append(image) + bboxes.extend(boxes_per_image) + results['images'] = images + + categories = [] + for i, cls in enumerate(class_names): + category = {} + category['supercategory'] = cls + category['name'] = cls + category['id'] = i + 1 + categories.append(category) + results['categories'] = categories + + annotations = [] + for i, box in enumerate(bboxes): + annotation = {} + annotation['area'] = box[-1] + annotation['category_id'] = box[-2] + annotation['image_id'] = box[-3] + annotation['iscrowd'] = box[-4] + annotation['bbox'] = box[:4] + annotation['id'] = i + annotations.append(annotation) + results['annotations'] = annotations + return results + +def preprocess_dr(dr_path, class_names): + image_ids = os.listdir(dr_path) + results = [] + for image_id in image_ids: + lines_list = file_lines_to_list(os.path.join(dr_path, image_id)) + image_id = os.path.splitext(image_id)[0] + for line in lines_list: + line_split = line.split() + confidence, left, top, right, bottom = line_split[-5:] + class_name = "" + for name in line_split[:-5]: + class_name += name + " " + class_name = class_name[:-1] + left, top, right, bottom = float(left), float(top), float(right), float(bottom) + result = {} + result["image_id"] = str(image_id) + result["category_id"] = class_names.index(class_name) + 1 + result["bbox"] = [left, top, right - left, bottom - top] + result["score"] = float(confidence) + results.append(result) + return results + +def get_coco_map(class_names, path): + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + GT_PATH = os.path.join(path, 'ground-truth') + DR_PATH = os.path.join(path, 'detection-results') + COCO_PATH = os.path.join(path, 'coco_eval') + + if not os.path.exists(COCO_PATH): + os.makedirs(COCO_PATH) + + GT_JSON_PATH = os.path.join(COCO_PATH, 'instances_gt.json') + DR_JSON_PATH = os.path.join(COCO_PATH, 'instances_dr.json') + + with open(GT_JSON_PATH, "w") as f: + results_gt = preprocess_gt(GT_PATH, class_names) + json.dump(results_gt, f, indent=4) + + with open(DR_JSON_PATH, "w") as f: + results_dr = preprocess_dr(DR_PATH, class_names) + json.dump(results_dr, f, indent=4) + + cocoGt = COCO(GT_JSON_PATH) + cocoDt = cocoGt.loadRes(DR_JSON_PATH) + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() diff --git a/TensorFlow/built-in/audio/Jasper_ID0020_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/audio/Jasper_ID0020_for_TensorFlow/test/train_full_1p.sh index 1fb76bae696dfa940a78123186bc64a353f23e7e..a6dc4fa174b275c6f703b9f4e9dce34118f76425 100644 --- a/TensorFlow/built-in/audio/Jasper_ID0020_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/built-in/audio/Jasper_ID0020_for_TensorFlow/test/train_full_1p.sh @@ -151,7 +151,7 @@ echo "E2E Training Duration sec : $e2e_time" #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' #获取性能数据，不需要修改 #吞吐量 diff --git a/TensorFlow/built-in/audio/WaveGlow_ID0024_for_TensorFlow/train.py b/TensorFlow/built-in/audio/WaveGlow_ID0024_for_TensorFlow/train.py index 376775a78a1684da948cf7ad75f1e8749fee488b..a502fe1c4cba23e809f9d09c0b543b07c0a2a6eb 100644 --- a/TensorFlow/built-in/audio/WaveGlow_ID0024_for_TensorFlow/train.py +++ b/TensorFlow/built-in/audio/WaveGlow_ID0024_for_TensorFlow/train.py @@ -223,7 +223,7 @@ def main(): print("#########gpu number:",args.ngpu) args.logdir = os.path.join(hparams.logdir_root, args.run_name) - if not os.path.exists(args.logdir): + if not os.path.exists(args.logdir) and deviceid == 0: os.makedirs(args.logdir) args.gen_wave_dir = os.path.join(args.logdir, 'wave') diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/LICENSE b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..12d255f8e0f049d3c3127e71788e219b86cdf55b --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/LICENSE @@ -0,0 +1,251 @@ + Apache License + Version 2.0, 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Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +CONTRIBUTION AGREEMENT + +By contributing to the BVLC/caffe repository through pull-request, comment, +or otherwise, the contributor releases their content to the +license and copyright terms herein. \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README.md b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b11a84220a7356fca01e775d20f2ceaa9f0a10cb --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README.md @@ -0,0 +1,84 @@ +English | [中文](README_zh-cn.md) + +# Ascend Video Processing + +This repository implements a Video Processing & Enhancement Framework on Ascend Platform, aiming to lower the barrier of implementing and deploying video restoraton and enhancement models, help users to build their own processing pipelines. + +We also provide some open-source enhancement models, as samples about how to use this framework for efficient training, online inference, and offline inference on Ascend platform. One can see the [src/networks/edvr.py](src/networks/edvr.py) file contains the classic video super-resolution model [EDVR](http://arxiv.org/abs/1905.02716) that can be trained and evaluated in tensorflow on Ascend NPU platform, which includes a ``deformable convolution`` operator implenmented exclusively on NPU. As well, we'll provide an example on how to inference with EDVR OM (offline models) on Ascend, and how to build a naive processing pipeline with video in and video out. + +## Environment + +- python3.7 +- training & online inference (with checkpoint file or PB file) + - Ascend 910 or Ascend 710 +- offline inference (with OM) + - Ascend 310 or Ascend 710 +## Requirements + +- tensorflow==1.15 +- opencv-python +- yacs +- tqdm + +## Customize Model +To construct your own model and fit the framework, you should define the model with base class ``src.networks.base_model.Base``, put it in ``src/networks`` folder, and that's it: + +```python +from src.networks.base_model import Base + +class YOUR_MODEL(Base): + pass +``` + +You can use your customized model by setting ``cfg.model.name=YOUR_MODEL`` in ``configs/models/YOUR_MODEL.py``. All the model details, training and inference details can as well be configured in this file, which will overide the default config terms in [src/utils/defaults.py](src/utils/defaults.py). + +## Training + +Enter the repository folder: + +```sh +cd AscendVideo +``` + +Modify the [scripts/env.sh](scripts/env.sh) to make sure you can import ``npu_bridge`` python package: +```sh +source scripts/env.sh +python3 -c "import npu_bridge" +``` + +Run training on a single device 0 with the configuration ``configs/models/YOUR_MODEL.py``: + +```sh +# On a single device 0 +bash scripts/run_train.sh 0 configs/models/YOUR_MODEL.py +``` + +Run training on two devices 1,2 with the configuration ``configs/models/YOUR_MODEL.py``: + +```sh +# On multiple devices, e.g., 1,2 +bash scripts/run_train.sh 1,2 configs/models/YOUR_MODEL.py +``` + +## Inference +Once you have trained the model, the checkpoint files will be saved in the output directory (specified by ``cfg.train.output_dir``). Each checkpoint consists of three files: +- ``YOUR_MODEL-10000.data****`` +- ``YOUR_MODEL-10000.meta`` +- ``YOUR_MODEL-10000.index`` + +Suppose the video frames lies in ``/path/to/frames``, where each frame is indexed following some pattern like: ``0001.png``, ``0002.png``, etc. It is easy to do inference with: + +```bash +bash scripts/run_inference.sh 0 configs/models/YOUR_MODEL.py /path/to/YOUR_MODEL-10000 /path/to/frames +``` + +The inference result will be saved in ``/path/to/frames_YOUR_MODEL``. + +### Freeze ckpt to PB +Checkpoint to PB file. +```shell +bash scripts/run_freeze.sh ckpt config.yaml +``` + + + diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README_zh-cn.md b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README_zh-cn.md new file mode 100644 index 0000000000000000000000000000000000000000..ec29bf1b5bae3fb5587d158e1cd4ff39b0711e3c --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/README_zh-cn.md @@ -0,0 +1,109 @@ +[English](README.md) | 中文 + +# 昇腾视频增强 +本仓库为基于昇腾平台的视频处理、修复和增强框架，目的是降低相关领域的开发者在昇腾平台上进行算法和模型研究、实现和部署模型的门槛，提升算法迭代的效率，帮助开发者建立自己的视频修复和增强流程。 + +本仓库会提供若干开源增强模型的样例，来引导开发者如何使用本框架和昇腾平台进行高效的训练、在线推理和离线推理等。本仓库支持多种不同的视频处理任务，如去噪、超分、插帧、HDR、人脸增强以及其他可以用模型或者非模型来完成的视频处理任务。开发者可参考[src/networks/edvr.py](src/networks/edvr.py)文件，来了解一个经典的视频超分模型[EDVR](http://arxiv.org/abs/1905.02716)是如何在昇腾平台上进行搭建、训练和评估的。 + +EDVR模型包含了一个特殊的算子``deformable_convolution``（可变卷积），昇腾平台有一个独占性的实现，对tensorflow上可变卷积的计算进行了一定优化。同时，我们会提供EDVR离线模型（Offline Model）以供开发者参考，如何在昇腾平台上进行离线推理，以及如何建立自己的视频增强端到端流程。 + +## 环境 +- Python版本：python3.7 +- 训练和在线推理硬件：Ascend 910 + +## 依赖项 +- tensorflow==1.15 +- opencv-python +- yacs +- tqdm + +## 自定义模型 +添加自定义模型比较简单，只需要继承``src.networks.base_model.Base``类创建一个新的模型类，将其放在``src/networks``下即可： + +```python +from src.networks.base_model import Base + +class YOUR_MODEL(Base): + # Define your own structure. + pass +``` + +然后通过``configs/models/YOUR_MODEL.py``作为配置文件来来调用自定义模型即可： +```yaml +model: + name: YOUR_MODEL +# other configurations +``` +该文件也可以配置模型结构或是训练、推理策略，程序将在[src/utils/defaults.py](src/utils/defaults.py)的基础上进行覆盖该配置文件。 + +## 训练 +进入目录： + +```sh +cd AscendVideo +``` + +根据硬件需要对环境变量文件[scripts/env.sh](scripts/env.sh)进行修改： +```sh +vim scripts/env.sh + +# 修改对应的环境变量，确保能import npu_bridge +``` + +在0号NPU设备上使用``configs/models/YOUR_MODEL.py``配置文件进行训练： + +```sh +# On a single device 0 +bash scripts/run_train.sh 0 configs/models/YOUR_MODEL.py +``` + +在1,2两个NPU设备上使用``configs/models/YOUR_MODEL.py``进行多卡训练: + +```sh +# On multiple devices, e.g., 1,2 +bash scripts/run_train.sh 1,2 configs/models/YOUR_MODEL.py +``` + +## 推理 +训练完成之后，输出目录（由``cfg.train.output_dir``指定）下会生成定间隔保存的checkpoint文件，例如: +- ``YOUR_MODEL-10000.data****`` +- ``YOUR_MODEL-10000.meta`` +- ``YOUR_MODEL-10000.index`` + +给定任意输入视频帧路径``/path/to/frames``，每一帧按顺序编号为``0001.png``,``0002.png``以此类推，则只需要使用如下命令即可进行在线推理： + +```bash +bash scripts/run_inference.sh 0 configs/models/YOUR_MODEL.py /path/to/YOUR_MODEL-10000 /path/to/frames +``` +推理结果会保存在``/path/to/frames_YOUR_MODEL``路径下。 + +### 模型固化 +将checkpoint固化为PB文件： +```shell +bash scripts/run_freeze.sh configs/models/YOUR_MODEL.py /path/to/YOUR_MODEL-10000 +``` +其中固化的输入placeholder的size可以通过修改``configs/models/YOUR_MODEL.py``来进行配置。 + +## 测试样片效果 +我们提供了若干用于测试增强效果的视频片段，并且给出了昇腾视频增强对于这些片段的处理效果，包括单一的去噪、插帧、人脸增强、HDR色彩增强和超分辨率等算法。 + +| 片段 | 分辨率 | 帧率 | 链接 | 备注 | +| ------------- | --- | --- | ------------------------------------------------------------ | --- | +| 超分原片 | 1080P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/City-1080p.mp4 | | +| 2倍超分 | 2160P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/City-1080p-2x_vsr.mp4 | | +| 4倍超分 | 4320P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/City-1080p-4x_vsr.mp4 | | +| 去噪原片 | 1080P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/City-Noisy-1080p.mp4 | | +| 去噪效果 | 1080P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/City-Noisy-1080p_Denoised.mp4 | | +| 人脸原片 | 1080P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Face.mp4 | | +| 人脸增强效果 | 1080P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Face-Enhancement.mp4 | | +| 插帧原片 | 1080P | 23.976 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Waterdrop-24FPS-1080p.mp4 | | +| 2倍插帧 | 1080P | 47.952 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Waterdrop-48FPS-1080p.mp4 | | +| 4倍插帧 | 1080P | 95.904 |https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Waterdrop-96FPS-1080p.mp4 | | +| SDR原片 | 2160P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Color-SDR-1080p.mp4 | | +| HDR无色彩增强 | 2160P | 25 | https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Color_HLG-1080p.mp4 | 需要播放器或者屏幕支持HLG | +| HDR昇腾色彩增强 | 2160P | 25 |https://obs-ascend-test.obs.cn-east-2.myhuaweicloud.com/vsr/Color-Enhanced-HLG-1080p.mp4 | 需要播放器或者屏幕支持HLG | + +## 离线推理参考 + +昇腾[Sample仓库](https://gitee.com/ascend/samples)提供了超分模型EDVR的[离线推理案例](https://gitee.com/ascend/samples/tree/master/python/level2_simple_inference/6_other/video_super_resolution)以供参考 + diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/codecs/default_sdr_x264.json b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/codecs/default_sdr_x264.json new file mode 100644 index 0000000000000000000000000000000000000000..e084257ba1211725a4ec82a2da782679397c752d --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/codecs/default_sdr_x264.json @@ -0,0 +1,9 @@ +{ + "codec": { + "-crf": "10", + "-c:v": "libx264", + "-pix_fmt": "yuv420p", + "-vf": "zscale=rangein=full:range=limited:transferin=709:matrixin=709:primariesin=709:matrix=709:transfer=709:primaries=709:agamma=0" + }, + "format": "mp4" +} \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/models/edvr_config.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/models/edvr_config.py new file mode 100644 index 0000000000000000000000000000000000000000..d38fe2627f78abe3737f2ec7a39acc96c4df96ae --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/configs/models/edvr_config.py @@ -0,0 +1,60 @@ +cfg = dict( + data=dict( + data_dir='/data2/vsr_datasets/reds', + train=dict( + batch_size=16, + input_size=[64, 64], + augmentation=dict( + apply=True, + interval_list=[1,2,3], + options=""" + RandomCrop: + input_dim: 4 + RandomTemporalReverse: + input_dim: 4 + RandomFlipLeftRight: + input_dim: 4 + RandomFlipUpDown: + input_dim: 4 + """, + ), + ), + ), + edvr=dict( + with_tsa=True, + mid_channels=64, + use_dcn=False, + num_groups=1, + num_deform_groups=1, + num_blocks_extraction=5, + num_blocks_reconstruction=10, + upsampling='bilinear', + align_corners=False + ), + model=dict( + content_loss_reduction='mean', + content_loss_type='l1', + factor_for_adapt_input=4, + name='EDVR', + num_net_input_frames=5, + num_net_output_frames=1, + scale=4, + scope='G' + ), + loss=dict( + content=dict( + loss_type='L1Loss', + loss_reduction='mean' + ), + ), + train=dict( + print_interval=100, + output_dir='output/edvr', + generator=dict( + lr_schedule=dict( + total_steps=[10000] + ) + ) + ), + log_file='edvr_train.log', +) \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/requirements.txt b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0cff5f86ffff8ad9465fc71c20b9c28d85c89d2 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/requirements.txt @@ -0,0 +1,4 @@ +tensorflow==1.15 +opencv-python +yacs +tqdm \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/8p.json b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/8p.json new file mode 100644 index 0000000000000000000000000000000000000000..4532a33910ee4aff2ccde7779255c5373f34ad88 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/8p.json @@ -0,0 +1,109 @@ +{ + "board_id": "0x002f", + "chip_info": "910", + "deploy_mode": "lab", + "group_count": "1", + "group_list": [ + { + "device_num": "8", + "server_num": "1", + "group_name": "", + "instance_count": "8", + "instance_list": [ + { + "devices": [ + { + "device_id": "0", + "device_ip": "192.168.100.101" + } + ], + "rank_id": "0", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "1", + "device_ip": "192.168.101.101" + } + ], + "rank_id": "1", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "2", + "device_ip": "192.168.102.101" + } + ], + "rank_id": "2", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "3", + "device_ip": "192.168.103.101" + } + ], + "rank_id": "3", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "4", + "device_ip": "192.168.100.100" + } + ], + "rank_id": "4", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "5", + "device_ip": "192.168.101.100" + } + ], + "rank_id": "5", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "6", + "device_ip": "192.168.102.100" + } + ], + "rank_id": "6", + "server_id": "172.17.1.120" + }, + { + "devices": [ + { + "device_id": "7", + "device_ip": "192.168.103.100" + } + ], + "rank_id": "7", + "server_id": "172.17.1.120" + } + ] + } + ], + "para_plane_nic_location": "device", + "para_plane_nic_name": [ + "eth0", + "eth1", + "eth2", + "eth3", + "eth4", + "eth5", + "eth6", + "eth7" + ], + "para_plane_nic_num": "8", + "status": "completed" +} diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/create_new_experiment.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/create_new_experiment.sh new file mode 100644 index 0000000000000000000000000000000000000000..dfef0967bd4b2f59c87b08eb28dd66f010b31464 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/create_new_experiment.sh @@ -0,0 +1,12 @@ +#!/bin/bash +new_env=$1 +cur_dir=`pwd` +root_dir=${cur_dir} + +if [ ! -d ${new_env} ];then + mkdir ${root_dir}/${new_env} + cd ${root_dir}/${new_env} + ln -s ../src src + ln -s ../configs configs + ln -s ../scripts scripts +fi diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/env.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/env.sh new file mode 100644 index 0000000000000000000000000000000000000000..789fb1a92be5248f26184f20edf2dcced7b3d5cd --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/env.sh @@ -0,0 +1,11 @@ +# !/bin/bash + +export CUSTOM_OP_LIB_PATH=/usr/local/Ascend/fwkacllib/ops/framework/built-in/tensorflow/ +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/:/usr/lib/:/usr/local/Ascend/fwkacllib/lib64/:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/usr/local/Ascend/add-ons/:/usr/local/Ascend/fwkacllib/lib64/plugin/opskernel:/usr/local/Ascend/fwkacllib/lib64/plugin/nnengine:/usr/local/Ascend/atc/lib64/plugin/opskernel:/usr/local/Ascend/atc/lib64/plugin/nnengine:/usr/local/Ascend/atc/lib64/stub:/usr/local/Ascend/acllib/lib64:/usr/local/python3.7/lib/:/usr/local/python3.7/lib/python3.7/site-packages/torch/lib/ +export PYTHONPATH=$PYTHONPATH:/usr/local/Ascend/atc/python/site-packages:/usr/local/Ascend/python/site-packages:/usr/local/Ascend/fwkacllib/python/site-packages:/usr/local/Ascend/fwkacllib/python/site-packages/auto_tune.egg/auto_tune:/usr/local/Ascend/fwkacllib/python/site-packages/schedule_search.egg:/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe:usr/local/Ascend/tfplugin/latest/tfplugin/python/site-packages:${PYTHONPATH} +export TOOLCHAIN_HOME=/usr/local/Ascend/toolkit +export PATH=$PATH:/usr/local/Ascend/fwkacllib/ccec_compiler/bin:/usr/local/Ascend/toolkit/bin:/usr/local/Ascend/fwkacllib/bin:/usr/local/Ascend/atc/bin:/usr/local/python3.7/bin/ +export ASCEND_OPP_PATH=/usr/local/Ascend/opp +export ASCEND_AICPU_PATH=/usr/local/Ascend +export SOC_VERSION=Ascend910 +export HCCL_CONNECT_TIMEOUT=600 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_8p.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..82bea58482c10cbcbb0a6a517eafe1a3fdedafcb --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_8p.sh @@ -0,0 +1,11 @@ +#!/bin/bash +cur_dir=`pwd` +root_dir=${cur_dir} + +mkdir data +for i in $(seq 0 7) +do + if [ ! -d "D$i" ];then + bash scripts/create_new_experiment.sh D${i} + fi +done diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_hccl_json.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_hccl_json.py new file mode 100644 index 0000000000000000000000000000000000000000..e0a6c2efdb659a8a2fad2e0420a9b563b7fc938b --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/prepare_hccl_json.py @@ -0,0 +1,41 @@ +import os +import sys +import copy +from collections import OrderedDict +import json + + +def parse_json(json_file): + with open(json_file, 'r') as f: + config = json.load(f, object_pairs_hook=OrderedDict) + return config + + +def generate_json(device_list, config, target_file): + new_config = copy.deepcopy(config) + device_insts = [] + insta_list = new_config["group_list"][0]["instance_list"] + rank = 0 + for inst in insta_list: + if inst["devices"][0]["device_id"] in device_list: + inst["rank_id"] = str(rank) + device_insts.append(inst) + rank += 1 + new_config["group_list"][0]["device_num"] = str(rank) + new_config["group_list"][0]["instance_count"] = str(rank) + new_config["group_list"][0]["instance_list"] = device_insts + + print(f'[INFO] Writing out hccl config json file to {target_file}') + with open(target_file, 'w') as f: + json.dump(new_config, f) + + +if __name__ == '__main__': + device_lists = sys.argv[1] + source_json_file = sys.argv[2] + target_file = sys.argv[3] + + device_lists = device_lists.strip().split(',') + config = parse_json(source_json_file) + + generate_json(device_lists, config, target_file) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_freeze.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_freeze.sh new file mode 100644 index 0000000000000000000000000000000000000000..d3bb74eb94bc7df02af04548d0382c4375cacee3 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_freeze.sh @@ -0,0 +1,38 @@ +#!/bin/bash +CKPT=$1 +CONFIG_FILE=$2 +DEVICE_ID=0 +DEVICE_RANK=1 + +source scripts/env.sh + +#export PRINT_MODEL=1 +export MOX_USE_NPU=1 +export FUSION_TENSOR_SIZE=2000000000 +export MOX_USE_TF_ESTIMATOR=0 +export MOX_USE_TDT=1 + +export HEARTBEAT=1 +export CONITNUE_TRAIN=true +export LOG_DIR=./log + +export ASCEND_GLOBAL_EVENT_ENABLE=0 +export ASCEND_GLOBAL_LOG_LEVEL=3 +export TF_CPP_MIN_LOG_LEVEL=3 + +# Turn profiling on +export JOB_ID=123456789 +export DEVICE_ID=${DEVICE_ID} +export DEVICE_INDEX=${DEVICE_ID} +export RANK_ID=${DEVICE_ID} +export RANK_SIZE=${DEVICE_RANK} +if [ ${DEVICE_RANK} -gt 1 ]; then + export RANK_TABLE_FILE=scripts/${DEVICE_RANK}p.json +fi + +rm -rf kernel_meta + +python3 src/main.py \ + --config-file ${CONFIG_FILE} \ + mode freeze \ + checkpoint ${CKPT} diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_inference.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..a20db0742dd92b32b49d37e69fd5f08f281bb5bb --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_inference.sh @@ -0,0 +1,179 @@ +#!/bin/bash + +devices=$1 +models=$2 +dir=$3 +codec_file=$4 + +resource_file='resource.json' +if [ -z "$4" ]; then + codec_file=configs/codecs/default_sdr_x264.json +fi +video_file_ext=$(cat ${codec_file} | python3 -c "import sys, json; print(json.load(sys.stdin)['format'])") + +# Set models +declare -A edvr=( +["config"]="configs/models/edvr_config.py" +["ckpt"]="outputs/edvr/TempoEDVR-280000" +) + +readarray -d , -t models <<< "${models}," +unset 'models[$((${#models[@]}-1))]' + +# Remove the last / if it exists +echo "$dir" | grep '/$' +if [ $? -eq 0 ] +then + dir=${dir%/} +fi + +# Set FPS +FPS=$(echo $dir | grep -Eo '[0-9]+[\.]?[0-9]+FPS' | grep -Eo '[0-9]+[\.]?[0-9]+') +FPS=$(awk -vp=$FPS -vq=1 'BEGIN{printf "%.3f" ,p * q}') + +# Check whether has been vfi +if test "${dir#*vfi}" != "${dir}" +then + FPS=$(awk -vp=${FPS} -vq=2 'BEGIN{printf "%0.3f" ,p * q}') +fi + +# Create temp txt file to record subvideo names +cur_dir=`pwd` +txt_file="temp.txt" +if [ -e "${dir_out}_videos/${txt_file}" ] +then + rm -f ${dir_out}_videos/${txt_file} +fi + +source scripts/env.sh + +function cmd() { + device_id=$1 + device_rank=$2 + rank_id=$3 + model_name=$4 + dir_in=$5 + dir_out=$6 + io_backend=$7 + + # Turn profiling on + export JOB_ID=123456789 + export DEVICE_ID=${device_id} + export DEVICE_INDEX=${device_id} + export RANK_ID=${rank_id} + export RANK_SIZE=${device_rank} + + export MOX_USE_NPU=1 + export FUSION_TENSOR_SIZE=2000000000 + export MOX_USE_TF_ESTIMATOR=0 + export MOX_USE_TDT=1 + + export HEARTBEAT=1 + export CONITNUE_TRAIN=true + export LOG_DIR=./log + + export ASCEND_GLOBAL_EVENT_LEVEL=0 + export ASCEND_GLOBAL_EVENT_ENABLE=0 + export ASCEND_GLOBAL_LOG_LEVEL=3 + export TF_CPP_MIN_LOG_LEVEL=3 + + rm -rf kernel_meta + rm -rf ~/ascend/log/plog + + declare -n model="$model_name" # model is a reference + python3 src/main.py \ + --config-file ${model["config"]} \ + mode inference \ + data.data_dir ${dir_in} \ + data.inference.auto_adapt_input True\ + inference.result_dir ${dir_out} \ + inference.io_backend ${io_backend} \ + inference.ffmpeg.video_filename ${rank_id}.${video_file_ext} \ + inference.ffmpeg.codec_file ${codec_file} \ + inference.ffmpeg.fps ${FPS} \ + env.rank_size ${RANK_SIZE} \ + checkpoint ${model["ckpt"]} \ + env.device 'npu' +} + +# read device id to list +function mfcb { local val="$4"; "$1"; eval "$2[$3]=\$val;"; }; +function val_ltrim { if [[ "$val" =~ ^[[:space:]]+ ]]; then val="${val:${#BASH_REMATH[0]}}"; fi; }; +function val_rtrim { if [[ "$val" =~ [[:space:]]+$ ]]; then val="${val:0:${#val}-${#BASH_REMATH[0]}}"; fi; }; +function val_trim { val_ltrim; val_rtrim; } + +if [[ -z "$1" ]]; then + echo "[INFO] device_id not set. Input argument could be like '1' or '0,1,2'." + echo "[INFO] Set device_id=0 by default." + device_list=0 + device_rank=1 +else + readarray -c1 -C 'mfcb val_trim device_list' -td, <<<"$devices,"; unset 'device_list[-1]'; declare -a device_list; + device_rank=${#device_list[@]} +fi +echo "[INFO] device_list: ${device_list[@]}" +echo "[INFO] device_rank: ${device_rank}" + + +cnt=0 +io_backend="disk" +for model_name in "${models[@]}"; do + if [[ "$model_name" =~ "vfi" ]]; then + # if model_name contains "vfi", multiply the fps + # bash does not support floating point + FPS=$(awk -vp=$FPS -vq=2 'BEGIN{printf "%.3f" ,p * q}') + fi + + if [[ "$var" =~ "hdr" && "$1" = "" ]]; then + codec_file=configs/codecs/exr2020_to_hlg_hdr_x264.json + video_file_ext=$(cat ${codec_file} | python3 -c "import sys, json; print(json.load(sys.stdin)['format'])") + fi + + cnt=$(( $cnt + 1 )) + + dir_out="${dir}_${model_name}" + if [ ! -d ${dir_out} ]; then + mkdir ${dir_out} + fi + + # set video output for the last model + if [ $cnt -eq ${#models[@]} ]; then + io_backend="disk:ffmpeg" + if [ ! -d "${dir_out}_videos" ]; then + mkdir ${dir_out}_videos + fi + fi + + if [ $device_rank -gt 1 ]; then + max_device_rank=`expr ${device_rank} - 1` + for d_id in ${!device_list[@]}; do + cd ${cur_dir} + bash scripts/create_new_experiment.sh D_${d_id} + cd D_${d_id} + # set video output for the last model + if [ $cnt -eq ${#models[@]} ]; then + # write video name to text file + echo "file ${dir_out}_videos/${d_id}.${video_file_ext}" >> ${dir_out}_videos/${txt_file} + fi + # inference + if [ $d_id -ne ${max_device_rank} ]; then + cmd ${device_list[$d_id]} ${device_rank} ${d_id} ${model_name} ${dir} ${dir_out} ${io_backend} & + else + cmd ${device_list[$d_id]} ${device_rank} ${d_id} ${model_name} ${dir} ${dir_out} ${io_backend} || exit 1 + fi + + done + # wait untill all jobs done + wait < <(jobs -p) + # concat all subvideos after the last model inference + if [ $cnt -eq ${#models[@]} ]; then + ffmpeg -y -f concat -safe 0 -i ${dir_out}_videos/${txt_file} -c copy ${dir_out}.${video_file_ext} + fi + else + cmd ${device_list[$d_id]} ${device_rank} ${device_list[$d_id]} ${model_name} ${dir} ${dir_out} ${io_backend} || exit 1 + mv ${dir_out}_videos/${device_list[$d_id]}.${video_file_ext} ${dir_out}.${video_file_ext} + fi + # update path + dir="${dir_out}" +done + diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_train.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_train.sh new file mode 100644 index 0000000000000000000000000000000000000000..583f8443828b5ef73923cb1f989defe569e8b360 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/run_train.sh @@ -0,0 +1,89 @@ +#!/bin/bash + +devices=$1 +config_file=$2 + +source scripts/env.sh + +function cmd() { + device_id=$1 + device_rank=$2 + rank_id=$3 + config_file=$4 + rank_table_file=$5 + + # Turn profiling on + export JOB_ID=123456789 + export DEVICE_ID=${device_id} + export DEVICE_INDEX=${device_id} + export RANK_ID=${rank_id} + export RANK_SIZE=${device_rank} + if [ -n "$rank_table_file" ]; then + export RANK_TABLE_FILE=${rank_table_file} + fi + + export MOX_USE_NPU=1 + export FUSION_TENSOR_SIZE=2000000000 + export MOX_USE_TF_ESTIMATOR=0 + export MOX_USE_TDT=1 + + export HEARTBEAT=1 + export CONITNUE_TRAIN=true + export LOG_DIR=./log + + export ASCEND_GLOBAL_EVENT_LEVEL=0 + export ASCEND_GLOBAL_EVENT_ENABLE=0 + export ASCEND_GLOBAL_LOG_LEVEL=3 + export TF_CPP_MIN_LOG_LEVEL=3 + + python3 src/main.py \ + --config-file ${config_file} \ + env.rank_size ${device_rank} \ + env.device 'npu' +} + +# read device id to list +function mfcb { local val="$4"; "$1"; eval "$2[$3]=\$val;"; }; +function val_ltrim { if [[ "$val" =~ ^[[:space:]]+ ]]; then val="${val:${#BASH_REMATH[0]}}"; fi; }; +function val_rtrim { if [[ "$val" =~ [[:space:]]+$ ]]; then val="${val:0:${#val}-${#BASH_REMATH[0]}}"; fi; }; +function val_trim { val_ltrim; val_rtrim; } + +if [[ -z "$1" ]]; then + echo "[INFO] device_id not set. Input argument could be like '1' or '0,1,2'." + echo "[INFO] Set device_id=0 by default." + device_list=0 + device_rank=1 +else + readarray -c1 -C 'mfcb val_trim device_list' -td, <<<"$devices,"; unset 'device_list[-1]'; declare -a device_list; + device_rank=${#device_list[@]} +fi +echo "[INFO] device_list: ${device_list[@]}" +echo "[INFO] device_rank: ${device_rank}" + +cur_dir=`pwd` +if [ $device_rank -gt 1 ]; then + source_json=scripts/8p.json + trimmed_dev_list=`echo ${device_list[@]} | tr -d ' '` + if [ ${device_rank} -eq 8 ]; then + target_json=$source_json + echo "[INFO] 8p using source hccl config file: ${target_json} ..." + else + target_json=scripts/${device_rank}p_${trimmed_dev_list}.json + echo "[INFO] (Re)Generating hccl config file: ${target_json} ..." + python3 scripts/prepare_hccl_json.py ${devices} ${source_json} ${target_json} + fi + + max_device_rank=`expr ${device_rank} - 1` + for d_id in ${!device_list[@]}; do + cd ${cur_dir} + bash scripts/create_new_experiment.sh D_${device_list[$d_id]} + cd D_${device_list[$d_id]} + if [ $d_id -ne ${max_device_rank} ]; then + cmd ${device_list[$d_id]} ${device_rank} ${d_id} ${config_file} ${target_json} & + else + cmd ${device_list[$d_id]} ${device_rank} ${d_id} ${config_file} ${target_json} && echo "[INFO] Train done." + fi + done +else + cmd ${device_list[$d_id]} ${device_rank} 0 ${config_file} +fi diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/video2frames.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/video2frames.sh new file mode 100644 index 0000000000000000000000000000000000000000..edec343ca80fa558e29b273c5dd1b129e5d51cf9 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/scripts/video2frames.sh @@ -0,0 +1,170 @@ +#!/bin/bash + +helpFunction() +{ + echo "" + echo "Usage: $0 [-h] [-i VIDEO_PATH] [-o OUTPUT_PATH] [-f FILTERS] [-l LOG_DIR] [-q]" + echo -e "\t-i: Input source video file." + echo -e "\t-o: Output frames directory." + echo -e "\t-f: Filters for deinterlacing the video when it's interlaced. Default 'bobweaver'." + echo -e "\t-r: Frame rate if interlaced. Default '1' " + echo -e "\t-l: Log directory." + echo -q "\t-q: Quiet mode. Will not print any information." + exit 1 # Exit script after printing help +} + +while getopts "i:o:f:l:r:hq" opt +do + case "$opt" in + i ) video_path="$OPTARG" ;; + o ) frames_dir="$OPTARG" ;; + f ) filter="$OPTARG" ;; + l ) log_dir="$OPTARG" ;; + r ) rate="$OPTARG" ;; + q ) quiet=true ;; + h ) helpFunction ;; # Print helpFunction in case parameter is non-existent + esac +done + +if [ x"$video_path" = x"" -o ! -f "$video_path" ]; then + echo "[ERROR] Invalid video path: $video_path" + helpFunction +fi + +if [ x"$quiet" = x"" ]; then + quiet=false +fi + +if [ x"$rate" = x"" ]; then + rate='1' +fi + +timestamp=$(date '+%Y%m%dR%H%M%S') +if [ x"$log_dir" = x"" ]; then + log_dir=/tmp/.mindvideo +fi + +if [ ! -d "$log_dir" ]; then + mkdir -p $log_dir +fi +report_log=${log_dir}/${timestamp}_probe.log +extract_log=${log_dir}/${timestamp}_video.log + +# ======================================================================================= +# check which type of videos: +# progressive, pseudo-interlaced (will be treated as progressive), truly interlaced +# ======================================================================================= +test_nframes=400 +export FFREPORT=file=$extract_log +if [ -e $report_log ]; then + rm $report_log +fi + +ffmpeg -report -i ${video_path} -vframes $test_nframes -vf idet -f null - 2> $report_log +wait < <(jobs -p) + +nframes=( $(cat $report_log | grep 'Multi frame detection: ' | grep -woP '(\d+)') ) +# nframes: [tff, bff, progressive, undetermined] + +n_frame_interlaced=$(awk -vp=${nframes[0]} -vq=${nframes[1]} 'BEGIN{printf "%d" ,p + q}') +# echo $n_frame_interlaced + +if [ $n_frame_interlaced -gt ${nframes[2]} ]; then + type=interlaced +else + type=progressive +fi + +# if it's progressive, use none filter regardless of the previous settings +if [ $type = "progressive" ]; then + filter_name=none +elif [ x"$filter" = x"" ]; then + # else if it's interlaced, used bobweaver as the default deintelacing filter + filter_name=bobweaver +else + filter_name=$filter +fi + +if [ $quiet = "false" ]; then + echo "[INFO] Video type: $type; Filter: ${filter_name}" +fi + +# ====================================================================================== +# extract frames from video with the given deinterlacing filter. +# record the fps first. +# ====================================================================================== +fps=$(ffprobe -v error -select_streams v -of default=noprint_wrappers=1:nokey=1 -show_entries stream=r_frame_rate $video_path) +fps=$(echo "print(f'{$fps:.3f}')" | python3) +frames_fps=$fps +#n_total_frames=$(ffprobe -v error -select_streams v:0 -count_packets -of default=noprint_wrappers=1:nokey=1 -show_entries stream=nb_read_packets $video_path) + +# determine the filter +if [ "$filter_name" = "bobweaver" ]; then + if [ $type = "interlaced" ] && [ $rate = '2' ] ; then + # deinterlace with 2x fps + filter_cmd="-vf bwdif=1:0:0" + frames_fps=$(echo "print(f'{2*$frames_fps:.3f}')" | python3) + else + filter_cmd="-vf bwdif=0:0:0" + fi +elif [ "$filter_name" = "yadif" ]; then + if [ $type = "interlaced" ] && [ $rate = '2' ] ; then + # deinterlace with 2x fps + filter_cmd="-vf yadif=1:0:0" + frames_fps=$(echo "print(f'{2*$frames_fps:.3f}')" | python3) + else + filter_cmd="-vf yadif=0:0:0" + fi +elif [ "$filter_name" = "QTGMC" ]; then + if [ $type = "interlaced" ] && [ $rate = '2' ] ; then + filter_cmd="50fps.vpy" + frames_fps=$(echo "print(f'{2*$frames_fps:.3f}')" | python3) + else + filter_cmd="25fps.vpy" + fi +elif [ "$filter_name" = "none" ]; then + filter_cmd="" +fi + +frames_dir=$frames_dir/${frames_fps}FPS_frames + +if [ ! -d "$frames_dir" ]; then + mkdir -p $frames_dir +fi + +# ============================================================================== +# check whether is HDR +# ============================================================================== +COLORS=$(ffprobe -show_streams -v error "${video_path}" |egrep "^color_transfer|^color_space=|^color_primaries=" |head -3) +for C in $COLORS; do + if [[ "$C" = "color_space="* ]]; then + COLORSPACE=${C##*=} + elif [[ "$C" = "color_transfer="* ]]; then + COLORTRANSFER=${C##*=} + elif [[ "$C" = "color_primaries="* ]]; then + COLORPRIMARIES=${C##*=} + fi +done + +if [ "${COLORSPACE}" = "bt2020nc" ] && [ "${COLORTRANSFER}" = "smpte2084" ] && [ "${COLORPRIMARIES}" = "bt2020" ]; then + ext='exr' +elif [ "${COLORSPACE}" = "bt2020nc" ] && [ "${COLORTRANSFER}" = "arib-std-b67" ] && [ "${COLORPRIMARIES}" = "bt2020" ]; then + ext='exr' +else + ext='png' +fi + +if [ $quiet = "false" ]; then + echo "[INFO] Extracting frames from ${video_path}. This may take a while." + echo "[INFO] Cmd: ffmpeg -i ${video_path} $filter_cmd $frames_dir/%08d.${ext}" +fi + +if [ "$filter_name" = "QTGMC" ]; then + # This is only valid when in x86 + vspipe --y4m $filter_cmd -a "video_path=${video_path}" - | ffmpeg -i pipe: $frames_dir/%08d.${ext} +else + ffmpeg -i ${video_path} $filter_cmd $frames_dir/%08d.${ext} +fi +wait < <(jobs -p) + +echo "$type, ${fps}, ${frames_fps}" \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed995e3d4b86bc4ed44ac806dca7afe1598bd5ce --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/__init__.py @@ -0,0 +1,160 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from src.dataloaders.dataloader import TfDataloader +from src.utils.exceptions import * +from src.utils.logger import logger +from src.utils.world import world + + +def build_train_dataloader(config, _world=None): + """ + Build train dataloader given config. + + Args: + config: yacs node, configuration. + + Returns: + generator of list[tensor]. + """ + + # Import the dataset classes only when needed to avoid import error + from src.dataloaders.train_dataset import ( + OfflineTrainDataset, + OnlineTrainDataset, + DummyTrainDataset, + MixtureDatasets, + ) + + # Support multi-dataset whose path are concated with ':' + # in cfg.data.data_dir + data_dir_list = config.data.data_dir.split(':') + task = config.task + online_degradation_mode = config.data.train.degradation.online + distributed = config.env.rank_size > 1 + device = config.env.device + batchsize = config.data.train.batch_size + + world_inst = _world or world + # _world should be initialized + if not world_inst.is_initialized: + raise WorldUninitializedError('World not initialized.') + + if config.debug_mode: + dataset_cls = DummyTrainDataset + elif online_degradation_mode: + dataset_cls = OnlineTrainDataset + else: + dataset_cls = OfflineTrainDataset + + if len(data_dir_list) > 1: + dataset = MixtureDatasets.from_datadir( + dataset_cls, data_dir_list, cfg=config) + else: + dataset = dataset_cls(data_dir=data_dir_list[0], cfg=config) + + dataloader = TfDataloader(dataset, batchsize, + distributed=distributed, + device=device) + return dataloader.batch_list + + +def build_test_dataloader(config, _world=None): + """ + Build inference dataloader given config. + + Args: + config: yacs node, configuration. + + Returns: + dict, contains the data term. + """ + from src.dataloaders.test_dataset import ( + VSRTestDataset, + DenoiseTestDataset, + VFITestDataset, + HDRTestDataset, + DummyTestDataset, + MixtureTestDataset, + ComposedTestDataset + ) + + TASK_MAP_TO_DATASET = { + 'vsr': VSRTestDataset, + 'denoise': DenoiseTestDataset, + 'vfi': VFITestDataset, + 'hdr': HDRTestDataset, + 'face': DenoiseTestDataset, + } + + data_dir_list = config.data.data_dir.split(':') + distributed = config.env.rank_size > 1 + task = config.task + world_inst = _world or world + # _world should be initialized + if not world_inst.is_initialized: + raise WorldUninitializedError('World not initialized.') + + assert task in TASK_MAP_TO_DATASET + + if config.debug_mode: + dataset_cls = DummyTestDataset + else: + dataset_cls = TASK_MAP_TO_DATASET[task] + + if config.debug_mode: + dataset = dataset_cls(data_dir=data_dir_list[0], cfg=config) + elif len(data_dir_list) > 1: + # For multi-dataset + dataset = MixtureTestDataset.from_datadir( + dataset_cls, data_dir_list, cfg=config) + else: + files = os.listdir(data_dir_list[0]) + if os.path.isdir(os.path.join(data_dir_list[0], files[0])): + # For dataset with multiple clips + dataset = ComposedTestDataset.from_datadir( + dataset_cls, data_dir_list[0], files, cfg=config + ) + else: + # Foe a single dataset with frames + dataset = dataset_cls(data_dir=data_dir_list[0], cfg=config) + + # Manually shard the dataset to inference on multiple devices. + if distributed: + dataset.shard(world_inst.rank_size, world_inst.rank_id) + return dataset + + +def build_dataloader(cfg, **kwargs): + """ + Build dataloader given scenario and configurations. + + Args: + cfg: yacs node, global configuration. + **kwargs: argument dicts. + """ + if cfg.mode in ['train', 'eval']: + dataloader = build_train_dataloader(cfg) + elif cfg.mode in 'inference': + dataloader = build_test_dataloader(cfg, **kwargs) + elif cfg.mode == 'freeze': + dataloader = None + else: + raise KeyError + return dataloader + + +__all__ = ['build_train_dataloader', 'build_test_dataloader', 'build_dataloader'] diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/dataloader.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..c66399343856674c0b4192adbae1b5be6c4fe5c9 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/dataloader.py @@ -0,0 +1,114 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + + +class TfDataloader: + """A wrapper of the tensorflow Dataset class. + + This class aims to make the dataset construction more convenient. Users + are only required to implement the dataset class and return the specific + information, then TfDataloader will wrap the dataset class and load the + data, similar to Pytorch dataset and dataloader. + + Args: + dataset: Dataset class. See `src.dataloaders.train_dataset` + batch_size: int + drop_remainder: boolean, whether drop the last remainder terms. + Necessary on Ascend NPU. Default is True. + distributed: boolean, whether to use distribute dataloader. + shuffle: boolean, whether to shuffle the dataset. + repeat: boolean, whether to repeat the dataset (usually in training). + device: str, hardware used for accelerating. Optional in ['npu', 'cpu'] + """ + def __init__(self, dataset, batch_size=2, drop_remainder=True, + distributed=False, shuffle=True, repeat=True, device='npu', + _world=None): + self.batch_size = batch_size + self.drop_remainder = drop_remainder + self.distributed = distributed + self.device = device + self.dataset = dataset + self.shuffle = shuffle + self.repeat = repeat + self.world = _world + self.sample_indices = list(range(len(self.dataset))) + + self.build_iterator() + + def get_item(self, index): + """ Tensorflow wrapper of the _get_item method of Dataset class + + Args: + index: int, called by tensorflow.data.Dataset.map function. + """ + # The dtype and shape are defined by the dataset. Otherwise, + # tf does not know the shape. + data = tf.numpy_function(lambda x: self.dataset[x], + [index], + self.dataset.data_dtype) + + for d, shape in zip(data, self.dataset.data_shape): + d.set_shape(tuple(shape)) + return data + + def build_iterator(self): + """ Build dataloader iterator """ + video_dataset = tf.data.Dataset.from_tensor_slices(self.sample_indices) + + if self.shuffle: + video_dataset = video_dataset.shuffle(len(self.dataset)) + + video_dataset = video_dataset.map(self.get_item, + num_parallel_calls=tf.data.experimental.AUTOTUNE) + video_dataset = video_dataset.batch(self.batch_size, + drop_remainder=self.drop_remainder) + + if self.repeat: + video_dataset = video_dataset.repeat() + + if self.distributed: + video_dataset = video_dataset.shard(self.world.rank_size, + self.world.rank_id) + + video_dataset = video_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) + + iterator = video_dataset.make_one_shot_iterator() + + # tensorflow will wrap all the numpy.ndarray as tensors + self.batch_list = iterator.get_next() + + +def build_dataloader(dataset, batch_size=2, drop_remainder=True, distributed=False, + shuffle=True, repeat=True, device='npu'): + """ + Build dataloader given the dataset. + + Args: + dataset: Dataset class. See `src.dataloaders.train_dataset` + batch_size: int + drop_remainder: boolean, whether drop the last remainder terms. + Necessary on Ascend NPU. Default is True. + distributed: boolean, whether to use distribute dataloader. + shuffle: boolean, whether to shuffle the dataset. + repeat: boolean, whether to repeat the dataset (usually in training). + device: str, hardware used for accelerating. Optional in ['npu', 'cpu'] + + Returns: + list[tensorlfow tensor] + """ + dataloader = TfDataloader(dataset, batch_size, drop_remainder, + distributed, shuffle, repeat, device) + return dataloader.batch_list diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/test_dataset.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/test_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a6c1a148eb43b7924b04e9c25509336b3ca9fd7a --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/test_dataset.py @@ -0,0 +1,668 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +from src.dataloaders.utils import ( + get_consecutive_frame_indices, + load_batch_image, + supported_file_format +) +from src.utils.file_io import imread +from src.utils.logger import logger + + +class _TestDataset: + """ The base class for training dataset. + + The derived classes should implement these functions: + _get_item: an indexing like item fetching method. + data_shape: returns the shape of each item produced by _get_item. + Shapes are like (t, h_lq, w_lq, c), (t, h_gt, w_gt, c). + data_dtype: returns the tensorflow dtype of each item produced + by _get_item. + + Args: + data_dir: str, top data directory of the test data. + Contains frames. + cfg: yacs node, global configuration. + """ + def __init__(self, data_dir, cfg): + """ Initialization of train dataset + + """ + self.cfg = cfg + self.scale = cfg.model.scale + + # Record the frame rate value, which will be used when determining + # the output filename. + self.frame_rate = 1 + self.num_lq_frames = cfg.model.num_net_input_frames + self.color_space = cfg.data.color_space + + # By default, all the frames will be infered. + if len(cfg.data.inference.subset_range) == 2: + # If given the frame index range to infer + min_index = cfg.data.inference.subset_range[0] + max_index = cfg.data.inference.subset_range[1] + + # Frame indices between [min_index, max_index] will be infered + logger.info(f'Inference range {min_index}, {max_index} in {data_dir}.') + is_subset = lambda x: min_index <= x <= max_index + elif cfg.data.inference.subset_list: + # If given the list of indices to infer + logger.info(f'Inference list {cfg.data.inference.subset_list} in {data_dir}.') + is_subset = lambda x: x in cfg.data.inference.subset_list + elif len(cfg.data.inference.subset_range) > 0: + # This is an invalid setting + logger.error(f'cfg.data.inference.subset_range should of length 2, ' + f'[min_index, max_index], ' + f'but is given {cfg.data.inference.subset_range}. ' + f'All the images will be inferred.') + is_subset = lambda x: True + else: + # This comp_fn will not be used, since `always_infer` is True + is_subset = lambda x: True + logger.info(f'Inference all images in {data_dir}.') + + def traverse_folder(dir): + file_list = list( + sorted( + filter( + lambda x: supported_file_format(x), + os.listdir(dir) + ) + ) + ) + num_frames = len(file_list) + base_index = int(file_list[0].split('.')[0]) + ext = file_list[0].split('.')[1] + file_meta = [dict(source_folder=dir, + filename=f, + num_frames=num_frames, + base_index=base_index, + ext=ext) + for f in file_list + if is_subset(int(f.split('.')[0]))] + return file_meta + + # Reseved value for nested fold structure. + # Will be used when save the results. + self._clipname = '' + + self.sample_list = traverse_folder(data_dir) + self.num_clips = 1 + + if len(self.sample_list) == 0: + raise FileNotFoundError(f'Found no files in {data_dir}') + + # Preload one lq sample to obtain the input shape + if self.sample_list: + # Record the ext. For VSR\Denoise\VFI tasks, the output will follow + # the input ext. For HDR, the input ext could be 'png', the output + # ext will be 'exr'. + self.ext = self.sample_list[0]['ext'] + self.output_ext = self.ext + + center_frame_meta = self.sample_list[0] + filename = center_frame_meta['filename'] + center_frame_index = int(filename[:-4]) + num_digits = len(filename[:-4]) + lq_path = center_frame_meta['source_folder'] + lq_file = os.path.join( + lq_path, + f'{center_frame_index:0{num_digits}d}.{self.ext}') + im = imread(lq_file) + self.lq_size = im.shape[:2] + + self.base_index = center_frame_meta['base_index'] + + @property + def raw_image_size(self): + """ + Returns the raw input image size (h, w). + + Returns: + tuple, the size of the input image + """ + return self.lq_size + + @property + def expect_output_file_ext(self): + """ + Returns the expected output file ext. For tasks other than HDR, + the output ext is the same with the input ext. For HDR, the output + will be 'exr'. + + Returns: + str, file extension. + """ + return self.output_ext + + @property + def expect_output_resolution(self): + """ Returns the expected output size (h, w). + + Returns: + tuple, the size of the output image + """ + return (self.scale * self.lq_size[0], self.scale * self.lq_size[1]) + + def __len__(self): + """ + Returns the number of samples to infer. When used in multi-device + inference. + + Returns: + int, the number of samples to infer. + """ + return len(self.sample_list) + + def __getitem__(self, item): + # No clue on the real input size, and thus does not require + # shape checking. + return self._get_item(item) + + def set_clip_name(self, clipname): + """ + Set the clip name for this dataset. + + Args: + clipname: str + """ + self._clipname = clipname + + def _shard_segment(self, rank_size, rank_id): + """ + Shard the data into ${rank_size} segments. For example, + case 1: + num_samples = 10, + rank_size = 3, + shard: [[0, 1, 2, 3], [4, 5, 6], [7, 8, 9]], + shard len: [4, 3, 3] + case 2: + num_samples = 10, + rank_size = 2, + shard: [[0, 1, 2, 3, 4]. [5, 6, 7, 8, 9]], + shard len: [5, 5] + Args: + rank_size: int + rank_id: int, [0, rank_size) + """ + num_samples = len(self.sample_list) + res = num_samples % rank_size + shard_size_base = int(num_samples // rank_size) + + if rank_id < res: + start_idx = (shard_size_base + 1) * rank_id + end_idx = start_idx + shard_size_base + 1 + else: + start_idx = (shard_size_base + 1) * res + \ + shard_size_base * (rank_id - res) + end_idx = start_idx + shard_size_base + + if rank_size == rank_id + 1: + end_idx = num_samples + logger.info(f'Data shard {start_idx} - {end_idx - 1} (total {num_samples})', + force=True) + + self.sample_list = list(self.sample_list[start_idx:end_idx]) + + def _shard_interleave(self, rank_size, rank_id): + """ + Shard the data into ${rank_size} interlaced segments. For example, + case 1: + num_samples = 10, + rank_size = 3, + shard: [[0, 3, 6, 9], [1, 4, 7], [2, 5, 8]], + shard len: [4, 3, 3] + case 2: + num_samples = 10, + rank_size = 2, + shard: [[0, 2, 4, 6, 8], [1, 3, 5, 7, 9]], + shard len: [5, 5] + Args: + rank_size: int + rank_id: int, [0, rank_size) + """ + start_idx = rank_id + self.sample_list = list(self.sample_list[start_idx::rank_size]) + + def shard(self, rank_size, rank_id, segment=True): + """ + Shard the sample list according to the rank_size and rank_id + + Args: + rank_size: int + rank_id: int, [0, rank_size) + segment: boolean, whether to shard into consecutive segments + or interlaced segments. Default 'True' + """ + if segment: + self._shard_segment(rank_size, rank_id) + else: + self._shard_interleave(rank_size, rank_id) + + def _get_item(self, item): + raise NotImplementedError + + +class VSRTestDataset(_TestDataset): + """ + Test dataset for VSR task. + """ + def _get_item(self, index): + center_frame_meta = self.sample_list[index] + folder = center_frame_meta['source_folder'] + filename = center_frame_meta['filename'] + num_frames = center_frame_meta['num_frames'] + base_index = center_frame_meta['base_index'] + center_frame_index = int(filename.split('.')[0]) + num_digits = len(filename.split('.')[0]) + + lq_indices = get_consecutive_frame_indices( + center_frame_index, + self.num_lq_frames, + num_frames, + base_index, interval=1, + pad_mode='reflect' + ) + lq_files = [os.path.join(folder, f'{ind:0{num_digits}d}.{self.ext}') + for ind in lq_indices] + lq = load_batch_image(lq_files, target_color_space=self.color_space) + + if self.cfg.data.normalized and not (self.ext == 'exr'): + lq = np.clip(lq / 255., 0., 1.) + + # Record the center frame id, which will be used when outputing the results. + center_frame_name = \ + f'{lq_indices[self.num_lq_frames//2]:0{num_digits}d}.{self.output_ext}' + + # If self._clipname is not empty, i.e., there exist several folders + # in the source lq folder + if self._clipname != '': + center_frame_name = os.path.join(self._clipname, center_frame_name) + + return dict(output_file=center_frame_name, lq=lq) + + +class DenoiseTestDataset(VSRTestDataset): + """ + Test dataset for Denoise task. + """ + def __init__(self, data_dir, cfg): + super().__init__(data_dir, cfg) + # Note that the output is the same size as the input in denoise. + self.scale = 1 + self.frame_rate = 1 + + +# DummyTestDataset for debug +class DummyTestDataset(_TestDataset): + """ + Dummy test daset for debugging. + """ + def __init__(self, data_dir, cfg): # pylint: disable=super-init-not-called + h = cfg.data.inference.input_size[0] + \ + (cfg.data.inference.patch_pad_size*2 + if cfg.data.inference.eval_using_patch else 0) + + w = cfg.data.inference.input_size[1] + \ + (cfg.data.inference.patch_pad_size*2 + if cfg.data.inference.eval_using_patch else 0) + + c = 1 if (cfg.data.color_space=='gray') else 3 + shape_lq = (cfg.model.num_net_input_frames, h, w, c) + self.sample_list = [np.zeros(shape_lq).astype(np.float32)] * 100 + logger.info(f'Using dummy test dataset with {len(self.sample_list)} ' + f'element (for debug only). with sizeof {shape_lq}') + self.lq_size = (h, w) + + def _get_item(self, index): + lq = self.sample_list[index] + return dict(output_file='dummy.png', lq=lq) + + +class VFITestDataset(VSRTestDataset): + """ + Test dataset for VFI task. + + The total number of output frames will be: + `self.num_sample_list * self.frame_rate` + where `self.num_sample_list + self.frame_rate - 1` frames are directly + copied from the input, and `(self.num_sample_list - 1) * (self.frame_rate - 1)` + frames are interpolated. + + Generally, if a model requires `M` input frames, and output `N*(self.frame_rate-1)` + frames each batch, then we set `self.num_lq_frames=M`, `self.num_interp_frames=N`, + and the num of key frames equals to `N+1`. + + We must be aware that not all input frames will be inserted with interpolated + frames. A model may require 4 input source frames [A, B, C, D], and interpolate + only 1 frame between B and C. The number of required input frames is indicated + by the `num_lq_frames`. Meanwhile in this case, only the center frames B and C + are `key frames` while A and D are just auxiliary information frames. The number + of `key frames` are given by `self.num_interp_frames + 1` with the assumption + that the key frames only lie in the center of the input frames. + """ + def __init__(self, data_dir, cfg): + super().__init__(data_dir, cfg) + # The frame rate is given by the model configuration. + self.frame_rate = cfg.model.frame_rate + # The number of the output frames in each batch, + # **not multiplying the frame_rate**. + self.num_interp_frames = cfg.model.num_net_output_frames + + self.num_final_digits = int( + np.ceil( + np.log10(self.frame_rate * len(self.sample_list)))) + self.scale = 1 + + def __len__(self): + return len(self.sample_list) - self.num_interp_frames + + def _get_item(self, index): + # Get the initial key frame metadata + key_frame_ids = [] + frame_meta = self.sample_list[index] + lq_path = frame_meta['source_folder'] + filename = frame_meta['filename'] + num_total_frames = frame_meta['num_frames'] + base_index = frame_meta['base_index'] + num_digits = len(filename.split('.')[0]) + start_frame_index = int(filename.split('.')[0]) + + # The next self.num_interp_frames+1 frames are key frames + key_frame_ids.append(start_frame_index) + for i in range(start_frame_index+1, start_frame_index+self.num_interp_frames+1): + key_frame_ids.append(i) + num_final_digits = max(self.num_final_digits, num_digits) + + # Assuming the key frames are in the center of input frames, + # get the auxiliary frames + lq_indices = get_consecutive_frame_indices( + key_frame_ids, + self.num_lq_frames, + num_total_frames, base_index, + interval=1, + pad_mode='replicate' + ) + + lq_files = [os.path.join(lq_path, f'{ind:0{num_digits}d}.{self.ext}') + for ind in lq_indices] + lq = load_batch_image(lq_files, target_color_space=self.color_space) + if self.cfg.data.normalized: + lq = np.clip(lq / 255., 0., 1.) + + def _format_output_filename(frame_id, _num_digits, ext): + output_file = f'{frame_id:0{_num_digits}d}.{ext}' + if self._clipname != '': + # Format the output file with `${clip}/00000.png` like pattern + output_file = os.path.join(self._clipname, output_file) + return output_file + + # Prepare input copies for VFI output. + # Record both the source-target filename, as well as the key frames data, + # in the dict `input_file_copy`: + # key: target_output_file + # value: [source_input_file, target_output_data] + # One can use the copy the source_input_file or write out the data to + # target_file. + input_file_copy = dict() + output_files = [] + for i, k_id in enumerate(key_frame_ids[:-1]): # leave the last key frame + source_file = os.path.join(lq_path, f'{k_id:0{num_digits}d}.{self.ext}') + + index_in_indices = lq_indices.index(k_id) + data = lq[index_in_indices] + + new_frame_id = base_index + (k_id - base_index) * self.frame_rate + target_file = _format_output_filename(new_frame_id, + num_final_digits, + self.output_ext) + + input_file_copy[target_file] = [source_file, data] + output_files.extend([_format_output_filename(new_frame_id+j+1, + num_final_digits, + self.output_ext) + for j in range(self.frame_rate-1)]) + + if index == len(self) - 1: # copy the last key frame only when reaching the end + source_file = os.path.join(lq_path, + f'{key_frame_ids[-1]:0{num_digits}d}.{self.ext}') + + index_in_indices = lq_indices.index(key_frame_ids[-1]) + data = lq[index_in_indices] + + new_frame_id = base_index + (key_frame_ids[-1] - base_index) * self.frame_rate + target_file = _format_output_filename( + new_frame_id, + num_final_digits, + self.output_ext) + input_file_copy[target_file] = [source_file, data] + + # copy the final frame + for i in range(self.frame_rate-1): + new_frame_id = new_frame_id + 1 + target_file = _format_output_filename( + new_frame_id, + num_final_digits, + self.output_ext) + input_file_copy[target_file] = [source_file, data] + + if len(output_files) == 1: + output_files = output_files[0] + + return dict(output_file=output_files, + lq=lq, + input_copies=input_file_copy) + + def _shard_segment(self, rank_size, rank_id): + num_samples = len(self.sample_list) + res = (num_samples - 1) % rank_size + shard_size_base = int((num_samples - 1) // rank_size) + + if rank_id < res: + start_idx = (shard_size_base + 1) * rank_id + # enclose the last as the key frame + end_idx = start_idx + (shard_size_base + 1) + 1 + else: + start_idx = (shard_size_base + 1) * res + shard_size_base * (rank_id - res) + # enclose the last as the key frame + end_idx = start_idx + shard_size_base + 1 + + if rank_size == rank_id + 1: + end_idx = num_samples + logger.info(f'Data shard {start_idx} - {end_idx - 1} (total {num_samples})', force=True) + self.sample_list = list(self.sample_list[start_idx:end_idx]) + self.num_samples_shard = len(self.sample_list) + self.shard_flag = True + + +class HDRTestDataset(VSRTestDataset): + """ + Test dataset for HDR task. The output ext should be set in + 'cfg.data.extension'. + """ + def __init__(self, data_dir, cfg): + super().__init__(data_dir, cfg) + self.output_ext = cfg.data.extension # regardless of the input ext + self.frame_rate = 1 + self.scale = 1 + + +class ComposedTestDataset(_TestDataset): + """ Test dataset for a test directory with multiple clips. + """ + def __init__(self): # pylint: disable=super-init-not-called + self._datasets = None + self.num_samples_list = [] + + @staticmethod + def from_datasets(*datasets): + """ + Construct composed dataset from a collection of sub dataset class. + + Args: + *datasets: list of test datasets. + """ + cls = ComposedTestDataset() + cls._datasets = list(datasets) + cls.num_samples_list = [len(d) for d in cls._datasets] + return + + @staticmethod + def from_datadir(subcls, data_dir, clip_list, cfg): + """ + Construct composed dataset from a collection of dataset folder. + + Args: + subcls: class type, task class + data_dir: str, top data folder + clip_list: list of str, clips in the data_dir + cfg: yacs node + """ + cls = ComposedTestDataset() + datasets = [] + for clip_name in clip_list: + data_clip = os.path.join(data_dir, clip_name) + sub_datasets = subcls(data_clip, cfg) + sub_datasets.set_clip_name(clip_name) + datasets.append(sub_datasets) + cls._datasets = datasets + cls.num_samples_list = [len(d) for d in cls._datasets] + return cls + + def get_datasets(self, index): + # Iterate over the datasets to locate the queried sample + dataset_id = 0 + for dataset_id, num in enumerate(self.num_samples_list): + if index - num < 0: + break + index -= num + return dataset_id, index + + def _get_item(self, item): + dataset_id, index = self.get_datasets(item) + data = self._datasets[dataset_id][index] + return data + + @property + def expect_task_output_meta_info(self): + return self._datasets[0].expect_task_output_meta_info + + @property + def raw_image_size(self): + return self._datasets[0].raw_image_size + + @property + def expect_output_file_ext(self): + return self._datasets[0].expect_output_file_ext + + def __len__(self): + return np.sum(self.num_samples_list) + + def shard(self, rank_size, rank_id): + # Shard not supported for now. + raise NotImplementedError('Composed dataset not support data shard.') + + +class MixtureTestDataset(_TestDataset): + """ + Test dataset for a test directory with multiple dataset folder. + """ + def __init__(self): # pylint: disable=super-init-not-called + self._datasets = None + self.num_samples_list = [0] + + @staticmethod + def from_datasets(*datasets): + """ + Construct mixture dataset from a list of test datasets. + + Args: + *datasets: list of test datasets instances. Should be return data + terms with the same dtype and shape. + + Returns: + a MixtureDatasets instance + """ + cls = MixtureTestDataset() + cls._datasets = list(datasets) + cls.num_samples_list = [len(d) for d in cls._datasets] + return + + @staticmethod + def from_datadir(subcls, data_dir_list, cfg): + """ + Construct mixture dataset from a list of data directories. + + Args: + subcls: test dataset class type + data_dir_list: list(str), each is a top directory of a dataset. + Should be return data terms with the same dtype and shape. + cfg: yacs Node, global configuration + + Returns: + a MixtureDatasets instance + """ + cls = MixtureTestDataset() + datasets = [] + for data_dir in data_dir_list: + files = os.listdir(data_dir) + if os.path.isdir(os.path.join(data_dir, files[0])): + sub_datasets = ComposedTestDataset.from_datadir( + subcls, + data_dir, + files, + cfg + ) + else: + sub_datasets = subcls(data_dir, cfg) + datasets.append(sub_datasets) + cls._datasets = list(datasets) + cls.num_samples_list = [len(d) for d in cls._datasets] + return cls + + @property + def data_dtype(self): + return self._datasets[0].data_dtype + + @property + def data_shape(self): + return self._datasets[0].data_shape + + def get_datasets(self, index): + dataset_id = 0 + for dataset_id, num in enumerate(self.num_samples_list): + if index - num < 0: + break + index -= num + return dataset_id, index + + def _get_item(self, item): + dataset_id, index = self.get_datasets(item) + return self._datasets[dataset_id][index] + + @property + def output_meta_info(self): + return self._datasets[0].output_meta_info + + @property + def raw_image_size(self): + return self._datasets[0].raw_image_size + + def __len__(self): + return np.sum(self.num_samples_list) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/train_dataset.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/train_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f2355103a2c72a3854e749a031eeb01d89df0f --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/train_dataset.py @@ -0,0 +1,553 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import random + +import cv2 +import numpy as np + +import tensorflow as tf +import yaml +from tqdm import tqdm + +from src.dataloaders.utils import ( + gen_pattern, + pad_list, + get_consecutive_frame_indices, + batch_dim_squeeze, + load_batch_image, + supported_file_format +) + +from src.utils.degradation import Degradation +from src.utils.transform import Compose +from src.utils.file_io import imread +from src.utils.logger import logger + + +class _TrainDataset: + """The base class for training dataset. + + The derived classes should implement these functions: + _get_item: an indexing like item fetching method. + data_shape: returns the shape of each item produced by _get_item. + Shapes are like (t, h_lq, w_lq, c), (t, h_gt, w_gt, c). + data_dtype: returns the tensorflow dtype of each item produced + by _get_item. + + Supported data folder structure: + 1. All train datasets class support this structure + data + `-- reds + |-- images + | |-- lq + | | |-- 000 + | | | |-- 00000000.png + | | | |-- 00000001.png + | | | |-- 00000002.png + | | | |-- ... + | | | `-- 00000099.png + | | `-- 001 + | | |-- 00000000.png + | | |-- 00000001.png + | | |-- 00000002.png + | | |-- ... + | | `-- 00000099.png + | `-- gt + | |-- 000 + | | |-- 00000000.png + | | |-- 00000001.png + | | |-- 00000002.png + | | |-- ... + | | `-- 00000099.png + | `-- 001 + | |-- 00000000.png + | |-- 00000001.png + | |-- 00000002.png + | |-- ... + | `-- 00000099.png + `-- sets + |-- train.json + `-- val.json + 2. Online datasets class support this: + reds_gt + |-- 000 + | |-- 00000000.png + | |-- 00000001.png + | |-- 00000002.png + | |-- ... + | `-- 00000099.png + `-- 001 + |-- 00000000.png + |-- 00000001.png + |-- 00000002.png + |-- ... + `-- 00000099.png + + Args: + data_dir: str, top data directory of the train dataset. + Should include the `images` and `sets` sub-folders. + cfg: yacs node, global configuration. + """ + def __init__(self, data_dir, cfg): + """ + Initialization of train dataset. + + """ + self.cfg = cfg + self.num_lq_frames = cfg.data.num_data_lq_frames + self.num_gt_frames = cfg.data.num_data_gt_frames + self.interval_list = cfg.data.train.augmentation.interval_list + self.augment = cfg.data.train.augmentation.apply + self.scale = cfg.model.scale + self.set_file = os.path.join(data_dir, cfg.data.train.set_file) + self.crop_size = cfg.data.train.input_size + self.color_space = cfg.data.color_space + + # TODO: change the data structure + clip_list = self.parse_datafolder_structure(data_dir, self.set_file) + self.sample_list = [] + + # Store all the frame metadata of all the clips in the folder + for vid in clip_list: + in_path = self.gt_path_pattern.format(vid) + file_list = list( + sorted( + filter( + lambda x: supported_file_format(x), + os.listdir(in_path) + ) + ) + ) + num_frames = len(file_list) + base_index = int(file_list[0].split('.')[0]) + ext = file_list[0].split('.')[1] + + # File metadata includes `clip` name, file name itself, total number + # of frames in the clip, the starting id of the frames, and file + # extension (png, exr, .etc). + file_meta = [dict(clip=vid, + filename=f, + num_frames=num_frames, + base_index=base_index, + ext=ext) + for f in file_list] + + self.sample_list.extend(file_meta) + + if len(self.sample_list) == 0: + raise FileNotFoundError(f'Found no files in {data_dir}') + else: + logger.info(f'Found {len(self.sample_list)} files in {data_dir}') + + def parse_datafolder_structure(self, data_dir, set_file): + """ + Parse the default dataset structure. + + Args: + data_dir: str, the top folder of the dataset. + set_file: str, the json indicating the clips (both lq and gt) + + Returns: + list of str, the names of the clips (in lq and corresponding gt) + """ + if not self.cfg.data.train.degradation.online: + self.lq_path_pattern = os.path.join(data_dir, 'lq', '{}') + self.gt_path_pattern = os.path.join(data_dir, 'gt', '{}') + + if os.path.exists(set_file): + clip_list = [] + with open(set_file, 'r') as f: + for line in f.readlines(): + clip_list.append(line.strip()) + else: + clip_list = sorted(os.listdir(os.path.join(data_dir, 'gt'))) + + return clip_list + + def __len__(self): + """ + Total number of samples for training. + + Returns: + int, the number of training samples + """ + return len(self.sample_list) + + def check_shape(self, data): + """ + Called after '_get_item' to check whether the real shapes are the + same with the expected in 'data_shape'. + + Args: + data: + + Returns: + + """ + for index, d, shape in zip(range(len(data)), data, self.data_shape): + assert tuple(d.shape) == tuple(shape), \ + f'Expect return data at pos {index} to have shape {shape}, ' \ + f'but got {d.shape}' + + @property + def data_shape(self): + """ + Returns the shape of each item produced by _get_item. Shapes + are like (t, h_lq, w_lq, c), (t, h_gt, w_gt, c). + + Returns: + tuple of shapes, each can be like (t, h, w, c) + """ + raise NotImplementedError + + @property + def data_dtype(self): + """ + Returns the tensorflow dtype of each item produced by _get_item. + + Returns: + tuple of data types, each can be like tf.float32 + """ + raise NotImplementedError + + def __getitem__(self, index): + data = self._get_item(index) + + self.check_shape(data) + + return data + + def _get_item(self, index): + """ An indexing-like item fetching method + + Args: + index: int + + Returns: + tuple of data terms (as numpy.ndarray) + """ + raise NotImplementedError + + +# DummyTrainDataset for debug +class DummyTrainDataset(_TrainDataset): + """ A dummy train dataset for debugging. + """ + def __init__(self, data_dir, cfg): # pylint: disable=super-init-not-called + b = cfg.data.train.batch_size + h = cfg.data.train.input_size[0] + w = cfg.data.train.input_size[1] + c = 1 if (cfg.data.color_space=='gray') else 3 + shape_lq = (cfg.data.num_data_lq_frames, h, w, c) + self.lq_shape = shape_lq + self.gt_shape = (cfg.data.num_data_gt_frames, h, w, 3) + + num_samples = 100 + self.sample_list = [np.zeros(self.lq_shape).astype(np.float32) + for _ in range(num_samples)] + self.sample_list_gt = [np.zeros(self.gt_shape).astype(np.float32) + for _ in range(num_samples)] + + def _get_item(self, index): + lq = self.sample_list[index] + gt = self.sample_list_gt[index] + return lq, gt + + @property + def data_dtype(self): + return tf.float32, tf.float32 + + @property + def data_shape(self): + return self.lq_shape, self.gt_shape + + +class OfflineTrainDataset(_TrainDataset): + """ + Offline degradation task training dataset. + Augmentation is always online. + """ + def __init__(self, data_dir, cfg): + super().__init__(data_dir, cfg) + self.num_channels = 3 if self.color_space != 'gray' else 1 + + # Load augmentation options from cfg + options = yaml.safe_load(cfg.data.train.augmentation.options) + self.augment_pipeline = Compose.from_cfgs( + options, + crop_size=self.crop_size, # source crop size + scales=(1, self.scale) # scale of each crop, corresponds to + # returned data terms. + ) + + @property + def data_shape(self): + h, w = self.crop_size # this is the input (lq) crop size + + lq_shape = (self.num_lq_frames, + h, + w, + self.num_channels) + + # Squeeze the batch dim if possible. Single image case + lq_shape = batch_dim_squeeze(lq_shape) + + gt_shape = (self.num_gt_frames, + h*self.scale, + w*self.scale, + self.num_channels) + gt_shape = batch_dim_squeeze(gt_shape) + + return lq_shape, gt_shape + + @property + def data_dtype(self): + return tf.float32, tf.float32 + + def _get_item(self, index): + # Get meta data. We take the `index` frame as the center frame + center_frame_meta = self.sample_list[index] + vid = center_frame_meta['clip'] + filename = center_frame_meta['filename'] + num_frames = center_frame_meta['num_frames'] + base_index = center_frame_meta['base_index'] + ext = center_frame_meta['ext'] + center_frame_index = int(filename[:-4]) + num_digits = len(filename[:-4]) + + # Frames interval augmentation + if self.augment: + interval = random.choice(self.interval_list) + else: + interval = 1 + + # Get the consecutive frame indices + lq_indices = get_consecutive_frame_indices( + center_frame_index, + self.num_lq_frames, + num_frames, # total number of frames in the clip + base_index, + interval=interval, + pad_mode='reflect') + + lq_files = [os.path.join(self.lq_path_pattern.format(vid), + f'{ind:0{num_digits}d}.{ext}') + for ind in lq_indices] + + gt_indices = get_consecutive_frame_indices( + center_frame_index, + self.num_gt_frames, + num_frames, + base_index, + interval=interval, + pad_mode='reflect') + + gt_files = [os.path.join(self.gt_path_pattern.format(vid), + f'{ind:0{num_digits}d}.{ext}') + for ind in gt_indices] + + lq = load_batch_image(lq_files, self.color_space) + gt = load_batch_image(gt_files, self.color_space) + + if self.augment: + lq, gt = self.augment_pipeline(lq, gt) + + if self.num_lq_frames == 1 and lq.shape[0] == 1: + lq = lq[0] + if self.num_gt_frames == 1 and gt.shape[0] == 1: + gt = gt[0] + + if self.cfg.data.normalized: + lq = np.clip(lq / 255., 0, 1) + gt = np.clip(gt / 255., 0, 1) + + return lq, gt + + +class OnlineTrainDataset(OfflineTrainDataset): + """ Online degradation task training dataset. + """ + def __init__(self, data_dir, cfg): + super().__init__(data_dir, cfg) + # Loading degradation from cfg: + # add noise, down-sampling, blur, etc. + options = yaml.safe_load(cfg.data.train.degradation.options) + assert isinstance(options, dict) + + # TODO: remove preset degradation + self.degradation_pipeline = get_degradation_model( + scale=self.scale, + version=cfg.data.train.degradation.online_version) + + options = yaml.safe_load(cfg.data.train.augmentation.options) + assert isinstance(options, dict) + + # Loading augmentation from cfg: + # random crop, random flip, random interval, etc. + self.augment_pipeline = transforms.Compose.from_cfgs( + options, + crop_size=self.crop_size, + scales=(self.scale, ) + ) + + # Loading gt enhancement from cfg: + # usm, etc. + self.gt_enhancement = cfg.data.train.gt_enhancement + if cfg.data.gt_enhancement: + self.gt_enhancement_module = get_degradation_model( + version='gt_enhancement' + ) + + def load_gt(self, im_files): + gt_list = [] + for i, _im in enumerate(im_files): + gt = imread(_im, self.color_space) + gt_list.append(gt) + return np.array(gt_list) + + def _get_item(self, index): + center_frame_meta = self.sample_list[index] + vid = center_frame_meta['clip'] + filename = center_frame_meta['filename'] + num_frames = center_frame_meta['num_frames'] + base_index = center_frame_meta['base_index'] + ext = center_frame_meta['ext'] + center_frame_index = int(filename[:-4]) + num_digits = len(filename[:-4]) + + if self.augment: + interval = random.choice(self.interval_list) + else: + interval = 1 + + # Should load num_lq_frames gt images + gt_indices = get_consecutive_frame_indices( + center_frame_index, + self.num_lq_frames, + num_frames, base_index, + interval=interval, + pad_mode='reflect' + ) + gt_files = [os.path.join(self.gt_path_pattern.format(vid), + f'{ind:0{num_digits}d}.{ext}') + for ind in gt_indices] + + gt = self.load_gt(gt_files) + if self.augment: + gt = self.augment_pipeline(gt)[0] + + # Do degradation after augmentation to reduce computation. + lq_list = self.degradation_pipeline.apply_batch( + np.array(gt), + allow_quantization=ext != 'exr' + ) + + lq = np.array(lq_list).astype(np.float32) + gt = gt.astype(np.float32) + + # Select the center num_gt_frames out + gt = gt[(self.num_lq_frames//2-self.num_gt_frames//2): + (self.num_lq_frames//2+self.num_gt_frames//2)+1] + + if self.gt_enhancement: + gt = [self.gt_enhancement_module.apply(_gt) for _gt in gt] + gt = np.array(gt) + + if self.num_lq_frames == 1: + lq = lq[0] + if self.num_gt_frames == 1: + gt = gt[0] + + if self.cfg.data.normalized: + lq = np.clip(lq / 255., 0, 1) + gt = np.clip(gt / 255., 0, 1) + return lq, gt + + +class MixtureDatasets(_TrainDataset): + """ + Mixture dataset containing multiple train datasets. + Could be constructed from a list of folders. + """ + def __init__(self): # pylint: disable=super-init-not-called + self._datasets = None + self.num_samples_list = [] + + @staticmethod + def from_datasets(*datasets): + """ + Construct mixture dataset from a list of train datasets. + + Args: + *datasets: list of OfflineTrainDataset or OnlineTrainDataset + instances. Should be return data terms with the same dtype and + shape. + + Returns: + a MixtureDatasets instance + """ + cls = MixtureDatasets() + cls._datasets = list(datasets) + cls.num_samples_list = [len(d) for d in cls._datasets] + return + + @staticmethod + def from_datadir(subcls, data_dir_list, cfg): + """ + Construct mixture dataset from a list of data directories. + + Args: + subcls: OfflineTrainDataset or OnlineTrainDataset type + data_dir_list: list(str), each is a top directory of a dataset. + Should be return data terms with the same dtype and shape. + cfg: yacs Node, global configuration + + Returns: + a MixtureDatasets instance + """ + cls = MixtureDatasets() + datasets = [] + for data_dir in data_dir_list: + sub_datasets = subcls(data_dir, cfg) + datasets.append(sub_datasets) + cls._datasets = list(datasets) + cls.num_samples_list = [len(d) for d in cls._datasets] + return cls + + @property + def data_dtype(self): + return self._datasets[0].data_dtype + + @property + def data_shape(self): + return self._datasets[0].data_shape + + def get_datasets(self, index): + # Iterate over the datasets to locate the queried sample + dataset_id = 0 + for dataset_id, num in enumerate(self.num_samples_list): + if index - num < 0: + break + index -= num + return dataset_id, index + + def _get_item(self, item): + dataset_id, index = self.get_datasets(item) + return self._datasets[dataset_id][index] + + def __len__(self): + return np.sum(self.num_samples_list) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/utils.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4724330eea7bec757e2f38bf6023f4139bcb29db --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/dataloaders/utils.py @@ -0,0 +1,205 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +from src.utils.file_io import imread +from src.utils.constant import VALID_FILE_EXT + + +def supported_file_format(filename): + """ + Check whether the image file is supported. + + Args: + filename: str + + Returns: + boolean + """ + ext = filename.split('.')[-1] + return ext in VALID_FILE_EXT + + +def gen_pattern(data_dir, meta, split='lq'): + """ + Generate image pattern given metadata and folder structure. + + Args: + data_dir: str, top dataset folder + meta: dict, loaded metadata from set file + split: str, name of data split + + Returns: + str, file pattern of the images + """ + if meta is None: + return os.path.join(data_dir, '{}') + else: + if meta['prefix']: + return os.path.join(data_dir, + 'images', + meta['{}_folder'.format(split)], + '{}') + else: + return os.path.join(data_dir, + 'images', + '{}', + meta['{}_folder'.format(split)]) + + +def pad_list(src, head_pad_size, tail_pad_size, mode): + """ + Pad the given list to target length. + + Args: + src: list[int], indicies of the frames + head_pad_size: int, length of pad at the head (before `src`) + tail_pad_size: int, length of pad at the tail (after `src`) + mode: str, pad mode. Optional in ['reflect', 'replicate'] + + Returns: + list[int], padded indices list which has target size + """ + num_src = len(src) + if mode == 'reflect': + head_pad_value = list(reversed(src[1:])) + tail_pad_value = list(reversed(src[:-1])) + elif mode == 'replicate': + head_pad_value = [src[0]] * head_pad_size + tail_pad_value = [src[-1]] * tail_pad_size + else: + raise NotImplementedError + src = head_pad_value + src + src = src[-(num_src + head_pad_size):] + tail_pad_value + src = src[:num_src+head_pad_size+tail_pad_size] + return src + + +def get_consecutive_frame_indices(given_frame_ids, num_frames_required, + max_frames, base_index=0, interval=1, + pad_mode='reflect'): + """ + Get consecutive indices given the center frame index/indices. + + There will be padding at the border of the list. Two typical cases + used in VSR and VFI model: + Case 1: + If given ids are like: + given_frame_ids=[1,2], + num_frames_required=4, + pad_mode='replicate' + should return [1,1,2,3]. [1, 2] are at the center. + Case 2: + If given ids are like: + given_frame_ids=3, + num_frames_required=5, + interval=2, + pad_mode='reflect' + should return [3,1,3,5,7]. [3] lies at the center. + + Args: + given_frame_ids: int or list[int], the center frame indices. + num_frames_required: int, number of the frames required. + max_frames: int, the total number of the frames in the dataset. + base_index: int, the base index of the frame. Default is 0. + iterval: int, the frame iterval. Default is 1. + pad_mode: str, the pad method if on the border. + Optional in ['reflect', 'replicate'] + + Returns: + list[int], length equals to num_frames_required + """ + + # Find the cosecutive frame indices + if isinstance(given_frame_ids, (list, tuple)): + # Currently only supported in vfi + assert num_frames_required % 2 == 0, \ + f'{len(given_frame_ids)} frame ids are given. ' \ + f'The required number of frames should be even.' + num_extra_frames = num_frames_required - len(given_frame_ids) + min_id = min(given_frame_ids) + max_id = max(given_frame_ids) + + else: + # Supported in other tasks + assert num_frames_required % 2 == 1, \ + f'Only the center frame id is given. ' \ + f'The required number of frames should be odd.' + min_id = max_id = given_frame_ids + num_extra_frames = num_frames_required + + assert min_id >= base_index and max_id < max_frames + base_index + + # Obtain the indices within the range [base_index, base_index + max_frames] + index = [] + left_pad = False + right_pad = False + for k in range(min_id - interval*(num_extra_frames//2), + max_id+interval*(num_extra_frames//2)+1, + interval): + if base_index > k: + left_pad = True + continue + elif k >= (base_index + max_frames): + right_pad = True + continue + else: + index.append(k) + + index_len = len(index) + # When the given frames are on the edge, perform padding, + if left_pad: + index = pad_list(index, num_frames_required-index_len, 0, pad_mode) + + if right_pad: + index = pad_list(index, 0, num_frames_required-index_len, pad_mode) + + return index + + +def batch_dim_squeeze(dim): + """ + Squeeze the batch dimension when possible. + + Args: + dim: list[int], shape of the tensor. + + Returns: + list[int], reduced dimension. + """ + return dim[1:] if dim[0] == 1 else dim + + +def load_batch_image(files, target_color_space, as_array=True): + """ + Load batch images, may return as np.ndarray or just a list. + + Args: + files: list[str], file paths to read. + target_color_space: str, color space to which the images are converted. + as_array: boolean, return as list or np.ndarray. + + Returns: + If as_array is True, return the images are np.ndarray. Else, return + as the list. + """ + + im = [] + for f in files: + assert os.path.exists(f), f"{f} not exists." + _im = imread(f, target_color_space).astype(np.float32) + im.append(_im) + return np.array(im) if as_array else im diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..98f6c71a50281f91278ec5234aa56c286dc333cf --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/__init__.py @@ -0,0 +1,43 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def build_engine(cfg): + """Returns the engine class given the mode in cfg. + + Args: + cfg: yacs node, global configuration. + + Returns: + engine class. + """ + mode = cfg.mode + ckpt = cfg.checkpoint + if mode == 'train': + from .trainer import SessionTrainer + return SessionTrainer + elif mode == 'inference': + from .inferencer import SessionInferencer, ModelFreeInferencer + if ckpt.endswith(".pb"): + return ModelFreeInferencer + else: + return SessionInferencer + elif mode == 'freeze': + from .freezer import SessionFreezer + return SessionFreezer + else: + raise NotImplementedError + + +__all__ = ['build_engine'] diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/freezer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/freezer.py new file mode 100644 index 0000000000000000000000000000000000000000..e675f4b460ad162ef3fe434719f37cf131e8c9a4 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/freezer.py @@ -0,0 +1,111 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np +import tensorflow as tf +from src.runner.saver import loose_loading +from src.runner.sess_config import get_sess_config +from src.utils.adapter import NetworkIOAdapter +from src.utils.logger import logger +from tensorflow.python.framework import graph_util + + +class _Freezer: + """Freezer engine to freeze ckpt to pb file. + + Args: + dataloader: None, will never be used. The input size will be determined + by configure. + network: network instance, will not be used in this mode. + cfg: yacs node, global configuration. + """ + def __init__(self, dataloader, network, cfg, **kwargs): + self.cfg = cfg + self.network = network + self.adapter = NetworkIOAdapter(cfg) # do not register_raw_size. Use the setting value + + # Different from the inference, we'll fix the input size. + # The fixed input size is given by: + # cfg.data.inference.best_patch_size[0] + pads_h + cfg.data.inference.patch_pad_size[0] + # cfg.data.inference.best_patch_size[1] + pads_w + cfg.data.inference.patch_pad_size[1] + pads_h, pads_w = self.adapter.cal_adapted_size(self.adapter.best_in_size) + self.adapter.limited_in_size = [self.adapter.best_in_size[0] + pads_h + self.adapter.eval_pad_size*2, + self.adapter.best_in_size[1] + pads_w + self.adapter.eval_pad_size*2] + self.adapter.register_raw_size(self.adapter.limited_in_size) + + self.network.build_graph((cfg.data.eval_batch_size, self.adapter.input_size)) + + def restore(self): + """ + Restore the graph from ckpt. + """ + raise NotImplementedError + + def run(self): + """ + Execute function to freeze the graph to pb. + """ + raise NotImplementedError + + +class SessionFreezer(_Freezer): + """ + A tf.Session based freezer engine. + """ + def __init__(self, dataloader, network, cfg): + super().__init__(dataloader, network, cfg) + sess_cfg = get_sess_config(cfg.device, + cfg.solver.xla, + cfg.solver.mix_precision, + False) + self.session = tf.Session(config=sess_cfg) + + def restore(self): + """ + Restore the requireed part of the graph given the ckpt. + """ + loose_loading(self.session, self.cfg.model.scope, self.cfg.output_dir, self.cfg.checkpoint) + + def run(self): + """ + Execute function to freeze the graph to pb. + """ + with self.session as sess: + tf.io.write_graph(sess.graph_def, self.cfg.checkpoint.rsplit('/', 1)[0], 'freeze_graph.pbtxt') + + logger.info('Loading trained model ...') + self.restore() + logger.info('Model loaded success.') + logger.info('Freeze model to pb files') + + pb_path = os.path.join(self.cfg.checkpoint + '.pb') + try: + if hasattr(self.network, 'inference_func'): + constant_graph = graph_util.convert_variables_to_constants( + sess, sess.graph_def, + self.network.output_node_name + ) + else: + constant_graph = graph_util.convert_variables_to_constants( + sess, sess.graph_def, + [self.network.output_node.name.split(':')[0]] + ) + with tf.gfile.FastGFile(pb_path, mode='wb') as f: + f.write(constant_graph.SerializeToString()) + logger.info('Model frozen success.') + except Exception as e: + logger.error('Failed to freeze model.') + logger.info(e) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/inferencer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/inferencer.py new file mode 100644 index 0000000000000000000000000000000000000000..cd96cf4b061e180af2eb82038f10aabb71e19017 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/inferencer.py @@ -0,0 +1,312 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import datetime +import os +import shutil +import time +from functools import partial +from multiprocessing import Manager + +import numpy as np +import tensorflow as tf +from src.runner.saver import loose_loading +from src.runner.sess_config import get_sess_config +from src.utils.adapter import NetworkIOAdapter +from src.utils.exceptions import * +from src.utils.file_io import ImageWriter, image_deprocess +from src.utils.logger import logger +from src.utils.moving_avg import MovingAvg +from src.utils.constant import FILE_EXT_TO_PIX_FMT +from src.utils.world import world +from tqdm import trange + + +class _Inferencer: + """Base inference engine. + + Args: + dataloader: dict, inference data dict produced by test dataset instance. + network: network instance, whose class should derive from + src.networks.base_model.Base . + cfg: yacs node, global configuration. + _world: world instance, could be given from the caller function, + or by default, the global world instance (see src.utils.world). + """ + def __init__(self, dataloader, network, cfg, _world=None): + self.device = cfg.env.device + self.is_distributed = cfg.env.rank_size > 1 + self.cfg = cfg + self.dataloader = dataloader + self.network = network + self.step_time = MovingAvg(smooth=0.9) + self.adapter = NetworkIOAdapter(cfg) + # _world should be initialized + self.world = _world or world + if not self.world.is_initialized: + raise WorldUninitializedError('World not initialized.') + + self._total = 0 + + def restore(self, *args, **kwargs): + """ + Restore parameters from ckpt. + """ + raise NotImplementedError + + def run(self): + """ + Execute inference steps. + """ + raise NotImplementedError + + +# Note: we use numpy dataloader instead of tf dataloader in inference +class SessionInferencer(_Inferencer): + """Session based inference engine. + + Args: + dataloader: dict, inference data dict produced by test dataset instance. + network: network instance, whose class should derive from + src.networks.base_model.Base . + cfg: yacs node, global configuration. + _world: world instance, could be given from the caller function, + or by default, the global world instance (see src.utils.world). + """ + def __init__(self, dataloader, network, cfg, _world=None): + super().__init__(dataloader, network, cfg, _world) + self.scale = cfg.model.scale + self.session = None + self.graph = None + + # Get expected output data information. Both are used for ffmpeg io-backend. + output_size = self.dataloader.expect_output_resolution + output_ext = self.dataloader.expect_output_file_ext + + pix_fmt = FILE_EXT_TO_PIX_FMT[output_ext] + + # Prepare image writer if is set. + if not self.cfg.inference.write_out: + logger.warn(f'You have set "write_out" to False, ' + f'hence there will be no outputs to {self.cfg.inference.io_backend}.') + else: + output_dir = self.cfg.inference.result_dir + output_dir = os.path.realpath(output_dir) + # By default, we write results to hard disk + self.image_deprocess_fn = partial( + image_deprocess, + source_color_space=self.cfg.data.color_space, + benormalized=self.cfg.data.normalized) + self.result_writer = ImageWriter( + output_dir, cfg, + benormalized=self.cfg.data.normalized, + source_color_space=self.cfg.data.color_space, + output_resolution=output_size, + pix_fmt=pix_fmt) + + def restore(self): + """Restore parameters from ckpt. + """ + if self.cfg.checkpoint == 'none': + # Reserved for tasks that are not performed using networks. + pass + elif (self.cfg.checkpoint == '' + and len(self.cfg.train.pretrained_scope_list) > 0): + # For models that consists of several sub-networks, e.g., vfi model + # with pretrained optical flow network. + assert len(self.cfg.train.pretrained_scope_list) == \ + len(self.cfg.train.pretrained_scope_ckpt) + for scope, ckpt in zip(self.cfg.train.pretrained_scope_list, + self.cfg.train.pretrained_scope_ckpt): + loose_loading(self.session, scope, '', ckpt) + return 0 + else: + # Commonly used branch. + return loose_loading(self.session, self.cfg.model.scope, + '', self.cfg.checkpoint) + + def network_preparation(self): + """Build network forward graph, and restor from ckpt. + """ + sess_cfg = get_sess_config(self.cfg.env.device, + self.cfg.session.xla, + self.cfg.session.mix_precision, + False) + self.session = tf.Session(config=sess_cfg) + + # Register the image raw size when inference to let the adapter decide + # whether to inference using patchwise strategy or as a whole. + self.adapter.register_raw_size(self.dataloader.raw_image_size) + + # Get the real adapted input size from adapter to build the graph. + self.network.build_graph(input_size=(self.cfg.data.inference.batch_size, + self.adapter.input_size)) + init_op = tf.group(tf.global_variables_initializer(), + tf.local_variables_initializer()) + self.session.run(init_op) + if self.cfg.debug_mode != 'zeroin': + self.restore() + + def run(self): + """Execute inference steps. + """ + self.network_preparation() + + # Dataset shard is done in building dataset, see dataloaders.__init__.py + self._total = len(self.dataloader) + + range_fn = partial(trange, position=self.world.rank_id, desc=f'On DeviceID {self.world.device_id}') + + if self.session is None: + raise SessionUndefinedError(f'{type(self).__name__}.session is not defined.') + + logger.info(f'Start inference.') + + if self.cfg.inference.write_out: + self.result_writer.initialize() + + for i in range_fn(self._total): + data_dict = self.dataloader[i] + st_time = time.time() + hq = self._inf_engine(data_dict) + once_time = time.time() - st_time + # Skip the first step since the elapse time is abnormal due to compilation. + if i > 0: + self.step_time.update(once_time) + + if self.cfg.inference.write_out: + self.write_out(data_dict['output_file'], hq, data_dict.get('input_copies', None)) + + if self.cfg.inference.write_out: + self.result_writer.finalize() + logger.info(f'\tInference time: {self.step_time.avg * 1000:.2f} ms/image') + + def _inf_engine(self, data_dict): + """Determine inference strategy. + """ + # TODO: support multiple feed dict. + lq = data_dict['lq'] + if hasattr(self.network, 'inference_func'): + # Reserved API if the processing of the network is not end-to-end. + # Pass through all the inputs, in case the model requires multiple-inputs. + data_dict['lq'] = self.adapter.adapt_input(data_dict['lq']) + hq = self.network.inference_func(self.session, data_dict, self.graph, self.adapter.mode) + hq = self.adapter.reverse_adapt(hq.squeeze()) + elif self.adapter.patch_mode: + patch_per_step = self.cfg.data.inference.batch_size + img_patches = self.adapter.extract_image_patches(lq, patch_per_step) + num_step = img_patches.shape[0] // patch_per_step + patch_hq = [] + for i in range(num_step): + batch_data = img_patches[i * patch_per_step:(i + 1) * patch_per_step] + if patch_per_step == 1 and batch_data.shape[0] != 1 and self.cfg.model.input_format_dimension == 5: + batch_data = batch_data[None, ...] + elif self.cfg.model.input_format_dimension == 4: + batch_data = np.reshape(batch_data, [-1, *batch_data.shape[2:]]) + _patch_hq = self._inf_func(batch_data) + patch_hq.extend(_patch_hq) + hq = self.adapter.stitching_patches_to_image(patch_hq) + else: + lq = self.adapter.adapt_input(lq) + hq = self._inf_func(lq[None]) + hq = self.adapter.reverse_adapt(hq.squeeze()) + return hq.squeeze() + + def _inf_func(self, lq): + """Real calling inference function. + + Args: + lq: numpy array, input array. + + Returns: + hq: numpy array, processd output array. + """ + # TODO: support multiple feed dict. + hq = self.session.run(self.network.output_node, feed_dict={self.network.input_node: lq}) + return hq + + def write_out(self, output_files, network_outputs, input_copies): + """Write out function. + """ + output_dict = dict() + + if isinstance(output_files, (list, tuple)): + assert len(output_files) == len(network_outputs) + network_outputs_ = [self.image_deprocess_fn(n, hdr=output_files[0].endswith('.exr')) + for n in network_outputs] + output_dict.update(dict(zip(output_files, network_outputs_))) + elif isinstance(output_files, str): + network_outputs_ = self.image_deprocess_fn(network_outputs, hdr=output_files.endswith('.exr')) + output_dict[output_files] = network_outputs_ + else: + raise NotImplementedError + + if input_copies is not None: + # deprocess the copied data + input_copies_deprocess = dict() + for k, v in input_copies.items(): + input_copies_deprocess[k] = [v[0], self.image_deprocess_fn(v[1], hdr=k.endswith('.exr'))] + output_dict.update(input_copies_deprocess) + + self.result_writer.write_out(output_dict) + + +class ModelFreeInferencer(SessionInferencer): + """ + Inferencer using pb file, without model python file. + """ + def restore(self): + """Restore from pb file. + + Returns: + graph: tf.graph, the forward tensorflow graph. + """ + with tf.gfile.GFile(self.cfg.checkpoint, "rb") as gf: + graph_def = tf.GraphDef() + graph_def.ParseFromString(gf.read()) + with tf.Graph().as_default() as graph: + tf.import_graph_def(graph_def, name="") + return graph + + def network_preparation(self): + """Build network forward graph, and restor from pb. Prepare adapter. + """ + sess_cfg = get_sess_config(self.cfg.env.device, + self.cfg.solver.xla, + self.cfg.solver.mix_precision, + False) + + # Load from PB + self.graph = self.restore() + self.session = tf.Session(config=sess_cfg, graph=self.graph) + + # Fix the real eval in size before register image raw size. + # This function will use the + # model.best_in_size + data.eval_padsize * 2 + # as the fixed eval in size + self.adapter.fix_eval_in_size() + self.adapter.register_raw_size(self.dataloader.raw_image_size) + + def _inf_func(self, lq): + """Real calling inference function. + + Args: + lq: numpy array, input array. + + Returns: + hq: numpy array, processd output array. + """ + hq = self.session.run(self.graph.get_tensor_by_name("SR_output:0"), + feed_dict={self.graph.get_tensor_by_name("L_input:0"): lq}) + return hq diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/trainer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..89b36c47e8a6dbe054bb96306ce026ee96040bfb --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/engine/trainer.py @@ -0,0 +1,499 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import datetime +import os +import time + +import numpy as np +import tensorflow as tf +from src.losses.modules.perceptual import load_perceptual_module +from src.runner.common import name_space +from src.runner.distributed_variables_broadcast import \ + broadcast_global_variables, allreduce_avg +from src.runner.helper import build_adversarial_train_helper +from src.runner.saver import strict_loading, loose_loading +from src.runner.sess_config import get_sess_config +from src.runner.solver import build_solver +from src.utils.exceptions import * +from src.utils.logger import logger +from src.utils.moving_avg import MovingAvg +from src.utils.world import world + + +class _Trainer: + """Base trainer class. + This class is for tensorflow for now. + + Args: + dataloader: list[tensor] generated from tf dataloader. See `src.dataloaders.dataloder` + for more information. + network: network instance whose class derives from Base network class. + cfg: yacs node, global configuration. + _world: World instance, option reserved for extension. By default, the trainer uses a + preset global `world` instance. + """ + def __init__(self, dataloader, network, cfg, _world=None): + self.device = cfg.env.device + self.is_distributed = cfg.env.rank_size > 1 + self.cfg = cfg + + self.dataloader = dataloader + self.network = network + self.g_train_op = None + self.d_train_op = None + self.g_solver = None + self.d_solver = None + + self.step_time = MovingAvg(smooth=0.9) + self.step_loss = MovingAvg(smooth=0.99) + + self.world = _world or world + if not self.world.is_initialized: + raise WorldUninitializedError('World not initialized.') + + # Call network.build_graph to construct the basic graph. + # Including dataloader, forward graph, and loss + self.network.build_graph(dataloader=self.dataloader) + + # Helper is to coordinate the adversarial training, i.e., + # whether to update the generator or the discriminator according to + # certain strategy. + self.helper = build_adversarial_train_helper(cfg) + + # Build the optimizers + self.build() + + def build(self): + """ + Top building function to prepare optimizers. + """ + self.build_g_optimizer() + + # Prepare discriminator optimizer if required + if self.cfg.loss.adversarial.loss_weight > 0.: + self.build_d_optimizer() + + # Use GLOBAL_VARIABLES to get both the weights and buffers. + # Do not use tf.GraphKeys.TRAINABLE_VARIABLES here, which will miss the + # bn buffers. + generator_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, + scope=self.cfg.model.scope) + self.saver = tf.train.Saver(var_list=generator_vars, + max_to_keep=10, + keep_checkpoint_every_n_hours=1) + + def build_g_optimizer(self): + """ + Build generator optimizer. + """ + # Build generator solver + self.g_solver = build_solver(self.cfg.train.generator.lr_schedule, + self.cfg.train.optimizer, + self.cfg.session.mix_precision, + self.cfg.train.loss_scale, + self.device, + self.is_distributed) + + # All generator losses are collected in name_space.GeneratorLoss scope. + # Add them to get the final generator loss. + losses_dict = name_space.get_collection(name_space.GeneratorLoss) + losses = tf.add_n(list(losses_dict.values())) + + name_space.add_to_collection(name_space.GeneratorLoss, 'loss_total', losses) + # TODO: encapsulate the learning rate + name_space.add_to_collection(name_space.GeneratorRunOp, 'g_lr', self.g_solver.lr) + + generator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, + scope=self.cfg.model.scope) + + g_train_op = self.g_solver.opt.minimize(losses, var_list=generator_vars) + + # bn buffer update after the optimization + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, + scope=self.cfg.model.scope) + if list(update_ops): + with tf.control_dependencies([g_train_op]): + g_train_op = tf.group(*update_ops) + + self.g_train_op = g_train_op + + # Add to name_space for later query + name_space.add_to_collection(name_space.GeneratorRunOp, 'g_train', self.g_train_op) + + def build_d_optimizer(self): + """ + Build discriminator optimizer. + """ + self.d_solver = build_solver(self.cfg.train.discriminator.lr_schedule, + self.cfg.train.optimizer, + self.cfg.session.mix_precision, + self.cfg.train.loss_scale, + self.device, + self.is_distributed) + # All discriminator losses are collected in name_space.DiscriminatorLoss scope. + # Add them to get the final discriminator loss. + losses_dict = name_space.get_collection(name_space.DiscriminatorLoss) + + self.d_loss = tf.add_n(list(losses_dict.values())) + name_space.add_to_collection(name_space.DiscriminatorRunOp, 'd_lr', self.d_solver.lr) + + discriminator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, + scope=name_space.DiscriminatorVarScope) + d_train_op = self.d_solver.opt.minimize(self.d_loss, var_list=discriminator_vars) + + # If parameter clip is applied, do it after optimization. + if self.cfg.loss.adversarial.parameter_clip: + amin, amax = self.cfg.loss.adversarial.parameter_clip_range + with tf.control_dependencies([d_train_op]): + d_train_op = tf.group([var.assign(tf.clip_by_value(var, amin, amax)) + for var in discriminator_vars]) + + # bn buffer update + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope=name_space.DiscriminatorVarScope) + if list(update_ops): + with tf.control_dependencies([d_train_op]): + d_train_op = tf.group(*update_ops) + + self.d_train_op = d_train_op + name_space.add_to_collection(name_space.DiscriminatorRunOp, 'd_train', self.d_train_op) + + def save(self, *args, **kwargs): + """ + Save ckpt during training every certain steps. + """ + raise NotImplementedError + + def print(self, *args, **kwargs): + """ + Print function to dump train information. + """ + raise NotImplementedError + + def restore(self): + """ + Restore from a ckpt for continual training. + """ + raise NotImplementedError + + def load_pretrained(self, *args, **kwargs): + """ + Load pretrained sub-networks for overall training and fine-tune. + """ + raise NotImplementedError + + def run(self): + """ + Execute function to run the train steps. + """ + raise NotImplementedError + + +class SessionTrainer(_Trainer): + """ + Tensorflow trainer using tf.Session. + """ + def __init__(self, dataloader, network, cfg): + super().__init__(dataloader, network, cfg) + sess_cfg = get_sess_config(cfg.env.device, + cfg.session.xla, + cfg.session.mix_precision, + cfg.env.rank_size>1) + self.session = tf.Session(config=sess_cfg) + + # TODO: refactor summary + if cfg.train.use_tensorboard: # for visualization when training drm + self.writer = tf.summary.FileWriter( + os.path.join(self.cfg.train.output_dir, 'summary'), + self.session.graph + ) + else: + self.writer = None + + def save(self, step): + """ + Save checkpoint on the step + + Args: + step: int + """ + if not os.path.exists(self.cfg.train.output_dir): + os.makedirs(self.cfg.train.output_dir) + + self.saver.save(self.session, + os.path.join(self.cfg.train.output_dir, + self.cfg.model.name), + global_step=step) + + def print(self, step, ops_result, loss_ops_result): + """ + Print train step information on the screen + + Args: + step: int, current train step. + ops_result: dict, data obtained by session.run. + loss_ops_result: dict, loss information + """ + loss_str = [f'{k}: {f"{v:3.3f}":>7}' for k, v in loss_ops_result.items()] + + fps = (self.cfg.data.train.batch_size + / (self.step_time.cur_val + 1e-6) + * self.cfg.env.rank_size) + eta = (self.g_solver.total_step - step) * self.step_time.avg + eta = str(datetime.timedelta(seconds=int(eta))) + + solver_info = [f'Step: [{step:>7d} / {self.g_solver.total_step}]'] + # If adversarial, print whether generator or discriminator is updated + if self.cfg.loss.adversarial.loss_weight > 0.: + adv_info = self.helper.info + g_update = adv_info['g_update'] + d_update = adv_info['d_update'] + + solver_info.append(f'g update: {f"{g_update}":>5}') + g_lr = ops_result['g_lr'] + solver_info.append(f'g lr: {f"{g_lr:.7f}":>6}') + + solver_info.append(f'd update: {f"{d_update}":>5}') + d_lr = ops_result['d_lr'] + solver_info.append(f'd lr: {f"{d_lr:.7f}":>6}') + else: + g_lr = ops_result['g_lr'] + solver_info.append(f'g lr: {f"{g_lr:.7f}":>6}') + + misc_info = [f'smooth_total: {f"{self.step_loss.smooth_avg:3.3f}":>7}', + f'step time: {f"{self.step_time.cur_val*1000:5.1f}":>7} ms', + f'fps: {f"{fps:3.2f}":>6}', + f'eta: {eta:>8}', + f'on device: {self.world.device_id:1d}'] + + print_info = ', '.join([*solver_info, *loss_str, *misc_info]) + logger.info(print_info) + + def load_pretrained(self, scope): + """ + Load part of the graph. + + This function is typically used in fine-tune, multi-stage training + scenarios. + + Args: + scope: str, top scope name for pretrained sub-graph. + """ + if self.cfg.checkpoint == '' and ( + len(self.cfg.train.pretrained_scope_list) > 0 + ): + assert len(self.cfg.train.pretrained_scope_list) == \ + len(self.cfg.model.pretrained_scope_ckpt) + for scope, ckpt in zip(self.cfg.train.pretrained_scope_list, + self.cfg.model.pretrained_scope_ckpt): + loose_loading(self.session, scope, + self.cfg.train.output_dir, ckpt) + else: + loose_loading(self.session, self.cfg.model.scope, + self.cfg.train.output_dir, self.cfg.checkpoint) + + def restore(self): + """ + Restore ckpt. + + This function is for continue training scenario. Thus every thing in + the generator will be loaded. + + Returns: + int, recover iteration to continue training. + """ + return strict_loading(self.session, + self.cfg.model.scope, + self.cfg.train.output_dir, + self.cfg.checkpoint) + + def run(self): + """ + Core function for the trainer to execute. + """ + # Initialization parameters. + init_op = tf.group(tf.global_variables_initializer(), + tf.local_variables_initializer()) + self.session.run(init_op) + + # Restore from ckpt if needed. + recover_step = 0 + if self.cfg.train.continue_training: + # For continue training. + recover_step = self.restore() + elif self.cfg.checkpoint != '' or ( + len(self.cfg.train.pretrained_scope_list) > 0): + # For multi-stage training, load pretrained model. + # Each trained with given scope. + self.load_pretrained(self.cfg.model.scope) + + # Load vgg-19 perceptual if needed. + if self.cfg.loss.perceptual.loss_weight > 0: + load_perceptual_module(self.session, self.cfg.loss.perceptual) + + # Synch all the nodes for initialization in distributed training. + if self.is_distributed: + logger.info(f'Broadcast variables from root rank') + broadcast_global_variables(self.session, + self.cfg.env.device, + self.cfg.env.root_rank) + + # Dump the train graph on the root node. + if self.world.is_root_rank: + tf.io.write_graph(self.session.graph_def, + self.cfg.train.output_dir, + 'train_graph.pbtxt') + logger.info(f'Start training.') + + # Train (may continue from recover step) + self._train(recover_step) + + def prepare_adv_adapt_op(self, d_loss_ops): + """ + Prepare auxiliary ops when adversarial training. + + Due to the insufficiency of Ascend platform in dynamic graph which is + common in adversarial training, we do the adaptive balance on session + run level instead of tf graph. To do this, we define a helper class to + determine whether to update generator and discriminator each step. + + On adaptive strategy which adjust the training step according to the + discriminator loss, we we must collect all the decision, aggregate and + sychronize the decision across the nodes. + + """ + + if self.cfg.loss.adversarial.loss_weight > 0. and ( + self.cfg.loss.adversarial.adaptive_strategy): + logger.info('Using adversarial training with adaptive strategy.') + logger.info('There will be some warm-start iterations for ' + 'discriminator, while the generator won\'t update.') + if self.is_distributed: + # In adaptive strategy, we should manually synchronize the + # discriminator losses across the devices. + logger.info('Distributed adversarial adaptive training. Generating ' + 'synchronize nodes.') + adv_helper_criteria = allreduce_avg( + d_loss_ops['discriminator'], + self.cfg.env.device, + self.world.rank_size + ) + else: + adv_helper_criteria = d_loss_ops['discriminator'] + else: + # To unify the interface, define a tf.no_op + adv_helper_criteria = tf.no_op() + return adv_helper_criteria + + def prepare_fetches(self): + """ + Prepare watched tensors. In each step, we want to know the + generator total loss, each part of generator losses, discriminator + total loss (if used), and some summary ops. + + Returns: + g_ops: dict, {op_name: op_tensor} of the generator. + d_ops: dict, {op_name: op_tensor} of the discriminator. + losses: dict, {loss_name: loss_tensor} for printing. + summary_ops: dict, {summary_name: summary_op} for visualization. + adv_helper_criteria: tensor, the criteria to tell whether update + generator or discriminator. May be a hccl operator. + """ + # prepare train ops, loss ops, summary ops + g_ops = name_space.get_collection(name_space.GeneratorRunOp) + g_loss_ops = name_space.get_collection(name_space.GeneratorLoss) + + d_ops = name_space.get_collection(name_space.DiscriminatorRunOp) + d_loss_ops = name_space.get_collection(name_space.DiscriminatorLoss) + + summary_ops = name_space.get_collection(name_space.Summary) + + adv_helper_criteria = self.prepare_adv_adapt_op(d_loss_ops) + + return g_ops, d_ops, {**g_loss_ops, **d_loss_ops}, summary_ops, adv_helper_criteria + + def prepare_feeds(self): + """ + Prepare feed dict for session run. + + Returns: + dict, will be fed to session run. + """ + # TODO: remove learning rate feed dict. + feed_dict = {self.g_solver.lr: self.g_solver.update_lr()} + if self.cfg.loss.adversarial.loss_weight > 0: + feed_dict[self.d_solver.lr] = self.d_solver.update_lr() + return feed_dict + + def _train(self, init_step=0): + """ + Train steps. + + Args: + init_step: int, the starting step of training. + """ + _g_ops, _d_ops, loss_ops, summary_ops, adv_helper_criteria = \ + self.prepare_fetches() + train_st = time.time() + for it in range(init_step, self.g_solver.total_step): + feed_dict = self.prepare_feeds() + + # In adversarial scenario, we use a helper instance to filter + # the truly evaluated ops. + real_g_ops, real_d_ops = self.helper.filter(_g_ops, _d_ops) + + st_time = time.time() + ops_result, loss_ops_result, adv_helper_criteria_result = \ + self.session.run([{**real_g_ops, **real_d_ops}, + loss_ops, + adv_helper_criteria], + feed_dict=feed_dict) + once_time = time.time() - st_time + + if self.world.is_root_rank: + if it > init_step: + # Skip the first print_interval steps, whose values + # might be abnormal + self.step_time.update(once_time) + total_loss = loss_ops_result['loss_total'] + self.step_loss.update(total_loss) + + if (it + 1) % self.cfg.train.print_interval == 0: + self.print(it + 1, ops_result, loss_ops_result) + + if (it + 1) % self.cfg.train.checkpoint_interval == 0: + self.save(it + 1) + + # Update adversarial helper function + self.helper.update_status(adv_helper_criteria_result, it+1) + + # TODO: support tensorboard, summary and evaluation. + # For tensorboard visualization + # if self.writer is not None and (it + 1) % 100 == 0: + # summary_merge = tf.summary.merge_all() + # summary_loss_result = self.session.run(summary_merge, feed_dict=feed_dict) + # self.writer.add_summary(summary_loss_result, it + 1) + + if (self.cfg.train.dump_intermediate == 'intermediate' + and (it + 1) % self.cfg.train.dump_intermediate == 0): + summary_ops_result = self.session.run(summary_ops) + # In distributed training, we should run summary ops on all the devices in + # order to synchronize. But only the root node will dump the data. + if self.world.is_root_rank: + self.network.dump_summary(it + 1, summary_ops_result) + + train_time = time.time() - train_st + time_mi = train_time / 60 + logger.info('Training finished. Average step time:{:.2f} ms, total elapse time: {:.1f} min.' + .format(np.mean(self.step_time.avg) * 1000, time_mi)) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed055f65e62da02cccdf6a4c4380c8a14e557770 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/__init__.py @@ -0,0 +1,20 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .act import * +from .norm import * +from .conv import * +from .dcn import * +from .linear import * +from .dropout import * diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/act.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/act.py new file mode 100644 index 0000000000000000000000000000000000000000..9bc380396e504ebd51f7d02657bdfc79bebd4483 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/act.py @@ -0,0 +1,74 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +from .base_layer import BaseLayer + + +__all__ = ['ActLayer'] + +class ActLayer(BaseLayer): + """Activation layer class. + + Args: + cfg: dict, should specify the activation `type` and other parameters. + name: str, scope name. + """ + def __init__(self, cfg, name=None): + super(ActLayer, self).__init__() + self.type = cfg.get('type').lower() + if self.type == 'leakyrelu': + self.alpha = cfg.get('alpha', 0.2) + elif self.type == 'prelu': + # see https://pytorch.org/docs/stable/generated/torch.nn.PReLU.html + # for explanation + self.channelwise = cfg.get('channelwise', True) + self.name = name + + def forward(self, x): + if self.type == 'relu': + return tf.nn.relu(x, name=self.name) + elif self.type == 'elu': + return tf.nn.elu(x, name=self.name) + elif self.type == 'prelu': + ndim = len(x.get_shape().as_list()) + if self.channelwise: + num_parameters = x.get_shape().as_list()[-1] + else: + num_parameters = 1 + + a = tf.get_variable( + name=self.name+'_prelu_a', + shape=(num_parameters, ), + dtype=x.dtype, + trainable=True, + initializer=tf.constant_initializer(0.25)) + + if self.channelwise: + a = tf.reshape(a, shape=tuple([1]*(ndim-1) + [num_parameters])) + neg_mask = tf.cast(tf.less(x, 0.), dtype=x.dtype) + neg_x = a * x # apply parameter `a` channel-wise + return x * (1. - neg_mask) + neg_x * neg_mask + else: + return tf.nn.leaky_relu(x, alpha=a, name=self.name) + elif self.type == 'tanh': + return tf.nn.tanh(x, name=self.name) + elif self.type == 'leakyrelu': + return tf.nn.leaky_relu(x, alpha=self.alpha, name=self.name) + elif self.type == 'softplus': + return tf.nn.softplus(x, name=self.name) + elif self.type == 'sigmoid': + return tf.nn.sigmoid(x, name=self.name) + else: + raise NotImplementedError diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/base_layer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/base_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..11e79581a651cafad04ab3b7f9a429a7ce842947 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/base_layer.py @@ -0,0 +1,33 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +class BaseLayer(object): + """Base layer class for all other common layers. + We'll use `Layer(args)(feat)` to call the layer. + """ + def __call__(self, *args, **kwargs): + return self.forward(*args, **kwargs) + + def forward(self, *args, **kwargs): + raise NotImplementedError + + def get_kernel(self): + raise NotImplementedError + + def get_bias(self): + raise NotImplementedError + + def get_buffer(self): + raise NotImplementedError diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/conv.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..4916bee0a42e0f57e05f02f8009b70bd678b9522 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/conv.py @@ -0,0 +1,420 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import tensorflow as tf +import numpy as np + +from .base_layer import BaseLayer + +from src.runner.initializer import get_initializer, calculate_fan +from src.utils.utils import to_pair +from src.ops.weight_regularzation import spectral_norm + +__all__ = ["Conv2D", "Conv3D", "Conv2DTranspose", "Conv3DTranspose"] + +class _ConvBaseLayer(BaseLayer): + """A base class of convolution layer. + + y = conv(x, weights) + bias + + Properties: + kernel: tensor, conv kernel. + bias: tensor, bias tensor. + """ + def __init__(self, + num_filters, + kernel_size=3, + strides=1, + dilations=1, + use_bias=True, + use_spectral_norm=False, + padding='same', + padding_mode='CONSTANT', + name='_conv_base'): + """ + Initialization function of convolution base class. + + Args: + num_filters: int, number of filters. + kernel_size: int or list[int], the kernel size. + strides: int or list[int], the stride size. + dilations: int or list[int], the kernel dilations. + use_bias: boolean, whether to use bias. Default True. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + padding: str or list[int]. If is given list of padding size, the + padding will be 'valid'. One can also pass in str such as + ('same', 'valid'). + padding_mode: str, indicating how to pad, i.e., REFLECT or CONSTANT. + name: str, variable scope name. + """ + self.num_filters = num_filters + self.kernel_size = to_pair(kernel_size, 2) + self.strides = to_pair(strides, 2) + self.dilation = to_pair(dilations, 2) + + self.name = name + self.use_bias = use_bias + self.use_spectral_norm = use_spectral_norm + self.padding = padding + self.padding_mode = padding_mode + + def __call__(self, x): + """ + Execute function of forward. + + Args: + x: tensor, input feature. + + Returns: + tensor, convolved feature. + """ + + # Get the data type of the input. + self.dtype = x.dtype + self.in_channels = x.get_shape().as_list()[-1] + + # Get the weight and bias initializers. + self.kernel_initializer = self.get_kernel_init(x) + self.bias_initializer = self.get_bias_init(x) + + # Apply forward. + with tf.variable_scope(self.name): + x = self.apply_padding(x) + x = self.forward(x) + + return x + + def apply_padding(self, x): + """ + Do padding_mode before convolution. In 'same' padding_mode, the padding_mode will be + conducted by convolution operator itself. + + Args: + x: tensor, input feature map. + + Returns: + tensor, padded feature map or the original one. + """ + # padding_mode for conv2d + if isinstance(self.padding, (list, tuple)): + if len(padding_mode) != 2: + raise ValueError('Invalid padding_mode') + padding_h, padding_w = padding_mode + padding_new = ((0,0), + (padding_h, padding_h), + (padding_w, padding_w), + (0,0)) + x = tf.pad(x, padding_new, mode=self.padding_mode.upper()) + self.padding = 'Valid' + elif self.padding_mode.upper() == 'REFLECT': + padding_h = (self.kernel_size[0]//2, self.kernel_size[0]//2) + padding_w = (self.kernel_size[1]//2, self.kernel_size[1]//2) + padding_new = ((0,0), padding_h, padding_w, (0,0)) + x = tf.pad(x, padding_new, mode=self.padding_mode.upper()) + self.padding = 'Valid' + return x + + def get_kernel_init(self, x): + """ + Get kernel initializer. This function is called after passing through + the input feature. We use 'kaiming_uniform' initializer. + + Args: + x: tensor, input feature map. + + Returns: + tensorflow initializer. + """ + kernel_initializer = get_initializer( + dict(type='kaiming_uniform', a=math.sqrt(5)), + self.in_channels, + self.num_filters, + self.kernel_size, + dtype=self.dtype) + return kernel_initializer + + def get_bias_init(self, x): + """ + Get bias initializer. This function is called after passing through + the input feature. + + Args: + x: tensor, input feature map. + + Returns: + tensorflow initializer. + """ + fan = calculate_fan(self.kernel_size, self.in_channels) + bound = 1 / math.sqrt(fan) + bias_initializer = tf.random_uniform_initializer( + -bound, + bound, + dtype=self.dtype) + return bias_initializer + + @property + def kernel(self): + w = tf.get_variable( + "kernel", + shape=[*self.kernel_size, self.in_channels, self.num_filters], + initializer=self.kernel_initializer, + regularizer=None, + dtype=self.dtype) + if self.use_spectral_norm: + w = spectral_norm(w) + return w + + @property + def bias(self): + bias = tf.get_variable( + "bias", + [self.num_filters], + initializer=self.bias_initializer, + dtype=self.dtype) + return bias + +class Conv2D(_ConvBaseLayer): + """A convolution2D class. + """ + def __init__(self, + num_filters, + kernel_size=(3, 3), + strides=(1, 1), + dilations=(1, 1), + use_bias=True, + use_spectral_norm=False, + padding='same', + padding_mode='CONSTANT', + name='Conv2D'): + super().__init__(num_filters, + kernel_size, + strides, + dilations, + use_bias, + use_spectral_norm, + padding, + padding_mode, + name) + + def forward(self, x): + """ + Forward computation of the convolution 2d. + + Args: + x: tensor, input feature map. + + Returns: + tensor + """ + x = tf.nn.conv2d( + input=x, + filter=self.kernel, + strides=[1, *self.strides, 1], + padding=self.padding.upper()) + if self.use_bias: + x = tf.nn.bias_add(x, self.bias) + return x + + +class Conv2DTranspose(Conv2D): + """A convolution transpose 2D class. + """ + def __init__(self, + num_filters, + kernel_size=(3, 3), + strides=(1, 1), + dilations=(1, 1), + use_bias=True, + use_spectral_norm=False, + padding='same', + padding_mode='CONSTANT', + name='Conv2DTranspose'): + super().__init__(num_filters, + kernel_size, + strides, + dilations, + use_bias, + use_spectral_norm, + padding, + padding_mode, + name) + + @property + def kernel(self): + # The kernel shape is (H_ksize, W_ksize, out_channels, in_channels), + # different from Conv2D. + w = tf.get_variable( + "kernel", + shape=[*self.kernel_size, self.num_filters, self.in_channels], + initializer=self.kernel_initializer, + regularizer=None, + dtype=self.dtype) + if self.use_spectral_norm: + w = spectral_norm(w) + return w + + def forward(self, x): + """Forward computation of the convolution transpose 2d. + + Args: + x: tensor, input feature map. + """ + n, h, w, c = x.shape.as_list() + output_shape = [n, + h * self.strides[0], + w * self.strides[1], + self.num_filters] + x = tf.nn.conv2d_transpose( + input=x, + filter=self.kernel, + output_shape=output_shape, + strides=[1, *self.strides, 1], + padding=self.padding.upper()) + if self.use_bias: + x = tf.nn.bias_add(x, self.bias) + return x + + +class Conv3D(_ConvBaseLayer): + """A convolution 3D class. + """ + def __init__(self, + num_filters, + kernel_size=(3, 3, 3), + strides=(1, 1, 1), + dilations=(1, 1, 1), + use_bias=True, + use_spectral_norm=False, + padding='same', + padding_mode='CONSTANT', + name='Conv3D'): + super().__init__(num_filters, + kernel_size, + strides, + dilations, + use_bias, + use_spectral_norm, + padding, + padding_mode, + name) + self.kernel_size = to_pair(kernel_size, 3) + self.strides = to_pair(strides, 3) + self.dilation = to_pair(dilations, 3) + + def apply_padding(self, x): + """Do padding_mode before convolution. + In 'same' padding_mode, the padding_mode will be conducted by + convolution operator itself. + + Args: + x: tensor, input feature map. + + Returns: + tensor, padded feature map or the original one. + """ + # padding_mode for conv3d + if type(self.padding) in [list, tuple]: + if len(self.padding) != 3: + raise ValueError('Invalid padding_mode') + padding_d, padding_h, padding_w = self.padding + padding_new = ((0,0), + (padding_d, padding_d), + (padding_h, padding_h), + (padding_w, padding_w), (0,0)) + self.padding = 'Valid' + x = tf.pad(x, padding_new, mode=self.padding_mode.upper()) + elif self.padding_mode.lower() == 'reflect': + padding_d = (self.kernel_size[0]//2, self.kernel_size[0]//2) + padding_h = (self.kernel_size[1]//2, self.kernel_size[1]//2) + padding_w = (self.kernel_size[2]//2, self.kernel_size[2]//2) + padding_new = ((0,0), padding_d, padding_h, padding_w, (0,0)) + x = tf.pad(x, padding_new, self.padding_mode.upper()) + self.padding = 'Valid' + return x + + def forward(self, x): + """Forward computation of the convolution 3d. + + Args: + x: tensor, input feature map. + """ + x = tf.nn.conv3d( + input=x, + filter=self.kernel, + strides=[1, *self.strides, 1], + padding=self.padding.upper()) + if self.use_bias: + x = tf.nn.bias_add(x, self.bias) + return x + + +class Conv3DTranspose(Conv3D): + """A convolution transpose 3D class. + """ + def __init__(self, + num_filters, + kernel_size=(3, 3, 3), + strides=(1, 1, 1), + dilations=(1, 1, 1), + use_bias=True, + use_spectral_norm=False, + padding='same', + padding_mode='CONSTANT', + name='Conv3DTranspose'): + super().__init__(num_filters, + kernel_size, + strides, + dilations, + use_bias, + use_spectral_norm, + padding, + padding_mode, + name) + + @property + def kernel(self): + # The kernel shape is (H_ksize, W_ksize, out_channels, in_channels), + # different from Conv3D. + w = tf.get_variable( + "kernel", + shape=[*self.kernel_size, self.num_filters, self.in_channels], + initializer=self.kernel_initializer, + regularizer=None, + dtype=self.dtype) + if self.use_spectral_norm: + w = spectral_norm(w) + return w + + def forward(self, x): + """Forward computation of the convolution transpose 3d. + + Args: + x: tensor, input feature map. + """ + n, h, w, c = x.shape.as_list() + output_shape = [n, + h * self.strides[0], + w * self.strides[1], + self.num_filters] + x = tf.nn.conv3d_transpose( + input=x, + filter=self.kernel, + output_shape=output_shape, + strides=[1, *self.strides, 1], + padding=self.padding.upper()) + if self.use_bias: + x = tf.nn.bias_add(x, self.bias) + return x diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dcn.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dcn.py new file mode 100644 index 0000000000000000000000000000000000000000..548dced8e5289994c0e19b81e55263ad6139de41 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dcn.py @@ -0,0 +1,376 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import tensorflow as tf +import numpy as np + +from .conv import Conv2D +from .base_layer import BaseLayer + +from src.utils.utils import to_pair +from src.utils.logger import logger + + +try: + from npu_bridge.tbe.npu_cube_ops import deformable_conv2d + OP_IMPL = 'npu' +except Exception: + logger.error('Failed to import NPU deformable_conv2d. ' + 'Please use the composed tf operator instead.' + '(This is NOT an actual error)') + OP_IMPL = 'tf' + + + +__all__ = ["DCNPack"] + +class DeformableConvLayer(BaseLayer): + """Deformable convolution layer. + + Args: + in_channels: int, number of channels of the input feature. + out_channels: int, number of channels of the output feature. + kernel_size: int or list[int] or tuple[int], kernel size of the conv + operation. + strides: int or list[int] or tuple[int], strides of the conv. + padding: str, options in ['same', 'valid']. Case insensitive. + dilations: int or list[int] or tuple[int], dilations of the conv. + use_bias: boolean, whether to add bias or not. Default True. + num_groups: int, number of convolution groups. + num_deform_groups: int, number of the groups of the offsets. + trainable: boolean, whether to train the parameters. + impl: str, which operator to use. Options in ['tf', 'npu']. If using + 'tf' version, the DCN will be composed of the tensorflow operators, + which may be memory and runtime inefficient. For Ascned platform, + we recommend to use npu deformable_conv2d instead. + """ + def __init__(self, + in_channels, + out_channels, + kernel_size, + strides=1, + padding='valid', + dilations=1, + use_bias=True, + num_groups=1, + num_deform_groups=1, + trainable=True, + impl='tf'): + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = to_pair(kernel_size, 2) + self.strides = to_pair(strides, 2) + self.padding = padding.lower() + self.dilations = to_pair(dilations, 2) + self.use_bias = use_bias + self.num_groups = num_groups + self.num_deform_groups = num_deform_groups + self.trainable = trainable + self.kernel_intermediate_shape = [] + self.build() + self.impl = impl + + def build(self): + """Prepare the weights and bias. + """ + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + initializer = tf.random_uniform_initializer(-stdv, stdv) + + self.kernel_intermediate_shape = [*self.kernel_size, self.in_channels//self.num_groups, self.out_channels//self.num_groups, self.num_groups] + + self.kernel = tf.get_variable( + "W", + [*self.kernel_size, self.in_channels//self.num_groups, self.out_channels], + initializer=initializer, + trainable=self.trainable) + if self.use_bias: + self.bias = tf.get_variable( + "b", + (self.out_channels,), + initializer=tf.constant_initializer(value=0.0), + trainable=self.trainable) + + def _cal_pads(self, ih, iw): + """Calculation padding given the input. + """ + if self.padding == 'same': + strh, strw = self.strides + kh, kw = self.kernel_size + dilh, dilw = self.dilations + tails_h = ih % strh + tails_w = iw % strw + dkh = dilh * (kh - 1) + 1 + dkw = dilw * (kw - 1) + 1 + pad_h = dkh - tails_h if tails_h > 0 else dkh - strh + pad_w = dkw - tails_w if tails_w > 0 else dkw - strw + pads = [pad_h // 2, pad_h // 2 + pad_h % 2, pad_w // 2, pad_w // 2 + pad_w % 2] + else: + pads = [0, 0, 0, 0] + return pads + + def forward(self, inputs, offset): + """Deformable Conv2d forward function. + """ + if self.impl == 'npu' and OP_IMPL == 'npu': + return self._forward_npu(inputs, offset) + else: + return self._forward_tf(inputs, offset) + + def _forward_npu(self, inputs, offset): + """Forward function of NPU deformable operator. + """ + _, ih, iw, _ = inputs.get_shape().as_list() + c = offset.get_shape().as_list()[3] + assert c == self.num_deform_groups*self.kernel_size[0]*self.kernel_size[1]*3 + offset_all = offset + + pads = self._cal_pads(ih, iw) + out = deformable_conv2d( + inputs, + self.kernel, + offset_all, + strides=[1] + list(self.strides) + [1], + pads=pads, + data_format='NHWC', + dilations=[1] + list(self.dilations) + [1], + groups=self.num_groups, + deformable_groups=self.num_deform_groups) + + if self.use_bias: + out = tf.nn.bias_add(out, self.bias) + return out + + def _forward_tf(self, inputs, offset): + """Forward function of tf composed deformable operator. + """ + def _get_in_bound_mask(x_, y_): + out_of_bound_x = tf.logical_or(tf.greater(x_, in_w-1), tf.less(x_, 0)) + out_of_bound_y = tf.logical_or(tf.greater(y_, in_h-1), tf.less(y_, 0)) + out_of_bound_mask = tf.logical_or(out_of_bound_x, out_of_bound_y) + return 1. - tf.to_float(out_of_bound_mask) + + inputs = self._pad_input(inputs) + bs, in_h, in_w, _ = inputs.get_shape().as_list() + bs, out_h, out_w, c = offset.get_shape().as_list() + + assert c == self.num_deform_groups*self.kernel_size[0]*self.kernel_size[1]*3 + c3 = c // 3 + + # get x, y axis offset. Swap the order to 'x,y' instead of 'y,x', align with npu dcn op + x_off = offset[:, :, :, :c3] + y_off = offset[:, :, :, c3:c3*2] + mask = offset[:, :, :, c3*2:] + + # input feature map gird coordinates + y, x = self._get_conv_indices(in_h, in_w) + y, x = [tf.to_float(i) for i in [y, x]] + y, x = [tf.tile(i, [1, 1, 1, self.num_deform_groups]) for i in [y, x]] + + # current deformable offsets + y, x = y + y_off, x + x_off + + # get four coordinates of points around (x, y) + y0, x0 = [tf.to_int32(tf.floor(i)) for i in [y, x]] + y1, x1 = y0 + 1, x0 + 1 + + # according to the strategy, prepare in_bound mask if use zero. + # In fact, gathernd NPU will take 0 if the index is out-of-bound, + # while CPU will throw an error. Therefore, do an explicit masking + m0 = _get_in_bound_mask(x0, y0) + m1 = _get_in_bound_mask(x1, y0) + m2 = _get_in_bound_mask(x0, y1) + m3 = _get_in_bound_mask(x1, y1) + + y_res = y - tf.to_float(y0) + x_res = x - tf.to_float(x0) + + w0_ori = (1. - y_res) * (1. - x_res) + w1_ori = (1. - y_res) * x_res + w2_ori = y_res * (1. - x_res) + w3_ori = y_res * x_res + + # clip the indices + y0_clip, y_clip, y1_clip = [tf.clip_by_value(i, 0, in_h - 1) for i in [y0, y, y1]] + x0_clip, x_clip, x1_clip = [tf.clip_by_value(i, 0, in_w - 1) for i in [x0, x, x1]] + + # get pixel values + indices = [[y0_clip, x0_clip], [y0_clip, x1_clip], [y1_clip, x0_clip], [y1_clip, x1_clip]] + p0, p1, p2, p3 = [self._get_pixel_values_at_point(inputs, i) for i in indices] + + # cast to float + x0_clip, x_clip, x1_clip, y0_clip, y_clip, y1_clip = [tf.to_float(i) for i in + [x0_clip, x_clip, x1_clip, y0_clip, y_clip, y1_clip]] + + # weights + w0 = m0 * w0_ori + w1 = m1 * w1_ori + w2 = m2 * w2_ori + w3 = m3 * w3_ori + + w0, w1, w2, w3 = [tf.reshape(i, [*i.get_shape()[:3], self.num_deform_groups, *self.kernel_size, 1]) + for i in [w0, w1, w2, w3]] + + # bilinear interpolation + pixels = tf.add_n([w0 * p0, w1 * p1, w2 * p2, w3 * p3]) + + if mask is not None: + pixels = tf.reshape(mask, [*mask.get_shape()[:3], self.num_deform_groups, *self.kernel_size, 1]) * pixels + + # reshape the "big" feature map + pixels = tf.transpose(pixels, [0,1,4,2,5,3,6]) + pixels = tf.reshape(pixels, [bs, out_h*self.kernel_size[0], out_w*self.kernel_size[1], -1]) + + # conv + kernel_reshaped = tf.reshape(self.kernel, self.kernel_intermediate_shape) + ich = pixels.shape[-1] // self.num_groups + out = tf.concat([tf.nn.conv2d( + pixels[:, :, :, i*ich:(i+1)*ich], + kernel_reshaped[:, :, :, :, i], + strides=self.kernel_size, + padding='VALID', + ) + for i in range(self.num_groups)], axis=-1) + + if self.use_bias: + out = tf.nn.bias_add(out, self.bias) + + return out + + def _pad_input(self, x): + """Pad the input before calculating the offsets. + """ + if self.padding == 'same': + ih, iw = x.get_shape().as_list()[1:3] + pads = self._cal_pads(ih, iw) + + if pads[0] + pads[1] + pads[2] + pads[3] != 0: + x = tf.pad(x, [[0, 0]] + [pads[:2]] + [pads[2:]] + [[0, 0]]) + + return x + + def _get_conv_indices(self, feat_h, feat_w): + """Get the x, y coordinates in the window when a filter sliding on the + feature map + """ + + x, y = tf.meshgrid(tf.range(feat_w), tf.range(feat_h)) + x, y = [tf.reshape(i, [1, *i.get_shape(), 1]) for i in [x, y]] # shape [1, h, w, 1] + x, y = [tf.image.extract_image_patches(i, + [1, *self.kernel_size, 1], + [1, *self.strides, 1], + [1, *self.dilations, 1], + 'VALID') + for i in [x, y]] # shape [1, out_h, out_w, filter_h * filter_w] + return y, x + + def _get_pixel_values_at_point(self, inputs, indices): + """Get pixel values at the given point. + """ + y, x = indices + bs, h, w, c = y.get_shape().as_list()[0: 4] + + if c % self.num_deform_groups != 0 or inputs.shape[-1] % self.num_deform_groups != 0: + raise ValueError + + per_group_offset_ch = c // self.num_deform_groups # kh*kw + per_group_input_ch = inputs.shape[-1] // self.num_deform_groups + batch_idx = tf.reshape(tf.range(0, bs), (bs, 1, 1, 1)) + b = tf.tile(batch_idx, (1, h, w, per_group_offset_ch)) + + outs = [] + for j in range(self.num_deform_groups): + pixel_idx = tf.stack([b, y[:, :, :, j*per_group_offset_ch:(j+1)*per_group_offset_ch], + x[:, :, :, j*per_group_offset_ch:(j+1)*per_group_offset_ch]], axis=-1) # [bs, h, w, per_group_offset_ch, 3] + outs.append(tf.gather_nd(inputs[:, :, :, j*per_group_input_ch:(j+1)*per_group_input_ch], pixel_idx)) + outs = tf.concat(outs, axis=-1) # [bs, h, w, per_group_offset_ch, cin] + + # reshape and transpose the outputs in order to align with the outer axis order + outs = tf.reshape(outs, [*outs.shape[:3], *self.kernel_size, self.num_deform_groups, -1]) + return tf.transpose(outs, [0,1,2,5,3,4,6]) + + +class DCNPack: + def __init__(self, + out_channels, + kernel_size=(3, 3), + strides=(1, 1), + padding='same', + dilations=(1, 1), + use_bias=True, + num_groups=1, + num_deform_groups=1, + name='DCN', + impl='npu'): + self.out_channels = out_channels + self.kernel_size = kernel_size + self.strides = strides + self.padding = padding + self.dilations = dilations + self.use_bias = use_bias + self.num_groups = num_groups + self.num_deform_groups = num_deform_groups + self.name = name + self.impl = impl + + def __call__(self, x, extra_feat): + with tf.variable_scope(self.name): + x = tf.cast(x, tf.float32) + + n_elem = (self.num_deform_groups + * self.kernel_size[0] + * self.kernel_size[1]) + + num_offset_channels = n_elem * 3 + + conv_offset = Conv2D(num_offset_channels, + kernel_size=self.kernel_size, + strides=self.strides, + padding=self.padding, + dilations=self.dilations, + use_bias=self.use_bias, + name='conv_offset')(extra_feat) + + conv_offset = tf.cast(conv_offset, tf.float32) + + # Get the modulation + modulation = tf.nn.sigmoid(conv_offset) + offset = conv_offset + + # Prepare a masking + weight = np.ones((1, 1, 1, num_offset_channels)).astype(np.float32) + weight[..., n_elem*2:] = 0. + weight = tf.convert_to_tensor(weight) + + # Make the n_elem*2 channels the offsets, the last n_elem channels + # the modulation. + input_offset_mask = weight * offset + (1. - weight) * modulation + + out = DeformableConvLayer( + in_channels=int(x.shape[-1]), + out_channels=self.out_channels, + kernel_size=self.kernel_size, + strides=self.strides, + padding=self.padding, + dilations=self.dilations, + use_bias=self.use_bias, + num_groups=self.num_groups, + num_deform_groups=self.num_deform_groups, + impl=self.impl)(x, input_offset_mask) + + return out diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dropout.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dropout.py new file mode 100644 index 0000000000000000000000000000000000000000..4dd5c9f65866828e2ed4791d2d8624411762b47b --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/dropout.py @@ -0,0 +1,60 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + +from src.layers.base_layer import BaseLayer +from src.utils.logger import logger + +try: + from npu_bridge.estimator import npu_ops + OP_IMPL = 'npu' +except Exception: + logger.error('Failed to import NPU dropout. Please use the composed tf operator instead.') + OP_IMPL = tf + + +__all__ = ["Dropout"] + +class Dropout(BaseLayer): + """Dropout layer. + + Use NPU high performance operator if possible. + + Args: + keep_prob: float, ranged in [0, 1], specifying the keeping probability + of the feature point. + """ + def __init__(self, keep_prob=0.1, name=None): + self.name = name + self.keep_prob = keep_prob + + def forward(self, input_tensor, training=False): + """Perform dropout. + """ + if not training: + return input_tensor + + with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE): + if OP_IMPL == tf: + output = tf.nn.dropout(input_tensor, self.keep_prob) + else: + if self.keep_prob is None or self.keep_prob == 1.0: + return input_tensor + + ##################modify for npu###################### + # Modify dropout for high performance + output = npu_ops.dropout(input_tensor, self.keep_prob) + ##################npu modify end###################### + return output diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/linear.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/linear.py new file mode 100644 index 0000000000000000000000000000000000000000..e02a2ce47655be7897a2845eae571e7b8a7f5ad8 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/linear.py @@ -0,0 +1,135 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +import tensorflow as tf + +from .base_layer import BaseLayer + +from src.ops.weight_regularzation import spectral_norm +from src.runner.initializer import get_initializer, calculate_fan + + +__all__ = ["Linear"] + +class Linear(BaseLayer): + """A linear layer. + + y = x*weigths + bias + + Args: + num_filters: int, number of filters in linear layer. + use_bias: boolean, whether to apply bias. Default True. + name: str, layer scope name. + use_spectral_norm: boolean, whether to use spectral normalization + on the weights. Default False. + trainable: boolean, whether weights and bias are trainable. + + Attributes: + kernel: tensor, linear kernel. + bias: tensor, bias tensor. + """ + def __init__(self, num_filters, use_bias=True, + name='Linear', use_spectral_norm=False, + trainable=True): + self.num_filters = num_filters + self.use_bias = use_bias + self.name = name + self.use_spectral_norm = use_spectral_norm + self.trainable = trainable + + def get_kernel_init(self, x): + """Get kernel initializer. + This function is called after passing through the input feature. + We use 'kaiming_uniform' initializer. + """ + kernel_initializer = get_initializer( + dict(type='kaiming_uniform', a=math.sqrt(5)), + self.in_channels, + self.num_filters, + (1, ), + dtype=self.dtype) + return kernel_initializer + + def get_bias_init(self, x): + """Get bias initializer. + This function is called after passing through the input feature. + """ + fan = calculate_fan((1, ), self.in_channels) + bound = 1 / math.sqrt(fan) + bias_initializer = tf.random_uniform_initializer( + -bound, + bound, + dtype=self.dtype) + return bias_initializer + + @property + def kernel(self): + w = tf.get_variable( + "kernel", + shape=[*self.kernel_size, self.in_channels, self.num_filters], + initializer=self.kernel_initializer, + regularizer=None, + dtype=self.dtype) + if self.use_spectral_norm: + w = spectral_norm(w) + return w + + @property + def bias(self): + bias = tf.get_variable( + "bias", + [self.num_filters], + initializer=self.bias_initializer, + dtype=self.dtype) + return bias + + def __call__(self, x): + """Execute function of forward. + + Args: + x: tensor, input feature. + + Returns: + tensor, feature. + """ + + # Get the data type of the input. + self.dtype = x.dtype + self.in_channels = x.get_shape().as_list()[-1] + + # Get the weight and bias initializers. + self.kernel_initializer = self.get_kernel_init(x) + self.bias_initializer = self.get_bias_init(x) + + # Apply forward. + with tf.variable_scope(self.name): + x = self.forward(x) + + return x + + def forward(self, x): + """Forward computation of the linear layer. + """ + x = tf.layers.dense( + x, + units=self.num_filters, + use_bias=self.use_bias, + kernel_initializer=self.kernel_initializer, + bias_initializer=self.bias_initializer, + trainable=self.trainable, + name=self.name, + ) + if self.use_bias: + x = tf.nn.bias_add(x, self.bias) + return x diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/norm.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/norm.py new file mode 100644 index 0000000000000000000000000000000000000000..cc33db42e42dfeb2248cfe0f9b331878108485c5 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/layers/norm.py @@ -0,0 +1,112 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +import tensorflow.contrib.slim as slim + +from .base_layer import BaseLayer + + +__all__ = ['NormLayer'] + +EPSILON = 1e-5 +DECAY = 0.99 + +def batch_norm(x, center=True, scale=True, is_train=True): + """Batch normalization function. + + y = (x - \mu) / \sigma * \gamma + \beta + + Args: + x: tensor, input feature map. + center: boolean, whether to use bias, i.e. beta. + scale: bollean, whether to use affine parameters, i.e. \gamma. + is_train: boolean, whether to update the buffers (moving average and var). + + Returns: + tensor, normalized feature map whose shape is the same with input. + """ + output = slim.batch_norm(x, decay=DECAY, center=center, scale=scale, + epsilon=EPSILON, updates_collections=tf.GraphKeys.UPDATE_OPS, + fused=False, is_training=is_train) + + return output + + +def instance_norm(x, center=True, scale=True, is_train=True): + """Apply instance normalization. + + Args: + x: tensor, input feature map. + center: boolean, whether to use bias, i.e. beta. + scale: bollean, whether to use affine parameters, i.e. \gamma. + is_train: boolean, whether to update the buffers (moving average and var). + + Returns: + tensor, normalized feature map whose shape is the same with input. + """ + return slim.instance_norm(x, center=center, scale=scale, epsilon=EPSILON, + trainable=is_train) + + +def layer_norm(x, center=True, scale=True, is_train=True): + """Apply layer normalization. + + Args: + x: tensor, input feature map. + center: boolean, whether to use bias, i.e. beta. + scale: bollean, whether to use affine parameters, i.e. \gamma. + is_train: boolean, whether to update the buffers (moving average and var). + + Returns: + tensor, normalized feature map whose shape is the same with input. + """ + return slim.layer_norm(x, center=True, scale=True, trainable=is_train) + + +NORM_FUNC = { + "bn": batch_norm, + "in": instance_norm, + "ln": layer_norm, +} + +class NormLayer(BaseLayer): + """ + Normalization layer class. + + Args: + norm_type: str, specifying the type of the norm layer. Possible choices: + ('bn', 'ln', 'in'). + center: boolean, whether to use bias, i.e. beta. + scale: bollean, whether to use affine parameters, i.e. \gamma. + is_train: boolean, whether to update the buffers (moving average and var). + + Raises: + ValueError, if the norm_type not in ('bn', 'ln', 'in') + """ + def __init__(self, norm_type, center=True, scale=True, is_train=True): + super(NormLayer, self).__init__() + if norm_type not in NORM_FUNC: + raise ValueError(f"Supported normalization layer type: {NORM_FUNC.keys()}, " + f"but is given {norm_type}") + self.fn = NORM_FUNC[norm_type] + self.is_train = is_train + self.center = center + self.scale = scale + + def forward(self, x): + return self.fn(x, + center=self.center, + scale=self.scale, + is_train=self.is_train) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/losses.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..8d8dd229167a6912927c4a59909f023e1f3abf23 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/losses.py @@ -0,0 +1,218 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tensorflow as tf + +from src.utils.logger import logger +from src.utils.klass import get_subclass_given_name + + +def get_loss(loss_type, pred, groundtruth, weight_map=None, **kwargs): + """Get the corresponding loss tensor given the loss type and the data pair. + + Args: + loss_type: str, type of loss class. + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + try: + klass = get_subclass_given_name(BaseLoss, loss_type) + except IndexError: + raise ValueError(f'Cannot find loss type {loss_type}.') + return klass()(pred, groundtruth, weight_map=None, **kwargs) + + +class BaseLoss(object): + def __call__(self, pred, grountruth, **kwargs): + raise NotImplementedError + + +class L1Loss(BaseLoss): + """Pixelwise L1-loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, weight_map=None, **kwargs): + loss = tf.abs(pred - groundtruth) + if weight_map is not None: + loss = loss * weight_map + return loss + + +class MarginalL1Loss(BaseLoss): + """Pixelwise L1-loss with margins. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + eps: scalar, a small scalar to margin out the values that are too small. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, eps=1e-6, weight_map=None, **kwargs): + + loss = tf.maximum(tf.abs(pred - groundtruth), eps) + if weight_map is not None: + loss = loss * weight_map + return loss + + +class L2Loss(BaseLoss): + """Pixelwise L2-loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, weight_map=None, **kwargs): + loss = tf.square(pred - groundtruth) + if weight_map is not None: + loss = loss * weight_map + return loss + + +class HuberLoss(BaseLoss): + """Pixelwise Huber loss, a.k.a. the smooth-l1 loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + delta: scalar, threshold to indicate where to change between l1 and l2. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, delta=1., weight_map=None, **kwargs): + res = pred - groundtruth + mask = tf.cast(tf.less(tf.abs(res), 1.), tf.float32) + lesser_region = 0.5 * l2_loss(pred, groundtruth) + greater_region = l1_loss(pred, groundtruth) - 0.5*delta**2 + loss = mask * lesser_region + (1. - mask) * greater_region + if weight_map is not None: + loss = loss * weight_map + return loss + + +# Alias smooth l1 loss. +SmoothL1Loss = HuberLoss + + +class CharbonnierLoss(BaseLoss): + """Pixelwise Charbonnier loss. A variant of L1-loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + eps: scalar, a small value to avoid inf or nan during sqrt. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, eps=1e-6, weight_map=None, **kwargs): + + loss = tf.sqrt((pred - groundtruth) ** 2 + eps) + if weight_map is not None: + loss = loss * weight_map + return loss + + +class MSELoss(BaseLoss): + """ + Pixelwise mse loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, weight_map=None, **kwargs): + + loss = tf.square(groundtruth - pred) + if weight_map is not None: + loss = loss * weight_map + + return loss + + +class FocalLoss(BaseLoss): + """Pixelwise FocalLoss. See https://arxiv.org/pdf/1708.02002.pdf + + Args:s + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + alpha: scalar, a small balance value. Default 0.25 as in the paper. + gamma: scalar, focusing parameter which is greater than 0. Default 2. + eps: scalar, a small value to avoid nan when tf.log. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, alpha=0.25, gamma=2, eps=1e-6, + weight_map=None, **kwargs): + + pt = tf.where(groundtruth, pred, 1.-pred) + loss = - alpha * tf.pow(1. - pt, gamma) * tf.log(tf.maximum(pt, eps)) + if weight_map is not None: + loss = loss * weight_map + return loss + + +class CosineDistanceLoss(BaseLoss): + """Pixelwise cosine distance loss. + + Args: + pred: tensor, the predictions. + groundtruth: tensor, the target tensor. + axis: int, which axis to do the normalization. + eps: scalar, a small value to avoid nan when tf.log. + weight_map: tensor or None. If given, the loss will be weighted. + + Returns: + tensor, whose shape is the same with the pred and groundtruth. + """ + def __call__(self, pred, groundtruth, axis=-1, eps=1e-6, weight_map=None, + **kwargs): + + prod = pred * groundtruth + prod = tf.reduce_sum(prod, axis=axis, keepdims=True) + pred_norm = tf.reduce_sum(tf.square(pred), axis=axis, keepdims=True) + gt_norm = tf.reduce_sum(tf.square(groundtruth), axis=axis, keepdims=True) + norm_scale = tf.sqrt(pred_norm * gt_norm + eps) + loss = 1. - prod / norm_scale + if weight_map is not None: + loss = loss * weight_map + return loss diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/adversarial.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/adversarial.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5b60ffcf05d34070bdb8389a4a58da3b0c3306 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/adversarial.py @@ -0,0 +1,303 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +import tensorflow as tf + +from src.losses.modules.gan import get_gan +from src.utils.klass import get_subclass_given_name +from src.runner.common import name_space + + +def build_adversarial_loss(hq, gt, cfg): + """hq and gt both in shape [b*num_out_frames, h, w, c] + """ + num_out_frames = cfg.data.num_data_gt_frames + adv_loss_type = cfg.loss.adversarial.loss_type + try: + loss_model = get_subclass_given_name(_BaseAdvLoss, + adv_loss_type)(cfg) + except IndexError: + logger.error(f'Cannot find adversarial loss type {adv_loss_type}.') + raise ValueError() + + hr_shape = gt.get_shape().as_list() + + # Check whether 3D network is used and reshape the tensor to 4D or 5D. + if '3D' in cfg.loss.adversarial.gan_type and len(hr_shape) == 4: + gt = tf.reshape(gt, [-1, num_out_frames, *hr_shape[1:]]) + hq = tf.reshape(hq, [-1, num_out_frames, *hr_shape[1:]]) + elif (not '3D' in cfg.loss.adversarial.gan_type) and len(hr_shape) != 4: + gt = tf.reshape(gt, [-1, *hr_shape[2:]]) + hq = tf.reshape(hq, [-1, *hr_shape[2:]]) + + return loss_model(real=gt, fake=hq) + + +class _BaseAdvLoss: + """Base adversarial loss class. + All the adv losses will be derived from the base class. + + After real data point and fake one forward through the discriminator, + the logits will be used to calculate the losses. + """ + def __init__(self, cfg): + reduction = cfg.loss.adversarial.loss_reduction + self.discriminator = get_gan(cfg) + self.reduction = reduction + self.cfg = cfg + + def __call__(self, real, fake): + """ + Forward the real and fake sample through the discriminator and + calculate the losses. + + Args: + real: tensor, 4D or 5D tensor for real samples. + fake: tensor, the same as real. Fake generated samples. + + Returns: + real_loss: scalar tensor, loss for the real sample. + fake_loss: scalar tensor, loss for the fake sample. + """ + + # Forward through the discriminators to get the logits. + fake_logit = self.discriminator(fake) + real_logit = self.discriminator(real) + + # Calculate the losses. + real_loss, fake_loss = self.forward(real_logit, fake_logit) + + # Cast to fp32 before reduction in case of precision loss on Ascend. + real_loss = tf.cast(real_loss, tf.float32) + fake_loss = tf.cast(fake_loss, tf.float32) + + if self.reduction == 'mean': + reduction_fn = tf.reduce_mean + elif self.reduction == 'sum': + reduction_fn = tf.reduce_sum + else: + raise NotImplementedError + + # Apply weights before output. + real_loss = reduction_fn(real_loss) * self.cfg.loss.adversarial.loss_weight + fake_loss = reduction_fn(fake_loss) * self.cfg.loss.adversarial.loss_weight + + name_space.add_to_collection(name_space.DiscriminatorLoss, + 'discriminator', real_loss) + name_space.add_to_collection(name_space.GeneratorLoss, + 'adversarial', fake_loss) + + return real_loss, fake_loss + + def forward(self, real_logit, fake_logit): + raise NotImplementedError + + +class VanillaAdvLoss(_BaseAdvLoss): + """ + Vanialla adversarial loss, i.e. + loss_d = E(log D) + E(log (1 - D(G))) + loss_g = - E(log D(G)) + """ + def forward(self, real_logit, fake_logit): + fake_loss = tf.nn.sigmoid_cross_entropy_with_logits( + logits=fake_logit, + labels=tf.ones_like(fake_logit)) + real_loss = tf.nn.sigmoid_cross_entropy_with_logits( + logits=real_logit, + labels=tf.ones_like(real_logit)) \ + + tf.nn.sigmoid_cross_entropy_with_logits( + logits=fake_logit, + labels=tf.zeros_like(fake_logit)) + + return real_loss, fake_loss + + +class HingeAdvLoss(_BaseAdvLoss): + """ + Hinge adversarial loss, i.e. + loss_d = E(max(0, 1 - D)) + E(max(0, 1 + D(G))) + loss_g = - E(D(G)) + """ + def forward(self, real_logit, fake_logit): + fake_loss = - fake_logit + real_loss = tf.nn.relu(1.0 - real_logit) + tf.nn.relu(1.0 + fake_logit) + return real_loss, fake_loss + + +class RSAdvLoss(VanillaAdvLoss): + """ + Relativistic adversarial loss, i.e. + loss_d = - E(log sigmoid(D - D(G))) + loss_g = - E(log sigmoid(D(G) - D)) + """ + # Relativistic Standard GAN + def forward(self, real_logit, fake_logit): + fake_loss = tf.nn.sigmoid_cross_entropy_with_logits( + logits=fake_logit, + labels=tf.ones_like(fake_logit)) + real_loss = tf.nn.sigmoid_cross_entropy_with_logits( + logits=real_logit, + labels=tf.ones_like(real_logit)) + + return real_loss, fake_loss + + def __call__(self, real, fake): + fake_logit = self.discriminator(fake) + real_logit = self.discriminator(real) + real_loss, fake_loss = self.forward(real_logit - fake_logit, + fake_logit - real_logit) + + real_loss = tf.cast(real_loss, tf.float32) + fake_loss = tf.cast(fake_loss, tf.float32) + + if self.reduction == 'mean': + reduction_fn = tf.reduce_mean + elif self.reduction == 'sum': + reduction_fn = tf.reduce_sum + else: + raise NotImplementedError + + real_loss = reduction_fn(real_loss) + fake_loss = reduction_fn(fake_loss) * self.cfg.loss.adversarial.loss_weight + + name_space.add_to_collection(name_space.DiscriminatorLoss, + 'discriminator', real_loss) + name_space.add_to_collection(name_space.GeneratorLoss, + 'adversarial', fake_loss) + + return real_loss, fake_loss + + +class RaSAdvLoss(VanillaAdvLoss): + """ + Relativistic adversarial loss, i.e. + loss_d = - E(log sigmoid(D - E(D(G)))) + E(log sigmoid(D(G) - E(D))) + loss_g = - E(log sigmoid(D(G) - E(D))) + E(log sigmoid(D - E(D(G)))) + """ + # Relativistic Average GAN + def forward(self, real_logit, fake_logit): + fake_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logit, + labels=tf.ones_like(fake_logit)) \ + + tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logit, + labels=tf.zeros_like(fake_logit)) + real_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logit, + labels=tf.ones_like(real_logit)) \ + + tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logit, + labels=tf.zeros_like(fake_logit)) + + return real_loss, fake_loss + + def __call__(self, real, fake): + fake_logit = self.discriminator(fake) + real_logit = self.discriminator(real) + real_loss, fake_loss = self.forward(real_logit - tf.reduce_mean(fake_logit, axis=0, keepdims=True), + fake_logit - tf.reduce_mean(real_logit, axis=0, keepdims=True)) + + real_loss = tf.cast(real_loss, tf.float32) + fake_loss = tf.cast(fake_loss, tf.float32) + + if self.reduction == 'mean': + reduction_fn = tf.reduce_mean + elif self.reduction == 'sum': + reduction_fn = tf.reduce_sum + else: + raise NotImplementedError + + real_loss = reduction_fn(real_loss) + fake_loss = reduction_fn(fake_loss) * self.cfg.loss.adversarial.loss_weight + + name_space.add_to_collection(name_space.DiscriminatorLoss, + 'discriminator', real_loss) + name_space.add_to_collection(name_space.GeneratorLoss, + 'adversarial', fake_loss) + + return real_loss, fake_loss + + +class LSAdvLoss(_BaseAdvLoss): + """ + Least-square adversarial loss, i.e. + loss_d = 0.5 * E((D - 1)**2) + 0.5 * E(D(G)**2) + loss_g = E((D(G) - 1)**2) + """ + def forward(self, real_logit, fake_logit): + fake_loss = tf.square(fake_logit - 1) + real_loss = 0.5 * tf.square(real_logit - 1) + 0.5 * tf.square(fake_logit) + + return real_loss, fake_loss + + +class WGANLoss(_BaseAdvLoss): + """ + Wesserstein adversarial loss, i.e. + loss_d = E(D) + E(1 - D(G)) + loss_g = - E(D(G)) + """ + def forward(self, real_logit, fake_logit): + fake_loss = - fake_logit + real_loss = fake_logit - real_logit + return real_loss, fake_loss + + +class WGAN_GP_Loss(WGANLoss): + """ + Wesserstein adversarial loss with gradient panelty, i.e. + loss_d = E(D) + E(1 - D(G)) + GP(D, D(G)) + loss_g = - E(D(G)) + """ + def gradient_penalty(self, real, fake): + b = real.get_shape().as_list()[0] + ndim = len(real.get_shape().as_list()) + shape = (b, ) + (1, ) * (ndim - 1) + alpha = tf.random.uniform(shape=shape) + interpolates = alpha * real + (1. - alpha) * fake + + interp_logit = self.discriminator(interpolates) + grads = tf.gradients( + interp_logit, + xs=interpolates + ) + gradient_penalty = tf.reduce_mean((grads-1.)**2) + return gradient_penalty + + def __call__(self, real, fake): + fake_logit = self.discriminator(fake) + real_logit = self.discriminator(real) + real_loss, fake_loss = self.forward(real_logit, fake_logit) + grad_penalty = self.gradient_penalty(real, fake) + + real_loss = tf.cast(real_loss, tf.float32) + fake_loss = tf.cast(fake_loss, tf.float32) + grad_penalty = tf.cast(grad_penalty, tf.float32) + + if self.reduction == 'mean': + reduction_fn = tf.reduce_mean + elif self.reduction == 'sum': + reduction_fn = tf.reduce_sum + else: + raise NotImplementedError + + real_loss = reduction_fn(real_loss) \ + + grad_penalty * self.cfg.loss.adversarial.grad_penalty_weight + fake_loss = reduction_fn(fake_loss) * self.cfg.loss.adversarial.loss_weight + + name_space.add_to_collection(name_space.DiscriminatorLoss, + 'discriminator', real_loss) + name_space.add_to_collection(name_space.GeneratorLoss, + 'adversarial', fake_loss) + + return real_loss, fake_loss diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/gan.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/gan.py new file mode 100644 index 0000000000000000000000000000000000000000..1045cde727a69a2b3f22bc3631c38d801b05d48b --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/gan.py @@ -0,0 +1,520 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +import tensorflow as tf + +from src.runner.common import name_space +from src.layers import Conv2D, Conv3D, NormLayer, ActLayer, Linear +from src.utils.klass import get_subclass_given_name + + +def get_gan(cfg): + """Get GAN instance given the configuration. + + Args: + cfg: yacs node, config for the GAN. + + Returns: + GAN instance. + """ + + try: + klass = get_subclass_given_name(BaseGAN, cfg.loss.adversarial.gan_type) + except IndexError: + logger.error(f'Cannot find GAN type {cfg.loss.adversarial.gan_type}.') + raise ValueError() + + return klass(cfg.loss.adversarial.mid_channels, cfg.loss.adversarial.norm_type) + + +class BaseGAN: + """Base GAN class. + """ + def __init__(self, scope=name_space.DiscriminatorVarScope): + self.scope = scope + + def __call__(self, input): + with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE): + return self.forward(input) + + def forward(self, input): + raise NotImplementedError + + +class VanillaGAN(BaseGAN): + """A vanilla discriminator for 4D feature map. + + Args: + mid_channels: int, multiplier of the channels in the middle layers. + norm_type: str, type of the normalization layer. + scope: str, discriminator scope name. + """ + def __init__(self, mid_channels=64, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.norm_type = norm_type + self.mid_channels = mid_channels + self.kernel_size = (3, 3) + + def conv_norm_act(self, inputs, output_channel, kernel_size, stride, norm_type, is_train, scope): + """A conv-norm-activation sequence. + """ + with tf.variable_scope(scope): + net = Conv2D(output_channel, kernel_size, stride, + use_bias=norm_type=='in', + name='conv', + use_spectral_norm=norm_type=='sn')(inputs) + net = NormLayer(norm_type, is_train=is_train)(net) + net = ActLayer(dict(type='leakyrelu', alpha=0.2), name='lrelu')(net) + return net + + def forward(self, input): + """Forward pass through the discriminator. + """ + # no batchnorm for the first layer, output size [in_h/2, in_w/2] + net = Conv2D(self.mid_channels, + kernel_size=self.kernel_size, + strides=(1, 1), + name='conv_first')(input) + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + + # The discriminator block part + # block 1, output size [in_h/4, in_w/4] + net = self.conv_norm_act(net, self.mid_channels, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_1') + # block 2, output size [in_h/8, in_w/8] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_2') + # block 3, output size [in_h/16, in_w/16] + net = self.conv_norm_act(net, self.mid_channels*3, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_3') + # block_4, output size [in_h/32, in_w/32] + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_4') + + # The dense layer 1 + b, h, w, c = net.get_shape().as_list() + net = tf.reshape(net, [b, h * w * c]) + net = Linear(256, name='linear1')(net) # channel-wise dense layer + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + net = Linear(1, name='linear_final')(net) # channel-wise dense layer + return net + + +class VanillaGAN3D(BaseGAN): + """A vanilla discriminator for 5D feature map. + + Args: + mid_channels: int, multiplier of the channels in the middle layers. + norm_type: str, type of the normalization layer. + scope: str, discriminator scope name. + """ + def __init__(self, mid_channels=32, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.norm_type = norm_type + self.mid_channels = mid_channels + self.kernel_size = (3, 5, 5) + + def conv_norm_act(self, inputs, output_channel, kernel_size, stride, norm_type, is_train, scope): + """A conv-norm-activation sequence. + """ + with tf.variable_scope(scope): + net = Conv3D(output_channel, kernel_size, stride, use_bias=norm_type=='in', name='conv', + use_spectral_norm=norm_type=='sn')(inputs) + net = NormLayer(norm_type, is_train=is_train)(net) + net = ActLayer(dict(type='leakyrelu', alpha=0.2), name='lrelu')(net) + return net + + def forward(self, input): + """Forward pass through the discriminator. + """ + + # no batchnorm for the first layer, output size [in_h/2, in_w/2] + net = Conv3D(self.mid_channels, kernel_size=self.kernel_size, strides=(1, 1, 1), + name='conv_first')(input) + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + + # The discriminator block part + # block 1, output size [in_h/4, in_w/4] + net = self.conv_norm_act(net, self.mid_channels, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_1') + # block 2, output size [in_h/8, in_w/8] + net = self.conv_norm_act(net, self.mid_channels, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_2') + # block 3, output size [in_h/16, in_w/16] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_3') + # block_4, output size [in_h/32, in_w/32] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_4') + # block_5, output size [in_h/64, in_w/64] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_5') + + # The dense layer 1 + b, t, h, w, c = net.get_shape().as_list() + net = tf.reshape(net, [b, t * h * w * c]) + net = Linear(256, name='linear1')(net) # channel-wise dense layer + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + net = Linear(1, name='linear_final')(net) # channel-wise dense layer + return net + + +class PatchGAN(BaseGAN): + """A PatchGAN discriminator for 4D feature map. + + Args: + mid_channels: int, multiplier of the channels in the middle layers. + norm_type: str, type of the normalization layer. + scope: str, discriminator scope name. + """ + def __init__(self, mid_channels=64, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.norm_type = norm_type + self.mid_channels = mid_channels + self.kernel_size = (3, 3) + + def conv_norm_act(self, inputs, output_channel, kernel_size, stride, norm_type, is_train, scope): + """A conv-norm-activation sequence. + """ + with tf.variable_scope(scope): + net = Conv2D(output_channel, kernel_size, stride, use_bias=norm_type=='in', name='conv', + use_spectral_norm=norm_type=='sn')(inputs) + net = NormLayer(norm_type, is_train=is_train)(net) + net = ActLayer(dict(type='leakyrelu', alpha=0.2), name='lrelu')(net) + return net + + def forward(self, input): + """Forward pass through the discriminator. + """ + + # no batchnorm for the first layer, output size [in_h/2, in_w/2] + net = Conv2D(self.mid_channels, kernel_size=self.kernel_size, + strides=(1, 1), name='conv_first')(input) + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + + # The discriminator block part + # block 1, output size [in_h/4, in_w/4] + net = self.conv_norm_act(net, self.mid_channels, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_1') + # block 2, output size [in_h/8, in_w/8] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_2') + # block 3, output size [in_h/16, in_w/16] + net = self.conv_norm_act(net, self.mid_channels*3, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_3') + # block_4, output size [in_h/32, in_w/32] + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (2, 2), self.norm_type, + True, 'disblock_4') + + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (1, 1), self.norm_type, + True, 'disblock_5') + net = Conv2D(self.mid_channels, kernel_size=(3, 3), strides=(1, 1), name='conv_last')(net) + return net + + +class PatchGAN3D(BaseGAN): + """A PatchGAN discriminator for 5D feature map. + + Args: + mid_channels: int, multiplier of the channels in the middle layers. + norm_type: str, type of the normalization layer. + scope: str, discriminator scope name. + """ + def __init__(self, mid_channels=64, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.norm_type = norm_type + self.mid_channels = mid_channels + self.kernel_size = (3, 5, 5) + + def conv_norm_act(self, inputs, output_channel, kernel_size, stride, norm_type, is_train, scope): + """ + A conv-norm-activation sequence. + """ + with tf.variable_scope(scope): + net = Conv3D(output_channel, kernel_size, stride, use_bias=norm_type=='in', name='conv', + use_spectral_norm=norm_type=='sn')(inputs) + net = NormLayer(norm_type, is_train=is_train)(net) + net = ActLayer(dict(type='leakyrelu', alpha=0.2), name='lrelu')(net) + return net + + def forward(self, input): + """ + Forward pass through the discriminator. + """ + # no batchnorm for the first layer, output size [in_h/2, in_w/2] + net = Conv3D(self.mid_channels, kernel_size=self.kernel_size, + strides=(1, 1, 1), name='conv_first')(input) + net = ActLayer(dict(type='leakyrelu', alpha=0.2))(net) + + # The discriminator block part + # block 1, output size [in_h/4, in_w/4] + net = self.conv_norm_act(net, self.mid_channels*2, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_1') + # block 2, output size [in_h/8, in_w/8] + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_3') + # block 3, output size [in_h/16, in_w/16] + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_5') + # block_4, output size [in_h/32, in_w/32] + net = self.conv_norm_act(net, self.mid_channels*4, + self.kernel_size, (1, 2, 2), self.norm_type, + True, 'disblock_7') + + net = Conv3D(self.mid_channels, kernel_size=self.kernel_size, + strides=(1, 1, 1), name='conv_last')(net) + # net = tf.reduce_mean(net, axis=1, keepdims=True) + return net + + +class BigGAN(BaseGAN): + """A BigGAN discriminator for 4D feature map. + + Args: + mid_channels: int, multiplier of the channels in the middle layers. + norm_type: str, type of the normalization layer. + scope: str, discriminator scope name. + """ + def __init__(self, mid_channels=64, norm_type='none', scope=name_space.DiscriminatorVarScope): + """ + Initialization function of the discriminator. + + + """ + super().__init__(scope) + self.ch = mid_channels + self.sn = False + self.layer_num = 4 + + def hw_flatten(self, x): + return tf.reshape(x, shape=[x.shape[0], -1, x.shape[-1]]) + + def down_sample(self, x): + return tf.layers.average_pooling2d(x, pool_size=2, strides=2, padding='SAME') + + def init_down_resblock(self, x_init, channels, use_bias=True, sn=False, scope='resblock'): + with tf.variable_scope(scope): + with tf.variable_scope('res1'): + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x_init) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + + with tf.variable_scope('res2'): + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + x = self.down_sample(x) + + with tf.variable_scope('shortcut'): + x_init = self.down_sample(x_init) + x_init = Conv2D(channels, kernel_size=(1, 1), use_bias=use_bias, use_spectral_norm=sn)(x_init) + + return x + x_init + + def down_resblock(self, x_init, channels, to_down=True, use_bias=True, sn=False, scope='resblock'): + with tf.variable_scope(scope): + init_channel = x_init.shape.as_list()[-1] + with tf.variable_scope('res1'): + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x_init) + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + + with tf.variable_scope('res2'): + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + if to_down: + x = self.down_sample(x) + + if to_down or init_channel != channels: + with tf.variable_scope('shortcut'): + x_init = Conv2D(channels, kernel_size=(1, 1), use_bias=use_bias, use_spectral_norm=sn)(x_init) + if to_down: + x_init = self.down_sample(x_init) + + return x + x_init + + def google_attention(self, x, channels, scope='attention'): + with tf.variable_scope(scope): + batch_size, height, width, num_channels = x.get_shape().as_list() + f = Conv2D(channels // 8, kernel_size=(1, 1), use_spectral_norm=self.sn, name='f')(x) # [bs, h, w, c'] + f = tf.layers.max_pooling2d(f, pool_size=2, strides=2, padding='SAME') + g = Conv2D(channels // 8, kernel_size=(1, 1), use_spectral_norm=self.sn, name='g')(x) # [bs, h, w, c'] + h = Conv2D(channels // 2, kernel_size=(1, 1), use_spectral_norm=self.sn, name='h')(x) # [bs, h, w, c] + h = tf.layers.max_pooling2d(h, pool_size=2, strides=2, padding='SAME') + + # N = h * w + s = tf.matmul(self.hw_flatten(g), self.hw_flatten(f), transpose_b=True) # # [bs, N, N] + + beta = tf.nn.softmax(s) # attention map + + o = tf.matmul(beta, self.hw_flatten(h)) # [bs, N, C] + gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0)) + + o = tf.reshape(o, shape=[batch_size, height, width, num_channels // 2]) # [bs, h, w, C] + o = Conv2D(channels, kernel_size=(1, 1), use_spectral_norm=self.sn, name='c')(o) # [bs, h, w, c] + x = gamma * o + x + + return x + + def forward(self, x): + """Forward pass through the discriminator. + """ + ch = self.ch + x = self.init_down_resblock(x, channels=ch, sn=self.sn, scope='resblock_0') + x = self.down_resblock(x, channels=ch * 2, sn=self.sn, scope='resblock_1') + + x = self.google_attention(x, channels=ch * 2, scope='self_attention') + + ch = ch * 2 + for i in range(self.layer_num): + if i == self.layer_num - 1: + x = self.down_resblock(x, channels=ch, sn=self.sn, to_down=False, scope='resblock_' + str(i+2)) + else: + x = self.down_resblock(x, channels=ch * 2, sn=self.sn, scope='resblock_' + str(i+2)) + ch = ch * 2 + + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + x = tf.reduce_sum(x, axis=[1, 2]) + x = Linear(1, name='linear')(x) + return x + +class MSPatchGAN(BaseGAN): + """ + A multi-scale PatchGAN discriminator for 4D feature map. + """ + def __init__(self, nf=64, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.nf = nf + + def patchGAN(self, x, n_layers, d_layers): + x = Conv2D(self.nf, kernel_size=(4, 4), strides=(2, 2), name='conv_first')(x) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + + for n in range(1, n_layers): + x = Conv2D(self.nf * min(2 ** n, 8), kernel_size=(4, 4), strides=(1, 1), name='conv' + str(n))(x) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + if n < d_layers: + x = Conv2D(self.nf * min(2 ** n, 8), kernel_size=(4, 4), strides=(2, 2), name='conv' + str(n) + '_down')(x) + x = Conv2D(1, kernel_size=(4, 4), strides=(1, 1), name='conv_last')(x) + return x + + def forward(self, x): + n, h, w, c = x.get_shape().as_list() + x_big = tf.image.resize_bilinear(x, size=(int(2*h), int(2*w)), align_corners=False, half_pixel_centers=False) + x_mid = x + x_sml = tf.image.resize_bilinear(x, size=(int(0.5*h), int(0.5*w)), align_corners=False, half_pixel_centers=False) + with tf.variable_scope('big', reuse=tf.AUTO_REUSE): + out_big = self.patchGAN(x_big, n_layers=3, d_layers=3) + with tf.variable_scope('mid', reuse=tf.AUTO_REUSE): + out_mid = self.patchGAN(x_mid, n_layers=3, d_layers=2) + with tf.variable_scope('sml', reuse=tf.AUTO_REUSE): + out_sml = self.patchGAN(x_sml, n_layers=3, d_layers=1) + x = tf.concat([out_big, out_mid, out_sml], axis=-1) + return x + + +class MSPatchBigGAN(BaseGAN): + """ + A multi-scale PatchBigGAN discriminator for 4D feature map. + """ + def __init__(self, nf=16, norm_type='in', scope=name_space.DiscriminatorVarScope): + super().__init__(scope) + self.nf = nf + self.sn = False + + def hw_flatten(self, x): + return tf.reshape(x, shape=[x.shape[0], -1, x.shape[-1]]) + + def down_sample(self, x): + return tf.layers.average_pooling2d(x, pool_size=2, strides=2, padding='SAME') + + def init_down_resblock(self, x_init, channels, use_bias=True, sn=False, scope='resblock'): + with tf.variable_scope(scope): + with tf.variable_scope('res1'): + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x_init) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + + with tf.variable_scope('res2'): + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + x = self.down_sample(x) + + with tf.variable_scope('shortcut'): + x_init = self.down_sample(x_init) + x_init = Conv2D(channels, kernel_size=(1, 1), use_bias=use_bias, use_spectral_norm=sn)(x_init) + + return x + x_init + + def down_resblock(self, x_init, channels, to_down=True, use_bias=True, sn=False, scope='resblock'): + with tf.variable_scope(scope): + init_channel = x_init.shape.as_list()[-1] + with tf.variable_scope('res1'): + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x_init) + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + + with tf.variable_scope('res2'): + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + x = Conv2D(channels, kernel_size=(3, 3), use_bias=use_bias, use_spectral_norm=sn)(x) + if to_down: + x = self.down_sample(x) + + if to_down or init_channel != channels: + with tf.variable_scope('shortcut'): + x_init = Conv2D(channels, kernel_size=(1, 1), use_bias=use_bias, use_spectral_norm=sn)(x_init) + if to_down: + x_init = self.down_sample(x_init) + + return x + x_init + + def patchGAN(self, x, n_layers, d_layers): + x = self.init_down_resblock(x, channels=self.nf, sn=self.sn, scope='resblock_0') + x = self.down_resblock(x, channels=self.nf * 2, sn=self.sn, scope='resblock_1') + for n in range(n_layers): + if n < d_layers: + x = self.down_resblock(x, channels=self.nf * min(2 ** n, 8), sn=self.sn, scope='resblock_' + str(n + 2)) + else: + x = self.down_resblock(x, channels=self.nf * min(2 ** n, 8), sn=self.sn, to_down=False, scope='resblock_' + str(n + 2)) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + x = Conv2D(self.nf * 8, kernel_size=(3, 3), strides=(1, 1), use_spectral_norm=self.sn, name='conv')(x) + x = ActLayer(dict(type='leakyrelu', alpha=0.2))(x) + x = Conv2D(1, strides=(1, 1), name='conv_last')(x) + return x + + def forward(self, x): + n, h, w, c = x.get_shape().as_list() + x_big = tf.image.resize_bilinear(x, size=(int(2*h), int(2*w)), align_corners=False, half_pixel_centers=False) + x_mid = x + x_sml = tf.image.resize_bilinear(x, size=(int(0.5*h), int(0.5*w)), align_corners=False, half_pixel_centers=False) + with tf.variable_scope('big', reuse=tf.AUTO_REUSE): + out_big = self.patchGAN(x_big, n_layers=3, d_layers=3) + with tf.variable_scope('mid', reuse=tf.AUTO_REUSE): + out_mid = self.patchGAN(x_mid, n_layers=3, d_layers=2) + with tf.variable_scope('sml', reuse=tf.AUTO_REUSE): + out_sml = self.patchGAN(x_sml, n_layers=3, d_layers=1) + x = tf.concat([out_big, out_mid, out_sml], axis=-1) + return x diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/perceptual.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/perceptual.py new file mode 100644 index 0000000000000000000000000000000000000000..4e8f4a27bac33d526d307afa81f0d33faefe8738 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/perceptual.py @@ -0,0 +1,181 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +import tensorflow as tf + +from src.runner.common import name_space +from src.utils.logger import logger + +from .vgg import VGG19_slim + + +def auto_download_pretrained(module='vgg', ckpt_path='./pretrained_modules'): + """Automatically download pretrained models. + + Args: + module: str, perceptual model name. + ckpt_path: str, where to save the downloaded ckpt file. + """ + import subprocess + if module in ['vgg', 'vgg_19']: + cmd0 = "wget http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz -O " + \ + os.path.join(ckpt_path, "vgg19.tar.gz") + cmd0 += ";tar -xvf " + os.path.join(ckpt_path, "vgg19.tar.gz") + " -C " + ckpt_path + \ + "; rm " + os.path.join(ckpt_path, "vgg19.tar.gz") + else: + raise NotImplementedError + + subprocess.call(cmd0, shell=True) + + +def load_perceptual_module(sess, module_cfg): + """Load perceptual module to the corresponding scope. + + Args: + sess: tf.Session instance. + module_cfg: yacs node, perceptual configuration. + """ + ckpt_dir = module_cfg.get('ckpt_dir', './pretrained_modules') + module = module_cfg.get('module', 'vgg_19') + if not os.path.exists(ckpt_dir): + os.makedirs(ckpt_dir, exist_ok=True) + + ckpt_file = os.path.join(ckpt_dir, f'{module}.ckpt') + if not os.path.exists(ckpt_file): + logger.info('No pretrained module. Downloading ...') + auto_download_pretrained(module, ckpt_dir) + + try: + logger.info('Loading pretrained perceptual module ...') + var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=module) + restore = tf.train.Saver(var_list) + restore.restore(sess, ckpt_file) + logger.info('Load pretrained perceptual module success.') + except Exception as e: + logger.error('Failed to load pretrained perceptual model.') + logger.info(e) + + +def build_perceptual_loss(generated, targets, module_cfg): + """Calculate the perceptual loss given the configuration. + + Args: + generated: tensor, the generated results. + targets: tensor, the groundtruth. + module_cfg: yacs node, perceptual configuration. + + Returns: + scalar tensor, the perceptual loss. + """ + module = module_cfg.get('module', 'vgg_19') + + # Convert to 4D shape. + gen_shape = generated.get_shape().as_list() + if len(gen_shape) == 5: + generated = tf.reshape(generated, shape=(-1, *gen_shape[2:])) + + tar_shape = targets.get_shape().as_list() + if len(tar_shape) == 5: + targets = tf.reshape(targets, shape=(-1, *tar_shape[2:])) + + # Get the intermediate resuls given the layer names. + if module == 'vgg_19': + with tf.name_scope('vgg_19'): + default_layer_labels = ['vgg_19/conv2/conv2_2', + 'vgg_19/conv3/conv3_4', + 'vgg_19/conv4/conv4_4', + 'vgg_19/conv5/conv5_4'] + default_layer_weights = [1., 1., 1., 1.] + layer_labels = module_cfg.get('layers', default_layer_labels) + layer_weights = module_cfg.get('layer_weights', default_layer_weights) + gen_fm = VGG19_slim(generated, reuse=tf.AUTO_REUSE, deep_list=layer_labels) + target_fm = VGG19_slim(targets, reuse=tf.AUTO_REUSE, deep_list=layer_labels) + else: + raise NotImplementedError + + # Compute the distance between the generated and groundtruth features. + with tf.variable_scope('perceptual_loss'): + loss = 0 + layer_n = len(layer_labels) + + for layer_i in range(layer_n): + cur_diff = tf.reduce_sum(gen_fm[layer_labels[layer_i]] * target_fm[layer_labels[layer_i]], axis=[3]) + # cosine similarity, -1~1, 1 best + cur_diff = 1.0 - tf.reduce_mean(cur_diff) # 0 ~ 2, 0 best + scaled_layer_loss = layer_weights[layer_i] * cur_diff + loss += scaled_layer_loss + + return loss + + +def build_content_style_loss(generated, targets, module_cfg): + """Calculate the perceptual style loss given the configuration. + + Args: + generated: tensor, the generated results. + targets: tensor, the groundtruth. + module_cfg: yacs node, perceptual configuration. + + Returns: + scalar tensor, the style loss. + """ + module = module_cfg.get('module', 'vgg_19') + + gen_shape = generated.get_shape().as_list() + if len(gen_shape) == 5: + generated = tf.reshape(generated, shape=(-1, *gen_shape[2:])) + + tar_shape = targets.get_shape().as_list() + if len(tar_shape) == 5: + targets = tf.reshape(targets, shape=(-1, *tar_shape[2:])) + + if module == 'vgg_19': + with tf.name_scope('vgg_19'): + default_layer_labels = ['vgg_19/conv2/conv2_2', + 'vgg_19/conv3/conv3_4', + 'vgg_19/conv4/conv4_4', + 'vgg_19/conv5/conv5_4'] + default_layer_weights = [1., 1., 1., 1.] + layer_labels = module_cfg.get('layers', default_layer_labels) + layer_weights = module_cfg.get('layers_weights', default_layer_weights) + print(layer_weights) + gen_fm = VGG19_slim(generated, reuse=tf.AUTO_REUSE, deep_list=layer_labels, norm_flag=False) + target_fm = VGG19_slim(targets, reuse=tf.AUTO_REUSE, deep_list=layer_labels, norm_flag=False) + else: + raise NotImplementedError + + with tf.variable_scope('perceptual_loss'): + loss = 0 + layer_n = len(layer_labels) + content_loss = 0 + style_loss = 0 + layers_content_weights = [0.008, 0.001, 0.03125, 40.0] + layer_style_weights = [0.002, 0.000008, 0.03125, 10000.0] + for layer_i in range(layer_n): + f1 = gen_fm[layer_labels[layer_i]] + f2 = target_fm[layer_labels[layer_i]] + content_loss += layers_content_weights[layer_i] * tf.reduce_mean(tf.square(f1 / 10.0 - f2 / 10.0)) + if layer_i > 2: + b,h,w,c = f1.shape + f1T = tf.reshape(f1, (b, h*w, c)) + f2T = tf.reshape(f2, (b, h*w, c)) + f1G = tf.matmul(f1T, f1T, transpose_a=True) + f2G = tf.matmul(f2T, f2T, transpose_a=True) + norm = tf.cast(100.0 * h * w, tf.float32) + style_loss += layer_style_weights[layer_i] * tf.reduce_mean(tf.square(f1G / norm - f2G / norm)) + loss = content_loss * 0.2 + style_loss * 0.08 + + return loss \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/vgg.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/vgg.py new file mode 100644 index 0000000000000000000000000000000000000000..536500a7a978c4dada5445a00590148bad5c2d44 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/losses/modules/vgg.py @@ -0,0 +1,88 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tensorflow as tf +import tensorflow.contrib.slim as slim + +VGG_MEAN = [123.68, 116.78, 103.94] + + +def vgg_19(inputs, + scope='vgg_19', + reuse=False): + """VGG19 model. + Borrowed from https://github.com/thunil/TecoGAN/blob/master/lib/ops.py#L287 + Changed from the Oxford Net VGG 19-Layers version E Example. + Note: Only offer features from conv1 until relu54, classification part is removed + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + scope: Optional scope for the variables. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_19', [inputs], reuse=reuse) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 2, slim.conv2d, 64, 3, scope='conv1', reuse=reuse) + net = slim.max_pool2d(net, [2, 2], scope='pool1') + + net = slim.repeat(net, 2, slim.conv2d, 128, 3, scope='conv2',reuse=reuse) + net = slim.max_pool2d(net, [2, 2], scope='pool2') + + net = slim.repeat(net, 4, slim.conv2d, 256, 3, scope='conv3', reuse=reuse) + net = slim.max_pool2d(net, [2, 2], scope='pool3') + + net = slim.repeat(net, 4, slim.conv2d, 512, 3, scope='conv4',reuse=reuse) + net = slim.max_pool2d(net, [2, 2], scope='pool4') + + net = slim.repeat(net, 4, slim.conv2d, 512, 3, scope='conv5',reuse=reuse) + net = slim.max_pool2d(net, [2, 2], scope='pool5') + + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + + return net, end_points + + +def VGG19_slim(input_fm, reuse, deep_list=None, norm_flag=True): + """Get the VGG19 features given the fm name. + Borrowed from https://github.com/thunil/TecoGAN/blob/master/lib/Teco.py#L5 + + Args: + input_fm: tensor, input feature map. + reuse: boolean, whether to reuse the scope variables. + deep_list: list[str], which features are to extract and used for calculation. + norm_flag: boolean, whether to normalize the feature map with Frobenius-norm. + """ + # deep_list, define the feature to extract + input_img_ab = input_fm * 255.0 - tf.constant(VGG_MEAN) + # model: + _, output = vgg_19(input_img_ab, reuse=reuse) + # feature maps: + results = {} + with tf.name_scope('vgg_norm'): + for key in output: + if (deep_list is None) or (key in deep_list): + orig_deep_feature = tf.cast(output[key], tf.float32) + if norm_flag: + orig_len = tf.sqrt(tf.reduce_sum(tf.square(orig_deep_feature), axis=[3], keepdims=True)+1e-12) + results[key] = orig_deep_feature / orig_len + else: + results[key] = orig_deep_feature + return results diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/main.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/main.py new file mode 100644 index 0000000000000000000000000000000000000000..96bb0fc6d3b4d1fb271abbaa69e9494fd3ce8af6 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/main.py @@ -0,0 +1,134 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import sys +import inspect +import importlib +from yacs.config import CfgNode + +import src.runner.npu_pkgs + +from src.utils.logger import logger as logger +from src.utils.defaults import cfg +from src.utils.world import world +from src.utils.utils import convert_dict_to_list +from src.networks import build_network +from src.engine import build_engine +from src.dataloaders import build_dataloader +from src.utils.constant import VALID_MODE + + +def get_args(): + """Get external arguments. + + Returns: + Namespace: the arguements to the whole program. + """ + parser = argparse.ArgumentParser(description="HiSi Ascend Video Processing Toolkit") + parser.add_argument( + "--config-file", + default="", + metavar="FILE", + help="path to config file", + type=str, + ) + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + + args = parser.parse_args() + + return args + + +def dump_cfg(_cfg): + """Dump config info to log file and stdout. + + Args: + _cfg: yacs node, the configuration. + """ + cfg_str = _cfg.dump() + if not os.path.exists(_cfg.train.output_dir): + os.makedirs(_cfg.train.output_dir, exist_ok=True) + dump_file = os.path.join(_cfg.train.output_dir, f"configure_{_cfg.mode}.yaml") + with open(dump_file, 'w') as f: + f.write(cfg_str) + logger.info(_cfg) + + +def processing(cfg): + """Processing function. + + This function supports training, inference and freeze engine. + + Args: + cfg: yacs node, global configuraton. + """ + world.initialize(device_type=cfg.env.device) + + if not cfg.log_file.startswith('/'): + log_file = os.path.join(cfg.train.output_dir, cfg.log_file) + else: + log_file = cfg.log_file + + # Silence all nodes other than the root node. + if world.is_root_rank: + logger.add_log_file(log_file) + else: + logger.silence = True + + if world.is_root_rank: + dump_cfg(cfg) + + # build networks + network = build_network(cfg) + + # build dataloader + dataloader = build_dataloader(cfg) + + # get engine + engine_type = build_engine(cfg) + engine = engine_type(dataloader, network, cfg) + engine.run() + + +def main(): + """Main entry function. + """ + args = get_args() + # Support either python config file with a dict, or a yaml file. + if args.config_file.endswith('.py'): + vars = {} + exec(open(args.config_file).read(), vars) + cfg.merge_from_other_cfg(CfgNode(vars['cfg'])) + elif config_module_name[-1] == 'yaml': + cfg.merge_from_file(args.config_file) + else: + raise ValueError() + + cfg.merge_from_list(args.opts) + cfg.freeze() + + assert cfg.mode in VALID_MODE + + processing(cfg) + + +if __name__ == '__main__': + main() diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/metrics/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/metrics/image_similarity.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/metrics/image_similarity.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb2608916c2bae58fd2e53dbdc628a69858de44 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/metrics/image_similarity.py @@ -0,0 +1,54 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + + +def ssim(a, b, max_val): + """Calculate SSIM of two images. + + Args: + a: tensor, 4D image tensor. + b: tensor, has the same shape with a. + max_val: scalar, the max value of tensor a and b. + """ + return tf.image.ssim(a, b, max_val, filter_size=11, + filter_sigma=1.5, k1=0.01, k2=0.03) + + +def ssim_multiscale(a, b, max_val): + """Calculate multi-scale SSIM of two images. + + Args: + a: tensor, 4D image tensor. + b: tensor, has the same shape with a. + max_val: scalar, the max value of tensor a and b. + """ + return tf.image.ssim_multiscale( + a, b, max_val, filter_size=11, + filter_sigma=1.5, k1=0.01, k2=0.03 + ) + + +def psnr(a, b, max_val): + """Calculate PSNR of two images. + + Args: + a: tensor, 4D image tensor. + b: tensor, has the same shape with a. + max_val: scalar, the max value of tensor a and b. + """ + return tf.image.psnr(a, b, max_val) + + diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b3aa40061e345225b58a78fd7aedd85388405485 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .conv_module import * +from .edvr_submodules import * +from .res_block import * +from .spade import * diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/conv_module.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/conv_module.py new file mode 100644 index 0000000000000000000000000000000000000000..3f33d3f53c02f54a39e95e530c8efe2a4017987f --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/conv_module.py @@ -0,0 +1,210 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf + +from src.layers import Conv2D, Conv3D, ActLayer, NormLayer, Conv2DTranspose + + +__all__ = ['Conv2DNormAct', 'Conv3DNormAct', 'Conv2DTransposeNormAct'] + + +class Conv2DNormAct: + """A base module consists of conv2d followed by norm and activation. + + Both normalization and activation layers are optional. + + Args: + num_filters: int, number of filters. + kernel_size: int or list[int], the kernel size. + strides: int or list[int], the stride size. + dilations: int or list[int], the kernel dilations. + padding: str or list[int]. If is given list of padding size, the + padding will be 'valid'. One can also pass in str such as + ('same', 'valid'). + padding_mode: str, indicating how to pad, i.e., REFLECT or CONSTANT. + + use_bias: boolean, whether to use bias. Default True. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + trainable: boolean, whether in training phase. If True, the buffers will + be add to UPDATE_OPS and update. + act_cfg: dict, specify the activation `type` and other parameters. + norm_cfg: dict, specify the normalization `type` and other parameters. + name: str, variable scope name. + """ + def __init__(self, num_filters, kernel_size=(3, 3), strides=(1, 1), + dilations=(1, 1), padding='same', padding_mode='CONSTANT', + use_bias=True, use_spectral_norm=False, trainable=True, + act_cfg=None, norm_cfg=None, + name='Conv2DModule'): + self.num_filters = num_filters + self.kernel_size = kernel_size + self.strides = strides + self.dilations = dilations + + self.padding = padding + self.padding_mode = padding_mode + + self.use_bias = use_bias + self.use_spectral_norm = use_spectral_norm + self.trainable = trainable + + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + + self.name = name + + def __call__(self, x): + with tf.variable_scope(self.name): + use_bias = self.use_bias if self.norm_cfg is None else False + x = Conv2D( + self.num_filters, kernel_size=self.kernel_size, strides=self.strides, + dilations=self.dilations, use_bias=self.use_bias, + use_spectral_norm=self.use_spectral_norm, + padding=self.padding, padding_mode=self.padding_mode, + name='Conv2D')(x) + + if self.norm_cfg is not None: + x = NormLayer(self.norm_cfg, is_train=self.trainable, name='Norm')(x) + + if self.act_cfg is not None: + x = ActLayer(self.act_cfg)(x) + return x + + +class Conv3DNormAct: + """A base module consists of conv3d followed by norm and activation. + + Both normalization and activation layers are optional. + + Args: + num_filters: int, number of filters. + kernel_size: int or list[int], the kernel size. + strides: int or list[int], the stride size. + dilations: int or list[int], the kernel dilations. + padding: str or list[int]. If is given list of padding size, the + padding will be 'valid'. One can also pass in str such as + ('same', 'valid'). + padding_mode: str, indicating how to pad, i.e., REFLECT or CONSTANT. + + use_bias: boolean, whether to use bias. Default True. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + trainable: boolean, whether in training phase. If True, the buffers will + be add to UPDATE_OPS and update. + act_cfg: dict, specify the activation `type` and other parameters. + norm_cfg: dict, specify the normalization `type` and other parameters. + name: str, variable scope name. + """ + def __init__(self, num_filters, kernel_size=(3, 3, 3), strides=(1, 1, 1), + dilations=(1, 1, 1), padding='same', padding_mode='CONSTANT', + use_bias=True, use_spectral_norm=False, trainable=True, + act_cfg=None, norm_cfg=None, + name='Conv3DModule'): + self.num_filters = num_filters + self.kernel_size = kernel_size + self.strides = strides + self.dilations = dilations + + self.padding = padding + self.padding_mode = padding_mode + + self.use_bias = use_bias + self.use_spectral_norm = use_spectral_norm + self.trainable = trainable + + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + + self.name = name + + def __call__(self, x): + with tf.variable_scope(self.name): + use_bias = self.use_bias if self.norm_cfg is None else False + x = Conv3D(self.num_filters, kernel_size=self.kernel_size, + strides=self.strides, dilations=self.dilations, + padding=self.padding, padding_mode=self.padding_mode, + use_bias=self.use_bias, use_spectral_norm=self.use_spectral_nor, + name='Conv3D')(x) + + if self.norm_cfg is not None: + x = NormLayer(self.norm_cfg, is_train=self.trainable, name='Norm')(x) + + if self.act_cfg is not None: + x = ActLayer(self.act_cfg)(x) + return x + + +class Conv2DTransposeNormAct: + """A base module consists of conv2d transpose followed by norm and activation. + + Both normalization and activation layers are optional. + + Args: + num_filters: int, number of filters. + kernel_size: int or list[int], the kernel size. + strides: int or list[int], the stride size. + dilations: int or list[int], the kernel dilations. + padding: str or list[int]. If is given list of padding size, the + padding will be 'valid'. One can also pass in str such as + ('same', 'valid'). + padding_mode: str, indicating how to pad, i.e., REFLECT or CONSTANT. + + use_bias: boolean, whether to use bias. Default True. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + trainable: boolean, whether in training phase. If True, the buffers will + be add to UPDATE_OPS and update. + act_cfg: dict, specify the activation `type` and other parameters. + norm_cfg: dict, specify the normalization `type` and other parameters. + name: str, variable scope name. + """ + def __init__(self, num_filters, kernel_size=(3, 3), strides=(1, 1), + dilations=(1, 1), padding='same', padding_mode='CONSTANT', + use_bias=True, use_spectral_norm=False, trainable=True, + act_cfg=None, norm_cfg=None, + name='Conv2DTransposeModule'): + self.num_filters = num_filters + self.kernel_size = kernel_size + self.strides = strides + self.dilations = dilations + + self.padding = padding + self.padding_mode = padding_mode + + self.use_bias = use_bias + self.use_spectral_norm = use_spectral_norm + self.trainable = trainable + + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + + self.name = name + + def __call__(self, x): + with tf.variable_scope(self.name): + use_bias = self.use_bias if self.norm_cfg is None else False + x = Conv2DTranspose( + self.num_filters, kernel_size=self.kernel_size, strides=self.strides, + dilations=self.dilations, use_bias=self.use_bias, + use_spectral_norm=self.use_spectral_norm, + padding=self.padding, padding_mode=self.padding_mode, + name='Conv2DTranspose')(x) + + if self.norm_cfg is not None: + x = NormLayer(self.norm_cfg, is_train=self.trainable, name='Norm')(x) + + if self.act_cfg is not None: + x = ActLayer(self.act_cfg)(x) + return x diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/edvr_submodules.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/edvr_submodules.py new file mode 100644 index 0000000000000000000000000000000000000000..df8c0e6eb3cee9fc1f7e2b6f56323dd0f023dd50 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/edvr_submodules.py @@ -0,0 +1,267 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +import tensorflow as tf + +from src.layers import Conv2D, Conv3D, ActLayer, DCNPack +from src.ops import resize + +from .conv_module import Conv2DNormAct + + +class PCDAlign(object): + """ + Pyramid, cascade and deformable alignment module in EDVR. + + Args: + num_feat: int, number of channels multiplier in the intermediate layers. + num_conv_groups: int, number of groups in convolution in dcn. + deformable_groups: int, number of groups in offsets in dcn. + dcn_impl: str, version of dcn operator. Possible choice in ('tf', 'npu'). + upsample_method: str, method of resize operator. Possible choice in + ('bilinear', 'bicubic'). + align_corners: boolean, used in resize. Whether to align corners during + resize. + """ + def __init__(self, num_feat=64, num_conv_groups=1, deformable_groups=1, + dcn_impl='npu', upsample_method='bilinear', align_corners=True): + self.mid_channels = num_feat + self.num_deform_groups = deformable_groups + self.num_groups = num_conv_groups + self.upsample_method = upsample_method + self.dcn_impl = dcn_impl + self.align_corners = align_corners + + def __call__(self, neighbor_feats, ref_feats, + act_cfg=dict(type='LeakyRelu', alpha=0.1), + name='pcd_align'): + """Forward pass of PCD module. + + Args: + neighbor_feats: list[tensor], the multi-scale feature maps of a + single neighbor frame. + ref_feats: list[tensor], the multi-scale feature maps of the center + frame. + act_cfg: dict, specify the activation `type` and other parameters. + name: str, variable scope name. + + Returns: + tensor, aligned multi-frame features. + """ + with tf.variable_scope(name, reuse=tf.AUTO_REUSE): + # The number of pyramid levels is 3. + assert len(neighbor_feats) == 3 and len(ref_feats) == 3, ( + 'The length of neighbor_feats and ref_feats must be both 3, ' + 'but got {} and {}'.format(len(neighbor_feats), len(ref_feats))) + + # Pyramids + upsampled_offset, upsampled_feat = None, None + for i in range(3, 0, -1): + with tf.variable_scope('level{}'.format(i)): + offset = tf.concat([neighbor_feats[i - 1], ref_feats[i - 1]], axis=-1) + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='offset_conv1')(offset) + if i == 3: + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='offset_conv2')(offset) + else: + offset = tf.concat([offset, upsampled_offset], axis=-1) + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='offset_conv2')(offset) + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='offset_conv3')(offset) + + feat = DCNPack(self.mid_channels, kernel_size=[3, 3], padding='same', + num_deform_groups=self.num_deform_groups, num_groups=self.num_groups, + name='dcn_l{}'.format(i), impl=self.dcn_impl, + )(neighbor_feats[i - 1], offset) + if i == 3: + feat = ActLayer(act_cfg)(feat) + else: + feat = tf.concat([feat, upsampled_feat], axis=-1) + feat = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg if i == 2 else None, + name='feat_conv')(feat) + + if i > 1: + # upsample offset and features + upsampled_offset = resize( + offset, size=[offset.shape[1] * 2, offset.shape[2] * 2], align_corners=self.align_corners, + name='upsample_offset{}'.format(i), method=self.upsample_method) + upsampled_offset = upsampled_offset * 2 + upsampled_feat = resize( + feat, size=[feat.shape[1] * 2, feat.shape[2] * 2], align_corners=self.align_corners, + name='upsample_feat{}'.format(i), method=self.upsample_method) + + # Cascading + offset = tf.concat([feat, ref_feats[0]], axis=-1) + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='cas_offset_conv1')(offset) + offset = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='cas_offset_conv2')(offset) + feat = DCNPack(self.mid_channels, kernel_size=[3, 3], padding='same', + num_deform_groups=self.num_deform_groups, name='dcn_cas', + impl=self.dcn_impl)(feat, offset) + feat = ActLayer(act_cfg)(feat) + + return feat + + +class PCWoDCN(object): + """ + A verbose pyramid and cascade module. + + Args: + num_feat: int, number of channels multiplier in the intermediate layers. + upsample_method: str, method of resize operator. Possible choice in + ('bilinear', 'bicubic'). + align_corners: boolean, used in resize. Whether to align corners during + resize. + """ + + def __init__(self, num_feat=64, upsample_method='bilinear', + align_corners=True): + self.mid_channels = num_feat + self.upsample_method = upsample_method + self.align_corners = align_corners + + def __call__(self, neighbor_feats, ref_feats, + act_cfg=dict(type='LeakyRelu', alpha=0.1), + name='pcd_align'): + """Forward pass of PCD module. + + Args: + neighbor_feats: list[tensor], the multi-scale feature maps of a + single neighbor frame. + ref_feats: list[tensor], the multi-scale feature maps of the center + frame. + act_cfg: dict, specify the activation `type` and other parameters. + name: str, variable scope name. + + Returns: + tensor, aligned multi-frame features. + """ + with tf.variable_scope(name, reuse=tf.AUTO_REUSE): + # The number of pyramid levels is 3. + assert len(neighbor_feats) == 3 and len(ref_feats) == 3, ( + 'The length of neighbor_feats and ref_feats must be both 3, ' + 'but got {} and {}'.format(len(neighbor_feats), len(ref_feats))) + + # Pyramids + upsampled_offset, upsampled_feat = None, None + for i in range(3, 0, -1): + with tf.variable_scope('level{}'.format(i)): + feat = Conv2DNormAct(self.mid_channels, kernel_size=[3, 3], + padding='same', name='pc_conv{}'.format(i))(neighbor_feats[i - 1]) + if i == 3: + feat = ActLayer(act_cfg)(feat) + else: + feat = tf.concat([feat, upsampled_feat], axis=-1) + feat = Conv2DNormAct(self.mid_channels, + act_cfg=act_cfg if i == 2 else None, + name='feat_conv')(feat) + + if i > 1: + upsampled_feat = resize( + feat, size=[feat.shape[1] * 2, feat.shape[2] * 2], + align_corners=self.align_corners, + name='upsample_feat{}'.format(i), + method=self.upsample_method) + + # Cascading + feat = Conv2DNormAct(self.mid_channels, kernel_size=[3, 3], + padding='same', name='dcn_cas')(feat) + feat = ActLayer(act_cfg)(feat) + + return feat + + +class TSAFusion(object): + """Fusiong of temporal and spatial attention. + + Args: + num_frames: int, number of input frames. + num_feat: int, multiplier of the filters number in the middle layers. + upsample_method: str, resize method. Possible choices in + ('bilinear', 'bicubic'). + align_corners: boolean, whether to align with corners when resize. + """ + def __init__(self, num_frames, num_feat, upsample_method='bilinear', + align_corners=True): + self.num_frames = num_frames + self.num_feat = num_feat + self.upsample_method = upsample_method + self.align_corners = align_corners + + def __call__(self, aligned_feat, act_cfg=dict(type='LeakyRelu', alpha=0.1)): + """Forward pass. + + Args: + aligned_feat: tensor + act_cfg: dict, specify the activation `type` and other parameters. + + Returns: + tensor, aggregated multi-frame features. + """ + with tf.variable_scope('tsa_fusion', reuse=tf.AUTO_REUSE): + # temporal attention + embedding_ref = Conv2D(self.num_feat, name='temporal_attn1')(aligned_feat[self.num_frames//2]) + + # corr_l = [] # correlation list + aligned_feat_list = [] + for i in range(self.num_frames): + emb = Conv2D(self.num_feat, name='temporal_attn2')(aligned_feat[i]) + emb = tf.cast(emb, tf.float32) + corr = tf.reduce_sum(emb * embedding_ref, axis=-1, keep_dims=True) # (n, h, w, 1) + # corr_l.append(corr) + + corr_prob = tf.nn.sigmoid(corr) + aligned_feat_list.append(corr_prob * aligned_feat[i]) + aligned_feat = tf.concat(aligned_feat_list, axis=-1) # (n, h, w, t*c) + feat = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), act_cfg=act_cfg, name='feat_fusion')(aligned_feat) + + # spatial attention + attn = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), act_cfg=act_cfg, name='spatial_attn1')(aligned_feat) + attn_max = tf.nn.max_pool2d(attn, 3, 2, 'SAME') + attn_avg = tf.nn.avg_pool(attn, 3, 2, 'SAME') + attn = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), + act_cfg=act_cfg, name='spatial_attn2')(tf.concat([attn_max, attn_avg], axis=-1)) + # pyramid levels + attn_level = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), act_cfg=act_cfg, name='spatial_attn_l1')(attn) + attn_max = tf.nn.max_pool2d(attn_level, 3, 2, 'SAME') + attn_avg = tf.nn.avg_pool(attn_level, 3, 2, 'SAME') + attn_level = Conv2DNormAct(self.num_feat, act_cfg=act_cfg, name='spatial_attn_l2')\ + (tf.concat([attn_max, attn_avg], axis=-1)) + attn_level = Conv2DNormAct(self.num_feat, act_cfg=act_cfg, name='spatial_attn_l3')(attn_level) + + attn_level = resize( + attn_level, size=[attn_level.shape[1] * 2, attn_level.shape[2] * 2], + align_corners=self.align_corners, + name='upsample1', method=self.upsample_method) + + attn = Conv2DNormAct(self.num_feat, act_cfg=act_cfg, name='spatial_attn3')(attn) + attn_level + attn = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), act_cfg=act_cfg, name='spatial_attn4')(attn) + + attn = resize( + attn, size=[attn.shape[1] * 2, attn.shape[2] * 2], + align_corners=self.align_corners, + name='upsample2', method=self.upsample_method) + attn = Conv2D(self.num_feat, name='spatial_attn5')(attn) + attn = Conv2DNormAct(self.num_feat, kernel_size=(1, 1), act_cfg=act_cfg, name='spatial_attn_add1')(attn) + attn_add = Conv2D(self.num_feat, kernel_size=(1, 1), name='spatial_attn_add2')(attn) + + attn = tf.cast(attn, tf.float32) + attn = tf.nn.sigmoid(attn) + + feat = tf.cast(feat, tf.float32) + attn_add = tf.cast(attn_add, tf.float32) + + # after initialization, * 2 makes (attn * 2) to be close to 1. + feat = feat * attn * 2 + attn_add + return feat diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/res_block.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/res_block.py new file mode 100644 index 0000000000000000000000000000000000000000..7d4870f6dc9aaf3122373bda031545f1f4ba64c6 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/res_block.py @@ -0,0 +1,145 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import tensorflow as tf +from src.layers import Conv2D, Conv3D, ActLayer, NormLayer +from src.utils.utils import to_pair + +from .conv_module import Conv2DNormAct, Conv3DNormAct + + +class ResBlock(object): + """A ResBlock class, consists of several conv blocks with bn. + + Args: + num_blocks: int, number of conv blocks. + mid_channels: int, number of the channels in the conv layers. + res_scale: float, a scalar that scale the residual. + act_cfg: dict, specify the activation `type` and other parameters. + norm_cfg: dict, specify the normalization `type` and other parameters. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + trainable: boolean, whether in training phase. If True, the buffers will + be add to UPDATE_OPS and update. + name: str, variable scope name. + """ + def __init__(self, num_blocks, mid_channels, res_scale=1.0, + act_cfg=dict(type='ReLU'), + norm_cfg=dict(type='bn'), + use_spectral_norm=False, + trainable=True, name='ResBlock'): + self.num_blocks = num_blocks + self.output_channel = mid_channels + self.res_scale = res_scale + self.name = name + self.trainable = trainable + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + self.use_spectral_norm = use_spectral_norm + + def shortcut_func(self, x): + """Shortcut path. + + May use a conv layer to change the number of channels. + """ + c_in = x.get_shape().as_list() + if c_in[-1] == self.output_channel: + return x + else: + return Conv2D(self.output_channel, + scale=self.scale, + name='conv_shortcut', + use_spectral_norm=self.use_spectral_norm)(x) + + def build_block(self, x, index): + """Build a basic conv block. + """ + identity = self.shortcut_func(x) + + out = Conv2DNormAct(self.output_channel, scale=scales[0], + act_cfg=self.act_cfg, norm_cfg=self.norm_cfg, + use_spectral_norm=self.use_spectral_norm, + name='conv{}a'.format(idx))(x) + out = Conv2DNormAct(self.output_channel, scale=scales[1], + norm_cfg=self.norm_cfg, + use_spectral_norm=self.use_spectral_norm, + name='conv{}b'.format(idx))(out) + + return identity + out * self.res_scale + + def __call__(self, x): + with tf.variable_scope(self.name) as scope: + for i in range(self.num_blocks): + x = self.build_block(x, i + 1) + return x + + +class ResBlockNoBN(object): + """A ResBlock class, consists of several conv blocks without bn. + + Args: + num_blocks: int, number of conv blocks. + mid_channels: int, number of the channels in the conv layers. + res_scale: float, a scalar that scale the residual. + act_cfg: dict, specify the activation `type` and other parameters. + norm_cfg: dict, specify the normalization `type` and other parameters. + use_spectral_norm: boolean, whether to use specatral normalization. + Default False. + trainable: boolean, whether in training phase. If True, the buffers will + be add to UPDATE_OPS and update. + name: str, variable scope name. + """ + def __init__(self, num_blocks, mid_channels, res_scale=1.0, + act_cfg=dict(type='ReLU'), dilation=1, + use_spectral_norm=False, trainable=True, + name='ResBlockNoBN'): + self.num_blocks = num_blocks + self.mid_channels = mid_channels + self.res_scale = res_scale + self.name = name + self.trainable = trainable + self.act_cfg = act_cfg + self.dilation = (dilation, dilation) + self.use_spectral_norm = use_spectral_norm + + def shortcut_func(self, x): + """Shortcut path. May use a conv layer to change the number of channels. + """ + c_in = x.get_shape().as_list() + if c_in[-1] == self.output_channel: + return x + else: + return Conv2D(self.output_channel, + scale=self.scale, + name='conv_shortcut', + use_spectral_norm=self.use_spectral_norm)(x) + + def build_block(self, x, idx): + """Build a basic conv block. + """ + out = Conv2D(self.mid_channels, + use_spectral_norm=self.use_spectral_norm, + name='conv{}a'.format(idx))(x) + out = ActLayer(self.act_cfg)(out) + out = Conv2D(self.mid_channels, + use_spectral_norm=self.use_spectral_norm, + name='conv{}b'.format(idx))(out) + return x + out * self.res_scale + + def __call__(self, x): + with tf.variable_scope(self.name) as scope: + for i in range(self.num_blocks): + x = self.build_block(x, i + 1) + return x diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/spade.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/spade.py new file mode 100644 index 0000000000000000000000000000000000000000..6a8d90be5193954a2d339c6353523993e2f8a929 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/modules/spade.py @@ -0,0 +1,150 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tensorflow as tf + +from src.layers import Conv2D, NormLayer, ActLayer + + +class SPADE(object): + """SPatially-Adaptive (DE)normalization. + + See https://arxiv.org/pdf/1903.07291.pdf. The forward pass is borrowed from + https://github.com/NVlabs/SPADE/blob/master/models/networks/normalization.py. + + Given the input input map \mathbf{h}, and a reference feature map \mathbf{m}, + the output activation value is : + ```math + \mathbf{y} = \gamma(\mathbf{m}) * \frac{\mathbf{h} - \mu}{\sigma} + \beta(\mathbf{m}) + ``` + where \gamma and \beta are both functions of \mathbf{m}. + + Args: + num_filters: int, number of filters of the output tensor. + kernel_size: int or list[int], kernel size of the conv layers. + num_hidden: int, number of filters in the middle layers which compute the + gamma and beta for normalization. + training: boolean, indicating whether in training phase or not. + norm_type: str, the type of normalization. + """ + def __init__(self, num_filters, kernel_size=(3,3), + num_hidden = 128, training=False, norm_type='in', + return_all=False, name='spade', + ver='v2'): + self.num_filters = num_filters + self.kernel_size = kernel_size + self.training = training + self.norm_type = norm_type + + + def spade(x, ref_feat, norm_nc, kernel_size=(3,3), name='spade', + nhidden = 128, training=False, norm_type='in', vis=False, + ver='v2'): + with tf.variable_scope(name): + # Part 1. generate parameter-free normalized activations + normalized = NormLayer(norm_type=self.norm_type, center=False, + scale=False, is_train=training)(x) + + # Part 2. produce scaling and bias conditioned on reference map + shape_x = x.get_shape().as_list() + shape_label = ref_feat.get_shape().as_list() + ref_feat = tf.image.resize_images(ref_feat, (shape_x[1], shape_x[2]), + method=tf.image.ResizeMethod.BILINEAR, + align_corners=True) + + if ver == 'v1': + actv = Conv2D(nhidden, kernel_size=kernel_size, padding='SAME', + strides=(1, 1), use_bias=True, trainable=True, + name='mlp_shared')(ref_feat) + actv = tf.nn.relu(actv) + else: + x_trans = Conv2D(shape_label[-1], kernel_size=kernel_size, + padding='SAME', strides=(1, 1), use_bias=True, + trainable=True, name='mlp_trans')(x) + actv = tf.nn.relu(ref_feat * x_trans) + actv = Conv2D(nhidden, kernel_size=kernel_size, padding='SAME', + strides=(1, 1), use_bias=True, trainable=True, + name='mlp_shared')(actv) + actv = tf.nn.relu(actv) + + gamma = Conv2D(norm_nc, kernel_size=kernel_size, padding='SAME', + strides=(1, 1), use_bias=True, trainable=True, + name='mlp_gamma')(actv) + beta = Conv2D(norm_nc, kernel_size=kernel_size, padding='SAME', + strides=(1, 1), use_bias=True, trainable=True, + name='mlp_beta')(actv) + + # apply scale and bias + out = normalized * (1 + gamma) + beta + if vis: + return out, gamma, beta + return out + + +class SPADEResBlock: + """ResBlock based on SPatially-Adaptive (DE)normalization. + + See https://arxiv.org/pdf/1903.07291.pdf. The forward pass is borrowed from + https://github.com/NVlabs/SPADE/blob/master/models/networks/normalization.py. + + Given the input input map \mathbf{h}, and a reference feature map \mathbf{m}, + the output activation value is : + ```math + \mathbf{y} = \gamma(\mathbf{m}) * \frac{\mathbf{h} - \mu}{\sigma} + \beta(\mathbf{m}) + ``` + where \gamma and \beta are both functions of \mathbf{m}. + + Args: + num_filters: int, number of filters of the output tensor. + kernel_size: int or list[int], kernel size of the conv layers. + nhidden: int, number of filters in the middle layers which compute the + gamma and beta for normalization. + training: boolean, indicating whether in training phase or not. + norm_type: str, the type of normalization. + """ + def __init__(self, fin, fout, trainable=True, spectral_norm=False, with_spade=True, name='spade_res_block'): + self.learned_short = fin != fout + self.fmiddle = min(fin, fout) + self.trainable = trainable + self.fin = fin + self.fout = fout + self.with_spade = with_spade + self.scope = name + + def __call__(self, x, ref): + # TODO: extend to[NTHWC] 5D input + with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE): + feat = x + if self.with_spade: + feat = spade(feat, ref, self.fin, kernel_size=(3, 3), name='spade1', training=self.trainable) + feat = Conv2D(self.fmiddle, kernel_size=(3, 3), strides=(1, 1), + padding='SAME', trainable=self.trainable, name='conv1')(feat) + feat = ActLayer(dict(type='leakyrelu', alpha=0.2))(feat) + + if self.with_spade: + feat = spade(feat, ref, self.fmiddle, kernel_size=(3, 3), name='spade2', training=self.trainable) + feat = Conv2D(self.fout, kernel_size=(3, 3), strides=(1, 1), + padding='SAME', trainable=self.trainable, name='conv2')(feat) + feat = ActLayer(dict(type='leakyrelu', alpha=0.2))(feat) + + short_cut = x + if self.learned_short: + if self.with_spade: + short_cut = spade(short_cut, ref, self.fin, kernel_size=(3, 3), + name='spade_shortcut', training=self.trainable) + short_cut = Conv2D(self.fout, kernel_size=(3, 3), strides=(1, 1), + padding='SAME', trainable=self.trainable, name='conv3')(short_cut) + + return short_cut + feat diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9543d0650499b3d49f5b743a3200dfcda3fd7a56 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/__init__.py @@ -0,0 +1,42 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import inspect +import importlib + +from .base_model import Base +from .register import registry + +model_dir = os.path.dirname(os.path.realpath(__file__)) +model_file = os.listdir(model_dir) + +# Automatically import all the defined class in the files under src.networks +__all__ = ['registry'] +for model in model_file: + module_name = model.split('.')[0] + if module_name in ['register', 'base_model', '__init__', 'VSR']: + continue + mod = importlib.import_module(f'.{module_name}', 'src.networks') + for name, obj in inspect.getmembers(mod, inspect.isclass): + if issubclass(obj, Base) and module_name not in __all__ and obj.__module__ == module_name: + __all__.append(name) + + +def build_network(cfg): + network = registry[cfg.model.name](cfg=cfg) + return network + + +__all__.append('build_network') diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/base_model.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..521eefbaa7301bd34376a3ef1eca8d73836fefbf --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/base_model.py @@ -0,0 +1,416 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import glob +import os + +import numpy as np +import tensorflow as tf +from src.losses.losses import get_loss +from src.losses.modules.adversarial import build_adversarial_loss +from src.losses.modules.perceptual import build_content_style_loss +from src.losses.modules.perceptual import build_perceptual_loss +from src.ops.edge import get_edges +from src.networks.register import RegisteredModel +from src.runner.common import name_space +from src.utils.logger import logger +from src.utils.utils import convert_to_dict + + +class Base(object, metaclass=RegisteredModel): + """Base class for all the video processing models. + + Attributes: + cfg: yacs CfgNode. Global configuration. + model_name: str, model name. + scale: int, output scale w.r.t input, e.g. EDVR output is 4x the scale + of the input. + num_net_input_frames: int, the total number of input frames to the + network. For EDVR, the default is 5. See src.utils.default. + num_net_output_frames: int, the number of output result frames by the + network. Default is 1. Also see src.utils.default. + num_data_lq_frames: int, the total number of lq frames in each case + generated by the datasets. Typically it can be the same with + ``num_net_input_frames``. But if one is to use temporal supervision + and there will be multiple output frames. In this case, see the + example below. + num_data_gt_frames: int, the total number of the hq frames in each case + produced by the dataset. Typically it is the same with + ``num_net_output_frames``. + input_color_space: int, the color space of the input frames. Default to + ``rgb``. + num_in_channels: int, number of the channels of the input frames. + Corresponds to ``input_color_space``. + is_train: boolean, whether the model is in training phase. Determined by + the ``cfg.mode``. + generative_model_scope: str, top scope name for the tensorflow graph. + Default value is 'G'. + output_dir: str, path to dump the summary. + + Example: + The most confusion configuration may be the ``num_**_frames``. Here is + an example of the basic scenario (multi-input frames and single center + output frame): + + Frame 1 -----> |---------| + Frame 2 -----> | | + Frame 3 -----> | network | -----> Frame 3' -----> Loss + Frame 4 -----> | | + Frame 5 -----> |---------| + + In this case, we have ``num_net_input_frames=5`` and + ``num_net_output_frames=1``. Also, since there is no temporal + supervision for the outputs, ``num_data_lq_frames=num_net_input_frames=5`` + and ``num_data_gt_frames=num_net_output_frames=1``, which is the EDVR + case. + + A second case is multi-input frames and multi-output frames: + + Frame 1 -----> |---------| -----> Frame 1' -----> |------| + Frame 2 -----> | | -----> Frame 2' -----> | | + Frame 3 -----> | network | -----> Frame 3' -----> | loss | -----> Loss + Frame 4 -----> | | -----> Frame 4' -----> | | + Frame 5 -----> |---------| -----> Frame 5' -----> |------| + + In this case, `num_data_lq_frames=num_net_input_frames=5`` and + ``num_data_gt_frames=num_net_output_frames=5``. + + Third case, multi-input frames, single center output frame and with + temporal supervision: + + Frame 1 -----> |---------| + Frame 2 -----> | | + Frame 3 -----> | | -----> Frame 3' -----> |------| + Frame 4 -----> | | -----> Frame 4' -----> | | + Frame 5 -----> | network | -----> Frame 5' -----> | loss | -----> Loss + Frame 6 -----> | | -----> Frame 6' -----> | | + Frame 7 -----> | | -----> Frame 7' -----> |------| + Frame 8 -----> | | + Frame 9 -----> |---------| + + In the 3rd case, ``num_data_lq_frames=9``, ``num_net_input_frames=5``, + ``num_data_gt_frames=5``, ``num_net_output_frames=1``, which satisfies: + + ``num_data_lq_frames = num_data_gt_frames + num_net_input_frames - num_net_output_frames`` + + During inference, the network is still multi-input frames and + single center output frame (same with the 1st case), while temporal + loss can be applied to the network during training. + + Args: + cfg: Configuration loaded from the *.yaml file. + """ + def __init__(self, cfg): + self.model_name = cfg.model.name + self.scale = cfg.model.scale + self.num_net_input_frames = cfg.model.num_net_input_frames + self.num_net_output_frames = cfg.model.num_net_output_frames + self.num_data_lq_frames = cfg.data.num_data_lq_frames + self.num_data_gt_frames = cfg.data.num_data_gt_frames + + self.input_color_space = cfg.data.color_space + self.num_in_channels = 3 + self.is_train = cfg.mode == 'train' + + self.cfg = cfg + self.lq = None # input low-quality + self.gt = None # groundtruth + self.hq = None # output high-quality + self.generative_model_scope = cfg.model.scope + self.output_dir = cfg.train.output_dir + + @property + def output_node(self): + """Obtain the default output result of the network + + Return: + A 4D [N, H, W, C] or 5D [N, T, H, W, C] tensorflow tensor. + """ + return self.hq + + @property + def input_node(self): + """Obtain the default input node of the network. + + Return: + A 4D [N, H, W, C] or 5D [N, T, H, W, C] tensor. + """ + return self.lq + + def parameters(self, scope=''): + """Obtain the trainable parameters given the scope. + + Args: + scope: str, the parameter scope. If is empty, return all the + parameters in the top scope ``self.generative_model_scope``. + + Return: + A list of parameter tensor in the given scope. + """ + if scope == '': + return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, + self.generative_model_scope) + else: + return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, + scope=scope) + + def calculate_content_loss(self, gt, hq): + """Compute the pixel-wise content loss. The loss will be added to + ``name_space.GeneratorLoss``. + + Args: + gt: tensor, predictions of the network. + hq: tensor, ground-truth of the training. + + """ + eps = self.cfg.loss.content.loss_margin + reduction = self.cfg.loss.content.loss_reduction + + loss = get_loss(self.cfg.loss.content.loss_type, gt, hq, eps=eps) + # reduction strategy is adjusted to ascend platform, to keep the + # gradient neither too large (in case of overflow) nor too small + # (in case gradient vanishing because of the CUBE operator). + if reduction == 'mean': + loss = tf.reduce_sum(tf.reduce_mean(loss, axis=[1, 2])) + elif reduction == 'sum': + loss = tf.reduce_mean(tf.reduce_sum(loss, axis=[1, 2, 3])) + else: + raise NotImplementedError + name_space.add_to_collection( + name_space.GeneratorLoss, + f'content {self.cfg.loss.content.loss_type}', + loss) + return loss + + def calculate_perceptual_loss(self, gt, hq): + """Compute perceptual loss. The loss will be added to + ``name_space.GeneratorLoss``. + + Args: + gt: tensor, predictions of the network. + hq: tensor, ground-truth of the training. + """ + # perceptual loss will be weighted in build_perceptual_loss + perceptual_config = convert_to_dict(self.cfg.loss.perceptual, []) + perceptual_loss = build_perceptual_loss(gt, hq, perceptual_config) + perceptual_loss = perceptual_loss * self.cfg.loss.perceptual.loss_weight + + name_space.add_to_collection( + name_space.GeneratorLoss, + 'perceptual', + perceptual_loss) + return perceptual_loss + + def calculate_border_loss(self, gt, hq): + """Compute edge loss. The loss will be added to ``name_space.GeneratorLoss``. + + Args: + gt: tensor, predictions of the network. + hq: tensor, ground-truth of the training. + """ + + hq_edge = get_edges(gt, method=self.cfg.loss.edge.method) + gt_edge = get_edges(hq, method=self.cfg.loss.edge.method) + edge_loss = get_loss(self.cfg.loss.content.loss_type, hq_edge, gt_edge) + edge_loss = tf.reduce_sum(tf.reduce_mean(edge_loss, axis=[1, 2])) + edge_loss = edge_loss * self.cfg.loss.edge.loss_weight + + name_space.add_to_collection( + name_space.GeneratorLoss, + 'edge', + edge_loss) + + return edge_loss + + def calculate_content_style_loss(self, gt, hq): + """Compute style loss. The loss will be added to + ``name_space.GeneratorLoss``. + + Args: + gt: tensor, predictions of the network. + hq: tensor, ground-truth of the training. + """ + # perceptual loss will be weighted in build_perceptual_loss + perceptual_config = convert_to_dict(self.cfg.loss.perceptual, []) + perceptual_loss = build_content_style_loss(gt, hq, perceptual_config) + perceptual_loss = perceptual_loss * self.cfg.loss.perceptual.loss_weight + + name_space.add_to_collection( + name_space.GeneratorLoss, + 'style', + perceptual_loss) + return perceptual_loss + + def calculate_adversarial_loss(self, gt, hq): + """Compute adversarial loss. The loss will be added to + ``name_space.GeneratorLoss``. + + Args: + gt: tensor, predictions of the network. + hq: tensor, ground-truth of the training. + """ + + # discriminator loss will be weighted and added to name_space in build_ + # adversarial_loss + _ = build_adversarial_loss(gt, hq, self.cfg) + + def build_losses(self, *args, **kwargs): + """Compute all the losses, including pixel-wise content loss (required), + perceptual and perceptual style loss (if loss_weight > 0), edge loss ( + if loss_weight > 0), and adversarial loss (if loss_weight > 0). + """ + # all losses should be added to name_space collections + gt = tf.cast(self.gt, tf.float32) + hq = tf.cast(self.hq, tf.float32) + + hq = tf.reshape(hq, gt.shape) + + _ = self.calculate_content_loss(gt, hq) + if self.cfg.loss.edge.loss_weight > 0: + _ = self.calculate_border_loss(gt, hq) + + if self.cfg.loss.perceptual.loss_weight > 0: + _ = self.calculate_perceptual_loss(gt, hq) + + if self.cfg.loss.adversarial.loss_weight > 0: + self.calculate_adversarial_loss(gt, hq) + + def build_metrics(self, *args, **kwargs): + # Reserved for evaluation. + pass + + def prepare_placeholder(self, size): + """Prepare placeholder for **inference** phase, given the input size. + + Args: + size: tuple/list, including [batchsize, (h, w)] + + Returns: + None + """ + # Note: this function is only for non-train mode + if self.lq is not None: + pass + b, spatial = size + + if self.cfg.model.input_format_dimension == 5: + if b is None or b < 0: + b = None + self.lq = tf.placeholder( + tf.float32, + shape=[b, + self.num_net_input_frames, + *spatial, + self.num_in_channels], + name='L_input') + elif self.cfg.model.input_format_dimension == 4: + # Mainly used for offline model inference for speeding up in the + # AIPP on Ascend 310 + if b is None or b < 0: + self.lq = tf.placeholder( + tf.float32, + shape=[None, + *spatial, + self.num_in_channels], + name='L_input') + else: + self.lq = tf.placeholder( + tf.float32, + shape=[b*self.num_net_input_frames, + *spatial, + self.num_in_channels], + name='L_input') + else: + raise ValueError(f'Input format dimension only support 4 or 5, ' + f'but got {self.cfg.model.input_format_dimension}') + + def build_graph(self, dataloader=None, input_size=None, *args, **kwargs): + """Build tensorflow graph, network building, loss calculation, metrics + calculation, etc. + + Args: + dataloader: tf.Datasets, in training or evaluation phase. + input_size: tuple or list, [b, (h, w)], for inference and freeze + phase. + + Returns: + None + """ + if self.cfg.mode in ['freeze', 'inference']: + assert input_size is not None + self.prepare_placeholder(input_size) + elif self.cfg.mode in ['train', 'eval']: + assert dataloader is not None + self.lq, self.gt = dataloader + else: + raise NotImplementedError + + # Forward propagation + self.hq = self.build_generator(self.lq) + + if self.cfg.mode == 'train': + name_space.add_to_collection(name_space.Summary, 'hq', self.gt) + self.build_losses() + elif self.cfg.mode == 'eval': + name_space.add_to_collection(name_space.Summary, 'hq', self.gt) + self.build_metrics() + + if self.cfg.mode in ['eval', 'inference', 'freeze'] and \ + self.cfg.model.convert_output_to_uint8: + self.hq = tf.cast( + tf.round( + tf.clip_by_value( + self.hq * 255., + 0., + 255.)), + tf.uint8 + ) + + # Setup the output node for inference without network file. + self.hq = tf.identity(self.hq, name='HQ_output') + + name_space.add_to_collections((name_space.Summary, + name_space.InputField), + 'lq', + self.lq) + name_space.add_to_collections((name_space.Summary, + name_space.OutputField), + 'gt', + self.hq) + + def build_generator(self, lq, *args, **kwargs): + """Building the forward network. This is the interface every derived + network class should implement. + + Args: + lq: tensor, input frames. 4D or 5D tensor. + + Returns: + None + """ + raise NotImplementedError + + def dump_summary(self, step, summary_dict): + """Function to visualize the intermediate training status. + In case, tensorboard is not available, one can use this function to + check the intermediate training or evaluation results. + + Args: + step: int, training step + summary_dict: dict, contains all the results. + + Returns: + None + """ + pass diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/edvr.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/edvr.py new file mode 100644 index 0000000000000000000000000000000000000000..135d7f14ca7d1b7ee51f8c85a526a719dc128965 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/edvr.py @@ -0,0 +1,170 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import os + +import numpy as np +import tensorflow as tf + +from src.layers import Conv2D, ActLayer +from src.modules import Conv2DNormAct, ResBlockNoBN +from src.ops import depth_to_space, resize, split +from src.modules.edvr_submodules import PCDAlign, TSAFusion, PCWoDCN +from src.networks.base_model import Base +from src.runner.common import name_space +from src.utils.file_io import imwrite + + +class EDVR(Base): + """EDVR video super-resolution network. + + Args: + cfg: yacs node. EDVR configures configured in edvr_config.py. + """ + def __init__(self, cfg): + super().__init__(cfg) + self.with_tsa = cfg.edvr.with_tsa + self.mid_channels = cfg.edvr.mid_channels + self.num_groups = cfg.edvr.num_groups + self.num_deform_groups = cfg.edvr.num_deform_groups + self.num_blocks_extraction = cfg.edvr.num_blocks_extraction + self.num_blocks_reconstruction = cfg.edvr.num_blocks_reconstruction + + if cfg.edvr.use_dcn: + self.align_module = PCDAlign(self.mid_channels, 1, self.num_deform_groups, + dcn_impl='npu', + upsample_method=self.cfg.edvr.upsampling, + align_corners=self.cfg.edvr.align_corners) + else: + self.align_module = PCWoDCN(self.mid_channels, + upsample_method=self.cfg.edvr.upsampling, + align_corners=self.cfg.edvr.align_corners) + + self.tsa_fusion_module = TSAFusion(self.num_net_input_frames, + self.mid_channels, + self.cfg.edvr.upsampling, + align_corners=self.cfg.edvr.align_corners) + + def feature_extraction(self, x, act_cfg=dict(type='LeakyRelu', alpha=0.1)): + # extract LR features + with tf.variable_scope('extraction', reuse=tf.AUTO_REUSE): + # L1 + # l1_feat = tf.reshape(x, [-1, x.shape[2], x.shape[3], x.shape[4]]) + l1_feat = Conv2D(self.mid_channels, name='conv_first')(x) + l1_feat = ActLayer(act_cfg)(l1_feat) + l1_feat = ResBlockNoBN(num_blocks=self.num_blocks_extraction, mid_channels=self.mid_channels)(l1_feat) + # L2 + l2_feat = Conv2DNormAct(self.mid_channels, strides=[2, 2], act_cfg=act_cfg, name='feat_l2_conv1')(l1_feat) + l2_feat = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='feat_l2_conv2')(l2_feat) + # L3 + l3_feat = Conv2DNormAct(self.mid_channels, strides=[2, 2], act_cfg=act_cfg, name='feat_l3_conv1')(l2_feat) + l3_feat = Conv2DNormAct(self.mid_channels, act_cfg=act_cfg, name='feat_l3_conv2')(l3_feat) + + return l1_feat, l2_feat, l3_feat + + def reconstruction(self, feat, x_center, act_cfg=dict(type='LeakyRelu', alpha=0.1)): + # reconstruction + out_channel = x_center.get_shape().as_list()[-1] + with tf.variable_scope('reconstruction', reuse=tf.AUTO_REUSE): + out = ResBlockNoBN(num_blocks=self.num_blocks_reconstruction, mid_channels=self.mid_channels)(feat) + level_upsample = int(math.log2(self.scale)) + for i in range(level_upsample): + out = Conv2D(self.mid_channels * 2 ** 2, name=f'upsample{i+1}')(out) + out = depth_to_space(out, 2) + out = Conv2D(self.mid_channels, name='conv_hr')(out) + out = ActLayer(act_cfg)(out) + out = Conv2D(out_channel, name='conv_last')(out) + + base = resize( + x_center, + size=[x_center.shape[1] * self.scale, x_center.shape[2] * self.scale], + align_corners=self.cfg.edvr.align_corners, + name='img_upsample', method=self.cfg.edvr.upsampling) + base = tf.cast(base, tf.float32) + out = tf.cast(out, tf.float32) + self.residual = out + out += base + + return out + + def build_generator(self, x): + # shape of x: [B,T_in,H,W,C] + with tf.variable_scope(self.generative_model_scope, reuse=tf.AUTO_REUSE): + if self.cfg.model.input_format_dimension == 4: + x_shape = x.get_shape().as_list() + x = tf.reshape(x, [-1, self.num_net_input_frames, *x_shape[1:]]) + + x_list = split(x, self.num_net_input_frames, axis=1, keep_dims=False) + x_center = x_list[self.num_net_input_frames//2] + + l1_feat_list = [] + l2_feat_list = [] + l3_feat_list = [] + for f in range(self.num_net_input_frames): + l1_feat, l2_feat, l3_feat = self.feature_extraction(x_list[f]) + l1_feat_list.append(l1_feat) + l2_feat_list.append(l2_feat) + l3_feat_list.append(l3_feat) + + ref_feats = [ + l1_feat_list[self.num_net_input_frames//2], + l2_feat_list[self.num_net_input_frames//2], + l3_feat_list[self.num_net_input_frames//2] + ] + aligned_feat = [] + + for i in range(self.num_net_input_frames): + neighbor_feats = [ + l1_feat_list[i], + l2_feat_list[i], + l3_feat_list[i] + ] + # aligned_feat.append(self.pcd_align(neighbor_feats, ref_feats)) + aligned_feat.append(self.align_module(neighbor_feats, ref_feats)) + + if self.with_tsa: + # feat = self.tsa_fusion(aligned_feat) + feat = self.tsa_fusion_module(aligned_feat) + else: + aligned_feat = tf.stack(aligned_feat, axis=1) # (n, t, h, w, c) + aligned_feat_shape = aligned_feat.get_shape().as_list() + last_dim = aligned_feat_shape[-1] * aligned_feat_shape[1] + aligned_feat = tf.transpose(aligned_feat, [0, 2, 3, 1, 4]) + aligned_feat = tf.reshape(aligned_feat, + [-1, aligned_feat.shape[1], aligned_feat.shape[2], last_dim]) + feat = Conv2D(self.mid_channels, kernel_size=[1, 1], name='fusion')(aligned_feat) + + # reconstruction + out = self.reconstruction(feat, x_center) + + return out + + def dump_summary(self, step, summary_dict): + # Keys of the summary dict correspond to the keys defined base_model "build_generator" + lr = summary_dict['lr'] + sr = summary_dict['sr'] + hr = summary_dict['hr'] + + os.makedirs(os.path.join(self.output_dir, 'intermediate'), exist_ok=True) + + output_file = os.path.join(self.output_dir, 'intermediate', f'step{step:06d}_lr.png') + imwrite(output_file, np.squeeze(lr[0, self.num_net_input_frames//2]), + source_color_space=self.cfg.data.color_space) + + output_file = os.path.join(self.output_dir, 'intermediate', f'step{step:06d}_hr.png') + imwrite(output_file, np.squeeze(hr[0]), source_color_space=self.cfg.data.color_space) + + output_file = os.path.join(self.output_dir, 'intermediate', f'step{step:06d}_sr.png') + imwrite(output_file, sr[0], source_color_space=self.cfg.data.color_space) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/register.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/register.py new file mode 100644 index 0000000000000000000000000000000000000000..530a60a9c75679a07b5936b1b8a3f98e05f36f21 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/networks/register.py @@ -0,0 +1,29 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +registry = {} + + +def register(cls): + registry[cls.__name__] = cls + return cls + + +class RegisteredModel(type): + """A class for model registration. + """ + def __new__(cls, clsname, bases, attrs): + newclass = super(RegisteredModel, cls).__new__(cls, clsname, bases, attrs) + register(newclass) # here is your register function + return newclass diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2d63ba3e71b68bd9b0d11278abdb5eb0808b3a8a --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .upsample import * +from .edge import * +from .slicing import * \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/edge.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/edge.py new file mode 100644 index 0000000000000000000000000000000000000000..d3af3b02c5d8b9acceb04ef0ec0cb9d0a16baf01 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/edge.py @@ -0,0 +1,110 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import tensorflow as tf +from scipy import signal +import random + + +__all__ = ['tf_gaussian_blur', 'get_edges'] + + +def gaussian_kernel(kernel_size, standard_dev): + """Returns a 2D Gaussian kernel array with side length size and a sigma of + signal. + + Args: + kernel_size: int. + standard_dev: float, scalar of the kernel width. + + Returns: + ndarray, a normalized np.ndarray of shape [kernel_size, kernel_size]. + """ + gkern1d = signal.gaussian(kernel_size, std=standard_dev).reshape(kernel_size, 1) + gkern2d = np.outer(gkern1d, gkern1d) + return (gkern2d/gkern2d.sum()) + + +def tf_gaussian_blur(x, kernel_size, standard_dev): + """Apply gaussian blur to tensor using tf interface. Only works for RGB or 3-channel + tensors. + + Args: + x: tensor, 4D. + kernel_size: int, blur kernel size. + standard_dev: float. + + Returns: + tensor, blured version of the input. + """ + gau_k = gaussian_kernel(kernel_size, standard_dev) + gau_0 = np.zeros_like(gau_k) + gau_list = np.float32( [ + [gau_k, gau_0, gau_0], + [gau_0, gau_k, gau_0], + [gau_0, gau_0, gau_k]] ) # only works for RGB images! + gau_wei = np.transpose(gau_list, [2,3,0,1]) + + fix_gkern = tf.constant(gau_wei, dtype=tf.float32, shape=[kernel_size, kernel_size, 3, 3], name='gauss_blurWeights' ) + # shape [batch_size, crop_h, crop_w, 3] + cur_data = tf.nn.conv2d(x, fix_gkern, strides=[1,1,1,1], padding="SAME", name='gauss_blur') + return cur_data + + +def get_edges(x, method='sobel', use_default=False): + """Get the edge map of a tensor x. + + Args: + x: tensor, input feature map, whose number of channels can be larger than 3. + method: str, which edge detector is used. + use_default: boolean, whether to use tensorflow default sobel edge detector. + + Returns: + tensor, the edge map of the input tensor. + """ + if method == 'sobel' and use_default: + edge = tf.image.sobel_edges(x) + output_h, output_w = tf.split(edge, 2, axis=-1) + output_h = tf.squeeze(output_h, axis=-1) + output_w = tf.squeeze(output_w, axis=-1) + edge_norm = tf.abs(output_h) * 0.5 + tf.abs(output_w) * 0.5 + elif method == 'sobel': + # Blur before apply sobel operator. + x = tf_gaussian_blur(x, 3, 1.2) + x = tf.reduce_mean(x, axis=-1, keep_dims=True) + kernel_h = [[-1, -2, -1], [0, 0, 0], [1, 2, 1]] + kernel_w = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]] + x_pad = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT') + c = x.get_shape().as_list()[-1] + conv_k_h = tf.constant(kernel_h, dtype=tf.float32, shape=(3, 3, 1, 1)) + conv_k_h = tf.tile(conv_k_h, (1, 1, c, 1)) + conv_k_w = tf.constant(kernel_w, dtype=tf.float32, shape=(3, 3, 1, 1)) + conv_k_w = tf.tile(conv_k_w, (1, 1, c, 1)) + output_h = tf.nn.depthwise_conv2d(x_pad, conv_k_h, strides=[1, 1, 1, 1], padding='VALID') + output_w = tf.nn.depthwise_conv2d(x_pad, conv_k_w, strides=[1, 1, 1, 1], padding='VALID') + edge_norm = tf.abs(output_h) * 0.5 + tf.abs(output_w) * 0.5 + elif method == 'laplacian': + # Blur before apply edge operator. + x = tf_gaussian_blur(x, 3, 1.2) + x = tf.reduce_mean(x, axis=-1, keep_dims=True) + kernel = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]] + conv_k = tf.constant(kernel, dtype=tf.float32, shape=(3, 3, 1, 1)) + x_pad = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], 'REFLECT') + output = tf.nn.depthwise_conv2d(x_pad, conv_k, strides=[1, 1, 1, 1], padding='VALID') + edge_norm = tf.abs(output) + else: + raise NotImplementedError + + return edge_norm \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/slicing.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/slicing.py new file mode 100644 index 0000000000000000000000000000000000000000..096f9e4c585697c1981fd2c78e6367ecf239ecff --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/slicing.py @@ -0,0 +1,42 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + +__all__ = ['split'] + + +def split(x, num_or_size_splits, axis=0, keep_dims=False): + """Split the tensor with possibly reduced dimension. + + Args: + x: tensor, the source tensor to split. + num_or_size_splits: int or list[int]. If is given `int`, specifying + the number of the splits; if given list[int], then the summation + of the sizes should equal to the length of the `axis` of x. + axis: int, which axis to split. + keep_dims: boolean, whether to reduce the `axis` dimension after split. + Dafault to False. + + Returns: + list[tensor] + """ + x_list = tf.split(x, num_or_size_splits, axis) + + if not keep_dims: + x_list2 = [tf.squeeze(x_, axis) for x_ in x_list] + return x_list2 + + return x_list + diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/upsample.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b32150360cd07d20e48ad1030ba5ec5fa76a6b --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/upsample.py @@ -0,0 +1,216 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + +from src.utils.utils import to_pair + +__all__ = [ + 'resize', 'depth_to_space', 'space_to_depth', + 'decimation_up', 'decimation_down' +] + + +def depth_to_space(x, scale, use_default=False, data_format='NHWC'): + """Rearrange data from depths to blocks of spatial data. + + Given a tensor of size [N, H, W, C], this operator converts the tensor + to size [N, H*scale, W*scale, C/(scale*scale)]. + + Args: + x: tensor, which has the shape [N, H, W, C] or [N, C, H, W]. + scale: int, specifying how many blocks the depths is rearrageed. Both + h and w dimension will be scaled up by this value. + use_default: boolean, use tensorflow default implementation. If False, + use a composed operator instead. Default False. + data_format: str, possible choices in ['NHWC', 'NCHW']. + + Returns: + tensor, which has the shape [N, H*scale, W*scale, C/(scale*scale)]. + """ + if use_default: + out = tf.nn.depth_to_space(x, scale, data_format=data_format) + elif data_format == 'NHWC': + b, h, w, c = x.get_shape().as_list() + c_scaled = c // (scale**2) + out = tf.reshape(x, [-1, h, w, scale, scale, c_scaled]) + out = tf.transpose(out, [0, 1, 3, 2, 4, 5]) + out = tf.reshape(out, [-1, h * scale, w * scale, c_scaled]) + elif data_format == 'NCHW': + b, c, h, w = x.get_shape().as_list() + c_scaled = c // (scale**2) + out = tf.reshape(x, [-1, scale, scale, c_scaled, h, w]) + out = tf.transpose(out, [0, 3, 4, 1, 5, 2]) + out = tf.reshape(out, [-1, c_scaled, h * scale, w * scale]) + else: + raise ValueError(f'Unknown data format `{data_format}`') + return out + + +def space_to_depth(x, scale, use_default=False, data_format='NHWC'): + """Rearrange data from blocks of spatial data to depths. + + Given a tensor of size [N, H, W, C], this operator converts the tensor + to size [N, H/scale, W/scale, C*(scale*scale)]. + + Args: + x: tensor, which has the shape [N, H, W, C] or [N, C, H, W]. + scale: int, specifying how many blocks the depths is rearrageed. Both + h and w dimension will be scaled down by this value. + use_default: boolean, use tensorflow default implementation. If False, + use a composed operator instead. Default False. + data_format: str, possible choices in ['NHWC', 'NCHW']. + + Returns: + tensor, which has the shape [N, H/scale, W/scale, C*(scale*scale)]. + """ + if use_default: + out = tf.nn.space_to_depth(x, scale, data_format=data_format) + elif data_format == 'NHWC': + b, h, w, c = x.get_shape().as_list() + c_scaled = c * (scale**2) + out = tf.reshape(x, [-1, h//scale, scale, w//scale, scale, c]) + out = tf.transpose(out, [0, 1, 3, 2, 4, 5]) + out = tf.reshape(out, [-1, h//scale, w//scale, c_scaled]) + elif data_format == 'NCHW': + b, c, h, w = x.get_shape().as_list() + c_scaled = c * (scale**2) + out = tf.reshape(x, [-1, c, h//scale, scale, w//scale, scale]) + out = tf.transpose(out, [0, 3, 5, 1, 2, 4]) + out = tf.reshape(out, [-1, c_scaled, h//scale, w//scale]) + else: + raise ValueError(f'Unknown data format `{data_format}`') + return out + + +def resize(x, size, align_corners=False, name=None, half_pixel_centers=False, method='bicubic'): + """Wrap of tensorflow resize function. + + Args: + x: tensor, which has the shape [N, H, W, C] or [N, C, H, W]. + size: list[int] of length 2, indicating the target size [H_target, W_target]. + align_corners: boolean, whether to align corners when resizing. + name: str, the name of the resize operation. + half_pixel_centers: boolean, whether use the half pixel as the center. + method: str, resize method. Possible choices in ('bicubic', 'bilinear', 'area') + + Return: + tensor, the resized version fo x which is of shape [N, H_target, W_target, C]. + """ + if method == 'bicubic': + upsampling = tf.image.resize_bicubic + elif method == 'bilinear': + upsampling = tf.image.resize_bilinear + elif method == 'area': + upsampling = tf.image.resize_area + return upsampling(x, size=size, align_corners=align_corners, name=name) + else: + raise ValueError + return upsampling(x, size=size, align_corners=align_corners, name=name, half_pixel_centers=half_pixel_centers) + + +def decimation_up(x, scale, data_format='NHWC'): + """Interpolate the tensor to target scale. + + Given a tensor of size [N, H, W, C], this operator converts the tensor + to size [N, H*scale, W*scale, C]. The interpolateed pixels will be filled + with zeros. + + For example, the entries of 2D tensor x is: + [[1, 2], + [3, 4]] + + When scale=3, the output will be: + [[1, 0, 0, 2, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0], + [3, 0, 0, 4, 0, 0], + [0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0]] + + Args: + x: tensor, which has the shape [N, H, W, C] or [N, C, H, W]. + scale: int, specifying the magnification the h and w. + data_format: str, possible choices in ['NHWC', 'NCHW']. + + Returns: + tensor, which has the shape [N, H*scale, W*scale, C]. + """ + x_shape = x.get_shape().as_list() + + scale = to_pair(scale, 2) + sh, sw = scale + + zeros = tf.zeros([*x_shape, sh*sw-1], dtype=x.dtype) + x_expand = tf.expand_dims(x, -1) + x_up = tf.concat([x_expand, zeros], axis=-1) + x_up = tf.reshape(x_up, shape=[*x_shape, sh, sw]) + if data_format == 'NCHW': + n, c, h, w = x_shape + x_up = tf.transpose(x_up, (0, 1, 2, 4, 3, 5)) + x_up = tf.reshape(x_up, [n, c, h*sh, w*sw]) + elif data_format == 'NHWC': + n, h, w, c = x_shape + x_up = tf.transpose(x_up, (0, 1, 4, 2, 5, 3)) + x_up = tf.reshape(x_up, [n, h*sh, w*sw, c]) + else: + raise ValueError + + return x_up + + +def decimation_down(x, scale, data_format='NCHW'): + """Decimation the tensor with target scale. + + Given a tensor of size [N, H, W, C], this operator converts the tensor + to size [N, H/scale, W/scale, C]. The values remained are the upper-left + corner value within each block. + + For example, the entries of 2D tensor x is: + [[ 1, 2, 3, 4], + [ 5, 6, 7, 8], + [ 9, 10, 11, 12], + [13, 14, 15, 16]] + + When scale=2, the output will be: + [[1, 3], + [9, 11]] + + Args: + x: tensor, which has the shape [N, H, W, C] or [N, C, H, W]. + scale: int, specifying the down magnification the h and w. + data_format: str, possible choices in ['NHWC', 'NCHW']. + + Returns: + tensor, which has the shape [N, H/scale, W/scale, C] + """ + x_shape = x.get_shape().as_list() + + scale = to_pair(scale, 2) + sh, sw = scale + + if data_format == 'NCHW': + b, c, h, w = x_shape + x_down = tf.reshape(x, [b, c, h//sh, sh, w//sw, sw]) + x_down = tf.slicing(x_down, (0, 0, 0, 0, 0, 0), (-1, -1, -1, 1, -1, 1)) + x_down = tf.squeeze(x_down, axis=(3, 5)) + elif data_format == 'NHWC': + b, h, w, c = x_shape + x_down = tf.reshape(x, [b, h//sh, sh, w//sw, sw, c]) + x_down = tf.slicing(x_down, (0, 0, 0, 0, 0, 0), (-1, -1, 1, -1, 1, -1)) + x_down = tf.squeeze(x_down, axis=(2, 4)) + else: + raise ValueError + + return x_down diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/weight_regularzation.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/weight_regularzation.py new file mode 100644 index 0000000000000000000000000000000000000000..cb902ba60c5a6fd2561298cb98993e3c6ecf9a8a --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/ops/weight_regularzation.py @@ -0,0 +1,53 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf + +def spectral_norm(w, iteration=1): + """Spectral normalization of kernels. + + Borrowed from https://github.com/taki0112/Spectral_Normalization-Tensorflow/blob/master/spectral_norm.py + + Args: + w: tensor, conv/linear layer kernel. + iteration: int, number of power iteration. + + Returns: + A normalized kernel tensor. + """ + w_shape = w.shape.as_list() + w = tf.reshape(w, [-1, w_shape[-1]]) + + u = tf.get_variable("spectral_norm_u", [1, w_shape[-1]], + initializer=tf.truncated_normal_initializer(), + trainable=False) + + u_hat = u + v_hat = None + for i in range(iteration): + # power iteration + # Usually iteration = 1 will be enough + v_ = tf.matmul(u_hat, tf.transpose(w)) + v_hat = tf.nn.l2_normalize(v_) + + u_ = tf.matmul(v_hat, w) + u_hat = tf.nn.l2_normalize(u_) + + sigma = tf.matmul(tf.matmul(v_hat, w), tf.transpose(u_hat)) + w_norm = w / sigma + + with tf.control_dependencies([u.assign(u_hat)]): + w_norm = tf.reshape(w_norm, w_shape) + + return w_norm \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..909661ab8a78390bd13b6c47bf89cfe47fac00c0 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/common.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/common.py new file mode 100644 index 0000000000000000000000000000000000000000..fd65820230913346c1a20b41bc09a4b3ee61c51d --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/common.py @@ -0,0 +1,87 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import OrderedDict + +from src.utils.klass import Singleton +from src.utils.logger import logger + + +class _NameSpace(metaclass=Singleton): + """A common name-space class to record, sort and retrieve the tensorflow ops. + + Attributes: + GeneratorLoss: the scope of the loss ops of the generator. + DiscriminatorLoss: the scope of the loss ops of the discriminator. + GeneratorVarScope: the scope of the variables in the generator. + DiscriminatorVarScope: the scope of the variables in the discriminator. + PerceptualVarScope: the scope of the variables in the perceptual module. + Summary: the scope of the summary ops. + GeneratorRunOp: the scope of the running ops, i.e. train_op, lr_update_op, + of the generator. + DiscriminatorRunOp: the scope of the running ops, i.e. train_op, lr_update_op, + of the discriminator. + InputField: the scope of the input tensor and ops. + OutputField: the scope of the output tensor and ops. + + Example: record the losses in the GeneratorLoss scope, retrieve and add them to get + the final total loss for training. + + >>> from src.runner.common import name_space + >>> l1_loss = compute_loss1(pred, gt) + >>> name_space.add_to_collection(name_space.GeneratorLoss, 'l1_loss', l1_loss) + >>> l2_loss = compute_loss2(pred, gt) + >>> name_space.add_to_collection(name_space.GeneratorLoss, 'l2_loss', l2_loss) + >>> ... + >>> losses_dict = name_space.get_collection(name_space.GeneratorLoss) + >>> total_loss = tf.add_n(list(losses_dict.values())) # l1_loss + l2_loss + """ + __scopes = dict( + GeneratorLoss='gen_loss', + DiscriminatorLoss='dis_loss', + GeneratorVarScope='gen_var', + DiscriminatorVarScope='dis_var', + PerceptualVarScope='percep_var', + Summary='summary', + GeneratorRunOp='gen_op', + DiscriminatorRunOp='dis_op', + InputField='input', + OutputField='output', + ) + + __collections = dict() + + def __init__(self): + for key, value in self.__scopes.items(): + setattr(self, key, value) + self.__collections[value] = OrderedDict() + + def add_to_collection(self, namespace, key, value): + assert namespace in self.__scopes.values() + if key in self.__collections[namespace]: + logger.warn(f'Key "{key}" has already exists in scope "{namespace}".') + self.__collections[namespace][key] = value + + def add_to_collections(self, namespaces, key, value): + assert isinstance(namespaces, (list, tuple)) + for name in namespaces: + self.add_to_collection(name, key, value) + + def get_collection(self, namespace): + return self.__collections[namespace] + + def get_op(self, namespace, opname): + return self.__collections[namespace][opname] + + +name_space = _NameSpace() diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/distributed_variables_broadcast.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/distributed_variables_broadcast.py new file mode 100644 index 0000000000000000000000000000000000000000..c8670c143b4fabd488168397d2a9fd1c523060a4 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/distributed_variables_broadcast.py @@ -0,0 +1,90 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from src.utils.logger import logger + + +def broadcast_global_variables(sess, device, root_rank=0): + """A helper function to broadcast the variables across the devices in + distributed training. + + Args: + sess: tf.Session instance. + device: str, possible choices in ('npu'). + root_rank: int, the root node rank of the cluster. Default 0. + + Raises: + ValueError, when device is not in ('npu'). + """ + if device == 'npu': + npu_broadcast(sess, root_rank) + else: + raise ValueError + + +def npu_broadcast(sess, root_rank=0): + """Broadcast the variables in NPU environment. + + We use hccl interface to do the broadcast. + + Args: + sess: tf.Session instance. + root_rank: int, the root node rank of the cluster. Default 0. + """ + from npu_bridge.hccl import hccl_ops + logger.info(f'Broadcast variables from root_rank {root_rank} ...') + op_list = [] + for var in tf.global_variables(): + if "float" in var.dtype.name: + outputs = hccl_ops.broadcast(tensor=[var], root_rank=root_rank) + if outputs is not None: + op_list.append(outputs[0].op) + op_list.append(tf.assign(var, outputs[0])) + bcast = tf.group(op_list) + sess.run(bcast) + + +def allreduce_avg(tensor, device, ranksize): + """A helper function to perform the reduce mean across the devices in + distributed engine. + + Args: + tensor: tensor to reduce average. + device: str, possible choices in ('npu'). + ranksize: int, the number of the nodes in the cluster. + + Raises: + ValueError, when device is not in ('npu'). + """ + if device == 'npu': + return npu_allreduce_avg(tensor, ranksize) + else: + raise NotImplementedError + + +def npu_allreduce_avg(tensor, ranksize): + """Reduce mean across the devices in NPU environment. + + Args: + tensor: tensor to reduce average. + ranksize: int, the number of the nodes in the cluster. + + Returns: + tensor, reduced average tensor. + """ + from npu_bridge.hccl import hccl_ops + # There is no 'mean' reduction in allreduce ops. Use 'sum' instead. + # See https://support.huaweicloud.com/mprtg-A800_9000_9010/atlasprtg_13_0024.html + tensor_reduced = hccl_ops.allreduce(tensor / ranksize, "sum") + return tensor_reduced diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/helper.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/helper.py new file mode 100644 index 0000000000000000000000000000000000000000..1677934b9e8fb3b711fd93db8e8a5f3faeddcf24 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/helper.py @@ -0,0 +1,213 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import os + +import tensorflow as tf +from src.utils.world import world + + +class _AdversarialTrainHelper: + """A helper for adversarial training. + + In each step, the generator and discriminator will produce losses. + The helper determines whether to update the G and D in the next + iteration. For example, in step `i`, if the D loss (already evaluated) + is found very small, which means that D might be too strong and should + halt for some steps, the helper will filter out the discriminator ops in + step `i+1` to keep the discriminator, then evaluate again in step `i+1` + to determine whether to update G and D in step `i+2`. + + Example: + >>> # Define the tf ops + >>> helper = _AdversarialTrainHelper() + >>> g_train_op, d_train_op = define_train() + >>> d_train_op_dict = {'d_op': d_train_op} + >>> g_train_op_dict = {'g_op': g_train_op} + >>> d_loss = compute_d_loss(fake, real) + >>> ... + >>> # in step i=0 + >>> i = 0 + >>> g_train_op_dict_real, d_train_op_dict_real = helper.filter(g_train_op_dict, d_train_op_dict) + >>> _, _, d_loss_eval = sess.run([g_train_op_dict_real, d_train_op_dict_real, d_loss]) + >>> helper.update_status(d_loss_eval, i+1) + >>> ... + >>> # in step i=1 + >>> i = 1 + >>> # decide whether to update G and D in step 1 according to the result in step 0. + >>> g_train_op_dict_real, d_train_op_dict_real = helper.filter(g_train_op_dict, d_train_op_dict) + >>> _, _, d_loss_eval = sess.run([g_train_op_dict, d_train_op_dict, d_loss]) + >>> # update status with the d_loss and step index. + >>> helper.update_status(d_loss_eval, i+1) + """ + def __init__(self): + self._called_once = False + self._info = dict() + + @property + def info(self): + return self._info + + def filter(self, g_ops_in, d_ops_in, *args, **kwargs): + if not self._called_once: + # For the first time, we must run all the operations on NPU + # to construct the whole graph. It is regardless of the update + # strategy. + g_update, d_update = True, True + self._called_once = True + else: + # Once called and initialized, use the configured strategy to + # check whether to update G and D. + g_update, d_update = self.check_state() + + # Post validation to make sure that at least one of G and D should + # update. + g_update, d_update = self.post_validation(g_update, d_update) + + # Save the decision + self._info = dict( + g_update=g_update, + d_update=d_update + ) + + g_ops = dict(**g_ops_in) + if not g_update: + g_ops.pop('g_train') + + d_ops = dict(**d_ops_in) + if not d_update and 'd_train' in d_ops_in: + d_ops.pop('d_train') + + return g_ops, d_ops + + def post_validation(self, g_update, d_update): + # Abnormal states when both g_update and d_update are false + if (not g_update) and (not d_update): + g_update = True + d_update = False + return g_update, d_update + + def check_state(self): + # This is where specific strategy should implement how to make decisions + # on the whether to update G and D. + raise NotImplementedError + + def update_status(self, *args, **kwargs): + # Record the step and the criteria value. + raise NotImplementedError + + def not_initialized(self): + raise ValueError(f'Helper has not been initialized.') + + +class ByPassTrainHelper(_AdversarialTrainHelper): + """A bypass train helper. + + The ops will not be filtered at all. + """ + def __init__(self, use_adv=False): + super().__init__() + self.use_adv = use_adv + + def check_state(self): + # g_update always True + # d_update according to self.use_adv + return True, self.use_adv + + def update_status(self, *args, **kwargs): + pass + + +class AdaptiveTrainHelper(_AdversarialTrainHelper): + """An adaptive train helper given the loss values. + """ + def __init__(self, d_threshold, g_threshold=None): + super().__init__() + self.d_threshold = d_threshold + self.g_threshold = g_threshold + self.previous_d_loss = None + self.previous_step = None + self.d_warmstarted = False + + def update_status(self, loss=None, step=None): + self.previous_d_loss = loss + self.previous_step = step + + def check_state(self): + if self.previous_d_loss is None: + self.not_initialized() + + d_update = self.previous_d_loss > self.d_threshold + if not self.d_warmstarted: + # Don't ever update generator when discriminator is not yet that strong. + # Once the discriminator is at first time strong enough, apply the dynamic update. + g_update = False + if not d_update: + self.d_warmstarted = True + elif self.g_threshold is None: + g_update = True + else: + g_update = self.previous_d_loss < self.g_threshold + + return g_update, d_update + + +class FixedStepTrainHelper(_AdversarialTrainHelper): + """A train helper with fixed interval. + """ + def __init__(self, g_update_interval=-1, d_update_interval=-1): + super().__init__() + self.g_update_interval = g_update_interval + self.d_update_interval = d_update_interval + self.previous_step = None + + def update_status(self, loss=None, step=None): + self.previous_step = step + + def check_state(self): + if self.previous_step is None: + self.not_initialized() + + g_update = (self.previous_step + 1) % self.g_update_interval == 0 + d_update = (self.previous_step + 1) % self.d_update_interval == 0 + + return g_update, d_update + + +def build_adversarial_train_helper(cfg): + """Build corresponding train helper given the configuration. + + Args: + cfg: yacs node, global configuration. + + Returns: + helper instance. + """ + if cfg.loss.adversarial.loss_weight > 0.: + if cfg.loss.adversarial.adaptive_strategy: + helper = AdaptiveTrainHelper(cfg.loss.adversarial.d_balance) + elif cfg.loss.adversarial.g_update_interval > 1 or cfg.loss.adversarial.d_update_interval > 1: + if cfg.loss.adversarial.g_update_interval > 1 and cfg.loss.adversarial.d_update_interval > 1: + raise ValueError('Either g update interval or d update interval should be 1.') + helper = FixedStepTrainHelper(cfg.loss.adversarial.g_update_interval, + cfg.loss.adversarial.d_update_interval) + else: + # no helper + helper = ByPassTrainHelper(use_adv=True) + else: + # no helper + helper = ByPassTrainHelper(use_adv=False) + + return helper diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/initializer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/initializer.py new file mode 100644 index 0000000000000000000000000000000000000000..1fd24bc4825998a1df1f3283d012cd9417db2e13 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/initializer.py @@ -0,0 +1,106 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import tensorflow as tf + + +def calculate_gain(nonlinearity, param=None): + """Calculate gain when initialization. + """ + if nonlinearity == 'sigmoid': + return 1 + elif nonlinearity == 'tanh': + return 5.0 / 3 + elif nonlinearity == 'relu': + return math.sqrt(2.0) + elif nonlinearity == 'elu': + return math.sqrt(1.55) + elif nonlinearity == 'leakyrelu': + if param is None: + negative_slope = 0.01 + elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float): + # True/False are instances of int, hence check above + negative_slope = param + else: + raise ValueError("negative_slope {} not a valid number".format(param)) + return math.sqrt(2.0 / (1 + negative_slope ** 2)) + else: + raise ValueError("Unsupported nonlinearity {}".format(nonlinearity)) + + +def calculate_fan(kernel_size, in_channels, out_channels=None, mode='fan_in'): + """Calculate fan when initialization. + """ + if mode == 'fan_in': + fan = in_channels + elif mode == 'fan_out': + fan = out_channels + else: + raise KeyError + for k in kernel_size: + fan *= k + return fan + + +def get_initializer(init_cfg, in_channels, out_channels, kernel_size, dtype=tf.dtypes.float32): + """Get initializer given the input/output channels and kernel_size. + + Args: + init_cfg: dict, specifying the initialization type and mode. + in_channels: int, specifying the number of the input channels. + out_channels: int, specifying the number of the ouput channels. + kernel_size: list[int], containing the kernel size of each dimension. + dtype: enum, specifying the data type of the initializer. + + Returns: + initializer instance. + """ + type = init_cfg.pop('type') + + if type == 'kaiming_uniform': + a = init_cfg.pop('a', 0) + mode = init_cfg.pop('mode', 'fan_in') + nonlinearity = init_cfg.pop('nonlinearity', 'leakyrelu') + fan = calculate_fan(kernel_size, in_channels, out_channels, mode) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + bound = math.sqrt(3.0) * std + initializer = tf.random_uniform_initializer(-bound, bound, dtype=dtype) + elif type == 'kaiming_normal': + a = init_cfg.pop('a', 0) + mode = init_cfg.pop('mode', 'fan_in') + nonlinearity = init_cfg.pop('nonlinearity', 'leakyrelu') + fan = calculate_fan(kernel_size, in_channels, out_channels, mode) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + initializer = tf.random_normal_initializer(0.0, std, dtype=dtype) + elif type == 'xavier_uniform': + gain = init_cfg.pop('gain', 1.) + fan_in = calculate_fan(kernel_size, in_channels, out_channels, 'fan_in') + fan_out = calculate_fan(kernel_size, in_channels, out_channels, 'fan_out') + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + a = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + initializer = tf.random_uniform_initializer(-a, a, dtype=dtype) + elif type == 'xavier_normal': + gain = init_cfg.pop('gain', 1.) + fan_in = calculate_fan(kernel_size, in_channels, out_channels, 'fan_in') + fan_out = calculate_fan(kernel_size, in_channels, out_channels, 'fan_out') + std = gain * math.sqrt(2.0 / float(fan_in + fan_out)) + initializer = tf.random_normal_initializer(0.0, std, dtype=dtype) + else: + raise NotImplementedError + + return initializer diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/loss_scaling.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/loss_scaling.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae2fd5a1ef10314624867a63acfaaffc55a32a1 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/loss_scaling.py @@ -0,0 +1,50 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf + + + +def npu_loss_scale_optimizer(opt, loss_scale, is_distributed=False): + """A wrap function of loss scaling optimizer for NPU tensorflow. + + Args: + opt: optimizer instance. + loss_scale: str, specifying the strategy to apply loss scaling. + Possible choices could be `off`: do not use loss scaling. + `d`: dynamic loss scaling, and `f*`: fixed loss scaling, + where `*` can be converted to an integer that specifies the + scale factor, `2^(int(*))`. + is_distributed: boolean, whether in distributed training. + + Returns: + a wrapped optimizer with loss scaling. + """ + from npu_bridge.estimator.npu.npu_loss_scale_optimizer import NPULossScaleOptimizer + from npu_bridge.estimator.npu.npu_loss_scale_manager import FixedLossScaleManager + from npu_bridge.estimator.npu.npu_loss_scale_manager import ExponentialUpdateLossScaleManager + if loss_scale == 'off': + pass + else: + if loss_scale.startswith('d'): + loss_scale_manager = \ + ExponentialUpdateLossScaleManager(init_loss_scale=2 ** 32, incr_every_n_steps=1000, + decr_every_n_nan_or_inf=2, decr_ratio=0.5) + elif loss_scale.startswith('f'): + scale_factor = int(loss_scale[2:]) + loss_scale_manager = FixedLossScaleManager(loss_scale=2 ** scale_factor) + else: + raise ValueError + opt = NPULossScaleOptimizer(opt, loss_scale_manager, is_distributed=is_distributed) + + return opt diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/lr_schedule.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/lr_schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..57960ee012b623bddf3abfc598d00bf2c5279789 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/lr_schedule.py @@ -0,0 +1,117 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math + +import tensorflow as tf + + +class BaseSchedule(object): + """Base class of learning rate schedule. + + Args: + base_lr: float, base learning rate at the beginning. + recover_step: int, recover step to continue training. + """ + def __init__(self, base_lr, recover_step=0): + self.lr = tf.placeholder(tf.float32, shape=[], name='learning_rate') + self.cur_step = recover_step + self.base_lr = base_lr + self.cur_lr = base_lr + + def __call__(self): + self.cur_step += 1 + return self.cur_lr + + +class CosineSchedule(BaseSchedule): + """A cosine learning rate schedule. + + Args: + base_lr: float, base learning rate at the beginning. + total_steps: list[int], the phased steps where the learning will be adjusted. + min_lr: float, minimum learning rate. + recover_step: int, recover step to contine training. + """ + def __init__(self, base_lr, total_steps, min_lr, recover_step=0): + super().__init__(base_lr, recover_step) + self.total_steps = total_steps[0] + self.min_lr = min_lr + self.alpha = min_lr / base_lr + + def __call__(self): + cosine_decay = 0.5 * (1 + math.cos(math.pi * self.cur_step / self.total_steps)) + decayed = (1 - self.alpha) * cosine_decay + self.alpha + self.cur_lr = self.base_lr * decayed + return super(CosineSchedule, self).__call__() + + +class RestartCosineSchedule(BaseSchedule): + """A cosine restart learning rate schedule. + + Args: + base_lr: float, base learning rate at the beginning. + total_steps: list[int], the phased steps where the learning will be adjusted. + restart_weights: list[float], the phased weigths which the learning will be adjusted to. + min_lr: float, minimum learning rate. + recover_step: int, recover step to contine training. + """ + def __init__(self, base_lr, total_steps, restart_weights, min_lr, recover_step=0): + super(RestartCosineSchedule, self).__init__(base_lr, recover_step) + self.total_steps = total_steps + self.restart_weights = restart_weights + self.min_lr = min_lr + self.alpha = min_lr / base_lr + + def _match_stage(self): + cur_step = self.cur_step + for total_steps, restart_weight in zip(self.total_steps, self.restart_weights): + if cur_step < total_steps: + return cur_step, total_steps, self.base_lr * restart_weight + else: + cur_step -= total_steps + raise ValueError('Should have stopped') + + def __call__(self): + cur_step, total_steps, base_lr = self._match_stage() + cosine_decay = 0.5 * (1 + math.cos(math.pi * cur_step / total_steps)) + decayed = (1 - self.alpha) * cosine_decay + self.alpha + self.cur_lr = base_lr * decayed + return super(RestartCosineSchedule, self).__call__() + + +def build_schedule(lr_cfg, recover_step=0): + """Build learning rate schedule. + + Args: + lr_cfg: dict, specifying the learning rate schedule type and its configuration. + recover_step: int, recover step to contine training. + + Returns: + A learning rate schedule instance. + """ + lr_type = lr_cfg.type.lower() + base_lr = lr_cfg.base_lr + total_steps = lr_cfg.total_steps + + if lr_type == 'cosine': + min_lr = lr_cfg.min_lr + return CosineSchedule(base_lr, total_steps, min_lr, recover_step) + elif lr_type == 'cosinerestart': + min_lr = lr_cfg.min_lr + restart_weights = lr_cfg.restart_weights + return RestartCosineSchedule(base_lr, total_steps, restart_weights, min_lr, recover_step) + elif lr_type == 'step': + raise NotImplementedError + else: + raise KeyError('Unkown type {}'.format(lr_type)) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/npu_pkgs.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/npu_pkgs.py new file mode 100644 index 0000000000000000000000000000000000000000..b9f54ad3cfe03efc0fe2d9d38ebe997cde355866 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/npu_pkgs.py @@ -0,0 +1,19 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from npu_bridge.estimator import npu_ops +from npu_bridge.estimator.npu.npu_config import NPURunConfig +from npu_bridge.estimator.npu.npu_estimator import NPUEstimator +from npu_bridge.estimator.npu.npu_optimizer import NPUDistributedOptimizer +from npu_bridge.estimator.npu.npu_optimizer import allreduce +from npu_bridge.hccl import hccl_ops diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/optimizer.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..584ec0c8f6098a1e666ca5a9e89401ee11938a6a --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/optimizer.py @@ -0,0 +1,76 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf + + +def build_optimizer(lr, opt_cfg, device, is_distributed, mix_precision, loss_scale): + """Build optimizer + + Args: + lr: learning rate schedule instance. + opt_cfg: dict, specifying the optimizer configuration. + device: str, specifying the device type. Possible choices in ('npu', 'cpu'). + is_distributed: boolean, whether in distributed learning. + mix_precision: boolean, whether to use mix precisioin. + loss_scale: str, specifying the strategy to apply loss scaling. + Possible choices could be `off`: do not use loss scaling. + `d`: dynamic loss scaling, and `f*`: fixed loss scaling, + where `*` can be converted to an integer that specifies the + scale factor, `2^(int(*))`. + + Returns: + An optimizer instance. + """ + opt_type = opt_cfg.type.lower() + + if opt_type == 'adam': + beta1 = opt_cfg.get('beta1', 0.9) + beta2 = opt_cfg.get('beta2', 0.999) + epsilon = opt_cfg.get('epsilon', 1e-08) + opt = tf.train.AdamOptimizer(lr, beta1=beta1, beta2=beta2, epsilon=epsilon) + elif opt_type == 'momentum': + momentum = opt_cfg.get('momentum', 0.9) + opt = tf.train.MomentumOptimizer(lr, momentum=momentum) + else: + raise KeyError('Unkown type {}'.format(opt_type)) + + if device == 'npu': + return npu_optimizer_wrapper(opt, mix_precision, loss_scale, is_distributed) + else: + return opt + + +def npu_optimizer_wrapper(opt, mix_precision, loss_scale, is_distributed=False): + """A wrapper function of optimizer on NPU. + + Args: + opt: optimizer instance. + is_distributed: boolean, whether in distributed learning. + mix_precision: boolean, whether to use mix precisioin. + loss_scale: str, specifying the strategy to apply loss scaling. + Possible choices could be `off`: do not use loss scaling. + `d`: dynamic loss scaling, and `f*`: fixed loss scaling, + where `*` can be converted to an integer that specifies the + scale factor, `2^(int(*))`. + + Returns: + An optimizer instance. + """ + from npu_bridge.estimator.npu.npu_optimizer import NPUDistributedOptimizer + from .loss_scaling import npu_loss_scale_optimizer + if is_distributed: + opt = NPUDistributedOptimizer(opt) + if mix_precision: + opt = npu_loss_scale_optimizer(opt, loss_scale, is_distributed) + return opt \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/saver.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/saver.py new file mode 100644 index 0000000000000000000000000000000000000000..f75eb0e18fe74a839c13361097ece36c0b4accd5 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/saver.py @@ -0,0 +1,137 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import re + +import tensorflow as tf +from tensorflow.python import pywrap_tensorflow + +from src.utils.logger import logger + + +def get_variables_in_checkpoint_file(file_name): + """Get all the variables given the checkpoint file + + Args: + file_name: str, ckpt file. + + Returns: + Dict of tensor name to tensor. + """ + try: + reader = pywrap_tensorflow.NewCheckpointReader(file_name) + var_to_shape_map = reader.get_variable_to_shape_map() + return var_to_shape_map + except Exception as e: # pylint: disable=broad-except + logger.error(str(e)) + if "corrupted compressed block contents" in str(e): + logger.error("It's likely that your checkpoint file has been compressed " + "with SNAPPY.") + + +def loading_variables(sess, variables, checkpoint, strict=False): + """Loading specific variables given session and checkpoint. + """ + if not strict: + var_dic = get_variables_in_checkpoint_file(checkpoint) + var_missing = [] + var_restore = [] + + for v in variables: + if v.name.split(':')[0] in var_dic: + var_restore.append(v) + logger.info('Match: {} {} {}/{}'.format( + v.name, + v.dtype, + v.shape, + var_dic[v.name.split(':')[0]])) + else: + logger.info('Miss: {} {}'.format(v.name, v.shape)) + var_missing.append(v.name) + assert len(variables) == len(var_restore) + len(var_missing) + + saver = tf.train.Saver(var_list=var_restore) + saver.restore(sess, checkpoint) + else: + saver = tf.train.Saver(var_list=variables) + saver.restore(sess, checkpoint) + logger.info("Loading checkpoints...{} Success".format(checkpoint)) + + # Get the step information in ckpt file, may be used for continual training. + recover_step = 0 + regex = re.compile('[A-Za-z.]*-([0-9]*).?[A-Za-z0-9]*$') + try: + b, = regex.search(checkpoint).groups() + if b is not None and b != '': + recover_step = int(b) + 1 + except: + pass + return recover_step + + +def restore(sess, var_list, directory, checkpoint, strict=False): + """Restore variables from ckpt. + """ + if os.path.exists(checkpoint + '.meta'): + logger.info(f'Found checkpoint {checkpoint}.') + ckpt_name = checkpoint + else: + logger.info(f'Cannot find checkpoint {checkpoint}. Searching in {directory} ...') + ckpt = tf.train.get_checkpoint_state(directory) + if ckpt and ckpt.model_checkpoint_path: + ckpt_name = os.path.basename(ckpt.model_checkpoint_path) + ckpt_name = os.path.join(directory, ckpt_name) + logger.info(f'Found checkpoint {ckpt_name}.') + else: + logger.error("Reading checkpoints... ERROR") + raise ValueError(f'Cannot find checkpoint in {directory}') + return loading_variables(sess, var_list, ckpt_name, strict=strict) + + +def strict_loading(sess, scope, directory, checkpoint): + """Strict loading **every single variable** in the scope. + """ + if scope == '': + logger.info(f"Reading checkpoints (no given scope) ...") + variables = tf.get_collection(tf.GraphKeys.VARIABLES) + else: + logger.info(f"Reading checkpoints for scope '{scope}' ...") + variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=scope) + return restore(sess, variables, directory, checkpoint, strict=True) + + +def loose_loading(sess, scope, directory, checkpoint): + """Loading variables in the scope, but allow missing keys or variables. + """ + if scope == '': + logger.info(f"Reading checkpoints (no given scope) ...") + variables = tf.get_collection(tf.GraphKeys.VARIABLES) + else: + logger.info(f"Reading checkpoints for scope '{scope}' ...") + variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=scope) + var_dic = get_variables_in_checkpoint_file(checkpoint) + var_missing = [] + var_restore = [] + + for v in variables: + loading_cond = v.name.split(':')[0] in var_dic and (scope in v.name.split(':')[0]) + if loading_cond: + var_restore.append(v) + logger.info('Match: {} {} {}/{}'.format(v.name, v.dtype, v.shape, var_dic[v.name.split(':')[0]])) + else: + logger.info('Miss: {} {}'.format(v.name, v.shape)) + var_missing.append(v.name) + assert len(variables) == len(var_restore) + len(var_missing) + return restore(sess, var_restore, directory, checkpoint, strict=True) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/sess_config.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/sess_config.py new file mode 100644 index 0000000000000000000000000000000000000000..7eeb1da8f6a0783303317374ecbb9a8c98b2659c --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/sess_config.py @@ -0,0 +1,87 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import tensorflow as tf + + +def _npu_config(mix_precision, is_distributed): + """Prepare NPU tf.Session config + + Args: + mix_precision: boolean, whether to use mix precision. + is_distributed: boolean, whether in distributed scenario. + + Returns: + A tf.ConfigProto instance. + """ + config = tf.ConfigProto() + custom_op = config.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["enable_data_pre_proc"].b = False + custom_op.parameter_map["mix_compile_mode"].b = False + custom_op.parameter_map["use_off_line"].b = True + custom_op.parameter_map["graph_memory_max_size"].s = \ + tf.compat.as_bytes(str(28*1024 * 1024 * 1024)) + custom_op.parameter_map["variable_memory_max_size"].s = \ + tf.compat.as_bytes(str(3*1024 * 1024 * 1024)) + + if mix_precision: + custom_op.parameter_map["precision_mode"].s = \ + tf.compat.as_bytes("allow_mix_precision") + if is_distributed: + config.graph_options.rewrite_options.optimizers.extend( + ["pruning", + "function", + "constfold", + "shape", + "arithmetic", + "loop", + "dependency", + "layout", + "memory", + "GradFusionOptimizer"]) + + from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig + config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + return config + + +def _cpu_config(): + """Prepare CPU tf.Session config + + Returns: + A tf.ConfigProto instance. + """ + return tf.ConfigProto() + + +def get_sess_config(device='npu', xla=False, mix_precision=True, is_distributed=False): + """Build session config. + + Args: + device: str, what type of hardware to use. + xla: boolean, whether to use xla. + mix_precision: boolean, whether to use mix precision. + is_distributed: boolean, whether in distributed scenario. + + Returns: + A tf.ConfigProto instance. + """ + if device == 'npu': + return _npu_config(mix_precision, is_distributed) + elif device == 'cpu': + return _cpu_config() + else: + raise KeyError('Unsupported device: {}'.format(device)) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/solver.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/solver.py new file mode 100644 index 0000000000000000000000000000000000000000..1ea18c091c6514424522dffdf52b8b61eb5ca9a7 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/runner/solver.py @@ -0,0 +1,86 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# Copyright (c) 2022 Huawei Technologies Co., Ltd +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from .lr_schedule import build_schedule +from .optimizer import build_optimizer + + +class Solver(object): + """Solver class including optimizer and learning-rate schedule. + + Args: + lr_cfg: yacs node, learning-rate schedule. + opt_cfg: yacs node, optimizer config. + device: str, valid options: ['npu', 'cpu'] + is_distributed: boolean, whether used in distributed training. + mix_precision: boolean, whether used mix precision during training. + loss_scale: boolean, whether use loss scaling to compensate the + precision loss during dtype conversion. + """ + def __init__(self, lr_cfg, opt_cfg, device, is_distributed, mix_precision, + loss_scale): + self.lr_schedule = build_schedule(lr_cfg) + self.opt = build_optimizer(self.lr_schedule.lr, opt_cfg, + device, + is_distributed, + mix_precision, + loss_scale) + self.total_step = sum(lr_cfg.total_steps) + + def update_lr(self): + """Update learning rate based on schedule and step. + """ + return self.lr_schedule() + + @property + def lr(self): + """Returns learning rate placeholder. + """ + return self.lr_schedule.lr + + @property + def cur_lr(self): + """Returns current learning rate. + """ + return self.lr_schedule.cur_lr + + +def build_solver(lr_cfg, optimizer_cfg, mix_precision, loss_scale, device, + is_distributed): + """Build solver for training. + + Args: + lr_cfg: yacs node, learning-rate schedule. + optimizer_cfg: yacs node, optimizer config. + device: str, valid options: ['npu', 'cpu'] + is_distributed: boolean, whether used in distributed training. + mix_precision: boolean, whether used mix precision during training. + loss_scale: boolean, whether use loss scaling to compensate the + precision loss during dtype conversion. + + Return: + A solver instance. + """ + assert device in ['npu', 'cpu'] + + return Solver(lr_cfg, + optimizer_cfg, + device, + is_distributed, + mix_precision, + loss_scale) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b6d1d7edd8e369641588e8a7721b78f402a0b63 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/__init__.py @@ -0,0 +1,14 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from src.utils.constant import * \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/adapter.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..22f24d40471465ef1378cbf8a3c037e43cbe35fa --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/adapter.py @@ -0,0 +1,393 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from itertools import product + +import numpy as np + +from .exceptions import * +from .logger import logger + + +def factor_ceiling(x, factor): + """Get the ceiling that is divisible by the factor. + """ + remain = x % factor + pad = 0 + if remain: + pad = factor - remain + x = x + pad + return x, pad + + +class NetworkIOAdapter: + """A network io adapter to process the input images when inference. + + Because of the memory limitation, we may not be able to process the whole + frame into a model in a single session.run. In this scenario, we use a + dispatch-process-stitching strategy to process the input frames. The + NetworkIOAdapter class is used to automatically make patches from the source + input frames, and stitching them together to get the whole result, during + which each patches can be overlapped. + + There are two basic modes in this adapter when using ckpt to inference: + 1. Inferece as a whole, where the model is able to process the whole image. + In this scenario, the only thing the adapter will do is to pad the input + frames to satisfy the network smallest feature map. If the smallest + feature map of the model is 1/N proportion to the original input, then + the size of the input frames should be divisible by N. Therefore, if + we find the original frame size does not satisfy this condition, the + adpater will pad the frames. After inference, the result will be trimmed + to the expected size. + + Example: + >>> adapter = NetworkIOAdapter(cfg) + >>> input_frames = get_data() # [N, T, H, W, C] + >>> adapter.register_raw_size(input_frames.shape[2:4]) + >>> print(adapter.patch_mode) + False + >>> padded_input = adapter.adapt_input(input_frames) + >>> result = sess.run(output_tensor, feed_dict={input_node: padded_input}) + >>> final_result = adapter.reverse_adapt(result) + + 2. Inference using patches. In this mode, we have to inference the original + input frames using split-and-conquer and then stitch them to the expected + result. The patch size can be configured by the cfg.data.inference.best_patch_size + for the efficient inference. We first pad the input frames to the factor + ceiling of the best_patch_size, thus the padded original image can be split + into several pieces. Then each patch is additionally padded with the + overlap size to avoid the discontinuity between two patch results. The + pad size of each patch should cover the size of the receptive field of the + network. The session will inference each padded patch, followed by stitching + method to aggregate the patch results to a complete one. The padded size + of the patch will be first trimmed off, and the patches will be concatenated + together. Finally, the corrsponding padded size of the whole image will be + trimmed to get the final result. + + Example: + >>> adapter = NetworkIOAdapter(cfg) + >>> input_frames = get_data() # [N, T, H, W, C] + >>> adapter.register_raw_size(input_frames.shape[2:4]) + >>> print(adapter.patch_mode) + True + >>> patches = adapter.extract_image_patches(input_frames) + >>> num_patches = len(patches) + >>> patch_per_step = cfg.data.inference.batch_size + >>> result_patches = [] + >>> for i in range(num_patches//patch_per_step): + ... _patch = sess.run(output_tensor, feed_dict={input_node: patches[i:i+patch_per_step]}) + ... result_patches.extend(_patch) + >>> final_result = adapter.stitching_patches_to_image(result_patches) + + **A special scenario is to infer with pb file**, where the graph is already + freezed. In this scenario, the input size is also fixed, and we use the + adapter to automatically determine how to inference. **One must aware that + the actual size of the input patch is**: + cfg.data.inference.input_size + cfg.data.inference.patch_pad_size * 2 + Therefore, one must ensure that the value above can be divisble by the network + feature map scale factor. + + Args: + cfg: yacs node, global configuration. + """ + def __init__(self, cfg): + self.cfg = cfg + + # network input settings + self.limited_in_size = cfg.data.inference.max_input_size + self.best_in_size = cfg.data.inference.best_patch_size + self.scale = cfg.model.scale + self.factor_for_adapt_input = cfg.model.factor_for_adapt_input + self.auto_mode = cfg.data.inference.auto_adapt_input + + if self.auto_mode and not self.limited_in_size: + raise ValueError('Max input size is required when in auto mode.') + if self.auto_mode == True: + self.mode = 'auto' + else: + self.mode = None + + if not self.best_in_size: + self.best_in_size = self.limited_in_size + + self.num_output_frames = cfg.model.num_net_output_frames + + # patch evaluation settings + self.eval_in_patch = cfg.data.inference.eval_using_patch + self.eval_pad_size = cfg.data.inference.patch_pad_size + # size of the input image, before adapted + self.eval_raw_size = [100000, 100000] + # size of the input patch when in patch mode, or the image size when in + # whole mode, before adapted + self.eval_in_size = cfg.data.inference.input_size + + self.fixed_in_size_flag = False + + # saved data for output, w.r.t. eval in patches strategy + self._network_direct_outsize = [] + self._stitching_mode_padsize = [] + self._patch_batch_pad = 0 + self._vtip_stitching_method = False + self._num_split = (0, 0) + + # saved data for reverse adapt, w.r.t. network input constrains + self._input_adapt_padsize = [0, 0] + + @property + def input_size(self): + # In patch mode, it should be padded size + if self.eval_in_patch: + pads_h, pads_w = self.eval_pad_size[0]*2, self.eval_pad_size[1]*2 + else: + # to keep inline with the original code, we should set + # eval_in_patch = eval_raw_size = raw image size + # when inference the whole image + pads_h, pads_w = self._input_adapt_padsize + + h = self.eval_in_size[0] + pads_h + w = self.eval_in_size[1] + pads_w + + return (h, w) + + @property + def patch_mode(self): + return self.eval_in_patch + + def adapt_input(self, lr): + #Used in whole image mode. + pads_h, pads_w = self._input_adapt_padsize + + if len(lr.shape) == 4: + pads = [[0, 0], + [pads_h//2, pads_h-pads_h//2], + [pads_w//2, pads_w-pads_w//2], + [0,0]] + else: + pads = [[0, 0], + [0, 0], + [pads_h//2, pads_h-pads_h//2], + [pads_w//2, pads_w-pads_w//2], + [0,0]] + + lr_pads = np.pad(lr, pads, mode='symmetric') + return lr_pads + + def reverse_adapt(self, data): + # Used in whole image mode + pads_h, pads_w = self._input_adapt_padsize + if data.ndim == 3: + h, w, c = data.shape + pads_t, pads_b = pads_h//2, pads_h-pads_h//2 + pads_l, pads_r = pads_w//2, pads_w-pads_w//2 + return data[pads_t*self.scale:h-pads_b*self.scale, + pads_l*self.scale:w-pads_r*self.scale] + elif data.ndim == 4: + _, h, w, c = data.shape + pads_t, pads_b = pads_h//2, pads_h-pads_h//2 + pads_l, pads_r = pads_w//2, pads_w-pads_w//2 + return data[:, + pads_t*self.scale:h-pads_b*self.scale, + pads_l*self.scale:w-pads_r*self.scale] + else: + raise ArrayDimensionError(f'Expect input data to have 3 or 4 ' + f'dimensions, but got {data.ndim}.') + + def fix_eval_in_size(self): + # Used for inference with PB file and the input size is fixed. + fixed_input_size = [self.best_in_size[0] + self.eval_pad_size[0] * 2, + self.best_in_size[1] + self.eval_pad_size[1] * 2] + pad_h, pad_w = self.cal_adapted_size(fixed_input_size) + assert pad_h == 0 and pad_w == 0, \ + f"Expect to have an input size that is divisible " \ + f"by {self.factor_for_adapt_input} when using a fixed input size, " \ + f"but got {fixed_input_size}. Must ensure that " \ + f"`model.best_in_size + data.eval_padsize*2` divisible by the factor." + self.eval_in_size = self.best_in_size + self.limited_in_size = fixed_input_size # Real input size + self.fixed_in_size_flag = True + + def register_raw_size(self, raw_size): + # Override the configured raw_size + self.eval_raw_size = raw_size + + logger.info(f'Automatically determine inference mode (whether patch or not).') + if self.mode == 'auto': + self.eval_in_size = raw_size + logger.info(f'auto inference mode.') + # In auto mode, the adapter will automatically define the input size + h, w = raw_size + limited_h, limited_w = self.limited_in_size + + if self.fixed_in_size_flag: + # Remember that in this case: + # self.limited_in_size = self.best_in_size + self.eval_padsize * 2 + # **We have also make sure that self.limited_in_size is divisible + # by the factor**. See self.fix_eval_in_size() + # Shall use a different logic to determine whether to eval in + # patch or not. + if h <= limited_h and w <= limited_w: + # If the raw input size equals to the fixed size + # (self.limited_in_size), eval in whole. + # self.limited_in_size = self.best_in_size + self.eval_padsize * 2 + # automatically ensures that self._input_adapt_padsize will be zero. + self.eval_in_patch = False + else: + # Else, use a patch mode no matter if the raw size is larger + # or smaller. To use the self.input_size interface consistent, + # set self.eval_in_size to best_in_size hence ensuring: + # self.limited_in_size = self.best_in_size + self.eval_padsize * 2 + # = self.eval_in_size + self.eval_padsize * 2 + self.eval_in_patch = True + self.eval_in_size = self.best_in_size + else: + if h * w > limited_w * limited_h: + self.eval_in_patch = True + self.eval_in_size = ( + factor_ceiling(min(h, self.best_in_size[0]), self.factor_for_adapt_input)[0], + factor_ceiling(min(w, self.best_in_size[1]), self.factor_for_adapt_input)[0], + ) + else: + self.eval_in_patch = False + + if self.eval_in_patch: + # Follow the config or the automatic setting + pass + else: + # Adapt the image input to fit the network requirements + if self.fixed_in_size_flag: + self._input_adapt_padsize = ( + (self.limited_in_size[0] - raw_size[0]), + (self.limited_in_size[1] - raw_size[1]), + ) + else: + # For whole image inference + self._input_adapt_padsize = self.cal_adapted_size(raw_size) + + if self._input_adapt_padsize[0]: + logger.info(f'Input height {raw_size[0]} is not a divisible by {self.factor_for_adapt_input}' + f', will be padded to {raw_size[0]+self._input_adapt_padsize[0]}') + + if self._input_adapt_padsize[1]: + logger.info(f'Input width {raw_size[1]} is not a divisible by {self.factor_for_adapt_input}' + f', will be padded to {raw_size[1]+self._input_adapt_padsize[1]}') + + logger.info(f'Inference adapter: ') + logger.info(f'\t Use patch: {f"{self.eval_in_patch}":>5}') + logger.info(f'\t Image Raw size: {self.eval_raw_size}') + logger.info(f'\tOriginal patch size: {self.eval_in_size}') + logger.info(f'\t Adapted input size: {self.input_size}') + + def cal_adapted_size(self, raw_size): + h, w = raw_size + # In case the input is not divisible by the factor + _, pad_h = factor_ceiling(h, self.factor_for_adapt_input) + _, pad_w = factor_ceiling(w, self.factor_for_adapt_input) + + return pad_h, pad_w + + def extract_image_patches(self, data, num_patches_per_step=1): + # This function is used in patch mode + return self._extract_image_patches_canonical(data, num_patches_per_step) + + def stitching_patches_to_image(self, data): + # This function is used in patch mode + return self._merge_patches_to_images_canonical(data) + + def _extract_image_patches_canonical(self, data, num_patches_per_step=1): + if data.ndim != 4: + raise ArrayDimensionError(f'Expect input data to have 4 dimensions, but got {data.ndim}.') + + _, h, w, _ = data.shape + ph, pw = self.eval_in_size + # image padding size + image_pad_right = int(float(w)/pw + 1) * pw - w + image_pad_bottom = int(float(h)/ph + 1) * ph - h + image_pad_right = 0 if image_pad_right == pw else image_pad_right + image_pad_bottom = 0 if image_pad_bottom == ph else image_pad_bottom + # patch padding size + patch_pad_top = patch_pad_bottom = self.eval_pad_size[0] + patch_pad_left = patch_pad_right = self.eval_pad_size[1] + + # pad image + pad_t = patch_pad_top + pad_b = patch_pad_bottom + image_pad_bottom + pad_l = patch_pad_left + pad_r = patch_pad_right + image_pad_right + img_paded = np.pad(data, ((0, 0), + (pad_t, pad_b), + (pad_l, pad_r), + (0, 0)), mode='symmetric') + + new_h, new_w = img_paded.shape[1:3] + self._network_direct_outsize = (self.num_output_frames, new_h*self.scale, new_w*self.scale, 3) + + # number of patches + num_split_y = (h + image_pad_bottom) // ph + num_split_x = (w + image_pad_right) // pw + self._num_split = (num_split_y, num_split_x) + + img_patches = [] + for split_j, split_i in product(range(num_split_y), range(num_split_x)): + # extract patches with extra pad size + patch_start_y = split_j * ph + patch_end_y = patch_start_y + ph + patch_pad_top + patch_pad_bottom + patch_start_x = split_i * pw + patch_end_x = patch_start_x + pw + patch_pad_left + patch_pad_right + img_patches.append(img_paded[:, patch_start_y:patch_end_y, patch_start_x:patch_end_x, :]) + + img_patches = np.array(img_patches) + num_patches = img_patches.shape[0] + batch_pad = (num_patches // num_patches_per_step + 1) * num_patches_per_step - num_patches + batch_pad = 0 if batch_pad == num_patches_per_step else batch_pad + self._patch_batch_pad = batch_pad + + # Concatenate all the patches in order. + if batch_pad > 0: + img_patches_padded = np.concatenate([ + img_patches, + np.zeros([batch_pad, *img_patches.shape[1:]], dtype=np.float32), + ], axis=0) + else: + img_patches_padded = img_patches + return img_patches_padded + + def _merge_patches_to_images_canonical(self, data): + # This is the reverse processing of the dispatching. + ph, pw = self.eval_in_size + num_split_y, num_split_x = self._num_split + sr_all = np.zeros(self._network_direct_outsize, dtype=np.float32) # [num_output_frames, h, w, c] + + h, w = self.eval_raw_size + patch_pad_top, patch_pad_left = self.eval_pad_size + patch_sr = np.array(data) # should be [num_patches, num_output_frames, h, w, c] + if patch_sr.ndim == 4 and self.num_output_frames == 1 and patch_sr.shape[1] != 1: + patch_sr = np.expand_dims(patch_sr, axis=1) + + patch_s_y = patch_pad_top * self.scale + patch_e_y = (patch_pad_top + ph) * self.scale + patch_s_x = patch_pad_left * self.scale + patch_e_x = (patch_pad_left + pw) * self.scale + patch_id = 0 + for split_j, split_i in product(range(num_split_y), range(num_split_x)): + im_s_y = split_j * ph * self.scale + im_e_y = im_s_y + ph * self.scale + im_s_x = split_i * pw * self.scale + im_e_x = im_s_x + pw * self.scale + sr_all[:, im_s_y:im_e_y, im_s_x:im_e_x] = patch_sr[patch_id, :, patch_s_y:patch_e_y, patch_s_x:patch_e_x] + patch_id += 1 + + # Trim the output to expected size. + sr_all = sr_all[:, :h*self.scale, :w*self.scale] + return sr_all.squeeze() diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/constant.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/constant.py new file mode 100644 index 0000000000000000000000000000000000000000..9156812f7c59e08658a735e6161eaaf8dc8ce34e --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/constant.py @@ -0,0 +1,47 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from enum import Enum + +VALID_COLORSPACE = {'rgb', 'bgr', 'lab', 'yuv', 'ycrcb', 'gray3d', 'gray', 'yuv', 'y'} +VALID_MODE = {'train', 'eval', 'inference', 'freeze'} +VALID_PARADIGM = {'dni'} +VALID_DEBUG_MODE = {'zeroin', 'intermediate'} +VALID_TASK = {'vsr', 'denoise', 'face', 'hdr', 'vfi'} + +# HDR +HDR_CODEC_PIX_FMT = 'gbrpf32le' +HDR_FILE_SUPPORTED_EXT = 'exr' + +SDR_CODEC_PIX_FMT = 'bgr24' +SDR_FILE_SUPPORTED_EXT = 'png' + +RESOURCE_FILE = r'src/resource.json' + +FILE_EXT_TO_PIX_FMT = { + HDR_FILE_SUPPORTED_EXT: HDR_CODEC_PIX_FMT, + SDR_FILE_SUPPORTED_EXT: SDR_CODEC_PIX_FMT, +} +VALID_FILE_EXT = FILE_EXT_TO_PIX_FMT.keys() + + +# io backend +class IO_BACKEND: + DISK = 'disk' + FFMPEG = 'ffmpeg' + + @classmethod + def CHECK_VALID(cls, io_backend): + assert io_backend in {cls.DISK, cls.FFMPEG}, \ + f'Invalid io backend {io_backend}' diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/defaults.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..30d51d8892845ca19a4aa9719c43e40820341423 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/defaults.py @@ -0,0 +1,269 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from yacs.config import CfgNode as CN + + +cfg = CN(new_allowed=True) + +cfg.mode = 'train' +cfg.task = 'vsr' # 'denoise', 'vsr' + +# ---------------------------------------------------------------------------- # +# Model (common) +# ---------------------------------------------------------------------------- # +cfg.model = CN(new_allowed=True) +cfg.model.name = 'EDVR' # Key for model +cfg.model.scope = 'G' # Scope key for parameters + +# for vfi +cfg.model.frame_rate = 2 + +# for vsr +cfg.model.scale = 4 + +# The input size as well as the placeholder will be adapted automatically. +# See base_model.py `_cal_input_size` function, and inferencer.py `adapt_input` function. +# The output will be reversed-adapted by the inferencer. +cfg.model.factor_for_adapt_input = 4 + +# The following num of frames are standalone defined to generalize to model configuration, +# e.g., extend to temporal, or extend to cascading of models: +# num_net_input_frames: the num frames of input LQ when model inference +# num_net_output_frames: the num frames of output SR when model inference +# num_data_lq_frames: the num frames of input LQ when dataloader in training +# num_data_gt_frames: the num frames of target GT when dataloader in training for supervision +cfg.model.num_net_input_frames = 5 +cfg.model.num_net_output_frames = 1 + +# Options for the input dimension +# 4: 4D tensor, with shape [b*frames, h, w, c], used when model frozen +# 5: 5D tensor, with shape [b, frames, h, w, c] +cfg.model.input_format_dimension = 5 +cfg.model.convert_output_to_uint8 = False + +# ---------------------------------------------------------------------------- # +# Loss (common) +# ---------------------------------------------------------------------------- # +cfg.loss = CN(new_allowed=True) +cfg.loss.content = CN(new_allowed=True) +cfg.loss.content.loss_type = 'L1Loss' +cfg.loss.content.loss_reduction = 'mean' +cfg.loss.content.loss_margin = 1e-6 + +# Loss (edge) +cfg.loss.edge = CN(new_allowed=True) +cfg.loss.edge.loss_weight = 0. +cfg.loss.edge.method = 'sobel' # ['sobel', 'laplacian'] + +# Loss (perceptual) +# perceptual module +cfg.loss.perceptual = CN(new_allowed=True) +cfg.loss.perceptual.loss_weight = 0. +cfg.loss.perceptual.module = 'vgg_19' +cfg.loss.perceptual.layers = ['vgg_19/conv2/conv2_2', + 'vgg_19/conv3/conv3_4', + 'vgg_19/conv4/conv4_4', + 'vgg_19/conv5/conv5_4'] +cfg.loss.perceptual.layers_weights = [1.0, 1.0, 1.0, 1.0] +# full ckpt file should be '${ckpt_dir}/${module}.ckpt' +cfg.loss.perceptual.ckpt_dir = './pretrained_modules' + +# Loss (adv) +cfg.loss.adversarial = CN(new_allowed=True) +cfg.loss.adversarial.loss_weight = 0. +cfg.loss.adversarial.adaptive_strategy = False +cfg.loss.adversarial.d_balance = 0.4 +cfg.loss.adversarial.gan_type = 'VanillaGAN' +cfg.loss.adversarial.grad_penalty_weight = 0.1 +cfg.loss.adversarial.g_update_interval = 1 +cfg.loss.adversarial.d_update_interval = 1 +cfg.loss.adversarial.loss_type = 'VanillaAdvLoss' +cfg.loss.adversarial.loss_reduction = 'mean' +cfg.loss.adversarial.norm_type = 'in' +cfg.loss.adversarial.mid_channels = 64 +cfg.loss.adversarial.parameter_clip = False +cfg.loss.adversarial.parameter_clip_range = [-0.01, 0.01] + +# ---------------------------------------------------------------------------- # +# Data (common) +# ---------------------------------------------------------------------------- # +cfg.data = CN(new_allowed=True) +# For mixture datasets, each should be separated with ':' +cfg.data.data_dir = 'data/reds' + +cfg.data.num_data_lq_frames = 5 +cfg.data.num_data_gt_frames = 1 +# File extension. For HDR, it should be 'exr'. For others, it would be 'png' +# Note: it's only used in inference dataset for now. +cfg.data.extension = 'png' +# ['bgr', 'rgb', 'lab'], default to `rgb` +cfg.data.color_space = 'rgb' +cfg.data.normalized = True + +# training +cfg.data.train = CN(new_allowed=True) +cfg.data.train.degradation = CN(new_allowed=True) +cfg.data.train.degradation.online = False +cfg.data.train.degradation.options = \ +""" +GaussianNoise: + input_dim: 4 + noise_level: 20 +IsotropicGaussianBlur2D: + input_dim: 4 + kernel_size: 15 + sigma: 10 +BicubicDownsampling: + input_dim: 4 + scale: 4 +batch_apply: False +""" +cfg.data.train.gt_enhancement = False +cfg.data.train.set_file = 'train.json' +cfg.data.train.batch_size = 4 +cfg.data.train.input_size = [64, 64] + +cfg.data.train.augmentation = CN(new_allowed=True) +cfg.data.train.augmentation.apply = True +cfg.data.train.augmentation.interval_list = [1, 2] +# Augmentation options, should be a doc-string (yml formatted), +# for example, the following. Note that in 'RandomCrop', the +# 'crop_size' and 'scale' will be further provided by the +# _TrainDataset class based on other configurations. Therefore, +# there is no need for users to explicitly provide these two +# parameters. The reason of such design is to avoid duplicate +# configure of the two parameters. +cfg.data.train.augmentation.options = \ +""" +RandomCrop: + input_dim: 4 +RandomTemporalReverse: + input_dim: 4 +RandomFlipLeftRight: + input_dim: 4 +RandomFlipUpDown: + input_dim: 4 +shuffle_transformers_order: False +""" + +# inference +cfg.data.inference = CN() +cfg.data.inference.auto_adapt_input = True +cfg.data.inference.batch_size = 1 +cfg.data.inference.input_size = [180, 320] +cfg.data.inference.eval_using_patch = False +cfg.data.inference.patch_pad_size = [32, 32] + +# Specify the max size of the input supported by the network. +# When releasing, the program will adaptively use different strategies on whether do +# inference with the whole image input or stitching. +cfg.data.inference.max_input_size = [540, 960] +cfg.data.inference.best_patch_size = [540, 640] + +# A subset of the given dataset for inference, (min_index, max_index). +# One should set the index **in** the file name, instead of the actual index of the +# file order. For example, the files are: +# - samples +# |- 0001.png (file list index 0) +# |- 0002.png (file list index 1) +# |- 0003.png (file list index 2) +# |- 0004.png (file list index 3) +# `- 0005.png (file list index 4) +# and the frames 0002.png - 0004.png are about to be inferred. Then the value of +# the following key should be [2, 4] (indices **in** the file name), rather than +# [1, 3] (indices of the file list). +cfg.data.inference.subset_range = [] +cfg.data.inference.subset_list = [] + +# ---------------------------------------------------------------------------- # +# Training (common) +# ---------------------------------------------------------------------------- # +cfg.train = CN(new_allowed=True) +cfg.train.training_scope = '' +cfg.train.pretrained_scope_list = [] +cfg.train.pretrained_scope_ckpt = [] + +cfg.train.optimizer = CN(new_allowed=True) +cfg.train.optimizer.type = 'Adam' + +# TODO: add options for optimizer +cfg.train.generator = CN(new_allowed=True) +cfg.train.generator.lr_schedule = CN(new_allowed=True) +cfg.train.generator.lr_schedule.type = 'CosineRestart' +cfg.train.generator.lr_schedule.base_lr = 4e-4 +cfg.train.generator.lr_schedule.total_steps = [10000] +cfg.train.generator.lr_schedule.restart_weights = [1, 0.5, 0.5, 0.5] +cfg.train.generator.lr_schedule.min_lr = 1e-7 + +# Discriminator lr schedule +cfg.train.discriminator = CN(new_allowed=True) +cfg.train.discriminator.lr_schedule = CN(new_allowed=True) +cfg.train.discriminator.lr_schedule.type = 'CosineRestart' +cfg.train.discriminator.lr_schedule.base_lr = 4e-4 +cfg.train.discriminator.lr_schedule.total_steps = [150000, 150000, 150000, 150000] +cfg.train.discriminator.lr_schedule.restart_weights = [1, 0.5, 0.5, 0.5] +cfg.train.discriminator.lr_schedule.min_lr = 1e-7 + +cfg.train.checkpoint_interval = 5000 +cfg.train.print_interval = 20 +cfg.train.loss_scale = 'off' + +cfg.train.use_tensorboard = False +cfg.train.dump_intermediate = False +cfg.train.dump_intermediate_interval = 2000 +cfg.train.continue_training = False + +cfg.train.output_dir = 'outputs/edvr' + +# ---------------------------------------------------------------------------- # +# Session +# ---------------------------------------------------------------------------- # +cfg.session = CN() +cfg.session.mix_precision = False +cfg.session.xla = False + +# ---------------------------------------------------------------------------- # +# Env +# ---------------------------------------------------------------------------- # +cfg.env = CN(new_allowed=True) +cfg.env.device = 'npu' +cfg.env.device_ids = [0] +cfg.env.rank_size = 1 +cfg.env.root_rank = 0 + +# ---------------------------------------------------------------------------- # +# Misc +# ---------------------------------------------------------------------------- # +cfg.debug_mode = False +cfg.log_file = '' +cfg.checkpoint = '' + +# ---------------------------------------------------------------------------- # +# Inference +# ---------------------------------------------------------------------------- # +cfg.inference = CN(new_allowed=True) +cfg.inference.write_out = True +cfg.inference.io_backend = 'disk' + +# disk scenario +cfg.inference.result_dir = '' +cfg.inference.writer_num_threads = 8 +cfg.inference.writer_queue_size = 64 # used in both disk and ffmpeg scenario + +# ffmpeg stream scenario +cfg.inference.ffmpeg = CN(new_allowed=True) +cfg.inference.ffmpeg.video_filename = 'test' +cfg.inference.ffmpeg.fps = 25. +cfg.inference.ffmpeg.codec_file = './config/codecs/default_x264.json' diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..493b218edcdb3f046f2416ffbee16b6694939ade --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/__init__.py @@ -0,0 +1,65 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .noise import * +from .scaling import * +from .blur import * + +from src.utils.klass import get_subclass_given_name + + +class Degradation(object): + """Composes several degradations together. + + Args: + transforms: list[Transform], list of joint transforms to compose. + """ + def __init__(self, degradations=None): + self.degradations = degradations + + @classmethod + def from_cfgs(cls, options, **kwargs): + """Construct augmentation pipeline from cfg dict. + + Args: + options: dict, pairs of {Transform_class_type: kwargs}. + kwargs: dict, additional kwargs. + + Returns: + A composed transform instance. + """ + + t = [] + for k, v in options.items(): + if k == 'RandomCrop': + # crop_size and scales are required terms + v['crop_size'] = kwargs['crop_size'] + v['scales'] = kwargs['scales'] + elif k == 'Scaling': + v['scales'] = kwargs['scales'] + _filter = get_subclass_given_name(Base, k) + t.append(_filter(**v)) + return cls(t) + + def __call__(self, *img): + for t in self.transforms: + img = t(*img) + return img + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/base.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/base.py new file mode 100644 index 0000000000000000000000000000000000000000..c0886c68c2b5858c7355e0d8bb75b474678d9f10 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/base.py @@ -0,0 +1,45 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import random +import time + + +class Base: + """Base class for degradation. + """ + def set_numpy_random_seed(self): + # Numpy random states are the same across all the mutli-processing. + # In order to maintain the randomness, use the system timestamp to + # manually set numpy seed every time this function is called. + np.random.seed(int(time.time() + random.random() * 1000000)) + + def check_input(self, x): + return isinstance(x, np.ndarray) and x.ndim == 3 + + def __call__(self, im): + self.set_numpy_random_seed() + if self.check_input(im): + return self.apply(im) + else: + raise ValueError(f'Expect input image to be 3D-array (HWC), but got {im.ndim}D-array.') + + def apply(self, im): + raise NotImplementedError + + def __repr__(self): + return f'{self.__class__.__name__}@{id(self)}' + + def __str__(self): + return self.__repr__() \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/blur.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/blur.py new file mode 100644 index 0000000000000000000000000000000000000000..b2328b29e3a82374b30640310e64c3c2f162dddf --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/blur.py @@ -0,0 +1,163 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import numpy as np +import cv2 +import time +import random +from scipy import special + +from src.utils.degradation.base import Base + + +class BlurKernel2D(Base): + """A common base class for blurness. + """ + def check_kernel(self): + if self.ksize[0] % 2 == 0 or self.ksize[1] % 2 == 0: + raise ValueError(f'Expect kernel size to be odd (1, 3, 5, etc.), but got {self.ksize}') + + +class AvgBlur2D(BlurKernel2D): + """A 2D average blur operator. + + Args: + k_size: int, blur kernel size, expected to be odd. + """ + def __init__(self, k_size): + assert k_size % 2 == 1 + k_size = (k_size, k_size) + self.k_size = k_size + + def apply(self, im): + return cv2.blur(im, self.k_size, borderType=cv2.BORDER_REFLECT_101) + + +class IsotropicGaussianBlur2D(BlurKernel2D): + """An isotropic Gaussian blur operator. + + Args: + kernel_size: int, blur kernel size, expected to be odd. + std: float, width of the kernel. + """ + def __init__(self, kernel_size, std): + assert kernel_size % 2 == 1 + self.kernel_size = (kernel_size, kernel_size) + self.std = std + + def apply(self, im): + if self.check_input(im): + return cv2.GaussianBlur(im, self.kernel_size, self.std, self.std, borderType=cv2.BORDER_REFLECT_101) + else: + raise ValueError + + +def gaussian(x, k, s): + return np.exp(-(x-(k-1)/2)**2/(2*s**2)) + + + +class AnisotropicGaussianBlur2D(BlurKernel2D): + """An anisotropic Gaussian blur operator. + + Reference to + https://github.com/cszn/USRNet/blob/4fb56deb80d655abb722ff83750ad3df163ef833/utils/utils_sisr.py#L129 + + Args: + kernel_size: int, blur kernel size, expected to be odd. + var: float, width of the kernel. + + """ + def __init__(self, kernel_size, var, angle, scale=1, noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + assert kernel_size % 2 == 1 + k_size = np.array([kernel_size, kernel_size]) + + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1, lambda_2 = var + # noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(angle), -np.sin(angle)], + [np.sin(angle), np.cos(angle)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5*(scale - 1) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X,Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z-MU + ZZ_t = ZZ.transpose(0,1,3,2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) + + # Normalize the kernel and return + self.kernel = raw_kernel / np.sum(raw_kernel) + + def apply(self, im): + return cv2.filter2D(im, -1, self.kernel) + + +def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): + """2D sinc filter. + + Borrowed from https://github.com/xinntao/BasicSR/blob/master/basicsr/data/degradations.py#L392 + Ref: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter + + Args: + cutoff: float, cutoff frequency in radians (pi is max) + kernel_size: int, horizontal and vertical size, must be odd. + pad_to: int, pad kernel size to desired size, must be odd or zero. + + Returns: + ndarray of [kernel_size, kernel_size], the sinc kernel. + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + kernel = np.fromfunction( + lambda x, y: cutoff * special.j1(cutoff * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) + kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) + kernel = kernel / np.sum(kernel) + if pad_to > kernel_size: + pad_size = (pad_to - kernel_size) // 2 + kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) + return kernel + + +class SincFilter(BlurKernel2D): + """A sinc filter. + + Args: + kernel_size: int, blur kernel size, expected to be odd. + omega_c: float, cutoff frequency in radians (pi is max) + """ + def __init__(self, kernel_size, omega_c): + self.kernel_size = kernel_size + self.omega_c = omega_c + self.kernel = circular_lowpass_kernel(self.omega_c, self.kernel_size, pad_to=False) + + def apply(self, im): + return cv2.filter2D(im, -1, self.kernel) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/noise.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/noise.py new file mode 100644 index 0000000000000000000000000000000000000000..ef24f2c6debb036496d052cfa7a6bde23a25d60d --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/noise.py @@ -0,0 +1,195 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import numpy as np +import cv2 +import random +import time + +from src.utils.degradation.base import Base + + +class NoiseAugmentation(Base): + """Noise addition class. + """ + def __init__(self, **kwargs): + self.max_value = 255. + self.min_value = 0. # input data should be in range [min_value, max_value] + self._data_format = 'hwc' # ['hwc', 'thwc'] + + def get_height(self, im): + return im.shape[self._data_format.index['h']] + + def get_width(self, im): + return im.shape[self._data_format.index['w']] + + def get_temp(self, im): + return im.shape[self._data_format.index['t']] + + @property + def data_format(self): + return self._data_format + + @data_format.setter + def data_format(self, target_format): + self._data_format = target_format.lower() + + def __call__(self, im, **kwargs): + # Input im should be in range [0, 255], either with np.uint8 or np.float32 dtype. + if self.check_input(im): + # Numpy random states are the same across all the mutli-processing. + # In order to maintain the randomness, use the system timestamp to + # manually set numpy seed every time this function is called. + self.set_numpy_random_seed() + im = self.apply(im) + im = np.clip(im.astype(np.float32), a_min=self.min_value, a_max=self.max_value) + return im + else: + raise ValueError(f'Expect input image to be [3D, 4D]-array, but got {im.ndim}D-array.') + + +class MultivarGaussianNoise(NoiseAugmentation): + """Multi-variate Gaussian noise. + + The noise in the channels is dependent. + + Args: + mean: float, the mean of the noise in each channel. + cor_var: ndarray, a 3x3 matrix of covariance. + """ + def __init__(self, mean=0., covar=None): + super().__init__() + assert covar is not None + self.mean = np.array([mean, mean, mean]) + self.cor_var = np.array(covar) + + def apply(self, clean_data, **kwargs): + shape = clean_data.shape + noise = np.random.multivariate_normal(self.mean, self.cor_var, size=shape[:-1]) + return clean_data + noise + + +class ChannelIndependentGaussianNoise(NoiseAugmentation): + """Channel indenpent Gaussian noise. + + The noise in the channels is independent. + + Args: + mean: float, the mean of the noise in each channel. + std: float, standard deviation of the noise. + """ + def __init__(self, mean=0., std=0.01): + super(ChannelIndependentGaussianNoise, self).__init__() + self.mean = mean + self.std = std + + def apply(self, clean_data, **kwargs): + shape = clean_data.shape + noise = self.std * np.random.randn(*shape) + self.mean + return clean_data + noise + + +class GrayscaleGaussianNoise(NoiseAugmentation): + """Single channel Gaussian noise. + + The noise in the channels is broadcast to all the channels. + + Args: + mean: float, the mean of the noise in each channel. + std: float, standard deviation of the noise. + """ + def __init__(self, mean=0., std=0.01): + super(GrayscaleGaussianNoise, self).__init__() + self.mean = mean + self.std = std + + def apply(self, clean_data, **kwargs): + shape = list(clean_data.shape) + shape[-1] = 1 + noise = self.std * np.random.randn(*shape) + self.mean + return clean_data + noise + + +class SaltPepperNoise(NoiseAugmentation): + """Salt and pepper noise. + + Args: + amount: float, total proportion of the noise pixels in the image. + salt_ratio: float, the proportion of the salt (white) in the noisy pixels. + """ + def __init__(self, amount=0.005, salt_ratio=0.5): + super().__init__() + self.amount = amount + self.salt_noise_ratio = salt_ratio + + def apply(self, clean_data, **kwargs): + h, w = clean_data.shape[:2] + # make a copy + noisy = np.array(clean_data) + + num_salt = np.ceil(self.amount * h * w * self.salt_noise_ratio) + coord = [np.random.randint(0, i - 1, int(num_salt)) for i in [h, w]] + noisy[tuple(coord)] = self.max_value + + num_pepper = np.ceil(self.amount * h * w * (1. - self.salt_noise_ratio)) + coord = [np.random.randint(0, i - 1, int(num_pepper)) for i in [h, w]] + noisy[tuple(coord)] = self.min_value + return noisy + + +class SpeckleNoise(NoiseAugmentation): + """Spekle noise. + """ + def apply(self, clean_data, **kwargs): + shape = clean_data.shape + gauss = np.random.randn(*shape) + noisy = clean_data + clean_data * gauss + return noisy + + +class JPEGCompressionNoise(NoiseAugmentation): + """JPEG compression noise. + + Args: + quality: int, ranged in [0, 100], controls the quality of the compressed + image. The lower the quality is, the worse the image looks like. + """ + def __init__(self, quality): + super(JPEGCompressionNoise, self).__init__() + self.quality = int(quality) + + def apply(self, clean_data, **kwargs): + clean_data = np.clip(clean_data, self.min_value, self.max_value).astype(np.uint8) + encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), self.quality] + result, encimg = cv2.imencode('.jpg', clean_data, encode_param) + noisy = cv2.imdecode(encimg, cv2.IMREAD_COLOR) + return noisy.astype(np.float32) + + +class PoissonNoise(NoiseAugmentation): + """Poisson noise. + + Reference https://stackoverflow.com/questions/19289470/adding-poisson-noise-to-an-image + https://github.com/xinntao/BasicSR/blob/master/basicsr/data/degradations.py#L587 + """ + def apply(self, clean_data, **kwargs): + # round and clip image for counting vals correctly + vals = len(np.unique(clean_data.astype(np.uint8))) + vals = 2**np.ceil(np.log2(vals)) + + img = np.clip(clean_data, self.min_value, self.max_value) / self.max_value + out = np.float32(np.random.poisson(img * vals) / float(vals)) + noise = (out - img) * self.max_value + + return clean_data + noise diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/scaling.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/scaling.py new file mode 100644 index 0000000000000000000000000000000000000000..febce3002eab445f9807c0ad7e058168bc6e04af --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/degradation/scaling.py @@ -0,0 +1,96 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import numpy as np +import cv2 +import time +import random +from scipy.interpolate import interp2d + +from src.utils.degradation.base import Base + + +class SpatialScaling(Base): + """Up- and down-sampling degradation. + + Args: + target_size: list[int], [W, H] specifying the size after resize. + scales: list[float], [Sw, Sh] specifying the scales of each dimension. + """ + def __init__(self, target_size, scales=None): + # Note: target_size and scales should be in [x, y] or [w, h] order + self.scales = scales + self.target_size = target_size + assert isinstance(self.target_size, tuple) + + +class NearestScaling(SpatialScaling): + """Nearest scaling up and down. + + Args: + kernel_width: int, kernel width to remedy the misalignment of nearest + sampling. + """ + def __init__(self, target_size, scales, kernel_width): + super(NearestScaling, self).__init__(target_size, scales) + kernel = cv2.getGaussianKernel(21, kernel_width) + self.kernel = kernel @ np.transpose(kernel) + self.kernel = self.shift_pixels(self.kernel, scales) + + def shift_pixels(self, x, scales, upper_left=False): + """shift pixel for super-resolution with different scale factors + + Args: + x: WxHxC or WxH, image or kernel + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (scales-1)*0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift[0] + y1 = yv + shift[1] + else: + x1 = xv - shift[0] + y1 = yv - shift[1] + + x1 = np.clip(x1, 0, w-1) + y1 = np.clip(y1, 0, h-1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + def apply(self, im): + im = cv2.filter2D(im, -1, self.kernel) + return cv2.resize(im, dsize=self.target_size, fx=0., fy=0., interpolation=cv2.INTER_NEAREST) + + +class BicubicScaling(SpatialScaling): + """Bicubic sampling. + """ + def apply(self, im): + return cv2.resize(im, dsize=self.target_size, fx=0., fy=0., interpolation=cv2.INTER_LINEAR) + + +class BilinearScaling(SpatialScaling): + """Bilinear sampling. + """ + def apply(self, im): + return cv2.resize(im, dsize=self.target_size, fx=0., fy=0., interpolation=cv2.INTER_CUBIC) diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/exceptions.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..b0e3c0404ca0e94b5cdec8267ba1d3bf47df5a54 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/exceptions.py @@ -0,0 +1,34 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class Error(Exception): + """Customize Exception class. + """ + pass + + +class WorldUninitializedError(Error): + pass + + +class SessionUndefinedError(Error): + pass + + +class ArrayDimensionError(Error): + pass + + +class DirectoryNotFoundError(Error): + pass diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/file_io.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/file_io.py new file mode 100644 index 0000000000000000000000000000000000000000..a9b8af3b5f04aa3b12c7ac850c249364ef2c0b6f --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/file_io.py @@ -0,0 +1,557 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import multiprocessing as mp +import os +import queue +import random +import shutil +import threading +import time +from functools import partial +from subprocess import Popen, PIPE + +import cv2 +import imageio +import numpy as np +from PIL import Image +from src.utils.klass import Singleton +from src.utils.logger import logger +from src.utils.constant import VALID_COLORSPACE, IO_BACKEND +from src.utils.utils import convert_to_dict + + +def imread(x, target_color_space='rgb'): + """Wrapped image read function. + + Support normal SDR png image, as well as HDR exr image. + + Args: + x: str, image file name. + target_color_space: str, what color space should the output image is in. + + Returns: + ndarray, an image of the target_color_space. + """ + target_color_space = target_color_space.lower() + assert target_color_space in VALID_COLORSPACE + + if x.endswith('.exr'): + # read hdr + im = cv2.imread(x, cv2.IMREAD_UNCHANGED) + else: + im = cv2.imread(x) + + # convert to grayscale if required. + if target_color_space == 'gray3d': + im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) + im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) + elif target_color_space == 'gray': + im = im[:,:,0:1] + + # data_format convert + if target_color_space in ['bgr', 'gray', 'gray3d']: + out = im + elif target_color_space == 'rgb': + out = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + elif target_color_space == 'lab': + out = cv2.cvtColor(im, cv2.COLOR_BGR2LAB) + elif target_color_space == 'ycrcb': + out = cv2.cvtColor(im, cv2.COLOR_BGR2YCrCb) + elif target_color_space == 'yuv': + out = cv2.cvtColor(im, cv2.COLOR_BGR2YUV) + elif target_color_space == 'y': + out = cv2.cvtColor(im, cv2.COLOR_BGR2YUV) + out = out[:,:,0:1] + else: + raise ValueError("Unknown data_format as {}, or maybe just mismatched!".format(target_color_space)) + + return out + + +def imwrite(name, x, source_color_space='rgb', benormalized=True): + """Wrapped image write function. + + Support normal SDR png image, as well as HDR exr image. + + Args: + name: str, output image file name. + x: ndarray, with shape [H, W, C]. + source_color_space: str, in what color space the source image is. + benormalized: boolean, whether the image is normalized. + """ + source_color_space = source_color_space.lower() + assert source_color_space in VALID_COLORSPACE + + hdr = name.endswith('.exr') + out = image_deprocess(x, source_color_space, benormalized, hdr) + if hdr: + hdr_image_write(name, out) + else: + sdr_image_write(name, out) + + +def image_deprocess(x, source_color_space='rgb', benormalized=True, hdr=False): + """Image deprocess function. + + Converts the normalized ndarray to a writable image by opencv. + """ + if hdr: + return hdr_image_deprocess(x, source_color_space) + else: + return sdr_image_deprocess(x, source_color_space, benormalized) + + +def hdr_image_deprocess(x, source_color_space='rgb'): + """Image deprocess function of HDR image. + + HDR image is always normalized. The only thing to do is to convert to another + color space. + """ + if source_color_space == 'rgb': + x = x[..., ::-1] + elif source_color_space == 'bgr': + pass + else: + raise NotImplementedError(f'HDR output does not support color-spaces other than RGB and BGR.') + return x + + +def sdr_image_deprocess(x, source_color_space='rgb', benormalized=True): + """Image deprocess function of SDR image. + + Converts the color space to 'bgr' for opencv to write out; denormalizes the + data to uint8. + """ + source_color_space = source_color_space.lower() + assert source_color_space in VALID_COLORSPACE + + if benormalized and source_color_space not in ['ycrcb', 'yuv', 'y']: + x[...] = x[...] * 255 + x = np.clip(x, 0., 255.) + + if source_color_space in ['bgr', 'gray']: + out = x + elif source_color_space == 'rgb': + out = cv2.cvtColor(x, cv2.COLOR_RGB2BGR, cv2.CV_32F) + elif source_color_space in ['lab', 'gray3d']: + x[:, :, 0:1] = x[:, :, 0:1] / 2.55 + x[:, :, 1:3] = x[:, :, 1:3] - 128. + out = cv2.cvtColor(x, cv2.COLOR_LAB2BGR, cv2.CV_32F) + out[...] = out[...] * 255. + elif source_color_space == 'ycrcb': + out = cv2.cvtColor(x, cv2.COLOR_YCrCb2BGR, cv2.CV_32F) + if benormalized: + out = np.clip(out * 255., 0., 255.) + elif source_color_space == 'yuv': + out = cv2.cvtColor(x, cv2.COLOR_YUV2BGR, cv2.CV_32F) + out = np.clip(out * 255, 0., 255.) + elif source_color_space == 'y': + out = np.clip(x * 255, 0., 255.) + else: + raise ValueError + + out = out.astype(np.uint8) + return out + + +def sdr_image_write(name, out): + # Just a wrapper. + cv2.imwrite(name, out) + + +def hdr_image_write(name, out): + # Save as half precision to for smaller file. + out = np.maximum(out, 0.) + cv2.imwrite(name, out, [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_HALF]) + +class HardDiskImageWriter: + """An image writer which saves the image data in a file on the hard disk. + + We use a queue and multi-thread to write the images to hard disk in the + background. + + Args: + max_num_threads: int, maximum number of the threads to use. + max_queue_size: int, maximum size of the queue to save the data in memory. + """ + def __init__(self, max_num_threads=1, max_queue_size=64): + self.queue = queue.Queue(max_queue_size) + self.threads_pool = [] + self.sentinel = (None, None) + self.max_num_threads = max_num_threads + self.notified = False + + def worker(self): + # Thread work to write out the images. + while True: + try: + elem = self.queue.get(True) + if id(elem) == id(self.sentinel): + self.end() + break + target_path, im_data = elem + hdr = target_path.endswith('.exr') + if hdr: + hdr_image_write(target_path, im_data) + else: + sdr_image_write(target_path, im_data) + except Exception as e: + if not self.notified: + self.notified = True + logger.error(f'Error when writing out images, {e}.') + pass + + def __del__(self): + # Wait until all the threads to join. + for t in self.threads_pool: + try: + t.join() + except: + pass + logger.info('Processing remaining elements') + + # Post check whether there are un-written. + while True: + try: + elem = self.queue.get(False) + assert id(elem) == id(self.sentinel), '[Warning] Remain elements in writing queue' + except queue.Empty: + break + + def put_to_queue(self, target_path, im_data): + # Put the target file name and the image data in the queue. + self.queue.put((target_path, im_data)) + if len(self.threads_pool) <= self.max_num_threads: + t = threading.Thread(target=self.worker, args=()) + t.start() + self.threads_pool.append(t) + + def end(self): + # Put sentinel in the queue to call exit. + self.queue.put(self.sentinel) + + +# https://github.com/imageio/imageio-ffmpeg/blob/f27b6cb31d4ed3fd436f3a22871b2b63d2384c80/imageio_ffmpeg/_utils.py#L55 +def _popen_kwargs(prevent_sigint=False): + startupinfo = None + preexec_fn = None + creationflags = 0 + if prevent_sigint: + # Prevent propagation of sigint (see #4) + # https://stackoverflow.com/questions/5045771 + preexec_fn = os.setpgrp # the _pre_exec does not seem to work + + falsy = ("", "0", "false", "no") + if os.getenv("IMAGEIO_FFMPEG_NO_PREVENT_SIGINT", "").lower() not in falsy: + # Unset preexec_fn to work around a strange hang on fork() (see #58) + preexec_fn = None + + return { + "startupinfo": startupinfo, + "creationflags": creationflags, + "preexec_fn": preexec_fn, + } + + +class FFMPEGStreamWriter: + """An image writer which saves the image data through ffmpeg stream to a video + file. + + Args: + video_filename: str, output target video file. + fps: str, vidoe fps for encoding. + output_param_file: str, codec config file for encoding. + output_resolution: list[int], the resolution [H, W] of the output video. + source_pix_fmt: str, the pixel format of the input to ffmpeg. When encoding + SDR vidoe, it should be `bgr24`. Or it should be 'gbrpf32le' for HDR. + ffmpeg_timeout: int, time limitation to prevent ffmpeg dies. + """ + def __init__(self, video_filename, fps='25', + output_param_file='./configs/codecs/default_x264.json', + output_resolution=None, + source_pix_fmt='bgr24', + ffmpeg_timeout=60): + + if output_resolution is None: + raise ValueError('Expect the output resolution, but got None.') + else: + assert len(output_resolution) == 2 + # W x H + s = f"{output_resolution[1]}x{output_resolution[0]}" + + with open(output_param_file, 'r') as fid: + output_params_dict = json.load(fid) + + # Input information. + vinput_opts = [ + '-r', str(fps), + '-f', 'rawvideo', + '-s', s, + '-pix_fmt', source_pix_fmt, + '-analyzeduration', str(2147483647), + '-probesize', str(2147483647), + ] + vinput_src = ['-i', '-'] + + # Output encoding information. + output_params = [] + vcodec = None + bitrate = None + pix_fmt = "yuv420p" + for k, v in output_params_dict["codec"].items(): + if k in ["-c:v", "-vcodec"]: + vcodec = v + elif k == '-bitrate': + bitrate = v + elif k == '-pix_fmt': + pix_fmt = v + else: + output_params += [k, v] + ext = output_params_dict.get("format", 'mp4') + + if bitrate is not None: + output_params += ['-bitrate', bitrate] + if vcodec is not None: + output_params += ['-c:v', vcodec] + output_params += ['-pix_fmt', pix_fmt] + + if not video_filename.endswith(ext): + video_filename = f'{video_filename}.{ext}' + + self.ffmpeg_timeout = ffmpeg_timeout + + self._basic_cmd = ['ffmpeg', '-y', + *vinput_opts, + *vinput_src, + *output_params, + video_filename, + ] + + def initialize(self): + # Use a generator to accept the image data without blocking the main + # processing. + self.write_gen = self._initialize_gen() + assert self.write_gen is not None + self.write_gen.send(None) + logger.info("Codec command:") + logger.info(self._basic_cmd) + + def _initialize_gen(self): + # Borrowed from imageio-ffmpeg + # https://github.com/imageio/imageio-ffmpeg/blob/master/imageio_ffmpeg/_io.py#L478 + stop_policy = 'timeout' + p = Popen( + self._basic_cmd, + stdin=PIPE, + stdout=None, + stderr=None, + **_popen_kwargs(prevent_sigint=True) + ) + + try: + while True: + frame = yield + try: + p.stdin.write(frame) + except Exception as err: + msg = ( + "{0:}\n\nFFMPEG COMMAND:\n{1:}\n\nFFMPEG STDERR " + "OUTPUT:\n".format(err, self._basic_cmd) + ) + stop_policy = "kill" + raise IOError(msg) + except GeneratorExit: + # Note that GeneratorExit does not inherit from Exception but BaseException + # Detect premature closing + raise + except Exception: + # Normal exceptions fall through + raise + except BaseException: + # Detect KeyboardInterrupt / SystemExit: don't wait for ffmpeg to quit + stop_policy = "kill" + raise + finally: + if p.poll() is None: + try: + p.stdin.close() + except Exception as err: # pragma: no cover + logger.warning("Error while attempting stop ffmpeg (w): " + str(err)) + + if stop_policy == "timeout": + # Wait until timeout, produce a warning and kill if it still exists + try: + etime = time.time() + self.ffmpeg_timeout + while (time.time() < etime) and p.poll() is None: + time.sleep(0.01) + finally: + if p.poll() is None: # pragma: no cover + logger.warn( + "We had to kill ffmpeg to stop it. " + + "Consider increasing ffmpeg_timeout, " + + "or setting it to zero (no timeout)." + ) + p.kill() + + elif stop_policy == "wait": + # Wait forever, kill if it if we're interrupted + try: + while p.poll() is None: + time.sleep(0.01) + finally: # the above can raise e.g. by ctrl-c or systemexit + if p.poll() is None: # pragma: no cover + p.kill() + + else: # stop_policy == "kill": + # Just kill it + p.kill() + # Just to be safe, wrap in try/except + try: + p.stdout.close() + except Exception: + pass + + def put_to_queue(self, target_path, im_data): + # target_path won't matter here + if im_data.dtype == np.float32: + # Notice that after the deprocess, everything is in bgr color space. + # HDR data, will use gbrpf32le pix_fmt. + # Make it [C, H, W] data format, and shift channels to [g, b, r]. + im_data = np.transpose(im_data[..., [1,0,2]], [2,0,1]) + # else is normal uint8 data. Use bgr24le pix_fmt and don't do anything + img_str = im_data.tobytes() + self.write_gen.send(img_str) + + def end(self): + self.write_gen.close() + + +class ImageWriter: + """A top class to handle the image writing. + + Multi-imagewriter is supported when writing out. This class contains all the + concrete writing instances, and deprocess the results passed by the engine and + feed to the writers. Multi-imagewriter can be configured using the + cfg.inference.io_backend where the backends are concatenated with ':'. For + example, setting: + + cfg.inference.io_backend = 'disk:ffmpeg' + + will use two image writer instance, one HardDiskImageWriter and the other + FFMPEGStreamWriter. + + Args: + output_dir: str, output top folder. + cfg: yacs node, global configuration. + source_color_space: str, in what color space the source image is. + benormalized: boolean, whether the image is normalized. + output_resolution: list[int], the resolution [H, W] of the output video. + pix_fmt: str, the pixel format of the input to ffmpeg. When encoding + SDR vidoe, it should be `bgr24`. Or it should be 'gbrpf32le' for HDR. + """ + def __init__(self, output_dir, cfg, benormalized=True, + source_color_space='bgr', output_resolution=None, + pix_fmt='bgr24' + ): + io_backends = cfg.inference.io_backend.split(':') + for ib in io_backends: + IO_BACKEND.CHECK_VALID(ib) + + self.io_backend = io_backends + self.cfg = cfg + + self.image_deprocess = partial(image_deprocess, + source_color_space=source_color_space, + benormalized=benormalized) + self.pix_fmt = pix_fmt + self.output_resolution = output_resolution + self.root_output_dir = output_dir + self.writers = [] + + for ib in io_backends: + self.add_writers(ib, self.root_output_dir) + + def add_writers(self, io_backend, root_dir=None): + # Add specific writers. + if root_dir is None: + root_dir = self.root_output_dir + + if io_backend == IO_BACKEND.DISK: + writer = HardDiskImageWriter(max_num_threads=self.cfg.inference.writer_num_threads, + max_queue_size=self.cfg.inference.writer_queue_size) + output_folder = root_dir + elif io_backend == IO_BACKEND.FFMPEG: + video_filename = os.path.join(f'{root_dir}_videos', self.cfg.inference.ffmpeg.video_filename) + writer = FFMPEGStreamWriter(video_filename=video_filename, + fps=self.cfg.inference.ffmpeg.fps, + output_param_file=self.cfg.inference.ffmpeg.codec_file, + source_pix_fmt=self.pix_fmt, + output_resolution=self.output_resolution, + ) + output_folder = f'{root_dir}_videos' + else: + raise NotImplementedError(f'{io_backend}') + + # Record both the writer instance and the output folder. + self.writers.append([writer, output_folder]) + + def initialize(self): + # Initialization and create folders if necessary. + logger.info(f'Using {self.io_backend} as the io backend.') + for writer_id, ib in enumerate(self.io_backend): + if ib in [IO_BACKEND.DISK, IO_BACKEND.FFMPEG]: + output_folder = self.writers[writer_id][1] + logger.info(f'For {ib} backend, the results will be written to {output_folder}') + os.makedirs(output_folder, exist_ok=True) + + if ib == IO_BACKEND.FFMPEG: + self.writers[writer_id][0].initialize() + + def finalize(self): + # Close the writers. + for writer, output_folder in self.writers: + writer.end() + + def write_out(self, output_data_dict): + output_data_dict = dict(sorted(output_data_dict.items(), key=lambda item: item[0])) + # Append image date to the writers after inference. + for target_file, data in output_data_dict.items(): + # without file copy: {output_file_name: ndarray} + # with file copy: {output_file_name: [source_file_name, ndarray]} + for backend_id, ib in enumerate(self.io_backend): + writer, output_folder = self.writers[backend_id] + target_file = os.path.join(output_folder, target_file) + if isinstance(data, np.ndarray): + # This scenario is only for multi-in single-out model, not include vfi + writer.put_to_queue(target_file, data) + elif isinstance(data, (list, tuple)): + # Mainly used in vfi scenario, or pipeline scenario. + if ib == IO_BACKEND.DISK: + # In the single vfi processing, we use shutil to copy the + # the original data instead of writing out from memory to disk. + # Yet we have not tested the performance of `writing out` strategy. + assert isinstance(data[0], str) + shutil.copy(data[0], target_file) + elif ib == IO_BACKEND.FFMPEG: + # This is used in ffmpeg stream, the first the target file to output + # while the second the output data. + assert isinstance(data[1], np.ndarray) + writer.put_to_queue(target_file, data[1]) + else: + raise NotImplementedError + else: + raise TypeError(f'Expect value `data` to be np.ndarray, or a list of [str, np.ndarray].' + f'But given {type(data)}') diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/klass.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/klass.py new file mode 100644 index 0000000000000000000000000000000000000000..dd15f29284d818583f3d18d2f6ab5a9d7b47396d --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/klass.py @@ -0,0 +1,45 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +def get_subclass_given_name(proto_type, sub_type_name): + """Get the subclass type given the name + + Look through all subclasses and select by type name. + + Args: + proto_type: base class. + sub_type_name: str, derived class name. + + Returns: + derived class type. + """ + subtype = [ + stp for stp in proto_type.__subclasses__() + if stp.__name__ == sub_type_name + ] + return subtype[0] + + +class Singleton(type): + """Singleton class type. + + A singleton class will only have one instance. + """ + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/logger.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..c8cfd4bdb709cb34fbb40ba20a3bd6812ce729ae --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/logger.py @@ -0,0 +1,91 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import logging +from src.utils.klass import Singleton + + +class Logger(metaclass=Singleton): + """A wrapper class of logging. + + Args: + level: enum, logging level. Default to logging.INFO + + Property: + silence: boolean, whether to mute the logger. Typically used in distributed + training and inference to silence the duplicated messages. + """ + def __init__(self, level=logging.INFO): + self.log_formatter = logging.Formatter( + "%(asctime)s [%(levelname)-5.5s] %(message)s" + ) + self._logger = logging.getLogger() + self._logger.setLevel(level) + self._logger.handlers.clear() + console_handler = logging.StreamHandler() + console_handler.setFormatter(self.log_formatter) + self._logger.addHandler(console_handler) + # A flag to control whether to output to stdout. + # Mainly used for distributed training, where only the root node will + # record the message. + self._silence = False + + def add_log_file(self, log_file): + """Add file handler. + + Args: + log_file: str, external log file to write out. + """ + if log_file is not None and log_file != '': + real_path = os.path.split(os.path.realpath(log_file))[0] + os.makedirs(real_path, exist_ok=True) + file_handler = logging.FileHandler(log_file) + file_handler.setFormatter(self.log_formatter) + self._logger.addHandler(file_handler) + self._logger.info(f'Log file: {log_file}') + + @property + def silence(self): + return self._silence + + @silence.setter + def silence(self, switch): + self._silence = switch + + def info(self, message, force=False): + if not self._silence or force: + self._logger.info(message) + + def warn(self, message, force=False): + if not self._silence or force: + self._logger.warning(message) + + def error(self, message, force=False): + if not self._silence or force: + self._logger.error(message) + + def fatal(self, message, force=False): + if not self._silence or force: + self._logger.fatal(message) + + def debug(self, message, force=False): + if not self._silence or force: + self._logger.debug(message) + + def warning(self, message, force=False): + self.warn(message, force) + + +logger = Logger() diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/moving_avg.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/moving_avg.py new file mode 100644 index 0000000000000000000000000000000000000000..0884c7f7a972369a2c9ef5615fe0f340df27c5c4 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/moving_avg.py @@ -0,0 +1,57 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +class MovingAvg: + """Class to record the buffering running statistics. + + Args: + smooth: float, a scalar in [0, 1] to smooth the statistics. + + Attributes: + sum: summation of historical data. + avg: average of historical data. + smooth_avg: smoothed average of historical data. + count: total number of historical data record. + cur_val: current data. + + Raises: + ValueError, when smooth is not between [0, 1]. + """ + def __init__(self, smooth=0.9): + if not (0. <= smooth <= 1.): + raise ValueError(f'Smooth value should be between [0, 1], ' + f'but is given {smooth}.') + self.smooth = smooth + self.clear() + + def update(self, val): + """Update statistics. + """ + self.cur_val = val + self.count += 1 + self.sum += val + self.avg = self.sum / self.count + if self.count == 1: + self.smooth_avg = val + else: + self.smooth_avg = self.smooth * self.smooth_avg + (1. - self.smooth) * val + + def clear(self): + """Clear all historical data. + """ + self.sum = 0. + self.avg = 0. + self.smooth_avg = 0. + self.count = 0 + self.cur_val = 0 diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/__init__.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..66f09226024d6b39187234c1231a83d1dc8a427e --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/__init__.py @@ -0,0 +1,69 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .joint_transforms import * +from src.utils.klass import get_subclass_given_name + + +class Compose(object): + """Composes several joint transforms together. + + Args: + transforms: list[Transform], list of joint transforms to compose. + """ + def __init__(self, transforms=None): + self.transforms = transforms + + @classmethod + def from_cfgs(cls, options, **kwargs): + """Construct augmentation pipeline from cfg dict. + + Args: + options: dict, pairs of {Transform_class_type: kwargs}. + kwargs: dict, additional kwargs. + + Returns: + A composed transform instance. + """ + + t = [] + for k, v in options.items(): + if k == 'RandomCrop': + # crop_size and scales are required terms + v['crop_size'] = kwargs['crop_size'] + v['scales'] = kwargs['scales'] + elif k == 'Scaling': + v['scales'] = kwargs['scales'] + + try: + _filter = get_subclass_given_name(_Transform, k) + except IndexError: + logger.error(f'Cannot find transform type {k}.') + raise ValueError() + + t.append(_filter(**v)) + return cls(t) + + def __call__(self, *img): + for t in self.transforms: + img = t(*img) + return img + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/joint_transforms.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/joint_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..4589a01316ca80c42f28ddd23c17c4d2785410ad --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/transform/joint_transforms.py @@ -0,0 +1,362 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import cv2 +import numpy as np +import random +import collections + + +__all__ = ['_Transform', 'RandomTemporalReverse', 'RandomFlipUpDown', + 'RandomFlipLeftRight', 'Scale', 'Resize', 'RandomCrop', + 'RandomDropChrominanceChannel', 'TempDistCrop', 'RandomSized', + 'RandomReverseColorChannel' +] + + +class _Transform(object): + """Base transform class. + """ + def __init__(self, input_dim=4): + self.input_dim = input_dim + + +class RandomTemporalReverse(_Transform): + """Random temporal reverse transform. + + This transform will reverse the multi-frame order. + """ + def __call__(self, *imgs): + if random.random() < 0.5: + imgs = [item[::-1] for item in imgs] + return imgs + + +class RandomFlipUpDown(_Transform): + """Random up-down flip transform. + + This transform will randomly flip the frames upside-down. + """ + def __init__(self, input_dim=3): + super().__init__(input_dim) + if input_dim == 3: + # HWC + self.fn = lambda x: x[::-1] + elif input_dim == 4: + # BHWC or DHWC + self.fn = lambda x: x[:, ::-1] + elif input_dim == 5: + # BDHWC + self.fn = lambda x: x[:, :, ::-1] + else: + raise NotImplementedError + + def __call__(self, *imgs): + if random.random() < 0.5: + imgs = [self.fn(item) for item in imgs] + return imgs + + +class RandomFlipLeftRight(_Transform): + """Random up-down flip transform. + + This transform will randomly flip the frames left-right. + """ + def __init__(self, input_dim=3): + super().__init__(input_dim) + if input_dim == 3: + # HWC + self.fn = lambda x: x[:, ::-1] + elif input_dim == 4: + # BHWC or DHWC + self.fn = lambda x: x[:, :, ::-1] + elif input_dim == 5: + # BDHWC + self.fn = lambda x: x[:, :, :, ::-1] + else: + raise NotImplementedError + + def __call__(self, *imgs): + if random.random() < 0.5: + imgs = [self.fn(item) for item in imgs] + return imgs + + +def _resize(img, new_size, input_dim, interpolation=cv2.INTER_LINEAR): + """Basic resize function. + """ + if img.shape[-1] == 1: + expand = True + else: + expand = False + if input_dim == 3: + img = cv2.resize(img, new_size, interpolation=interpolation) + elif input_dim == 4: + img = [cv2.resize(item, new_size, interpolation=interpolation) for item in img] + img = np.stack(img, axis=0) + else: + raise ValueError('Resize: image dimension must be in [3, 4]') + + if expand: + img = np.expand_dims(img, -1) + + return img + + +class Scale(_Transform): + """Resize the inputs given the scale. + """ + def __init__(self, input_dim, scales, interpolations=None): + super().__init__(input_dim) + self.scale = scales + self.interpolations = interpolations + + def __call__(self, *imgs): + h, w = imgs[0].shape[-3:-1] + ow = int(self.scale * w) + oh = int(self.scale * h) + if self.interpolations is None: + imgs = [_resize(item, (ow, oh), self.input_dim) + for item in imgs] + else: + imgs = [_resize(item, (ow, oh), self.input_dim, interpolation=interpolation) + for item, interpolation in zip(imgs, self.interpolations)] + return imgs + + +class Resize(_Transform): + """Resize the inputs given the target size. + """ + def __init__(self, input_dim, size, interpolations=None): + super().__init__(input_dim) + self.size = size + self.interpolations = interpolations + + def __call__(self, *imgs): + new_size = tuple([self.size, self.size]) + if self.interpolations is None: + imgs = [_resize(item, new_size, self.input_dim) + for item in imgs] + else: + imgs = [_resize(item, new_size, self.input_dim, interpolation=interpolation) + for item, interpolation in zip(imgs, self.interpolations)] + return imgs + + +class RandomCrop(_Transform): + """Random crop the images into patches. + + This function is for multiple input array, i.e. [lr_array, lr2_array, ..., hd_array]. + We want the corresponding crops of these inputs. Therefore, the function accepts + a base crop_size and the scales of the base crop_size that correspond to the + expected output patch size of each input array. + + For example, in 4x super resolution we want the training input has the size + [64, 64], and the output thus [256, 256] size. Therefore when cropping the paired + lr and gt data, the corresponding regions of lr and gt are to be cropped. We + can use a RandomCrop transform with: + + Example: + >>> lr, gt = get_data() # suppose 4D tensors of data format DHWC + >>> tr = RandomCrop(input_dim=4, crop_size=(64, 64), scales=(1, 4)) + >>> lr_crop, gt_crop = tr([lr, gt]) + + Args: + input_dim: int, dimension of each input. + crop_size: list[int], the base size [H, W] of the patch. + scales: list[int], the scales of the crop for each input. + bbox: list[int], the bounding box of the crop, [H_ul, W_ul, H_br, W_br], + where H_ul and W_ul are the height and width of upper-left pixel, + H_br and W_br are the height and width of the bottom-right pixel. + """ + def __init__(self, input_dim, crop_size, scales, bbox=None): + super().__init__(input_dim) + # Notice, this transformation always based on the first element of the images + self._h = crop_size[0] + self._w = crop_size[1] + self.scales = scales + self.bbox = bbox + + def crop(self, im, ymin, xmin, ymax, xmax): + if self.input_dim == 3: + # HWC + return im[ymin:ymax, xmin:xmax] + elif self.input_dim == 4: + # DHWC or BHWC + return im[:, ymin:ymax, xmin:xmax] + elif self.input_dim == 5: + # BDHWC + return im[:, :, ymin:ymax, xmin:xmax] + + def __call__(self, *imgs): + assert len(self.scales) == len(imgs) + h, w = imgs[0].shape[-3:-1] + if self.bbox is None: + h_st, h_ed, w_st, w_ed = 0, h, 0, w + else: + h_st, h_ed, w_st, w_ed = self.bbox + xmin = random.randint(w_st, w_ed - self._w * self.scales[0]) + ymin = random.randint(h_st, h_ed - self._h * self.scales[0]) + + augs = [] + for scale, im in zip(self.scales, imgs): + y_st = ymin * scale // self.scales[0] + y_ed = y_st + self._h * scale + x_st = xmin * scale // self.scales[0] + x_ed = x_st + self._w * scale + + patch = self.crop(im, y_st, x_st, y_ed, x_ed) + assert patch.shape[-3:-1] == (self._h * scale, self._w * scale), \ + f'Expect cropped patch to have size {(self._h * scale, self._w * scale)},' \ + f' but got {patch.shape[-3:-1]} (might be out of range). ' \ + f'For information, im has shape {im.shape}, crop range y: {y_st}-{y_ed}, x: {x_st}-{x_ed}.' + augs.append(patch) + return augs + + +class RandomDropChrominanceChannel(_Transform): + """Randomly drop chrominance channels. The luminance channel will be replicated. + """ + def _to_grayscale_3channel(self, x): + single_x = x[..., 0:1] + return np.concatenate([single_x, single_x, single_x], axis=-1) + + def __call__(self, *imgs): + if random.random() < 0.5: + imgs = [self._to_grayscale_3channel(item) for item in imgs] + return imgs + + +class TempDistCrop(_Transform): + '''Crop video frames with disturbed bboxes along temporal dimension + ''' + def __init__(self, input_dim, crop_size, scales, dist=0.01, no_padding=True, crop_range=None): + super(TempDistCrop, self).__init__(input_dim) + self._h = crop_size[0] + self._w = crop_size[1] + self.dist = dist + self.scales = scales + self.no_padding = no_padding + self.crop_range = crop_range + + def crop(self, imgs, T, ymins, xmins, ymaxs, xmaxs): + assert self.input_dim == 4 + if imgs.shape[0] == 1: + c_idx = T//2 + return imgs[:, ymins[c_idx]:ymaxs[c_idx], xmins[c_idx]:xmaxs[c_idx]] + else: + res = [] + for i in range(T): + res.append(imgs[i, ymins[i]:ymaxs[i], xmins[i]:xmaxs[i]]) + + return np.stack(res, axis=0) + + def pos_disturbe(self, T, ymin, xmin, H, W): + xmins, ymins = [], [] + x_bias, y_bias = np.random.random(size=T), np.random.random(size=T) + x_bias = ((x_bias*2-1)*self.dist * self._w).astype(np.int) + y_bias = ((y_bias*2-1)*self.dist * self._h).astype(np.int) + for i in range(T): + xmins.append(np.clip(xmin+x_bias[i], 0, W - self._w)) + ymins.append(np.clip(ymin+y_bias[i], 0, H - self._h)) + + return np.array(xmins), np.array(ymins) + + def _pad(self, imgs): + h, w = imgs.shape[-3:-1] + if self.no_padding: + if w < self._w or h < self._h: + if w < h: + ow = self._w + oh = int(self._w * h / w) + else: + oh = self._h + ow = int(self._h * w / h) + if self.input_dim == 3: + imgs = cv2.resize(imgs, (ow, oh)) + elif self.input_dim == 4: + imgs = [cv2.resize(item, (ow, oh)) for item in imgs] + else: + raise ValueError('TempDistCrop: image dimension must be in [3, 4]') + else: + pad_h = max(self._h - h, 0) + pad_w = max(self._w - w, 0) + imgs = [np.pad(item, ((0, pad_h), (0, pad_w), (0, 0)), 'constant') + for item in imgs] + if self.input_dim == 3: + imgs = np.pad(imgs, ((0, pad_h), (0, pad_w), (0, 0)), 'constant') + elif self.input_dim == 4: + imgs = [np.pad(item, ((0, pad_h), (0, pad_w), (0, 0)), 'constant') + for item in imgs] + else: + raise ValueError('TempDistCrop: image dimension must be in [3, 4]') + return imgs + + def __call__(self, *imgs): + assert len(self.scales) == len(imgs) + # imgs = [self._pad(item) for item in imgs] + h, w = imgs[0].shape[-3:-1] + bedge = 140 + if self.crop_range is None: + # h_st, h_ed, w_st, w_ed = 0, h, 0, w + h_st, h_ed, w_st, w_ed = bedge, h-bedge, 0, w + else: + h_st, h_ed, w_st, w_ed = self.crop_range + xmin = random.randint(w_st, w_ed - self._w * self.scales[0]) + ymin = random.randint(h_st, h_ed - self._h * self.scales[0]) + T = imgs[0].shape[0] + xmins, ymins = self.pos_disturbe(T, ymin, xmin, h, w) + + augs = [] + for scale, im in zip(self.scales, imgs): + y_st = ymins * scale + y_ed = (ymins + self._h) * scale + x_st = xmins * scale + x_ed = (xmins + self._w) * scale + + augs.append(self.crop(im, T, y_st, x_st, y_ed, x_ed)) + return augs + + +class RandomSized(_Transform): + """Random resize the input with a ranged scale. + """ + def __init__(self, input_dim=3, scale=(0.8, 1.2), interpolations=None): + super().__init__(input_dim) + self.scale = scale + self.interpolations = interpolations + + def __call__(self, *imgs): + org_h, org_w = imgs[0].shape[-3:-1] + r = random.uniform(self.scale[0], self.scale[1]) + w = int(r * org_w) + h = int(r * org_h) + + new_size = tuple([w, h]) + if self.interpolations is None: + imgs = [_resize(item, new_size, self.input_dim, interpolation=cv2.INTER_LINEAR) + for item in imgs] + else: + imgs = [_resize(item, new_size, self.input_dim, interpolation=interpolation) + for item, interpolation in zip(imgs, self.interpolations)] + return imgs + + +class RandomReverseColorChannel(_Transform): + """Randomly shift the color channel. + """ + def __call__(self, *imgs): + if random.random() < 0.5: + imgs = [item[..., ::-1] for item in imgs] + return imgs diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/utils.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d96e6be70424cbcfdc8ff812f41f6abe95779202 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/utils.py @@ -0,0 +1,90 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from itertools import chain +import numpy as np +from yacs.config import CfgNode, _VALID_TYPES, _assert_with_logging, _valid_type + + +def to_pair(x, num_reps): + """Make paired of the int. + + Args: + x: int + num_reps: int, number of replicate of the input x. + + Return: + list[int], where each value is a copy of the input x. + """ + if isinstance(x, list) or isinstance(x, tuple): + assert len(x) == num_reps + elif isinstance(x, int): + x = [x] * num_reps + else: + raise ValueError + return x + + +def convert_to_dict(cfg_node, key_list): + """Convert yacs node to dict. + + Usage: + # a is a yacs node + a_as_dict = convert_to_dict(a, []) + + Args: + cfg_node: a yacs node. + key_list: list[str], the key name in the dict. + + Return: + dict, a dict version of the config node. + """ + if not isinstance(cfg_node, CfgNode): + _assert_with_logging( + _valid_type(cfg_node), + "Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list), type(cfg_node), _VALID_TYPES + ), + ) + return cfg_node + else: + cfg_dict = dict(cfg_node) + for k, v in cfg_dict.items(): + cfg_dict[k] = convert_to_dict(v, key_list + [k]) + return cfg_dict + + +def convert_dict_to_list(cfg_dict, prefix_key=None): + """Convert a dict to list + """ + if prefix_key and prefix_key == '': + prefix_key = None + + cfg_list = [] + + for k, v in cfg_dict.items(): + cur_key = f'{prefix_key}.{k}' if prefix_key is not None else k + if isinstance(v, dict): + cfg_list_sub = convert_dict_to_list(v, prefix_key=cur_key) + cfg_list.extend(cfg_list_sub) + # new_keys = [f'{k}.{sub_k}' for sub_k in cfg_list_sub[::2]] + # values = cfg_list_sub[1::2] + # cfg_list.extend(list(chain(*zip(new_keys, values)))) + elif isinstance(v, (list, tuple)): + cfg_list.extend([cur_key, f'{v}']) + else: + cfg_list.extend([cur_key, v]) + # cfg_list.extend([cur_key, f'{v}']) + return cfg_list diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/world.py b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/world.py new file mode 100644 index 0000000000000000000000000000000000000000..73b92950595022dc24273752f1f83c0a7b5ce502 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/src/utils/world.py @@ -0,0 +1,174 @@ +# Copyright 2022 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import os +import shutil + +from .exceptions import WorldUninitializedError +from .logger import logger + + + +def _setup_npu_env(remove_kernel_meta=True, device_id=None, rank_id=None, rank_size=None): + """Setup NPU environment variables. + """ + os.environ['FUSION_TENSOR_SIZE'] = '20000000' + os.environ['JOB_ID'] = '12345678' + os.environ['MOX_USE_NPU'] = '1' + os.environ['MOX_USE_TDT'] = '1' + os.environ['MOX_USE_TF_ESTIMATOR'] = '0' + os.environ['HEARTBEAT'] = '1' + os.environ['CONTINUE_TRAIN'] = 'true' + os.environ['LOG_DIR'] = './log' + os.environ['ASCEND_GLOBAL_EVENT_LEVEL'] = '0' + os.environ['ASCEND_GLOBAL_EVENT_ENABLE'] = '0' + os.environ['ASCEND_GLOBAL_LOG_LEVEL'] = '3' + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + + if device_id is not None: + os.environ['DEVICE_ID'] = str(device_id) + os.environ['ASCEND_DEVICE_ID'] = str(device_id) + if rank_id is not None: + os.environ['RANK_ID'] = str(rank_id) + if rank_size is not None: + os.environ['RANK_SIZE'] = str(rank_size) + + +class World: + """A class to keep all the cluster information. + + This class controls how the distributed training and inference is organized, + when running on multi-device. + + Args: + root_rank_id: int, the root node of the cluster. + + Properties: + is_initialized: a boolean flag to indicate whether the cluster information + is intialized. + device_type: the type of the devices in the cluster. + device_id: the physical index of the device used. + rank_size: the number of the devices used in the cluster. + rank_id: the index of the device used in the cluster, ranged in [0, rank_size). + is_root_rank: a boolean value to indicate that the device is regarded as the + root node. Only root node will print messages and save ckpt during training. + """ + def __init__(self, root_rank_id=0): + self.root_rank_id = root_rank_id + self._device_id = None + self._rank_id = None + self._rank_size = None + self._device_type = None + + self._initialized = False + + def initialize(self, device_type, device_id=None, + rank_id=None, rank_size=None, setup_npu_env=True): + """Initialize cluster information by environment variables or the input. + """ + if device_id is None or rank_id is None or rank_size is None: + self.init_by_environ() + else: + self._device_id = int(device_id) + self._rank_id = int(rank_id) + self._rank_size = int(rank_size) + + # initialize some other env + if setup_npu_env: + _setup_npu_env(remove_kernel_meta=True, + device_id=device_id, + rank_id=rank_id, + rank_size=rank_size) + + self._device_type = device_type + + if self._rank_size == 1: + # Force the single device as root_rank + self._rank_id = 0 + + self._initialized = True + + @property + def is_initialized(self): + return self._initialized + + def init_by_environ(self): + """Initialize cluster using environment variables. + """ + try: + self._device_id = int(os.environ['DEVICE_ID']) + except KeyError: + logger.error("Environ 'DEVICE_ID' not defined. Use default value DEVICE_ID=0.") + self._device_id = 0 + except ValueError: + logger.error(f"Environ 'DEVICE_ID' {os.environ['DEVICE_ID']} cannot converted to int. " + "Use default value DEVICE_ID=0.") + self._device_id = 0 + + try: + self._rank_id = int(os.environ['RANK_ID']) + except KeyError: + logger.error("Environ 'RANK_ID' not defined. Use default value RANK_ID=0.") + self._rank_id = 0 + except ValueError: + logger.error(f"Environ 'RANK_ID' {os.environ['RANK_ID']} cannot converted to int. " + "Use default value RANK_ID=0.") + self._rank_id = 0 + + try: + self._rank_size = int(os.environ['RANK_SIZE']) + except KeyError: + logger.error("Environ 'RANK_SIZE' not defined. Use default value RANK_SIZE=1.") + self._rank_size = 1 + except ValueError: + logger.error(f"Environ 'RANK_SIZE' {os.environ['RANK_SIZE']} cannot converted to int. " + "Use default value RANK_SIZE=1.") + self._rank_size = 1 + + self._initialized = True + + @property + def device_type(self): + if self._device_type is None: + raise WorldUninitializedError('World not initialized.') + return self._device_type + + @property + def device_id(self): + if self._device_id is None: + raise WorldUninitializedError('World not initialized.') + return self._device_id + + @property + def rank_id(self): + if self._rank_id is None: + raise WorldUninitializedError('World not initialized.') + return self._rank_id + + @property + def rank_size(self): + if self._rank_size is None: + raise WorldUninitializedError('World not initialized.') + return self._rank_size + + @property + def is_root_rank(self): + if self._rank_id is None: + raise WorldUninitializedError('World not initialized.') + return self._rank_id == self.root_rank_id + + +# Global instance. +world = World() diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_full_1p.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_full_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..39f41a129d5b4d5e1a6a69b99fe67714ad7639d9 --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_full_1p.sh @@ -0,0 +1,129 @@ +#!/bin/bash +# source env.sh +#当前路径,不需要修改 +cur_path=`pwd` +export ASCEND_SLOG_PRINT_TO_STDOUT=1 +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +export DEVICE_ID=$ASCEND_DEVICE_ID +RANK_ID_START=0 + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 +# export ASCEND_GLOBAL_LOG_LEVEL=3 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="AscendVideo_EDVR_ID3085_for_Tensorflow" +#训练batch_size +batch_size=16 +#训练step +output_dir='test/output' + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1p.sh " + echo " " + echo "parameter explain: + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) +cd $cur_path/../ +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + fi + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup python3.7 $cur_path/../src/main.py \ + --config-file $cur_path/../configs/models/edvr_config.py \ + train.checkpoint_interval 5000 \ + train.print_interval 100 \ + data.data_dir ${data_path} \ + env.rank_size 1 \ + train.output_dir ${output_dir} \ + train.generator.lr_schedule.total_steps 150000,150000,150000,150000 > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +FPS=`grep 'fps:' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk -F "fps:" '{print $2}' | awk -F "," '{print $1}' | tail -n +2| awk '{sum+=$1} END {print sum/NR}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#稳定性精度看护结果汇总 +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`echo "scale=2;${batch_size} * ${RANK_SIZE} * 1000 / ${FPS}"|bc` + +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep 'Step:' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss_total:" '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..915c060786af2837b076225062858beaeaf16b6f --- /dev/null +++ b/TensorFlow/built-in/cv/Video_enhancement/AscendVideo_EDVR_ID3085_for_Tensorflow/test/train_performance_1p.sh @@ -0,0 +1,128 @@ +#!/bin/bash +# source env.sh +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +export DEVICE_ID=$ASCEND_DEVICE_ID +RANK_ID_START=0 + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 +export ASCEND_GLOBAL_LOG_LEVEL=3 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="AscendVideo_EDVR_ID3085_for_Tensorflow" +#训练batch_size +batch_size=16 +#训练step +output_dir='test/output' + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1p.sh " + echo " " + echo "parameter explain: + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) +cd $cur_path/../ +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + fi + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup python3.7 $cur_path/../src/main.py \ + --config-file $cur_path/../configs/models/edvr_config.py \ + train.checkpoint_interval 5000 \ + train.print_interval 10 \ + data.data_dir ${data_path} \ + env.rank_size 1 \ + train.output_dir ${output_dir} \ + train.generator.lr_schedule.total_steps 10000, > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +FPS=`grep 'fps:' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk -F "fps:" '{print $2}' | awk -F "," '{print $1}' | tail -n +2| awk '{sum+=$1} END {print sum/NR}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#稳定性精度看护结果汇总 +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`echo "scale=2;${batch_size} * ${RANK_SIZE} * 1000 / ${FPS}"|bc` + +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep 'Step:' $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "loss_total:" '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendcv/runner/sess_config.py b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendcv/runner/sess_config.py index 9325b851d35389a0c9c4eccd3ab7f4f37654d60c..ed8c1fbc0eb7a7093194560e613745eda79846fd 100644 --- a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendcv/runner/sess_config.py +++ b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendcv/runner/sess_config.py @@ -12,18 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf +from npu_bridge.npu_init import * def _npu_config(mix_precision, is_distributed): config = tf.ConfigProto() custom_op = config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" - custom_op.parameter_map["enable_data_pre_proc"].b = False custom_op.parameter_map["mix_compile_mode"].b = False custom_op.parameter_map["use_off_line"].b = True custom_op.parameter_map["graph_memory_max_size"].s = tf.compat.as_bytes(str(28*1024 * 1024 * 1024)) custom_op.parameter_map["variable_memory_max_size"].s = tf.compat.as_bytes(str(3*1024 * 1024 * 1024)) custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") + custom_op.parameter_map["enable_data_pre_proc"].b = True + custom_op.parameter_map["iterations_per_loop"].i = 10 + config = npu_config_proto(config_proto=config) #if mix_precision: # custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") if is_distributed: diff --git a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendvsr/models/base_model.py b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendvsr/models/base_model.py index bff76f3153038f634f833b187d4ff3daefb885a2..cef442af6b656f53fd1e067ec65fd5b8399f4cec 100644 --- a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendvsr/models/base_model.py +++ b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/ascendvsr/models/base_model.py @@ -7,6 +7,7 @@ import json import re import tensorflow as tf from tqdm import trange +from npu_bridge.npu_init import * from ascendcv.runner.solver import build_solver from ascendcv.utils.writer import ImageWriter @@ -168,6 +169,7 @@ class VSR(object): self.saver = tf.train.Saver(max_to_keep=100, keep_checkpoint_every_n_hours=1) ave_loss = None + train_op = util.set_iteration_per_loop(sess, train_op, 10) st_time = time.time() for it in range(recover_step, solver.total_step): if self.read_mode == 'python': @@ -188,7 +190,7 @@ class VSR(object): if (it + 1) % self.solver.print_interval == 0 and \ not (npu_distributed and int(os.environ['DEVICE_ID']) != self.cfg.root_rank): - ave_time = once_time / self.solver.print_interval + ave_time = once_time / self.solver.print_interval / 10 fps = self.batch_size / ave_time * self.cfg.rank_size print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), 'Step:{}, lr:{:.8f}, loss:{:.08f}, session time:{:.2f}ms, session fps:{:.2f}, device_id: {}'.format( diff --git a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh index 7a1e537fc34eb6f1b7a817b42623cd2db0f2ec53..c64f63e125329b1d51d0779527eb6f5d62210114 100644 --- a/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/built-in/cv/Video_enhancement/EDVR_ID0056_for_TensorFlow/test/train_performance_1p.sh @@ -2,7 +2,7 @@ # source env.sh #当前路径,不需要修改 cur_path=`pwd` -export ASCEND_SLOG_PRINT_TO_STDOUT=1 +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 #集合通信参数,不需要修改 export RANK_SIZE=1 diff --git a/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/README.txt b/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/README.txt deleted file mode 100644 index 5f176019472ffe937b0110dd6b6bd15886a05021..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/README.txt +++ /dev/null @@ -1,97 +0,0 @@ -************************************************************************** -AVA Dataset - June 2012 Release - -For detailed information, please refer to: - -“AVA: A Large-Scale Database for Aesthetic Visual Analysis”. Naila Murray, - Luca Marchesotti, Florent Perronnin, CVPR 2012. - -Contacts: -Naila Murray (nmurray [at] cvc [dot] uab [dot] es) -Luca Marchesotti (Luca [dot] Marchesotti [at] xrce [dot] xerox [dot] com) -************************************************************************** - -This package contains: - -1. AVA.txt -2. tags.txt -3. challenges.txt -4. aesthetic image lists: lists of train and test images used for aesthetics -experiments. -5. style image lists: lists of train and test images used for style -experiments. - -************************************************************************** -Content of AVA.txt -************************************************************************** - -Column 1: Index - -Column 2: Image ID - -Columns 3 - 12: Counts of aesthetics ratings on a scale of 1-10. Column 3 -has counts of ratings of 1 and column 12 has counts of ratings of 10. - -Columns 13 - 14: Semantic tag IDs. There are 66 IDs ranging from 1 to 66. -The file tags.txt contains the textual tag corresponding to the numerical -id. Each image has between 0 and 2 tags. Images with less than 2 tags have -a "0" in place of the missing tag(s). - -Column 15: Challenge ID. The file challenges.txt contains the name of -the challenge corresponding to each ID. - -************************************************************************** -Aesthetics image Lists -************************************************************************** - -The aesthetics_image_lists directory contains files with the IDs of images -used for training and testing generic aesthetics classifiers. There were: - -1. small scale (ss) experiments with few training images. -2. large scale (ls) experiments with many training images. - -The directory also contains lists of training and testing images used for -content (or category)-dependent classifiers. The categories are: animal, -architecture, cityscape, floral, food/drink, landscape, portrait, and -still-life. - -************************************************************************** -Style image Lists -************************************************************************** - -The style_image_lists directory contains files with the IDs of images -used for training and testing photographic style classifiers. The files are: - -1. train.jpgl - list of IDs of training images -2. test.jpgl - list of IDs of testing images -3. styles.txt - numeric style IDs and their associated photographic styles. -4. train.lab - annotations for images in train.jpgl consisting of numeric -style IDs. -5. test.multilab - binary annotations for images in test.jpgl. There are 14 -columns corresponding to the 14 possible styles so that, for example, a 1 -in column 3 means that the image has been labeled with the 3rd style listed -in styles.txt - -Note that the training images are single-labeled, but the test images are -multilabeled. - -************************************************************************** -How to obtain the images? -************************************************************************** - -The URLs for the images are constructed as: - - http://www.dpchallenge.com/image.php?IMAGE_ID= - -e.g., - - http://www.dpchallenge.com/image.php?IMAGE_ID=359334 - - -************************************************************************** -Copyright Considerations -************************************************************************** - -Rights to all images are retained by the photographers/dpchallenge. This -is why the image files are not included in the database. Please respect -the copyright and refrain from redistributing images or data. diff --git a/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/aesthetics_image_lists/animal_test.jpgl b/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/aesthetics_image_lists/animal_test.jpgl deleted file mode 100644 index d6e67437be7b6b5fd394f3667cf0a7a385e6ffb2..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/aesthetics_image_lists/animal_test.jpgl +++ /dev/null @@ -1,2500 +0,0 @@ -629797 -902498 -588000 -448695 -237907 -314147 -306944 -486521 -545526 -126116 -28784 -415178 -713715 -234538 -53043 -497944 -722101 -843320 -334499 -156437 -185173 -413104 -639709 -384014 -438926 -758327 -349734 -722028 -822511 -731886 -534103 -538205 -715540 -120710 -598172 -555465 -427476 -864374 -40090 -200880 -926003 -232781 -596783 -778425 -605838 -645466 -323224 -743468 -799937 -445875 -649970 -941842 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deleted file mode 100644 index 834e49af64700d578c60c4b66dbf1a943fed266c..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/cv/detection/NIMA_ID0158_for_TensorFlow/AVA_dataset/tags.txt +++ /dev/null @@ -1,66 +0,0 @@ -1 Abstract -24 Action -31 Advertisement -66 Analog -19 Animals -20 Architecture -43 Astrophotography -57 Birds -21 Black and White -51 Blur -64 Camera Phones -16 Candid -50 Children -2 Cityscape -34 Digital Art -37 Diptych / Triptych -49 DPChallenge GTGs -12 Emotive -4 Family -3 Fashion -63 Fish Eye -38 Floral -40 Food and Drink -53 High Dynamic Range (HDR) -45 History -58 Horror -5 Humorous -46 Infrared -65 Insects, etc -6 Interior -14 Landscape -62 Lensbaby -22 Macro -56 Maternity -44 Military -59 Music -15 Nature -26 Nude -55 Overlays -33 Panoramic -13 Performance -32 Persuasive -52 Photo-Impressionism -25 Photojournalism -60 Pinhole/Zone Plate -30 Political -17 Portraiture -27 Rural -41 Science and Technology -35 Seascapes -47 Self Portrait -7 Sky -8 Snapshot -9 Sports -18 Still Life -61 Street -29 Studio -54 Texture Library -48 Textures -36 Traditional Art -39 Transportation -23 Travel -10 Urban -11 Vintage -28 Water -42 Wedding diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/Dockerfile b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/Dockerfile index 6076e6fcde50a005f94e3d57f1c03ec2d7b448aa..0dd97d97fd08921d2ac594e511efce5c452e4b79 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/Dockerfile +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/Dockerfile @@ -1,9 +1,7 @@ ARG FROM_IMAGE_NAME=ascend-tensorflow-arm:20.1.0 FROM ${FROM_IMAGE_NAME} - +USER root RUN apt -y install libgl1-mesa-glx COPY requirements.txt . RUN pip3.7 install -r requirements.txt - -RUN ln -s /usr/bin/python3.7 /usr/bin/python3 \ No newline at end of file diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/docker_start.sh b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/docker_start.sh index e99bbe7685ba6f302f694e54a5d15ebe0f1c6b08..949698ab8238841d4b1d1adef4cf6387b0642e2f 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/docker_start.sh +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/docker_start.sh @@ -1,5 +1,19 @@ #!/bin/bash +#Copyright 2022 Huawei Technologies Co., Ltd + +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at + +#http://www.apache.org/licenses/LICENSE-2.0 + +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. + docker_image=$1 data_dir=$2 model_dir=$3 diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/infer/docker_start_infer.sh b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/infer/docker_start_infer.sh index 72889067a499eb55e93cac635d4b00454799524a..69f4fbbf409fdef7a78bc6c3a6d484f93bdec952 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/infer/docker_start_infer.sh +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/infer/docker_start_infer.sh @@ -38,7 +38,7 @@ function param_check() { param_check -docker run -it \ +docker run -it -u root \ --device=/dev/davinci0 \ --device=/dev/davinci_manager \ --device=/dev/devmm_svm \ diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_multi.py b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_multi.py index 2d7d2032f135a8f9330f61eeffd7f99ff50d093a..7ae30ea0cbadcb0115bad2844c437896d8996223 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_multi.py +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_multi.py @@ -45,7 +45,7 @@ if not os.path.exists(save_dir): if not os.path.exists(log_dir): os.makedirs(log_dir) -work_path = '/cache/user-job-dir/YoloV3_for_TensorFlow' +work_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), "../") ### Some paths train_file = os.path.join(work_path, './modelarts/coco2014_trainval_modelarts.txt') # The path of the training txt file. diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_single.py b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_single.py index 3cf9c95680169187bfb77d8251d09177f4173576..7f5d4bd356c4835bdfaeda5be64088b23627a90f 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_single.py +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/modelarts/args_modelarts_single.py @@ -45,7 +45,7 @@ if not os.path.exists(save_dir): if not os.path.exists(log_dir): os.makedirs(log_dir) -work_path = '/cache/user-job-dir/YoloV3_for_TensorFlow' +work_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), "../") ### Some paths train_file = os.path.join(work_path, './modelarts/coco2014_trainval_modelarts.txt') # The path of the training txt file. diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh index 714d7a8b5ddbaa3291cebf414bbff110e202a79f..ad5504a6d7fbb1e61758be164d293b9a02d19b1b 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_1p.sh @@ -135,6 +135,7 @@ do --mode single \ --data_url $data_path/coco \ --train_url ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ + --total_epoches 1 \ --over_dump ${over_dump} \ --over_dump_path ${over_dump_path} \ > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_8p.sh b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_8p.sh index 2d7e9b1c5e1df38e2afd4f664087c6374ff7d270..79e72a8da8dad4b8ff3dce733fc8f2538d976ed9 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_8p.sh +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/test/train_performance_8p.sh @@ -152,6 +152,7 @@ do --mode single \ --data_url $data_path/coco \ --train_url ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ + --total_epoches 1 \ --over_dump ${over_dump} \ --over_dump_path ${over_dump_path} \ > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/train.py b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/train.py index 9fdc9b34e8a921e006f19e303db69325e84fe114..2348373849d32bebe03a546faeb4c475cd495c5a 100644 --- a/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/train.py +++ b/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow/train.py @@ -71,7 +71,8 @@ parser.add_argument("--save_dir", default='./training/', help="path of ckpt.") parser.add_argument("--batch_size", type=int, default=16, help="batchsize.") - +parser.add_argument("--total_epoches", type=int, default=200, + help="epoches of train.") # modify for npu overflow start # enable overflow parser.add_argument("--over_dump", type=str, default="False", @@ -108,7 +109,8 @@ if args_input.save_dir: args.save_dir = args_input.save_dir if args_input.batch_size: args.batch_size = args_input.batch_size - +if args_input.total_epoches: + args.total_epoches = args_input.total_epoches print('setting train mode %s.' % args_input.mode) # setting loggers diff --git a/TensorFlow/built-in/cv/image_classification/AM3_ID1260_for_TensorFlow/datasets/mini_imagenet_class_label_dict3.txt b/TensorFlow/built-in/cv/image_classification/AM3_ID1260_for_TensorFlow/datasets/mini_imagenet_class_label_dict3.txt deleted file mode 100644 index 0d93f41323c8f320ae043d931d2774548139cc71..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/cv/image_classification/AM3_ID1260_for_TensorFlow/datasets/mini_imagenet_class_label_dict3.txt +++ /dev/null @@ -1,1000 +0,0 @@ -n02119789 1 kit_fox -n02100735 2 English_setter -n02110185 3 Siberian_husky -n02096294 4 Australian_terrier -n02102040 5 English_springer -n02066245 6 grey_whale -n02509815 7 lesser_panda -n02124075 8 Egyptian_cat -n02417914 9 ibex -n02123394 10 Persian_cat -n02125311 11 cougar -n02423022 12 gazelle -n02346627 13 porcupine -n02077923 14 sea_lion -n02110063 15 malamute -n02447366 16 badger -n02109047 17 Great_Dane -n02089867 18 Walker_hound -n02102177 19 Welsh_springer_spaniel -n02091134 20 whippet -n02092002 21 Scottish_deerhound -n02071294 22 killer_whale -n02442845 23 mink -n02504458 24 African_elephant -n02092339 25 Weimaraner -n02098105 26 soft-coated_wheaten_terrier -n02096437 27 Dandie_Dinmont_terrier -n02114712 28 red_wolf -n02105641 29 Old_English_sheepdog -n02128925 30 jaguar -n02091635 31 otter_hound -n02088466 32 bloodhound -n02096051 33 Airedale -n02117135 34 hyena -n02138441 35 meerkat -n02097130 36 giant_schnauzer -n02493509 37 titi -n02457408 38 three-toed_sloth -n02389026 39 sorrel -n02443484 40 black-footed_ferret -n02110341 41 dalmatian -n02089078 42 black-and-tan_coonhound -n02086910 43 papillon -n02445715 44 skunk -n02093256 45 Staffordshire_bullterrier -n02113978 46 Mexican_hairless -n02106382 47 Bouvier_des_Flandres -n02441942 48 weasel -n02113712 49 miniature_poodle -n02113186 50 Cardigan -n02105162 51 malinois -n02415577 52 bighorn -n02356798 53 fox_squirrel -n02488702 54 colobus -n02123159 55 tiger_cat -n02098413 56 Lhasa -n02422699 57 impala -n02114855 58 coyote -n02094433 59 Yorkshire_terrier -n02111277 60 Newfoundland -n02132136 61 brown_bear -n02119022 62 red_fox -n02091467 63 Norwegian_elkhound -n02106550 64 Rottweiler -n02422106 65 hartebeest -n02091831 66 Saluki -n02120505 67 grey_fox -n02104365 68 schipperke -n02086079 69 Pekinese -n02112706 70 Brabancon_griffon -n02098286 71 West_Highland_white_terrier -n02095889 72 Sealyham_terrier -n02484975 73 guenon -n02137549 74 mongoose -n02500267 75 indri -n02129604 76 tiger -n02090721 77 Irish_wolfhound -n02396427 78 wild_boar -n02108000 79 EntleBucher -n02391049 80 zebra -n02412080 81 ram -n02108915 82 French_bulldog -n02480495 83 orangutan -n02110806 84 basenji -n02128385 85 leopard -n02107683 86 Bernese_mountain_dog -n02085936 87 Maltese_dog -n02094114 88 Norfolk_terrier -n02087046 89 toy_terrier -n02100583 90 vizsla -n02096177 91 cairn -n02494079 92 squirrel_monkey -n02105056 93 groenendael -n02101556 94 clumber -n02123597 95 Siamese_cat -n02481823 96 chimpanzee -n02105505 97 komondor -n02088094 98 Afghan_hound -n02085782 99 Japanese_spaniel -n02489166 100 proboscis_monkey -n02364673 101 guinea_pig -n02114548 102 white_wolf -n02134084 103 ice_bear -n02480855 104 gorilla -n02090622 105 borzoi -n02113624 106 toy_poodle -n02093859 107 Kerry_blue_terrier -n02403003 108 ox -n02097298 109 Scotch_terrier -n02108551 110 Tibetan_mastiff -n02493793 111 spider_monkey -n02107142 112 Doberman -n02096585 113 Boston_bull -n02107574 114 Greater_Swiss_Mountain_dog -n02107908 115 Appenzeller -n02086240 116 Shih-Tzu -n02102973 117 Irish_water_spaniel -n02112018 118 Pomeranian -n02093647 119 Bedlington_terrier -n02397096 120 warthog -n02437312 121 Arabian_camel -n02483708 122 siamang -n02097047 123 miniature_schnauzer -n02106030 124 collie -n02099601 125 golden_retriever -n02093991 126 Irish_terrier -n02110627 127 affenpinscher -n02106166 128 Border_collie -n02326432 129 hare -n02108089 130 boxer -n02097658 131 silky_terrier -n02088364 132 beagle -n02111129 133 Leonberg -n02100236 134 German_short-haired_pointer -n02486261 135 patas -n02115913 136 dhole -n02486410 137 baboon -n02487347 138 macaque -n02099849 139 Chesapeake_Bay_retriever -n02108422 140 bull_mastiff -n02104029 141 kuvasz -n02492035 142 capuchin -n02110958 143 pug -n02099429 144 curly-coated_retriever -n02094258 145 Norwich_terrier -n02099267 146 flat-coated_retriever -n02395406 147 hog -n02112350 148 keeshond -n02109961 149 Eskimo_dog -n02101388 150 Brittany_spaniel -n02113799 151 standard_poodle -n02095570 152 Lakeland_terrier -n02128757 153 snow_leopard -n02101006 154 Gordon_setter -n02115641 155 dingo -n02097209 156 standard_schnauzer -n02342885 157 hamster -n02097474 158 Tibetan_terrier -n02120079 159 Arctic_fox -n02095314 160 wire-haired_fox_terrier -n02088238 161 basset -n02408429 162 water_buffalo -n02133161 163 American_black_bear -n02328150 164 Angora -n02410509 165 bison -n02492660 166 howler_monkey -n02398521 167 hippopotamus -n02112137 168 chow -n02510455 169 giant_panda -n02093428 170 American_Staffordshire_terrier -n02105855 171 Shetland_sheepdog -n02111500 172 Great_Pyrenees -n02085620 173 Chihuahua -n02123045 174 tabby -n02490219 175 marmoset -n02099712 176 Labrador_retriever -n02109525 177 Saint_Bernard -n02454379 178 armadillo -n02111889 179 Samoyed -n02088632 180 bluetick -n02090379 181 redbone -n02443114 182 polecat -n02361337 183 marmot -n02105412 184 kelpie -n02483362 185 gibbon -n02437616 186 llama -n02107312 187 miniature_pinscher -n02325366 188 wood_rabbit -n02091032 189 Italian_greyhound -n02129165 190 lion -n02102318 191 cocker_spaniel -n02100877 192 Irish_setter -n02074367 193 dugong -n02504013 194 Indian_elephant -n02363005 195 beaver -n02102480 196 Sussex_spaniel -n02113023 197 Pembroke -n02086646 198 Blenheim_spaniel -n02497673 199 Madagascar_cat -n02087394 200 Rhodesian_ridgeback -n02127052 201 lynx -n02116738 202 African_hunting_dog -n02488291 203 langur -n02091244 204 Ibizan_hound -n02114367 205 timber_wolf -n02130308 206 cheetah -n02089973 207 English_foxhound -n02105251 208 briard -n02134418 209 sloth_bear -n02093754 210 Border_terrier -n02106662 211 German_shepherd -n02444819 212 otter -n01882714 213 koala -n01871265 214 tusker -n01872401 215 echidna -n01877812 216 wallaby -n01873310 217 platypus -n01883070 218 wombat -n04086273 219 revolver -n04507155 220 umbrella -n04147183 221 schooner -n04254680 222 soccer_ball -n02672831 223 accordion -n02219486 224 ant -n02317335 225 starfish -n01968897 226 chambered_nautilus -n03452741 227 grand_piano -n03642806 228 laptop -n07745940 229 strawberry -n02690373 230 airliner -n04552348 231 warplane -n02692877 232 airship -n02782093 233 balloon -n04266014 234 space_shuttle -n03344393 235 fireboat -n03447447 236 gondola -n04273569 237 speedboat -n03662601 238 lifeboat -n02951358 239 canoe -n04612504 240 yawl -n02981792 241 catamaran -n04483307 242 trimaran -n03095699 243 container_ship -n03673027 244 liner -n03947888 245 pirate -n02687172 246 aircraft_carrier -n04347754 247 submarine -n04606251 248 wreck -n03478589 249 half_track -n04389033 250 tank -n03773504 251 missile -n02860847 252 bobsled -n03218198 253 dogsled -n02835271 254 tandem_bicycle -n03792782 255 mountain_bike -n03393912 256 freight_car -n03895866 257 passenger_car -n02797295 258 barrow -n04204347 259 shopping_cart -n03791053 260 motor_scooter -n03384352 261 forklift -n03272562 262 electric_locomotive -n04310018 263 steam_locomotive -n02704792 264 amphibian -n02701002 265 ambulance -n02814533 266 beach_wagon -n02930766 267 cab -n03100240 268 convertible -n03594945 269 jeep -n03670208 270 limousine -n03770679 271 minivan -n03777568 272 Model_T -n04037443 273 racer -n04285008 274 sports_car -n03444034 275 go-kart -n03445924 276 golfcart -n03785016 277 moped -n04252225 278 snowplow -n03345487 279 fire_engine -n03417042 280 garbage_truck -n03930630 281 pickup -n04461696 282 tow_truck -n04467665 283 trailer_truck -n03796401 284 moving_van -n03977966 285 police_van -n04065272 286 recreational_vehicle -n04335435 287 streetcar -n04252077 288 snowmobile -n04465501 289 tractor -n03776460 290 mobile_home -n04482393 291 tricycle -n04509417 292 unicycle -n03538406 293 horse_cart -n03599486 294 ricksha_rickshaw -n03868242 295 oxcart -n02804414 296 bassinet -n03125729 297 cradle -n03131574 298 crib -n03388549 299 four-poster -n02870880 300 bookcase -n03018349 301 china_cabinet -n03742115 302 medicine_chest -n03016953 303 chiffonier -n04380533 304 table_lamp -n03337140 305 file -n03891251 306 park_bench -n02791124 307 barber_chair -n04429376 308 throne -n03376595 309 folding_chair -n04099969 310 rocking_chair -n04344873 311 studio_couch -n04447861 312 toilet_seat -n03179701 313 desk -n03982430 314 pool_table -n03201208 315 dining_table -n03290653 316 entertainment_center -n04550184 317 wardrobe -n07742313 318 Granny_Smith -n07747607 319 orange -n07749582 320 lemon -n07753113 321 fig -n07753275 322 pineapple -n07753592 323 banana -n07754684 324 jackfruit -n07760859 325 custard_apple -n07768694 326 pomegranate -n12267677 327 acorn -n12620546 328 hip -n13133613 329 ear -n11879895 330 rapeseed -n12144580 331 corn -n12768682 332 buckeye -n03854065 333 organ -n04515003 334 upright -n03017168 335 chime -n03249569 336 drum -n03447721 337 gong -n03720891 338 maraca -n03721384 339 marimba -n04311174 340 steel_drum -n02787622 341 banjo -n02992211 342 cello -n04536866 343 violin -n03495258 344 harp -n02676566 345 acoustic_guitar -n03272010 346 electric_guitar -n03110669 347 cornet -n03394916 348 French_horn -n04487394 349 trombone -n03494278 350 harmonica -n03840681 351 ocarina -n03884397 352 panpipe -n02804610 353 bassoon -n03838899 354 oboe -n04141076 355 sax -n03372029 356 flute -n11939491 357 daisy -n12057211 358 yellow_lady's_slipper -n09246464 359 cliff -n09468604 360 valley -n09193705 361 alp -n09472597 362 volcano -n09399592 363 promontory -n09421951 364 sandbar -n09256479 365 coral_reef -n09332890 366 lakeside -n09428293 367 seashore -n09288635 368 geyser -n03498962 369 hatchet -n03041632 370 cleaver -n03658185 371 letter_opener -n03954731 372 plane -n03995372 373 power_drill -n03649909 374 lawn_mower -n03481172 375 hammer -n03109150 376 corkscrew -n02951585 377 can_opener -n03970156 378 plunger -n04154565 379 screwdriver -n04208210 380 shovel -n03967562 381 plow -n03000684 382 chain_saw -n01514668 383 cock -n01514859 384 hen -n01518878 385 ostrich -n01530575 386 brambling -n01531178 387 goldfinch -n01532829 388 house_finch -n01534433 389 junco -n01537544 390 indigo_bunting -n01558993 391 robin -n01560419 392 bulbul -n01580077 393 jay -n01582220 394 magpie -n01592084 395 chickadee -n01601694 396 water_ouzel -n01608432 397 kite -n01614925 398 bald_eagle -n01616318 399 vulture -n01622779 400 great_grey_owl -n01795545 401 black_grouse -n01796340 402 ptarmigan -n01797886 403 ruffed_grouse -n01798484 404 prairie_chicken -n01806143 405 peacock -n01806567 406 quail -n01807496 407 partridge -n01817953 408 African_grey -n01818515 409 macaw -n01819313 410 sulphur-crested_cockatoo -n01820546 411 lorikeet -n01824575 412 coucal -n01828970 413 bee_eater -n01829413 414 hornbill -n01833805 415 hummingbird -n01843065 416 jacamar -n01843383 417 toucan -n01847000 418 drake -n01855032 419 red-breasted_merganser -n01855672 420 goose -n01860187 421 black_swan -n02002556 422 white_stork -n02002724 423 black_stork -n02006656 424 spoonbill -n02007558 425 flamingo -n02009912 426 American_egret -n02009229 427 little_blue_heron -n02011460 428 bittern -n02012849 429 crane -n02013706 430 Aramus_pictus -n02018207 431 American_coot -n02018795 432 bustard -n02025239 433 ruddy_turnstone -n02027492 434 red-backed_sandpiper -n02028035 435 redshank -n02033041 436 dowitcher -n02037110 437 oystercatcher -n02017213 438 European_gallinule -n02051845 439 pelican -n02056570 440 king_penguin -n02058221 441 albatross -n01484850 442 great_white_shark -n01491361 443 tiger_shark -n01494475 444 hammerhead -n01496331 445 electric_ray -n01498041 446 stingray -n02514041 447 snoek -n02536864 448 coho -n01440764 449 tench -n01443537 450 goldfish -n02526121 451 eel -n02606052 452 rock_beauty -n02607072 453 anemone_fish -n02643566 454 lionfish -n02655020 455 puffer -n02640242 456 sturgeon -n02641379 457 gar -n01664065 458 loggerhead -n01665541 459 leatherback_turtle -n01667114 460 mud_turtle -n01667778 461 terrapin -n01669191 462 box_turtle -n01675722 463 banded_gecko -n01677366 464 common_iguana -n01682714 465 American_chameleon -n01685808 466 whiptail -n01687978 467 agama -n01688243 468 frilled_lizard -n01689811 469 alligator_lizard -n01692333 470 Gila_monster -n01693334 471 green_lizard -n01694178 472 African_chameleon -n01695060 473 Komodo_dragon -n01704323 474 triceratops -n01697457 475 African_crocodile -n01698640 476 American_alligator -n01728572 477 thunder_snake -n01728920 478 ringneck_snake -n01729322 479 hognose_snake -n01729977 480 green_snake -n01734418 481 king_snake -n01735189 482 garter_snake -n01737021 483 water_snake -n01739381 484 vine_snake -n01740131 485 night_snake -n01742172 486 boa_constrictor -n01744401 487 rock_python -n01748264 488 Indian_cobra -n01749939 489 green_mamba -n01751748 490 sea_snake -n01753488 491 horned_viper -n01755581 492 diamondback -n01756291 493 sidewinder -n01629819 494 European_fire_salamander -n01630670 495 common_newt -n01631663 496 eft -n01632458 497 spotted_salamander -n01632777 498 axolotl -n01641577 499 bullfrog -n01644373 500 tree_frog -n01644900 501 tailed_frog -n04579432 502 whistle -n04592741 503 wing -n03876231 504 paintbrush -n03483316 505 hand_blower -n03868863 506 oxygen_mask -n04251144 507 snorkel -n03691459 508 loudspeaker -n03759954 509 microphone -n04152593 510 screen -n03793489 511 mouse -n03271574 512 electric_fan -n03843555 513 oil_filter -n04332243 514 strainer -n04265275 515 space_heater -n04330267 516 stove -n03467068 517 guillotine -n02794156 518 barometer -n04118776 519 rule -n03841143 520 odometer -n04141975 521 scale -n02708093 522 analog_clock -n03196217 523 digital_clock -n04548280 524 wall_clock -n03544143 525 hourglass -n04355338 526 sundial -n03891332 527 parking_meter -n04328186 528 stopwatch -n03197337 529 digital_watch -n04317175 530 stethoscope -n04376876 531 syringe -n03706229 532 magnetic_compass -n02841315 533 binoculars -n04009552 534 projector -n04356056 535 sunglasses -n03692522 536 loupe -n04044716 537 radio_telescope -n02879718 538 bow -n02950826 539 cannon -n02749479 540 assault_rifle -n04090263 541 rifle -n04008634 542 projectile -n03085013 543 computer_keyboard -n04505470 544 typewriter_keyboard -n03126707 545 crane -n03666591 546 lighter -n02666196 547 abacus -n02977058 548 cash_machine -n04238763 549 slide_rule -n03180011 550 desktop_computer -n03485407 551 hand-held_computer -n03832673 552 notebook -n06359193 553 web_site -n03496892 554 harvester -n04428191 555 thresher -n04004767 556 printer -n04243546 557 slot -n04525305 558 vending_machine -n04179913 559 sewing_machine -n03602883 560 joystick -n04372370 561 switch -n03532672 562 hook -n02974003 563 car_wheel -n03874293 564 paddlewheel -n03944341 565 pinwheel -n03992509 566 potter's_wheel -n03425413 567 gas_pump -n02966193 568 carousel -n04371774 569 swing -n04067472 570 reel -n04040759 571 radiator -n04019541 572 puck -n03492542 573 hard_disc -n04355933 574 sunglass -n03929660 575 pick -n02965783 576 car_mirror -n04258138 577 solar_dish -n04074963 578 remote_control -n03208938 579 disk_brake -n02910353 580 buckle -n03476684 581 hair_slide -n03627232 582 knot -n03075370 583 combination_lock -n03874599 584 padlock -n03804744 585 nail -n04127249 586 safety_pin -n04153751 587 screw -n03803284 588 muzzle -n04162706 589 seat_belt -n04228054 590 ski -n02948072 591 candle -n03590841 592 jack-o'-lantern -n04286575 593 spotlight -n04456115 594 torch -n03814639 595 neck_brace -n03933933 596 pier -n04485082 597 tripod -n03733131 598 maypole -n03794056 599 mousetrap -n04275548 600 spider_web -n01768244 601 trilobite -n01770081 602 harvestman -n01770393 603 scorpion -n01773157 604 black_and_gold_garden_spider -n01773549 605 barn_spider -n01773797 606 garden_spider -n01774384 607 black_widow -n01774750 608 tarantula -n01775062 609 wolf_spider -n01776313 610 tick -n01784675 611 centipede -n01990800 612 isopod -n01978287 613 Dungeness_crab -n01978455 614 rock_crab -n01980166 615 fiddler_crab -n01981276 616 king_crab -n01983481 617 American_lobster -n01984695 618 spiny_lobster -n01985128 619 crayfish -n01986214 620 hermit_crab -n02165105 621 tiger_beetle -n02165456 622 ladybug -n02167151 623 ground_beetle -n02168699 624 long-horned_beetle -n02169497 625 leaf_beetle -n02172182 626 dung_beetle -n02174001 627 rhinoceros_beetle -n02177972 628 weevil -n02190166 629 fly -n02206856 630 bee -n02226429 631 grasshopper -n02229544 632 cricket -n02231487 633 walking_stick -n02233338 634 cockroach -n02236044 635 mantis -n02256656 636 cicada -n02259212 637 leafhopper -n02264363 638 lacewing -n02268443 639 dragonfly -n02268853 640 damselfly -n02276258 641 admiral -n02277742 642 ringlet -n02279972 643 monarch -n02280649 644 cabbage_butterfly -n02281406 645 sulphur_butterfly -n02281787 646 lycaenid_butterfly -n01910747 647 jellyfish -n01914609 648 sea_anemone -n01917289 649 brain_coral -n01924916 650 flatworm -n01930112 651 nematode -n01943899 652 conch -n01944390 653 snail -n01945685 654 slug -n01950731 655 sea_slug -n01955084 656 chiton -n02319095 657 sea_urchin -n02321529 658 sea_cucumber -n03584829 659 iron -n03297495 660 espresso_maker -n03761084 661 microwave -n03259280 662 Dutch_oven -n04111531 663 rotisserie -n04442312 664 toaster -n04542943 665 waffle_iron -n04517823 666 vacuum -n03207941 667 dishwasher -n04070727 668 refrigerator -n04554684 669 washer -n03133878 670 Crock_Pot -n03400231 671 frying_pan -n04596742 672 wok -n02939185 673 caldron -n03063689 674 coffeepot -n04398044 675 teapot -n04270147 676 spatula -n02699494 677 altar -n04486054 678 triumphal_arch -n03899768 679 patio -n04311004 680 steel_arch_bridge -n04366367 681 suspension_bridge -n04532670 682 viaduct -n02793495 683 barn -n03457902 684 greenhouse -n03877845 685 palace -n03781244 686 monastery -n03661043 687 library -n02727426 688 apiary -n02859443 689 boathouse -n03028079 690 church -n03788195 691 mosque -n04346328 692 stupa -n03956157 693 planetarium -n04081281 694 restaurant -n03032252 695 cinema -n03529860 696 home_theater -n03697007 697 lumbermill -n03065424 698 coil -n03837869 699 obelisk -n04458633 700 totem_pole -n02980441 701 castle -n04005630 702 prison -n03461385 703 grocery_store -n02776631 704 bakery -n02791270 705 barbershop -n02871525 706 bookshop -n02927161 707 butcher_shop -n03089624 708 confectionery -n04200800 709 shoe_shop -n04443257 710 tobacco_shop -n04462240 711 toyshop -n03388043 712 fountain -n03042490 713 cliff_dwelling -n04613696 714 yurt -n03216828 715 dock -n02892201 716 brass -n03743016 717 megalith -n02788148 718 bannister -n02894605 719 breakwater -n03160309 720 dam -n03000134 721 chainlink_fence -n03930313 722 picket_fence -n04604644 723 worm_fence -n04326547 724 stone_wall -n03459775 725 grille -n04239074 726 sliding_door -n04501370 727 turnstile -n03792972 728 mountain_tent -n04149813 729 scoreboard -n03530642 730 honeycomb -n03961711 731 plate_rack -n03903868 732 pedestal -n02814860 733 beacon -n07711569 734 mashed_potato -n07720875 735 bell_pepper -n07714571 736 head_cabbage -n07714990 737 broccoli -n07715103 738 cauliflower -n07716358 739 zucchini -n07716906 740 spaghetti_squash -n07717410 741 acorn_squash -n07717556 742 butternut_squash -n07718472 743 cucumber -n07718747 744 artichoke -n07730033 745 cardoon -n07734744 746 mushroom -n04209239 747 shower_curtain -n03594734 748 jean -n02971356 749 carton -n03485794 750 handkerchief -n04133789 751 sandal -n02747177 752 ashcan -n04125021 753 safe -n07579787 754 plate -n03814906 755 necklace -n03134739 756 croquet_ball -n03404251 757 fur_coat -n04423845 758 thimble -n03877472 759 pajama -n04120489 760 running_shoe -n03062245 761 cocktail_shaker -n03014705 762 chest -n03717622 763 manhole_cover -n03777754 764 modem -n04493381 765 tub -n04476259 766 tray -n02777292 767 balance_beam -n07693725 768 bagel -n03998194 769 prayer_rug -n03617480 770 kimono -n07590611 771 hot_pot -n04579145 772 whiskey_jug -n03623198 773 knee_pad -n07248320 774 book_jacket -n04277352 775 spindle -n04229816 776 ski_mask -n02823428 777 beer_bottle -n03127747 778 crash_helmet -n02877765 779 bottlecap -n04435653 780 tile_roof -n03724870 781 mask -n03710637 782 maillot -n03920288 783 Petri_dish -n03379051 784 football_helmet -n02807133 785 bathing_cap -n04399382 786 teddy -n03527444 787 holster -n03983396 788 pop_bottle -n03924679 789 photocopier -n04532106 790 vestment -n06785654 791 crossword_puzzle -n03445777 792 golf_ball -n07613480 793 trifle -n04350905 794 suit -n04562935 795 water_tower -n03325584 796 feather_boa -n03045698 797 cloak -n07892512 798 red_wine -n03250847 799 drumstick -n04192698 800 shield -n03026506 801 Christmas_stocking -n03534580 802 hoopskirt -n07565083 803 menu -n04296562 804 stage -n02869837 805 bonnet -n07871810 806 meat_loaf -n02799071 807 baseball -n03314780 808 face_powder -n04141327 809 scabbard -n04357314 810 sunscreen -n02823750 811 beer_glass -n13052670 812 hen_of_the_woods -n07583066 813 guacamole -n03637318 814 lampshade -n04599235 815 wool -n07802026 816 hay -n02883205 817 bow_tie -n03709823 818 mailbag -n04560804 819 water_jug -n02909870 820 bucket -n03207743 821 dishrag -n04263257 822 soup_bowl -n07932039 823 eggnog -n03786901 824 mortar -n04479046 825 trench_coat -n03873416 826 paddle -n02999410 827 chain -n04367480 828 swab -n03775546 829 mixing_bowl -n07875152 830 potpie -n04591713 831 wine_bottle -n04201297 832 shoji -n02916936 833 bulletproof_vest -n03240683 834 drilling_platform -n02840245 835 binder -n02963159 836 cardigan -n04370456 837 sweatshirt -n03991062 838 pot -n02843684 839 birdhouse -n03482405 840 hamper -n03942813 841 ping-pong_ball -n03908618 842 pencil_box -n03902125 843 pay-phone -n07584110 844 consomme -n02730930 845 apron -n04023962 846 punching_bag -n02769748 847 backpack -n10148035 848 groom -n02817516 849 bearskin -n03908714 850 pencil_sharpener -n02906734 851 broom -n03788365 852 mosquito_net -n02667093 853 abaya -n03787032 854 mortarboard -n03980874 855 poncho -n03141823 856 crutch -n03976467 857 Polaroid_camera -n04264628 858 space_bar -n07930864 859 cup -n04039381 860 racket -n06874185 861 traffic_light -n04033901 862 quill -n04041544 863 radio -n07860988 864 dough -n03146219 865 cuirass -n03763968 866 military_uniform -n03676483 867 lipstick -n04209133 868 shower_cap -n03782006 869 monitor -n03857828 870 oscilloscope -n03775071 871 mitten -n02892767 872 brassiere -n07684084 873 French_loaf -n04522168 874 vase -n03764736 875 milk_can -n04118538 876 rugby_ball -n03887697 877 paper_towel -n13044778 878 earthstar -n03291819 879 envelope -n03770439 880 miniskirt -n03124170 881 cowboy_hat -n04487081 882 trolleybus -n03916031 883 perfume -n02808440 884 bathtub -n07697537 885 hotdog -n12985857 886 coral_fungus -n02917067 887 bullet_train -n03938244 888 pillow -n15075141 889 toilet_tissue -n02978881 890 cassette -n02966687 891 carpenter's_kit -n03633091 892 ladle -n13040303 893 stinkhorn -n03690938 894 lotion -n03476991 895 hair_spray -n02669723 896 academic_gown -n03220513 897 dome -n03127925 898 crate -n04584207 899 wig -n07880968 900 burrito -n03937543 901 pill_bottle -n03000247 902 chain_mail -n04418357 903 theater_curtain -n04590129 904 window_shade -n02795169 905 barrel -n04553703 906 washbasin -n02783161 907 ballpoint -n02802426 908 basketball -n02808304 909 bath_towel -n03124043 910 cowboy_boot -n03450230 911 gown -n04589890 912 window_screen -n12998815 913 agaric -n02992529 914 cellular_telephone -n03825788 915 nipple -n02790996 916 barbell -n03710193 917 mailbox -n03630383 918 lab_coat -n03347037 919 fire_screen -n03769881 920 minibus -n03871628 921 packet -n03733281 922 maze -n03976657 923 pole -n03535780 924 horizontal_bar -n04259630 925 sombrero -n03929855 926 pickelhaube -n04049303 927 rain_barrel -n04548362 928 wallet -n02979186 929 cassette_player -n06596364 930 comic_book -n03935335 931 piggy_bank -n06794110 932 street_sign -n02825657 933 bell_cote -n03388183 934 fountain_pen -n04591157 935 Windsor_tie -n04540053 936 volleyball -n03866082 937 overskirt -n04136333 938 sarong -n04026417 939 purse -n02865351 940 bolo_tie -n02834397 941 bib -n03888257 942 parachute -n04235860 943 sleeping_bag -n04404412 944 television -n04371430 945 swimming_trunks -n03733805 946 measuring_cup -n07920052 947 espresso -n07873807 948 pizza -n02895154 949 breastplate -n04204238 950 shopping_basket -n04597913 951 wooden_spoon -n04131690 952 saltshaker -n07836838 953 chocolate_sauce -n09835506 954 ballplayer -n03443371 955 goblet -n13037406 956 gyromitra -n04336792 957 stretcher -n04557648 958 water_bottle -n03187595 959 dial_telephone -n04254120 960 soap_dispenser -n03595614 961 jersey -n04146614 962 school_bus -n03598930 963 jigsaw_puzzle -n03958227 964 plastic_bag -n04069434 965 reflex_camera -n03188531 966 diaper -n02786058 967 Band_Aid -n07615774 968 ice_lolly -n04525038 969 velvet -n04409515 970 tennis_ball -n03424325 971 gasmask -n03223299 972 doormat -n03680355 973 Loafer -n07614500 974 ice_cream -n07695742 975 pretzel -n04033995 976 quilt -n03710721 977 maillot -n04392985 978 tape_player -n03047690 979 clog -n03584254 980 iPod -n13054560 981 bolete -n10565667 982 scuba_diver -n03950228 983 pitcher -n03729826 984 matchstick -n02837789 985 bikini -n04254777 986 sock -n02988304 987 CD_player -n03657121 988 lens_cap -n04417672 989 thatch -n04523525 990 vault -n02815834 991 beaker -n09229709 992 bubble -n07697313 993 cheeseburger -n03888605 994 parallel_bars -n03355925 995 flagpole -n03063599 996 coffee_mug -n04116512 997 rubber_eraser -n04325704 998 stole -n07831146 999 carbonara -n03255030 1000 dumbbell \ No newline at end of file diff --git a/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/set_ranktable.py b/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/set_ranktable.py new file mode 100644 index 0000000000000000000000000000000000000000..c25b51462c5df2325462786688d4a206ee29fb9a --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/set_ranktable.py @@ -0,0 +1,1740 @@ +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('-n', '--npu_nums', type=int, default='2', help='nums of npu') +parser.add_argument('-c', '--conf_path', type=str, default='./', help='the path of server_info') +FLAGS = parser.parse_args() + +import json +import os +server = [] +server_conf = [] +server_list = ["0", "1", "2", "3", "4", "5", "6", "7"] +if os.path.isdir(FLAGS.conf_path): + for f in os.listdir(FLAGS.conf_path): + if (f.split("_")[-1]).split(".")[0] in server_list and (f.split("_")[-1]).split(".")[1] == 'info' and f.split("_")[0] == 'server': + server_conf.append(f) + + + + + + +rank_address = [] +for i in range(FLAGS.npu_nums): + for x in server_conf: + if (x.split("_")[-1]).split(".")[0] == str(i): + server.append(x.split("_")[1]) + l = FLAGS.conf_path + "/" + x + with open(l, "r") as a: + s = a.readlines() + for s_ in s: + if 'address_0' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_1' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_2' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_3' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_4' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_5' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_6' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_7' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + +if FLAGS.npu_nums == 1: + rank = { + "server_count":"1", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 2: + rank = { + "server_count":"2", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]} + ], + + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 3: + rank = { + "server_count":"3", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 4: + rank = { + "server_count":"4", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 5: + rank = { + "server_count":"5", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + +elif FLAGS.npu_nums == 6: + rank = { + "server_count":"6", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 7: + rank = { + "server_count":"7", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +elif FLAGS.npu_nums == 8: + rank = { + "server_count":"8", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]}, + { + "server_id":server[7], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[56], + "rank_id":"56" + }, + { + "device_id":"1", + "device_ip":rank_address[57], + "rank_id":"57" + }, + { + "device_id":"2", + "device_ip":rank_address[58], + "rank_id":"58" + }, + { + "device_id":"3", + "device_ip":rank_address[59], + "rank_id":"59" + }, + { + "device_id":"4", + "device_ip":rank_address[60], + "rank_id":"60" + }, + { + "device_id":"5", + "device_ip":rank_address[61], + "rank_id":"61" + }, + { + "device_id":"6", + "device_ip":rank_address[62], + "rank_id":"62" + }, + { + "device_id":"7", + "device_ip":rank_address[63], + "rank_id":"63" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +with open("rank_table.json", "w") as f: + json.dump(rank, f) + + + + + + diff --git a/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/train_performance_16p.sh b/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/train_performance_16p.sh new file mode 100644 index 0000000000000000000000000000000000000000..2c22303b31d5b7840b66693ba46a639842e30aff --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/Densenet_3D_ID0121_for_TensorFlow/test/train_performance_16p.sh @@ -0,0 +1,106 @@ +#!/bin/bash + + +cur_path=`pwd`/.. +RANK_ID_START=0 +export RANK_ID=0 +export RANK_SIZE=16 +export JOB_ID=888886 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 +export ASCEND_GLOBAL_LOG_LEVEL=3 + +#基础参数 需要模型审视修改 +#网络名称，同目录名称 +Network="Densenet_3D_ID0121_for_TensorFlow" +batch_size=2 +#维测参数，precision_mode需要模型审视修改 +autotune=False + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --autotune* ]];then + autotune=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --server_index* ]];then + server_index=`echo ${para#*=}` + elif [[ $para == --conf_path* ]];then + conf_path=`echo ${para#*=}` + fi +done + +# 自动生成ranktable的脚本 +rank_size=8 +nohup python3 set_ranktable.py --npu_nums=$((RANK_SIZE/rank_size)) --conf_path=$conf_path +wait +export RANK_TABLE_FILE=${cur_path}/test/rank_table.json + +start=$(date +%s) +for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++)); +do + # 设置环境变量 + export RANK_ID=$RANK_ID + export DEVICE_INDEX=`expr ${RANK_ID} - $((rank_size*server_index))` + export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + echo "DEVICE ID: $ASCEND_DEVICE_ID" + #进入训练脚本目录，需要模型审视修改 + if [ -d $cur_path/test/output/$ASCEND_DEVICE_ID ];then + rm -rf $cur_path/test/output/$ASCEND_DEVICE_ID + mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID + else + mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID + fi + cd $cur_path + python3 train.py -bs 2 -mn dense24 -sp dense24_correction -nc True -e 1 -r ${data_path} -per True -mul_rank_size=$RANK_SIZE -mul_device_id=$RANK_ID > $cur_path/test/output/$ASCEND_DEVICE_ID/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait +end=$(date +%s) +e2etime=$(( $end - $start )) +step_sec=`grep -a 'epoch-patient' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${ASCEND_DEVICE_ID}.log |awk 'END {print $16}'` +ActualFPS=`awk 'BEGIN{printf "%.2f\n",'${RANK_SIZE}'*'${batch_size}'/'$step_sec'}'` +echo "--------Final Result ----------" +echo "Final Performance ms/step : $ActualFPS" +echo "Final Training Duration sec : $e2etime" +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +grep 'patient acc:' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${ASCEND_DEVICE_ID}.log|awk '{print $6}'|sed 's/,//g' >> $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt +#最后一个迭代acc值，不需要修改 +train_accuracy=`awk 'END {print}' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt` + +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +#稳定性精度看护结果汇总 + +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据 +#单迭代训练时长，不需要修改 +TrainingTime=`grep "time cust:" $cur_path/test/output/$ASCEND_DEVICE_ID/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $16}'` +#ActualFPS=`echo "scale=2;${BatchSize} / ${TrainingTime}"|bc` + +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep 'patient loss:' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'|sed 's/,//g' >> $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2etime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/ReduceMeanD.json b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/ReduceMeanD.json new file mode 100644 index 0000000000000000000000000000000000000000..6de932d0c6b151ecb1ffca1a26d3af4e0feca412 --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/ReduceMeanD.json @@ -0,0 +1,17 @@ +{ + "black-list":{ + "to-remove":[ + ], + "to-add":[ + ] + }, + "white-list":{ + "to-remove":[ + ], + "to-add":[ + "ReduceMeanD","ReduceMean" + ] + }, + "gray-list":{ + } +} \ No newline at end of file diff --git a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/efficientnet_model.py b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/efficientnet_model.py index 8319a3514587c8bb1fafdee192533e9a47f0019f..3f34d6a38d3a167986d4c90e5960ed8c63f07855 100644 --- a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/efficientnet_model.py +++ b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/efficientnet_model.py @@ -29,7 +29,7 @@ import numpy as np import six from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf - +from npu_bridge.estimator import npu_ops import utils GlobalParams = collections.namedtuple('GlobalParams', [ @@ -636,7 +636,8 @@ class Model(tf.keras.Model): self.endpoints['pooled_features'] = outputs if not pooled_features_only: if self._dropout: - outputs = self._dropout(outputs, training=training) + #outputs = self._dropout(outputs, training=training) + outputs = npu_ops.dropout(outputs, keep_prob = 1.0 - self._global_params.drop_connect_rate) self.endpoints['global_pool'] = outputs if self._fc: outputs = tf.squeeze(outputs, self._spatial_dims) @@ -647,7 +648,8 @@ class Model(tf.keras.Model): self.endpoints['pooled_features'] = outputs if not pooled_features_only: if self._dropout: - outputs = self._dropout(outputs, training=training) + #outputs = self._dropout(outputs, training=training) + outputs = npu_ops.dropout(outputs, keep_prob = 1.0 - self._global_params.drop_connect_rate) self.endpoints['global_pool'] = outputs if self._fc: outputs = self._fc(outputs) diff --git a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/main_npu.py b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/main_npu.py index f4b7fb74ec725bf1ea2fbecd3e8e660a669d761d..e8131ad312aedde72f252b52619b2fc0b7e71c74 100644 --- a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/main_npu.py +++ b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/efficientnet/main_npu.py @@ -692,6 +692,7 @@ def main(unused_argv): session_config=estimator_config, model_dir=FLAGS.model_dir, iterations_per_loop=FLAGS.iterations_per_loop, + modify_mixlist='./efficientnet/ReduceMeanD.json', keep_checkpoint_max=5) # for NPU diff --git a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_1p.sh index 037b342b51a43dae1879688557c9bc021ab7e738..943ceae25b48e18e2132ff896f8fec49a4c101f8 100644 --- a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_1p.sh @@ -92,6 +92,9 @@ if [[ $data_path == "" ]];then exit 1 fi +#修改参数 +sed -i "695s|./efficientnet/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../efficientnet/main_npu.py + #训练开始时间，不需要修改 start_time=$(date +%s) cd $cur_path/../ @@ -117,8 +120,8 @@ do --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ --mode=train \ --train_batch_size=256 \ - --train_steps=250 \ - --iterations_per_loop=10 \ + --train_steps=500 \ + --iterations_per_loop=100 \ --model_name=efficientnet-b0 > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -127,6 +130,9 @@ wait end_time=$(date +%s) e2e_time=$(( $end_time - $start_time )) +#恢复参数 +sed -i "695s|${cur_path}/../ReduceMeanD.json|./efficientnet/ReduceMeanD.json|g" $cur_path/../efficientnet/main_npu.py + #结果打印，不需要修改 echo "------------------ Final result ------------------" #输出性能FPS，需要模型审视修改 diff --git a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_8p.sh b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_8p.sh index 087e7104498773af4229c0a98e3a7b2ede4f5e49..8502e079e3dc5fd811164d3bd602cd2089118483 100644 --- a/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_8p.sh +++ b/TensorFlow/built-in/cv/image_classification/EfficientNet_B0_ID0009_for_TensorFlow/test/train_performance_8p.sh @@ -98,6 +98,9 @@ if [[ $data_path == "" ]];then exit 1 fi +#修改参数 +sed -i "695s|./efficientnet/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../efficientnet/main_npu.py + #autotune时，先开启autotune执行单P训练，不需要修改 if [[ $autotune == True ]]; then train_full_1p.sh --autotune=$autotune --data_path=$data_path @@ -142,8 +145,8 @@ do --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ --mode=train_and_eval \ --train_batch_size=256 \ - --train_steps=250 \ - --iterations_per_loop=10 \ + --train_steps=500 \ + --iterations_per_loop=100 \ --steps_per_eval=31250 \ --eval_batch_size=128 \ --base_learning_rate=0.2 \ @@ -155,6 +158,9 @@ wait end_time=$(date +%s) e2e_time=$(( $end_time - $start_time )) +#恢复参数 +sed -i "695s|${cur_path}/../ReduceMeanD.json|./efficientnet/ReduceMeanD.json|g" $cur_path/../efficientnet/main_npu.py + #结果打印，不需要修改 echo "------------------ Final result ------------------" #输出性能FPS，需要模型审视修改 diff --git a/TensorFlow/built-in/cv/image_classification/Oct-ResNet_ID0251_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/cv/image_classification/Oct-ResNet_ID0251_for_TensorFlow/test/train_full_1p.sh index e9100627aea62b026ecb378f049293ae86748095..ef4ae989b3606ce99bcc5d1d19427e05b15169ed 100644 --- a/TensorFlow/built-in/cv/image_classification/Oct-ResNet_ID0251_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/built-in/cv/image_classification/Oct-ResNet_ID0251_for_TensorFlow/test/train_full_1p.sh @@ -155,7 +155,7 @@ FPS=`awk 'BEGIN {printf "%.2f\n",'${batch_size}'*'${batch_per_sec}'}'` echo "Final Performance images/sec : $FPS" #输出训练精度,需要模型审视修改 -train_accuracy=`grep "acc =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | tail -n 1 | awk -F " " '{print $9}'` +train_accuracy=`grep "acc =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | tail -n 1 | tr -d ','| awk -F " " '{print $9}'` #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/ReduceMeanD.json b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/ReduceMeanD.json new file mode 100644 index 0000000000000000000000000000000000000000..6de932d0c6b151ecb1ffca1a26d3af4e0feca412 --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/ReduceMeanD.json @@ -0,0 +1,17 @@ +{ + "black-list":{ + "to-remove":[ + ], + "to-add":[ + ] + }, + "white-list":{ + "to-remove":[ + ], + "to-add":[ + "ReduceMeanD","ReduceMean" + ] + }, + "gray-list":{ + } +} \ No newline at end of file diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/gpu_base_trainer.py b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/gpu_base_trainer.py index d42c68ec57bafe02426a8e38d0ceaef22f56a64e..9a517ee1985239e05b9d9f73578011a183916f41 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/gpu_base_trainer.py +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/src/trainers/gpu_base_trainer.py @@ -100,7 +100,16 @@ class GPUBaseTrain(object): run_config = NPURunConfig(dump_config=dump_config, hcom_parallel=True, precision_mode="allow_mix_precision", save_summary_steps=0, log_step_count_steps=None, enable_data_pre_proc=True,save_checkpoints_secs=1e9, session_config=session_config, model_dir = self.config['model_dir'], iterations_per_loop=self.config['iterations_per_loop']) else: if self.config['debug'] : - run_config = NPURunConfig(hcom_parallel=True, precision_mode="allow_mix_precision", enable_data_pre_proc=True, save_checkpoints_steps=112590, session_config=session_config, model_dir = self.config['model_dir'], iterations_per_loop=self.config['iterations_per_loop'], keep_checkpoint_max=5) + run_config = NPURunConfig(hcom_parallel=True, + precision_mode="allow_mix_precision", + enable_data_pre_proc=True, + save_checkpoints_steps=112590, + session_config=session_config, + model_dir = self.config['model_dir'], + iterations_per_loop=self.config['iterations_per_loop'], + keep_checkpoint_max=5, + enable_small_channel=1, + modify_mixlist='./src/trainers/ReduceMeanD.json') else : run_config = NPURunConfig(hcom_parallel=True, precision_mode="allow_mix_precision", save_summary_steps=0, log_step_count_steps=None, enable_data_pre_proc=True,save_checkpoints_secs=1e9, session_config=session_config, model_dir = self.config['model_dir'], iterations_per_loop=self.config['iterations_per_loop']) diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/set_ranktable.py b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/set_ranktable.py new file mode 100644 index 0000000000000000000000000000000000000000..c25b51462c5df2325462786688d4a206ee29fb9a --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/set_ranktable.py @@ -0,0 +1,1740 @@ +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('-n', '--npu_nums', type=int, default='2', help='nums of npu') +parser.add_argument('-c', '--conf_path', type=str, default='./', help='the path of server_info') +FLAGS = parser.parse_args() + +import json +import os +server = [] +server_conf = [] +server_list = ["0", "1", "2", "3", "4", "5", "6", "7"] +if os.path.isdir(FLAGS.conf_path): + for f in os.listdir(FLAGS.conf_path): + if (f.split("_")[-1]).split(".")[0] in server_list and (f.split("_")[-1]).split(".")[1] == 'info' and f.split("_")[0] == 'server': + server_conf.append(f) + + + + + + +rank_address = [] +for i in range(FLAGS.npu_nums): + for x in server_conf: + if (x.split("_")[-1]).split(".")[0] == str(i): + server.append(x.split("_")[1]) + l = FLAGS.conf_path + "/" + x + with open(l, "r") as a: + s = a.readlines() + for s_ in s: + if 'address_0' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_1' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_2' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_3' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_4' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_5' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_6' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_7' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + +if FLAGS.npu_nums == 1: + rank = { + "server_count":"1", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 2: + rank = { + "server_count":"2", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]} + ], + + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 3: + rank = { + "server_count":"3", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 4: + rank = { + "server_count":"4", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 5: + rank = { + "server_count":"5", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + +elif FLAGS.npu_nums == 6: + rank = { + "server_count":"6", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 7: + rank = { + "server_count":"7", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +elif FLAGS.npu_nums == 8: + rank = { + "server_count":"8", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]}, + { + "server_id":server[7], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[56], + "rank_id":"56" + }, + { + "device_id":"1", + "device_ip":rank_address[57], + "rank_id":"57" + }, + { + "device_id":"2", + "device_ip":rank_address[58], + "rank_id":"58" + }, + { + "device_id":"3", + "device_ip":rank_address[59], + "rank_id":"59" + }, + { + "device_id":"4", + "device_ip":rank_address[60], + "rank_id":"60" + }, + { + "device_id":"5", + "device_ip":rank_address[61], + "rank_id":"61" + }, + { + "device_id":"6", + "device_ip":rank_address[62], + "rank_id":"62" + }, + { + "device_id":"7", + "device_ip":rank_address[63], + "rank_id":"63" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +with open("rank_table.json", "w") as f: + json.dump(rank, f) + + + + + + diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh index 2107632c35bd6d37b43ef24bc298451c6a97249c..30fd680e6e538521e855940e1add830d8f4f591f 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_1p.sh @@ -96,6 +96,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_256bs_1p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_256bs_1p.py +sed -i "112s|./src/trainers/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #训练开始时间，不需要修改 @@ -135,6 +136,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_1p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_1p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_8p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_8p.sh index 024f66aaa686ce46d78c8c77e668d2555b8d80c0..35f1881cc11d8b41c6e8a52066b6807fb6d60c2e 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_8p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_8p.sh @@ -101,6 +101,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_256bs_8p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_256bs_8p.py +sed -i "112s|./src/trainers/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #autotune时，先开启autotune执行单P训练，不需要修改 @@ -159,6 +160,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_8p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_8p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_16p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_16p.sh new file mode 100644 index 0000000000000000000000000000000000000000..e18ba26d3a76b015705a20244462cc8473204e60 --- /dev/null +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_16p.sh @@ -0,0 +1,209 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=16 +export JOB_ID=99990001 +export RANK_ID=8 +export SLOG_PRINT_TO_STDOUT=0 +export HCCL_CONNECT_TIMEOUT=600 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 +export ASCEND_GLOBAL_LOG_LEVEL=3 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="ResNet50_ID0058_for_TensorFlow" +#训练epoch +train_epochs=1 +#训练batch_size +batch_size=256 +#训练step +train_steps=2000 +#学习率 +learning_rate= + +#维测参数，precision_mode需要模型审视修改 +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False +autotune=False + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_1p.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --autotune whether to enable autotune, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --autotune* ]];then + autotune=`echo ${para#*=}` + mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak + mv $install_path/fwkacllib/data/tiling/Ascend910/custom $install_path/fwkacllib/data/tiling/Ascend910/custom_bak + autotune_dump_path=${cur_path}/output/autotune_dump + mkdir -p ${autotune_dump_path}/GA + mkdir -p ${autotune_dump_path}/rl + cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/ + cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/ + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --server_index* ]];then + server_index=`echo ${para#*=}` + elif [[ $para == --conf_path* ]];then + conf_path=`echo ${para#*=}` + elif [[ $para == --bind_core* ]]; then + bind_core=`echo ${para#*=}` + name_bind="_bindcore" + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +# 自动生成ranktable的脚本 +rank_size=8 +nohup python3 set_ranktable.py --npu_nums=$((RANK_SIZE/rank_size)) --conf_path=$conf_path +wait +export RANK_TABLE_FILE=${cur_path}/rank_table.json + + +#修改参数 +sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py +sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py + +cp data_loader.py $cur_path/../src/data_loader/resnet50/ +#autotune时，先开启autotune执行单P训练，不需要修改 +if [[ $autotune == True ]]; then + train_full_1p.sh --autotune=$autotune --data_path=$data_path + wait + autotune=False +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 +cd $cur_path/../ +for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export DEVICE_INDEX=`expr ${RANK_ID} - $((rank_size*server_index))` + export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + fi + + # 绑核，不需要的绑核的模型删除，需要模型审视修改 + corenum=`cat /proc/cpuinfo |grep "processor"|wc -l` + let a=ASCEND_DEVICE_ID*${corenum}/8 + let b=ASCEND_DEVICE_ID+1 + let c=b*${corenum}/8-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + if [ "x${bind_core}" != x ];then + bind_core="taskset -c $a-$c" + fi + nohup ${bind_core} python3.7 ${cur_path}/../src/mains/res50.py --config_file=res50_256bs_HW192_8p \ + --max_train_steps=${train_steps} \ + --iterations_per_loop=100 \ + --debug=True \ + --eval=False \ + --model_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/d_solution/ckpt${ASCEND_DEVICE_ID} >> ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#参数改回 +sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py +sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +FPS=`cat ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | grep "FPS:" | awk -F "FPS:" '{print $2}' | awk -F " loss:" '{print $1}' | tail -n +2 | awk '{sum+=$1} END {print sum*2/NR}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#打印，不需要修改 +echo "E2E Training Duration sec : $e2e_time" + +#稳定性精度看护结果汇总 +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p_hw192'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "FPS:" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss: " '{print $2}' | awk -F "total" '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_1p.sh index 9c2c2c93e845d58ae3231e4c5e29912d3105650f..b70f86f1ab0dd204be34b7ef8d2a06a143522c0a 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_1p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_1p.sh @@ -96,6 +96,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_256bs_HW192_1p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_256bs_HW192_1p.py +sed -i "112s|./src/trainers/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #训练开始时间，不需要修改 @@ -135,6 +136,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_1p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_1p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_8p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_8p.sh index 7046b5c50cd2819c96797c67e0a5ba005b6b1311..a32343d7684739180b568cbc40946fbe0a9ec01f 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_8p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs256_hw192_8p.sh @@ -101,6 +101,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py +sed -i "112s|./src/trainers/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #autotune时，先开启autotune执行单P训练，不需要修改 @@ -159,6 +160,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_256bs_HW192_8p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_1p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_1p.sh index 6c08021b499cdd6fcc6dbfefd9397ce13e0833a1..551f6d7ef7fcc7199d35e817e6247c2557bfc0af 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_1p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_1p.sh @@ -96,6 +96,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_32bs_1p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_32bs_1p.py +sed -i "112s|./src/trainers/ReduceMeanD.jsonn|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #训练开始时间，不需要修改 @@ -135,6 +136,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_32bs_1p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_32bs_1p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_8p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_8p.sh index e18a92d993c952020ecf4ac43f12075e9bf9e31f..d6b1f66d5e722f0fc0b8184f926177b29c7e0354 100644 --- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_8p.sh +++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_performance_bs32_8p.sh @@ -101,6 +101,7 @@ fi #修改参数 sed -i "50s|PATH_TO_BE_CONFIGURED|${data_path}|g" $cur_path/../src/configs/res50_32bs_8p.py sed -i "107s|PATH_TO_BE_CONFIGURED|${cur_path}/output/0/d\_solution/ckpt0|g" $cur_path/../src/configs/res50_32bs_8p.py +sed -i "112s|./src/trainers/ReduceMeanD.json|${cur_path}/../ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py cp data_loader.py $cur_path/../src/data_loader/resnet50/ #autotune时，先开启autotune执行单P训练，不需要修改 @@ -159,6 +160,7 @@ e2e_time=$(( $end_time - $start_time )) #参数改回 sed -i "50s|${data_path}|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_32bs_8p.py sed -i "107s|${cur_path}/output/0/d\_solution/ckpt0|PATH_TO_BE_CONFIGURED|g" $cur_path/../src/configs/res50_32bs_8p.py +sed -i "112s|${cur_path}/../ReduceMeanD.json|./src/trainers/ReduceMeanD.json|g" $cur_path/../src/trainers/gpu_base_trainer.py #结果打印，不需要修改 echo "------------------ Final result ------------------" diff --git a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/main_npu.py b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/main_npu.py index f0cfc1c366126831f74ebe8e6b05ff4ba1b1454b..fd1bc90d78820c6d682984cc72b0bb4a08781f25 100644 --- a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/main_npu.py +++ b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/main_npu.py @@ -39,8 +39,8 @@ from dataset.data_loader import Dataset, CLASSES from runtime.hooks import get_hooks, ProfilingHook, TrainingHook from runtime.arguments import PARSER from runtime.setup import prepare_model_dir, build_estimator, set_flags, get_logger -#from hccl.split.api import set_split_strategy_by_idx -#set_split_strategy_by_idx([1,90,99]) +from hccl.split.api import set_split_strategy_by_idx +set_split_strategy_by_idx([1,90,99]) def parse_evaluation_results(result): data = {CLASSES[i]: result[CLASSES[i]] for i in range(len(CLASSES))} diff --git a/TensorFlow/built-in/cv/image_synthesis/DCGAN_ID2196_for_TensorFlow/web/index.html b/TensorFlow/built-in/cv/image_synthesis/DCGAN_ID2196_for_TensorFlow/web/index.html deleted file mode 100644 index 6ea2fe6158ffbc6f72e303c305ec055980f885e5..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/cv/image_synthesis/DCGAN_ID2196_for_TensorFlow/web/index.html +++ /dev/null @@ -1,427 +0,0 @@ - - - - - - - - - - - - - - - - - - - Neural Face | 프사 뉴럴 - - - - - - - - - - - - - - - - - - -
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프사 뉴럴

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Neural Face

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프사 뉴럴은 Facebook AI Research에서 개발한 Deep Convolutional Generative Adversarial Networks (DCGAN) 이라는 기계 학습 모델을 사용해 만들어졌습니다.

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프사 뉴럴은 얼굴 사진을 만드는 인공 지능이며
이 페이지에 나오는 모든 사람들은 이세상에 존재하지 않습니다.

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Neural Face uses Deep Convolutional Generative Adversarial Networks (DCGAN), which is developed by Facebook AI Research.

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Neural Face is an Artificial Intelligence which generates face images
and all images in this page are not REAL.

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Image Generation

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프사 뉴럴은 0에서 1 사이의 100개의 숫자 z로 사람의 이미지를 만들어내는 인공지능입니다.

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1. 아래에 보이는 100개의 픽셀을 z의 각 숫자를 나타냅니다.
2. 만들어진 사진 위에 마우스를 올리면 사진에 사용된 z가 보입니다.
3. 만들어진 이미지를 누르시면 그 이미지의 z가 복사됩니다.

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Neural Face uses a vector z that consists of 100 real numbers ranging from 0 to 1.

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1. Each pixel in the below pallete represents a value in z.
2. If you hover your mouse over an image, z for that image will be displayed.
3. If you click an image, z will be copied to the palette.

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(브라우저 성능에 따라 1~10초가 걸립니다)

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(Might take 1 to 10 seconds depending on your browser)

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프사 뉴럴을 불러오고 있습니다...

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Neural Face is preparing to draw...

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알고리즘

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Algorithm

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프사 뉴럴의 핵심 모델인 DCGAN은 두 개의 인공 신경망으로 구성되어 있으며, 각각

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1. 사진을 만들어내는 생성자 (G)
2. 진짜 사진과 생성자가 만든 사진을 구분하는 구분자 (D)

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라고 부릅니다.

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두 신경망은 수많은 이미지를 반복적으로 보면서 생성자는 구분자를 속이기 위해, 구분자는 생성자가 만든 사진을 판별하기 위해 학습합니다. 이러한 학습 방법을 적대적 학습 (Adversarial Learning)이라고 하며, 생성자와 구분자를 도둑과 경찰로 비유하기도 합니다.

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DCGAN, which is the core of Neural Face, consists of two different neural networks which are:

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1. Generator (G) that generates an image
2. Discriminator (D) that discriminate real images from generated images

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Two neural networks compete as one tries to deceive the other. This kind of learning is called Adversarial Learning. Because of this, Generator and Discriminator are described as a thief and police, respectively.

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생성자와 구분자는 여러 가지 인공 신경망 종류 중에서 각각 Deconvolutional Network (DNN)과 Convolutional Neural Network (CNN)로 구현되어 있습니다. CNN은 수백 개의 픽셀로 이루어진 이미지를 작은 차원의 숫자들 (z)로 잘 요약할 수 있는 필터를 배우는 인공 신경망이며, DNN은 이렇게 작아진 차원의 숫자들로 원래 이미지를 복원하는 필터를 배우는 신경망입니다.

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구분자는 인공 신경망에 실제 이미지를 넣은 결과를 1로, 만들어진 이미지의 결과는 0으로 구분하도록 학습합니다. 반대로 생성자는 Gaussian Distribution을 따르는 z라는 확률 변수를 두고, 사람의 이미지의 확률 분포를 z를 사용해 계산합니다. 이렇게 만들어진 이미지를 구분자가 실제 이미지라고 잘못 판단하도록 계속 학습합니다.

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Generator and Discriminator consist of Deconvolutional Network (DNN) and Convolutional Neural Network (CNN). CNN is a neural network which encodes the hundreds of pixels of an image into a vector of small dimensions (z) which is a summary of the image. DNN is a network that learns filters to recover the original image from z.

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When a real image is given, Discriminator should output 1 or 0 for whether the image was generated from Generator. In the contrast, Generator generates an image from z, which follows a Gaussian Distribution, and tries to figure out the distribution of human images from z. In this way, a Generator tries to cheat Discriminator into making a wrong decision.

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Results

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프사 뉴럴를 학습시키기 위해 인터넷에 10만 개 이상의 사진들을 모았고 이 사진들에서 얼굴 사진만 잘라서 얼굴 데이터 셋을 만들었습니다. 코드는 최근에 구글에서 공개한 TensorFlow로 구현했으며 GTX 980 Ti를 사용하여 이틀간 학습시켰습니다.

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아래는 초기 학습 단계에서 프사 뉴럴이 정해진 z로 얼굴 사진을 만들어 가는 과정을 보여줍니다.

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More than 100K images are crawled from online communities and those images are cropped by using openface which is a face recognition framework. Neural Face is implemented with TensorFlow and a GTX 980 Ti is used to train for two days.

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Below is a series of images generated by Generator with a fixed z between the first and the fith epoch of training.

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생성자가 사용하는 z는 -1에서 1 사이의 Gaussian Distribution을 따르는 확률 변수이며, 평균값인 0으로 이미지를 만들게 되면, 프사 뉴럴이 생각하는 평균적인 얼굴을 알 수 있습니다.

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The vector z has real values from -1 to 1 and it follows the Gaussian Distribution. We can see the most common face that is interpreted by Neural Face using 0 as all values of z.

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평균값 0에서 랜덤한 차원의 값을 조금씩 바꾸면 아래와 같은 변화를 볼 수 있습니다.

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The below images are generated by changing the values of z continuously, starting from the average value (0) to -1 or 1.

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아래의 사진들은 100차원의 z 값 중에서 임의의 차원들을 -1부터 1까지 바꾸면서 생성자 신경망에 넣은 결과이며, 점점 미소를 짓거나, 안경이 생기거나, 흑백 사진이 되거나, 성별이 바뀌는 등의 결과를 확인하실 수 있습니다.

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The below images are generated by changing ten different values of z from -1 to 1. People in the images vary in characteristics such as smiling, wearing glasses, turning into black and white images, and changing into different sex.

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프사 뉴럴의 코드는 이곳에 공개되어 있습니다.

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The code of Neural Face can be found here.

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Misc.

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마지막으로 튜링 테스트를 해 보겠습니다 :)
아래 사진 중에서 진짜 사진은 무엇일까요?

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(마우스로 클릭하면 정답이 보입니다)

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Lastly, let's conduct a Turing Test :)
Can you guess which are the real images?

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(Answer will be showed if you click an image)

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Other Projects

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Taehoon Kim

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@carpedm20

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- - - - - - - - - - - - - - - - - - - - diff --git a/TensorFlow/built-in/cv/image_synthesis/VAE-GAN_ID1800_for_TensorFlow/utils.py b/TensorFlow/built-in/cv/image_synthesis/VAE-GAN_ID1800_for_TensorFlow/utils.py index 5e8fc04028a4709b590cf3ade1f753acf796ea97..2529eb3685a92725365d4a44f583adb578dc67cf 100644 --- a/TensorFlow/built-in/cv/image_synthesis/VAE-GAN_ID1800_for_TensorFlow/utils.py +++ b/TensorFlow/built-in/cv/image_synthesis/VAE-GAN_ID1800_for_TensorFlow/utils.py @@ -45,7 +45,8 @@ def encoder(input_tensor, output_size): net = layers.conv2d(net, 32, 5, stride=2) net = layers.conv2d(net, 64, 5, stride=2) net = layers.conv2d(net, 128, 5, stride=2, padding='VALID') - net = layers.dropout(net, keep_prob=0.9) + # net = layers.dropout(net, keep_prob=0.9) + net = npu_ops.dropout(net, keep_prob=0.9) net = layers.flatten(net) return layers.fully_connected(net, output_size, activation_fn=None) diff --git a/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/albert/albert_config/vocab.txt b/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/albert/albert_config/vocab.txt deleted file mode 100644 index ca4f9781030019ab9b253c6dcb8c7878b6dc87a5..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/nlp/Albert_ID0632_for_TensorFlow/albert/albert_config/vocab.txt +++ /dev/null @@ -1,21128 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] -[unused74] -[unused75] -[unused76] -[unused77] -[unused78] -[unused79] -[unused80] -[unused81] -[unused82] -[unused83] -[unused84] -[unused85] -[unused86] -[unused87] -[unused88] -[unused89] -[unused90] -[unused91] -[unused92] -[unused93] -[unused94] -[unused95] -[unused96] -[unused97] -[unused98] -[unused99] -[UNK] -[CLS] -[SEP] -[MASK] - - -! -" -# -$ -% -& -' -( -) -* -+ -, -- -. -/ -0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -: -; -< -= -> -? -@ -[ -\ -] -^ -_ -a -b -c -d -e -f -g -h -i -j -k -l -m -n -o -p -q -r -s -t -u -v -w -x -y -z -{ -| -} -~ -£ -¤ -¥ -§ -© -« -® -° -± -² -³ -µ -· -¹ -º -» -¼ -× -ß -æ -÷ -ø -đ -ŋ -ɔ -ə -ɡ -ʰ -ˇ -ˈ -ˊ -ˋ -ˍ -ː -˙ -˚ -ˢ -α -β -γ -δ -ε -η -θ -ι -κ -λ -μ -ν -ο -π -ρ -ς -σ -τ -υ -φ -χ -ψ -ω -а -б -в -г -д -е -ж -з -и -к -л -м -н -о -п -р -с -т -у -ф -х -ц -ч -ш -ы -ь -я -і -ا -ب -ة -ت -د -ر -س -ع -ل -م -ن -ه -و -ي -۩ -ก -ง -น -ม -ย -ร -อ -า -เ -๑ -་ -ღ -ᄀ -ᄁ -ᄂ -ᄃ -ᄅ -ᄆ -ᄇ -ᄈ -ᄉ -ᄋ -ᄌ -ᄎ -ᄏ -ᄐ -ᄑ -ᄒ -ᅡ -ᅢ -ᅣ -ᅥ -ᅦ -ᅧ -ᅨ -ᅩ -ᅪ -ᅬ -ᅭ -ᅮ -ᅯ -ᅲ -ᅳ -ᅴ -ᅵ -ᆨ -ᆫ -ᆯ -ᆷ -ᆸ -ᆺ -ᆻ -ᆼ -ᗜ -ᵃ -ᵉ -ᵍ -ᵏ -ᵐ -ᵒ -ᵘ -‖ -„ -† -• -‥ -‧ -
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a/TensorFlow/built-in/nlp/Bert-NER_ID0797_for_TensorFlow/output/result_dir/label_test.txt +++ /dev/null @@ -1,40610 +0,0 @@ -S O O -- O O -J B-PER B-LOC -GE O O -L B-ORG O -W O O -, O O -CH B-ORG B-PER -IN O O -S B-LOC O -DE O O -. O O -Na B-LOC B-PER -La I-PER I-PER -AL B-LOC B-LOC -, O O -United B-LOC B-LOC -Arab I-LOC I-LOC -Emirates I-LOC I-LOC -1996 O O -Japan B-LOC B-LOC -began O O -the O O -defence O O -of O O -their O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -title O O -with O O -a O O -lucky O O -2 O O -win O O -against O O -Syria B-LOC B-LOC -in O O -a O O -Group O O -C O O -championship O O -match O O -on O O -Friday O O -. O O -But O O -China B-LOC B-LOC -saw O O -their O O -luck O O -desert O O -them O O -in O O -the O O -second O O -match O O -of O O -the O O -group O O -, O O -crashing O O -to O O -a O O -surprise O O -2 O O -defeat O O -to O O -newcomer O O -Uzbekistan B-LOC B-LOC -. O O -China B-LOC B-LOC -controlled O O -most O O -of O O -the O O -match O O -and O O -saw O O -several O O -chances O O -missed O O -until O O -the O O -78 O O -minute O O -when O O -U B-MISC B-MISC -striker O O -Igor B-PER B-PER -S I-PER I-PER -took O O -advantage O O -of O O -a O O -mi O O -defensive O O -header O O -to O O -lo O O -the O O -ball O O -over O O -the O O -advancing O O -Chinese B-MISC B-MISC -keeper O O -and O O -into O O -an O O -empty O O -net O O -. O O -Ole B-PER B-PER -S I-PER I-PER -made O O -sure O O -of O O -the O O -win O O -in O O -injury O O -time O O -, O O -hitting O O -an O O -un O O -left O O -foot O O -shot O O -from O O -just O O -outside O O -the O O -area O O -. O O -The O O -former O O -Soviet B-MISC B-MISC -republic O O -was O O -playing O O -in O O -an O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -finals O O -tie O O -for O O -the O O -first O O -time O O -. O O -Despite O O -winning O O -the O O -Asian B-MISC B-MISC -Games I-MISC I-MISC -title O O -two O O -years O O -ago O O -, O O -Uzbekistan B-LOC B-LOC -are O O -in O O -the O O -finals O O -as O O -outsider O O -. O O -Two O O -goals O O -from O O -defensive O O -errors O O -in O O -the O O -last O O -six O O -minutes O O -allowed O O -Japan B-LOC B-LOC -to O O -come O O -from O O -behind O O -and O O -collect O O -all O O -three O O -points O O -from O O -their O O -opening O O -meeting O O -against O O -Syria B-LOC B-LOC -. O O -Ta B-PER B-PER -Ta I-PER I-PER -scored O O -the O O -winner O O -in O O -the O O -88 O O -minute O O -, O O -rising O O -to O O -head O O -a O O -Hi B-PER B-PER -Yan I-PER I-PER -cross O O -towards O O -the O O -Syrian B-MISC B-MISC -goal O O -which O O -goalkeeper O O -Salem B-PER B-PER -Bit I-PER I-PER -appeared O O -to O O -have O O -covered O O -but O O -then O O -allowed O O -to O O -slip O O -into O O -the O O -net O O -. O O -It O O -was O O -the O O -second O O -costly O O -b O O -by O O -Syria B-LOC B-LOC -in O O -four O O -minutes O O -. O O -De O O -Hassan B-PER B-PER -Abbas I-PER I-PER -rose O O -to O O -intercept O O -a O O -long O O -ball O O -into O O -the O O -area O O -in O O -the O O -84 O O -minute O O -but O O -only O O -managed O O -to O O -diver O O -it O O -into O O -the O O -top O O -corner O O -of O O -Bit B-ORG B-PER -' O O -goal O O -. O O -Na B-PER B-PER -Jo I-PER I-PER -had O O -given O O -Syria B-LOC B-LOC -the O O -lead O O -with O O -a O O -well O O -header O O -in O O -the O O -seventh O O -minute O O -. O O -Japan B-LOC B-LOC -then O O -laid O O -siege O O -to O O -the O O -Syrian B-MISC B-MISC -penalty O O -area O O -for O O -most O O -of O O -the O O -game O O -but O O -rarely O O -breach O O -the O O -Syrian B-MISC B-MISC -defence O O -. O O -Bit B-ORG B-PER -pulled O O -off O O -fine O O -saves O O -whenever O O -they O O -did O O -. O O -Japan B-LOC B-LOC -coach O O -Shu B-PER B-PER -Ka I-PER I-PER -said O O -: O O -' O O -' O O -The O O -Syrian B-MISC B-MISC -own O O -goal O O -proved O O -lucky O O -for O O -us O O -. O O -The O O -Syrian B-MISC B-MISC -scored O O -early O O -and O O -then O O -played O O -defensive O O -and O O -adopted O O -long O O -balls O O -which O O -made O O -it O O -hard O O -for O O -us O O -. O O -' O O -' O O -Japan B-LOC B-LOC -, O O -co O O -of O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -in O O -2002 O O -and O O -ranked O O -20th O O -in O O -the O O -world O O -by O O -FIFA B-ORG B-ORG -, O O -are O O -favourite O O -to O O -regain O O -their O O -title O O -here O O -. O O -Host O O -UAE B-LOC B-LOC -play O O -Kuwait B-LOC B-LOC -and O O -South B-LOC B-LOC -Korea I-LOC I-LOC -take O O -on O O -Indonesia B-LOC B-LOC -on O O -Saturday O O -in O O -Group O O -A O O -matches O O -. O O -All O O -four O O -teams O O -are O O -level O O -with O O -one O O -point O O -each O O -from O O -one O O -game O O -. O O -R B-ORG B-ORG -UN I-ORG I-ORG -- O O -C B-ORG B-PER -BA O O -F O O -IT O B-LOC -A O O -A O O -Y O O -. O O -ROM B-LOC B-LOC -1996 O O -Italy B-LOC B-LOC -recalled O O -Marcel B-PER B-PER -Cut I-PER I-PER -on O O -Friday O O -for O O -their O O -friendly O O -against O O -Scotland B-LOC B-LOC -at O O -Murray B-LOC B-LOC -more O O -than O O -a O O -year O O -after O O -the O O -30 O O -wing O O -announced O O -he O O -was O O -retiring O O -following O O -differences O O -over O O -selection O O -. O O -Cut B-PER B-PER -, O O -who O O -trainer O O -George B-PER B-PER -Co I-PER I-PER -said O O -was O O -certain O O -to O O -play O O -on O O -Saturday O O -week O O -, O O -was O O -named O O -in O O -a O O -21 O O -squad O O -lacking O O -only O O -two O O -of O O -the O O -team O O -beaten O O -54 O O -by O O -England B-LOC B-LOC -at O O -T B-LOC B-LOC -last O O -month O O -. O O -Stefano B-PER B-PER -Bo I-PER I-PER -is O O -out O O -through O O -illness O O -and O O -Co B-PER B-PER -said O O -he O O -had O O -dropped O O -back O O -row O O -Co B-PER B-PER -Co I-PER I-PER -, O O -who O O -had O O -been O O -recalled O O -for O O -the O O -England B-LOC B-LOC -game O O -after O O -five O O -years O O -out O O -of O O -the O O -national O O -team O O -. O O -Cut B-PER B-PER -announced O O -his O O -retirement O O -after O O -the O O -1995 O B-MISC -World B-MISC I-MISC -Cup I-MISC I-MISC -, O O -where O O -he O O -took O O -issue O O -with O O -being O O -dropped O O -from O O -the O O -Italy B-LOC B-LOC -side O O -that O O -faced O O -England B-LOC B-LOC -in O O -the O O -pool O O -stages O O -. O O -Co B-PER B-PER -said O O -he O O -had O O -approached O O -the O O -player O O -two O O -months O O -ago O O -about O O -a O O -comeback O O -. O O -" O O -He O O -ended O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -on O O -the O O -wrong O O -note O O -, O O -" O O -Co B-PER B-PER -said O O -. O O -" O O -I O O -thought O O -it O O -would O O -be O O -useful O O -to O O -have O O -him O O -back O O -and O O -he O O -said O O -he O O -would O O -be O O -available O O -. O O -I O O -think O O -now O O -is O O -the O O -right O O -time O O -for O O -him O O -to O O -return O O -. O O -" O O -Squad O O -: O O -Javier B-PER B-PER -Per I-PER I-PER -, O O -Paolo B-PER B-PER -V I-PER I-PER -, O O -Marcel B-PER B-PER -Cut I-PER I-PER -, O O -Ivan B-PER B-PER -Francesca I-PER I-PER -, O O -Lea B-PER B-PER -Man I-PER I-PER -, O O -Diego B-PER B-PER -Dom I-PER I-PER -, O O -Francesco B-PER B-PER -Ma I-PER I-PER -, O O -Alessandro B-PER B-PER -T I-PER I-PER -, O O -Or B-PER B-PER -Ara I-PER I-PER -, O O -Andrea B-PER B-PER -S I-PER I-PER -, O O -Mass B-PER B-PER -G I-PER I-PER -, O O -Carlo B-PER B-PER -Ch I-PER I-PER -, O O -Walter B-PER B-PER -C I-PER I-PER -, O O -Franco B-PER B-PER -Pro I-PER I-PER -C I-PER I-PER -, O O -Carlo B-PER B-PER -Or I-PER I-PER -, O O -Mass B-PER B-PER -Cut I-PER I-PER -, O O -G B-PER B-PER -C I-PER I-PER -, O O -G B-PER B-PER -G I-PER I-PER -, O O -Nicola B-PER B-PER -S O O -- O O -LA O O -GO O O -G O O -J B-PER B-LOC -W O O -O O O -S B-LOC B-LOC -. O O -AL B-LOC B-LOC -, O O -United B-LOC B-LOC -Arab I-LOC I-LOC -Emirates I-LOC I-LOC -1996 O O -Two O O -goals O O -in O O -the O O -last O O -six O O -minutes O O -gave O O -holders O O -Japan B-LOC B-LOC -an O O -un O O -2 O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -victory O O -over O O -Syria B-LOC B-LOC -on O O -Friday O O -. O O -Ta B-PER B-PER -Ta I-PER I-PER -headed O O -the O O -winner O O -in O O -the O O -88 O O -minute O O -of O O -the O O -group O O -C O O -game O O -after O O -goalkeeper O O -Salem B-PER B-PER -Bit I-PER I-PER -spoiled O O -a O O -mistake O O -display O O -by O O -allowing O O -the O O -ball O O -to O O -slip O O -under O O -his O O -body O O -. O O -It O O -was O O -the O O -second O O -Syrian B-MISC B-MISC -defensive O O -b O O -in O O -four O O -minutes O O -. O O -De O O -Hassan B-PER B-PER -Abbas I-PER I-PER -rose O O -to O O -intercept O O -a O O -long O O -ball O O -into O O -the O O -area O O -in O O -the O O -84 O O -minute O O -but O O -only O O -managed O O -to O O -diver O O -it O O -into O O -the O O -top O O -corner O O -of O O -Bit B-ORG B-PER -' O O -goal O O -. O O -Syria B-LOC B-LOC -had O O -taken O O -the O O -lead O O -from O O -their O O -first O O -serious O O -attack O O -in O O -the O O -seventh O O -minute O O -. O O -Na B-PER B-PER -Jo I-PER I-PER -headed O O -a O O -cross O O -from O O -the O O -right O O -by O O -Am B-PER B-PER -A I-PER I-PER -into O O -the O O -top O O -right O O -corner O O -of O O -Ken B-PER B-PER -Shi I-PER I-PER -' O O -goal O O -. O O -Japan B-LOC B-LOC -then O O -laid O O -siege O O -to O O -the O O -Syrian B-MISC B-MISC -penalty O O -area O O -and O O -had O O -a O O -goal O O -di O O -for O O -offs O O -in O O -the O O -16th O O -minute O O -. O O -A O O -minute O O -later O O -, O O -Bit B-ORG B-PER -produced O O -a O O -good O O -double O O -save O O -, O O -first O O -from O O -Ka B-PER B-PER -Mi I-PER I-PER -' O O -header O O -and O O -then O O -blocked O O -a O O -Ta B-PER B-PER -follow O O -shot O O -. O O -Bit B-PER B-PER -saved O O -well O O -again O O -from O O -Mi B-PER B-PER -in O O -the O O -37th O O -minute O O -, O O -par O O -away O O -his O O -header O O -from O O -a O O -corner O O -. O O -Japan B-LOC B-LOC -started O O -the O O -second O O -half O O -brightly O O -but O O -Bit B-ORG B-PER -denied O O -them O O -an O O -equal O O -when O O -he O O -dive O O -to O O -his O O -right O O -to O O -save O O -Na B-PER B-PER -So I-PER I-PER -' O O -low O O -drive O O -in O O -the O O -53 O O -minute O O -. O O -Japan B-LOC B-LOC -: O O -19 O O -- O O -Ken B-PER B-PER -Shi I-PER I-PER -, O O -2 O O -- O O -Hi B-PER B-PER -Yan I-PER I-PER -, O O -3 O O -- O O -Na B-PER B-PER -So I-PER I-PER -, O O -4 O O -- O O -Ma B-PER B-PER -I I-PER I-PER -, O O -5 O O -- O O -Nor B-PER B-PER -O I-PER I-PER -, O O -6 O O -- O O -Mo B-PER B-PER -Ya I-PER I-PER -, O O -8 O O -- O O -Ma B-PER B-PER -Mae I-PER I-PER -( O O -7 O O -- O O -Ya B-PER B-PER -Honda I-PER I-PER -71 O O -) O O -, O O -9 O O -- O O -Ta B-PER B-PER -Ta I-PER I-PER -, O O -10 O O -- O O -Hi B-PER B-PER -Nana I-PER I-PER -, O O -11 O O -- O O -Ka B-PER B-PER -Mi I-PER I-PER -, O O -15 O O -- O O -Hi B-PER B-PER -Mo I-PER I-PER -( O O -14 O O -- O O -Ma B-PER B-PER -Ok I-PER I-PER -75 O O -) O O -. O O -Syria B-LOC B-LOC -: O O -24 O O -- O O -Salem B-PER B-PER -Bit I-PER I-PER -, O O -3 O O -- O O -Bach B-PER B-PER -Sr I-PER I-PER -; O O -4 O O -- O O -Hassan B-PER B-PER -Abbas I-PER I-PER -, O O -5 O O -- O O -Ta B-PER B-PER -J I-PER I-PER -, O O -6 O O -- O O -Am B-PER B-PER -A I-PER I-PER -( O O -9 O O -- O O -Lou B-PER B-PER -Tale I-PER I-PER -69 O O -) O O -, O O -8 O O -- O O -Ni B-PER B-PER -al I-PER I-PER -, O O -10 O O -- O O -Mohammed B-PER B-PER -A I-PER I-PER -, O O -12 O O -- O O -Ali B-PER B-PER -Di I-PER I-PER -, O O -13 O O -- O O -Abdul B-PER B-PER -La I-PER I-PER -He I-PER I-PER -( O O -17 O O -- O O -Am B-PER B-PER -R I-PER I-PER -46 O O -) O O -, O O -14 O O -- O O -K B-PER B-PER -Z I-PER I-PER -; O O -16 O O -- O O -Na B-PER B-PER -Jo I-PER I-PER -. O O -F O O -SK O B-MISC -C I-MISC I-MISC -M O O -R O O -. O O -T B-LOC B-LOC -, O O -France B-LOC B-LOC -1996 O O -Results O O -of O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -freestyle O O -skiing O O -m O O -competition O O -on O O -Friday O O -: O O -Men O O -1 O O -Je B-PER B-PER -Ron I-PER I-PER -( O O -Sweden B-LOC B-LOC -) O O -25 O O -points O O -2 O O -Andrei B-PER B-PER -Ivan I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -24 O O -3 O O -Ryan B-PER B-PER -Johnson I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -24 O O -4 O O -Jean B-PER B-PER -Brass I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -24 O O -5 O O -Ko B-PER B-PER -Hole I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -23 O O -6 O O -Je B-PER B-PER -Col I-PER I-PER -( O O -France B-LOC B-LOC -) O O -23 O O -7 O O -Jim B-PER B-PER -Moran I-PER I-PER -( O O -U B-LOC B-LOC -) O O -23 O O -8 O O -Dominic B-PER B-PER -G I-PER I-PER -( O O -Canada B-LOC B-LOC -AL B-LOC B-LOC -, O O -United B-LOC B-LOC -Arab I-LOC I-LOC -Emirates I-LOC I-LOC -1996 O O -Results O O -of O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -group O O -C O O -matches O O -played O O -on O O -Friday O O -: O O -Japan B-LOC B-LOC -2 O O -Syria B-LOC B-LOC -1 O O -( O O -halftime O O -0 O O -) O O -Score O O -: O O -Japan B-LOC B-LOC -- O O -Hassan B-PER B-PER -Abbas I-PER I-PER -84 O O -own O O -goal O O -, O O -Ta B-PER B-PER -Ta I-PER I-PER -88 O O -. O O -Syria B-LOC B-LOC -- O O -Na B-PER B-PER -Jo I-PER I-PER -7 O O -Attendance O O -: O O -10 O O -. O O -China B-LOC B-LOC -0 O O -Uzbekistan B-LOC B-LOC -2 O O -( O O -halftime O O -0 O O -) O O -Score O O -: O O -S B-PER B-PER -Igor I-PER I-PER -78 O O -, O O -S B-PER B-PER -Ole B-PER I-PER -90 O O -At O O -: O O -3 O O -Standing O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -Uzbekistan B-LOC B-LOC -1 O O -1 O O -0 O O -0 O O -2 O O -0 O O -3 O O -Japan B-LOC B-LOC -1 O O -1 O O -0 O O -0 O O -2 O O -1 O O -3 O O -Syria B-LOC B-LOC -1 O O -0 O O -0 O O -1 O O -1 O O -2 O O -0 O O -China B-LOC B-LOC -1 O O -0 O O -0 O O -1 O O -0 O O -2 O O -0 O O -CR O O -- O O -PA B-LOC B-LOC -V O O -NE B-LOC B-LOC -Z I-LOC I-LOC -ONE O O -SC O O -. O O -[ O O -CO O O -14 O O -GM B-MISC B-MISC -] O O -S B-LOC B-LOC -, O O -Pakistan B-LOC B-LOC -1996 O O -Score O O -in O O -the O O -second O O -one O O -cricket O O -international O O -between O O -Pakistan B-LOC B-LOC -and O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -on O O -Friday O O -: O O -Pakistan B-LOC B-LOC -Sa B-PER B-PER -An I-PER I-PER -run O O -out O O -91 O O -( O O -correct O O -from O O -90 O O -) O O -Z B-PER B-PER -El I-PER I-PER -b O O -Cairns B-PER B-PER -86 O O -( O O -correct O O -from O O -87 O O -) O O -I B-PER B-PER -Ahmad I-PER I-PER -c O O -S B-PER B-PER -b O O -Vaughan B-PER B-PER -59 O O -In B-PER B-PER -Ha I-PER I-PER -s O O -G B-PER B-PER -b O O -As B-PER B-PER -2 O O -Was B-PER B-PER -A I-PER I-PER -b O O -Harris B-PER B-PER -4 O O -Shah B-PER B-PER -A I-PER I-PER -b O O -Harris B-PER B-PER -2 O O -Mo B-PER B-PER -Khan I-PER I-PER -c O O -As B-PER B-PER -b O O -Harris B-PER B-PER -1 O O -W B-PER B-PER -You I-PER I-PER -Bowling O O -: O O -Was B-PER B-PER -A I-PER I-PER -8 O O -( O O -9 O O -, O O -1 O O -) O O -, O O -W B-PER B-PER -You I-PER I-PER -6 O O -( O O -2 O O -, O O -1 O O -) O O -, O O -Sa B-PER B-PER -Mu I-PER I-PER -8 O O -, O O -Mu B-PER B-PER -Ahmad I-PER I-PER -10 O O -( O O -1 O O -) O O -, O O -Shah B-PER B-PER -A I-PER I-PER -7 O O -, O O -Sal B-PER B-PER -Malik I-PER I-PER -2 O O -, O O -I B-PER B-PER -Ahmad I-PER I-PER -0 O O -. O O -Re O O -: O O -Pakistan B-LOC B-LOC -won O O -by O O -46 O O -runs O O -. O O -Third O O -one O O -match O O -: O O -December O O -8 O O -, O O -in O O -Karachi B-LOC B-LOC -. O O -S O O -- O O -E B-MISC B-MISC -F B-MISC I-MISC -C I-MISC I-MISC -SEC O O -R O O -R O O -. O O -L B-LOC B-LOC -1996 O O -Re O O -of O O -an O O -English B-MISC B-MISC -F B-MISC I-MISC -Challenge I-MISC I-MISC -Cup I-MISC B-MISC -second O O -round O O -match O O -on O O -Friday O O -: O O -Plymouth B-ORG B-ORG -4 O O -Exeter B-ORG B-ORG -1 O O -S O O -- O O -B B-MISC B-PER -BA O O -L O O -. O O -L B-LOC B-LOC -1996 O O -Dutch B-MISC B-MISC -forward O O -Reggie B-PER B-PER -B I-PER I-PER -had O O -his O O -in O O -suspension O O -lifted O O -by O O -FIFA B-ORG B-ORG -on O O -Friday O O -and O O -was O O -set O O -to O O -make O O -his O O -Sheffield B-ORG B-ORG -Wednesday I-ORG I-ORG -comeback O O -against O O -Liverpool B-ORG B-ORG -on O O -Saturday O O -. O O -B B-PER B-PER -missed O O -his O O -club O O -' O O -last O O -two O O -games O O -after O O -FIFA B-ORG B-ORG -slapped O O -a O O -worldwide O O -ban O O -on O O -him O O -for O O -appearing O O -to O O -sign O O -contracts O O -for O O -both O O -Wednesday B-ORG B-ORG -and O O -U B-ORG B-ORG -while O O -he O O -was O O -playing O O -for O O -Fe B-ORG B-ORG -. O O -FIFA B-ORG B-ORG -' O O -players O O -' O O -status O O -committee O O -, O O -meeting O O -in O O -Barcelona B-LOC B-LOC -, O O -decided O O -that O O -although O O -the O O -U B-MISC B-ORG -document O O -was O O -basically O O -valid O O -, O O -it O O -could O O -not O O -be O O -legally O O -protected O O -. O O -The O O -committee O O -said O O -the O O -Italian B-MISC B-MISC -club O O -had O O -violated O O -regulations O O -by O O -failing O O -to O O -inform O O -Fe B-ORG B-ORG -, O O -with O O -whom O O -the O O -player O O -was O O -under O O -contract O O -. O O -B B-ORG B-PER -was O O -fined O O -75 O O -Swiss B-MISC B-MISC -f O O -( O O -$ O O -57 O O -) O O -for O O -failing O O -to O O -inform O O -the O O -En B-ORG B-MISC -club O O -of O O -his O O -previous O O -commitment O O -to O O -U B-ORG B-ORG -. O O -S O O -- O O -L O B-ORG -' O O -B B-PER B-PER -F O O -F O O -PA O O -IN O O -FA O O -F O O -. O O -L B-LOC B-LOC -1996 O O -Leeds B-ORG B-ORG -' O O -England B-LOC B-LOC -under O O -striker O O -Lee B-PER B-PER -Bow I-PER I-PER -was O O -fined O O -4 O O -pounds O O -( O O -$ O O -7 O O -) O O -on O O -Friday O O -for O O -hurling O O -chairs O O -at O O -restaurant O O -staff O O -during O O -a O O -disturbance O O -at O O -a O O -McDonald B-ORG B-ORG -' I-ORG I-ORG -fast O O -restaurant O O -. O O -Bow B-PER B-PER -, O O -19 O O -, O O -who O O -was O O -caught O O -in O O -the O O -act O O -by O O -security O O -cameras O O -, O O -pleaded O O -guilty O O -to O O -a O O -charge O O -of O O -a O O -at O O -a O O -court O O -in O O -London B-LOC B-LOC -. O O -He O O -was O O -fined O O -and O O -ordered O O -to O O -pay O O -a O O -total O O -of O O -175 O O -pounds O O -to O O -two O O -members O O -of O O -staff O O -injured O O -in O O -the O O -f O O -in O O -an O O -east O O -London I-LOC B-LOC -restaurant O O -in O O -October O O -. O O -Leeds B-ORG B-ORG -had O O -already O O -fined O O -Bow B-PER B-PER -4 O O -pounds O O -( O O -$ O O -6 O O -) O O -and O O -warned O O -him O O -a O O -repeat O O -of O O -his O O -criminal O O -behaviour O O -could O O -cost O O -him O O -his O O -place O O -in O O -the O O -side O O -. O O -Bow B-PER B-PER -, O O -who O O -moved O O -to O O -the O O -Yorkshire B-LOC B-LOC -club O O -in O O -August O O -for O O -3 O O -million O O -pounds O O -( O O -$ O O -5 O O -million O O -) O O -, O O -was O O -expected O O -to O O -play O O -against O O -Middlesbrough B-ORG B-ORG -on O O -Saturday O O -. O O -BA O O -- O O -EU B-MISC B-MISC -ST O O -. O O -L B-LOC B-LOC -1996 O O -Standing O O -in O O -the O O -men O O -' O O -Euro B-MISC B-MISC -basketball O O -championship O O -after O O -Thursday O O -' O O -matches O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -lost O O -, O O -points O O -) O O -: O O -Group O O -A O O -CS B-ORG B-ORG -Moscow I-ORG I-ORG -( O O -Russia B-LOC B-LOC -9 O O -6 O O -3 O O -15 O O -Stefan B-ORG B-ORG -Milan I-ORG I-ORG -( O O -Italy B-LOC B-LOC -) O O -9 O O -6 O O -3 O O -15 O O -Maccabi B-ORG B-ORG -Tel I-ORG I-ORG -Aviv I-ORG I-ORG -( O O -Israel B-LOC B-LOC -) O O -9 O O -5 O O -4 O O -14 O O -U B-ORG B-ORG -S I-ORG I-ORG -( O O -Turkey B-LOC B-LOC -) O O -9 O O -4 O O -5 O O -13 O O -Lim B-ORG B-ORG -( O O -France B-LOC B-LOC -) O O -9 O O -3 O O -6 O O -12 O O -Pan B-ORG B-ORG -( O O -Greece B-LOC B-LOC -) O O -9 O O -3 O O -6 O O -12 O O -Group O O -B O O -Teams B-ORG B-ORG -Bologna I-ORG I-ORG -( O O -Italy B-LOC B-LOC -) O O -9 O O -7 O O -2 O O -16 O O -Olympia B-ORG B-ORG -( O O -Greece B-LOC B-LOC -) O O -9 O O -5 O O -4 O O -14 O O -C B-ORG B-ORG -Robert B-PER B-PER -Kit I-PER I-PER -L B-LOC B-LOC -1996 O O -Centre O O -Jason B-PER B-PER -Little I-PER I-PER -will O O -miss O O -Australia B-LOC B-LOC -' O O -end O O -fixture O O -against O O -the O O -Bar B-ORG B-ORG -at O O -T B-LOC B-LOC -on O O -Saturday O O -. O O -Little B-PER B-PER -has O O -opted O O -not O O -to O O -risk O O -a O O -the O O -knee O O -injury O O -which O O -ruled O O -him O O -out O O -of O O -a O O -large O O -chunk O O -of O O -the O O -tour O O -and O O -is O O -replaced O O -by O O -fellow O O -Queensland B-MISC B-MISC -Daniel B-PER B-PER -Herbert I-PER I-PER -. O O -Owen B-PER B-PER -Fine I-PER I-PER -has O O -recovered O O -from O O -the O O -knocks O O -he O O -took O O -in O O -last O O -weekend O O -' O O -test O O -against O O -Wales B-LOC B-LOC -and O O -retains O O -his O O -place O O -in O O -the O O -back O O -ahead O O -of O O -Daniel B-PER B-PER -Man I-PER I-PER -. O O -The O O -Wall B-ORG B-ORG -have O O -their O O -sights O O -set O O -on O O -a O O -13th O O -successive O O -victory O O -to O O -end O O -their O O -European B-MISC B-MISC -tour O O -with O O -a O O -100 O O -percent O O -record O O -but O O -also O O -want O O -to O O -turn O O -on O O -the O O -style O O -and O O -provide O O -David B-PER B-PER -Camp I-PER I-PER -with O O -a O O -fitting O O -send O O -in O O -his O O -final O O -match O O -in O O -Australian B-MISC B-MISC -colours O O -. O O -The O O -Wall B-ORG B-ORG -currently O O -have O O -no O O -plans O O -to O O -make O O -any O O -special O O -presentation O O -to O O -the O O -34 O O -winger O O -but O O -a O O -full O O -house O O -of O O -75 O O -spectators O O -will O O -still O O -gather O O -in O O -the O O -hope O O -of O O -witness O O -one O O -last O O -moment O O -of O O -magic O O -. O O -Camp B-PER B-PER -will O O -be O O -up O O -against O O -a O O -familiar O O -foe O O -in O O -the O O -shape O O -of O O -Bar B-ORG B-ORG -captain O O -Rob B-PER B-PER -Andrew I-PER I-PER -, O O -the O O -man O O -who O O -kicked O O -Australia B-LOC B-LOC -to O O -defeat O O -with O O -a O O -last O O -drop O O -in O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -quarter O O -in O O -Cape B-LOC B-LOC -Town I-LOC I-LOC -. O O -" O O -Campo B-PER B-PER -has O O -a O O -massive O O -following O O -in O O -this O O -country O O -and O O -has O O -had O O -the O O -public O O -with O O -him O O -ever O O -since O O -he O O -first O O -played O O -here O O -in O O -1984 O O -, O O -" O O -said O O -Andrew B-PER B-PER -, O O -also O O -likely O O -to O O -be O O -making O O -his O O -final O O -T B-ORG B-LOC -appearance O O -. O O -On O O -tour O O -, O O -Australia B-LOC B-LOC -have O O -won O O -all O O -four O O -tests O O -against O O -Italy B-LOC B-LOC -, O O -Scotland B-LOC B-LOC -, O O -Ireland B-LOC B-LOC -and O O -Wales B-LOC B-LOC -, O O -and O O -scored O O -41 O O -points O O -at O O -an O O -average O O -of O O -almost O O -35 O O -points O O -a O O -game O O -. O O -League O O -duties O O -restricted O O -the O O -Bar B-ORG B-ORG -' O O -select O O -options O O -but O O -they O O -still O O -b O O -13 O O -international O O -including O O -England B-LOC B-LOC -full O O -Tim B-PER B-PER -St I-PER I-PER -and O O -recalled O O -wing O O -Tony B-PER B-PER -Underwood I-PER I-PER -, O O -plus O O -All B-ORG B-ORG -Black I-ORG I-ORG -forwards O O -Ian B-PER B-PER -Jones I-PER I-PER -and O O -Nor B-PER B-PER -Hewitt I-PER I-PER -. O O -Teams O O -: O O -Bar O B-ORG -- O O -15 O O -- O O -Tim B-PER B-PER -St I-PER I-PER -( O O -England B-LOC B-LOC -) O O -; O O -14 O O -- O O -Nigel B-PER B-PER -Walker I-PER I-PER -( O O -Wales B-LOC B-LOC -) O O -, O O -13 O O -- O O -Allan B-PER B-PER -Bat I-PER I-PER -( O O -Wales B-LOC B-LOC -) O O -, O O -12 O O -- O O -Gregor B-PER B-PER -Townsend I-PER I-PER -( O O -Scotland B-LOC B-LOC -) O O -, O O -11 O O -- O O -Tony B-PER B-PER -Underwood I-PER I-PER -( O O -England B-LOC B-LOC -) O O -; O O -10 O O -- O O -Rob B-PER B-PER -Andrew I-PER I-PER -( O O -England B-LOC B-LOC -) O O -, O O -9 O O -- O O -Rob B-PER B-PER -How I-PER I-PER -( O O -Wales B-LOC B-LOC -) O O -; O O -8 O O -- O O -Scott B-PER B-PER -Quinn I-PER I-PER -( O O -Wales B-LOC B-LOC -) O O -, O O -7 O O -- O O -Neil B-PER B-PER -Back I-PER I-PER -( O O -England B-LOC B-LOC -) O O -, O O -6 O O -- O O -Dale B-PER B-PER -M I-PER I-PER -( O O -Pont B-LOC B-LOC -) O O -, O O -5 O O -- O O -Ian B-PER B-PER -Jones I-PER I-PER -( O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -) O O -, O O -4 O O -- O O -Craig B-PER B-PER -Quinn I-PER I-PER -( O O -Wales B-LOC B-LOC -) O O -, O O -3 O O -- O O -Darren B-PER B-PER -G I-PER I-PER -( O O -Leicester B-LOC B-LOC -) O O -Australia B-LOC B-LOC -- O O -15 O O -- O O -Matthew B-PER B-PER -Burke I-PER I-PER -; O O -14 O O -- O O -Joe B-PER B-PER -R I-PER I-PER -, O O -13 O O -- O O -Daniel B-PER B-PER -Herbert I-PER I-PER -, O O -12 O O -- O O -Tim B-PER B-PER -Ho I-PER I-PER -( O O -captain O O -) O O -, O O -11 O O -- O O -David B-PER B-PER -Camp I-PER I-PER -; O O -10 O O -- O O -Pat B-PER B-PER -Howard I-PER I-PER -, O O -9 O O -- O O -Sam B-PER B-PER -Payne I-PER I-PER -; O O -8 O O -- O O -Michael B-PER B-PER -B I-PER I-PER -, O O -7 O O -- O O -David B-PER B-PER -Wilson I-PER I-PER -, O O -6 O O -- O O -Owen B-PER B-PER -Fine I-PER I-PER -, O O -5 O O -- O O -David B-PER B-PER -G I-PER I-PER -, O O -4 O O -- O O -Tim B-PER B-PER -Gavin I-PER I-PER -, O O -3 O O -- O O -Andrew B-PER B-PER -Blade I-PER I-PER -, O O -2 O O -- O O -Marco B-PER B-PER -Cap I-PER I-PER -, O O -1 O O -- O O -Dan B-PER B-PER -Crowley I-PER I-PER -. O O -GO O O -- O O -Z B-MISC B-MISC -O I-MISC I-MISC -SEC O O -R O O -SC O O -. O O -H B-LOC B-LOC -1996 O O -Leading O O -second O O -round O O -scores O O -in O O -the O O -Zimbabwe B-MISC B-MISC -Open I-MISC I-MISC -at O O -the O O -par O O -Chapman B-LOC B-LOC -Golf I-LOC I-LOC -Club I-LOC I-LOC -on O O -Friday O O -( O O -South B-MISC B-MISC -African I-MISC I-MISC -unless O O -stated O O -) O O -: O O -132 O O -Des B-PER B-PER -Te I-PER I-PER -65 O O -67 O O -133 O O -Mark B-PER B-PER -M I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -72 O O -61 O O -134 O O -Steve B-PER B-PER -van I-PER I-PER -V I-PER I-PER -65 O O -69 O O -136 O O -Nick B-PER B-PER -Price I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -68 O O -68 O O -, O O -Justin B-PER B-PER -Ho I-PER I-PER -71 O O -65 O O -, O O -Andrew B-PER B-PER -Pitt I-PER I-PER -( O O -U B-LOC B-LOC -) O O -69 O O -67 O O -138 O O -Mark B-PER B-PER -C I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -69 O O -69 O O -, O O -Mark B-PER B-PER -Mu I-PER I-PER -71 O O -67 O O -139 O O -He B-PER B-PER -S I-PER I-PER -75 O O -64 O O -, O O -Andrew B-PER B-PER -Park I-PER I-PER -72 O O -67 O O -140 O O -Sc B-PER B-PER -van I-PER I-PER -der I-PER I-PER -Me I-PER I-PER -B B-LOC B-LOC -1996 O O -Romania B-LOC B-LOC -trainer O O -Ang B-PER B-PER -I I-PER I-PER -called O O -up O O -three O O -un O O -players O O -on O O -Friday O O -in O O -his O O -squad O O -to O O -face O O -Macedonia B-LOC B-LOC -next O O -week O O -in O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -qualifier O O -. O O -Mid O O -Vale B-PER B-PER -Stefan I-PER I-PER -and O O -striker O O -V B-PER B-PER -Ion I-PER I-PER -of O O -O B-ORG B-ORG -Gala I-ORG I-ORG -and O O -defender O O -Liv B-PER B-PER -C I-PER I-PER -of O O -National B-ORG B-ORG -Bucharest I-ORG I-ORG -are O O -the O O -newcomer O O -for O O -the O O -European B-MISC B-MISC -group O O -eight O O -clash O O -in O O -Macedonia B-LOC B-LOC -on O O -December O O -14 O O -. O O -I B-PER B-PER -said O O -he O O -had O O -picked O O -them O O -because O O -of O O -their O O -good O O -performances O O -in O O -the O O -domestic O O -championship O O -in O O -which O O -National B-ORG B-ORG -Bucharest I-ORG I-ORG -are O O -top O O -and O O -O B-ORG B-ORG -Gala I-ORG I-ORG -third O O -. O O -" O O -I O O -think O O -it O O -' O O -fair O O -to O O -give O O -them O O -a O O -chance O O -, O O -" O O -he O O -told O O -reporters O O -. O O -League O O -title O O -St B-ORG B-ORG -Bucharest I-ORG I-ORG -, O O -who O O -finished O O -bottom O O -of O O -their O O -Champions B-MISC B-MISC -' I-MISC I-MISC -League I-MISC I-MISC -group O O -in O O -the O O -European B-MISC B-MISC -Cup I-MISC I-MISC -, O O -have O O -only O O -two O O -players O O -in O O -the O O -squad O O -. O O -Attack O O -midfielder O O -Adrian B-PER B-PER -Il I-PER I-PER -, O O -who O O -recently O O -moved O O -from O O -St B-ORG B-ORG -to O O -Turkish B-MISC B-MISC -club O O -Gala B-ORG B-ORG -, O O -is O O -ruled O O -out O O -after O O -two O O -yellow O O -offences O O -. O O -Squad O O -: O O -Goal O O -- O O -Bo B-PER B-PER -St I-PER I-PER -, O O -F B-PER B-PER -P I-PER I-PER -. O O -De O O -- O O -Dan B-PER B-PER -Pet I-PER I-PER -, O O -Daniel B-PER B-PER -Pro I-PER I-PER -, O O -Anton B-PER B-PER -Do I-PER I-PER -, O O -Co B-PER B-PER -Pa I-PER I-PER -, O O -Liv B-PER B-PER -C I-PER I-PER -, O O -T B-PER B-PER -Se I-PER I-PER -, O O -I B-PER B-PER -Fi I-PER I-PER -. O O -Mid O O -- O O -G B-PER B-PER -Ha I-PER I-PER -, O O -G B-PER B-PER -Pope I-PER I-PER -, O O -Con B-PER B-PER -G I-PER I-PER -, O O -Vale B-PER B-PER -Stefan I-PER I-PER -, O O -Ba B-PER B-PER -Pan I-PER I-PER -, O O -Do B-PER B-PER -Mu I-PER I-PER -, O O -O B-PER B-PER -Sting I-PER I-PER -. O O -Forward O O -- O O -I B-PER B-PER -Vlad I-PER I-PER -, O O -G B-PER B-PER -C I-PER I-PER -, O O -Ion B-PER B-PER -Dan I-PER I-PER -, O O -V B-PER B-PER -Ion I-PER I-PER -. O O -R O B-ORG -S O O -- O O -BR B-MISC B-MISC -CH O O -R O O -. O O -R B-LOC B-LOC -DE I-LOC I-LOC -J I-LOC I-LOC -1996 O O -Results O O -of O O -Brazilian B-MISC B-MISC -soccer O O -championship O O -semifinal O O -, O O -first O O -leg O O -matches O O -on O O -Thursday O O -. O O -Go B-ORG B-ORG -1 O O -G B-ORG B-ORG -3 O O -Port B-ORG B-ORG -1 O O -At B-ORG B-ORG -Mine I-ORG I-ORG -0 O O -CR O O -- O O -LA B-ORG B-PER -E O O -AN O O -MI O O -D O O -. O O -Robert B-PER B-PER -G I-PER I-PER -ME B-LOC B-LOC -1996 O O -Australia B-LOC B-LOC -gave O O -Brian B-PER B-PER -Lara I-PER I-PER -another O O -reason O O -to O O -be O O -miserable O O -when O O -they O O -beat O O -West B-LOC B-LOC -Indies I-LOC I-LOC -by O O -five O O -wickets O O -in O O -the O O -opening O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -match O O -on O O -Friday O O -. O O -Lara B-PER B-PER -, O O -discipline O O -for O O -misconduct O O -on O O -Wednesday O O -, O O -was O O -dismissed O O -for O O -five O O -to O O -extend O O -a O O -disappointing O O -run O O -of O O -form O O -on O O -tour O O -. O O -Australia B-LOC B-LOC -, O O -who O O -hold O O -a O O -2 O O -lead O O -in O O -the O O -five O O -test O O -series O O -, O O -overhaul O O -West B-LOC B-LOC -Indies I-LOC I-LOC -' O O -total O O -of O O -172 O O -all O O -out O O -with O O -eight O O -balls O O -to O O -spare O O -to O O -end O O -a O O -run O O -of O O -six O O -successive O O -one O O -defeats O O -. O O -All O O -Greg B-PER B-PER -B I-PER I-PER -steered O O -his O O -side O O -to O O -a O O -comfortable O O -victory O O -with O O -an O O -unbeaten O O -57 O O -in O O -90 O O -balls O O -to O O -the O O -delight O O -of O O -the O O -42 O O -crowd O O -. O O -Man O O -match O O -B B-PER B-PER -came O O -to O O -the O O -wicket O O -with O O -the O O -total O O -on O O -70 O O -for O O -two O O -and O O -hit O O -three O O -four O O -during O O -an O O -un O O -innings O O -lasting O O -129 O O -minutes O O -. O O -His O O -crucial O O -fifth O O -partnership O O -with O O -fellow O O -all O O -Stuart B-PER B-PER -Law I-PER I-PER -, O O -who O O -scored O O -21 O O -, O O -added O O -71 O O -off O O -85 O O -balls O O -. O O -Lara B-PER B-PER -looked O O -out O O -of O O -touch O O -during O O -his O O -brief O O -stay O O -at O O -the O O -c O O -before O O -chip O O -a O O -simple O O -catch O O -to O O -Shane B-PER B-PER -War I-PER I-PER -at O O -mid O O -. O O -West B-LOC B-LOC -Indies I-LOC I-LOC -tour O O -manager O O -Clive B-PER B-PER -Lloyd I-PER I-PER -has O O -a O O -for O O -Lara B-PER B-PER -' O O -behaviour O O -on O O -Tuesday O O -. O O -He O O -( O O -Lara B-PER B-PER -) O O -had O O -told O O -Australia B-LOC B-LOC -coach O O -Geoff B-PER B-PER -Marsh I-PER I-PER -that O O -wicket O O -Ian B-PER B-PER -He I-PER I-PER -was O O -un O O -in O O -the O O -visitors O O -' O O -dressing O O -room O O -. O O -The O O -Melbourne B-LOC B-LOC -crowd O O -were O O -clearly O O -angered O O -by O O -the O O -incident O O -, O O -loudly O O -j O O -the O O -West B-LOC B-LOC -Indies I-LOC I-LOC -vice O O -as O O -he O O -walked O O -to O O -the O O -middle O O -. O O -It O O -was O O -left O O -to O O -fellow O O -left O O -Shi B-PER B-PER -Chan I-PER I-PER -to O O -hold O O -the O O -innings O O -together O O -with O O -a O O -g O O -54 O O -despite O O -the O O -hand O O -of O O -an O O -injured O O -groin O O -. O O -Chan B-PER B-PER -was O O -forced O O -to O O -rely O O -on O O -a O O -runner O O -for O O -most O O -of O O -his O O -innings O O -after O O -hurting O O -himself O O -as O O -he O O -s O O -back O O -to O O -his O O -c O O -to O O -avoid O O -being O O -run O O -out O O -. O O -Pakistan B-LOC B-LOC -, O O -who O O -arrive O O -in O O -Australia B-LOC B-LOC -later O O -this O O -month O O -, O O -are O O -the O O -other O O -team O O -competing O O -in O O -the O O -World B-MISC B-MISC -Series I-MISC I-MISC -tournament O O -. O O -CR O O -- O O -AU B-LOC B-LOC -V O O -W O B-LOC -IN O I-LOC -W O B-MISC -SE O I-MISC -SC O O -. O O -ME B-LOC B-LOC -1996 O O -Score O O -in O O -the O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -match O O -between O O -Australia B-LOC B-LOC -and O O -West B-LOC B-LOC -Indies I-LOC I-LOC -on O O -Friday O O -: O O -West B-LOC B-LOC -Indies I-LOC I-LOC -S B-PER B-PER -Campbell I-PER I-PER -c O O -He B-PER B-PER -b O O -Gillespie B-PER B-PER -31 O O -R B-PER B-PER -Samuel I-PER I-PER -c O O -M B-PER B-PER -W I-PER I-PER -b O O -Gillespie B-PER B-PER -7 O O -B B-PER B-PER -Lara I-PER I-PER -c O O -War B-PER B-PER -b O O -Moody B-PER B-PER -5 O O -S B-PER B-PER -Chan I-PER I-PER -c O O -He B-PER B-PER -b O O -B B-PER B-PER -54 O O -C B-PER B-PER -Hooper I-PER I-PER -run O O -out O O -7 O O -J B-PER B-PER -Adams I-PER I-PER -lb O O -b O O -Moody B-PER B-PER -5 O O -J B-PER B-PER -Murray I-PER I-PER -c O O -B B-PER B-PER -b O O -War B-PER B-PER -24 O O -N B-PER B-PER -McLean I-PER I-PER -c O O -and O O -b O O -M B-PER B-PER -W I-PER I-PER -7 O O -K B-PER B-PER -Benjamin I-PER I-PER -b O O -War B-PER B-PER -8 O O -C B-PER B-PER -Ambrose I-PER I-PER -run O O -out O O -2 O O -C B-PER B-PER -Walsh I-PER I-PER -not O O -out O O -Bowling O O -: O O -Re B-PER B-PER -10 O O -( O O -n O O -) O O -, O O -Gillespie B-PER B-PER -10 O O -, O O -Moody B-PER B-PER -10 O O -, O O -B B-PER B-PER -6 O O -, O O -War B-PER B-PER -10 O O -( O O -w O O -) O O -, O O -M B-PER B-PER -W I-PER I-PER -3 O O -. O O -Australia B-LOC B-LOC -M B-PER B-PER -Taylor I-PER I-PER -b O O -McLean B-PER B-PER -29 O O -M B-PER B-PER -W I-PER I-PER -c O O -Murray B-PER B-PER -b O O -Benjamin B-PER B-PER -27 O O -R B-PER B-PER -Pont I-PER I-PER -lb O O -McLean B-PER B-PER -5 O O -G B-PER B-PER -B I-PER I-PER -not O O -out O O -57 O O -M B-PER B-PER -Be I-PER I-PER -s O O -Murray B-PER B-PER -b O O -Hooper B-PER B-PER -3 O O -S B-PER B-PER -Law I-PER I-PER -b O O -Hooper B-PER B-PER -21 O O -T B-PER B-PER -Moody I-PER I-PER -not O O -out O O -3 O O -Extra O O -( O O -lb O O -n O O -w O O -) O O -28 O O -Total O O -( O O -for O O -five O O -wickets O O -, O O -48 O O -overs O O -) O O -173 O O -Fall O O -of O O -wickets O O -: O O -1 O O -2 O O -3 O O -4 O O -5 O O -. O O -Did O O -not O O -bat O O -: O O -I B-PER B-PER -He I-PER I-PER -, O O -P B-PER B-PER -Re I-PER I-PER -, O O -S B-PER B-PER -War I-PER I-PER -, O O -J B-PER B-PER -Gillespie I-PER I-PER -. O O -Bowling O O -: O O -Ambrose B-PER B-PER -10 O O -( O O -2 O O -1 O O -) O O -, O O -Walsh B-PER B-PER -9 O O -( O O -4 O O -) O O -, O O -Benjamin B-PER B-PER -9 O O -( O O -1 O O -1 O O -) O O -, O O -Hooper B-PER B-PER -10 O O -( O O -1 O O -) O O -, O O -McLean B-PER B-PER -10 O O -( O O -1 O O -) O O -. O O -Re O O -: O O -Australia B-LOC B-LOC -won O O -by O O -five O O -wickets O O -. O O -CR O O -- O O -AU B-LOC B-LOC -B O O -W O B-LOC -IN O I-LOC -B O O -F O O -W O O -. O O -ME B-LOC B-LOC -1996 O O -Australia B-LOC B-LOC -beat O O -West B-LOC B-LOC -Indies I-LOC I-LOC -by O O -five O O -wickets O O -in O O -a O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -match O O -at O O -the O O -Melbourne B-LOC B-LOC -Cricket I-LOC I-LOC -Ground I-LOC I-LOC -on O O -Friday O O -. O O -Score O O -: O O -West B-LOC B-LOC -Indies I-LOC I-LOC -172 O O -all O O -out O O -in O O -49 O O -overs O O -( O O -Shi B-PER B-PER -Chan I-PER I-PER -54 O O -) O O -; O O -Australia B-LOC B-LOC -173 O O -in O O -48 O O -overs O O -( O O -Greg B-PER B-PER -B I-PER I-PER -57 O O -not O O -out O O -) O O -. O O -CR O O -- O O -W O B-LOC -IN O I-LOC -172 O O -AL O O -O O O -IN O O -49 O O -O O O -V O O -AU B-LOC B-LOC -. O O -ME B-LOC B-LOC -1996 O O -West B-LOC B-LOC -Indies I-LOC I-LOC -were O O -all O O -out O O -for O O -172 O O -off O O -49 O O -overs O O -in O O -the O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -match O O -against O O -Australia B-LOC B-LOC -on O O -Friday O O -. O O -CR O O -- O O -SH B-PER B-MISC -SH O I-MISC -SC O O -. O O -H B-LOC B-LOC -, O O -Australia B-LOC B-LOC -1996 O O -Score O O -on O O -the O O -first O O -day O O -of O O -the O O -four O O -Sheffield B-MISC B-MISC -Shield I-MISC I-MISC -match O O -between O O -Tasmania B-LOC B-LOC -and O O -Victoria B-LOC B-LOC -at O O -Belle B-LOC B-LOC -Oval I-LOC I-LOC -on O O -Friday O O -: O O -Tasmania B-LOC B-LOC -35 O O -for O O -three O O -( O O -David B-PER B-PER -Bo I-PER I-PER -106 O O -not O O -out O O -, O O -Shaun B-PER B-PER -Young I-PER I-PER -86 O O -not O O -out O O -, O O -Michael B-PER B-PER -Di I-PER I-PER -119 O O -) O O -v O O -Victoria B-LOC B-ORG -. O O -CR O O -- O O -LA B-ORG B-PER -S O O -M O O -AU B-MISC O -TO O O -MI O O -. O O -ME B-LOC B-LOC -1996 O O -West B-LOC B-LOC -Indies I-LOC I-LOC -batsman O O -Brian B-PER B-PER -Lara I-PER I-PER -suffered O O -another O O -blow O O -to O O -his O O -Australian B-MISC B-MISC -tour O O -, O O -after O O -already O O -being O O -discipline O O -for O O -misconduct O O -, O O -when O O -he O O -was O O -dismissed O O -cheap O O -in O O -the O O -first O O -limited O O -overs O O -match O O -against O O -Australia B-LOC B-LOC -on O O -Friday O O -. O O -Lara B-PER B-PER -, O O -who O O -earned O O -a O O -stern O O -re O O -from O O -his O O -own O O -tour O O -management O O -after O O -an O O -angry O O -out O O -against O O -Australia B-LOC B-LOC -wicket O O -Ian B-PER B-PER -He I-PER I-PER -, O O -scored O O -five O O -to O O -pro O O -a O O -run O O -of O O -poor O O -form O O -with O O -the O O -bat O O -. O O -The O O -West B-LOC B-LOC -Indies I-LOC I-LOC -vice O O -struggled O O -for O O -timing O O -during O O -his O O -36 O O -stay O O -at O O -the O O -c O O -before O O -chip O O -a O O -ball O O -from O O -medium O O -pace O O -Tom B-PER B-PER -Moody I-PER I-PER -straight O O -to O O -Shane B-PER B-PER -War I-PER I-PER -at O O -mid O O -. O O -West B-LOC B-LOC -Indies I-LOC I-LOC -were O O -53 O O -for O O -two O O -in O O -15 O O -overs O O -when O O -rain O O -stopped O O -play O O -at O O -the O O -Melbourne B-LOC B-LOC -Cricket I-LOC I-LOC -Ground I-LOC I-LOC -after O O -captain O O -Courtney B-PER B-PER -Walsh I-PER I-PER -won O O -the O O -toss O O -and O O -elected O O -to O O -bat O O -. O O -Lara B-PER B-PER -' O O -out O O -three O O -days O O -ago O O -has O O -clearly O O -turned O O -some O O -of O O -the O O -Australian B-MISC B-MISC -public O O -against O O -him O O -. O O -As O O -he O O -walked O O -to O O -the O O -wicket O O -he O O -was O O -greeted O O -by O O -loud O O -j O O -from O O -sections O O -of O O -the O O -crowd O O -. O O -On O O -several O O -occasions O O -during O O -his O O -innings O O -, O O -the O O -crowd O O -joined O O -together O O -in O O -a O O -series O O -of O O -o O O -chant O O -against O O -him O O -. O O -Tour B-MISC O -manager O O -Clive B-PER B-PER -Lloyd I-PER I-PER -on O O -Wednesday O O -a O O -for O O -Lara B-PER B-PER -' O O -behaviour O O -in O O -confront O O -Australia B-LOC B-LOC -coach O O -Geoff B-PER B-PER -Marsh I-PER I-PER -in O O -the O O -opposition O O -dressing O O -room O O -to O O -protest O O -against O O -his O O -dismissal O O -in O O -the O O -second O O -test O O -on O O -Tuesday O O -. O O -Lloyd B-PER B-PER -did O O -not O O -say O O -what O O -form O O -the O O -discipline O O -would O O -take O O -. O O -Lara B-PER B-PER -, O O -who O O -holds O O -the O O -record O O -for O O -the O O -highest O O -score O O -in O O -test O O -and O O -first O O -cricket O O -, O O -was O O -unhappy O O -about O O -He B-PER B-PER -' O O -role O O -in O O -the O O -incident O O -and O O -questioned O O -whether O O -the O O -ball O O -had O O -carried O O -to O O -the O O -Australia B-LOC B-LOC -keeper O O -. O O -Australia B-LOC B-LOC -went O O -on O O -to O O -win O O -the O O -match O O -at O O -the O O -Sydney B-LOC B-LOC -Cricket I-LOC I-LOC -Ground I-LOC I-LOC -by O O -124 O O -runs O O -to O O -take O O -a O O -two O O -lead O O -in O O -the O O -five O O -series O O -after O O -Lara B-PER B-PER -failed O O -in O O -both O O -innings O O -. O O -Lara B-PER B-PER -has O O -yet O O -to O O -score O O -a O O -century O O -since O O -West B-LOC B-LOC -Indies I-ORG I-LOC -arrived O O -in O O -Australia B-LOC B-LOC -five O O -weeks O O -ago O O -. O O -Both O O -West B-LOC B-LOC -Indies I-LOC I-LOC -and O O -Australia B-LOC B-LOC -team O O -management O O -have O O -played O O -down O O -the O O -incident O O -, O O -stress O O -that O O -relations O O -between O O -the O O -two O O -sides O O -have O O -not O O -been O O -adverse O O -affected O O -. O O -Pakistan B-LOC B-LOC -, O O -who O O -arrive O O -next O O -week O O -, O O -are O O -the O O -third O O -team O O -in O O -the O O -triangular O O -World B-MISC B-MISC -Series I-MISC I-MISC -tournament O O -. O O -CR O O -- O O -W O B-LOC -IN O I-LOC -TO O O -BA O O -A O O -W O O -THE O O -TO O O -. O O -ME B-LOC B-LOC -1996 O O -West B-LOC B-LOC -Indies I-LOC I-LOC -captain O O -Courtney B-PER B-PER -Walsh I-PER I-PER -elected O O -to O O -bat O O -after O O -winning O O -the O O -toss O O -in O O -the O O -first O O -match O O -in O O -the O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -competition O O -against O O -Australia B-LOC B-LOC -at O O -the O O -Melbourne B-LOC B-LOC -Cricket I-LOC O -Ground I-LOC O -on O O -Friday O O -. O O -Teams O O -: O O -Australia B-LOC B-LOC -- O O -Mark B-PER B-PER -Taylor I-PER I-PER -( O O -captain O O -) O O -, O O -Mark B-PER B-PER -W I-PER I-PER -, O O -Ricky B-PER B-PER -Pont I-PER I-PER -, O O -Greg B-PER B-PER -B I-PER I-PER -, O O -Michael B-PER B-PER -Be I-PER I-PER -, O O -Stuart B-PER B-PER -Law I-PER I-PER -, O O -Tom B-PER B-PER -Moody I-PER I-PER -, O O -Ian B-PER B-PER -He I-PER I-PER -, O O -Paul B-PER B-PER -Re I-PER I-PER -, O O -Shane B-PER B-PER -War I-PER I-PER -, O O -Jason B-PER B-PER -Gillespie I-PER I-PER -, O O -Glenn B-PER B-PER -McGrath I-PER I-PER -12th O O -man O O -. O O -West B-LOC B-LOC -Indies I-LOC I-LOC -- O O -She B-PER B-PER -Campbell I-PER I-PER -, O O -Robert B-PER B-PER -Samuel I-PER I-PER -, O O -Brian B-PER B-PER -Lara I-PER I-PER -, O O -Shi B-PER B-PER -Chan I-PER I-PER -, O O -Carl B-PER B-PER -Hooper I-PER I-PER -, O O -Jimmy B-PER B-PER -Adams I-PER I-PER -, O O -Junior B-PER B-PER -Murray I-PER I-PER -, O O -Nixon B-PER B-PER -McLean I-PER I-PER -, O O -Kenneth B-PER B-PER -Benjamin I-PER I-PER -, O O -C B-PER B-PER -Ambrose I-PER I-PER -, O O -Courtney B-PER B-PER -Walsh I-PER I-PER -( O O -captain O O -) O O -, O O -Roland B-PER B-PER -Hold I-PER I-PER -12th O O -man O O -. O O -BA O O -- O O -W B-MISC B-MISC -G B-MISC I-MISC -PR I-MISC I-MISC -R O O -. O O -BA B-LOC B-LOC -1996 O O -Results O O -in O O -last O O -of O O -the O O -group O O -matches O O -at O O -the O O -World B-MISC B-MISC -Grand B-MISC I-MISC -Prix I-MISC I-MISC -badminton O O -finals O O -on O O -Friday O O -: O O -Men O O -' O O -singles O O -Group O O -B O O -Chen B-PER B-PER -Gang I-PER I-PER -( O O -China B-LOC B-LOC -) O O -beat O O -Martin B-PER B-PER -Lo I-PER I-PER -Hansen I-PER I-PER -( O O -Denmark B-LOC B-LOC -) O O -15 O O -15 O O -Dong B-PER B-PER -Ji I-PER I-PER -( O O -China B-LOC B-LOC -) O O -beat O O -Thomas B-PER B-PER -St I-PER I-PER -( O O -Denmark B-LOC B-LOC -) O O -15 O O -15 O O -In B-PER B-PER -W I-PER I-PER -( O O -Indonesia B-LOC B-LOC -) O O -beat O O -On B-PER B-PER -E I-PER I-PER -Ho I-PER I-PER -( O O -Malaysia B-LOC B-LOC -) O O -5 O O -15 O O -15 O O -Group O O -C O O -Sun B-PER B-PER -Jun I-PER I-PER -( O O -China B-LOC B-LOC -) O O -beat O O -Rashid B-PER B-PER -Side I-PER I-PER -( O O -Malaysia B-LOC B-LOC -) O O -15 O O -17 O O -Her B-PER B-PER -Susan I-PER I-PER -( O O -Semifinals O O -( O O -on O O -Saturday O O -) O O -: O O -Su B-PER B-PER -Susan I-PER I-PER -( O O -Indonesia B-LOC B-LOC -) O O -v O O -Cam B-PER B-PER -Martin I-PER I-PER -( O O -Denmark B-LOC B-LOC -) O O -; O O -Ye B-PER B-PER -Zhao I-PER I-PER -( O O -China B-LOC B-LOC -) O O -v O O -Gong B-PER B-PER -Z I-PER I-PER -( O O -China B-LOC B-LOC -) O O -. O O -S O O -- O O -AR B-MISC B-MISC -CO O O -W O O -A B-MISC B-MISC -C I-MISC I-MISC -W O I-MISC -' O I-MISC -C I-MISC I-MISC -. O O -CA B-LOC B-LOC -1996 O O -Re O O -of O O -the O O -second O O -leg O O -of O O -the O O -African B-MISC B-MISC -Cup I-MISC I-MISC -Winners I-MISC I-MISC -' I-MISC I-MISC -Cup I-MISC I-MISC -final O O -at O O -the O O -National B-LOC B-LOC -stadium O I-LOC -on O O -Friday O O -: O O -Arab B-ORG B-ORG -Con I-ORG I-ORG -( O O -Egypt B-LOC B-LOC -) O O -4 O O -So B-ORG B-ORG -( O O -Z B-LOC B-LOC -) O O -0 O O -( O O -halftime O O -2 O O -) O O -Score O O -: O O -Al B-PER B-PER -Ash I-PER I-PER -7 O O -, O O -56 O O -penalty O O -, O O -Mohamed B-PER B-PER -O I-PER I-PER -24 O O -, O O -73 O O -Con O O -won O O -4 O O -on O O -aggregate O O -. O O -NHL B-ORG B-ORG -I I-MISC O -H O O -- O O -ST O O -A O O -T O O -' O O -GA O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Standing O O -of O O -National B-MISC B-ORG -Hockey I-MISC I-ORG -League I-MISC B-ORG -teams O O -after O O -games O O -played O O -on O O -Thursday O O -( O O -ta O O -under O O -won O O -, O O -lost O O -, O O -tied O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -EA B-MISC O -CO O O -NO O O -D O O -W O O -L O O -T O O -G O O -GA B-ORG O -PT I-ORG O -H I-ORG B-ORG -12 O O -7 O O -6 O O -77 O O -76 O O -30 O O -B B-ORG B-ORG -13 O O -12 O O -1 O O -77 O O -76 O O -27 O O -B B-ORG B-ORG -10 O O -11 O O -4 O O -74 O O -84 O O -24 O O -M B-ORG B-ORG -10 O O -14 O O -4 O O -96 O O -103 O O -24 O O -P B-ORG B-ORG -9 O O -13 O O -3 O O -81 O O -91 O O -21 O O -O B-ORG B-ORG -[ O O -CO O O -08 O O -GM B-MISC B-MISC -] O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -( O O -Co O O -headline O O -from O O -NBA B-ORG B-ORG -to O O -NHL B-ORG B-ORG -and O O -correct O O -team O O -name O O -in O O -second O O -result O O -from O O -La B-ORG B-ORG -C I-ORG I-ORG -to O O -N B-ORG B-ORG -Islanders I-ORG I-ORG -. O O -) O O -Results O O -of O O -National B-MISC B-ORG -Hockey I-MISC I-ORG -League I-MISC B-ORG -games O O -on O O -Thursday O O -( O O -home O O -team O O -in O O -CA O O -) O O -: O O -Hartford B-ORG B-ORG -4 O O -B B-ORG B-ORG -2 O O -FL B-ORG B-ORG -4 O O -N B-ORG B-ORG -Islanders I-ORG I-ORG -2 O O -NE B-ORG B-ORG -J I-ORG I-ORG -2 O O -Calgary B-ORG B-ORG -1 O O -Phoenix B-ORG B-ORG -3 O O -ST B-ORG B-ORG -L I-ORG I-ORG -0 O O -Tampa B-ORG B-ORG -Bay I-ORG I-ORG -2 O O -L B-ORG B-ORG -AN I-ORG I-ORG -1 O O -NFL B-ORG B-ORG -AM I-MISC O -F O O -C B-PER O -EA O B-ORG -TO O O -ST O O -IN O O -P B-MISC O -H O O -. O O -IN B-LOC B-LOC -1996 O O -The O O -injury O O -Indianapolis B-ORG B-ORG -Colts I-ORG I-ORG -lost O O -another O O -quarterback O O -on O O -Thursday O O -but O O -last O O -year O O -' O O -AFC B-MISC O -finalists O O -rallied O O -together O O -to O O -shoot O O -down O O -the O O -Philadelphia B-ORG B-ORG -Eagles I-ORG I-ORG -37 O O -in O O -a O O -show O O -of O O -playoff O O -contender O O -. O O -Marshall B-PER B-PER -F I-PER I-PER -rushed O O -for O O -101 O O -yards O O -and O O -two O O -touchdowns O O -and O O -Jason B-PER B-PER -Bel I-PER I-PER -returned O O -an O O -interception O O -44 O O -yards O O -for O O -a O O -score O O -as O O -the O O -Colts B-ORG B-ORG -improved O O -to O O -8 O O -, O O -the O O -same O O -mark O O -as O O -the O O -Eagles B-ORG B-ORG -, O O -who O O -lost O O -for O O -the O O -fourth O O -time O O -in O O -five O O -games O O -. O O -Paul B-PER B-PER -Justin I-PER I-PER -, O O -starting O O -for O O -the O O -side O O -Jim B-PER B-PER -Ha I-PER I-PER -, O O -was O O -14 O O -for O O -144 O O -yards O O -and O O -a O O -touchdown O O -for O O -the O O -the O O -Colts B-ORG B-ORG -, O O -who O O -played O O -their O O -last O O -home O O -game O O -of O O -the O O -season O O -. O O -Indianapolis B-LOC B-LOC -closes O O -with O O -games O O -at O O -Kansas B-LOC B-LOC -City I-LOC I-LOC -and O O -Cincinnati B-LOC B-LOC -. O O -The O O -Eagles B-ORG B-ORG -were O O -held O O -without O O -a O O -touchdown O O -until O O -the O O -final O O -five O O -seconds O O -. O O -Philadelphia B-LOC B-LOC -, O O -which O O -fell O O -from O O -an O O -NFC B-MISC O -East I-MISC O -tie O O -with O O -the O O -Dallas B-ORG B-ORG -Cowboys I-ORG I-ORG -and O O -Washington B-ORG B-ORG -Redskins I-ORG I-ORG -, O O -go O O -on O O -the O O -road O O -against O O -the O O -New B-ORG B-ORG -York I-ORG I-ORG -Jets I-ORG I-ORG -and O O -then O O -entertain O O -Arizona B-LOC B-ORG -. O O -The O O -loss O O -by O O -Philadelphia B-ORG B-ORG -allowed O O -the O O -idle O O -Green B-ORG B-ORG -Bay I-ORG I-ORG -Packers I-ORG I-ORG -( O O -10 O O -) O O -to O O -c O O -the O O -first O O -NFC B-MISC O -playoff O O -berth O O -. O O -The O O -Colts B-ORG B-ORG -won O O -despite O O -the O O -absence O O -of O O -injured O O -starting O O -defensive O O -tackle O O -Tony B-PER B-PER -Sir I-PER I-PER -, O O -corner O O -Ray B-PER B-PER -Buchanan I-PER I-PER -and O O -linebacker O O -Quentin B-PER B-PER -Cory I-PER I-PER -. O O -F B-PER B-PER -carried O O -16 O O -times O O -, O O -including O O -a O O -13 O O -TD O O -run O O -in O O -the O O -first O O -quarter O O -and O O -a O O -seven O O -score O O -early O O -in O O -the O O -final O O -period O O -. O O -Justin B-PER B-PER -made O O -his O O -second O O -straight O O -start O O -for O O -Ha B-PER B-PER -, O O -who O O -has O O -a O O -knee O O -injury O O -. O O -Justin B-PER B-PER -suffered O O -a O O -s O O -right O O -shoulder O O -in O O -the O O -third O O -quarter O O -and O O -did O O -not O O -return O O -. O O -Third O O -Ke B-PER B-PER -Bell I-PER I-PER -, O O -a O O -1988 O O -draft O O -choice O O -of O O -the O O -Miami B-ORG B-ORG -Dolphins I-ORG I-ORG -, O O -made O O -his O O -NFL B-ORG B-ORG -debut O O -and O O -was O O -5 O O -for O O -75 O O -yards O O -, O O -including O O -a O O -20 O O -scoring O O -strike O O -to O O -Marvin B-PER B-PER -Harrison I-PER I-PER -in O O -the O O -third O O -period O O -. O O -A O O -39 O O -interference O O -penalty O O -against O O -Philadelphia B-LOC B-LOC -' O O -Troy B-PER B-PER -Vincent I-PER I-PER -set O O -up O O -F B-PER B-PER -' O O -first O O -score O O -around O O -left O O -end O O -that O O -capped O O -an O O -80 O O -march O O -5 O O -into O O -the O O -game O O -and O O -the O O -r O O -was O O -on O O -. O O -Eagles B-ORG B-ORG -quarterback O O -Ty B-PER B-PER -Det I-PER I-PER -was O O -17 O O -for O O -182 O O -yards O O -before O O -he O O -was O O -bench O O -. O O -Ricky B-PER B-PER -Watt I-PER I-PER -, O O -who O O -leads O O -the O O -NFC B-ORG O -in O O -rushing O O -, O O -left O O -the O O -game O O -after O O -getting O O -knee O O -to O O -the O O -helmet O O -after O O -gaining O O -33 O O -yards O O -on O O -seven O O -carries O O -. O O -NBA B-ORG B-ORG -BA O O -- O O -ST O O -A O O -T O O -' O O -GA O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Standing O O -of O O -National B-MISC B-ORG -Basketball I-MISC B-ORG -Association I-MISC I-ORG -teams O O -after O O -games O O -played O O -on O O -Thursday O O -( O O -ta O O -under O O -won O O -, O O -lost O O -, O O -percentage O O -, O O -games O O -behind O O -) O O -: O O -EA B-MISC O -CO O O -AT B-ORG B-LOC -D O O -W O O -L O O -PC O O -GB O O -MI B-ORG B-ORG -14 O O -4 O O -. O O -- O O -NE B-ORG B-ORG -Y I-ORG I-ORG -10 O O -6 O O -. O O -3 O O -OR B-ORG B-ORG -8 O O -6 O O -. O O -4 O O -WA B-ORG B-ORG -7 O O -9 O O -. O O -6 O O -P B-ORG B-ORG -7 O O -10 O O -. O O -6 O O -1 O O -B B-ORG B-ORG -4 O O -12 O O -. O O -9 O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -National B-MISC B-ORG -Football I-MISC I-ORG -League I-MISC I-ORG -standings O O -after O O -Thursday O O -' O O -game O O -( O O -ta O O -under O O -won O O -, O O -lost O O -, O O -tied O O -, O O -points O O -for O O -and O O -points O O -against O O -) O O -: O O -AM B-MISC B-MISC -F I-MISC O -CO O O -EA B-ORG O -D I-MISC O -W O O -L O O -T O O -P O O -PA O O -NE B-ORG B-ORG -E I-ORG I-ORG -9 O O -4 O O -0 O O -35 O O -26 O O -B B-ORG B-ORG -9 O O -4 O O -0 O O -26 O O -215 O O -IN B-ORG B-ORG -8 O O -6 O O -0 O O -26 O O -28 O O -MI B-ORG B-ORG -6 O O -7 O O -0 O O -285 O O -26 O O -NY B-ORG B-ORG -J I-ORG I-ORG -1 O O -12 O O -0 O O -221 O O -36 O O -CE B-ORG O -D I-MISC O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Re O O -of O O -National B-MISC B-ORG -Football I-MISC B-LOC -League I-MISC B-LOC -game O O -on O O -Thursday O O -( O O -home O O -team O O -in O O -CA O O -) O O -: O O -IN B-ORG B-ORG -37 O O -Philadelphia B-ORG B-ORG -10 O O -NCAA B-MISC B-ORG -AM I-MISC O -F O B-MISC -ST O I-MISC -' O O -PA O B-PER -F O O -R O O -L B-ORG B-MISC -A O I-MISC -W O O -. O O -H B-LOC B-LOC -1996 O O -Ohio B-ORG B-ORG -State I-ORG I-ORG -left O O -tackle O O -Orlando B-PER B-PER -Pace I-PER I-PER -became O O -the O O -first O O -repeat O O -winner O O -of O O -the O O -Lombard B-MISC B-MISC -Award I-MISC I-MISC -Thursday O O -night O O -when O O -the O O -R B-ORG B-ORG -Club I-ORG I-ORG -of I-ORG O -Houston I-ORG B-LOC -again O O -honoured O O -him O O -as O O -college O O -football O O -' O O -line O O -of O O -the O O -year O O -. O O -Pace B-PER B-PER -, O O -a O O -junior O O -, O O -helped O O -Ohio B-ORG B-ORG -State I-ORG I-ORG -to O O -a O O -10 O O -record O O -and O O -a O O -berth O O -in O O -the O O -Rose B-MISC B-MISC -Bowl I-MISC I-MISC -against O O -Arizona B-ORG B-ORG -State I-ORG I-ORG -. O O -He O O -was O O -the O O -most O O -dominant O O -offensive O O -line O O -in O O -the O O -country O O -and O O -also O O -played O O -defensive O O -line O O -in O O -goal O O -situations O O -. O O -Last O O -year O O -, O O -Pace B-PER B-PER -became O O -the O O -first O O -sophomore O O -to O O -win O O -the O O -award O O -since O O -its O O -inception O O -in O O -1970 O O -. O O -Pace B-PER B-PER -out O O -three O O -senior O O -finalists O O -- O O -Virginia B-ORG B-ORG -Tech I-ORG I-ORG -defensive O O -end O O -Cornell B-PER B-PER -Brown I-PER I-PER -, O O -Arizona B-ORG B-ORG -State I-ORG I-ORG -offensive O O -tackle O O -Juan B-PER B-PER -R I-PER I-PER -and O O -defensive O O -end O O -Jared B-PER B-PER -Tom I-PER I-PER -of O O -Nebraska B-LOC B-ORG -. O O -The O O -Lombard B-MISC B-MISC -Award I-MISC I-MISC -is O O -presented O O -to O O -the O O -college O O -line O O -who O O -, O O -in O O -addition O O -to O O -outstanding O O -effort O O -on O O -the O O -field O O -, O O -best O O -ex O O -the O O -characteristics O O -and O O -discipline O O -of O O -Vince B-PER B-PER -Lombard I-PER I-PER -, O O -legendary O O -coach O O -of O O -the O O -Green B-ORG B-ORG -Bay I-ORG I-ORG -Packers I-ORG I-ORG -. O O -S O O -- O O -D B-MISC B-MISC -F O O -D O O -R O O -/ O O -ST O O -. O O -AM B-LOC B-LOC -1996 O O -Re O O -of O O -Dutch B-MISC B-MISC -first O O -division O O -soccer O O -match O O -played O O -on O O -Friday O O -: O O -R B-ORG B-ORG -W I-ORG I-ORG -1 O O -Willem B-ORG B-ORG -II I-ORG I-ORG -T I-ORG I-ORG -2 O O -Standing O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -PS B-ORG B-ORG -Ein I-ORG I-ORG -18 O O -13 O O -3 O O -2 O O -52 O O -14 O O -42 O O -Fe B-ORG B-ORG -17 O O -11 O O -3 O O -3 O O -29 O O -20 O O -36 O O -T B-ORG B-ORG -En I-ORG I-ORG -18 O O -10 O O -4 O O -4 O O -28 O O -15 O O -34 O O -G B-ORG B-ORG -Do I-ORG I-ORG -18 O O -9 O O -3 O O -6 O O -29 O O -22 O O -30 O O -V B-ORG B-ORG -A I-ORG I-ORG -18 O O -8 O O -5 O O -5 O O -29 O O -21 O O -29 O O -B B-LOC B-LOC -1996 O O -Results O O -of O O -German B-MISC B-MISC -first O O -division O O -soccer O O -matches O O -played O O -on O O -Friday O O -: O O -Bo B-ORG B-ORG -2 O O -Bay B-ORG B-ORG -Lev I-ORG I-ORG -2 O O -We B-ORG B-ORG -Bremen I-ORG I-ORG -1 O O -1860 B-ORG B-ORG -Munich I-ORG I-ORG -1 O O -Karl B-ORG B-ORG -3 O O -Freiburg B-ORG B-ORG -0 O O -Sc B-ORG B-ORG -2 O O -Hans B-ORG B-ORG -R I-ORG I-ORG -0 O O -Standing O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -goals O O -against O O -points O O -) O O -: O O -Bay B-ORG B-ORG -Lev I-ORG I-ORG -17 O O -10 O O -4 O O -3 O O -38 O O -22 O O -34 O O -Bayern B-ORG B-ORG -Munich I-ORG I-ORG -16 O O -9 O O -6 O O -1 O O -26 O O -14 O O -33 O O -V B-ORG B-ORG -Stuttgart I-ORG I-ORG -16 O O -9 O O -4 O O -3 O O -39 O O -17 O O -31 O O -Bo B-ORG B-ORG -Dortmund I-ORG I-ORG -16 O O -9 O O -4 O O -3 O O -33 O O -17 O O -31 O O -Karl B-ORG B-ORG -17 O O -8 O O -PA B-LOC B-LOC -1996 O O -Su O O -of O O -French B-MISC B-MISC -first O O -division O O -matches O O -on O O -Friday O O -: O O -Len B-ORG B-ORG -0 O O -Nan B-ORG B-ORG -4 O O -( O O -J B-PER B-PER -N I-PER I-PER -7 O O -, O O -Claude B-PER B-PER -Make I-PER I-PER -42 O O -, O O -Jocelyn B-PER B-PER -Go I-PER B-PER -67 O O -, O O -Christophe B-PER B-PER -Pig I-PER I-PER -72 O O -) O O -. O O -Half O O -0 O O -. O O -Attendance O O -: O O -15 O O -. O O -Paris B-ORG B-ORG -St I-ORG I-ORG -Germain I-ORG I-ORG -1 O O -( O O -Bruno B-PER B-PER -N I-PER I-PER -2 O O -) O O -Nancy B-ORG B-ORG -2 O O -( O O -Paul B-PER B-PER -Fischer I-PER I-PER -70 O O -, O O -Phil B-PER B-PER -Gray I-PER I-PER -89 O O -) O O -. O O -1 O O -. O O -30 O O -. O O -S O O -- O O -D B-MISC B-MISC -F O O -D O O -S O O -. O O -AM B-LOC B-LOC -1996 O O -Su O O -of O O -Dutch B-MISC B-MISC -first O O -division O O -soccer O O -match O O -played O O -on O O -Friday O O -: O O -R B-ORG B-ORG -W I-ORG I-ORG -1 O O -( O O -Star B-ORG O -76 O O -) O O -Willem B-ORG B-ORG -II I-ORG I-ORG -T I-ORG I-ORG -2 O O -( O O -Ko B-PER B-PER -45 O O -, O O -Van B-PER B-PER -der I-PER I-PER -V I-PER I-PER -77 O O -) O O -. O O -Half O O -0 O O -. O O -Attendance O O -5 O O -. O O -S O O -- O O -F B-MISC B-MISC -L O O -ST O O -. O O -PA B-LOC B-LOC -1996 O O -Standing O O -in O O -the O O -French B-MISC B-MISC -first O O -division O O -after O O -Friday O O -' O O -matches O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -against O O -, O O -points O O -) O O -: O O -Paris B-ORG B-ORG -Saint I-ORG I-ORG -21 O O -12 O O -6 O O -3 O O -34 O O -15 O O -42 O O -Monaco B-ORG B-ORG -20 O O -12 O O -5 O O -3 O O -36 O O -16 O O -41 O O -Bordeaux B-ORG B-ORG -20 O O -9 O O -7 O O -4 O O -29 O O -21 O O -34 O O -Strasbourg B-ORG B-ORG -20 O O -11 O O -1 O O -8 O O -27 O O -27 O O -34 O O -Ba B-ORG B-ORG -20 O O -9 O O -6 O O -5 O O -27 O O -22 O O -33 O O -Au B-ORG B-ORG -20 O O -8 O O -8 O O -4 O O -26 O O -12 O O -32 O O -Metz B-ORG B-ORG -20 O O -8 O O -7 O O -5 O O -21 O O -16 O O -31 O O -Nan B-ORG B-ORG -21 O O -7 O O -9 O O -5 O O -41 O O -25 O O -30 O O -G B-ORG B-ORG -20 O O -7 O O -7 O O -6 O O -18 O O -18 O O -28 O O -Lille B-ORG B-ORG -20 O O -7 O O -7 O O -PA B-LOC B-LOC -1996 O O -Results O O -of O O -French B-MISC B-MISC -first O O -division O O -matches O O -on O O -Friday O O -: O O -Len B-ORG B-ORG -0 O O -Nan B-ORG B-ORG -4 O O -Paris B-ORG B-ORG -St I-ORG I-ORG -Germain I-ORG I-ORG -1 O O -Nancy B-ORG B-ORG -2 O O -S O O -- O O -GE B-MISC B-MISC -F O O -D O O -S O O -. O O -B B-LOC B-LOC -1996 O O -Su O O -of O O -matches O O -played O O -in O O -the O O -German B-MISC B-MISC -first O O -division O O -on O O -Friday O O -: O O -Bo B-ORG B-ORG -2 O O -( O O -Stick B-PER B-PER -30th O O -pen O O -, O O -W B-PER B-PER -89 O O -) O O -Bay B-ORG B-ORG -Lev I-ORG I-ORG -2 O O -( O O -Ki B-PER B-PER -18th O O -, O O -Ram B-PER B-PER -56 O O -) O O -. O O -Half O O -1 O O -. O O -Attendance O O -: O O -24 O O -We B-ORG B-ORG -Bremen I-ORG I-ORG -1 O O -( O O -Bo B-PER B-PER -85 O O -) O O -1860 B-ORG B-ORG -Munich I-ORG I-ORG -1 O O -( O O -Bo B-PER B-PER -8th O O -) O O -. O O -Half O O -0 O O -. O O -Attendance O O -33 O O -Karl B-ORG B-LOC -3 O O -( O O -Reich B-PER B-PER -29th O O -, O O -Carl B-PER B-PER -44 O O -, O O -Dundee B-PER B-ORG -69 O O -) O O -Freiburg B-ORG B-LOC -0 O O -. O O -Half O O -2 O O -. O O -Attendance O O -33 O O -Sc B-ORG B-ORG -2 O O -( O O -Mu B-PER B-PER -2nd O O -and O O -27th O O -) O O -Hans B-ORG B-ORG -R I-ORG I-ORG -0 O O -. O O -Half O O -2 O O -. O O -Attendance O O -29 O O -T O O -- O O -G B-MISC B-MISC -SL I-MISC I-MISC -C I-MISC I-MISC -Q O O -R O O -. O O -M B-LOC B-LOC -, O O -Germany B-LOC B-LOC -1996 O O -Quarter O O -results O O -at O O -the O O -$ O O -6 O O -million O O -Grand B-MISC B-MISC -Slam I-MISC I-MISC -Cup I-MISC I-MISC -tennis O O -tournament O O -on O O -Friday O O -: O O -Go B-PER B-PER -Ivan I-PER I-PER -( O O -Croatia B-LOC B-LOC -) O O -beat O O -Mark B-PER B-PER -Wood I-PER I-PER -( O O -Australia B-LOC B-LOC -) O O -6 O O -6 O O -Ye B-PER B-PER -Ka I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -beat O O -Jim B-PER B-PER -Co I-PER I-PER -( O O -U B-LOC B-LOC -) O O -2 O O -6 O O -8 O O -S O O -- O O -W O B-PER -H O O -DE O O -P O B-LOC -OF O O -CO B-LOC B-PER -. O O -L B-LOC B-LOC -1996 O O -Portugal B-LOC B-LOC -called O O -up O O -Porto B-ORG B-ORG -central O O -defender O O -Jo B-PER B-PER -Manuel I-PER I-PER -Pi I-PER I-PER -on O O -Friday O O -to O O -face O O -Germany B-LOC B-LOC -in O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -qualifier O O -in O O -place O O -of O O -injured O O -club O O -colleague O O -Jorge B-PER B-PER -Costa I-PER I-PER -, O O -who O O -is O O -still O O -nursing O O -a O O -broken O O -nose O O -after O O -being O O -head O O -by O O -Liberia B-MISC B-MISC -striker O O -Georg B-PER B-PER -We I-PER I-PER -. O O -Costa B-PER B-PER -has O O -not O O -played O O -since O O -being O O -struck O O -by O O -the O O -AC B-ORG B-ORG -Milan I-ORG I-ORG -forward O O -after O O -a O O -bad O O -European B-MISC B-MISC -Champions I-MISC I-MISC -' I-MISC I-MISC -League I-MISC I-MISC -game O O -on O O -November O O -27 O O -. O O -Portugal B-LOC B-LOC -lead O O -European B-MISC B-MISC -qualifying O O -group O O -nine O O -with O O -seven O O -points O O -from O O -four O O -games O O -, O O -one O O -more O O -than O O -Ukraine B-LOC B-LOC -and O O -three O O -more O O -than O O -Germany B-LOC B-LOC -, O O -who O O -have O O -only O O -played O O -twice O O -. O O -The O O -Portuguese B-MISC B-MISC -host O O -Germany B-LOC B-LOC -on O O -December O O -14 O O -. O O -Squad O O -: O O -Goal O O -- O O -V B-PER B-PER -Bai I-PER I-PER -( O O -Barcelona B-ORG B-ORG -, O O -Spain B-LOC B-LOC -) O O -, O O -R B-PER B-PER -Co I-PER I-PER -( O O -B B-ORG B-ORG -) O O -: O O -De O O -- O O -Paul B-PER B-PER -Santos I-PER I-PER -( O O -Porto B-ORG B-ORG -) O O -, O O -Sergio B-PER B-PER -Con I-PER I-PER -( O O -Porto B-ORG B-ORG -) O O -, O O -Jo B-PER B-PER -Manuel I-PER I-PER -Pi I-PER I-PER -( O O -Porto B-ORG B-ORG -) O O -, O O -Ocean B-PER B-PER -Cruz I-PER I-PER -( O O -Sporting B-ORG B-ORG -) O O -, O O -Fernando B-PER B-PER -Co I-PER I-PER -( O O -Barcelona B-ORG B-ORG -) O O -, O O -Held B-PER B-PER -C I-PER I-PER -( O O -Deportivo B-ORG B-ORG -Co I-ORG I-ORG -, O O -Spain B-LOC B-LOC -) O O -, O O -Di B-PER B-PER -Te I-PER I-PER -( O O -Juventus B-ORG B-ORG -, O O -Italy B-LOC B-LOC -) O O -, O O -Carlos B-PER B-PER -Secret I-PER I-PER -( O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -, O O -Spain B-LOC B-LOC -) O O -: O O -Mid O O -- O O -R B-PER B-PER -Barr I-PER I-PER -( O O -Porto B-ORG B-ORG -) O O -, O O -Jose B-PER B-PER -Barr I-PER I-PER -( O O -Porto B-ORG B-ORG -) O O -, O O -Luis B-PER B-PER -Fi I-PER I-PER -S O O -SH O O -ON O O -R O B-ORG -MA O I-ORG -V O O -BA O B-ORG -. O O -MA B-LOC B-LOC -1996 O O -William B-PER B-PER -Hill I-PER I-PER -betting O O -on O O -Saturday O O -' O O -Spanish B-MISC B-MISC -first O O -division O O -match O O -between O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -and O O -Barcelona B-ORG B-ORG -: O O -To O O -win O O -: O O -6 O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -; O O -7 O O -Barcelona B-ORG B-ORG -Draw O O -: O O -9 O O -Co O O -score O O -: O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -to O O -win O O -Barcelona B-ORG B-ORG -to O O -win O O -1 O O -13 O O -1 O O -15 O O -2 O O -9 O O -2 O O -12 O O -2 O O -8 O O -2 O O -10 O O -3 O O -20 O O -3 O O -28 O O -3 O O -16 O O -3 O O -22 O O -3 O O -25 O O -3 O O -25 O O -MA B-LOC B-LOC -1996 O O -Spanish B-MISC B-MISC -police O O -will O O -breath O O -fans O O -at O O -the O O -gates O O -of O O -the O O -Santiago B-LOC B-LOC -Bern I-LOC I-LOC -stadium O I-LOC -and O O -ban O O -drunk O O -supporters O O -from O O -Saturday O O -' O O -big O O -Real B-ORG B-MISC -Madrid I-ORG I-MISC -game O O -, O O -the O O -Madrid B-ORG B-LOC -daily O O -El B-ORG B-ORG -Mu I-ORG I-ORG -said O O -on O O -Friday O O -. O O -Although O O -there O O -are O O -no O O -known O O -precedent O O -in O O -the O O -country O O -, O O -the O O -action O O -is O O -en O O -in O O -Spanish B-MISC B-MISC -legislation O O -governing O O -sports O O -events O O -. O O -T O O -for O O -the O O -game O O -s O O -that O O -supporters O O -will O O -be O O -barred O O -if O O -they O O -are O O -" O O -under O O -the O O -effects O O -of O O -alcohol O O -" O O -. O O -S O O -- O O -SP B-MISC B-MISC -F O O -D O O -ST O O -. O O -MA B-LOC B-LOC -1996 O O -Standing O O -in O O -the O O -Spanish B-MISC B-MISC -first O O -division O O -ahead O O -of O O -this O O -weekend O O -' O O -games O O -. O O -( O O -ta O O -under O O -games O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -against O O -, O O -points O O -) O O -: O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -15 O O -10 O O -5 O O -0 O O -31 O O -12 O O -35 O O -Barcelona B-ORG B-ORG -15 O O -10 O O -4 O O -1 O O -46 O O -19 O O -34 O O -Deportivo B-ORG B-ORG -Co I-ORG I-ORG -15 O O -9 O O -6 O O -0 O O -23 O O -7 O O -33 O O -Real B-ORG B-ORG -Bet I-ORG I-ORG -15 O O -8 O O -5 O O -2 O O -28 O O -13 O O -29 O O -At B-ORG B-ORG -Madrid I-ORG I-ORG -15 O O -8 O O -3 O O -4 O O -26 O O -17 O O -27 O O -Athletic B-ORG B-ORG -B I-ORG I-ORG -15 O O -7 O O -4 O O -4 O O -28 O O -22 O O -25 O O -Real B-ORG B-ORG -So I-ORG I-ORG -15 O O -7 O O -3 O O -5 O O -20 O O -18 O O -24 O O -Val B-ORG B-ORG -15 O O -7 O O -3 O O -5 O O -19 O O -18 O O -24 O O -Racing B-ORG B-ORG -Santa I-ORG I-ORG -15 O O -5 O O -7 O O -3 O O -15 O O -15 O O -22 O O -Ray B-ORG B-ORG -Valle I-ORG I-ORG -15 O O -5 O O -5 O O -5 O O -21 O O -19 O O -20 O O -Valencia B-ORG B-ORG -15 O O -MA B-LOC B-LOC -1996 O O -Spain B-LOC B-LOC -coach O O -Javier B-PER B-PER -Clement I-PER I-PER -has O O -added O O -un O O -Deportivo B-ORG B-ORG -Co I-ORG I-ORG -midfielder O O -Armand B-PER B-PER -Alvarez I-PER I-PER -to O O -his O O -squad O O -for O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -qualifier O O -against O O -Yugoslavia B-LOC B-LOC -on O O -December O O -14 O O -. O O -" O O -I O O -do O O -n O O -believe O O -it O O -. O O -I O O -thought O O -it O O -was O O -a O O -joke O O -, O O -" O O -said O O -Armand B-PER B-PER -who O O -replaces O O -injured O O -At B-ORG B-ORG -Madrid I-ORG I-ORG -play O O -Jose B-PER B-PER -Luis I-PER I-PER -Cam I-PER I-PER -. O O -S O O -- O O -FIFA B-ORG B-ORG -B O O -H B-PER B-PER -ST O O -B O O -W O B-PER -. O O -ROM B-LOC B-LOC -1996 O O -FIFA B-ORG B-ORG -chairman O O -Jo B-PER B-PER -Have I-PER I-PER -said O O -on O O -Friday O O -he O O -would O O -personally O O -present O O -AC B-ORG B-ORG -Milan I-ORG I-ORG -George I-PER B-PER -We I-PER I-PER -with O O -world O O -soccer O O -' O O -fair O O -play O O -award O O -despite O O -the O O -striker O O -' O O -attack O O -on O O -Porto B-ORG B-ORG -captain O O -Jorge B-PER B-PER -Costa I-PER I-PER -. O O -In O O -an O O -interview O O -with O O -the O O -Italian B-MISC B-MISC -newspaper O O -G B-ORG B-ORG -dell I-ORG I-ORG -Sport I-ORG I-ORG -, O O -he O O -was O O -quoted O O -as O O -saying O O -We B-PER B-PER -had O O -been O O -provoked O O -into O O -the O O -assault O O -which O O -left O O -Costa B-PER B-PER -with O O -a O O -broken O O -nose O O -. O O -" O O -FIFA B-ORG B-ORG -has O O -named O O -the O O -Liberia B-MISC B-MISC -for O O -its O O -1996 O O -Fair B-MISC O -Play I-MISC O -award O O -and O O -it O O -is O O -not O O -going O O -to O O -change O O -its O O -decision O O -, O O -" O O -Have B-PER B-PER -said O O -. O O -" O O -A O O -reaction O O -, O O -provoked O O -, O O -cannot O O -erase O O -10 O O -years O O -of O O -loyalty O O -everywhere O O -and O O -in O O -every O O -competition O O -. O O -" O O -I O O -will O O -be O O -happy O O -to O O -give O O -him O O -the O O -award O O -personally O O -on O O -January O O -20 O O -in O O -Lisbon B-LOC B-LOC -and O O -I O O -' O O -confident O O -that O O -Costa B-PER B-PER -himself O O -will O O -be O O -there O O -beside O O -me O O -on O O -that O O -day O O -to O O -shake O O -his O O -hand O O -. O O -" O O -We B-PER B-PER -was O O -suspended O O -for O O -one O O -match O O -by O O -UEFA B-ORG B-ORG -, O O -European B-MISC B-MISC -soccer O O -' O O -governing O O -body O O -, O O -pending O O -a O O -full O O -investigation O O -. O O -The O O -incident O O -took O O -place O O -in O O -the O O -players O O -' O O -tunnel O O -after O O -a O O -European B-MISC B-MISC -Champions I-MISC I-MISC -' I-MISC I-MISC -League I-MISC I-MISC -match O O -on O O -November O O -20 O O -. O O -We B-PER B-PER -has O O -admitted O O -head O O -butt O O -Costa B-PER B-PER -but O O -said O O -he O O -reacted O O -to O O -racist O O -ta O O -. O O -He O O -has O O -offered O O -to O O -a O O -if O O -Costa B-PER B-PER -acknowledge O O -the O O -pro O O -. O O -Costa B-PER B-PER -, O O -who O O -needed O O -surgery O O -on O O -his O O -nose O O -, O O -has O O -not O O -accepted O O -the O O -offer O O -and O O -was O O -reported O O -to O O -be O O -considering O O -su O O -We B-PER B-PER -. O O -We B-PER B-PER -served O O -out O O -his O O -suspension O O -during O O -Milan B-ORG B-ORG -' O O -2 O O -home O O -defeat O O -by O O -Rosen B-ORG B-ORG -of O O -Norway B-LOC B-LOC -on O O -Wednesday O O -. O O -The O O -defeat O O -put O O -the O O -Italians B-MISC B-MISC -out O O -of O O -the O O -Euro B-MISC B-MISC -Cup I-MISC I-MISC -. O O -G B-LOC O -W O O -T O O -MA O B-ORG -UN O I-ORG -FA O O -IN O O -AU B-LOC B-LOC -. O O -VI B-LOC B-LOC -1996 O O -Two O O -Manchester B-ORG B-ORG -United I-ORG I-ORG -soccer O O -fans O O -were O O -wounded O O -by O O -unidentified O O -gun O O -on O O -Friday O O -and O O -taken O O -to O O -hospital O O -in O O -the O O -Austrian B-MISC B-MISC -capital O O -, O O -police O O -said O O -. O O -" O O -The O O -four O O -B B-MISC B-MISC -were O O -shot O O -at O O -from O O -a O O -Mercedes B-MISC B-MISC -car O O -at O O -around O O -1 O O -a O O -, O O -" O O -a O O -spoke O O -told O O -Re B-ORG B-ORG -. O O -The O O -two O O -men O O -were O O -hit O O -in O O -the O O -p O O -and O O -leg O O -. O O -Police O O -said O O -their O O -lives O O -were O O -not O O -in O O -danger O O -. O O -The O O -fans O O -, O O -in O O -Austria B-LOC B-LOC -to O O -watch O O -their O O -team O O -play O O -Rapid B-ORG B-ORG -Vienna I-ORG I-ORG -last O O -Wednesday O O -, O O -may O O -have O O -been O O -involved O O -in O O -a O O -pub O O -bra O O -earlier O O -, O O -the O O -spoke O O -said O O -. O O -Manchester B-ORG B-ORG -United I-ORG I-ORG -won O O -2 O O -. O O -S O O -- O O -IT B-MISC B-MISC -F O O -D O O -MA O O -T O O -W O O -. O O -ROM B-LOC B-LOC -1996 O O -Italian B-MISC B-MISC -Serie I-MISC B-MISC -A I-MISC I-MISC -games O O -to O O -be O O -played O O -on O O -Sunday O O -( O O -league O O -positions O O -in O O -parent O O -, O O -all O O -kick O O -off O O -times O O -GM B-MISC B-MISC -) O O -: O O -Bologna B-ORG B-ORG -( O O -4 O O -) O O -v O O -Pi B-ORG B-ORG -( O O -13 O O -) O O -133 O O -Along O O -with O O -leaders O O -Vice B-ORG B-ORG -, O O -fourth O O -Bologna B-ORG B-ORG -represent O O -the O O -biggest O O -surprise O O -of O O -this O O -Italian B-MISC B-MISC -autumn O O -. O O -Led O O -as O O -usual O O -by O O -S B-MISC B-MISC -Ken B-PER B-PER -Anders I-PER I-PER -and O O -Russian B-MISC B-MISC -Igor B-PER B-PER -Ko I-PER I-PER -in O O -attack O O -, O O -Bologna B-ORG B-ORG -can O O -expect O O -a O O -tough O O -home O O -match O O -against O O -a O O -Pi B-ORG B-ORG -side O O -still O O -ex O O -after O O -a O O -3 O O -league O O -win O O -over O O -AC B-ORG B-ORG -Milan I-ORG I-ORG -last O O -Sunday O O -. O O -C B-ORG B-ORG -( O O -16 O O -) O O -v O O -Reg B-ORG B-ORG -( O O -18 O O -) O O -153 O O -C B-ORG B-ORG -start O O -favourite O O -in O O -this O O -relegation O O -scrap O O -following O O -draws O O -with O O -Na B-ORG B-ORG -and O O -Inter B-ORG B-ORG -in O O -last O O -two O O -out O O -but O O -will O O -be O O -without O O -suspended O O -Swiss B-MISC B-MISC -defender O O -Ramon B-PER B-PER -Vega I-PER I-PER -. O O -Bottom O O -team O O -Reg B-ORG B-ORG -are O O -also O O -without O O -a O O -suspended O O -defender O O -, O O -German B-MISC B-MISC -Diet B-PER B-PER -Be I-PER I-PER -. O O -Fi B-ORG B-ORG -( O O -10 O O -) O O -v O O -Peru B-ORG B-ORG -( O O -8 O O -) O O -133 O O -Fi B-ORG B-ORG -will O O -be O O -without O O -three O O -suspended O O -players O O -- O O -defenders O O -Daniel B-PER B-PER -Car I-PER I-PER -and O O -Lorenzo B-PER B-PER -Amor I-PER I-PER -and O O -midfielder O O -Emilia B-PER B-PER -Big I-PER I-PER -- O O -for O O -a O O -difficult O O -home O O -match O O -against O O -unpredictable O O -, O O -attack O O -Peru B-ORG B-ORG -led O O -by O O -in O O -C B-MISC B-MISC -striker O O -Milan B-PER B-PER -Rap I-PER I-PER -and O O -the O O -experienced O O -Faust B-PER B-PER -Pi I-PER I-PER -. O O -La B-ORG B-ORG -( O O -12 O O -) O O -v O O -AS B-ORG B-ORG -Roma I-ORG I-ORG -( O O -7 O O -) O O -1930 O O -Poor O O -man O O -' O O -Roman B-MISC B-MISC -derby O O -in O O -what O O -has O O -been O O -a O O -miserable O O -season O O -for O O -both O O -Rome B-LOC B-LOC -teams O O -, O O -already O O -eliminated O O -from O O -the O O -Italian B-MISC B-MISC -and O O -UEFA B-MISC B-MISC -Cups I-MISC I-MISC -. O O -La B-ORG B-ORG -have O O -injury O O -doubts O O -about O O -striker O O -Pier B-PER B-PER -C I-PER I-PER -, O O -Czech B-MISC B-LOC -midfielder O O -Pavel B-PER B-PER -Ned I-PER I-PER -and O O -defender O O -Paolo B-PER B-PER -Negro I-PER I-PER -, O O -while O O -Roma B-ORG B-ORG -present O O -a O O -full O O -strength O O -side O O -led O O -by O O -Argentine B-MISC B-MISC -Abel B-PER B-PER -Ba I-PER I-PER -, O O -Marco B-PER B-PER -Del I-PER I-PER -and O O -Francesco B-PER B-PER -To I-PER I-PER -in O O -attack O O -. O O -AC B-ORG B-ORG -Milan I-ORG I-ORG -( O O -9 O O -) O O -v O O -U B-ORG B-ORG -( O O -11 O O -) O O -133 O O -Can O O -Milan B-ORG B-ORG -sink O O -any O O -further O O -? O O -Following O O -their O O -mid O O -Champions B-MISC B-MISC -' I-MISC I-MISC -League I-MISC I-MISC -elimination O O -by O O -Norwegian B-MISC B-MISC -side O O -Rosen B-ORG B-ORG -, O O -a O O -morale O O -win O O -is O O -badly O O -needed O O -. O O -Liberia B-MISC B-MISC -striker O O -George B-PER B-PER -We I-PER I-PER -makes O O -a O O -welcome O O -return O O -for O O -Milan B-ORG B-ORG -alongside O O -Roberto B-PER B-PER -Ba I-PER I-PER -, O O -with O O -Montenegrin B-MISC B-MISC -De B-PER B-PER -Sa I-PER I-PER -in O O -midfield O O -. O O -Good O O -news O O -for O O -Milan B-ORG B-ORG -is O O -that O O -U B-ORG B-ORG -' O O -German B-MISC B-MISC -striker O O -Oliver B-PER B-PER -B I-PER I-PER -is O O -out O O -through O O -injury O O -. O O -Na B-ORG B-ORG -( O O -5 O O -) O O -v O O -Verona B-ORG B-ORG -( O O -17 O O -) O O -133 O O -In O O -Na B-ORG B-ORG -should O O -prove O O -too O O -strong O O -for O O -second O O -from O O -bottom O O -Verona B-ORG B-ORG -despite O O -the O O -absence O O -of O O -their O O -suspended O O -Argentine B-MISC B-MISC -defender O O -Roberto B-PER B-PER -A I-PER I-PER -. O O -Verona B-ORG B-ORG -' O O -slim O O -chances O O -have O O -been O O -further O O -reduced O O -by O O -a O O -knee O O -injury O O -to O O -their O O -experienced O O -midfielder O O -E B-PER B-PER -Co I-PER I-PER -. O O -Parma B-ORG B-ORG -( O O -14 O O -) O O -v O O -At B-ORG B-ORG -( O O -15 O O -) O O -133 O O -Parma B-ORG B-ORG -may O O -field O O -new O O -signing O O -, O O -C B-MISC B-MISC -midfielder O O -Mario B-PER B-PER -Stan I-PER I-PER -, O O -in O O -an O O -attempt O O -to O O -lift O O -a O O -miserable O O -season O O -which O O -has O O -seen O O -them O O -go O O -without O O -a O O -win O O -since O O -a O O -1 O O -triumph O O -over O O -C B-ORG B-ORG -eight O O -weeks O O -ago O O -. O O -Parma B-ORG B-ORG -' O O -French B-MISC B-MISC -midfielder O O -Daniel B-PER B-PER -Bravo I-PER I-PER -and O O -defender O O -F B-PER B-PER -Can I-PER I-PER -are O O -suspended O O -while O O -Argentine B-MISC B-MISC -N B-PER B-PER -Sen I-PER I-PER -is O O -out O O -through O O -injury O O -. O O -At B-ORG B-ORG -look O O -to O O -Fi B-PER B-PER -In I-PER I-PER -, O O -scorer O O -of O O -eight O O -goals O O -. O O -Sam B-ORG B-ORG -( O O -6 O O -) O O -v O O -Juventus B-ORG B-ORG -( O O -3 O O -) O O -133 O O -All O O -Juventus B-ORG B-ORG -field O O -their O O -most O O -recent O O -signing O O -, O O -Portuguese B-MISC B-MISC -defender O O -Di B-PER B-PER -, O O -while O O -Alessandro B-PER B-PER -Del I-PER I-PER -Pier I-PER I-PER -and O O -C B-PER B-MISC -Al B-PER B-PER -Bo I-PER I-PER -lead O O -the O O -attack O O -. O O -The O O -new O O -world O O -club O O -champions O O -may O O -prove O O -too O O -strong O O -for O O -a O O -Sam B-ORG B-ORG -side O O -led O O -by O O -captain O O -Roberto B-PER B-PER -Man I-PER I-PER -but O O -missing O O -injured O O -French B-MISC B-MISC -midfielder O O -Pierre B-PER B-PER -Lai I-PER I-PER -. O O -Vice B-ORG B-ORG -( O O -1 O O -) O O -v O O -Inter B-ORG B-ORG -( O O -2 O O -) O O -133 O O -Not O O -exactly O O -a O O -clash O O -of O O -the O O -t O O -but O O -an O O -in O O -match O O -nonetheless O O -. O O -Full O O -strength O O -Vice B-ORG B-ORG -, O O -led O O -by O O -Uruguayan B-MISC B-MISC -Marcel B-PER B-PER -O I-PER I-PER -, O O -may O O -continue O O -their O O -surprise O O -run O O -at O O -the O O -top O O -against O O -an O O -Inter B-ORG B-ORG -side O O -that O O -has O O -been O O -less O O -than O O -impressive O O -in O O -three O O -successive O O -home O O -draws O O -. O O -Inter B-ORG B-ORG -will O O -be O O -without O O -suspended O O -French B-MISC B-MISC -defender O O -Jo B-PER B-PER -Anglo I-PER I-PER -and O O -injured O O -Chilean B-MISC B-MISC -striker O O -Ivan B-PER B-PER -Z I-PER I-PER -. O O -BA O O -- O O -EU B-MISC B-MISC -R O O -. O O -BR B-LOC B-LOC -1996 O O -Re O O -of O O -a O O -Euro B-MISC B-MISC -basketball O O -match O O -on O O -Thursday O O -: O O -Group O O -B O O -In O O -Cha B-LOC B-LOC -: O O -Cha B-ORG B-ORG -( O O -Belgium B-LOC B-LOC -) O O -75 O O -E B-ORG B-ORG -Madrid I-ORG I-ORG -( O O -Spain B-LOC B-LOC -) O O -82 O O -( O O -34 O O -) O O -Leading O O -scorer O O -: O O -Cha B-ORG B-ORG -- O O -Eric B-PER B-PER -C I-PER I-PER -18 O O -, O O -Ron B-PER B-PER -Ellis I-PER I-PER -18 O O -, O O -Jacques B-PER B-PER -St I-PER I-PER -14 O O -E B-ORG B-ORG -- O O -Harper B-PER B-PER -Williams I-PER I-PER -20 O O -, O O -Chad B-PER B-PER -Thompson I-PER I-PER -17 O O -, O O -Juan B-PER B-PER -Ai I-PER I-PER -14 O O -Group O O -D O O -In O O -Belgrade B-LOC B-LOC -: O O -Part B-ORG B-ORG -Belgrade I-ORG I-ORG -( O O -Yugoslavia B-LOC B-LOC -) O O -78 O O -Kind B-ORG B-ORG -Bologna I-ORG I-ORG -( O O -Italy B-LOC B-LOC -) O O -70 O O -( O O -halftime O O -44 O O -) O O -Leading O O -scorer O O -: O O -Part B-ORG B-ORG -- O O -B B-LOC B-LOC -, O O -India B-LOC B-LOC -1996 O O -World O O -number O O -two O O -Rodney B-PER B-PER -E I-PER I-PER -moved O O -within O O -sight O O -of O O -his O O -fifth O O -title O O -of O O -the O O -year O O -on O O -Friday O O -when O O -he O O -hurried O O -in O O -only O O -40 O O -minutes O O -to O O -the O O -final O O -of O O -the O O -richest O O -squash O O -tournament O O -outside O O -the O O -World B-MISC B-MISC -Open I-MISC I-MISC -, O O -the O O -$ O O -105 O O -Ma B-MISC B-MISC -International I-MISC I-MISC -. O O -The O O -Australian B-MISC B-MISC -brushed O O -aside O O -un O O -English B-MISC B-MISC -Mark B-PER B-PER -Cairns I-PER I-PER -15 O O -15 O O -15 O O -. O O -Top O O -E B-PER B-PER -now O O -meets O O -title O O -Peter B-PER B-PER -Nico I-PER I-PER -of O O -Scotland B-LOC B-LOC -who O O -over O O -Simon B-PER B-PER -Park I-PER I-PER -of O O -England B-LOC B-LOC -15 O O -15 O O -15 O O -. O O -Nico B-PER B-PER -was O O -full O O -of O O -praise O O -for O O -his O O -opponent O O -who O O -has O O -battled O O -test O O -cancer O O -to O O -return O O -to O O -the O O -circuit O O -. O O -" O O -He O O -' O O -a O O -remarkably O O -courage O O -player O O -, O O -" O O -said O O -Nico B-PER B-PER -. O O -S O O -- O O -MA B-MISC B-MISC -IN O I-MISC -SE O O -R O O -. O O -B B-LOC B-LOC -, O O -India B-LOC B-LOC -1996 O O -Results O O -of O O -semifinals O O -in O O -the O O -Ma B-MISC B-MISC -International I-MISC I-MISC -squash O O -tournament O O -on O O -Friday O O -: O O -Peter B-PER B-PER -Nico I-PER I-PER -( O O -Scotland B-LOC B-LOC -) O O -beat O O -Simon B-PER B-PER -Park I-PER I-PER -( O O -England B-LOC B-LOC -) O O -15 O O -15 O O -15 O O -Rodney B-PER B-PER -E I-PER I-PER -( O O -Australia B-LOC B-LOC -) O O -beat O O -Mark B-PER B-PER -Cairns I-PER I-PER -( O O -England B-LOC B-LOC -) O O -15 O O -15 O O -15 O O -. O O -Final O O -: O O -Nico B-PER B-PER -v O O -E B-ORG B-PER -, O O -on O O -Saturday O O -. O O -G B-ORG O -K O O -F O O -IN O O -S B-ORG B-MISC -' O O -Z B-ORG B-MISC -PR O O -. O O -D B-LOC B-PER -, O O -South B-LOC B-LOC -Africa I-LOC I-LOC -1996 O O -At O O -least O O -four O O -people O O -have O O -been O O -shot O O -dead O O -in O O -two O O -suspected O O -political O O -attacks O O -in O O -South B-LOC B-LOC -Africa I-LOC I-LOC -' O O -volatile O O -Z B-MISC B-MISC -heart O O -, O O -police O O -said O O -on O O -Friday O O -. O O -A O O -police O O -spokesman O O -said O O -two O O -youths O O -believed O O -to O O -be O O -supporters O O -of O O -President O O -Nelson B-PER B-PER -Man I-PER I-PER -' O O -African B-ORG B-ORG -National I-ORG I-ORG -Congress I-ORG I-ORG -( O O -AN B-ORG B-ORG -) O O -had O O -been O O -killed O O -when O O -unknown O O -gun O O -opened O O -fire O O -at O O -the O O -rural O O -settlement O O -of O O -I B-LOC B-LOC -on O O -K B-LOC B-LOC -province O O -' O O -south O O -coast O O -on O O -Thursday O O -night O O -. O O -The O O -victims O O -were O O -18 O O -and O O -20 O O -, O O -he O O -said O O -, O O -adding O O -one O O -other O O -youth O O -had O O -been O O -wounded O O -in O O -the O O -shooting O O -. O O -In O O -another O O -attack O O -, O O -also O O -on O O -the O O -province O O -' O O -south O O -coast O O -on O O -Thursday O O -night O O -, O O -two O O -men O O -were O O -shot O O -dead O O -near O O -Um B-LOC B-LOC -. O O -" O O -We O O -suspect O O -that O O -these O O -killings O O -are O O -linked O O -to O O -politics O O -, O O -" O O -spokesman O O -Ba B-PER B-PER -Na I-PER I-PER -told O O -Re B-ORG B-ORG -. O O -There O O -had O O -been O O -no O O -arrests O O -. O O -The O O -killings O O -came O O -just O O -hours O O -after O O -violence O O -monitors O O -said O O -they O O -were O O -not O O -optimistic O O -of O O -a O O -peaceful O O -f O O -season O O -in O O -K B-LOC B-LOC -and O O -pointed O O -the O O -south O O -coast O O -region O O -where O O -18 O O -people O O -were O O -massacre O O -last O O -Christmas O O -as O O -one O O -of O O -potential O O -hot O O -spots O O -. O O -They O O -said O O -the O O -recent O O -l O O -in O O -political O O -feud O O -could O O -be O O -upset O O -as O O -thousands O O -of O O -migrant O O -workers O O -, O O -some O O -tense O O -with O O -g O O -br O O -in O O -the O O -cities O O -and O O -keen O O -to O O -settle O O -old O O -scores O O -, O O -flock O O -back O O -to O O -their O O -home O O -villages O O -. O O -More O O -than O O -14 O O -people O O -have O O -lost O O -their O O -lives O O -in O O -over O O -a O O -decade O O -of O O -political O O -turf O O -wars O O -between O O -the O O -AN B-ORG B-ORG -and O O -Z B-MISC B-MISC -Chief O O -Man B-PER B-PER -But I-PER I-PER -' O O -In B-ORG B-ORG -Freedom I-ORG I-ORG -Party I-ORG I-ORG -in O O -the O O -province O O -. O O -H B-PER B-PER -PR O O -C B-MISC B-MISC -N O O -AL O B-PER -AS O O -F O O -. O O -K B-PER B-PER -G I-PER I-PER -PR B-LOC B-LOC -1996 O O -Czech B-MISC B-LOC -President O O -V B-PER B-PER -Have I-PER I-PER -on O O -Friday O O -welcomed O O -the O O -appointment O O -of O O -Madeleine B-PER B-PER -Al I-PER I-PER -, O O -who O O -is O O -of O O -Czech B-MISC B-LOC -extraction O O -, O O -as O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -' O O -first O O -woman O O -Secretary O O -of O O -State O O -. O O -In O O -a O O -statement O O -Have B-PER B-PER -, O O -who O O -is O O -recovering O O -from O O -cancer O O -surgery O O -, O O -said O O -: O O -" O O -Madeleine B-PER B-PER -Al I-PER I-PER -is O O -a O O -distinguished O O -friend O O -, O O -a O O -tested O O -diplomat O O -, O O -and O O -a O O -true O O -American B-MISC B-MISC -of O O -fine O O -origins O O -. O O -" O O -" O O -I O O -look O O -forward O O -to O O -continuing O O -our O O -good O O -relations O O -. O O -with O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -and O O -with O O -the O O -first O O -woman O O -ever O O -to O O -hold O O -the O O -position O O -of O O -Secretary O O -of O O -State O O -. O O -I O O -wish O O -her O O -well O O -, O O -" O O -Have B-PER B-PER -said O O -in O O -a O O -statement O O -to O O -Re B-ORG B-ORG -. O O -Have B-PER B-PER -, O O -who O O -helped O O -lead O O -the O O -" O O -velvet O O -revolution O O -" O O -that O O -ou O O -the O O -Communist B-MISC B-MISC -regime O O -in O O -Prague B-LOC B-LOC -in O O -1989 O O -, O O -invited O O -Al B-PER B-PER -, O O -then O O -working O O -for O O -a O O -private O O -foreign O O -policy O O -think O O -tank O O -, O O -to O O -advise O O -his O O -new O O -democratic O O -government O O -in O O -1990 O O -. O O -Have B-PER B-PER -had O O -a O O -small O O -ma O O -t O O -removed O O -from O O -his O O -lung O O -on O O -Monday O O -and O O -is O O -recovering O O -in O O -hospital O O -. O O -Al B-PER B-PER -, O O -born O O -Marie B-PER B-PER -Ko I-PER I-PER -to O O -a O O -Czechoslovak B-MISC B-MISC -diplomat O O -in O O -1937 O O -, O O -fled O O -with O O -her O O -family O O -to O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -after O O -the O O -Communists B-MISC B-MISC -came O O -to O O -power O O -in O O -a O O -coup O O -in O O -1948 O O -. O O -As O O -an O O -academic O O -, O O -Al B-PER B-PER -studied O O -and O O -lectured O O -on O O -Europe B-LOC B-LOC -' O O -20th O O -century O O -problems O O -before O O -becoming O O -U B-LOC B-LOC -ambassador O O -to O O -the O O -United B-ORG B-ORG -Nations I-ORG I-ORG -. O O -Czech B-MISC B-LOC -diplomat O O -, O O -seeking O O -to O O -have O O -their O O -country O O -included O O -in O O -the O O -expected O O -expansion O O -of O O -NATO B-ORG B-ORG -, O O -praised O O -the O O -selection O O -of O O -Al B-PER B-PER -, O O -known O O -to O O -be O O -a O O -strong O O -supporter O O -of O O -alliance O O -' O O -integration O O -of O O -former O O -So B-MISC B-MISC -countries O O -. O O -" O O -The O O -nomination O O -. O O -is O O -a O O -clear O O -signal O O -that O O -one O O -key O O -of O O -the O O -lines O O -of O O -foreign O O -policy O O -will O O -be O O -the O O -strengthening O O -of O O -the O O -trans B-MISC B-MISC -cooperation O O -, O O -a O O -creation O O -of O O -strategic O O -partnership O O -between O O -Europe B-LOC B-LOC -and O O -the O O -US B-LOC B-LOC -, O O -" O O -Foreign O O -Minister O O -Josef B-PER B-PER -Z I-PER I-PER -told O O -Re B-ORG B-ORG -. O O -" O O -( O O -Al B-PER B-PER -) O O -is O O -a O O -convinced O O -advocate O O -of O O -NATO B-ORG B-ORG -en O O -and O O -of O O -stab O O -of O O -security O O -structures O O -. O O -" O O -Czech B-MISC B-LOC -ambassador O O -to O O -the O O -United B-ORG B-ORG -Nations I-ORG I-ORG -, O O -Ka B-PER B-PER -Ko I-PER I-PER -, O O -told O O -the O O -daily O O -M B-ORG B-ORG -Front I-ORG I-ORG -D I-ORG I-ORG -that O O -Al B-PER B-PER -" O O -is O O -a O O -little O O -light O O -in O O -our O O -diplomatic O O -heaven O O -, O O -" O O -but O O -warned O O -against O O -expecting O O -her O O -to O O -ex O O -any O O -influence O O -in O O -favour O O -of O O -the O O -Czech B-MISC B-MISC -. O O -RA B-PER B-ORG -ROM B-ORG I-ORG -A O O -H O O -AT O O -4 O O -PM O O -. O O -B B-LOC B-LOC -1996 O O -Radio B-ORG B-ORG -Romania I-ORG I-ORG -news O O -headlines O O -: O O -* O O -The O O -Democratic B-ORG B-MISC -Convention I-ORG I-MISC -signed O O -an O O -agreement O O -on O O -government O O -and O O -parliamentary O O -support O O -with O O -its O O -coalition O O -partners O O -the O O -Social B-ORG B-ORG -Democratic I-ORG I-ORG -Union I-ORG I-ORG -and O O -the O O -Hungarian B-ORG B-ORG -Democratic I-ORG I-ORG -Union I-ORG I-ORG -( O O -U B-ORG B-ORG -) O O -. O O -The O O -ceremony O O -was O O -attended O O -by O O -President O O -Emil B-PER B-PER -Constantine I-PER I-PER -. O O -* O O -The O O -three O O -parties O O -in O O -the O O -government O O -coalition O O -have O O -committed O O -themselves O O -to O O -a O O -real O O -reform O O -of O O -Romania B-LOC B-LOC -' O O -economy O O -, O O -Constantine B-PER B-PER -said O O -after O O -the O O -ceremony O O -. O O -* O O -The O O -U B-ORG B-ORG -wants O O -to O O -contribute O O -to O O -social O O -reform O O -and O O -economic O O -revival O O -in O O -Romania B-LOC B-LOC -, O O -union O O -leader O O -Mark B-PER B-PER -Bel I-PER I-PER -said O O -. O O -* O O -The O O -international O O -airport O O -in O O -Tim B-LOC B-LOC -and O O -the O O -domestic O O -airports O O -in O O -Ara B-LOC B-LOC -, O O -Or B-LOC B-LOC -and O O -Si B-LOC B-LOC -were O O -closed O O -due O O -to O O -fog O O -. O O -- O O -Bucharest B-ORG B-ORG -News I-ORG I-ORG -40 O O -312 O O -C B-MISC B-MISC -VI O O -SE O O -W O O -DE O O -AT O O -PA B-MISC O -CO O O -. O O -PR B-LOC B-LOC -1996 O O -Saturday O O -' O O -national O O -congress O O -of O O -the O O -ruling O O -Czech B-ORG B-ORG -Civic I-ORG I-ORG -Democratic I-ORG I-ORG -Party I-ORG I-ORG -( O O -O B-ORG B-ORG -) O O -will O O -discuss O O -making O O -the O O -party O O -more O O -efficient O O -and O O -transparent O O -, O O -Foreign O O -Minister O O -and O O -O B-ORG B-ORG -vice O O -Josef B-PER B-PER -Z I-PER I-PER -, O O -said O O -on O O -Friday O O -. O O -" O O -Modern O O -and O O -more O O -pro O O -of O O -the O O -party O O -' O O -structure O O -, O O -having O O -financing O O -of O O -the O O -party O O -be O O -more O O -transparent O O -. O O -are O O -absolutely O O -fundamental O O -, O O -" O O -Z B-PER B-PER -, O O -who O O -is O O -also O O -vice O O -in O O -the O O -government O O -, O O -told O O -Re B-ORG B-ORG -. O O -He O O -said O O -after O O -June O O -general O O -elections O O -in O O -which O O -the O O -ruling O O -three O O -coalition O O -lost O O -its O O -parliamentary O O -majority O O -, O O -the O O -O B-ORG B-ORG -executive O O -, O O -led O O -by O O -Prime O O -Minister O O -V B-PER B-PER -Klaus I-PER I-PER -, O O -had O O -developed O O -proposals O O -on O O -these O O -subjects O O -to O O -present O O -at O O -the O O -congress O O -on O O -Saturday O O -in O O -the O O -Czech B-MISC B-LOC -second O O -city O O -B B-LOC B-LOC -. O O -" O O -I O O -am O O -convinced O O -, O O -that O O -the O O -congress O O -will O O -tackle O O -these O O -proposals O O -, O O -" O O -he O O -said O O -. O O -The O O -O B-ORG B-ORG -, O O -a O O -party O O -in O O -which O O -Klaus B-PER B-PER -often O O -tries O O -to O O -em O O -the O O -style O O -of O O -former O O -British B-MISC B-MISC -Prime O O -Minister O O -Margaret B-PER B-PER -Thatcher I-PER I-PER -, O O -has O O -been O O -in O O -control O O -of O O -Czech B-MISC B-LOC -politics O O -since O O -winning O O -general O O -elections O O -in O O -1992 O O -. O O -Z B-ORG B-PER -in O O -the O O -summer O O -led O O -calls O O -for O O -the O O -party O O -and O O -its O O -leadership O O -to O O -listen O O -to O O -more O O -diverse O O -opinions O O -, O O -a O O -thin O O -criticism O O -of O O -Klaus B-PER B-PER -who O O -has O O -spear O O -the O O -country O O -' O O -post B-MISC B-MISC -economic O O -reforms O O -. O O -The O O -party O O -, O O -led O O -by O O -the O O -vigorously O O -Klaus B-PER B-PER -, O O -took O O -32 O O -of O O -81 O O -seats O O -after O O -late O O -November O O -runoff O O -elections O O -to O O -the O O -new O O -upper O O -house O O -of O O -Czech B-MISC B-LOC -parliament O O -. O O -But O O -after O O -the O O -first O O -round O O -vote O O -a O O -week O O -before O O -, O O -the O O -O B-ORG B-ORG -had O O -the O O -potential O O -to O O -win O O -as O O -many O O -79 O O -seats O O -. O O -Klaus B-PER B-PER -and O O -his O O -coalition O O -lost O O -its O O -majority O O -in O O -parliament O O -in O O -June O O -lower O O -house O O -elections O O -after O O -the O O -left O O -opposition O O -consolidated O O -, O O -putting O O -the O O -centre O O -Social B-ORG B-MISC -Democrats I-ORG I-MISC -in O O -a O O -strong O O -second O O -position O O -. O O -- O O -Prague B-ORG B-ORG -News I-ORG I-ORG -42 O O -P O B-LOC -GO O O -M O O -F O O -P O O -S O B-MISC -ACC O O -. O O -Marc B-PER B-PER -G I-PER I-PER -WA B-LOC B-LOC -1996 O O -Poland B-LOC B-LOC -said O O -on O O -Friday O O -that O O -Swiss B-MISC B-MISC -bank O O -accounts O O -, O O -which O O -in O O -many O O -cases O O -belonged O O -to O O -Polish B-MISC B-MISC -Jews I-MISC B-MISC -who O O -died O O -in O O -the O O -Holocaust B-MISC B-MISC -, O O -were O O -used O O -in O O -debt O O -settlements O O -between O O -the O O -two O O -countries O O -after O O -the O O -World B-MISC B-MISC -War I-MISC I-MISC -Two I-MISC I-MISC -. O O -Foreign O O -Minister O O -Darius B-PER B-PER -Rosa I-PER I-PER -, O O -un O O -first O O -findings O O -of O O -a O O -special O O -government O O -commission O O -, O O -said O O -that O O -in O O -1970s O O -the O O -then O O -communist O O -Poland B-LOC B-LOC -received O O -460 O O -Swiss B-MISC B-MISC -f O O -from O O -the O O -accounts O O -. O O -" O O -In O O -1970s O O -, O O -Poland B-LOC B-LOC -received O O -from O O -un O O -accounts O O -in O O -Switzerland B-LOC B-LOC -a O O -sum O O -of O O -460 O O -f O O -. O O -What O O -was O O -its O O -right O O -( O O -to O O -the O O -money O O -) O O -. O O -do O O -not O O -know O O -, O O -" O O -Rosa B-PER B-PER -told O O -a O O -news O O -conference O O -. O O -Switzerland B-LOC B-LOC -stands O O -accused O O -by O O -Senator O O -Al B-PER B-PER -D I-PER I-PER -, O O -chairman O O -of O O -the O O -powerful O O -U B-ORG B-ORG -Senate I-ORG I-ORG -Banking I-ORG I-ORG -Committee I-ORG I-ORG -, O O -of O O -agreeing O O -to O O -give O O -money O O -to O O -Poland B-LOC B-LOC -from O O -un O O -bank O O -accounts O O -of O O -Polish B-MISC B-MISC -citizens O O -, O O -as O O -part O O -of O O -an O O -accord O O -on O O -com O O -Swiss B-MISC B-MISC -nationals O O -whose O O -assets O O -had O O -been O O -seized O O -in O O -communist O O -Poland B-LOC B-LOC -. O O -Many O O -of O O -these O O -citizens O O -were O O -Jews B-MISC B-MISC -murdered O O -during O O -the O O -war O O -, O O -when O O -Nazi B-MISC B-MISC -German B-MISC B-MISC -invaders O O -killed O O -most O O -of O O -Poland B-LOC B-LOC -' O O -3 O O -million O O -Jews B-MISC B-MISC -. O O -Rosa B-PER B-PER -did O O -not O O -say O O -whether O O -the O O -payment O O -in O O -1970s O O -was O O -part O O -of O O -the O O -1949 O O -agreement O O -between O O -Warsaw B-LOC B-LOC -and O O -Switzerland B-LOC B-LOC -on O O -compensation O O -to O O -Swiss B-MISC B-MISC -citizens O O -whose O O -assets O O -were O O -seized O O -by O O -the O O -Soviet B-MISC B-MISC -communists O O -authorities O O -after O O -World B-MISC B-MISC -War I-MISC I-MISC -Two I-MISC I-MISC -. O O -" O O -I O O -expect O O -that O O -the O O -commission O O -will O O -finish O O -gathering O O -information O O -within O O -two O O -to O O -three O O -weeks O O -and O O -then O O -more O O -details O O -will O O -be O O -provided O O -, O O -" O O -Rosa B-PER B-PER -said O O -. O O -Rosa B-PER B-PER -confirmed O O -that O O -the O O -1949 O O -agreement O O -had O O -provided O O -for O O -granting O O -Switzerland B-LOC B-LOC -about O O -53 O O -million O O -f O O -and O O -most O O -of O O -this O O -sum O O -was O O -re O O -with O O -coal O O -exports O O -. O O -He O O -said O O -, O O -however O O -, O O -that O O -Switzerland B-LOC B-LOC -did O O -get O O -about O O -16 O O -f O O -from O O -the O O -so O O -" O O -dead O O -accounts O O -" O O -as O O -part O O -of O O -the O O -compensation O O -. O O -" O O -About O O -16 O O -f O O -were O O -seized O O -from O O -accounts O O -of O O -four O O -or O O -five O O -Polish B-MISC B-MISC -citizens O O -, O O -whose O O -data O O -we O O -do O O -not O O -precisely O O -know O O -. O O -The O O -issue O O -is O O -of O O -moral O O -and O O -legal O O -nature O O -, O O -because O O -its O O -financial O O -significance O O -is O O -small O O -, O O -" O O -Rosa B-PER B-PER -said O O -. O O -Under O O -pressure O O -from O O -international O O -Jewish B-MISC B-MISC -organisations O O -, O O -Swiss B-MISC B-MISC -government O O -has O O -devised O O -a O O -plan O O -to O O -pay O O -out O O -millions O O -of O O -dollars O O -in O O -un O O -bank O O -accounts O O -as O O -a O O -con O O -gesture O O -toward O O -Holocaust B-MISC B-MISC -victims O O -. O O -The O O -conservative O O -Radical B-MISC B-ORG -Democrats I-ORG I-ORG -( O O -F B-ORG B-ORG -) O O -have O O -said O O -they O O -would O O -ask O O -parliament O O -next O O -week O O -to O O -order O O -Swiss B-MISC B-MISC -banks O O -to O O -put O O -some O O -40 O O -million O O -Swiss B-MISC B-MISC -f O O -( O O -$ O O -31 O O -million O O -) O O -in O O -dormant O O -wealth O O -into O O -a O O -fund O O -ear O O -for O O -Jewish B-MISC B-MISC -groups O O -and O O -charitable O O -organisations O O -. O O -But O O -Swiss B-MISC B-MISC -banks O O -and O O -the O O -country O O -' O O -Jewish B-MISC B-MISC -community O O -voiced O O -doubts O O -whether O O -the O O -plan O O -would O O -work O O -. O O -IN O B-MISC -SE O O -NO O O -B O O -97 O O -NE O O -R O O -. O O -Steven B-PER B-PER -Si I-PER I-PER -WA B-LOC B-LOC -1996 O O -Polish B-MISC B-MISC -br O O -Z B-PER B-ORG -' O O -1996 O O -profit O O -s O O -may O O -last O O -into O O -next O O -year O O -due O O -in O O -part O O -to O O -he O O -de O O -charges O O -, O O -but O O -recent O O -high O O -investment O O -should O O -help O O -the O O -firm O O -defend O O -its O O -10 O O -market O O -share O O -, O O -the O O -firm O O -' O O -chief O O -executive O O -said O O -. O O -Company O O -President O O -Jean B-PER B-PER -van I-PER I-PER -Box I-PER I-PER -told O O -Re B-ORG B-ORG -in O O -an O O -interview O O -on O O -Friday O O -that O O -the O O -firm O O -, O O -whose O O -net O O -profit O O -fell O O -77 O O -percent O O -in O O -the O O -first O O -10 O O -months O O -of O O -1996 O O -despite O O -a O O -30 O O -rise O O -in O O -sales O O -, O O -might O O -only O O -post O O -slightly O O -better O O -profits O O -in O O -1997 O O -before O O -having O O -a O O -chance O O -to O O -make O O -a O O -more O O -significant O O -turn O O -. O O -So O O -far O O -this O O -year O O -Z B-ORG B-ORG -, O O -whose O O -full O O -name O O -is O O -Z B-ORG B-ORG -Pi I-ORG I-ORG -w I-ORG I-ORG -Z I-ORG I-ORG -SA I-ORG I-ORG -, O O -has O O -net O O -six O O -million O O -z O O -on O O -sales O O -of O O -224 O O -million O O -z O O -. O O -It O O -has O O -produced O O -1 O O -million O O -he O O -. O O -Van B-PER B-PER -Box I-ORG I-PER -would O O -not O O -say O O -how O O -much O O -higher O O -1997 O O -profits O O -or O O -market O O -share O O -could O O -be O O -but O O -said O O -sales O O -of O O -leading O O -Polish B-MISC B-MISC -br O O -should O O -rise O O -as O O -the O O -country O O -' O O -young O O -urban O O -professionals O O -gradually O O -switch O O -from O O -vodka O O -to O O -beer O O -. O O -" O O -The O O -perspective O O -on O O -growth O O -is O O -such O O -that O O -reasonably O O -we O O -can O O -think O O -that O O -somewhere O O -between O O -65 O O -and O O -80 O O -litre O O -per O O -year O O -is O O -certainly O O -reach O O -, O O -" O O -van B-PER O -Box I-PER B-PER -said O O -on O O -Polish B-MISC B-MISC -per O O -beer O O -consumption O O -, O O -currently O O -around O O -40 O O -litre O O -. O O -He O O -said O O -the O O -65 O O -level O O -could O O -be O O -reached O O -in O O -the O O -next O O -ten O O -years O O -and O O -make O O -Poland B-LOC B-LOC -, O O -with O O -its O O -40 O O -population O O -, O O -Europe B-LOC B-LOC -' O O -third O O -largest O O -beer O O -market O O -after O O -Germany B-LOC B-LOC -and O O -Britain B-LOC B-LOC -. O O -Van B-PER B-PER -Box I-PER I-PER -said O O -Poland B-LOC B-LOC -' O O -top O O -five O O -br O O -, O O -which O O -produce O O -about O O -55 O O -percent O O -of O O -the O O -country O O -' O O -beer O O -, O O -could O O -all O O -raise O O -market O O -share O O -as O O -some O O -of O O -the O O -numerous O O -small O O -br O O -fall O O -to O O -competition O O -from O O -the O O -large O O -br O O -with O O -foreign O O -investors O O -. O O -Z B-ORG B-ORG -is O O -31 O O -owned O O -by O O -He B-ORG B-ORG -while O O -Carl B-ORG B-ORG -has O O -the O O -same O O -amount O O -in O O -Ok B-LOC B-ORG -. O O -Earlier O O -this O O -year O O -South B-ORG B-ORG -African I-ORG I-ORG -Brewer I-ORG I-ORG -Ltd I-ORG I-ORG -( O O -SA B-ORG B-ORG -) O O -bought O O -strategic O O -stakes O O -in O O -the O O -un O O -Le B-ORG B-ORG -and O O -Ty B-ORG B-ORG -br O O -, O O -which O O -together O O -hold O O -more O O -than O O -20 O O -percent O O -of O O -the O O -market O O -, O O -and O O -Australia B-LOC B-LOC -' O O -B B-ORG B-ORG -B I-ORG I-ORG -has O O -a O O -controlling O O -stake O O -in O O -Poland B-LOC B-LOC -' O O -large O O -t O O -brewery O O -, O O -El B-ORG B-ORG -Company I-ORG I-ORG -Ltd I-ORG I-ORG -( O O -E B-ORG B-ORG -) O O -. O O -Van B-PER B-PER -Box I-PER I-PER -said O O -the O O -tough O O -competition O O -had O O -prevented O O -Z B-PER B-ORG -from O O -raising O O -prices O O -in O O -line O O -with O O -inflation O O -, O O -which O O -had O O -added O O -to O O -the O O -pressure O O -on O O -the O O -firm O O -' O O -margins O O -. O O -He O O -said O O -advertising O O -costs O O -would O O -also O O -increase O O -in O O -the O O -fight O O -for O O -market O O -share O O -. O O -But O O -he O O -said O O -the O O -company O O -' O O -investment O O -of O O -more O O -than O O -$ O O -100 O O -million O O -already O O -this O O -decade O O -, O O -largely O O -in O O -production O O -, O O -would O O -help O O -position O O -it O O -to O O -compete O O -with O O -such O O -competitors O O -as O O -br O O -from O O -the O O -neighbouring O O -Czech B-LOC B-LOC -Republic I-LOC I-LOC -. O O -Some O O -analysts O O -say O O -cheaper O O -but O O -high O O -Czech B-MISC B-LOC -imports O O -could O O -invade O O -Poland B-LOC B-LOC -once O O -ta O O -for O O -CE B-ORG B-ORG -countries O O -are O O -lifted O O -in O O -1998 O O -, O O -but O O -van B-PER O -Box I-PER B-PER -says O O -such O O -a O O -threat O O -might O O -be O O -exaggerated O O -despite O O -the O O -Czech B-MISC B-LOC -beer O O -market O O -' O O -over O O -. O O -" O O -I O O -think O O -Polish B-MISC B-MISC -consumers O O -in O O -general O O -are O O -quite O O -proud O O -of O O -their O O -beers O O -- O O -and O O -I O O -' O O -speaking O O -about O O -all O O -the O O -brands O O -- O O -and O O -as O O -we O O -make O O -good O O -beers O O -. O O -I O O -think O O -that O O -this O O -fi O O -to O O -our O O -beers O O -is O O -a O O -factor O O -which O O -can O O -limit O O -the O O -Czech B-MISC B-LOC -beers O O -, O O -" O O -he O O -said O O -. O O -Van B-PER B-PER -Box I-PER I-PER -said O O -Z B-ORG B-ORG -had O O -its O O -eye O O -on O O -Ok B-ORG B-ORG -, O O -which O O -has O O -said O O -it O O -would O O -start O O -producing O O -Carl B-ORG B-ORG -beer O O -next O O -year O O -, O O -but O O -that O O -Z B-ORG B-ORG -' O O -potential O O -production O O -of O O -He B-ORG B-ORG -was O O -a O O -medium O O -possibility O O -rather O O -than O O -a O O -short O O -one O O -. O O -He O O -said O O -his O O -firm O O -would O O -be O O -better O O -off O O -concentrating O O -on O O -its O O -leading O O -brand O O -, O O -Z B-MISC B-ORG -Full I-MISC B-MISC -Light I-MISC I-MISC -, O O -which O O -accounts O O -for O O -85 O O -percent O O -of O O -sales O O -and O O -is O O -the O O -country O O -' O O -largest O O -brand O O -. O O -" O O -You O O -will O O -not O O -win O O -the O O -war O O -of O O -the O O -Polish B-MISC B-MISC -beer O O -market O O -with O O -imported O O -international O O -brands O O -, O O -" O O -van B-PER O -Box I-PER B-PER -said O O -, O O -adding O O -that O O -He B-ORG B-ORG -would O O -remain O O -an O O -up O O -import O O -in O O -Poland B-LOC B-LOC -. O O -Van B-PER B-PER -Box I-PER I-PER -also O O -said O O -Z B-ORG B-ORG -would O O -be O O -boost O O -by O O -its O O -recent O O -shed O O -of O O -soft O O -drinks O O -which O O -only O O -accounted O O -for O O -about O O -three O O -percent O O -of O O -the O O -firm O O -' O O -overall O O -sales O O -and O O -for O O -which O O -7 O O -million O O -z O O -in O O -provisions O O -had O O -already O O -been O O -made O O -. O O -- O O -Warsaw B-ORG B-ORG -News I-ORG I-ORG -+ O O -22 O O -65 O O -97 O O -H B-ORG B-PER -H O O -T O O -A O O -CO O O -W O O -. O O -PR B-LOC B-LOC -1996 O O -Doctors O O -performed O O -an O O -emergency O O -t O O -to O O -help O O -Czech B-MISC B-LOC -President O O -V B-PER B-PER -Have I-PER I-PER -breathe O O -after O O -cancer O O -surgery O O -on O O -his O O -lungs O O -earlier O O -this O O -week O O -, O O -a O O -spokesman O O -said O O -on O O -Friday O O -. O O -He O O -said O O -that O O -the O O -procedure O O -to O O -insert O O -a O O -device O O -into O O -Have B-PER B-PER -' O O -throat O O -, O O -done O O -after O O -his O O -breathing O O -worse O O -on O O -Thursday O O -, O O -had O O -helped O O -, O O -and O O -the O O -president O O -' O O -condition O O -significantly O O -improved O O -. O O -" O O -A O O -worse O O -in O O -the O O -president O O -' O O -lung O O -functions O O -took O O -place O O -yesterday O O -, O O -" O O -presidential O O -spokesman O O -La B-PER B-PER -Space I-PER I-PER -said O O -in O O -a O O -statement O O -. O O -" O O -A O O -t O O -was O O -performed O O -and O O -supportive O O -breathing O O -was O O -installed O O -through O O -the O O -help O O -of O O -a O O -breathing O O -device O O -, O O -" O O -he O O -said O O -. O O -" O O -After O O -these O O -steps O O -, O O -the O O -president O O -' O O -condition O O -sign O O -improved O O -. O O -" O O -Have B-PER B-PER -has O O -been O O -recovering O O -from O O -surgery O O -on O O -Monday O O -which O O -removed O O -a O O -small O O -ma O O -t O O -and O O -half O O -of O O -his O O -right O O -lung O O -. O O -Doctors O O -after O O -the O O -operation O O -said O O -that O O -they O O -had O O -caught O O -the O O -cancer O O -early O O -, O O -and O O -that O O -Have B-PER B-PER -could O O -fully O O -recover O O -from O O -the O O -surgery O O -within O O -six O O -weeks O O -. O O -His O O -spokesman O O -said O O -on O O -Thursday O O -that O O -Have B-PER B-PER -, O O -60 O O -and O O -a O O -heavy O O -smoke O O -, O O -had O O -also O O -developed O O -a O O -slight O O -case O O -of O O -pneumonia O O -in O O -the O O -left O O -lung O O -. O O -UK B-LOC B-MISC -open O O -skies O O -talks O O -end O O -, O O -no O O -date O O -to O O -restart O O -. O O -L B-LOC B-LOC -1996 O O -The O O -UK B-ORG B-LOC -Department I-ORG B-ORG -of I-ORG I-ORG -Transport I-ORG I-ORG -on O O -Friday O O -said O O -that O O -the O O -latest O O -round O O -of O O -" O O -open O O -skies O O -" O O -talks O O -with O O -the O O -U B-LOC B-LOC -had O O -ended O O -with O O -no O O -deal O O -on O O -liberal O O -the O O -trans O O -flight O O -market O O -and O O -no O O -date O O -set O O -for O O -when O O -talks O O -would O O -restart O O -. O O -A O O -spokesman O O -for O O -the O O -D B-ORG B-ORG -told O O -Re B-ORG B-ORG -" O O -We O O -have O O -had O O -talks O O -towards O O -concluding O O -a O O -new O O -air O O -service O O -agreement O O -which O O -would O O -produce O O -liberal O O -. O O -useful O O -progress O O -was O O -made O O -on O O -a O O -number O O -of O O -issues O O -, O O -but O O -not O O -all O O -. O O -No O O -date O O -has O O -been O O -set O O -for O O -further O O -talks O O -. O O -" O O -Tam B-ORG B-ORG -Tim I-ORG I-ORG -at O O -$ O O -15 O O -in O O -London B-LOC B-LOC -. O O -L B-LOC B-LOC -1996 O O -PT B-ORG B-ORG -Tam I-ORG I-ORG -Tim I-ORG I-ORG -closed O O -at O O -$ O O -15 O O -per O O -G B-ORG O -in O O -London B-LOC B-LOC -on O O -Friday O O -. O O -It O O -recorded O O -the O O -day O O -' O O -low O O -of O O -$ O O -15 O O -and O O -the O O -day O O -' O O -high O O -of O O -$ O O -15 O O -. O O -It O O -closed O O -at O O -$ O O -15 O O -on O O -Thursday O O -. O O -One O O -Global B-ORG O -De I-ORG O -Re I-ORG O -represents O O -10 O O -common O O -shares O O -. O O -- O O -Jakarta B-LOC B-LOC -news O O -+ O O -38 O O -Tel B-ORG B-ORG -at O O -$ O O -35 O O -in O O -London B-LOC B-LOC -. O O -L B-LOC B-LOC -1996 O O -PT B-ORG B-ORG -Tel I-ORG I-ORG -Indonesia I-ORG I-ORG -( O O -Tel B-ORG B-ORG -) O O -closed O O -at O O -$ O O -35 O O -in O O -London B-LOC B-LOC -on O O -Friday O O -. O O -It O O -recorded O O -the O O -day O O -' O O -low O O -of O O -$ O O -34 O O -and O O -the O O -day O O -' O O -high O O -of O O -$ O O -35 O O -. O O -Its O O -previous O O -close O O -on O O -Thursday O O -as O O -$ O O -35 O O -. O O -One O O -AD B-ORG O -represents O O -20 O O -ordinary O O -shares O O -- O O -Jakarta B-LOC B-LOC -news O O -+ O O -38 O O -. O O -Woman O O -charged O O -over O O -N B-LOC B-LOC -Ireland I-LOC I-LOC -arms O O -find O O -. O O -B B-LOC B-LOC -1996 O O -A O O -woman O O -was O O -charged O O -on O O -Friday O O -with O O -terrorist O O -offences O O -after O O -three O O -Irish B-ORG B-ORG -Republican I-ORG I-ORG -Army I-ORG I-ORG -mortar O O -bombs O O -were O O -found O O -in O O -a O O -Belfast B-LOC B-LOC -house O O -, O O -police O O -said O O -. O O -Police O O -said O O -the O O -bombs O O -were O O -found O O -hidden O O -with O O -in O O -and O O -ammunition O O -that O O -were O O -blocked O O -up O O -behind O O -a O O -kitchen O O -wall O O -. O O -The O O -35 O O -woman O O -was O O -charged O O -with O O -possession O O -of O O -explosives O O -with O O -intent O O -to O O -end O O -life O O -and O O -making O O -a O O -house O O -available O O -for O O -the O O -purpose O O -of O O -terrorism O O -, O O -police O O -said O O -. O O -She O O -will O O -appear O O -in O O -court O O -on O O -Saturday O O -. O O -Her O O -name O O -was O O -not O O -released O O -. O O -Security O O -forces O O -said O O -the O O -bombs O O -may O O -have O O -been O O -intended O O -for O O -use O O -in O O -a O O -pre O O -bombing O O -campaign O O -by O O -the O O -guerrilla O O -group O O -that O O -is O O -battling O O -to O O -ou O O -Britain B-LOC B-LOC -from O O -Northern B-LOC B-LOC -Ireland I-LOC I-LOC -. O O -Britain B-LOC B-LOC -sets O O -conditions O O -to O O -clear O O -American B-MISC B-MISC -alliance O O -. O O -Edna B-PER B-PER -Fe I-PER I-PER -L B-LOC B-LOC -1996 O O -The O O -British B-MISC B-MISC -government O O -warned O O -Friday O O -that O O -it O O -would O O -refer O O -the O O -proposed O O -trans B-MISC B-MISC -alliance O O -between O O -British B-ORG B-ORG -Airways I-ORG I-ORG -P I-ORG I-ORG -and O O -American B-ORG B-ORG -Airlines I-ORG I-ORG -to O O -Britain B-LOC B-LOC -' O O -Mon B-ORG B-ORG -and I-ORG I-ORG -Me I-ORG I-ORG -Commission I-ORG I-ORG -unless O O -the O O -carriers O O -com O O -with O O -a O O -number O O -of O O -conditions O O -. O O -Trade O B-ORG -and I-ORG I-ORG -Industry I-ORG I-ORG -Secretary O O -Ian B-PER B-PER -Lang I-PER I-PER -added O O -that O O -even O O -if O O -the O O -conditions O O -were O O -met O O -by O O -both O O -airlines O O -, O O -final O O -clearance O O -would O O -hi O O -on O O -an O O -open O O -skies O O -deal O O -between O O -Britain B-LOC B-LOC -and O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -to O O -liberal O O -trans B-MISC B-MISC -air O O -traffic O O -, O O -which O O -would O O -create O O -greater O O -competition O O -on O O -the O O -routes O O -. O O -Lang B-PER B-PER -said O O -he O O -supported O O -conditions O O -proposed O O -by O O -Britain B-LOC B-LOC -' O O -Office B-ORG B-ORG -of I-ORG I-ORG -Fair I-ORG I-ORG -Trading I-ORG I-ORG -, O O -which O O -was O O -asked O O -to O O -examine O O -the O O -case O O -last O O -month O O -. O O -" O O -I O O -agree O O -. O O -that O O -without O O -suitable O O -undertaking O O -the O O -alliance O O -would O O -be O O -likely O O -to O O -lead O O -to O O -a O O -significant O O -loss O O -of O O -actual O O -and O O -potential O O -passengers O O -, O O -on O O -those O O -routes O O -where O O -BA B-ORG B-ORG -and O O -AA B-ORG B-ORG -currently O O -compete O O -and O O -for O O -all O O -passengers O O -on O O -the O O -trans B-MISC B-MISC -market O O -route O O -between O O -the O O -UK B-LOC B-LOC -and O O -U B-LOC B-LOC -, O O -" O O -he O O -said O O -. O O -His O O -comments O O -came O O -just O O -minutes O O -after O O -the O O -latest O O -set O O -of O O -open O O -skies O O -talks O O -ended O O -in O O -London B-LOC B-LOC -with O O -no O O -deal O O -signed O O -. O O -Industry O O -sources O O -said O O -there O O -was O O -no O O -new O O -date O O -for O O -fresh O O -talks O O -and O O -blamed O O -the O O -dead O O -on O O -uncertainty O O -over O O -whether O O -the O O -British B-MISC B-MISC -Airways I-MISC I-MISC -deal O O -would O O -be O O -cleared O O -. O O -The O O -conditions O O -for O O -clearance O O -of O O -the O O -alliance O O -were O O -that O O -British B-ORG B-ORG -Airways I-ORG I-ORG -and O O -American B-ORG B-ORG -drop O O -168 O O -slots O O -at O O -London B-LOC B-LOC -Heath I-LOC I-LOC -airport O O -, O O -the O O -busiest O O -in O O -Europe B-LOC B-LOC -. O O -American B-MISC B-ORG -' O O -parent O O -, O O -AM B-ORG B-ORG -Corp I-ORG I-ORG -, O O -said O O -it O O -did O O -not O O -view O O -the O O -terms O O -as O O -a O O -" O O -deal O O -break O O -. O O -" O O -However O O -, O O -it O O -called O O -the O O -conditions O O -" O O -more O O -severe O O -" O O -than O O -those O O -imposed O O -by O O -other O O -regulatory O O -authorities O O -on O O -similar O O -airline O O -alliances O O -. O O -British B-ORG B-ORG -Airways I-ORG I-ORG -' O O -initial O O -response O O -was O O -that O O -" O O -un O O -dive O O -of O O -slots O O -is O O -unprecedented O O -and O O -if O O -done O O -it O O -must O O -be O O -on O O -the O O -basis O O -of O O -fair O O -market O O -value O O -. O O -" O O -It O O -added O O -that O O -it O O -would O O -be O O -" O O -prepared O O -to O O -take O O -reasonable O O -steps O O -to O O -assist O O -the O O -introduction O O -of O O -additional O O -competition O O -. O O -" O O -The O O -government O O -also O O -wants O O -British B-ORG B-ORG -Airways I-ORG I-ORG -to O O -drop O O -a O O -clause O O -in O O -its O O -agreement O O -with O O -USA B-ORG B-ORG -that O O -bars O O -it O O -from O O -competing O O -on O O -trans B-MISC B-MISC -routes O O -, O O -and O O -said O O -both O O -British B-ORG B-ORG -Airways I-ORG I-ORG -and O O -American B-ORG B-ORG -should O O -be O O -prepared O O -to O O -reduce O O -services O O -on O O -the O O -London B-LOC B-LOC -to O O -Dallas B-LOC B-LOC -Worth O I-LOC -route O O -in O O -the O O -event O O -that O O -a O O -new O O -en O O -wishes O O -to O O -enter O O -. O O -It O O -also O O -suggested O O -losing O O -some O O -slots O O -on O O -the O O -London B-LOC B-MISC -route O O -. O O -The O O -Office B-ORG B-ORG -of I-ORG I-ORG -Fair I-ORG I-ORG -Trade I-ORG I-ORG -called O O -for O O -British B-ORG B-ORG -Airways I-ORG I-ORG -/ O O -American B-ORG B-ORG -to O O -allow O O -third O O -access O O -to O O -their O O -joint O O -frequent O O -fly O O -programme O O -where O O -the O O -applicant O O -does O O -not O O -have O O -access O O -to O O -an O O -equivalent O O -programme O O -. O O -Lang B-PER B-PER -said O O -responses O O -should O O -be O O -made O O -to O O -the O O -Office B-ORG B-ORG -of I-ORG I-ORG -Fair I-ORG I-ORG -Trading I-ORG I-ORG -by O O -Jan O O -10 O O -, O O -1997 O O -. O O -Me O O -oil O O -products O O -mostly O O -lower O O -as O O -El B-MISC B-ORG -strike O O -ends O O -. O O -L B-LOC B-LOC -1996 O O -Mediterranean B-MISC O -oil O O -products O O -were O O -steady O O -to O O -mostly O O -lower O O -on O O -Friday O O -after O O -El B-ORG B-ORG -re O O -workers O O -voted O O -to O O -end O O -their O O -nine O O -strike O O -. O O -Gas O O -oil O O -erased O O -Thursday O O -' O O -gains O O -, O O -p O O -$ O O -5 O O -a O O -ton O O -in O O -line O O -with O O -the O O -screen O O -. O O -Volume O O -was O O -very O O -thin O O -and O O -market O O -remained O O -long O O -, O O -with O O -premium O O -down O O -$ O O -1 O O -at O O -about O O -high O O -c O O -quotes O O -+ O O -basis O O -Genoa B-LOC B-ORG -. O O -" O O -The O O -sharp O O -moves O O -on O O -the O O -screen O O -make O O -everyone O O -nervous O O -, O O -" O O -a O O -trader O O -said O O -. O O -Trade O O -were O O -discussed O O -in O O -0 O O -, O O -0 O O -and O O -one O O -percent O O -heating O O -oil O O -into O O -Syria B-LOC B-LOC -and O O -Lebanon B-LOC B-LOC -and O O -there O O -were O O -fresh O O -in O O -from O O -France B-LOC B-LOC -and O O -Spain B-LOC B-LOC -for O O -low O O -su O O -diesel O O -. O O -Interest O O -remains O O -focus O O -on O O -a O O -tender O O -by O O -India B-LOC B-LOC -for O O -a O O -second O O -purchase O O -of O O -high O O -speed O O -diesel O O -for O O -January O O -delivery O O -. O O -Fuel O O -oil O O -lost O O -ground O O -sharply O O -with O O -weaker O O -crude O O -, O O -but O O -also O O -suffered O O -from O O -some O O -pricing O O -pressure O O -. O O -High O O -su O O -cracked O O -fuel O O -lost O O -about O O -$ O O -3 O O -to O O -$ O O -109 O O -f O O -Me B-ORG O -with O O -several O O -cargo O O -threatening O O -to O O -over O O -the O O -market O O -. O O -The O O -chance O O -of O O -material O O -heading O O -north O O -, O O -talked O O -earlier O O -this O O -week O O -, O O -may O O -be O O -in O O -j O O -now O O -since O O -American B-MISC B-MISC -fuel O O -oil O O -is O O -expected O O -to O O -head O O -trans O O -following O O -out O O -at O O -two O O -co O O -units O O -in O O -the O O -U B-LOC B-LOC -. O O -Up O O -to O O -165 O O -tonnes O O -of O O -fuel O O -will O O -have O O -to O O -find O O -a O O -new O O -home O O -and O O -with O O -the O O -a O O -from O O -the O O -U B-LOC B-LOC -to O O -Europe B-LOC B-LOC -open O O -Rotterdam B-LOC B-LOC -is O O -a O O -prime O O -candidate O O -. O O -Low O O -su O O -prices O O -were O O -lower O O -with O O -c O O -Me B-MISC O -p O O -in O O -the O O -mid O O -to O O -low O O -$ O O -140 O O -. O O -Gas O O -prices O O -fell O O -after O O -striking O O -El B-ORG B-ORG -re O O -workers O O -voted O O -to O O -go O O -back O O -to O O -work O O -, O O -traders O O -said O O -. O O -But O O -an O O -open O O -a O O -to O O -the O O -U B-LOC B-LOC -and O O -tight O O -Italian B-MISC B-MISC -supplies O O -after O O -El B-PER B-ORG -scooped O O -up O O -Me B-MISC O -material O O -over O O -the O O -last O O -week O O -, O O -continued O O -to O O -under O O -prices O O -into O O -next O O -week O O -. O O -New O O -men O O -scare O O -hits O O -Britain B-LOC B-LOC -. O O -L B-LOC B-LOC -1996 O O -A O O -boy O O -has O O -died O O -from O O -men O O -and O O -a O O -girl O O -from O O -the O O -same O O -school O O -has O O -contracted O O -the O O -disease O O -in O O -the O O -second O O -such O O -scare O O -to O O -hit O O -Britain B-LOC B-LOC -in O O -as O O -many O O -weeks O O -, O O -health O O -authorities O O -said O O -on O O -Friday O O -. O O -The O O -16 O O -who O O -attended O O -Sale B-LOC B-ORG -Grammar I-LOC I-ORG -School I-LOC I-ORG -in O O -the O O -northern O O -England B-LOC B-LOC -city O O -of O O -Manchester B-LOC B-LOC -died O O -less O O -than O O -a O O -day O O -after O O -becoming O O -ill O O -. O O -The O O -15 O O -girl O O -is O O -also O O -suffering O O -from O O -the O O -disease O O -and O O -hospital O O -officials O O -described O O -her O O -condition O O -as O O -serious O O -. O O -" O O -At O O -the O O -moment O O -there O O -is O O -no O O -evidence O O -the O O -two O O -cases O O -are O O -linked O O -. O O -However O O -, O O -we O O -are O O -assuming O O -they O O -are O O -as O O -a O O -pre O O -for O O -the O O -time O O -being O O -, O O -" O O -a O O -spoke O O -said O O -. O O -The O O -more O O -than O O -1 O O -students O O -at O O -the O O -school O O -are O O -being O O -given O O -anti O O -as O O -a O O -pre O O -. O O -Wales B-LOC B-LOC -g O O -with O O -its O O -own O O -cluster O O -of O O -men O O -cases O O -on O O -a O O -university O O -campus O O -in O O -Cardiff B-LOC B-LOC -. O O -At O O -least O O -two O O -people O O -have O O -died O O -and O O -hundreds O O -have O O -been O O -v O O -in O O -an O O -effort O O -to O O -contain O O -the O O -virus O O -. O O -In O O -Scotland B-LOC B-LOC -, O O -eight O O -people O O -have O O -died O O -and O O -hundreds O O -more O O -are O O -fighting O O -a O O -widespread O O -food O O -outbreak O O -. O O -A O O -health O O -authority O O -spoke O O -said O O -78 O O -people O O -suspected O O -of O O -having O O -the O O -disease O O -, O O -including O O -64 O O -confirmed O O -cases O O -, O O -were O O -still O O -being O O -treated O O -. O O -Three O O -were O O -listed O O -in O O -poor O O -condition O O -. O O -More O O -than O O -290 O O -people O O -have O O -reported O O -symptoms O O -in O O -Lana B-LOC B-LOC -county O O -, O O -the O O -worst O O -area O O -, O O -since O O -the O O -outbreak O O -first O O -came O O -to O O -light O O -after O O -people O O -ate O O -ta O O -meat O O -pie O O -at O O -a O O -pension O O -' O O -lunch O O -. O O -Major B-PER B-PER -' O O -office O B-MISC -still O O -have O O -majority O O -. O O -L B-LOC B-LOC -1996 O O -British B-MISC B-MISC -Prime O O -Minister O O -John B-PER B-PER -Major I-PER I-PER -' O O -office O O -said O O -on O O -Friday O O -that O O -rebel O O -Conservative B-MISC B-MISC -MP O O -Sir O O -John B-PER B-PER -Go I-PER I-PER -had O O -not O O -" O O -resigned O O -the O O -whip O O -" O O -( O O -quit O O -the O O -parliamentary O O -party O O -) O O -and O O -the O O -government O O -still O O -had O O -a O O -majority O O -in O O -the O O -65 O O -parliament O O -. O O -" O O -He O O -( O O -Go B-PER B-PER -) O O -is O O -the O O -right O O -not O O -to O O -cooperate O O -, O O -but O O -he O O -has O O -not O O -resigned O O -the O O -whip O O -. O O -The O O -government O O -still O O -has O O -a O O -majority O O -, O O -" O O -a O O -spokesman O O -from O O -Major B-PER B-PER -' O O -office O O -in O O -Down B-LOC B-LOC -Street I-LOC I-LOC -said O O -. O O -Go B-PER B-PER -' O O -office O O -said O O -later O O -the O O -MP O O -would O O -not O O -feel O O -himself O O -obliged O O -to O O -vote O O -with O O -the O O -government O O -. O O -He O O -said O O -at O O -one O O -point O O -during O O -a O O -press O O -conference O O -: O O -" O O -I O O -have O O -seen O O -my O O -whip O O -( O O -party O O -manager O O -) O O -for O O -next O O -week O O -which O O -, O O -of O O -course O O -, O O -does O O -n O O -mean O O -very O O -much O O -to O O -me O O -now O O -. O O -" O O -Before O O -Go B-PER B-PER -' O O -statement O O -, O O -Major B-PER B-PER -had O O -a O O -one O O -majority O O -in O O -the O O -65 O O -House B-ORG B-ORG -of I-ORG I-ORG -Commons I-ORG I-ORG -lower O O -house O O -of O O -parliament O O -. O O -In O O -his O O -formal O O -statement O O -, O O -Go B-PER B-PER -said O O -: O O -" O O -I O O -am O O -today O O -withdrawing O O -my O O -cooperation O O -from O O -the O O -government O O -and O O -shall O O -not O O -treat O O -the O O -" O O -whip O O -' O O -as O O -either O O -a O O -summon O O -to O O -attend O O -the O O -House B-ORG B-ORG -of I-ORG I-ORG -Commons I-ORG I-ORG -or O O -as O O -placing O O -me O O -under O O -any O O -obligation O O -to O O -vote O O -as O O -advised O O -. O O -" O O -Go B-PER B-PER -resigned O O -over O O -a O O -hospital O O -closure O O -in O O -his O O -constituency O O -. O O -Electronic B-ORG B-ORG -Data I-ORG I-ORG -bags O O -flight O O -data O O -contract O O -. O O -L B-LOC B-LOC -1996 O O -Information O O -technology O O -firm O O -Electronic B-ORG B-ORG -Data I-ORG I-ORG -Systems I-ORG I-ORG -said O O -on O O -Friday O O -it O O -had O O -bag O O -a O O -contract O O -for O O -the O O -first O O -air O O -traffic O O -control O O -project O O -being O O -funded O O -under O O -the O O -Private B-ORG O -Finance I-ORG O -Initiative I-ORG O -. O O -In O O -a O O -statement O O -, O O -E B-ORG B-ORG -said O O -the O O -contract O O -would O O -be O O -in O O -the O O -region O O -of O O -50 O O -million O O -s O O -. O O -The O O -contract O O -involved O O -up O O -the O O -flight O O -data O O -processing O O -system O O -at O O -the O O -Ocean B-LOC B-LOC -Control I-ORG I-LOC -Centre I-LOC I-LOC -in O O -Pre B-LOC B-LOC -in O O -south O O -west O O -Scotland B-LOC B-LOC -for O O -National B-ORG B-ORG -Air I-ORG I-ORG -Traffic I-ORG I-ORG -Services I-ORG I-ORG -Ltd I-ORG I-ORG -( O O -N B-ORG B-ORG -) O O -, O O -subsidiary O O -of O O -the O O -Civil B-ORG B-ORG -Aviation I-ORG I-ORG -Authority I-ORG I-ORG -. O O -The O O -system O O -is O O -responsible O O -for O O -the O O -control O O -of O O -aircraft O O -flying O O -trans O O -routes O O -from O O -Europe B-LOC B-LOC -and O O -North B-LOC B-LOC -America I-LOC I-LOC -. O O -The O O -system O O -, O O -which O O -would O O -use O O -satellite O O -technology O O -, O O -is O O -scheduled O O -to O O -enter O O -service O O -in O O -2000 O O -. O O -- O O -London B-ORG B-ORG -News I-ORG I-ORG -+ O O -77 O O -R B-ORG B-ORG -- O O -Cricket O O -- O O -Play O O -restart O O -in O O -Australia B-LOC B-MISC -Indies I-LOC I-MISC -match O O -. O O -ME B-LOC B-LOC -1996 O O -Play O O -restart O O -in O O -the O O -first O O -World B-MISC B-MISC -Series I-MISC I-MISC -limited O O -overs O O -match O O -between O O -West B-LOC B-LOC -Indies I-LOC I-LOC -and O O -Australia B-LOC B-LOC -after O O -a O O -rain O O -delay O O -of O O -50 O O -minutes O O -on O O -Friday O O -. O O -West B-LOC B-LOC -Indies I-LOC I-LOC -resumed O O -their O O -innings O O -on O O -53 O O -for O O -two O O -with O O -opener O O -She B-PER B-PER -Campbell I-PER I-PER -on O O -25 O O -and O O -Shi B-PER B-PER -Chan I-PER I-PER -10 O O -. O O -Rain O O -earlier O O -delayed O O -the O O -start O O -of O O -play O O -by O O -30 O O -minutes O O -. O O -- O O -Sydney B-ORG B-ORG -News I-ORG I-ORG -61 O O -93 O O -Cricket O O -- O O -Pakistan B-LOC B-LOC -beat O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -by O O -46 O O -runs O O -. O O -S B-LOC B-LOC -, O O -Pakistan B-LOC B-LOC -1996 O O -Pakistan B-LOC B-LOC -beat O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -by O O -46 O O -runs O O -on O O -Friday O O -to O O -take O O -an O O -un O O -2 O O -lead O O -in O O -the O O -three O O -one O O -series O O -. O O -Score O O -: O O -Pakistan B-LOC B-LOC -27 O O -, O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -231 O O -Manitoba B-ORG B-ORG -Po I-ORG I-ORG -forward O O -contract O O -PM O O -prices O O -- O O -Dec O O -6 O O -. O O -W B-LOC B-LOC -1996 O O -Manitoba B-ORG B-ORG -Po I-ORG I-ORG -closing O O -forward O O -contract O O -prices O O -in O O -Canadian B-MISC B-MISC -dollars O O -per O O -hundred O O -lbs O O -( O O -C B-MISC O -) O O -for O O -Dec O O -6 O O -including O O -minimum O O -guaranteed O O -price O O -- O O -CO O O -PR O O -C O O -PM O O -C O O -PM O O -C O O -RA O O -D O O -PM O O -C O O -F O O -MI O O -AT O O -123 O O -CS O O -Feb O O -97 O O -79 O O -79 O O -75 O O -77 O O -Mar O O -97 O O -76 O O -76 O O -72 O O -73 O O -Apr O O -97 O O -74 O O -74 O O -( O O -( O O -Winnipeg B-LOC B-LOC -bureau O O -204 O O -) O O -) O O -Canadian B-ORG B-MISC -West I-ORG O -Coast I-ORG O -V I-ORG O -Lo I-ORG O -- O O -CW B-ORG O -. O O -W B-LOC B-LOC -1996 O O -The O O -Canadian B-ORG B-ORG -W I-ORG I-ORG -Board I-ORG I-ORG -reported O O -six O O -ships O O -loading O O -, O O -10 O O -waiting O O -and O O -four O O -due O O -at O O -the O O -Canadian B-LOC B-MISC -West I-LOC O -Coast I-LOC O -, O O -as O O -of O O -Friday O O -. O O -The O O -longest O O -wait O O -to O O -load O O -on O O -the O O -West B-LOC O -Coast I-LOC O -was O O -13 O O -days O O -. O O -Two O O -ship O O -loaded O O -in O O -Thunder B-LOC B-LOC -Bay I-LOC I-LOC -, O O -one O O -waited O O -and O O -seven O O -were O O -due O O -. O O -Two O O -ships O O -loaded O O -on O O -the O O -East B-LOC O -Coast I-LOC O -, O O -three O O -waited O O -to O O -load O O -, O O -six O O -were O O -due O O -. O O -Port B-ORG O -Lo I-ORG O -Waiting O O -Vancouver B-ORG B-LOC -5 O O -7 O O -Prince B-ORG B-LOC -Rupert I-ORG I-LOC -1 O O -3 O O -( O O -( O O -Gilbert B-PER B-PER -Le I-PER I-PER -G I-PER I-PER -204 O O -94 O O -35 O O -) O O -) O O -New B-LOC B-LOC -York I-LOC I-LOC -time O O -fixtures O O -- O O -Dec O O -6 O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -No O O -new O O -fixtures O O -reported O O -from O O -New B-LOC B-LOC -York I-LOC I-LOC -. O O -- O O -New B-ORG B-ORG -York I-ORG I-ORG -Co I-ORG I-ORG -Des I-ORG I-ORG -+ O O -212 O O -85 O O -1640 O O -New B-LOC B-LOC -York I-LOC I-LOC -coal O O -/ O O -ore O O -/ O O -scrap O O -fixtures O O -- O O -Dec O O -6 O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -OR B-ORG O -- O O -Maritime B-ORG B-MISC -Queen I-ORG I-MISC -70 O O -tonnes O O -Dam B-ORG B-LOC -/ O O -Ka B-LOC B-LOC -20 O O -$ O O -5 O O -fi O O -35 O O -/ O O -30 O O -China B-ORG B-ORG -Steel I-ORG I-ORG -. O O -- O O -New B-ORG B-ORG -York I-ORG I-ORG -Co I-ORG I-ORG -Des I-ORG O -+ O O -212 O O -85 O O -1640 O O -Clean O O -tanker O O -fixtures O O -and O O -en O O -- O O -232 O O -GM B-ORG B-MISC -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -F O O -- O O -W B-LOC O -H O O -- O O -Dani B-PER B-MISC -28 O O -16 O O -Car B-ORG B-LOC -/ O O -up O O -W O O -Mo B-ORG B-ORG -. O O -- O O -New B-ORG B-ORG -York I-ORG I-ORG -Co I-ORG I-ORG -Des I-ORG O -, O O -212 O O -Dirty O O -tanker O O -fixtures O O -and O O -en O O -- O O -231 O O -GM B-ORG B-MISC -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -MI B-LOC O -/ O O -R B-LOC B-LOC -SE I-LOC I-LOC -- O O -Thai B-MISC B-MISC -Resource O I-MISC -264 O O -31 O O -Ra B-LOC B-LOC -Tan I-LOC I-LOC -/ O O -Red B-LOC B-LOC -Sea I-LOC I-LOC -W O O -Mo B-ORG B-ORG -. O O -ME O B-MISC -- O O -Lu B-ORG B-MISC -I I-ORG I-MISC -85 O O -25 O O -Sid B-ORG B-LOC -K I-LOC I-LOC -/ O O -Augusta B-LOC B-LOC -W O O -Ex B-ORG B-ORG -. O O -S B-ORG B-MISC -139 O O -17 O O -Sid B-ORG B-LOC -K I-LOC I-LOC -/ O O -Augusta B-LOC B-LOC -W O O -Ex B-ORG B-ORG -. O O -Me B-ORG B-MISC -77 O O -17 O O -Baja B-LOC B-LOC -/ O O -F B-LOC B-LOC -W O O -Ex B-ORG B-ORG -. O O -- O O -New B-ORG B-ORG -York I-ORG I-ORG -Co I-ORG I-ORG -Des I-ORG I-ORG -+ O O -212 O O -85 O O -1640 O O -NYC B-ORG B-MISC -Jan O O -re O O -has O O -its O O -1st O O -Euro B-MISC B-MISC -floating O O -rate O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -New B-LOC B-LOC -York I-LOC I-LOC -City I-LOC I-LOC -on O O -Friday O O -said O O -that O O -it O O -planned O O -a O O -$ O O -77 O O -million O O -re O O -for O O -January O O -that O O -will O O -include O O -its O O -first O O -floating O O -rate O O -issue O O -of O O -taxa O O -debt O O -for O O -European B-MISC B-MISC -investors O O -. O O -A O O -city O O -official O O -, O O -who O O -declined O O -to O O -be O O -named O O -, O O -explained O O -that O O -Goldman B-ORG B-ORG -, I-ORG I-ORG -Sachs B-ORG I-ORG -, O O -which O O -this O O -summer O O -was O O -demo O O -to O O -the O O -second O O -tier O O -of O O -the O O -s O O -, O O -proposed O O -the O O -floating O O -rate O O -issue O O -and O O -as O O -a O O -result O O -was O O -promoted O O -to O O -book O O -runner O O -for O O -this O O -offering O O -. O O -By O O -selling O O -the O O -floating O O -rate O O -debt O O -, O O -the O O -city O O -hopes O O -to O O -establish O O -a O O -bench O O -, O O -the O O -city O O -official O O -said O O -, O O -adding O O -that O O -it O O -needed O O -a O O -large O O -deal O O -to O O -accomplish O O -this O O -objective O O -. O O -The O O -city O O -in O O -late O O -June O O -sold O O -its O O -first O O -issue O O -of O O -Euro B-MISC B-MISC -, O O -a O O -strategy O O -that O O -it O O -says O O -saved O O -it O O -$ O O -500 O O -in O O -interest O O -costs O O -, O O -and O O -it O O -has O O -been O O -trying O O -to O O -build O O -on O O -this O O -strategy O O -of O O -expanding O O -the O O -pool O O -of O O -potential O O -investors O O -since O O -then O O -. O O -In O O -November O O -, O O -New B-LOC B-LOC -York I-LOC I-LOC -City I-LOC I-LOC -said O O -it O O -became O O -the O O -first O O -U B-LOC B-LOC -municipality O O -to O O -offer O O -bonds O O -for O O -sale O O -in O O -European B-MISC B-MISC -markets O O -by O O -competitive O O -bidding O O -as O O -it O O -listed O O -taxa O O -bonds O O -on O O -the O O -London B-ORG B-ORG -Stock I-ORG I-ORG -Exchange I-ORG I-ORG -. O O -The O O -re O O -planned O O -for O O -January O O -also O O -includes O O -a O O -$ O O -47 O O -million O O -tax O O -offering O O -. O O -No O O -specific O O -date O O -in O O -January O O -has O O -been O O -selected O O -for O O -the O O -debt O O -sale O O -, O O -the O O -official O O -added O O -. O O -- O O -Joan B-PER B-PER -G I-PER I-PER -, O O -212 O O -USD B-ORG B-ORG -gross O O -cut O O -hide O O -and O O -off O O -value O O -. O O -DE B-LOC B-LOC -M I-LOC I-LOC -1996 O O -The O O -hide O O -and O O -off O O -value O O -from O O -a O O -typical O O -slaughter O O -steer O O -for O O -Friday O O -was O O -estimated O O -at O O -$ O O -9 O O -per O O -c O O -live O O -, O O -d O O -0 O O -when O O -compared O O -to O O -Thursday O O -' O O -value O O -. O O -- O O -USD B-ORG B-ORG -Wall B-ORG B-LOC -St I-ORG I-LOC -s O O -about O O -Santa B-LOC B-LOC -Fe I-LOC I-LOC -sa O O -. O O -Brendan B-PER B-PER -In I-PER I-PER -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Homes B-ORG B-ORG -Mining I-ORG I-ORG -Co I-ORG I-ORG -tops O O -Wall B-LOC B-LOC -Street I-LOC I-LOC -' O O -list O O -as O O -the O O -most O O -likely O O -white O O -knight O O -buyer O O -for O O -Santa B-ORG B-ORG -Fe I-ORG I-ORG -Pacific I-ORG I-ORG -Gold I-ORG I-ORG -Corp I-ORG I-ORG -if O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -rejects O O -un O O -suit O O -New B-ORG B-ORG -Mining I-ORG I-ORG -Corp I-ORG I-ORG -. O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -is O O -so O O -far O O -mum O O -on O O -the O O -more O O -than O O -$ O O -2 O O -billion O O -stock O O -swap O O -takeover O O -proposal O O -from O O -New B-ORG B-ORG -, O O -announced O O -Thursday O O -. O O -Wall B-LOC B-LOC -Street I-LOC I-LOC -, O O -since O O -the O O -bid O O -, O O -has O O -speculated O O -that O O -any O O -deal O O -between O O -New B-LOC B-ORG -and O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -would O O -be O O -a O O -" O O -bear O O -hug O O -, O O -" O O -or O O -a O O -reluctantly O O -negotiated O O -agreement O O -where O O -the O O -buyer O O -is O O -not O O -necessarily O O -a O O -friendly O O -suit O O -. O O -New B-ORG B-ORG -said O O -the O O -companies O O -have O O -had O O -previous O O -contact O O -, O O -though O O -declined O O -to O O -detail O O -the O O -encounters O O -. O O -Ana O O -predict O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -will O O -go O O -to O O -the O O -highest O O -bid O O -, O O -and O O -that O O -if O O -a O O -rival O O -buyer O O -is O O -found O O -, O O -New B-ORG B-ORG -may O O -not O O -be O O -able O O -to O O -match O O -its O O -offer O O -. O O -They O O -said O O -the O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -deal O O -, O O -which O O -includes O O -desirable O O -Nevada B-LOC B-LOC -mining O O -territory O O -, O O -would O O -only O O -pay O O -for O O -New B-PER B-ORG -longer O O -term O O -. O O -New B-ORG B-ORG -, O O -in O O -fact O O -, O O -will O O -not O O -benefit O O -from O O -the O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -acquisition O O -on O O -an O O -earnings O O -basis O O -for O O -at O O -least O O -two O O -years O O -, O O -which O O -also O O -limits O O -its O O -capacity O O -to O O -raise O O -its O O -offer O O -. O O -Any O O -deal O O -, O O -friendly O O -or O O -hostile O O -, O O -would O O -almost O O -assured O O -be O O -a O O -stock O O -swap O O -, O O -which O O -is O O -necessary O O -to O O -preserve O O -the O O -tax O O -, O O -pool O O -accounting O O -, O O -they O O -said O O -. O O -Ana O O -and O O -a O O -immediately O O -ruled O O -out O O -Barr B-ORG B-ORG -Gold I-ORG I-ORG -Corp I-ORG I-ORG -and O O -B B-ORG B-ORG -Mine I-ORG I-ORG -Ltd I-ORG I-ORG -as O O -Santa B-MISC B-ORG -Fe I-ORG I-ORG -sa O O -because O O -they O O -are O O -locked O O -in O O -negotiations O O -over O O -their O O -splitting O O -Indonesia B-LOC B-LOC -' O O -Bus B-LOC B-ORG -vast O O -gold O O -deposit O O -. O O -Place B-ORG B-ORG -Dome I-ORG I-ORG -Inc I-ORG I-ORG -too O O -was O O -considered O O -un O O -because O O -it O O -is O O -focusing O O -on O O -geographic O O -expansion O O -in O O -areas O O -that O O -do O O -match O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -' O O -Nevada B-LOC B-LOC -, O O -South B-LOC B-LOC -America I-LOC I-LOC -and O O -Central B-LOC B-LOC -Asia I-LOC I-LOC -presence O O -, O O -they O O -said O O -. O O -A O O -Homes B-ORG B-ORG -spokesman O O -was O O -not O O -immediately O O -available O O -to O O -comment O O -on O O -speculation O O -that O O -it O O -tops O O -the O O -list O O -. O O -Homes B-ORG B-ORG -, O O -based O O -in O O -San B-LOC B-LOC -Francisco I-LOC I-LOC -, O O -operates O O -gold O O -mines O O -in O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -, O O -Australia B-LOC B-LOC -, O O -Chile B-LOC B-LOC -and O O -Canada B-LOC B-LOC -. O O -E O O -in O O -1995 O O -were O O -$ O O -0 O O -per O O -share O O -, O O -or O O -$ O O -30 O O -million O O -, O O -on O O -revenues O O -of O O -$ O O -74 O O -million O O -. O O -Santa B-ORG B-ORG -Fe I-ORG I-ORG -is O O -headquartered O O -Albuquerque B-LOC B-LOC -, O O -N B-LOC B-LOC -and O O -reported O O -1995 O O -earnings O O -of O O -$ O O -0 O O -per O O -share O O -, O O -or O O -$ O O -40 O O -million O O -, O O -on O O -revenues O O -of O O -$ O O -350 O O -million O O -. O O -Santa B-ORG B-ORG -Fe I-ORG I-ORG -has O O -mining O O -and O O -exploration O O -operations O O -in O O -Nevada B-LOC B-LOC -, O O -California B-LOC B-LOC -, O O -Montana B-LOC B-LOC -, O O -Canada B-LOC B-LOC -, O O -Brazil B-LOC B-LOC -, O O -Australia B-LOC B-LOC -, O O -Chile B-LOC B-LOC -, O O -Ka B-LOC B-LOC -, O O -Mexico B-LOC B-LOC -and O O -Ghana B-LOC B-LOC -. O O -Pain B-ORG B-ORG -analyst O O -Marc B-PER B-PER -Cohen I-PER I-PER -said O O -he O O -lowered O O -his O O -rating O O -on O O -New B-ORG B-ORG -to O O -neutral O O -from O O -attractive O O -today O O -because O O -if O O -New B-ORG B-ORG -merged O O -with O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -, O O -investors O O -would O O -have O O -to O O -wait O O -until O O -the O O -second O O -half O O -of O O -1998 O O -to O O -realize O O -earnings O O -a O O -. O O -" O O -I O O -think O O -Homes B-PER B-ORG -could O O -come O O -in O O -as O O -a O O -white O O -knight O O -, O O -but O O -how O O -much O O -is O O -someone O O -willing O O -to O O -come O O -in O O -above O O -the O O -New B-LOC B-ORG -number O O -. O O -One O O -would O O -have O O -to O O -out O O -by O O -at O O -least O O -15 O O -percent O O -, O O -but O O -there O O -is O O -going O O -to O O -be O O -a O O -( O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -) O O -deal O O -with O O -someone O O -, O O -" O O -he O O -said O O -. O O -" O O -Long O O -term O O -, O O -two O O -to O O -three O O -years O O -out O O -, O O -( O O -a O O -New B-ORG B-MISC -Fe O I-MISC -deal O O -) O O -is O O -positive O O -, O O -it O O -does O O -all O O -the O O -right O O -things O O -. O O -But O O -in O O -the O O -near O O -it O O -is O O -, O O -at O O -worst O O -, O O -neutral O O -, O O -" O O -the O O -analyst O O -added O O -. O O -New B-ORG B-ORG -proposed O O -to O O -Santa B-LOC B-LOC -Fe I-LOC I-LOC -a O O -stock O O -merger O O -at O O -a O O -ratio O O -of O O -0 O O -New B-ORG B-ORG -shares O O -for O O -each O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -shares O O -. O O -In O O -Friday O O -New B-ORG B-ORG -York I-ORG I-ORG -Stock I-ORG I-ORG -Exchange I-ORG I-ORG -trade O O -, O O -New B-PER B-ORG -was O O -off O O -1 O O -to O O -46 O O -while O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -added O O -1 O O -to O O -15 O O -. O O -" O O -New B-ORG B-ORG -said O O -it O O -wants O O -to O O -discuss O O -a O O -friendly O O -deal O O -with O O -Santa B-LOC B-ORG -Fe I-LOC I-ORG -, O O -which O O -is O O -almost O O -always O O -a O O -e O O -for O O -' O O -We O O -have O O -more O O -money O O -in O O -our O O -pocket O O -, O O -' O O -" O O -said O O -an O O -a O O -, O O -referring O O -to O O -a O O -possible O O -sweet O O -bid O O -from O O -New B-PER B-ORG -. O O -Two O O -other O O -a O O -called O O -New B-PER B-ORG -' O O -move O O -a O O -" O O -a O O -32 O O -cent O O -bid O O -" O O -because O O -there O O -is O O -no O O -formal O O -tender O O -offer O O -, O O -only O O -the O O -proposal O O -letter O O -" O O -mail O O -" O O -to O O -Santa B-ORG B-ORG -Fe I-LOC I-ORG -' O O -board O O -. O O -- O O -Wall B-ORG B-ORG -Street I-ORG I-ORG -Des I-ORG I-ORG -, O O -212 O O -. O O -Russ B-PER B-ORG -Be I-PER I-ORG -president O O -to O O -retire O O -in O O -July O O -. O O -O B-LOC B-LOC -, O O -N B-LOC B-LOC -1996 O O -Russ B-ORG B-ORG -Be I-ORG I-ORG -and I-ORG I-ORG -Co I-ORG I-ORG -Inc I-ORG I-ORG -said O O -on O O -Friday O O -that O O -A B-PER B-PER -C I-PER I-PER -Cooke I-PER I-PER -will O O -retire O O -as O O -president O O -and O O -chief O O -operating O O -officer O O -effective O O -July O O -1 O O -, O O -1997 O O -. O O -Cooke B-PER B-PER -will O O -provide O O -consulting O O -services O O -to O O -the O O -company O O -through O O -July O O -1 O O -, O O -1998 O O -, O O -and O O -will O O -continue O O -to O O -serve O O -as O O -a O O -director O O -, O O -the O O -toy O O -and O O -gift O O -maker O O -said O O -. O O -Zimbabwe B-LOC B-LOC -execute O O -convicted O O -murderer O O -. O O -H B-LOC B-LOC -1996 O O -Zimbabwe B-LOC B-LOC -hanged O O -a O O -convicted O O -murderer O O -on O O -Friday O O -, O O -bringing O O -to O O -eight O O -the O O -number O O -of O O -executions O O -carried O O -out O O -in O O -the O O -past O O -year O O -. O O -A O O -statement O O -said O O -Pi B-PER B-PER -Sin I-PER I-PER -N I-PER I-PER -was O O -hanged O O -at O O -dawn O O -. O O -President O O -Robert B-PER B-PER -Mu I-PER I-PER -' O O -government O O -has O O -resisted O O -pressure O O -from O O -local O O -and O O -international O O -human O O -rights O O -groups O O -to O O -a O O -the O O -death O O -sentence O O -. O O -Multi O O -commander O O -going O O -back O O -to O O -east O O -Z B-LOC B-LOC -. O O -Jonathan B-PER B-PER -Wright I-PER I-PER -N B-LOC B-LOC -1996 O O -The O O -Canadian B-MISC B-MISC -general O O -in O O -charge O O -of O O -a O O -multinational O O -force O O -for O O -eastern O O -Z B-LOC B-LOC -said O O -on O O -Friday O O -he O O -was O O -going O O -back O O -to O O -Z B-LOC B-LOC -for O O -more O O -information O O -about O O -the O O -p O O -of O O -about O O -165 O O -Rwanda B-MISC B-MISC -refugees O O -ad O O -in O O -the O O -countryside O O -. O O -Lieutenant O O -Maurice B-PER B-PER -Bari I-PER I-PER -told O O -a O O -news O O -conference O O -in O O -Nairobi B-LOC B-LOC -his O O -main O O -concern O O -was O O -for O O -a O O -large O O -group O O -of O O -about O O -150 O O -refugees O O -living O O -off O O -the O O -land O O -in O O -a O O -valley O O -about O O -65 O O -km O O -( O O -40 O O -miles O O -) O O -west O O -of O O -the O O -eastern O O -city O O -of O O -Go B-LOC B-LOC -. O O -If O O -he O O -decided O O -it O O -was O O -necessary O O -and O O -safe O O -for O O -the O O -air O O -, O O -he O O -would O O -not O O -hesitate O O -to O O -order O O -air O O -of O O -food O O -for O O -the O O -refugees O O -, O O -even O O -against O O -the O O -wishes O O -of O O -the O O -government O O -in O O -Ki B-LOC B-LOC -and O O -the O O -Z B-MISC B-MISC -rebels O O -who O O -control O O -much O O -of O O -eastern O O -Z B-LOC B-LOC -, O O -he O O -said O O -. O O -" O O -Tomorrow O O -I O O -' O O -going O O -into O O -Rwanda B-LOC B-LOC -and O O -my O O -intention O O -is O O -to O O -go O O -across O O -into O O -eastern O O -Z B-LOC B-LOC -and O O -try O O -to O O -find O O -out O O -for O O -the O O -second O O -time O O -what O O -the O O -situation O O -is O O -on O O -the O O -ground O O -, O O -" O O -he O O -said O O -. O O -General O O -Bari B-PER B-PER -saw O O -rebel O O -leader O O -Laurent B-PER B-PER -Ka I-PER I-PER -in O O -Go B-LOC B-LOC -last O O -week O O -but O O -the O O -rebels O O -told O O -him O O -the O O -crisis O O -was O O -over O O -because O O -most O O -of O O -the O O -Rwanda B-MISC B-MISC -refugees O O -have O O -already O O -gone O O -home O O -. O O -The O O -rebels O O -do O O -not O O -want O O -the O O -multinational O O -force O O -to O O -deploy O O -on O O -the O O -ground O O -, O O -for O O -fear O O -it O O -might O O -help O O -the O O -Z B-MISC B-MISC -army O O -regain O O -control O O -of O O -the O O -area O O -. O O -Ki B-LOC B-LOC -oppose O O -air O O -, O O -apparently O O -because O O -the O O -food O O -could O O -fall O O -into O O -the O O -hands O O -of O O -the O O -rebels O O -and O O -their O O -local O O -supporters O O -. O O -Canadian B-MISC B-MISC -Defence O O -Minister O O -Doug B-PER B-PER -Young I-PER I-PER -said O O -on O O -Thursday O O -that O O -the O O -multinational O O -force O O -would O O -probably O O -not O O -have O O -to O O -make O O -food O O -air O O -or O O -intervene O O -mi O O -in O O -any O O -major O O -way O O -. O O -" O O -It O O -does O O -n O O -look O O -as O O -though O O -they O O -( O O -air O O -) O O -are O O -going O O -to O O -be O O -required O O -in O O -any O O -significant O O -way O O -because O O -the O O -NGOs O O -( O O -non O O -organisations O O -) O O -are O O -in O O -that O O -area O O -on O O -the O O -border O O -between O O -Z B-LOC B-LOC -and O O -Rwanda B-LOC B-LOC -, O O -" O O -Young B-PER B-PER -told O O -reporters O O -. O O -But O O -General O O -Bari B-PER B-PER -said O O -it O O -would O O -be O O -premature O O -to O O -rule O O -out O O -any O O -course O O -of O O -action O O -until O O -he O O -had O O -more O O -information O O -. O O -" O O -We O O -hope O O -that O O -if O O -the O O -front O O -moves O O -forward O O -or O O -stab O O -then O O -we O O -will O O -have O O -access O O -( O O -to O O -the O O -large O O -group O O -of O O -refugees O O -) O O -with O O -reconnaissance O O -or O O -humanitarian O O -agencies O O -. O O -" O O -If O O -they O O -ca O O -n O O -move O O -because O O -they O O -are O O -too O O -weak O O -, O O -then O O -we O O -will O O -probably O O -consider O O -very O O -seriously O O -using O O -air O O -delivery O O -means O O -( O O -air O O -) O O -. O O -' O O -complex O O -, O O -it O O -' O O -dangerous O O -for O O -the O O -air O O -crew O O -that O O -fly O O -in O O -there O O -and O O -it O O -will O O -have O O -to O O -be O O -absolutely O O -necessary O O -. O O -If O O -it O O -is O O -necessary O O -, O O -I O O -w O O -n O O -hesitate O O -to O O -use O O -it O O -, O O -" O O -he O O -said O O -. O O -Ask O O -if O O -he O O -would O O -di O O -the O O -objections O O -of O O -the O O -Z B-MISC B-MISC -government O O -, O O -he O O -said O O -: O O -" O O -It O O -would O O -have O O -to O O -be O O -in O O -the O O -last O O -resort O O -. O O -It O O -would O O -have O O -to O O -mean O O -that O O -tens O O -of O O -thousands O O -of O O -lives O O -are O O -in O O -danger O O -. O O -Do O O -you O O -think O O -that O O -I O O -would O O -have O O -a O O -conscience O O -problem O O -doing O O -it O O -or O O -not O O -at O O -that O O -time O O -? O O -And O O -my O O -mandate O O -is O O -also O O -under O O -Chapter O O -Seven O O -to O O -operate O O -in O O -eastern O O -Z B-LOC B-LOC -. O O -" O O -Under O O -Chapter O O -Seven O O -of O O -the O O -U B-ORG B-ORG -charter O O -, O O -the O O -Security B-ORG B-ORG -Council I-ORG I-ORG -has O O -wide O O -powers O O -to O O -preserve O O -peace O O -and O O -security O O -. O O -" O O -I O O -know O O -their O O -( O O -the O O -Z B-MISC B-MISC -government O O -' O O -) O O -position O O -and O O -I O O -know O O -it O O -' O O -very O O -delicate O O -and O O -we O O -are O O -very O O -sensitive O O -to O O -their O O -position O O -also O O -, O O -" O O -the O O -general O O -added O O -. O O -He O O -denied O O -that O O -his O O -contacts O O -, O O -criticised O O -by O O -Ki B-LOC B-LOC -, O O -with O O -the O O -Z B-MISC B-MISC -rebels O O -amounted O O -to O O -negotiations O O -. O O -" O O -I O O -do O O -n O O -negotiate O O -, O O -" O O -he O O -said O O -. O O -" O O -I O O -coordinate O O -with O O -those O O -who O O -are O O -holding O O -ground O O -and O O -that O O -' O O -a O O -wise O O -thing O O -to O O -do O O -. O O -When O O -we O O -do O O -n O O -know O O -where O O -the O O -front O O -is O O -, O O -we O O -do O O -n O O -know O O -what O O -the O O -risk O O -is O O -. O O -" O O -Bari B-PER B-PER -said O O -that O O -apart O O -from O O -the O O -group O O -of O O -150 O O -, O O -U B-LOC B-LOC -and O O -British B-MISC B-MISC -reconnaissance O O -plans O O -had O O -tracked O O -two O O -much O O -smaller O O -groups O O -of O O -refugees O O -- O O -one O O -of O O -up O O -to O O -1 O O -north O O -of O O -the O O -town O O -of O O -Ma B-LOC O -and O O -one O O -of O O -up O O -to O O -8 O O -on O O -the O O -road O O -from O O -B B-LOC B-LOC -west O O -to O O -Kind B-LOC B-LOC -. O O -The O O -Ki B-LOC B-LOC -office O O -of O O -the O O -medical O O -charity O O -Me B-ORG B-ORG -sa I-ORG I-ORG -Frontier I-ORG I-ORG -said O O -on O O -Friday O O -that O O -more O O -than O O -100 O O -refugees O O -were O O -t O O -northwest O O -from O O -the O O -Go B-LOC B-LOC -area O O -and O O -many O O -of O O -them O O -were O O -now O O -in O O -the O O -town O O -of O O -W B-LOC B-LOC -. O O -The O O -general O O -did O O -not O O -mention O O -these O O -refugees O O -, O O -who O O -are O O -on O O -the O O -outer O O -limit O O -of O O -the O O -strip O O -the O O -planes O O -have O O -been O O -checking O O -. O O -Mauritius B-LOC B-LOC -put O O -on O O -cyclone O O -alert O O -. O O -P B-LOC B-LOC -L I-LOC I-LOC -1996 O O -Ma B-MISC B-MISC -authorities O O -put O O -the O O -Indian B-LOC B-LOC -Ocean I-LOC I-LOC -island O O -on O O -cyclone O O -alert O O -on O O -Friday O O -. O O -The O O -weather O O -services O O -office O O -said O O -the O O -centre O O -of O O -the O O -intense O O -tropical O O -cyclone O O -Daniel B-PER B-MISC -was O O -570 O O -km O O -( O O -310 O O -miles O O -) O O -north O O -by O O -northwest O O -of O O -the O O -island O O -on O O -Friday O O -afternoon O O -and O O -was O O -moving O O -south O O -by O O -southwest O O -at O O -eight O O -km O O -an O O -hour O O -( O O -four O O -knots O O -) O O -. O O -Although O O -not O O -threatening O O -Mauritius B-LOC B-LOC -directly O O -, O O -it O O -is O O -coming O O -closer O O -to O O -the O O -island O O -and O O -could O O -change O O -direction O O -, O O -it O O -added O O -. O O -Wind O O -up O O -to O O -75 O O -km O O -an O O -hour O O -( O O -40 O O -knots O O -) O O -could O O -blow O O -over O O -Mauritius B-LOC B-LOC -during O O -the O O -night O O -of O O -Friday O O -to O O -Saturday O O -, O O -it O O -said O O -. O O -The O O -weather O O -in O O -the O O -capital O O -Port B-LOC B-LOC -Louis I-LOC I-LOC -was O O -heavily O O -cloud O O -on O O -Friday O O -afternoon O O -with O O -occasional O O -shower O O -. O O -The O O -northeastern O O -coast O O -of O O -the O O -nearby O O -island O O -of O O -Madagascar B-LOC B-LOC -has O O -also O O -gone O O -on O O -alert O O -. O O -U B-ORG B-ORG -evacuate O O -staff O O -from O O -Central B-LOC B-LOC -African I-LOC I-LOC -Republic I-LOC I-LOC -. O O -AB B-LOC B-LOC -1996 O O -The O O -United B-ORG B-ORG -Nations I-ORG I-ORG -evacuated O O -its O O -staff O O -in O O -the O O -Central B-LOC B-LOC -African I-LOC I-LOC -Republic I-LOC I-LOC -on O O -Friday O O -because O O -of O O -mounting O O -violence O O -in O O -a O O -two O O -army O O -m O O -in O O -the O O -capital O O -, O O -a O O -U B-ORG B-ORG -official O O -said O O -. O O -The O O -official O O -from O O -the O O -U B-ORG B-ORG -refugee O O -agency O O -UN B-ORG B-ORG -said O O -a O O -chartered O O -plane O O -had O O -picked O O -up O O -the O O -staff O O -from O O -Bang B-LOC B-LOC -and O O -was O O -heading O O -for O O -A B-LOC B-LOC -, O O -Ivory B-LOC B-LOC -Coast I-LOC I-LOC -. O O -Senegal B-LOC B-LOC -proposes O O -foreign O O -minister O O -for O O -U B-ORG B-ORG -post O O -. O O -D B-LOC B-LOC -1996 O O -Senegal B-LOC B-LOC -' O O -President O O -Abd B-PER B-PER -Di I-PER I-PER -said O O -on O O -Friday O O -he O O -was O O -proposing O O -his O O -foreign O O -minister O O -Mo B-PER B-PER -Ni I-PER I-PER -for O O -the O O -post O O -of O O -United B-ORG B-ORG -Nations I-ORG I-ORG -secretary O O -. O O -Di B-PER B-PER -announced O O -his O O -intention O O -to O O -reporters O O -when O O -he O O -returned O O -from O O -the O O -Franco B-MISC B-MISC -summit O O -in O O -Burkina B-LOC B-LOC -Faso I-LOC I-LOC -where O O -an O O -African B-MISC B-MISC -successor O O -to O O -Secretary O O -Bo B-PER B-PER -Bo I-PER I-PER -was O O -discussed O O -. O O -The O O -United B-LOC B-LOC -States I-LOC I-LOC -has O O -veto O O -a O O -second O O -term O O -for O O -the O O -Egyptian B-MISC B-MISC -but O O -left O O -the O O -door O O -open O O -for O O -another O O -African B-MISC B-MISC -candidate O O -. O O -" O O -If O O -Africa B-LOC B-LOC -does O O -not O O -wish O O -to O O -lose O O -its O O -turn O O -we O O -have O O -to O O -act O O -fast O O -, O O -" O O -Di B-PER B-PER -said O O -. O O -" O O -Some O O -of O O -my O O -brother O O -heads O O -of O O -state O O -asked O O -me O O -if O O -I O O -would O O -n O O -nominate O O -Mo B-PER B-PER -Ni I-PER I-PER -. O O -I O O -see O O -in O O -him O O -the O O -profile O O -of O O -a O O -secret O O -of O O -the O O -United B-ORG B-ORG -Nations I-ORG I-ORG -and O O -I O O -have O O -given O O -my O O -endorsement O O -. O O -" O O -Ex O O -, O O -son O O -killed O O -in O O -Central B-LOC B-LOC -Africa I-LOC I-LOC -unrest O O -. O O -Raphael B-PER B-PER -Ko I-PER I-PER -BA B-LOC B-LOC -1996 O O -A O O -former O O -cabinet O O -minister O O -in O O -Central B-LOC B-LOC -African I-LOC I-LOC -Republic I-LOC I-LOC -and O O -his O O -son O O -were O O -abducted O O -from O O -their O O -home O O -and O O -murdered O O -in O O -growing O O -ethnic O O -violence O O -in O O -the O O -capital O O -Bang B-LOC B-LOC -, O O -a O O -government O O -minister O O -said O O -on O O -Friday O O -. O O -With O O -violence O O -spiral O O -out O O -of O O -control O O -, O O -France B-LOC B-LOC -voiced O O -backing O O -for O O -the O O -elected O O -Bang B-LOC B-LOC -government O O -but O O -said O O -its O O -troops O O -based O O -in O O -the O O -former O O -colony O O -under O O -defence O O -pact O O -would O O -not O O -help O O -it O O -combat O O -army O O -m O O -. O O -" O O -France B-LOC B-LOC -cannot O O -be O O -involved O O -in O O -the O O -domestic O O -political O O -debate O O -, O O -" O O -President O O -Jacques B-PER B-PER -Chi I-PER I-PER -told O O -a O O -news O O -conference O O -at O O -the O O -end O O -of O O -a O O -Franco B-MISC B-MISC -summit O O -in O O -Burkina B-LOC B-LOC -Faso I-LOC I-LOC -. O O -" O O -French B-MISC B-MISC -troops O O -may O O -only O O -take O O -part O O -in O O -maintaining O O -order O O -to O O -avoid O O -major O O -abuses O O -and O O -protect O O -foreign O O -communities O O -, O O -" O O -he O O -said O O -. O O -Public B-ORG O -Service I-ORG O -Minister O O -David B-PER B-PER -Do I-PER I-PER -, O O -who O O -is O O -the O O -head O O -of O O -the O O -national O O -Red B-ORG B-ORG -Cross I-ORG I-ORG -, O O -told O O -Re B-ORG B-ORG -he O O -had O O -seen O O -the O O -bodies O O -of O O -former O O -interior O O -minister O O -Christophe B-PER B-PER -G I-PER I-PER -and O O -his O O -son O O -, O O -who O O -was O O -not O O -named O O -. O O -Witness O O -said O O -they O O -had O O -been O O -seized O O -by O O -troops O O -loyal O O -to O O -President O O -Ang B-PER B-PER -Pat I-PER I-PER -at O O -dawn O O -on O O -Thursday O O -when O O -they O O -clashed O O -with O O -soldiers O O -staging O O -a O O -m O O -since O O -November O O -16 O O -. O O -G B-PER B-PER -is O O -from O O -the O O -Ya B-LOC B-MISC -tribe O O -to O O -which O O -most O O -of O O -the O O -rebel O O -soldiers O O -belong O O -. O O -The O O -uprising O O -began O O -over O O -pay O O -demands O O -but O O -has O O -turned O O -into O O -a O O -campaign O O -to O O -top O O -Pat B-PER B-PER -, O O -spark O O -ethnic O O -violence O O -and O O -dividing O O -the O O -capital O O -. O O -The O O -former O O -minister O O -and O O -his O O -son O O -had O O -been O O -taken O O -from O O -their O O -home O O -close O O -to O O -the O O -presidential O O -palace O O -, O O -which O O -is O O -guarded O O -by O O -loyal O O -soldiers O O -backed O O -by O O -French B-MISC B-MISC -troops O O -based O O -in O O -Bang B-LOC B-LOC -. O O -The O O -bodies O O -were O O -found O O -on O O -Thursday O O -in O O -an O O -open O O -field O O -about O O -two O O -km O O -( O O -one O O -mile O O -) O O -further O O -away O O -, O O -said O O -Do B-PER B-PER -and O O -other O O -witnesses O O -. O O -The O O -men O O -were O O -seized O O -as O O -loyal O O -forces O O -and O O -French B-MISC B-MISC -troops O O -fought O O -gun O O -with O O -m O O -who O O -fired O O -rockets O O -into O O -the O O -city O O -centre O O -. O O -A O O -French B-MISC B-MISC -hotel O O -was O O -slightly O O -damaged O O -. O O -Ya B-MISC B-MISC -are O O -ho O O -in O O -stronghold O O -districts O O -of O O -Pat B-LOC B-PER -' O O -Bay B-LOC B-MISC -people O O -while O O -other O O -tribes O O -have O O -fled O O -areas O O -in O O -rebel O O -hands O O -. O O -Road O O -have O O -been O O -erected O O -in O O -city O O -districts O O -while O O -central O O -Bang B-LOC B-LOC -, O O -which O O -is O O -patrol O O -by O O -French B-MISC B-MISC -troops O O -with O O -tanks O O -, O O -is O O -deserted O O -. O O -Shop O O -and O O -businesses O O -have O O -remained O O -shut O O -this O O -week O O -. O O -The O O -Franco B-MISC B-MISC -summit O O -decided O O -to O O -send O O -a O O -mission O O -Bang B-LOC B-LOC -to O O -seek O O -ways O O -of O O -containing O O -the O O -m O O -and O O -a O O -threat O O -of O O -civil O O -war O O -. O O -Chi B-PER B-PER -said O O -Burkina B-LOC B-LOC -Faso I-LOC I-LOC -President O O -B B-PER B-PER -Co I-PER I-PER -would O O -visit O O -Bang B-LOC B-LOC -" O O -in O O -the O O -coming O O -hours O O -" O O -with O O -the O O -heads O O -of O O -state O O -of O O -Gabon B-LOC B-LOC -, O O -Mali B-LOC B-LOC -and O O -Chad B-LOC B-LOC -to O O -try O O -and O O -establish O O -dialogue O O -between O O -authorities O O -and O O -rebels O O -. O O -The O O -m O O -forced O O -Pat B-PER B-PER -to O O -miss O O -the O O -summit O O -. O O -His O O -spokesman O O -had O O -predicted O O -the O O -meeting O O -to O O -send O O -an O O -assessment O O -mission O O -. O O -Pat B-PER B-PER -, O O -who O O -won O O -Central B-LOC B-LOC -Africa I-LOC I-LOC -' O O -first O O -multi O O -elections O O -, O O -refuses O O -to O O -resign O O -. O O -Church B-MISC O -me O O -attempts O O -hit O O -dead O O -over O O -rebel O O -demands O O -for O O -his O O -departure O O -. O O -Soldiers O O -staged O O -m O O -in O O -April O O -and O O -May O O -, O O -with O O -French B-MISC B-MISC -troops O O -stepping O O -in O O -with O O -tanks O O -and O O -helicopters O O -to O O -que O O -the O O -more O O -serious O O -second O O -uprising O O -. O O -Pat B-PER B-PER -offered O O -concessions O O -and O O -am O O -to O O -rebels O O -before O O -the O O -May O O -rebellion O O -ended O O -after O O -rebels O O -lo O O -the O O -city O O -centre O O -. O O -Rebels O O -a O O -Pat B-PER B-PER -of O O -tribal O O -and O O -of O O -arm O O -his O O -civilian O O -supporters O O -and O O -hired O O -guns O O -from O O -Sudan B-LOC B-LOC -and O O -Chad B-LOC B-LOC -. O O -Mu O O -have O O -vowed O O -to O O -di O O -all O O -civilians O O -and O O -to O O -chase O O -out O O -the O O -foreign O O -forces O O -kn O O -as O O -Co B-LOC B-MISC -. O O -Hospital O O -sources O O -and O O -witnesses O O -said O O -about O O -10 O O -people O O -were O O -known O O -to O O -have O O -been O O -killed O O -in O O -the O O -more O O -than O O -two O O -weeks O O -of O O -fighting O O -, O O -including O O -two O O -rebels O O -killed O O -in O O -Thursday O O -' O O -clashes O O -. O O -An O O -und O O -number O O -of O O -people O O -are O O -reported O O -to O O -have O O -been O O -abducted O O -and O O -killed O O -outside O O -the O O -town O O -by O O -tribal O O -v O O -groups O O -. O O -In O O -Thursday O O -' O O -fighting O O -, O O -French B-MISC B-MISC -troops O O -fired O O -back O O -as O O -m O O -trying O O -to O O -break O O -out O O -of O O -their O O -stronghold O O -rain O O -mortar O O -shells O O -on O O -the O O -city O O -centre O O -. O O -Five O O -die O O -as O O -SA B-MISC B-MISC -crop O O -plane O O -hits O O -pickup O O -. O O -J B-LOC B-LOC -1996 O O -Five O O -people O O -were O O -killed O O -when O O -a O O -crop O O -plane O O -preparing O O -for O O -takeoff O O -crashed O O -into O O -a O O -light O O -delivery O O -vehicle O O -in O O -South B-LOC B-LOC -Africa I-LOC I-LOC -' O O -North B-LOC B-LOC -West I-LOC I-LOC -region O O -, O O -state O O -radio O O -reported O O -on O O -Friday O O -. O O -The O O -freak O O -accident O O -occurred O O -in O O -Ma B-LOC B-LOC -on O O -Thursday O O -. O O -The O O -pilot O O -survived O O -the O O -crash O O -, O O -but O O -the O O -driver O O -and O O -passengers O O -of O O -the O O -van O O -were O O -killed O O -. O O -W O O -- O O -Con O O -at O O -C B-LOC B-LOC -airports O O -- O O -Dec O O -6 O O -. O O -M B-LOC B-LOC -1996 O O -No O O -weather O O -closure O O -of O O -C B-LOC B-LOC -airports O O -are O O -expected O O -on O O -December O O -7 O O -and O O -8 O O -, O O -the O O -Russian B-ORG B-ORG -Weather I-ORG I-ORG -Service I-ORG I-ORG -said O O -on O O -Friday O O -. O O -- O O -Moscow B-ORG B-ORG -News I-ORG I-ORG -+ O O -94 O O -85 O O -Skin O O -attack O O -Bratislava B-LOC B-LOC -Rabbi O O -- O O -police O O -. O O -BR B-LOC B-LOC -1996 O O -Four O O -skin O O -attacked O O -and O O -insulted O O -the O O -rabbi O O -of O O -Bratislava B-LOC B-LOC -, O O -Bar B-PER B-PER -Meyer I-PER I-PER -, O O -in O O -the O O -city O O -centre O O -on O O -Friday O O -, O O -but O O -he O O -escaped O O -un O O -, O O -a O O -police O O -spokesman O O -told O O -Re B-ORG B-ORG -. O O -" O O -A O O -group O O -of O O -four O O -skin O O -attacked O O -the O O -rabbi O O -, O O -one O O -kicked O O -him O O -in O O -the O O -hand O O -but O O -caused O O -no O O -injury O O -, O O -" O O -the O O -spokesman O O -said O O -. O O -" O O -All O O -four O O -attackers O O -were O O -app O O -and O O -two O O -have O O -been O O -detained O O -, O O -" O O -the O O -spokesman O O -added O O -He O O -was O O -unable O O -to O O -give O O -more O O -details O O -. O O -" O O -The O O -further O O -procedure O O -is O O -now O O -in O O -the O O -hands O O -of O O -the O O -local O O -police O O -investigator O O -, O O -" O O -the O O -spokesman O O -said O O -. O O -It O O -was O O -the O O -second O O -attack O O -by O O -skin O O -in O O -two O O -years O O -on O O -Meyer B-PER B-PER -, O O -an O O -American B-MISC B-MISC -. O O -Meyer B-PER B-PER -was O O -not O O -available O O -for O O -comment O O -. O O -Albanian B-MISC B-MISC -jailed O O -for O O -threat O O -of O O -bomb O O -suicide O O -. O O -T B-LOC B-LOC -1996 O O -An O O -Albanian B-MISC B-MISC -court O O -on O O -Friday O O -sentenced O O -a O O -man O O -who O O -threatened O O -to O O -blow O O -himself O O -up O O -outside O O -President O O -Sal B-PER B-PER -Be I-PER I-PER -' O O -office O O -to O O -13 O O -years O O -in O O -jail O O -for O O -guerrilla O O -action O O -and O O -illegal O O -possession O O -of O O -arms O O -. O O -B B-PER B-PER -last O O -April O O -said O O -he O O -would O O -blow O O -himself O O -up O O -outside O O -the O O -presidential O O -palace O O -unless O O -he O O -was O O -allowed O O -to O O -speak O O -to O O -Be B-PER B-PER -, O O -who O O -was O O -at O O -the O O -time O O -meeting O O -Italian B-MISC B-MISC -President O O -Oscar B-PER B-PER -Luigi I-PER I-PER -Sc I-PER I-PER -. O O -B B-PER B-PER -was O O -over O O -by O O -riot O O -police O O -less O O -than O O -one O O -hour O O -after O O -he O O -began O O -his O O -action O O -. O O -" O O -Eva O O -all O O -the O O -conditions O O -of O O -the O O -case O O -the O O -court O O -thinks O O -the O O -sentence O O -should O O -be O O -lower O O -than O O -the O O -minimum O O -( O O -15 O O -years O O -) O O -, O O -" O O -Tirana B-LOC B-LOC -judge O O -Q B-PER B-PER -G I-PER I-PER -added O O -. O O -The O O -defendant O O -denied O O -the O O -charges O O -, O O -saying O O -his O O -action O O -was O O -intended O O -to O O -urge O O -the O O -authorities O O -to O O -give O O -him O O -a O O -$ O O -20 O O -loan O O -. O O -Medical O O -experts O O -had O O -concluded O O -B B-PER B-PER -was O O -mentally O O -unstable O O -but O O -fully O O -responsible O O -for O O -the O O -act O O -he O O -had O O -committed O O -, O O -G B-PER B-PER -said O O -. O O -Polish B-MISC B-MISC -ex O O -president O O -to O O -visit O O -Pope B-PER O -. O O -WA B-LOC B-LOC -1996 O O -Poland B-LOC B-LOC -' O O -ex O O -President O O -Al B-PER B-PER -K I-PER I-PER -is O O -likely O O -to O O -visit O O -Polish B-MISC O -Pope O O -John B-PER B-PER -Paul I-PER I-PER -in O O -early O O -1997 O O -despite O O -uneasy O O -relations O O -between O O -the O O -Vatican B-LOC B-LOC -and O O -Warsaw B-LOC B-LOC -, O O -the O O -foreign O O -minister O O -said O O -on O O -Friday O O -. O O -" O O -President O O -K B-PER B-PER -plans O O -to O O -visit O O -Italy B-LOC B-LOC -on O O -a O O -invitation O O -from O O -President O O -Oscar B-PER B-PER -Sc I-PER I-PER -. O O -A O O -meeting O O -with O O -the O O -Pope B-MISC O -is O O -also O O -planned O O -, O O -" O O -Darius B-PER B-PER -Rosa I-PER I-PER -told O O -a O O -news O O -conference O O -. O O -Rosa B-PER B-PER -said O O -that O O -the O O -atmosphere O O -of O O -the O O -meeting O O -, O O -if O O -it O O -takes O O -place O O -, O O -would O O -largely O O -depend O O -on O O -the O O -progress O O -in O O -talks O O -on O O -rat O O -of O O -a O O -treaty O O -between O O -Warsaw B-LOC B-LOC -and O O -the O O -Vatican B-LOC B-LOC -. O O -@ O O -The O O -rat O O -of O O -the O O -treaty O O -, O O -which O O -was O O -signed O O -in O O -1993 O O -by O O -the O O -then O O -right O O -government O O -, O O -is O O -being O O -delayed O O -by O O -an O O -ex O O -party O O -, O O -which O O -won O O -parliamentary O O -elections O O -in O O -the O O -same O O -year O O -and O O -now O O -dominate O O -parliament O O -. O O -The O O -party O O -, O O -the O O -Democratic B-ORG B-ORG -Left I-ORG I-ORG -Alliance I-ORG I-ORG -, O O -says O O -the O O -agreement O O -would O O -give O O -the O O -Catholic B-ORG B-ORG -Church I-ORG I-ORG -too O O -much O O -influence O O -over O O -life O O -in O O -Poland B-LOC B-LOC -and O O -could O O -in O O -on O O -rights O O -of O O -other O O -religious O O -groups O O -and O O -non O O -. O O -The O O -relations O O -with O O -the O O -Vatican B-LOC B-LOC -have O O -also O O -been O O -sour O O -by O O -a O O -recent O O -relaxation O O -of O O -Poland B-LOC B-LOC -' O O -anti O O -rules O O -, O O -which O O -K B-PER B-PER -signed O O -into O O -law O O -last O O -month O O -. O O -Russia B-LOC B-LOC -warns O O -Nor B-PER B-ORG -, O O -not O O -expected O O -to O O -liquid O O -it O O -. O O -Lynn B-PER B-PER -Browning I-PER I-PER -M B-LOC B-LOC -1996 O O -Russian B-MISC B-MISC -Finance O O -Minister O O -Alexander B-PER B-PER -Liv I-PER I-PER -warned O O -financially O O -Nor B-ORG B-ORG -Nick I-ORG I-ORG -on O O -Friday O O -that O O -it O O -must O O -pay O O -over O O -taxes O O -, O O -but O O -analysts O O -said O O -the O O -firm O O -would O O -not O O -be O O -liquid O O -or O O -that O O -its O O -would O O -assets O O -would O O -be O O -frozen O O -. O O -" O O -Nor B-ORG B-ORG -really O O -is O O -a O O -big O O -debt O O -, O O -both O O -to O O -the O O -federal O O -and O O -regional O O -budget O O -, O O -" O O -said O O -Konstantin B-PER B-PER -Ch I-PER I-PER -, O O -e O O -analyst O O -at O O -Moscow B-LOC B-LOC -broker O O -R B-ORG B-ORG -Plus I-ORG I-ORG -and O O -a O O -Nor B-ORG B-ORG -watch O O -. O O -" O O -Liv B-PER B-PER -' O O -words O O -are O O -an O O -attempt O O -to O O -put O O -pressure O O -on O O -the O O -company O O -. O O -" O O -The O O -official O O -It B-ORG B-ORG -news O O -agency O O -quoted O O -Liv B-PER B-PER -as O O -telling O O -parliamentary O O -deputies O O -that O O -RA B-ORG B-ORG -Nor I-ORG I-ORG -Nike I-ORG I-ORG -0 O O -had O O -to O O -pay O O -its O O -tax O O -a O O -and O O -that O O -bankruptcy O O -procedures O O -applied O O -to O O -the O O -metals O O -group O O -. O O -" O O -If O O -it O O -was O O -an O O -un O O -statement O O -and O O -a O O -bolt O O -out O O -of O O -the O O -blue O O -, O O -then O O -it O O -obviously O O -means O O -something O O -, O O -" O O -said O O -Christopher B-PER B-PER -Gran I-PER I-PER -, O O -chief O O -economist O O -at O O -United B-ORG B-ORG -City I-ORG I-ORG -Bank I-ORG I-ORG -in O O -Moscow B-LOC B-LOC -. O O -" O O -But O O -if O O -it O O -was O O -a O O -response O O -to O O -a O O -deputy O O -' O O -question O O -that O O -was O O -essentially O O -loaded O O -, O O -then O O -it O O -was O O -the O O -only O O -answer O O -he O O -could O O -have O O -given O O -. O O -" O O -Russian B-MISC B-MISC -tax O O -and O O -cabinet O O -authorities O O -, O O -under O O -pressure O O -from O O -the O O -International B-ORG B-ORG -Mon I-ORG I-ORG -Fund I-ORG I-ORG -to O O -boost O O -tax O O -revenues O O -as O O -a O O -condition O O -for O O -receiving O O -payments O O -of O O -a O O -$ O O -10 O O -billion O O -, O O -three O O -loan O O -to O O -Moscow B-LOC B-LOC -, O O -have O O -been O O -striking O O -fear O O -into O O -the O O -hearts O O -of O O -some O O -of O O -Russia B-LOC B-LOC -' O O -most O O -prominent O O -industrial O O -firms O O -by O O -saying O O -they O O -must O O -pay O O -up O O -or O O -face O O -liquid O O -. O O -" O O -They O O -could O O -freeze O O -metal O O -, O O -but O O -it O O -' O O -not O O -a O O -long O O -solution O O -to O O -the O O -problem O O -and O O -would O O -n O O -put O O -money O O -in O O -the O O -budget O O -, O O -" O O -Ch B-PER B-PER -said O O -. O O -" O O -I O O -do O O -n O O -think O O -they O O -would O O -do O O -that O O -. O O -" O O -En O O -social O O -infrastructure O O -in O O -the O O -icy O O -Far B-LOC O -North I-LOC O -where O O -Nor B-ORG B-ORG -is O O -based O O -depend O O -on O O -the O O -company O O -, O O -and O O -Moscow B-LOC B-LOC -has O O -said O O -it O O -has O O -no O O -finances O O -to O O -re O O -hundreds O O -of O O -thousands O O -of O O -people O O -- O O -an O O -expenditure O O -which O O -could O O -far O O -outs O O -Nor B-ORG B-ORG -' O O -debts O O -. O O -Nor B-ORG B-ORG -officials O O -declined O O -to O O -comment O O -. O O -Ana O O -said O O -the O O -government O O -, O O -while O O -anxious O O -about O O -Nor B-ORG B-ORG -' O O -debts O O -, O O -is O O -highly O O -unlikely O O -to O O -bring O O -the O O -nickel O O -, O O -copper O O -, O O -co O O -, O O -platinum O O -and O O -platinum O O -group O O -metals O O -producer O O -to O O -its O O -knees O O -or O O -take O O -measures O O -that O O -could O O -significantly O O -affect O O -output O O -. O O -But O O -it O O -also O O -wants O O -Nor B-ORG B-ORG -, O O -the O O -world O O -' O O -second O O -nickel O O -producer O O -, O O -to O O -clean O O -up O O -its O O -act O O -. O O -" O O -The O O -procedure O O -of O O -bankruptcy O O -will O O -be O O -applied O O -, O O -" O O -Ta B-ORG B-ORG -quoted O O -Liv B-PER B-PER -as O O -telling O O -Du B-ORG B-ORG -deputies O O -about O O -Nor B-ORG B-ORG -. O O -It O O -indirectly O O -quoted O O -him O O -as O O -saying O O -Nor B-PER B-ORG -should O O -first O O -pay O O -salary O O -a O O -, O O -which O O -in O O -the O O -past O O -have O O -led O O -to O O -worker O O -strikes O O -. O O -" O O -It O O -is O O -unlikely O O -that O O -Nor B-ORG B-ORG -will O O -pay O O -these O O -debts O O -in O O -the O O -near O O -- O O -the O O -company O O -will O O -remain O O -a O O -debt O O -in O O -the O O -near O O -future O O -, O O -" O O -Ch B-PER B-PER -said O O -. O O -He O O -estimated O O -the O O -company O O -' O O -regional O O -debts O O -at O O -least O O -one O O -trillion O O -r O O -and O O -said O O -30 O O -percent O O -of O O -the O O -giant O O -K B-ORG B-LOC -regional O O -budget O O -was O O -fuel O O -by O O -Nor B-ORG B-ORG -money O O -. O O -Nor B-ORG B-ORG -' O O -new O O -majority O O -shareholder O O -, O O -Russian B-MISC B-MISC -com O O -bank O O -Un B-ORG B-ORG -, O O -has O O -said O O -it O O -is O O -re O O -metal O O -exports O O -through O O -Inter B-ORG B-ORG -in O O -order O O -to O O -boost O O -revenues O O -. O O -But O O -the O O -changes O O -have O O -yet O O -to O O -improve O O -significantly O O -Nor B-PER B-ORG -' O O -situation O O -. O O -" O O -Un B-ORG B-ORG -has O O -inherited O O -a O O -mountain O O -and O O -whether O O -or O O -not O O -they O O -climb O O -out O O -and O O -over O O -it O O -remains O O -to O O -be O O -seen O O -, O O -" O O -said O O -one O O -metals O O -source O O -. O O -Nor B-ORG B-ORG -said O O -in O O -September O O -that O O -it O O -total O O -debts O O -, O O -including O O -unpaid O O -salaries O O -to O O -workers O O -, O O -were O O -13 O O -trillion O O -r O O -. O O -The O O -company O O -said O O -last O O -month O O -that O O -it O O -had O O -worked O O -out O O -a O O -tax O O -payment O O -schedule O O -with O O -authorities O O -, O O -after O O -regional O O -tax O O -officials O O -threatened O O -to O O -seize O O -some O O -nickel O O -and O O -copper O O -assets O O -. O O -- O O -Moscow B-ORG B-ORG -News I-ORG I-ORG -, O O -+ O O -94 O O -85 O O -Estonian B-MISC B-MISC -Tallinn B-ORG B-ORG -Pan I-ORG I-ORG -11 O O -net O O -46 O O -m O O -k O O -. O O -T B-LOC B-LOC -1996 O O -Tallinn B-ORG B-ORG -Pan I-ORG I-ORG -, O O -one O O -of O O -the O O -largest O O -banks O O -in O O -Estonia B-LOC B-LOC -, O O -made O O -a O O -11 O O -1996 O O -net O O -profit O O -of O O -46 O O -million O O -k O O -, O O -the O O -bank O O -said O O -on O O -Friday O O -. O O -It O O -said O O -in O O -a O O -statement O O -that O O -it O O -made O O -profits O O -of O O -4 O O -million O O -k O O -in O O -November O O -. O O -The O O -bank O O -made O O -a O O -profit O O -of O O -20 O O -million O O -k O O -in O O -the O O -first O O -half O O -of O O -the O O -year O O -. O O -Tallinn B-ORG B-ORG -Pan I-ORG I-ORG -said O O -its O O -assets O O -rose O O -17 O O -million O O -k O O -to O O -1 O O -billion O O -k O O -. O O -De O O -deposits O O -rose O O -to O O -85 O O -million O O -k O O -from O O -83 O O -million O O -k O O -and O O -time O O -deposits O O -increased O O -to O O -295 O O -million O O -k O O -from O O -285 O O -million O O -k O O -. O O -- O O -Riga B-ORG B-ORG -News I-ORG I-ORG -, O O -+ O O -72 O O -52 O O -Russia B-LOC B-LOC -ready O O -for O O -construct O O -work O O -with O O -Al B-ORG B-PER -. O O -M B-LOC B-LOC -1996 O O -Russia B-LOC B-LOC -said O O -on O O -Friday O O -it O O -expected O O -a O O -construct O O -relationship O O -with O O -Madeleine B-PER B-PER -Al I-PER I-PER -, O O -nominated O O -by O O -U B-LOC B-LOC -President O O -Bill B-PER B-PER -Clinton I-PER I-PER -to O O -be O O -Secretary O O -of O O -State O O -. O O -Inter B-ORG B-ORG -news O O -agency O O -quoted O O -First O O -Deputy O O -Foreign O O -Minister O O -Igor B-PER B-PER -Ivan I-PER I-PER -as O O -saying O O -Moscow B-LOC B-LOC -was O O -ready O O -for O O -" O O -most O O -active O O -and O O -construct O O -" O O -work O O -with O O -Al B-ORG B-PER -. O O -But O O -he O O -noted O O -that O O -policy O O -would O O -be O O -shaped O O -by O O -Clinton B-PER B-PER -and O O -President O O -Boris B-PER B-PER -Ye I-PER I-PER -. O O -Clinton B-PER B-PER -and O O -Ye B-PER B-PER -are O O -due O O -to O O -meet O O -next O O -March O O -for O O -their O O -first O O -summit O O -since O O -both O O -were O O -re O O -. O O -" O O -Our O O -countries O O -' O O -leaders O O -have O O -agreed O O -to O O -meet O O -in O O -March O O -, O O -1997 O O -. O O -The O O -Russian B-MISC B-MISC -foreign O O -ministry O O -believes O O -the O O -new O O -directions O O -in O O -the O O -development O O -of O O -Russian B-MISC B-MISC -relations O O -will O O -be O O -worked O O -out O O -there O O -, O O -" O O -Ivan B-PER B-PER -told O O -Inter B-ORG B-ORG -. O O -Inter B-ORG B-ORG -, O O -out O O -Al B-PER B-PER -' O O -biography O O -, O O -pointed O O -out O O -that O O -she O O -had O O -defended O O -Washington B-LOC B-LOC -' O O -interests O O -fiercely O O -as O O -U B-LOC B-LOC -ambassador O O -to O O -the O O -United B-ORG B-ORG -Nations I-ORG I-ORG -and O O -that O O -this O O -had O O -included O O -actively O O -supporting O O -NATO B-ORG B-ORG -' O O -plans O O -to O O -expand O O -eastward O O -. O O -Russia B-LOC B-LOC -oppose O O -NATO B-ORG B-ORG -' O O -plans O O -to O O -take O O -in O O -countries O O -of O O -eastern O O -and O O -central O O -Europe B-LOC B-LOC -which O O -used O O -to O O -be O O -part O O -of O O -the O O -Soviet B-MISC B-MISC -Warsaw B-ORG B-MISC -Pact I-ORG I-MISC -, O O -saying O O -such O O -moves O O -would O O -threaten O O -its O O -security O O -. O O -Ye B-PER B-PER -plans O O -return O O -to O O -K B-LOC B-LOC -for O O -Dec O O -25 O O -- O O -speaker O O -. O O -M B-LOC B-LOC -1996 O O -Russian B-MISC B-MISC -President O O -Boris B-PER B-PER -Ye I-PER I-PER -, O O -who O O -had O O -heart O O -bypass O O -surgery O O -a O O -month O O -ago O O -, O O -plans O O -to O O -return O O -to O O -work O O -on O O -December O O -25 O O -, O O -the O O -head O O -of O O -the O O -upper O O -chamber O O -of O O -parliament O O -told O O -Inter B-ORG B-ORG -news O O -agency O O -on O O -Friday O O -. O O -" O O -Today O O -he O O -is O O -a O O -mobile O O -, O O -energetic O O -man O O -with O O -lots O O -of O O -colour O O -in O O -his O O -cheeks O O -, O O -" O O -said O O -Ye B-PER B-PER -St I-PER I-PER -who O O -met O O -Ye B-PER B-PER -, O O -65 O O -, O O -on O O -Friday O O -at O O -a O O -country O O -residence O O -. O O -" O O -He O O -told O O -me O O -that O O -he O O -had O O -lost O O -20 O O -kg O O -( O O -44 O O -lbs O O -) O O -which O O -is O O -natural O O -after O O -such O O -an O O -operation O O -. O O -" O O -December O O -25 O O -, O O -a O O -normal O O -working O O -day O O -in O O -Russia B-LOC B-LOC -, O O -is O O -the O O -fifth O O -anniversary O O -of O O -Ye B-PER B-PER -' O O -arrival O O -in O O -the O O -K B-LOC B-LOC -. O O -He O O -took O O -over O O -there O O -, O O -and O O -took O O -control O O -of O O -the O O -red O O -button O O -controlling O O -nuclear O O -arms O O -, O O -in O O -December O O -1991 O O -when O O -Mikhail B-PER B-PER -Go I-PER I-PER -resigned O O -, O O -marking O O -the O O -end O O -of O O -the O O -Soviet B-LOC B-LOC -Union I-LOC I-LOC -. O O -Ye B-PER B-PER -has O O -been O O -shown O O -a O O -few O O -times O O -on O O -television O O -since O O -his O O -q O O -bypass O O -on O O -November O O -5 O O -but O O -has O O -yet O O -to O O -deliver O O -any O O -major O O -television O O -or O O -radio O O -address O O -to O O -the O O -nation O O -. O O -Sur O O -Ren B-PER B-PER -A I-PER I-PER -who O O -led O O -the O O -operation O O -, O O -told O O -It B-ORG B-ORG -news O O -agency O O -Ye B-PER B-PER -was O O -working O O -up O O -to O O -four O O -hours O O -a O O -day O O -at O O -his O O -residence O O -. O O -Bomb O O -explode O O -outside O O -home O O -of O O -expelled O O -Slovak B-MISC B-MISC -MP O O -. O O -BR B-LOC B-LOC -1996 O O -A O O -bomb O O -exploded O O -on O O -Friday O O -outside O O -the O O -home O O -of O O -a O O -Slovak B-MISC B-MISC -politician O O -expelled O O -from O O -parliament O O -after O O -he O O -quit O O -the O O -ruling O O -party O O -, O O -complaining O O -of O O -a O O -lack O O -of O O -democracy O O -in O O -the O O -country O O -. O O -The O O -official O O -T B-ORG B-ORG -news O O -agency O O -said O O -the O O -explosion O O -blew O O -out O O -all O O -ground O O -floor O O -windows O O -of O O -Fr B-PER B-PER -G I-PER I-PER -' O O -family O O -home O O -in O O -Gala B-LOC B-LOC -, O O -western O O -Slovakia B-LOC B-LOC -, O O -and O O -damaged O O -the O O -main O O -entrance O O -, O O -but O O -no O O -was O O -injured O O -. O O -G B-PER B-PER -, O O -formerly O O -a O O -member O O -of O O -Prime O O -Minister O O -Vladimir B-PER B-PER -Me I-PER I-PER -' O O -ruling O O -Movement B-ORG B-ORG -for I-ORG I-ORG -a I-ORG I-ORG -Democratic I-ORG I-ORG -Slovakia I-ORG I-ORG -, O O -was O O -stripped O O -of O O -his O O -parliamentary O O -mandate O O -on O O -Wednesday O O -after O O -leaving O O -the O O -party O O -last O O -month O O -in O O -protest O O -over O O -what O O -he O O -said O O -was O O -a O O -lack O O -of O O -democracy O O -in O O -the O O -country O O -. O O -He O O -said O O -he O O -had O O -been O O -receiving O O -anonymous O O -death O O -threats O O -since O O -making O O -the O O -move O O -. O O -" O O -This O O -was O O -an O O -act O O -of O O -terrorism O O -and O O -now O O -I O O -fear O O -not O O -only O O -for O O -my O O -own O O -life O O -, O O -but O O -also O O -of O O -that O O -of O O -my O O -wife O O -and O O -children O O -, O O -" O O -he O O -told O O -T B-ORG B-ORG -. O O -G B-PER B-PER -' O O -family O O -was O O -sleeping O O -in O O -a O O -bedroom O O -at O O -the O O -back O O -of O O -the O O -house O O -and O O -were O O -un O O -by O O -the O O -blast O O -. O O -It O O -was O O -not O O -immediately O O -clear O O -who O O -was O O -behind O O -the O O -blast O O -. O O -Bomb O O -explode O O -at O O -mosque O O -in O O -central O O -Bulgaria B-LOC B-LOC -. O O -S B-LOC B-LOC -1996 O O -A O O -bomb O O -exploded O O -on O O -Friday O O -at O O -a O O -mosque O O -in O O -the O O -central O O -Bulgarian B-MISC B-MISC -town O O -of O O -Kazan B-LOC B-LOC -, O O -causing O O -damage O O -but O O -no O O -injuries O O -, O O -state O O -radio O O -said O O -. O O -V O O -crime O O -has O O -so O O -since O O -the O O -collapse O O -of O O -communism O O -in O O -1989 O O -as O O -Bulgaria B-LOC B-LOC -moves O O -to O O -a O O -market O O -economy O O -. O O -Bomb O O -are O O -often O O -carried O O -out O O -by O O -criminals O O -to O O -settle O O -scores O O -but O O -the O O -motive O O -in O O -this O O -case O O -was O O -not O O -immediately O O -clear O O -. O O -Some O O -residents O O -of O O -the O O -Kazan B-LOC B-LOC -area O O -are O O -Mo B-MISC B-MISC -who O O -converted O O -to O O -Islam B-MISC B-MISC -during O O -Ottoman B-MISC O -Turkish B-MISC B-MISC -rule O O -. O O -The O O -majority O O -in O O -Bulgaria B-LOC B-LOC -are O O -Christians O B-MISC -. O O -The O O -radio O O -quoted O O -police O O -as O O -saying O O -the O O -blast O O -broke O O -windows O O -and O O -shattered O O -the O O -door O O -of O O -the O O -mosque O O -. O O -Hungary B-LOC B-LOC -o O O -/ O O -n O O -rates O O -end O O -up O O -before O O -Dec O O -10 O O -tax O O -payment O O -. O O -B B-LOC B-LOC -1996 O O -Hungarian B-MISC B-MISC -overnight O O -interest O O -rates O O -closed O O -higher O O -on O O -Friday O O -as O O -market O O -liquid O O -tightened O O -before O O -the O O -December O O -10 O O -social O O -security O O -contribution O O -payment O O -deadline O O -, O O -dealers O O -said O O -. O O -" O O -The O O -banks O O -are O O -already O O -preparing O O -for O O -the O O -December O O -10 O O -tax O O -payment O O -, O O -" O O -said O O -Budapest B-ORG B-LOC -Bank I-ORG O -' O O -Sand B-PER B-PER -To I-PER I-PER -. O O -" O O -They O O -expect O O -a O O -larger O O -payment O O -. O O -" O O -The O O -overnight O O -market O O -opened O O -at O O -22 O O -/ O O -22 O O -percent O O -, O O -then O O -substantial O O -money O O -was O O -taken O O -up O O -at O O -22 O O -percent O O -. O O -But O O -later O O -, O O -rates O O -dropped O O -and O O -closed O O -at O O -22 O O -/ O O -22 O O -as O O -a O O -large O O -bank O O -finished O O -borrow O O -money O O -. O O -On O O -Thursday O O -, O O -overnight O O -rates O O -moved O O -between O O -21 O O -and O O -22 O O -. O O -Deal O O -said O O -liquid O O -could O O -tighten O O -further O O -early O O -next O O -week O O -as O O -the O O -social O O -security O O -contribution O O -payments O O -date O O -approaches O O -. O O -- O O -Sand B-PER B-PER -Pet I-PER I-PER -, O O -Budapest B-LOC B-LOC -news O O -( O O -36 O O -1 O O -) O O -32 O O -404 O O -Mexico B-LOC B-LOC -stocks O O -off O O -low O O -but O O -still O O -hit O O -by O O -Greens B-ORG B-PER -. O O -ME B-ORG B-LOC -C I-ORG I-LOC -Mexican B-MISC B-MISC -stocks O O -closed O O -sharply O O -lower O O -Friday O O -, O O -but O O -had O O -made O O -a O O -tentative O O -recovery O O -as O O -initial O O -panic O O -and O O -vol O O -a O O -. O O -" O O -It O O -was O O -Greens B-PER B-PER -at O O -first O O -. O O -Then O O -once O O -we O O -saw O O -the O O -Dow B-MISC B-MISC -( O O -Jones B-MISC B-MISC -industrial O O -average O O -) O O -was O O -not O O -about O O -to O O -crash O O -, O O -some O O -buyers O O -stepped O O -in O O -, O O -" O O -said O O -a O O -trader O O -, O O -referring O O -to O O -Federal B-ORG B-ORG -Reserve I-ORG I-ORG -Chairman O O -Alan B-PER B-PER -Greens I-PER I-PER -, O O -whose O O -comments O O -that O O -assets O O -were O O -" O O -irrational O O -ex O O -" O O -upset O O -financial O O -markets O O -worldwide O O -. O O -The O O -blue O O -IP B-MISC B-MISC -index O O -ended O O -down O O -1 O O -points O O -, O O -or O O -43 O O -percent O O -, O O -at O O -3 O O -. O O -Volume O O -was O O -regular O O -at O O -74 O O -million O O -shares O O -traded O O -. O O -Mexican B-MISC B-MISC -stocks O O -were O O -also O O -hurt O O -by O O -U B-LOC B-LOC -long O O -bond O O -rates O O -which O O -had O O -begun O O -to O O -rise O O -before O O -Greens B-PER B-PER -' O O -comments O O -and O O -were O O -in O O -by O O -employment O O -data O O -released O O -before O O -trade O O -began O O -in O O -Mexico B-LOC B-LOC -. O O -Yi O O -on O O -U B-LOC B-LOC -30 O O -Treasury B-ORG B-ORG -bonds O O -were O O -6 O O -percent O O -when O O -stock O O -trading O O -closed O O -in O O -Mexico B-LOC B-LOC -, O O -unchanged O O -from O O -Thursday O O -. O O -On O O -the O O -broad O O -market O O -, O O -107 O O -stocks O O -changed O O -hands O O -, O O -of O O -which O O -loser O O -well O O -outnumbered O O -winners O O -by O O -75 O O -to O O -13 O O -. O O -Trade O O -noted O O -the O O -lack O O -of O O -blue O O -chips O O -or O O -stocks O O -traded O O -at O O -significant O O -volume O O -among O O -the O O -gain O O -. O O -Si B-ORG B-ORG -, O O -the O O -steel O O -arm O O -of O O -the O O -debt O O -Side B-ORG B-ORG -group O O -headed O O -the O O -loser O O -, O O -off O O -7 O O -cent O O -( O O -1 O O -cent O O -) O O -at O O -1 O O -p O O -( O O -18 O O -cents O O -) O O -. O O -Side B-ORG B-ORG -fell O O -4 O O -cent O O -( O O -1 O O -cent O O -) O O -to O O -95 O O -cent O O -( O O -12 O O -cents O O -) O O -. O O -Trade O O -also O O -remarked O O -that O O -Mexican B-MISC B-MISC -AD B-MISC B-MISC -suffered O O -in O O -New B-LOC B-LOC -York I-LOC I-LOC -. O O -Heavyweight O O -Tel B-ORG B-ORG -and O O -Tel B-ORG B-ORG -ended O O -off O O -25 O O -cents O O -and O O -75 O O -cents O O -, O O -respectively O O -, O O -at O O -$ O O -31 O O -and O O -$ O O -25 O O -. O O -" O O -Falling O O -share O O -prices O O -in O O -New B-LOC B-LOC -York I-LOC I-LOC -do O O -n O O -hurt O O -Mexico B-LOC B-LOC -as O O -long O O -as O O -it O O -happens O O -gradually O O -, O O -as O O -earlier O O -this O O -week O O -. O O -It O O -' O O -a O O -sudden O O -p O O -that O O -takes O O -its O O -toll O O -, O O -" O O -said O O -Carlos B-PER B-PER -Ponce I-PER I-PER -, O O -research O O -director O O -at O O -Santa B-LOC B-LOC -. O O -Trade O O -and O O -analysts O O -differed O O -as O O -to O O -how O O -firm O O -the O O -relative O O -recovery O O -on O O -Friday O O -was O O -. O O -" O O -Some O O -buyers O O -stepped O O -in O O -, O O -but O O -the O O -market O O -was O O -not O O -very O O -convinced O O -. O O -Volume O O -was O O -lack O O -, O O -" O O -said O O -one O O -trader O O -. O O -" O O -The O O -market O O -' O O -very O O -healthy O O -, O O -we O O -' O O -buying O O -, O O -" O O -said O O -another O O -trader O O -. O O -Ponce B-PER B-PER -said O O -shares O O -were O O -certainly O O -attractive O O -priced O O -in O O -Mexico B-LOC B-LOC -, O O -but O O -would O O -not O O -appreciate O O -until O O -foreign O O -buyers O O -stepped O O -in O O -, O O -which O O -they O O -had O O -yet O O -to O O -do O O -. O O -' O O -Plastic O O -surgery O O -gets O O -boost O O -in O O -Brazil B-LOC B-LOC -. O O -Simon B-PER B-PER -de I-PER I-PER -Lo I-PER I-PER -R B-LOC B-LOC -DE I-LOC I-LOC -J I-LOC I-LOC -1996 O O -Plastic O O -surgery O O -is O O -boom O O -, O O -especially O O -among O O -men O O -, O O -as O O -Brazilian B-MISC B-MISC -spend O O -much O O -of O O -their O O -new O O -wealth O O -on O O -the O O -latest O O -beauty O O -treatments O O -, O O -said O O -the O O -organise O O -of O O -a O O -four O O -international O O -plastic O O -surgery O O -conference O O -that O O -opened O O -on O O -Friday O O -. O O -The O O -number O O -of O O -plastic O O -surge O O -in O O -Brazil B-LOC B-LOC -has O O -jumped O O -30 O O -percent O O -to O O -an O O -estimated O O -150 O O -this O O -year O O -since O O -an O O -anti O O -plan O O -was O O -introduced O O -in O O -July O O -1994 O O -, O O -Far B-PER B-PER -Ha I-PER I-PER -, O O -the O O -president O O -of O O -the O O -Brazilian B-ORG B-ORG -Plastic I-ORG I-ORG -Surgery I-ORG I-ORG -Society I-ORG I-ORG -( O O -S B-ORG B-ORG -) O O -, O O -said O O -. O O -The O O -number O O -of O O -operations O O -on O O -men O O -increased O O -even O O -more O O -- O O -by O O -80 O O -percent O O -, O O -from O O -8 O O -in O O -1994 O O -to O O -15 O O -in O O -1995 O O -, O O -he O O -said O O -. O O -" O O -Brazil B-LOC B-LOC -ranks O O -right O O -at O O -the O O -top O O -for O O -plastic O O -surgery O O -with O O -respect O O -to O O -the O O -number O O -of O O -surgeon O O -, O O -the O O -number O O -of O O -patients O O -, O O -number O O -of O O -operations O O -, O O -number O O -of O O -conferences O O -. O O -Our O O -statistics O O -are O O -the O O -highest O O -for O O -everything O O -, O O -" O O -Ha B-PER B-PER -said O O -. O O -" O O -We O O -believe O O -the O O -increase O O -in O O -plastic O O -surge O O -for O O -men O O -results O O -from O O -the O O -difficulties O O -in O O -the O O -job O O -market O O -. O O -People O O -need O O -to O O -have O O -a O O -more O O -youthful O O -look O O -to O O -compete O O -in O O -the O O -job O O -market O O -, O O -given O O -the O O -profound O O -changes O O -in O O -Latin B-LOC B-LOC -America I-LOC I-LOC -' O O -economy O O -. O O -" O O -A O O -controlled O O -exchange O O -rate O O -, O O -trade O O -liberal O O -and O O -tight O O -monetary O O -policies O O -have O O -also O O -dramatically O O -curb O O -inflation O O -, O O -making O O -more O O -money O O -available O O -for O O -co O O -surgery O O -. O O -Brazil B-LOC B-LOC -has O O -been O O -at O O -the O O -forefront O O -in O O -plastic O O -surgery O O -for O O -decades O O -and O O -is O O -home O O -to O O -one O O -of O O -the O O -most O O -famous O O -surgeon O O -, O O -I B-PER B-PER -Pit I-PER I-PER -. O O -There O O -are O O -6 O O -plastic O O -surgeon O O -there O O -, O O -of O O -which O O -4 O O -have O O -qualified O O -to O O -be O O -members O O -of O O -the O O -S B-ORG B-ORG -. O O -Every O O -year O O -, O O -500 O O -new O O -plastic O O -surgeon O O -graduate O O -in O O -Brazil B-LOC B-LOC -and O O -medical O O -students O O -from O O -all O O -over O O -the O O -world O O -come O O -to O O -study O O -there O O -. O O -Ha B-PER B-PER -attributes O O -Brazil B-LOC B-LOC -' O O -fascination O O -with O O -plastic O O -surgery O O -not O O -to O O -excessive O O -van O O -but O O -to O O -the O O -country O O -' O O -mix O O -and O O -match O O -of O O -different O O -races O O -, O O -which O O -can O O -create O O -physical O O -dish O O -. O O -" O O -What O O -happens O O -is O O -the O O -nose O O -sometimes O O -does O O -n O O -match O O -the O O -mouth O O -or O O -the O O -butt O O -do O O -n O O -match O O -with O O -the O O -legs O O -, O O -" O O -he O O -said O O -. O O -Brazil B-LOC B-LOC -' O O -most O O -sought O O -beauty O O -treatment O O -is O O -lip O O -in O O -which O O -fat O O -is O O -sucked O O -away O O -from O O -areas O O -of O O -the O O -body O O -, O O -with O O -about O O -30 O O -operations O O -a O O -year O O -at O O -a O O -cost O O -of O O -$ O O -3 O O -to O O -$ O O -4 O O -each O O -. O O -St O O -t O O -and O O -breast O O -operations O O -are O O -also O O -popular O O -since O O -the O O -tropical O O -climate O O -calls O O -for O O -flesh O O -fashion O O -, O O -but O O -unlike O O -women O O -elsewhere O O -Brazilian B-MISC B-MISC -tend O O -to O O -have O O -breast O O -reduction O O -and O O -butt O O -imp O O -. O O -" O O -The O O -women O O -who O O -want O O -to O O -reduce O O -their O O -breasts O O -here O O -would O O -probably O O -want O O -to O O -increase O O -them O O -in O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -, O O -" O O -S B-ORG B-ORG -Vice O O -Oswald B-PER B-PER -Sal I-PER I-PER -said O O -. O O -" O O -Beauty O O -ideals O O -and O O -cultures O O -are O O -different O O -in O O -every O O -country O O -. O O -" O O -Plastic O O -surgery O O -scare O O -like O O -the O O -case O O -in O O -which O O -Brazilian B-MISC B-MISC -model O O -Claudia B-PER B-PER -Liz I-PER I-PER -fell O O -into O O -a O O -coma O O -after O O -being O O -an O O -for O O -a O O -lip O O -in O O -October O O -are O O -not O O -much O O -of O O -a O O -de O O -. O O -Sal B-ORG B-PER -said O O -operations O O -fell O O -30 O O -percent O O -immediately O O -after O O -that O O -case O O -but O O -the O O -rate O O -was O O -back O O -to O O -normal O O -now O O -. O O -Daily O O -Argentine B-MISC B-MISC -grain O O -fixing O O -- O O -Cam B-ORG O -A I-ORG O -. O O -B B-LOC B-LOC -AI I-LOC I-LOC -1996 O O -A O O -December O O -5 O O -price O O -fix O O -: O O -Buenos B-LOC B-LOC -Aires I-LOC I-LOC -Que B-ORG B-LOC -Rosario I-ORG B-LOC -Ba I-ORG B-LOC -Blanc I-ORG I-LOC -O I-ORG O -un O O -un O O -un O O -un O O -W O O -121 O O -130 O O -121 O O -121 O O -Mai B-ORG O -( O O -Flint B-LOC O -) O O -113 O O -114 O O -113 O O -112 O O -Mai B-ORG O -( O O -Den B-ORG O -) O O -113 O O -114 O O -113 O O -112 O O -So B-ORG O -un O O -un O O -un O O -un O O -Mill B-ORG O -un O O -un O O -90 O O -un O O -So B-ORG O -28 O O -un O O -28 O O -un O O -Suns B-ORG O -219 O O -216 O O -220 O O -216 O O -- O O -Buenos B-ORG B-ORG -Aires I-ORG I-ORG -News I-ORG I-ORG -+ O O -31 O O -Mexican B-MISC B-MISC -daily O O -port O O -ME B-LOC B-LOC -C I-LOC I-LOC -1996 O O -All O O -major O O -ports O O -were O O -open O O -as O O -of O O -1000 O O -local O O -/ O O -1600 O O -GM B-MISC B-MISC -, O O -the O O -Communications B-ORG B-ORG -and I-ORG I-ORG -Transportation I-ORG I-ORG -Ministry I-ORG I-ORG -said O O -in O O -a O O -daily O O -update O O -. O O -Tam B-LOC B-LOC -port O O -authorities O O -said O O -fishing O O -restrictions O O -were O O -in O O -place O O -in O O -an O O -area O O -adjacent O O -to O O -the O O -port O O -because O O -of O O -a O O -g O O -study O O -being O O -carried O O -out O O -in O O -deep O O -waters O O -of O O -the O O -region O O -from O O -the O O -ship O O -Ken B-ORG B-MISC -. O O -The O O -ministry O O -updated O O -port O O -conditions O O -and O O -shipping O O -warnings O O -for O O -the O O -Gulf B-LOC B-LOC -of I-LOC I-LOC -Mexico I-LOC I-LOC -, O O -Caribbean B-LOC B-LOC -and O O -Pacific B-LOC B-LOC -Coast I-LOC I-LOC -. O O -- O O -Pacific B-LOC B-LOC -Coast I-LOC I-LOC -: O O -Light O O -rains O O -along O O -the O O -coast O O -of O O -southern O O -Baja B-LOC B-LOC -California I-LOC I-LOC -and O O -Sin B-LOC B-LOC -, O O -with O O -the O O -rest O O -of O O -the O O -coast O O -seeing O O -clear O O -skies O O -. O O -Wind O O -from O O -the O O -northeast O O -of O O -10 O O -to O O -15 O O -knots O O -( O O -19 O O -to O O -28 O O -kilometers O O -/ O O -11 O O -to O O -17 O O -miles O O -per O O -hour O O -) O O -. O O -A O O -new O O -front O O -is O O -seen O O -emerging O O -during O O -the O O -course O O -of O O -Friday O O -, O O -affecting O O -the O O -north O O -of O O -the O O -Baja B-LOC B-LOC -California I-LOC I-LOC -peninsula O O -and O O -Son B-LOC B-LOC -state O O -, O O -bringing O O -lower O O -temperatures O O -, O O -light O O -rains O O -and O O -waves O O -up O O -to O O -six O O -feet O O -. O O -- O O -Gulf B-LOC B-LOC -of I-LOC I-LOC -Mexico I-LOC I-LOC -: O O -Cold O O -front O O -bringing O O -light O O -rains O O -to O O -the O O -coast O O -of O O -Tam B-LOC B-LOC -, O O -but O O -with O O -the O O -rest O O -of O O -the O O -Gulf B-LOC B-LOC -in O O -clear O O -skies O O -. O O -Wind O O -from O O -the O O -northeast O O -at O O -10 O O -to O O -15 O O -knots O O -( O O -19 O O -to O O -28 O O -kilometers O O -/ O O -11 O O -to O O -17 O O -miles O O -per O O -hour O O -) O O -. O O -- O O -Caribbean B-LOC B-LOC -: O O -Tropical O O -air O O -carrying O O -s O O -light O O -rains O O -over O O -the O O -coast O O -of O O -Q B-LOC B-LOC -R I-LOC I-LOC -state O O -. O O -Wind O O -from O O -the O O -northeast O O -at O O -10 O O -to O O -15 O O -knots O O -with O O -waves O O -three O O -to O O -five O O -feet O O -high O O -. O O -- O O -Chris B-PER B-PER -As I-PER I-PER -, O O -Mexico B-LOC B-LOC -City I-LOC I-LOC -news O O -+ O O -72 O O -Brazil B-LOC B-LOC -exam O O -cheat O O -caught O O -using O O -" O O -page O O -" O O -watches O O -. O O -R B-LOC B-LOC -DE I-LOC I-LOC -J I-LOC I-LOC -1996 O O -Brazilian B-MISC B-MISC -students O O -have O O -been O O -caught O O -cheating O O -in O O -university O O -entrance O O -exams O O -by O O -using O O -digital O O -watches O O -which O O -gave O O -the O O -correct O O -answers O O -to O O -test O O -questions O O -, O O -a O O -newspaper O O -said O O -on O O -Friday O O -. O O -Rio B-LOC B-LOC -de I-LOC I-LOC -Janeiro I-LOC I-LOC -state O O -university O O -officials O O -discovered O O -students O O -were O O -paying O O -15 O O -re O O -( O O -$ O O -14 O O -) O O -for O O -the O O -special O O -watches O O -, O O -which O O -operated O O -like O O -a O O -telephone O O -page O O -to O O -receive O O -correct O O -answers O O -, O O -O B-ORG B-ORG -G I-ORG I-ORG -said O O -. O O -Seven O O -students O O -were O O -found O O -with O O -the O O -watches O O -and O O -disqualified O O -, O O -O B-ORG B-ORG -G I-ORG I-ORG -said O O -. O O -Chile B-LOC B-LOC -, O O -Mexico B-LOC B-LOC -to O O -seek O O -to O O -broad O O -trade O O -deal O O -. O O -SA B-LOC B-PER -1996 O O -Chile B-LOC B-LOC -and O O -Mexico B-LOC B-LOC -will O O -start O O -negotiations O O -next O O -year O O -to O O -broad O O -their O O -free O O -trade O O -agreement O O -to O O -include O O -services O O -and O O -investments O O -, O O -Finance O B-ORG -Minister O O -Eduardo B-PER B-PER -An I-PER I-PER -said O O -. O O -Chile B-LOC B-LOC -hopes O O -to O O -broad O O -the O O -treaty O O -signed O O -in O O -1994 O O -beyond O O -reduction O O -of O O -ta O O -on O O -imports O O -and O O -exports O O -and O O -add O O -provisions O O -covering O O -services O O -and O O -investment O O -codes O O -, O O -said O O -An B-PER B-PER -. O O -Both O O -areas O O -tend O O -to O O -more O O -laden O O -with O O -friction O O -in O O -free O O -trade O O -negotiations O O -than O O -ta O O -reduction O O -. O O -' O O -' O O -In O O -January O O -or O O -February O O -, O O -we O O -' O O -have O O -some O O -very O O -close O O -contacts O O -with O O -Mexico B-LOC B-LOC -to O O -add O O -the O O -issue O O -of O O -services O O -and O O -advance O O -on O O -the O O -issue O O -of O O -investments O O -, O O -' O O -' O O -An B-PER B-PER -told O O -reporters O O -after O O -signing O O -a O O -free O O -trade O O -deal O O -with O O -Canada B-LOC B-LOC -. O O -' O O -' O O -We O O -want O O -to O O -give O O -the O O -treaty O O -between O O -Mexico B-LOC B-LOC -and O O -Chile B-LOC B-LOC -greater O O -depth O O -and O O -coverage O O -than O O -it O O -has O O -now O O -. O O -It O O -' O O -very O O -good O O -now O O -, O O -but O O -it O O -practically O O -only O O -covers O O -trade O O -in O O -goods O O -, O O -' O O -' O O -he O O -said O O -. O O -An B-PER B-PER -also O O -said O O -he O O -was O O -confident O O -the O O -Chilean B-ORG B-MISC -Congress I-ORG O -would O O -rat O O -the O O -treaty O O -with O O -Congress B-ORG O -quickly O O -. O O -' O O -' O O -The O O -reactions O O -from O O -business O O -and O O -unions O O -which O O -I O O -have O O -seen O O -have O O -been O O -almost O O -unanimously O O -positive O O -, O O -so O O -I O O -do O O -n O O -see O O -any O O -problem O O -, O O -' O O -' O O -he O O -said O O -. O O -- O O -Roger B-PER B-PER -At I-PER I-PER -, O O -Santiago B-LOC B-LOC -news O O -+ O O -x O O -Indonesia B-LOC B-LOC -' O O -Bel B-PER B-PER -leaves O O -for O O -Nobel B-MISC B-MISC -award O O -ceremony O O -. O O -D B-LOC B-LOC -, O O -East B-LOC B-LOC -Timor I-LOC I-LOC -1996 O O -East B-MISC B-MISC -Timor I-MISC I-MISC -Roman B-MISC B-MISC -Catholic I-MISC I-MISC -Bishop O O -Carlos B-PER B-PER -Bel I-PER I-PER -left O O -Di B-LOC B-LOC -on O O -Friday O O -on O O -his O O -way O O -to O O -Norway B-LOC B-LOC -for O O -the O O -awards O O -ceremony O O -as O O -co O O -of O O -the O O -1996 O O -Nobel B-MISC B-MISC -Peace I-MISC I-MISC -Prize I-MISC I-MISC -. O O -Witness O O -said O O -Bel B-PER B-PER -left O O -the O O -territory O O -for O O -the O O -Indonesian B-MISC B-MISC -capital O O -Jakarta B-LOC B-LOC -accompanied O O -by O O -five O O -other O O -people O O -. O O -It O O -was O O -not O O -immediately O O -known O O -when O O -he O O -would O O -arrive O O -in O O -Oslo B-LOC B-LOC -. O O -The O O -bishop O O -will O O -jointly O O -receive O O -the O O -Nobel B-MISC B-MISC -award O O -next O O -Tuesday O O -with O O -East B-MISC B-MISC -Timor I-MISC I-MISC -activist O O -Jose B-PER B-PER -Ramos I-PER I-PER -Ho I-PER I-PER -, O O -who O O -lives O O -in O O -self O O -in O O -Australia B-LOC B-LOC -. O O -The O O -Indonesian B-MISC B-MISC -government O O -has O O -condemned O O -the O O -inclusion O O -of O O -Ramos B-PER B-PER -Ho I-PER I-PER -in O O -the O O -award O O -, O O -and O O -Foreign O O -Minister O O -Ali B-PER B-PER -Al I-PER I-PER -said O O -on O O -Friday O O -that O O -Indonesia B-LOC B-LOC -would O O -not O O -be O O -represented O O -officially O O -at O O -the O O -ceremony O O -in O O -the O O -Norwegian B-MISC B-MISC -capital O O -. O O -" O O -I O O -sincere O O -believe O O -that O O -this O O -unfortunate O O -choice O O -in O O -giving O O -the O O -honour O O -to O O -such O O -a O O -controversial O O -figure O O -as O O -Ramos B-PER B-PER -Ho I-PER I-PER -. O O -will O O -ex O O -the O O -problem O O -in O O -finding O O -a O O -solution O O -( O O -to O O -East B-LOC B-LOC -Timor I-LOC I-LOC -) O O -, O O -" O O -Al B-PER B-PER -said O O -on O O -Friday O O -. O O -He O O -was O O -responding O O -to O O -questions O O -at O O -a O O -news O O -conference O O -called O O -to O O -discuss O O -next O O -week O O -' O O -ministerial O O -meeting O O -of O O -the O O -Organisation B-ORG B-ORG -of I-ORG I-ORG -the I-ORG I-ORG -Islamic I-ORG I-ORG -Conference I-ORG I-ORG -( O O -O B-ORG B-ORG -) O O -in O O -Jakarta B-LOC B-LOC -. O O -Ramos B-PER B-PER -Ho I-PER I-PER -has O O -been O O -a O O -vocal O O -leader O O -of O O -the O O -opposition O O -to O O -Jakarta B-LOC B-LOC -' O O -rule O O -in O O -the O O -territory O O -. O O -Bel B-PER B-PER -and O O -Ramos B-PER B-PER -Ho I-PER I-PER -were O O -aware O O -the O O -prize O O -for O O -their O O -efforts O O -to O O -secure O O -a O O -peaceful O O -solution O O -to O O -the O O -issue O O -of O O -East B-LOC B-LOC -Timor I-LOC I-LOC -, O O -a O O -former O O -Portuguese B-MISC B-MISC -colony O O -which O O -Indonesia B-LOC B-LOC -invaded O O -in O O -1975 O O -and O O -annexed O O -the O O -following O O -year O O -. O O -The O O -United B-ORG B-ORG -Nations I-ORG I-ORG -has O O -never O O -recognised O O -Jakarta B-LOC B-LOC -' O O -move O O -. O O -Al B-PER B-PER -said O O -the O O -government O O -' O O -position O O -on O O -the O O -Nobel B-MISC B-MISC -Peace I-MISC I-MISC -Prize I-MISC I-MISC -would O O -have O O -been O O -different O O -if O O -it O O -had O O -been O O -awarded O O -solely O O -to O O -Bel B-PER B-PER -. O O -Ask O O -if O O -the O O -Indonesian B-MISC B-MISC -ambassador O O -to O O -Norway B-LOC B-LOC -would O O -have O O -attended O O -the O O -ceremony O O -if O O -only O O -Bel B-PER B-PER -had O O -been O O -involved O O -, O O -Al B-PER B-PER -replied O O -: O O -" O O -Probably O O -, O O -yes O O -, O O -but O O -that O O -is O O -a O O -h O O -question O O -. O O -" O O -Al B-PER B-PER -said O O -on O O -Tuesday O O -that O O -on O O -his O O -way O O -back O O -from O O -Oslo B-LOC B-LOC -, O O -Bel B-PER B-PER -would O O -visit O O -the O O -Vatican B-LOC B-LOC -to O O -see O O -the O O -Pope B-MISC O -, O O -and O O -would O O -also O O -meet O O -German B-MISC B-MISC -Chancellor O O -Helmut B-PER B-PER -Ko I-PER I-PER -in O O -Bonn B-LOC B-LOC -. O O -Ko B-PER B-PER -had O O -wanted O O -to O O -meet O O -Bel B-PER B-PER -during O O -the O O -chancellor O O -' O O -official O O -visit O O -to O O -Indonesia B-LOC B-LOC -last O O -month O O -, O O -but O O -the O O -bishop O O -was O O -too O O -busy O O -in O O -East B-LOC B-LOC -Timor I-LOC I-LOC -to O O -come O O -to O O -Jakarta B-LOC B-LOC -. O O -China B-LOC B-LOC -to O O -open O O -port O O -in O O -Hai B-LOC B-LOC -to O O -foreign O O -ships O O -. O O -B B-LOC B-LOC -1996 O O -China B-LOC B-LOC -' O O -State B-ORG B-ORG -Council I-ORG I-ORG -, O O -or O O -cabinet O O -, O O -has O O -given O O -a O O -port O O -in O O -the O O -southern O O -province O O -of O O -Hai B-LOC B-LOC -permission O O -to O O -open O O -to O O -foreign O O -vessels O O -, O O -the O O -Xi B-ORG B-ORG -news O O -agency O O -said O O -on O O -Friday O O -. O O -Xi B-PER B-ORG -did O O -not O O -say O O -when O O -Qing B-LOC B-LOC -port O O -in O O -Wen B-LOC B-LOC -city O O -would O O -be O O -opened O O -to O O -foreign O O -vessels O O -. O O -Wen B-LOC B-LOC -has O O -built O O -a O O -berth O O -for O O -5 O O -dead O O -container O O -ships O O -at O O -the O O -port O O -and O O -invested O O -34 O O -million O O -y O O -( O O -$ O O -4 O O -million O O -) O O -to O O -d O O -the O O -harbour O O -, O O -Xi B-PER B-ORG -said O O -. O O -It O O -gave O O -no O O -further O O -details O O -. O O -( O O -$ O O -1 O O -= O O -8 O O -y O O -) O O -Government O O -di O O -protest O O -with O O -water O O -cannons O O -. O O -RA B-LOC B-LOC -1996 O O -Burmese B-MISC B-MISC -troops O O -and O O -riot O O -police O O -moved O O -in O O -to O O -di O O -a O O -student O O -street O O -protest O O -at O O -a O O -suburban O O -road O O -junction O O -near O O -the O O -Ra B-ORG B-ORG -( I-LOC I-ORG -Ra B-LOC I-ORG -) I-ORG I-ORG -University I-ORG I-ORG -early O O -on O O -Saturday O O -, O O -witnesses O O -said O O -. O O -They O O -said O O -police O O -and O O -troops O O -used O O -water O O -cannons O O -from O O -fire O O -engines O O -to O O -sub O O -about O O -120 O O -university O O -students O O -sitting O O -in O O -at O O -the O O -centre O O -of O O -the O O -junction O O -at O O -about O O -3 O O -a O O -before O O -they O O -moved O O -in O O -to O O -round O O -them O O -up O O -. O O -The O O -students O O -, O O -who O O -had O O -staged O O -an O O -11 O O -protest O O -at O O -the O O -junction O O -in O O -northern O O -Ra B-LOC B-LOC -, O O -were O O -taken O O -away O O -in O O -three O O -vehicles O O -. O O -The O O -witnesses O O -said O O -some O O -of O O -the O O -students O O -were O O -hit O O -with O O -bat O O -while O O -they O O -were O O -herd O O -onto O O -the O O -vehicles O O -and O O -it O O -was O O -believed O O -they O O -were O O -taken O O -to O O -the O O -In B-LOC B-LOC -prison O O -in O O -suburban O O -Ra B-LOC B-LOC -. O O -The O O -protesting O O -students O O -, O O -mostly O O -from O O -Ra B-LOC B-ORG -University I-ORG I-ORG -, O O -were O O -demanding O O -the O O -right O O -to O O -organise O O -independent O O -unions O O -on O O -campuses O O -and O O -the O O -release O O -of O O -about O O -80 O O -student O O -leaders O O -currently O O -in O O -jail O O -. O O -They O O -were O O -among O O -500 O O -students O O -who O O -started O O -demonstrating O O -at O O -the O O -intersection O O -on O O -late O O -Friday O O -afternoon O O -. O O -The O O -protest O O -was O O -the O O -second O O -major O O -one O O -in O O -five O O -days O O -in O O -the O O -capital O O -. O O -Burmese B-MISC B-MISC -students O O -march O O -briefly O O -out O O -of O O -campus O O -. O O -V B-PER B-PER -Amor I-PER I-PER -RA B-LOC B-LOC -1996 O O -About O O -200 O O -Burmese B-MISC B-MISC -students O O -marched O O -briefly O O -from O O -troubled O O -Yang B-ORG B-ORG -Institute I-ORG I-ORG -of I-ORG I-ORG -Technology I-ORG I-ORG -in O O -northern O O -Ra B-LOC B-LOC -on O O -Friday O O -towards O O -the O O -University B-ORG B-ORG -of I-ORG I-ORG -Yang I-ORG I-ORG -six O O -km O O -( O O -four O O -miles O O -) O O -away O O -, O O -and O O -returned O O -to O O -their O O -campus O O -, O O -witnesses O O -said O O -. O O -Seven O O -truck O O -of O O -armed O O -riot O O -police O O -and O O -three O O -fire O O -engines O O -were O O -on O O -stand O O -at O O -one O O -of O O -the O O -junction O O -near O O -the O O -institute O O -. O O -There O O -were O O -no O O -clashes O O -. O O -" O O -They O O -are O O -now O O -back O O -in O O -the O O -Y B-LOC B-ORG -campus O O -, O O -" O O -an O O -institute O O -official O O -who O O -declined O O -to O O -be O O -identified O O -told O O -Re B-ORG B-ORG -by O O -telephone O O -. O O -One O O -of O O -two O O -roads O O -leading O O -to O O -the O O -University B-ORG B-ORG -of I-ORG I-ORG -Yang I-ORG I-ORG -from O O -the O O -institute O O -had O O -been O O -closed O O -by O O -authorities O O -. O O -But O O -about O O -300 O O -university O O -students O O -were O O -still O O -gathered O O -outside O O -the O O -gates O O -of O O -their O O -campus O O -, O O -witnesses O O -said O O -. O O -They O O -were O O -singing O O -peacefully O O -. O O -On O O -Monday O O -and O O -Tuesday O O -, O O -students O O -from O O -the O O -institute O O -and O O -the O O -university O O -launched O O -protests O O -against O O -what O O -they O O -said O O -was O O -unfair O O -handling O O -by O O -police O O -of O O -a O O -bra O O -between O O -some O O -of O O -their O O -colleagues O O -and O O -restaurant O O -owners O O -in O O -October O O -. O O -On O O -Tuesday O O -and O O -Wednesday O O -, O O -opposition O O -leader O O -Au B-PER B-PER -San I-PER I-PER -Su I-PER I-PER -K I-PER I-PER -was O O -restricted O O -to O O -her O O -home O O -by O O -the O O -military O O -government O O -to O O -prevent O O -her O O -from O O -being O O -drawn O O -into O O -the O O -protests O O -. O O -She O O -was O O -allowed O O -to O O -move O O -freely O O -on O O -Thursday O O -. O O -The O O -protest O O -culminated O O -at O O -dawn O O -on O O -Tuesday O O -with O O -several O O -hundred O O -students O O -being O O -detained O O -briefly O O -by O O -police O O -in O O -central O O -Ra B-LOC B-LOC -. O O -The O O -street O O -protests O O -were O O -the O O -biggest O O -seen O O -in O O -the O O -capital O O -since O O -the O O -student O O -pro O O -demonstrations O O -of O O -September O O -1988 O O -when O O -the O O -j O O -crushed O O -the O O -uprising O O -. O O -Thousands O O -were O O -killed O O -or O O -imprisoned O O -. O O -Earlier O O -on O O -Friday O O -some O O -of O O -the O O -students O O -, O O -who O O -were O O -held O O -briefly O O -by O O -police O O -during O O -the O O -protests O O -earlier O O -this O O -week O O -, O O -said O O -they O O -were O O -still O O -di O O -with O O -the O O -military O O -government O O -. O O -They O O -told O O -Re B-ORG B-ORG -they O O -were O O -unhappy O O -that O O -the O O -ruling O O -State B-ORG B-ORG -Law I-ORG I-ORG -and I-ORG I-ORG -Order I-ORG I-ORG -Restoration I-ORG I-ORG -Council I-ORG I-ORG -( O O -SL B-ORG B-ORG -) O O -had O O -not O O -he O O -their O O -calls O O -for O O -the O O -right O O -to O O -organise O O -independent O O -unions O O -on O O -campus O O -. O O -" O O -We O O -still O O -want O O -government O O -answers O O -to O O -our O O -demands O O -. O O -We O O -want O O -police O O -punishment O O -to O O -be O O -published O O -in O O -newspapers O O -, O O -" O O -one O O -student O O -said O O -. O O -But O O -the O O -students O O -stressed O O -their O O -protests O O -were O O -non O O -and O O -they O O -had O O -no O O -contact O O -with O O -Su B-PER B-PER -K I-PER I-PER -' O O -National B-ORG B-ORG -League I-ORG I-ORG -for I-ORG I-ORG -Democracy I-ORG I-ORG -( O O -NL B-ORG B-ORG -) O O -. O O -Su B-PER B-PER -K I-PER I-PER -, O O -a O O -Nobel B-MISC B-MISC -la O O -and O O -daughter O O -of O O -independence O O -hero O O -Au B-PER B-PER -San I-PER I-PER -, O O -and O O -key O O -NL B-ORG B-ORG -officials O O -have O O -also O O -denied O O -any O O -link O O -with O O -the O O -students O O -. O O -But O O -they O O -have O O -said O O -both O O -parties O O -had O O -a O O -" O O -moral O O -link O O -" O O -in O O -that O O -they O O -were O O -against O O -police O O -brutality O O -and O O -injustice O O -. O O -The O O -students O O -also O O -demanded O O -the O O -government O O -announce O O -punishment O O -met O O -out O O -to O O -policemen O O -who O O -they O O -said O O -had O O -man O O -students O O -involved O O -in O O -a O O -bra O O -with O O -some O O -restaurant O O -owners O O -near O O -the O O -Yang B-LOC B-LOC -institute O O -in O O -October O O -. O O -The O O -students O O -appealed O O -to O O -the O O -government O O -not O O -to O O -close O O -the O O -institute O O -because O O -of O O -their O O -latest O O -demonstration O O -. O O -The O O -institute O O -was O O -shut O O -for O O -nearly O O -two O O -years O O -after O O -the O O -1988 O O -uprising O O -. O O -On O O -Friday O O -, O O -the O O -road O O -leading O O -to O O -Su B-PER B-PER -K I-PER I-PER -' O O -lakes O O -residence O O -in O O -central O O -Ra B-LOC B-LOC -remained O O -closed O O -by O O -police O O -. O O -Union O O -leaders O O -outrage O O -by O O -W B-ORG B-ORG -s O O -to O O -IL B-ORG B-ORG -head O O -. O O -S B-LOC B-LOC -1996 O O -International O O -trade O O -union O O -leaders O O -on O O -Friday O O -expressed O O -outrage O O -that O O -the O O -head O O -of O O -the O O -International B-ORG B-ORG -Labour I-ORG I-ORG -Organisation I-ORG I-ORG -( O O -IL B-ORG B-ORG -) O O -had O O -been O O -barred O O -from O O -speaking O O -at O O -next O O -week O O -' O O -W B-ORG B-ORG -meeting O O -in O O -Singapore B-LOC B-LOC -. O O -Bill B-PER B-PER -Jordan I-PER I-PER -, O O -general O O -secretary O O -of O O -the O O -International B-ORG B-ORG -Confederation I-ORG I-ORG -of I-ORG I-ORG -Free I-ORG I-ORG -Trade I-ORG I-ORG -Union I-ORG I-ORG -( O O -I B-ORG B-ORG -) O O -, O O -told O O -a O O -news O O -conference O O -the O O -withdrawal O O -of O O -a O O -W B-ORG B-ORG -invitation O O -to O O -IL B-ORG B-ORG -director O O -general O O -Michel B-PER B-PER -Hansen I-PER I-PER -was O O -" O O -outrage O O -behaviour O O -on O O -the O O -part O O -of O O -an O O -organisation O O -that O O -wants O O -to O O -command O O -respect O O -in O O -the O O -world O O -" O O -. O O -Jordan B-LOC B-PER -said O O -a O O -small O O -group O O -of O O -developing O O -nations O O -that O O -oppose O O -linking O O -trade O O -talks O O -and O O -labour O O -conditions O O -had O O -pressure O O -World B-ORG B-ORG -Trade I-ORG I-ORG -Organisation I-ORG I-ORG -( O O -W B-ORG B-ORG -) O O -officials O O -to O O -prevent O O -Hansen B-PER O -from O O -taking O O -the O O -platform O O -to O O -urge O O -such O O -links O O -. O O -" O O -It O O -is O O -to O O -their O O -shame O O -that O O -those O O -who O O -are O O -responsible O O -for O O -encouraging O O -this O O -meeting O O -responded O O -( O O -to O O -the O O -pressure O O -) O O -in O O -si O O -him O O -, O O -" O O -Jordan B-LOC B-PER -said O O -after O O -the O O -opening O O -of O O -an O O -I B-ORG B-ORG -conference O O -on O O -international O O -labour O O -standards O O -and O O -trade O O -. O O -The O O -three O O -trade O O -union O O -conference O O -in O O -Singapore B-LOC B-LOC -hopes O O -to O O -push O O -labour O O -issues O O -onto O O -the O O -W B-ORG B-ORG -agenda O O -. O O -Jordan B-LOC B-PER -said O O -the O O -W B-ORG B-ORG -' O O -credibility O O -was O O -at O O -stake O O -over O O -the O O -issue O O -of O O -trade O O -and O O -labour O O -. O O -The O O -I B-ORG B-ORG -said O O -it O O -wanted O O -the O O -W B-ORG B-ORG -conference O O -beginning O O -on O O -Monday O O -to O O -out O O -forced O O -and O O -child O O -labour O O -, O O -end O O -discrimination O O -in O O -hiring O O -, O O -and O O -guarantee O O -the O O -right O O -to O O -join O O -a O O -union O O -. O O -Bill B-PER B-PER -Brett I-PER I-PER -, O O -chairman O O -of O O -the O O -IL B-ORG B-ORG -Workers I-ORG I-ORG -Group I-ORG I-ORG -, O O -told O O -Re B-ORG B-ORG -before O O -the O O -news O O -conference O O -he O O -was O O -" O O -not O O -too O O -surprised O O -, O O -but O O -very O O -disappointed O O -" O O -that O O -the O O -speaking O O -invitation O O -had O O -been O O -withdrawn O O -. O O -" O O -Some O O -governments O O -are O O -very O O -determined O O -to O O -stop O O -the O O -issue O O -( O O -of O O -trade O O -and O O -labour O O -rights O O -) O O -being O O -discussed O O -, O O -" O O -he O O -said O O -, O O -adding O O -that O O -the O O -Association B-ORG B-ORG -of I-ORG I-ORG -Southeast I-ORG I-ORG -Asian I-ORG I-ORG -Nations I-ORG I-ORG -( O O -AS B-ORG B-ORG -) O O -seemed O O -particularly O O -hostile O O -to O O -the O O -IL B-ORG B-ORG -agenda O O -. O O -AS B-ORG B-ORG -groups O O -Brunei B-LOC B-LOC -, O O -Indonesia B-LOC B-LOC -, O O -Malaysia B-LOC B-LOC -, O O -the O O -Philippines B-LOC B-LOC -, O O -Singapore B-LOC B-LOC -, O O -Thailand B-LOC B-LOC -and O O -Vietnam B-LOC B-LOC -. O O -The O O -IL B-ORG B-ORG -wants O O -a O O -trade O O -and O O -labour O O -rights O O -" O O -social O O -clause O O -" O O -included O O -in O O -the O O -final O O -ministerial O O -statement O O -issued O O -by O O -W B-ORG B-ORG -leaders O O -at O O -the O O -end O O -of O O -the O O -meeting O O -, O O -the O O -organization O O -' O O -first O O -ministerial O O -gathering O O -. O O -Speaking O O -to O O -I B-ORG B-ORG -delegates O O -, O O -Richard B-PER B-PER -E I-PER I-PER -, O O -director O O -of O O -the O O -W B-ORG B-ORG -secret O O -, O O -said O O -the O O -W B-ORG B-ORG -was O O -capable O O -of O O -making O O -a O O -significant O O -contribution O O -to O O -governmental O O -efforts O O -to O O -solve O O -social O O -problems O O -. O O -But O O -he O O -said O O -the O O -W B-ORG B-ORG -' O O -organisation O O -structure O O -made O O -it O O -difficult O O -to O O -impose O O -on O O -its O O -members O O -a O O -social O O -clause O O -such O O -as O O -that O O -called O O -for O O -by O O -the O O -IL B-ORG B-ORG -. O O -Indian B-MISC B-MISC -rubber O O -demand O O -seen O O -outs O O -production O O -. O O -S B-LOC B-LOC -1996 O O -Indian B-MISC B-MISC -rubber O O -demand O O -is O O -seen O O -out O O -local O O -production O O -in O O -the O O -1996 O O -April O O -/ O O -March O O -season O O -and O O -the O O -trend O O -will O O -per O O -way O O -into O O -the O O -next O O -century O O -, O O -the O O -chairman O O -of O O -the O O -R B-ORG B-ORG -Board I-ORG I-ORG -of I-ORG I-ORG -India I-ORG I-ORG -said O O -on O O -Friday O O -. O O -K B-PER B-PER -Matthew I-PER I-PER -said O O -at O O -the O O -Asia B-ORG B-MISC -R I-ORG I-MISC -Markets I-ORG I-MISC -meeting O I-MISC -here O O -Indian B-MISC B-MISC -production O O -of O O -natural O O -rubber O O -in O O -1996 O O -will O O -reach O O -54 O O -tonnes O O -against O O -projected O O -demand O O -of O O -57 O O -tonnes O O -, O O -a O O -gap O O -of O O -31 O O -tonnes O O -. O O -For O O -synthetic O O -rubber O O -, O O -production O O -reached O O -68 O O -tonnes O O -in O O -1995 O O -while O O -consumption O O -in O O -the O O -same O O -season O O -hit O O -134 O O -tonnes O O -, O O -Matthew B-PER B-PER -added O O -. O O -" O O -Though O O -schemes O O -designed O O -to O O -realise O O -further O O -increases O O -in O O -the O O -production O O -of O O -natural O O -rubber O O -are O O -being O O -operated O O -successfully O O -, O O -the O O -demand O O -gap O O -is O O -expected O O -to O O -widen O O -, O O -" O O -he O O -said O O -. O O -Indian B-MISC B-MISC -synthetic O O -rubber O O -output O O -is O O -not O O -expected O O -to O O -rise O O -significantly O O -in O O -the O O -next O O -season O O -but O O -demand O O -will O O -rise O O -to O O -145 O O -tonnes O O -. O O -Matthew B-PER B-PER -estimates O O -that O O -by O O -the O O -2000 O O -season O O -, O O -the O O -gap O O -between O O -natural O O -rubber O O -output O O -and O O -consumption O O -will O O -rise O O -to O O -51 O O -tonnes O O -and O O -31 O O -tonnes O O -in O O -2010 O O -. O O -Natural O O -rubber O O -production O O -will O O -go O O -up O O -to O O -69 O O -tonnes O O -in O O -2000 O O -against O O -consumption O O -of O O -74 O O -tonnes O O -. O O -In O O -2010 O O -, O O -domestic O O -demand O O -should O O -rise O O -further O O -to O O -1 O O -million O O -tonnes O O -while O O -production O O -will O O -only O O -reach O O -about O O -91 O O -tonnes O O -. O O -One O O -way O O -to O O -bridge O O -the O O -widening O O -gap O O -is O O -to O O -put O O -more O O -land O O -under O O -cultivation O O -which O O -the O O -R B-ORG B-ORG -Board I-ORG I-ORG -official O O -estimates O O -will O O -reach O O -220 O O -hectares O O -between O O -now O O -and O O -the O O -year O O -2003 O O -although O O -Matthew B-PER B-PER -said O O -this O O -may O O -not O O -be O O -possible O O -at O O -this O O -time O O -in O O -India B-LOC B-LOC -. O O -" O O -The O O -development O O -objective O O -for O O -the O O -rubber O O -plantation O O -industry O O -will O O -be O O -to O O -increase O O -production O O -to O O -the O O -best O O -extent O O -possibly O O -with O O -a O O -view O O -to O O -mini O O -imports O O -of O O -natural O O -rubber O O -, O O -" O O -he O O -said O O -. O O -- O O -Singapore B-ORG B-ORG -News I-ORG I-ORG -( O O -65 O O -) O O -Japan B-LOC B-LOC -' O O -authorities O O -seen O O -seeking O O -to O O -re O O -in O O -dollar O O -. O O -George B-PER B-PER -Ni I-PER I-PER -TO B-LOC B-LOC -1996 O O -Co O O -by O O -Japan B-LOC B-LOC -' O O -tight O O -central O O -bank O O -chief O O -and O O -an O O -influential O O -top O O -bureau O O -are O O -further O O -signs O O -that O O -the O O -nation O O -' O O -authorities O O -want O O -to O O -keep O O -the O O -dollar O O -at O O -current O O -levels O O -, O O -market O O -sources O O -said O O -on O O -Friday O O -. O O -In O O -a O O -rare O O -expression O O -of O O -a O O -view O O -on O O -cu O O -by O O -the O O -Bank B-ORG B-ORG -of I-ORG I-ORG -Japan I-ORG I-ORG -( O O -B B-ORG B-ORG -) O O -governor O O -, O O -Ya B-PER B-PER -Mat I-PER I-PER -was O O -quoted O O -in O O -Japan B-LOC B-LOC -' O O -leading O O -economic O O -daily O O -on O O -Friday O O -as O O -saying O O -that O O -he O O -sees O O -no O O -further O O -, O O -immediate O O -fall O O -in O O -the O O -ye O O -. O O -This O O -followed O O -a O O -widely O O -watched O O -television O O -appearance O O -late O O -on O O -Thursday O O -by O O -E B-PER B-PER -Sa I-PER I-PER -, O O -a O O -high O O -Finance B-ORG B-ORG -Ministry I-ORG I-ORG -official O O -, O O -who O O -denied O O -he O O -had O O -said O O -he O O -wants O O -to O O -guide O O -the O O -dollar O O -lower O O -to O O -between O O -108 O O -and O O -110 O O -ye O O -. O O -But O O -many O O -in O O -the O O -market O O -thought O O -Sa B-PER B-PER -' O O -real O O -intentions O O -lay O O -elsewhere O O -, O O -and O O -took O O -more O O -notice O O -of O O -his O O -comments O O -about O O -the O O -U B-LOC B-LOC -government O O -' O O -stance O O -on O O -the O O -dollar O O -, O O -dealers O O -said O O -. O O -" O O -I O O -think O O -his O O -views O O -on O O -( O O -U B-LOC B-ORG -Treasury O I-ORG -Secretary O O -Robert B-PER B-PER -) O O -Rubin B-PER B-PER -' O O -comments O O -were O O -indeed O O -what O O -he O O -himself O O -thinks O O -about O O -the O O -dollar O O -, O O -" O O -said O O -Hank B-PER B-PER -Note I-PER I-PER -, O O -chief O O -dealer O O -at O O -Su B-ORG B-ORG -Bank I-ORG I-ORG -. O O -Ask O O -about O O -Rubin B-PER B-PER -' O O -comment O O -that O O -a O O -strong O O -dollar O O -was O O -in O O -U B-LOC B-LOC -interests O O -, O O -Sa B-PER B-PER -said O O -the O O -remark O O -does O O -not O O -necessarily O O -mean O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -supports O O -a O O -stronger O O -dollar O O -. O O -" O O -It O O -' O O -a O O -strong O O -dollar O O -, O O -not O O -stronger O O -. O O -In O O -that O O -sense O O -, O O -the O O -comments O O -are O O -not O O -pointing O O -to O O -a O O -certain O O -direction O O -, O O -" O O -he O O -said O O -. O O -" O O -It O O -shows O O -that O O -Sa B-PER B-PER -is O O -not O O -for O O -a O O -stronger O O -dollar O O -either O O -, O O -" O O -said O O -Su B-PER B-ORG -' O O -Note B-MISC B-PER -. O O -Ta B-PER B-PER -Sa I-PER I-PER -, O O -first O O -vice O O -president O O -at O O -Union B-ORG B-ORG -Bank I-ORG I-ORG -of I-ORG I-ORG -Switzerland I-ORG I-ORG -in O O -Tokyo B-LOC B-LOC -, O O -said O O -: O O -" O O -Maybe O O -a O O -dollar O O -at O O -104 O O -ye O O -is O O -not O O -acceptable O O -( O O -to O O -Sa B-PER B-PER -) O O -, O O -but O O -it O O -may O O -be O O -okay O O -at O O -the O O -current O O -level O O -, O O -at O O -the O O -lower O O -end O O -of O O -112 O O -ye O O -. O O -" O O -Market O O -participants O O -have O O -kept O O -a O O -close O O -eye O O -on O O -Sa B-PER B-PER -, O O -chief O O -of O O -the O O -ministry O O -' O O -International B-ORG B-ORG -Finance I-ORG I-ORG -Bureau I-ORG I-ORG -, O O -as O O -a O O -comment O O -he O O -made O O -in O O -November O O -after O O -the O O -dollar O O -hit O O -this O O -year O O -' O O -high O O -of O O -114 O O -ye O O -pushed O O -the O O -currency O O -down O O -sharply O O -. O O -He O O -had O O -said O O -then O O -that O O -the O O -market O O -' O O -view O O -on O O -Japan B-LOC B-LOC -' O O -economy O O -was O O -too O O -p O O -and O O -that O O -he O O -believed O O -it O O -was O O -stronger O O -than O O -the O O -market O O -thought O O -. O O -Deal O O -have O O -come O O -to O O -refer O O -to O O -115 O O -ye O O -as O O -the O O -" O O -Sa B-MISC B-PER -ceiling O O -" O O -for O O -the O O -dollar O O -following O O -the O O -remark O O -. O O -Ad O O -to O O -the O O -comments O O -by O O -" O O -Mr O B-PER -Ye B-PER I-PER -" O O -, O O -as O O -Sa B-PER B-PER -is O O -known O O -for O O -his O O -prominence O O -in O O -the O O -currency O O -market O O -, O O -was O O -B B-ORG B-ORG -governor O O -Mat B-PER B-PER -' O O -remark O O -. O O -" O O -The O O -recent O O -level O O -of O O -the O O -ye O O -exchange O O -rate O O -has O O -been O O -stable O O -, O O -and O O -it O O -does O O -not O O -appear O O -to O O -be O O -moving O O -towards O O -a O O -further O O -de O O -of O O -the O O -ye O O -immediately O O -, O O -so O O -import O O -prices O O -are O O -likely O O -to O O -stab O O -at O O -current O O -levels O O -, O O -" O O -Mat B-PER B-PER -said O O -in O O -an O O -interview O O -with O O -the O O -Ni B-ORG B-ORG -Ke I-ORG I-ORG -Shi I-ORG I-ORG -. O O -" O O -The O O -fact O O -that O O -he O O -touched O O -on O O -the O O -issue O O -of O O -inflation O O -triggered O O -by O O -import O O -prices O O -shows O O -that O O -the O O -B B-ORG B-ORG -does O O -not O O -want O O -a O O -further O O -de O O -of O O -the O O -ye O O -, O O -past O O -115 O O -ye O O -, O O -" O O -said O O -Ya B-PER B-PER -Ka I-PER I-PER -, O O -chief O O -fore O O -manager O O -at O O -Toy B-ORG B-ORG -Trust I-ORG I-ORG -& I-ORG I-ORG -Banking I-ORG I-ORG -Co I-ORG I-ORG -. O O -Some O O -said O O -the O O -central O O -bank O O -may O O -have O O -been O O -concerned O O -a O O -weaker O O -ye O O -would O O -lead O O -to O O -un O O -p O O -about O O -Japan B-LOC B-LOC -' O O -economy O O -. O O -" O O -There O O -was O O -concern O O -that O O -foreign O O -investors O O -may O O -sell O O -Japanese B-MISC B-MISC -stocks O O -if O O -the O O -dollar O O -goes O O -above O O -115 O O -ye O O -. O O -The O O -B B-ORG B-ORG -does O O -not O O -want O O -the O O -ye O O -' O O -weakness O O -to O O -lead O O -to O O -p O O -over O O -the O O -economy O O -, O O -" O O -said O O -Tai B-PER B-PER -Tanaka I-PER I-PER -, O O -market O O -s O O -with O O -Credit B-ORG B-ORG -Su I-ORG I-ORG -in O O -Tokyo B-LOC B-LOC -. O O -Senior O O -B B-ORG B-ORG -officials O O -have O O -separately O O -said O O -financial O O -markets O O -' O O -views O O -on O O -the O O -economy O O -have O O -been O O -too O O -negative O O -. O O -" O O -I O O -realise O O -there O O -are O O -negative O O -views O O -in O O -the O O -markets O O -about O O -the O O -impact O O -of O O -the O O -consumption O O -tax O O -hike O O -and O O -drop O O -in O O -public O O -spending O O -, O O -but O O -the O O -markets O O -appear O O -to O O -be O O -ex O O -the O O -magnitude O O -of O O -the O O -negative O O -impact O O -, O O -" O O -a O O -senior O O -B B-ORG B-ORG -official O O -told O O -Re B-ORG B-ORG -on O O -Friday O O -. O O -The O O -consumption O O -tax O O -is O O -due O O -to O O -raised O O -in O O -April O O -from O O -three O O -to O O -five O O -percent O O -. O O -Lebanon B-LOC B-LOC -sentences O O -pro B-MISC B-MISC -warlord O O -to O O -death O O -. O O -Hai B-PER B-ORG -Had I-PER I-ORG -B B-LOC B-LOC -1996 O O -A O O -Lebanese B-MISC B-MISC -military O O -court O O -on O O -Friday O O -sentenced O O -to O O -death O O -in O O -absent O O -the O O -commander O O -of O O -Israel B-LOC B-LOC -' O O -sur O O -militia O O -in O O -south O O -Lebanon B-LOC B-LOC -on O O -treason O O -charges O O -. O O -The O O -court O O -convicted O O -General O O -Antoine B-PER B-PER -La I-PER I-PER -, O O -head O O -of O O -the O O -South B-ORG B-ORG -Lebanon I-ORG I-ORG -Army I-ORG I-ORG -( O O -SL B-ORG B-ORG -) O O -, O O -of O O -collaborating O O -with O O -Israel B-LOC B-LOC -with O O -which O O -Lebanon B-LOC B-LOC -is O O -officially O O -at O O -war O O -. O O -La B-PER B-PER -, O O -a O O -69 O O -former O O -Lebanese B-MISC B-MISC -army O O -major O O -, O O -heads O O -the O O -3 O O -SL B-ORG B-ORG -militia O O -which O O -helps O O -Israel B-LOC B-LOC -hold O O -a O O -border O O -zone O O -in O O -south O O -Lebanon B-LOC B-LOC -to O O -ward O O -off O O -cross O O -guerrilla O O -raids O O -on O O -northern O O -Israel B-LOC B-LOC -. O O -La B-PER B-PER -said O O -a O O -few O O -days O O -after O O -the O O -trial O O -began O O -on O O -February O O -16 O O -that O O -Lebanese B-MISC B-MISC -authorities O O -must O O -drop O O -the O O -charges O O -or O O -risk O O -blocking O O -any O O -peace O O -deal O O -with O O -the O O -Jewish B-MISC B-MISC -state O O -. O O -He O O -said O O -Israel B-LOC B-LOC -was O O -capable O O -of O O -press O O -Lebanon B-LOC B-LOC -' O O -Syrian B-MISC B-MISC -government O O -to O O -stop O O -the O O -legal O O -pursuit O O -. O O -The O O -charges O O -against O O -La B-PER B-PER -were O O -: O O -forming O O -a O O -hostile O O -army O O -, O O -carrying O O -weapons O O -on O O -Israel B-LOC B-LOC -' O O -side O O -, O O -helping O O -Israel B-LOC B-LOC -strip O O -off O O -a O O -part O O -of O O -Lebanese B-MISC B-MISC -territory O O -by O O -violence O O -, O O -forming O O -an O O -armed O O -gang O O -, O O -killing O O -or O O -trying O O -to O O -kill O O -Lebanese B-MISC B-MISC -civilians O O -by O O -artillery O O -shell O O -and O O -kidnapping O O -Lebanese B-MISC B-MISC -citizens O O -for O O -long O O -periods O O -. O O -Shortly O O -before O O -La B-PER B-PER -' O O -trial O O -began O O -, O O -a O O -Beirut B-LOC B-LOC -military O O -prosecutor O O -charged O O -another O O -89 O O -former O O -Lebanese B-MISC B-MISC -army O O -soldiers O O -with O O -collaborating O O -with O O -Israel B-LOC B-LOC -. O O -No O O -date O O -has O O -been O O -set O O -for O O -the O O -trial O O -of O O -the O O -men O O -, O O -all O O -members O O -of O O -the O O -SL B-ORG B-ORG -living O O -in O O -the O O -Israeli B-MISC B-MISC -zone O O -in O O -south O O -Lebanon B-LOC B-LOC -. O O -Israel B-LOC B-LOC -and O O -La B-LOC B-PER -have O O -repeatedly O O -demanded O O -safety O O -guarantees O O -for O O -the O O -SL B-ORG B-ORG -- O O -a O O -mixed O O -Christian B-MISC B-MISC -Mo B-MISC B-MISC -force O O -- O O -which O O -the O O -Jewish B-MISC B-MISC -states O O -regards O O -as O O -loyal O O -allies O O -. O O -Israel B-LOC B-LOC -has O O -said O O -the O O -Lebanese B-MISC B-MISC -army O O -must O O -incorporate O O -the O O -SL B-ORG B-ORG -fighters O O -into O O -its O O -ranks O O -as O O -an O O -army O O -brigade O O -as O O -a O O -condition O O -for O O -peace O O -. O O -But O O -Lebanese B-MISC B-MISC -political O O -analysts O O -have O O -said O O -that O O -would O O -be O O -out O O -of O O -the O O -question O O -and O O -Lebanese B-MISC B-MISC -authorities O O -pre O O -the O O -issue O O -by O O -taking O O -legal O O -action O O -against O O -La B-PER B-PER -. O O -Former O O -Israeli B-MISC B-MISC -Prime O O -Minister O O -Shi B-PER B-PER -Per I-PER I-PER -, O O -calling O O -La B-PER B-PER -" O O -a O O -great O O -Lebanese B-MISC B-MISC -pat O O -" O O -, O O -said O O -earlier O O -this O O -year O O -Lebanon B-LOC B-LOC -had O O -insulted O O -the O O -SL B-ORG B-ORG -commander O O -by O O -ordering O O -his O O -arrest O O -on O O -the O O -treason O O -charges O O -. O O -Per B-PER B-PER -, O O -who O O -was O O -ou O O -in O O -May O O -by O O -right O O -Israeli B-MISC B-MISC -leader O O -Benjamin B-PER B-PER -Net I-PER I-PER -, O O -said O O -there O O -could O O -not O O -be O O -real O O -negotiations O O -with O O -Lebanon B-LOC B-LOC -" O O -unless O O -it O O -will O O -stop O O -the O O -ma O O -of O O -the O O -SL B-ORG B-ORG -and O O -its O O -commanders O O -. O O -" O O -The O O -Beirut B-LOC B-LOC -military O O -court O O -also O O -sentenced O O -to O O -life O O -in O O -jail O O -in O O -absent O O -E B-PER B-PER -Sa I-PER I-PER -, O O -former O O -head O O -of O O -the O O -pro B-MISC B-MISC -Guardians I-ORG B-ORG -of I-ORG I-ORG -the I-ORG I-ORG -Cedar I-ORG I-ORG -, O O -a O O -small O O -right O O -Christian B-MISC B-MISC -civil O O -war O O -militia O O -. O O -Sa B-PER B-PER -, O O -whose O O -trial O O -was O O -concurrent O O -with O O -La B-PER B-PER -' O O -, O O -was O O -convicted O O -of O O -" O O -contact O O -the O O -Israeli B-MISC B-MISC -enemy O O -, O O -passing O O -information O O -to O O -Israel B-LOC B-LOC -and O O -undertaking O O -hostile O O -acts O O -against O O -Lebanon B-LOC B-LOC -" O O -. O O -Texas B-LOC B-LOC -/ O O -w O O -Ok B-LOC B-LOC -fed O O -cattle O O -market O O -thin O O -at O O -$ O O -67 O O -- O O -USD O B-ORG -. O O -AM B-LOC B-LOC -1996 O O -Trade O O -was O O -slow O O -in O O -the O O -Pan B-LOC B-LOC -area O O -Friday O O -, O O -USD B-ORG B-ORG -said O O -. O O -S O O -steer O O -and O O -he O O -were O O -$ O O -1 O O -per O O -c O O -lower O O -. O O -Fe O O -reporting O O -moderate O O -inquiry O O -. O O -Sales O O -reported O O -on O O -8 O O -head O O -slaughter O O -steer O O -and O O -1 O O -he O O -; O O -following O O -weekly O O -movement O O -of O O -71 O O -head O O -. O O -Note O O -- O O -all O O -cattle O O -prices O O -based O O -on O O -net O O -weights O O -F O O -the O O -feed O O -after O O -a O O -4 O O -percent O O -shrink O O -. O O -S B-ORG O -St I-ORG O -- O O -Select O O -and O O -Choice O O -2 O O -115 O O -lbs O O -67 O O -. O O -S B-ORG O -He I-ORG O -- O O -Select O O -and O O -Choice O O -2 O O -105 O O -lbs O O -67 O O -. O O -Con O O -- O O -9 O O -Week O O -A O O -- O O -None O O -Year O O -A O O -- O O -None O O -( O O -( O O -Chicago B-LOC B-LOC -news O O -312 O O -40 O O -87 O O -) O O -) O O -USD B-ORG B-ORG -daily O O -cattle O O -and O O -ho O O -slaughter O O -- O O -Dec O O -6 O O -. O O -E O O -daily O O -livestock O O -slaughter O O -under O O -Fed B-MISC B-ORG -inspection O O -- O O -USD B-ORG B-ORG -CA I-ORG O -CA O O -H O O -Friday O O -12 O O -( O O -est O O -) O O -132 O O -7 O O -35 O O -Week O O -ago O O -( O O -est O O -) O O -130 O O -6 O O -34 O O -Year O O -ago O O -( O O -act O O -) O O -132 O O -6 O O -33 O O -Saturday O O -12 O O -( O O -est O O -) O O -38 O O -0 O O -18 O O -Week O O -to O O -date O O -( O O -est O O -) O O -68 O O -31 O O -1 O O -Same O O -Period O O -Last O O -Week O O -( O O -est O O -) O O -60 O O -26 O O -1 O O -Same O O -Period O O -Previous O O -day O O -estimated O O -St B-ORG O -and O O -He B-ORG O -Co I-ORG O -and I-ORG O -Bull B-ORG O -Thursday O O -100 O O -33 O O -BA O O -- O O -Hartford B-LOC B-LOC -, O O -Con B-LOC B-LOC -. O O -, O O -$ O O -11 O O -m O O -. O O -C O O -OF I-ORG O -H I-ORG B-LOC -, O O -CO B-LOC B-LOC -R O O -: O O -$ O O -25 O O -GE O O -O O O -B O O -M O B-ORG -' O I-ORG -: O O -A O O -/ O O -A1 O O -S B-ORG B-ORG -: O O -AAA O O -/ O O -AA O O -Del O O -Date O O -: O O -12 O O -F O B-ORG -IN O O -Mat O O -Ba O O -Co O O -List O O -12 O O -1 O O -6 O O -4 O O -12 O O -57 O O -4 O O -4 O O -12 O O -265 O O -4 O O -4 O O -12 O O -625 O O -T B-LOC B-LOC -, O O -F B-LOC B-LOC -1996 O O -Fourteen O O -years O O -after O O -he O O -b O O -and O O -shot O O -a O O -man O O -whose O O -trailer O O -home O O -he O O -robbed O O -in O O -1982 O O -, O O -John B-PER B-PER -Mills I-PER I-PER -Jr I-PER O -. O O -, O O -41 O O -, O O -was O O -put O O -to O O -death O O -in O O -Florida B-LOC B-LOC -' O O -electric O O -chair O O -Friday O O -. O O -As O O -Glenn B-PER B-PER -Law I-PER I-PER -, O O -a O O -rural O O -Florida B-LOC B-LOC -minister O O -who O O -is O O -the O O -victim O O -' O O -father O O -, O O -looked O O -on O O -, O O -Mills B-PER B-PER -was O O -pronounced O O -dead O O -at O O -7 O O -a O O -E O O -( O O -121 O O -GM B-MISC B-MISC -) O O -for O O -the O O -murder O O -of O O -Lester B-PER B-PER -Law I-PER I-PER -. O O -Speaking O O -in O O -Arabic B-MISC B-MISC -, O O -Mills B-PER B-PER -made O O -a O O -final O O -statement O O -before O O -an O O -anonymous O O -citizen O O -flipped O O -the O O -switch O O -that O O -sent O O -2000 O O -vol O O -of O O -electricity O O -through O O -his O O -body O O -, O O -said O O -Department B-ORG B-ORG -of I-ORG I-ORG -Co I-ORG I-ORG -spokesman O O -Eugene B-PER B-PER -Morris I-PER I-PER -, O O -who O O -was O O -present O O -at O O -the O O -execution O O -. O O -" O O -I O O -bear O O -witness O O -that O O -there O O -is O O -no O O -God B-PER B-PER -but O O -Allah B-PER B-PER -and O O -I O O -bear O O -witness O O -that O O -the O O -prophet O O -Mohammed B-PER B-PER -is O O -the O O -messenger O O -of O O -God B-PER B-PER -, O O -" O O -he O O -quoted O O -Mills B-PER B-PER -as O O -saying O O -. O O -Prison O O -officials O O -said O O -they O O -had O O -no O O -record O O -of O O -Mills B-PER B-PER -' O O -official O O -conversion O O -, O O -but O O -they O O -said O O -that O O -, O O -on O O -May O O -14 O O -, O O -1991 O O -, O O -he O O -had O O -asked O O -that O O -a O O -new O O -name O O -, O O -Yu B-PER B-PER -Abdullah I-PER I-PER -Mu I-PER I-PER -, O O -be O O -added O O -to O O -his O O -prison O O -file O O -, O O -which O O -is O O -usually O O -an O O -indication O O -of O O -a O O -conversion O O -to O O -Islam B-MISC B-MISC -. O O -Mills B-PER B-PER -is O O -the O O -38th O O -person O O -to O O -die O O -in O O -Florida B-LOC B-LOC -' O O -electric O O -chair O O -since O O -the O O -U B-ORG B-ORG -Supreme I-ORG I-ORG -Court I-ORG I-ORG -reversed O O -itself O O -in O O -1976 O O -and O O -legal O O -the O O -death O O -penalty O O -. O O -Prison O O -officials O O -said O O -Mills B-PER B-PER -made O O -no O O -special O O -request O O -for O O -a O O -last O O -meal O O -and O O -did O O -not O O -eat O O -the O O -steak O O -, O O -fried O O -potatoes O O -and O O -orange O O -juice O O -offered O O -him O O -. O O -He O O -spent O O -Thursday O O -with O O -family O O -members O O -and O O -his O O -spiritual O O -adviser O O -, O O -Morris B-PER B-PER -said O O -. O O -Mills B-PER B-PER -was O O -scheduled O O -to O O -die O O -Wednesday O O -but O O -had O O -his O O -sentence O O -temporarily O O -postponed O O -by O O -the O O -Florida B-ORG B-ORG -Supreme I-ORG I-ORG -Court I-ORG I-ORG -. O O -On O O -Thursday O O -, O O -the O O -11th B-ORG O -Circuit B-ORG O -U I-ORG B-ORG -Court I-ORG I-ORG -of I-ORG I-ORG -Appeals I-ORG I-ORG -in O O -Atlanta B-LOC B-LOC -denied O O -his O O -appeal O O -in O O -federal O O -court O O -. O O -In O O -March O O -1982 O O -, O O -Mills B-PER B-PER -and O O -a O O -Michael B-PER B-PER -Frederick I-PER I-PER -knocked O O -on O O -the O O -door O O -of O O -Lester B-PER B-PER -Law I-PER I-PER -' O O -trailer O O -in O O -an O O -attempt O O -to O O -r O O -it O O -, O O -police O O -said O O -. O O -Lester B-PER B-PER -Law I-PER I-PER -was O O -taken O O -to O O -a O O -nearby O O -airs O O -where O O -he O O -was O O -b O O -with O O -a O O -tire O O -iron O O -. O O -Mills B-PER B-PER -then O O -fired O O -two O O -shots O O -that O O -killed O O -Law B-PER B-PER -as O O -the O O -victim O O -tried O O -to O O -run O O -away O O -. O O -Frederick B-PER B-PER -is O O -serving O O -a O O -34 O O -sentence O O -. O O -New B-LOC B-LOC -York I-LOC I-LOC -grain O O -freight O O -fixtures O O -- O O -Dec O O -5 O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Man B-ORG O -50 O O -tonnes O O -so O O -US B-LOC O -/ O O -China B-LOC B-LOC -10 O O -$ O O -23 O O -10 O O -/ O O -4 O O -Gee B-ORG O -. O O -- O O -New B-ORG B-ORG -York I-ORG I-ORG -Co I-ORG I-ORG -Des I-ORG I-ORG -+ O O -212 O O -85 O O -1640 O O -Iowa B-ORG B-LOC -Min I-ORG B-LOC -fed O O -cattle O O -market O O -quiet O O -, O O -no O O -sales O B-MISC -. O O -DE B-LOC B-LOC -M I-LOC I-LOC -1996 O O -S O O -steer O O -and O O -he O O -not O O -tested O O -, O O -compared O O -with O O -Thursday O O -' O O -close O O -, O O -USD B-ORG B-ORG -said O O -. O O -Trade O O -slow O O -. O O -De O O -and O O -seller O O -interest O O -light O O -. O O -Off O O -light O O -. O O -St B-ORG O -- O O -Select B-MISC O -and O O -Choice B-MISC O -2 O O -no O O -sales O O -. O O -He B-ORG O -- O O -Select B-MISC O -and O O -Choice O O -2 O O -no O O -sales O O -. O O -Car B-PER O -Ba I-ORG O -( O O -weight O O -only O O -) O O -Compared O O -Thursday O O -Close O O -- O O -S O O -steer O O -and O O -he O O -not O O -tested O O -. O O -St B-ORG O -- O O -Select B-MISC O -and O O -Choice B-MISC O -2 O O -no O O -sales O O -. O O -Holstein B-PER O -- O O -( O O -weight O O -only O O -) O O -Select O O -to O O -mostly O O -Choice B-MISC O -2 O O -125 O O -lbs O O -no O O -sales O O -. O O -Holstein B-MISC O -- O O -( O O -grade O O -and O O -weight O O -) O O -Choice O O -2 O O -125 O O -lbs O O -no O O -sales O O -Select O O -2 O O -125 O O -lbs O O -no O O -sales O O -. O O -He B-ORG O -- O O -Select B-MISC O -and O O -Choice O O -2 O O -no O O -sales O O -. O O -Con O O -- O O -None O O -Week O O -A O O -- O O -800 O O -Year O O -A O O -- O O -900 O O -W O O -to O O -Date O O -- O O -None O O -Week O O -A O O -- O O -800 O O -Year O O -A O O -- O O -900 O O -( O O -( O O -Chicago B-LOC B-LOC -news O O -312 O O -) O O -) O O -Man O O -stole O O -pigs O O -, O O -tipped O O -strip O O -, O O -gets O O -10 O O -years O O -. O O -AP B-LOC B-LOC -, O O -W B-LOC B-LOC -. O O -1996 O O -A O O -farm O O -used O O -the O O -proceeds O O -from O O -stolen O O -pigs O O -to O O -lavish O O -tips O O -on O O -dancers O O -at O O -strip O O -clubs O O -and O O -offered O O -one O O -$ O O -3 O O -to O O -pay O O -for O O -breast O O -imp O O -surgery O O -, O O -authorities O O -said O O -Friday O O -. O O -In O O -sent O O -Dar B-PER B-PER -V I-PER I-PER -, O O -38 O O -, O O -to O O -a O O -10 O O -prison O O -term O O -on O O -Thursday O O -, O O -Out B-LOC B-LOC -County I-ORG I-LOC -Circuit I-ORG O -Court I-ORG O -Judge O O -Dennis B-PER B-PER -Lu I-PER I-PER -said O O -he O O -was O O -" O O -a O O -thief O O -by O O -habit O O -. O O -" O O -" O O -You O O -are O O -self O O -. O O -You O O -are O O -na O O -, O O -" O O -Lu B-PER O -said O O -at O O -the O O -sent O O -, O O -adding O O -V B-PER B-PER -should O O -pay O O -rest O O -of O O -more O O -than O O -$ O O -100 O O -to O O -the O O -farming O O -family O O -who O O -had O O -hired O O -him O O -. O O -V B-PER B-PER -, O O -who O O -was O O -already O O -on O O -probation O O -for O O -prior O O -pig O O -theft O O -, O O -pleaded O O -that O O -he O O -was O O -trying O O -to O O -pay O O -bills O O -for O O -his O O -ex O O -and O O -children O O -. O O -But O O -the O O -court O O -heard O O -that O O -re O O -showed O O -much O O -of O O -the O O -money O O -went O O -to O O -dancers O O -at O O -strip O O -clubs O O -where O O -he O O -was O O -known O O -as O O -a O O -big O O -tip O O -. O O -One O O -strip O O -said O O -V B-PER B-PER -offered O O -to O O -give O O -her O O -$ O O -3 O O -for O O -breast O O -imp O O -surgery O O -. O O -Canadian B-MISC B-MISC -grain O O -statistics O O -weekly O O -. O O -CH B-ORG B-LOC -, O O -Dec O O -6 O O -( O O -Re B-ORG B-ORG -) O O -Statistics O O -for O O -the O O -week O O -ending O O -December O O -1 O O -in O O -000 O O -' O O -tonnes O O -. O O -- O O -Canadian B-ORG B-ORG -G I-ORG I-ORG -Commission I-ORG I-ORG -V O O -Su I-ORG O -Farmers I-ORG O -Del I-ORG O -C O O -W O O -Y O O -A O O -C O O -W O O -Y O O -to O O -Date O O -Y O O -A O O -W O O -43 O O -390 O O -288 O O -62 O O -55 O O -Du B-ORG O -116 O O -122 O O -44 O O -96 O O -106 O O -O B-ORG O -286 O O -28 O O -31 O O -93 O O -58 O O -Bar B-ORG O -107 O O -110 O O -178 O O -253 O O -1897 O O -R B-ORG O -44 O O -86 O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -NY B-ORG B-ORG -Henry B-ORG B-LOC -Hu I-ORG I-LOC -na O O -future O O -settled O O -significantly O O -lower O O -Friday O O -, O O -pressure O O -early O O -by O O -profit O O -taking O O -and O O -driven O O -even O O -lower O O -late O O -by O O -the O O -National B-ORG B-ORG -Weather I-ORG I-ORG -Service I-ORG I-ORG -' O O -bear O O -six O O -to O O -10 O O -day O O -forecast O O -, O O -sources O O -said O O -. O O -January O O -ended O O -29 O O -cents O O -lower O O -at O O -$ O O -3 O O -per O O -million O O -British B-MISC B-MISC -thermal O O -units O O -after O O -dipping O O -to O O -a O O -low O O -of O O -$ O O -3 O O -. O O -Feb O O -settled O O -down O O -22 O O -cents O O -at O O -$ O O -3 O O -. O O -Most O O -others O O -also O O -were O O -down O O -. O O -" O O -Weather O O -forecast O O -have O O -been O O -sketch O O -. O O -Now O O -the O O -National B-ORG B-ORG -Weather I-ORG I-ORG -Service I-ORG I-ORG -is O O -calling O O -for O O -above O O -temperatures O O -in O O -more O O -than O O -half O O -of O O -the O O -U B-LOC B-LOC -, O O -" O O -one O O -future O O -trader O O -said O O -. O O -In O O -its O O -forecast O O -, O O -the O O -NW B-ORG B-ORG -said O O -it O O -expects O O -above O O -temperatures O O -" O O -over O O -the O O -lower O O -48 O O -states O O -" O O -from O O -December O O -12 O O -through O O -December O O -16 O O -. O O -With O O -more O O -room O O -to O O -the O O -down O O -anticipated O O -early O O -next O O -week O O -, O O -traders O O -said O O -support O O -in O O -January O O -was O O -at O O -$ O O -3 O O -, O O -then O O -$ O O -3 O O -. O O -The O O -next O O -backs O O -were O O -seen O O -at O O -$ O O -3 O O -and O O -$ O O -3 O O -, O O -the O O -low O O -set O O -on O O -November O O -21 O O -. O O -Resistance O O -was O O -p O O -at O O -the O O -new O O -contract O O -high O O -of O O -$ O O -3 O O -. O O -In O O -the O O -cash O O -market O O -, O O -Gulf B-LOC B-LOC -Coast I-LOC I-LOC -prices O O -were O O -around O O -$ O O -3 O O -shortly O O -before O O -nomination O O -deadline O O -. O O -Mid B-MISC O -prices O O -were O O -similarly O O -lower O O -in O O -the O O -$ O O -3 O O -New B-LOC B-LOC -York I-LOC I-LOC -city O O -gate O O -gas O O -slipped O O -into O O -the O O -$ O O -4 O O -, O O -down O O -almost O O -15 O O -cents O O -. O O -NY B-ORG B-ORG -said O O -an O O -estimated O O -35 O O -Hu B-MISC O -contracts O O -traded O O -, O O -down O O -from O O -Thursday O O -' O O -revised O O -tally O O -of O O -43 O O -. O O -NY B-ORG B-ORG -Alberta I-ORG B-LOC -na O O -remained O O -un O O -, O O -with O O -January O O -settling O O -at O O -$ O O -1 O O -, O O -off O O -10 O O -cents O O -from O O -Thursday O O -. O O -Physical O O -prices O O -for O O -the O O -weekend O O -at O O -the O O -A B-ORG B-ORG -storage O O -hub O O -were O O -also O O -down O O -about O O -10 O O -cents O O -in O O -the O O -C B-MISC B-MISC -1 O O -per O O -gig O O -, O O -or O O -$ O O -1 O O -per O O -mm O O -range O O -, O O -pressure O O -by O O -un O O -mild O O -weather O O -in O O -western O O -Canada B-LOC B-LOC -. O O -NY B-MISC B-ORG -Per I-MISC B-MISC -na O O -, O O -also O O -un O O -, O O -ended O O -10 O O -cents O O -lower O O -at O O -$ O O -2 O O -. O O -In O O -con O O -with O O -future O O -, O O -Per B-MISC B-MISC -cash O O -prices O O -for O O -the O O -weekend O O -fell O O -more O O -than O O -10 O O -cents O O -to O O -the O O -high O O -- O O -$ O O -3 O O -On O O -the O O -KC B-MISC B-ORG -, O O -January O O -finished O O -26 O O -cents O O -lower O O -at O O -$ O O -3 O O -after O O -dipping O O -to O O -a O O -low O O -of O O -$ O O -3 O O -earlier O O -in O O -the O O -session O O -. O O -February O O -was O O -down O O -22 O O -cents O O -at O O -the O O -close O O -, O O -while O O -other O O -def O O -were O O -4 O O -to O O -nine O O -cents O O -lower O O -. O O -The O O -East B-MISC O -/ O O -West B-LOC O -spread O O -narrowed O O -by O O -3 O O -cents O O -to O O -13 O O -cents O O -( O O -NY B-MISC B-ORG -premium O O -) O O -. O O -Physical O O -prices O O -at O O -W B-LOC B-LOC -for O O -the O O -weekend O O -lost O O -more O O -than O O -15 O O -cents O O -to O O -the O O -low O O -$ O O -3 O O -as O O -mild O O -weather O O -moved O O -into O O -the O O -Southwest B-LOC O -. O O -- O O -H B-PER B-PER -M I-PER I-PER -, O O -New B-ORG B-ORG -York I-ORG I-ORG -Power I-ORG I-ORG -Des I-ORG I-ORG -+ O O -U B-LOC B-LOC -barge O O -lightly O O -quoted O O -on O O -call O O -session O O -. O O -ST B-LOC B-LOC -L O I-LOC -1996 O O -U B-LOC B-LOC -barge O O -rates O O -were O O -lightly O O -quoted O O -Friday O O -on O O -the O O -St B-ORG B-LOC -Louis I-ORG I-LOC -Merchant I-ORG O -Exchange I-ORG O -call O O -session O O -. O O -No O O -barge O O -traded O O -versus O O -no O O -trades O O -Thursday O O -. O O -- O O -Two O O -barge O O -next O O -week O O -Illinois B-LOC B-LOC -bid O O -at O O -a O O -steady O O -130 O O -percent O O -of O O -ta O O -, O O -offered O O -at O O -135 O O -percent O O -. O O -- O O -One O O -barge O O -, O O -week O O -of O O -December O O -15 O O -, O O -lower O O -Ohio B-LOC B-LOC -bid O O -2 O O -points O O -higher O O -at O O -105 O O -percent O O -, O O -no O O -offer O O -. O O -Two O O -barge O O -, O O -week O O -of O O -January O O -5 O O -, O O -Illinois B-LOC B-LOC -, O O -offered O O -five O O -points O O -lower O O -at O O -195 O O -percent O O -, O O -bid O O -at O O -150 O O -percent O O -. O O -- O O -Five O O -barge O O -, O O -30 O O -open O O -, O O -mid O B-MISC -( O O -McGregor B-LOC B-PER -and O O -south O O -) O O -bid O O -at O O -160 O O -percent O O -, O O -offered O O -at O O -170 O O -percent O O -, O O -no O O -comparisons O O -. O O -- O O -36 O O -barge O O -, O O -two O O -each O O -week O O -May O O -, O O -Illinois B-LOC B-LOC -, O O -offered O O -at O O -130 O O -percent O O -of O O -ta O O -, O O -no O O -bid O O -or O O -comparison O O -. O O -- O O -36 O O -barge O O -, O O -two O O -each O O -week O O -May O O -, O O -mid B-LOC B-MISC -offered O O -at O O -a O O -steady O O -135 O O -percent O O -, O O -bid O O -at O O -120 O O -percent O O -( O O -basis O O -one O O -each O O -week O O -) O O -. O O -- O O -Chicago B-LOC B-LOC -news O O -312 O O -87 O O -CB B-ORG B-ORG -grain O O -/ O O -oils O O -re O O -and O O -shipment O O -. O O -CH B-LOC B-LOC -1996 O O -G O O -and O O -so O O -re O O -and O O -shipment O O -, O O -in O O -bush O O -, O O -at O O -delivery O O -locations O O -for O O -the O O -previous O O -trading O O -day O O -, O O -according O O -to O O -the O O -Chicago B-ORG B-ORG -Board I-ORG I-ORG -of I-ORG I-ORG -Trade I-ORG I-ORG -- O O -Re O O -Ship I-ORG O -W I-ORG O -Chicago I-ORG B-LOC -0 O O -0 O O -St B-ORG B-LOC -Louis O I-LOC -21 O O -0 O O -Toledo B-ORG B-LOC -61 O O -0 O O -Co B-ORG O -Chicago I-ORG B-LOC -78 O O -0 O O -St B-ORG B-LOC -Louis O I-LOC -217 O O -75 O O -Toledo B-ORG B-LOC -285 O O -56 O O -O B-ORG O -Chicago I-ORG B-LOC -0 O O -0 O O -Minneapolis B-ORG B-LOC -306 O O -153 O O -So B-ORG O -Chicago I-ORG B-LOC -8 O O -48 O O -St B-ORG B-LOC -Louis I-ORG I-LOC -253 O O -WA B-LOC B-LOC -1996 O O -President O O -Clinton B-PER B-PER -aims O O -to O O -hold O O -more O O -news O O -conferences O O -in O O -his O O -second O O -term O O -and O O -will O O -have O O -one O O -Dec O O -13 O O -, O O -the O O -White B-LOC B-LOC -House I-LOC I-LOC -said O O -Friday O O -. O O -The O O -president O O -had O O -only O O -two O O -formal O O -, O O -full O O -news O O -conferences O O -last O O -year O O -, O O -one O O -in O O -January O O -and O O -one O O -after O O -he O O -won O O -re O O -in O O -November O O -, O O -although O O -he O O -had O O -various O O -other O O -limited O O -sessions O O -with O O -the O O -press O O -. O O -White B-LOC B-LOC -House I-LOC I-LOC -spokesman O O -Mike B-PER B-PER -M I-PER I-PER -said O O -Clinton B-PER B-PER -" O O -plans O O -to O O -have O O -regular O O -news O O -conferences O O -" O O -during O O -his O O -second O O -term O O -. O O -But O O -when O O -asked O O -how O O -frequent O O -these O O -would O O -be O O -, O O -he O O -was O O -e O O -, O O -saying O O -, O O -" O O -periodic O O -, O O -occasional O O -. O O -" O O -" O O -He O O -enjoys O O -the O O -give O O -and O O -take O O -" O O -with O O -reporters O O -, O O -the O O -spokesman O O -added O O -. O O -Action O O -Performance I-ORG O -to O O -acquire O O -firms O O -. O O -T B-LOC B-LOC -, O O -Ari B-LOC B-LOC -. O O -1996 O O -Action B-ORG B-ORG -Performance I-ORG I-ORG -Co I-ORG I-ORG -Inc I-ORG I-ORG -said O O -Friday O O -it O O -has O O -agreed O O -to O O -acquire O O -Motors B-ORG B-ORG -Tradition I-ORG I-ORG -Ltd I-ORG I-ORG -and O O -Creative B-ORG B-ORG -Marketing I-ORG I-ORG -& I-ORG I-ORG -Promotion I-ORG I-ORG -Inc I-ORG I-ORG -for O O -about O O -$ O O -13 O O -million O O -in O O -cash O O -and O O -stock O O -. O O -The O O -two O O -firms O O -to O O -be O O -acquired O O -have O O -about O O -$ O O -25 O O -million O O -in O O -annual O O -revenues O O -from O O -the O O -design O O -, O O -manufacture O O -and O O -sale O O -and O O -distribution O O -of O O -licensed O O -motors O O -products O O -. O O -The O O -deal O O -is O O -expected O O -to O O -close O O -by O O -the O O -end O O -of O O -the O O -year O O -subject O O -to O O -due O O -di O O -and O O -other O O -customary O O -closing O O -conditions O O -. O O -Half O O -of O O -dog O O -bites O O -provoked O O -, O O -says O O -American B-MISC B-MISC -ve O O -. O O -CH B-LOC B-LOC -1996 O O -As O O -many O O -as O O -1 O O -million O O -dog O O -bites O O -are O O -recorded O O -in O O -the O O -United B-LOC B-LOC -States I-LOC I-LOC -every O O -year O O -and O O -half O O -of O O -them O O -are O O -provoked O O -by O O -humans O O -, O O -a O O -ve O O -told O O -fellow O O -animal O O -doctors O O -on O O -Friday O O -. O O -The O O -Human B-ORG O -Society I-ORG O -of I-ORG O -the I-ORG O -United I-ORG B-LOC -States I-ORG I-LOC -estimates O O -that O O -between O O -500 O O -and O O -one O O -million O O -bites O O -are O O -delivered O O -by O O -dogs O O -each O O -year O O -, O O -more O O -than O O -half O O -of O O -which O O -are O O -suffered O O -by O O -children O O -. O O -" O O -Most O O -bites O O -can O O -be O O -prevented O O -by O O -teaching O O -children O O -how O O -to O O -respect O O -a O O -dog O O -, O O -" O O -Michael B-PER B-PER -Co I-PER I-PER -of O O -the O O -Glen B-LOC B-ORG -Animal I-LOC I-ORG -Hospital I-LOC I-ORG -in O O -Columbus B-LOC B-LOC -, O O -Ohio I-LOC B-LOC -, O O -told O O -the O O -annual O O -meeting O O -of O O -the O O -American B-ORG B-ORG -Veterinary I-ORG I-ORG -Medical I-ORG I-ORG -Association I-ORG I-ORG -. O O -" O O -Let O O -' O O -not O O -let O O -our O O -kids O O -jump O O -on O O -them O O -or O O -crawl O O -on O O -them O O -. O O -Dogs O O -and O O -children O O -do O O -n O O -have O O -to O O -have O O -an O O -interaction O O -. O O -Let O O -' O O -respect O O -their O O -territories O O -, O O -" O O -he O O -said O O -. O O -Co B-PER B-PER -said O O -50 O O -percent O O -of O O -reported O O -bites O O -were O O -provoked O O -by O O -a O O -person O O -and O O -60 O O -percent O O -were O O -suffered O O -by O O -children O O -. O O -He O O -also O O -estimated O O -that O O -only O O -25 O O -percent O O -of O O -bites O O -were O O -reported O O -because O O -medical O O -attention O O -was O O -not O O -needed O O -. O O -Don B-PER B-PER -R I-PER I-PER -, O O -president O O -of O O -the O O -National B-ORG B-ORG -Animal I-ORG I-ORG -Control I-ORG I-ORG -Association I-ORG I-ORG -, O O -said O O -aggressive O O -in O O -dogs O O -was O O -related O O -more O O -to O O -gender O O -than O O -breed O O -and O O -a O O -male O O -dog O O -that O O -had O O -not O O -been O O -ne O O -was O O -three O O -times O O -more O O -likely O O -to O O -bite O O -than O O -an O O -un O O -female O O -. O O -The O O -five O O -breeds O O -credited O O -with O O -the O O -most O O -incidents O O -were O O -ch O O -ch O O -, O O -R B-MISC B-MISC -, O O -German B-MISC B-MISC -she O I-MISC -, O O -cock O B-MISC -span O I-MISC -and O O -Dal B-MISC B-MISC -. O O -" O O -The O O -trends O O -in O O -dog O O -bites O O -by O O -particular O O -breeds O O -have O O -more O O -to O O -do O O -with O O -f O O -pets O O -owned O O -by O O -individuals O O -who O O -need O O -to O O -have O O -something O O -unique O O -. O O -Speaking O O -strictly O O -of O O -dogs O O -, O O -15 O O -years O O -ago O O -the O O -mac O O -f O O -pet O O -was O O -a O O -Do B-MISC B-MISC -. O O -Today O O -, O O -R B-MISC B-MISC -are O O -on O O -the O O -way O O -up O O -, O O -" O O -R B-PER B-PER -said O O -. O O -If O O -approached O O -by O O -a O O -stray O O -dog O O -, O O -children O O -should O O -be O O -taught O O -to O O -stand O O -still O O -with O O -fists O O -folded O O -underneath O O -the O O -neck O O -, O O -elbows O O -in O O -, O O -and O O -gaze O O -forward O O -until O O -the O O -dog O O -goes O O -away O O -. O O -Iowa B-MISC B-LOC -Min I-MISC B-LOC -feed O O -cattle O O -market O O -not O O -tested O O -USD B-ORG B-ORG -. O O -DE B-LOC B-LOC -M I-LOC I-LOC -1996 O O -St O O -and O O -he O O -were O O -not O O -tested O O -, O O -compared O O -with O O -Thursday O O -' O O -close O O -, O O -USD B-ORG B-ORG -said O O -. O O -Report O O -sales O O -for O O -Fr B-MISC O -None O O -. O O -Week O O -to O O -Date O O -- O O -None O O -. O O -( O O -( O O -Chicago B-LOC B-LOC -news O O -312 O O -) O O -) O O -Nebraska B-LOC B-LOC -fed O O -cattle O O -round O O -- O O -USD B-ORG B-ORG -. O O -O B-LOC B-LOC -1996 O O -S O O -steer O O -and O O -he O O -were O O -not O O -established O O -Thursday O O -. O O -De O O -limited O O -. O O -Se O O -interest O O -light O O -. O O -- O O -USD B-ORG B-ORG -Thursday O O -200 O O -Last O O -Week O O -Holiday O O -Last O O -Year O O -N O O -/ O O -A O O -Week O O -to O O -Date O O -3 O O -S B-MISC O -P O O -L O O -W O O -800 O O -S O O -P O O -L O O -Y O O -N O O -/ O O -A O O -Dr O O -Ba O O -Del O O -not O O -well O O -tested O O -. O O -Dr O O -Ba O O -St O O -: O O -Few O O -Select O O -and O O -Choice O O -2 O O -, O O -1200 O O -lbs O O -112 O O -; O O -load O O -early O O -114 O O -. O O -Dr O O -Ba O O -He O O -: O O -Few O O -Select O O -and O O -Choice O O -2 O O -, O O -1100 O O -lbs O O -112 O O -. O O -Four O O -Africans B-MISC B-MISC -said O O -to O O -v O O -for O O -top O O -U B-ORG B-ORG -post O O -. O O -Evelyn B-PER B-PER -Leopold I-PER I-PER -UN B-LOC B-ORG -N I-LOC I-ORG -1996 O O -Four O O -African B-MISC B-MISC -states O O -are O O -ready O O -to O O -nominate O O -candidates O O -for O O -the O O -post O O -of O O -U B-ORG B-ORG -secretary O O -on O O -Friday O O -now O O -that O O -Bo B-PER B-PER -Bo I-PER I-PER -has O O -temporarily O O -put O O -aside O O -his O O -bid O O -for O O -re O O -. O O -The O O -nominees O O -, O O -according O O -to O O -diplomat O O -, O O -are O O -: O O -Ko B-PER B-PER -Anna I-PER I-PER -of O O -Ghana B-LOC B-LOC -, O O -the O O -U B-ORG B-ORG -under O O -for O O -peace O O -; O O -Ahmed B-PER B-PER -O I-PER I-PER -Abd I-PER I-PER -of O O -Ma B-LOC B-LOC -, O O -the O O -former O O -U B-ORG B-ORG -special O O -en O O -for O O -B B-LOC B-LOC -; O O -Amar B-PER B-PER -E I-PER I-PER -of O O -the O O -Ivory B-LOC B-LOC -Coast I-LOC I-LOC -, O O -its O O -foreign O O -minister O O -and O O -the O O -U B-ORG B-ORG -General I-ORG I-ORG -Assembly I-ORG I-ORG -president O O -in O O -1994 O O -; O O -and O O -Ham B-PER B-PER -Al I-PER I-PER -of O O -Niger B-LOC B-LOC -, O O -the O O -secretary O O -of O O -the O O -Organisation B-ORG B-ORG -of I-ORG I-ORG -the I-ORG I-ORG -Islamic I-ORG I-ORG -Conference I-ORG I-ORG -. O O -Representatives O O -of O O -the O O -U B-ORG B-ORG -missions O O -of O O -Ghana B-LOC B-LOC -, O O -the O O -Ivory B-LOC B-LOC -Coast I-LOC I-LOC -, O O -Ma B-LOC B-LOC -and O O -Niger B-LOC B-LOC -have O O -scheduled O O -a O O -meeting O O -with O O -Security B-ORG B-ORG -Council I-ORG I-ORG -president O O -Paolo B-PER B-PER -Fu I-PER I-PER -of O O -Italy B-LOC B-LOC -to O O -hand O O -in O O -the O O -nominations O O -in O O -writing O O -, O O -the O O -en O O -said O O -. O O -It O O -was O O -not O O -known O O -if O O -other O O -candidates O O -would O O -step O O -forward O O -. O O -Diploma O O -said O O -General O O -Joseph B-PER B-PER -G I-PER I-PER -of O O -Nigeria B-LOC B-LOC -, O O -a O O -U B-ORG B-ORG -General I-ORG I-ORG -Assembly I-ORG I-ORG -president O O -in O O -1989 O O -, O O -was O O -putting O O -forth O O -his O O -own O O -candidacy O O -without O O -being O O -nominated O O -by O O -his O O -country O O -. O O -Bo B-PER B-PER -on O O -Wednesday O O -opened O O -the O O -door O O -for O O -other O O -Africans B-MISC B-MISC -to O O -contest O O -his O O -job O O -by O O -saying O O -he O O -was O O -su O O -temporarily O O -his O O -candidacy O O -but O O -was O O -not O O -withdrawing O O -completely O O -from O O -the O O -race O O -. O O -His O O -supporters O O -said O O -this O O -meant O O -he O O -remained O O -a O O -candidate O O -in O O -case O O -the O O -race O O -reached O O -an O O -imp O O -. O O -The O O -United B-LOC B-LOC -States I-LOC I-LOC -on O O -Nov O O -19 O O -veto O O -his O O -bid O O -for O O -re O O -while O O -the O O -other O O -14 O O -Security B-ORG B-ORG -Council I-ORG I-ORG -members O O -supported O O -him O O -. O O -The O O -move O O -by O O -the O O -African B-MISC B-MISC -states O O -means O O -that O O -the O O -council O O -could O O -begin O O -voting O O -on O O -candidates O O -next O O -week O O -, O O -a O O -procedure O O -that O O -could O O -either O O -result O O -in O O -a O O -decision O O -or O O -turn O O -into O O -a O O -bitter O O -fight O O -with O O -veto O O -against O O -each O O -nominee O O -. O O -The O O -Security B-ORG B-ORG -Council I-ORG I-ORG -has O O -to O O -vote O O -on O O -a O O -new O O -secretary O O -and O O -then O O -seek O O -the O O -endorsement O O -of O O -the O O -185 O O -General B-ORG B-ORG -Assembly I-ORG I-ORG -before O O -December O O -31 O O -when O O -Bo B-PER B-PER -' O O -term O O -ex O O -. O O -Spain B-LOC B-LOC -' O O -police O O -seize O O -petrol O O -bombs O O -, O O -arrest O O -five O O -. O O -MA B-LOC B-LOC -1996 O O -Spanish B-MISC B-MISC -police O O -said O O -on O O -Friday O O -they O O -had O O -arrested O O -five O O -people O O -and O O -seized O O -more O O -than O O -90 O O -petrol O O -bombs O O -during O O -disturbance O O -after O O -a O O -protest O O -in O O -the O O -Basque B-MISC B-MISC -country O O -against O O -Spain B-LOC B-LOC -' O O -constitution O O -. O O -Hood O O -protesters O O -threw O O -burning O O -bottles O O -and O O -other O O -objects O O -at O O -police O O -in O O -Pam B-LOC B-LOC -after O O -the O O -protest O O -organised O O -by O O -Herr B-PER B-ORG -Bat I-PER I-ORG -, O O -the O O -political O O -wing O O -of O O -Basque B-MISC B-MISC -se O O -group O O -ET B-ORG B-ORG -. O O -Police O O -also O O -confiscated O O -eight O O -kg O O -( O O -18 O O -lb O O -) O O -of O O -screw O O -, O O -b O O -and O O -spray O O -paint O O -cans O O -. O O -The O O -protest O O -, O O -which O O -attracted O O -several O O -thousand O O -supporters O O -, O O -coincided O O -with O O -the O O -18th O O -anniversary O O -of O O -Spain B-LOC B-LOC -' O O -constitution O O -. O O -Mussolini B-PER B-PER -' O O -granddaughter O O -re O O -far O O -party O O -. O O -ROM B-LOC B-LOC -1996 O O -Al B-PER B-PER -Mussolini I-PER I-PER -, O O -the O O -granddaughter O O -of O O -Italy B-LOC B-LOC -' O O -F B-MISC O -dictator O O -Benito B-PER B-PER -Mussolini I-PER I-PER -, O O -said O O -on O O -Friday O O -she O O -had O O -rejoined O O -the O O -far O O -National B-ORG B-ORG -Alliance I-ORG I-ORG -( O O -AN B-ORG B-ORG -) O O -party O O -she O O -quit O O -over O O -policy O O -differences O O -last O O -month O O -. O O -" O O -I O O -' O O -gone O O -back O O -, O O -" O O -she O O -told O O -a O O -radio O O -show O O -shortly O O -after O O -AN B-ORG B-ORG -leader O O -G B-PER B-PER -Fin I-PER I-PER -, O O -who O O -was O O -being O O -interviewed O O -on O O -the O O -programme O O -, O O -said O O -the O O -row O O -had O O -been O O -resolved O O -. O O -" O O -He O O -did O O -n O O -want O O -to O O -lose O O -me O O -and O O -I O O -did O O -n O O -want O O -to O O -lose O O -him O O -. O O -" O O -Fin B-PER B-PER -told O O -state O O -radio O O -RA B-ORG B-LOC -he O O -met O O -Mussolini B-PER B-PER -thanks O O -to O O -the O O -good O O -offices O O -of O O -Giuseppe B-PER B-PER -Ta I-PER I-PER -, O O -AN B-ORG B-ORG -' O O -leader O O -in O O -the O O -Chamber B-ORG B-ORG -of I-ORG I-ORG -Deputies I-ORG I-ORG -( O O -lower O O -house O O -) O O -, O O -and O O -had O O -overcome O O -their O O -differences O O -. O O -Mussolini B-PER B-PER -, O O -33 O O -, O O -resigned O O -from O O -the O O -parliamentary O O -party O O -group O O -for O O -what O O -she O O -said O O -were O O -strictly O O -political O O -reasons O O -. O O -The O O -fiery O O -politician O O -, O O -who O O -is O O -also O O -a O O -niece O O -of O O -screen O O -star O O -Sophia B-PER B-PER -Lo I-PER I-PER -, O O -had O O -accused O O -AN B-ORG B-ORG -leaders O O -of O O -s O O -internal O O -party O O -debate O O -. O O -Mussolini B-PER B-PER -, O O -who O O -sits O O -in O O -the O O -Chamber B-ORG B-ORG -, O O -told O O -La B-ORG B-ORG -St I-ORG I-ORG -newspaper O O -last O O -month O O -after O O -quit O O -AN B-ORG B-ORG -' O O -parliamentary O O -party O O -that O O -she O O -was O O -considering O O -joining O O -the O O -neo B-MISC O -Social B-ORG B-ORG -Movement I-ORG I-ORG -( O O -MS B-ORG B-ORG -) O O -formed O O -by O O -some O O -of O O -the O O -Du B-PER O -' O O -World B-MISC B-MISC -War I-MISC I-MISC -Two I-MISC I-MISC -followers O O -. O O -German B-MISC B-MISC -Santa B-PER B-PER -in O O -bank O O -nearly O O -gets O O -arrested O O -. O O -H B-LOC B-LOC -, O O -Germany B-LOC B-LOC -1996 O O -A O O -Santa B-MISC B-PER -Claus I-MISC I-PER -distributing O O -presents O O -to O O -workers O O -in O O -a O O -German B-MISC B-MISC -bank O O -on O O -Friday O O -nearly O O -ended O O -up O O -behind O O -bars O O -when O O -a O O -passing O O -police O O -patrol O O -thought O O -he O O -was O O -a O O -r O O -in O O -disguise O O -. O O -The O O -man O O -, O O -doing O O -his O O -rounds O O -in O O -the O O -northern O O -city O O -of O O -Hanover B-LOC B-LOC -on O O -the O O -day O O -when O O -German B-MISC B-MISC -children O O -traditionally O O -receive O O -small O O -presents O O -from O O -Saint B-PER B-PER -Nicholas I-PER I-PER -, O O -convinced O O -police O O -eventually O O -that O O -he O O -was O O -genuine O O -. O O -Italy B-LOC B-LOC -commission O O -concludes O O -1997 O O -budget O O -examination O O -. O O -ROM B-LOC B-LOC -1996 O O -The O O -Italian B-MISC B-MISC -upper O O -house O O -Senate B-ORG B-ORG -budget O O -commission O O -has O O -concluded O O -its O O -examination O O -of O O -Italy B-LOC B-LOC -' O O -1997 O O -budget O O -, O O -and O O -it O O -will O O -approve O O -the O O -measure O O -officially O O -by O O -Saturday O O -. O O -From O O -Tuesday O O -, O O -the O O -full O O -assembly O O -of O O -the O O -Senate B-ORG B-ORG -will O O -start O O -its O O -examination O O -of O O -the O O -financial O O -package O O -. O O -- O O -Milan B-LOC B-LOC -news O O -+ O O -66 O O -EU B-LOC B-ORG -, O O -Poland B-LOC B-LOC -agree O O -on O O -oil O O -import O O -ta O O -. O O -BR B-LOC B-LOC -1996 O O -The O O -European B-ORG B-ORG -Union I-ORG I-ORG -and O O -Poland B-LOC B-LOC -have O O -resolved O O -disagreements O O -over O O -a O O -new O O -Polish B-MISC B-MISC -oil O O -import O O -regime O O -, O O -the O O -European B-ORG B-ORG -Commission I-ORG I-ORG -said O O -on O O -Friday O O -. O O -The O O -EU B-ORG B-ORG -had O O -objected O O -to O O -increases O O -in O O -Polish B-MISC B-MISC -ta O O -on O O -imports O O -of O O -gasoline O O -and O O -gas O O -products O O -introduced O O -on O O -January O O -1 O O -, O O -1996 O O -, O O -saying O O -they O O -con O O -levels O O -en O O -in O O -the O O -so O O -Europe B-LOC B-LOC -Agreement I-MISC O -between O O -the O O -EU B-ORG B-ORG -and O O -Poland B-LOC B-LOC -. O O -The O O -increases O O -were O O -aimed O O -at O O -protecting O O -the O O -Polish B-MISC B-MISC -market O O -while O O -helping O O -to O O -modern O O -the O O -local O O -oil O O -industry O O -. O O -" O O -The O O -EU B-ORG B-ORG -and O O -Poland B-LOC B-LOC -have O O -now O O -reached O O -a O O -final O O -settlement O O -regarding O O -issues O O -related O O -to O O -the O O -Polish B-MISC B-MISC -import O O -regime O O -in O O -the O O -oils O O -sector O O -, O O -" O O -the O O -Commission B-ORG B-ORG -said O O -in O O -a O O -statement O O -. O O -Under O O -the O O -agreement O O -, O O -Poland B-LOC B-LOC -will O O -a O O -all O O -oil O O -import O O -ta O O -by O O -2001 O O -, O O -remove O O -all O O -oil O O -price O O -controls O O -and O O -end O O -quantitative O O -restrictions O O -on O O -imports O O -by O O -January O O -1 O O -, O O -1997 O O -. O O -The O O -agreement O O -includes O O -the O O -early O O -p O O -and O O -modern O O -of O O -Polish B-MISC B-MISC -oil O O -re O O -, O O -which O O -will O O -be O O -obliged O O -to O O -offer O O -equal O O -treatment O O -to O O -all O O -buyers O O -. O O -The O O -EU B-ORG B-ORG -and O O -Poland B-LOC B-LOC -will O O -monitor O O -the O O -settlement O O -at O O -six O O -meetings O O -. O O -Hindu B-MISC B-MISC -party O O -forces O O -India B-LOC B-LOC -parliament O O -to O O -ad O O -. O O -NE B-LOC B-LOC -DE I-LOC I-LOC -1996 O O -Hindu B-MISC B-MISC -nationalists O O -forced O O -ad O O -of O O -India B-LOC B-LOC -' O O -lower O O -house O O -of O O -parliament O O -on O O -Friday O O -, O O -in O O -protest O O -against O O -a O O -proposal O O -to O O -observe O O -a O O -minute O O -' O O -silence O O -over O O -the O O -destruction O O -of O O -a O O -mosque O O -by O O -a O O -Hindu B-MISC B-MISC -mob O O -in O O -1992 O O -. O O -Members O O -of O O -the O O -Hindu B-MISC B-MISC -nationalist O O -Bharatiya B-ORG B-ORG -Janata I-ORG I-ORG -Party I-ORG I-ORG -( O O -B B-ORG B-ORG -) O O -shouted O O -pro B-MISC B-MISC -slogan O O -in O O -the O O -house O O -after O O -a O O -communist O O -deputy O O -made O O -the O O -proposal O O -in O O -re O O -of O O -the O O -Ba B-LOC O -mosque O O -, O O -which O O -was O O -r O O -on O O -December O O -6 O O -, O O -1992 O O -. O O -The O O -house O O -was O O -first O O -ad O O -for O O -two O O -hours O O -. O O -When O O -it O O -re O O -, O O -B B-ORG B-ORG -deputies O O -resumed O O -the O O -slogan O O -, O O -and O O -deputy O O -speaker O O -Sur B-PER B-PER -B I-PER I-PER -suspended O O -work O O -until O O -Monday O O -. O O -The O O -destruction O O -of O O -the O O -16th O O -mosque O O -in O O -the O O -northern O O -Indian B-MISC B-MISC -town O O -of O O -A B-LOC B-LOC -triggered O O -nationwide O O -Hindu B-MISC B-MISC -violence O O -in O O -which O O -more O O -than O O -3 O O -people O O -were O O -killed O O -. O O -Indian B-MISC B-MISC -officials O O -blame O O -revenge O O -Mo B-MISC B-MISC -underworld O O -gangs O O -in O O -Bombay B-LOC B-LOC -for O O -a O O -string O O -of O O -bombings O O -in O O -the O O -city O O -three O O -months O O -later O O -that O O -killed O O -260 O O -people O O -. O O -The O O -B B-ORG B-ORG -backs O O -a O O -hard O O -Hindu B-MISC B-MISC -campaign O O -to O O -build O O -a O O -temple O O -at O O -the O O -site O O -of O O -the O O -mosque O O -, O O -which O O -Hindus B-MISC B-MISC -believe O O -was O O -the O O -birthplace O O -of O O -the O O -Lord O O -Rama B-PER B-PER -. O O -The O O -campaign O O -cat O O -B B-ORG B-ORG -from O O -the O O -political O O -fringe O O -to O O -become O O -India B-LOC B-LOC -' O O -main O O -opposition O O -party O O -in O O -1991 O O -. O O -Indian B-MISC B-MISC -Sept O O -crude O O -oil O O -output O O -falls O O -to O O -2 O O -m O O -T O O -. O O -NE B-LOC B-LOC -DE I-LOC I-LOC -1996 O O -India B-LOC B-LOC -' O O -crude O O -petroleum O O -output O O -fell O O -to O O -2 O O -million O O -tonnes O O -in O O -September O O -from O O -2 O O -million O O -in O O -the O O -same O O -month O O -in O O -1995 O O -, O O -the O O -government O O -said O O -on O O -Friday O O -. O O -ST O O -O I-ORG O -Sept O O -Sept O O -Apr O O -Apr O O -1996 O O -1995 O O -1996 O O -1995 O O -C O O -petroleum O O -2 O O -2 O O -15 O O -17 O O -Petroleum O O -products O O -4 O O -5 O O -30 O O -29 O O -Note O O -- O O -Figure O O -are O O -in O O -thousands O O -of O O -tonnes O O -and O O -preliminary O O -. O O -L B-MISC B-LOC -CH O O -MA O O -GO O O -ON O O -W O O -W O O -W O O -. O O -BR B-LOC B-LOC -1996 O O -Luxembourg B-LOC B-LOC -' O O -traditional O O -Christmas O O -market O O -, O O -which O O -starts O O -on O O -Saturday O O -and O O -runs O O -to O O -December O O -24 O O -, O O -has O O -taken O O -to O O -the O O -world O O -wide O O -web O O -as O O -a O O -way O O -of O O -public O O -its O O -activities O O -. O O -The O O -web O O -site O O -( O O -http O O -) O O -gives O O -details O O -of O O -the O O -market O O -' O O -concert O O -programme O O -as O O -well O O -as O O -its O O -various O O -retailers O O -. O O -- O O -Brussels B-ORG B-ORG -News I-ORG I-ORG -+ O O -2 O O -287 O O -68 O O -, O O -F O O -+ O O -2 O O -230 O O -77 O O -London B-LOC B-LOC -coal O O -/ O O -ore O O -fixtures O O -. O O -L B-LOC B-LOC -1996 O O -CO B-LOC O -- O O -La B-LOC B-MISC -Peak I-LOC I-MISC -- O O -120 O O -tones O O -coal O O -Hay B-LOC B-LOC -Point I-LOC I-LOC -or O O -Newcastle B-LOC B-LOC -/ O O -Ka B-LOC B-LOC -20 O O -$ O O -5 O O -and O O -$ O O -5 O O -fi O O -respectively O O -40 O O -/ O O -28 O O -s O O -China B-ORG B-ORG -Steel I-ORG I-ORG -. O O -Royal B-ORG B-MISC -C I-ORG I-MISC -- O O -77 O O -tonnes O O -coal O O -Mara B-LOC B-LOC -/ O O -F B-LOC B-LOC -19 O O -$ O O -9 O O -fi O O -20 O O -s O O -/ O O -25 O O -s O O -Co B-ORG B-ORG -and O I-ORG -C B-ORG I-ORG -. O O -OR B-ORG O -- O O -I B-ORG O -T I-ORG O -- O O -70 O O -tonnes O O -Dam B-ORG B-LOC -/ O O -Ka B-LOC B-LOC -20 O O -$ O O -5 O O -fi O O -35 O O -s O O -/ O O -30 O O -s O O -China B-ORG B-ORG -Steel I-ORG I-ORG -. O O -UK B-LOC B-LOC -book O O -length O O -Conservative B-MISC B-MISC -victory O O -odds O O -. O O -L B-LOC B-LOC -1996 O O -UK B-LOC B-LOC -book O O -William B-PER B-PER -Hill I-PER I-PER -said O O -on O O -Friday O O -they O O -have O O -length O O -the O O -odds O O -of O O -a O O -Conservative B-MISC B-MISC -victory O O -in O O -the O O -next O O -general O O -election O O -from O O -9 O O -to O O -5 O O -. O O -William B-PER B-PER -Hill I-PER I-PER -said O O -the O O -odds O O -were O O -the O O -longest O O -they O O -had O O -been O O -for O O -six O O -months O O -. O O -The O O -Labour B-ORG B-ORG -opposition O O -are O O -now O O -1 O O -favourite O O -, O O -it O O -said O O -. O O -The O O -election O O -must O O -be O O -held O O -by O O -May O O -. O O -- O O -London B-ORG B-ORG -News I-ORG I-ORG -+ O O -171 O O -54 O O -Italy B-LOC B-LOC -tops O O -week O O -of O O -me O O -bond O O -returns O O -- O O -Sal B-PER B-ORG -. O O -L B-LOC B-LOC -1996 O O -High O O -Italy B-LOC B-LOC -topped O O -the O O -league O O -in O O -a O O -week O O -of O O -me O O -returns O O -on O O -government O O -bonds O O -, O O -Sal B-ORG B-ORG -Brothers I-ORG I-ORG -said O O -on O O -Friday O O -. O O -In O O -local O O -currency O O -terms O O -, O O -Italian B-MISC B-MISC -BT B-ORG O -offered O O -returns O O -of O O -0 O O -percent O O -in O O -the O O -week O O -ended O O -on O O -Thursday O O -, O O -with O O -fellow O O -high O O -Sweden B-LOC B-LOC -close O O -behind O O -on O O -0 O O -percent O O -. O O -The O O -weekly O O -government O O -bond O O -index O O -rose O O -0 O O -percent O O -in O O -local O O -currency O O -terms O O -. O O -France B-LOC B-LOC -managed O O -third O O -place O O -with O O -0 O O -percent O O -in O O -the O O -16 O O -world O O -government O O -bond O O -index O O -. O O -Canada B-LOC B-LOC -' O O -were O O -the O O -worst O O -performing O O -bonds O O -. O O -They O O -lost O O -2 O O -percent O O -, O O -depressed O O -by O O -a O O -wave O O -of O O -new O O -Canadian B-MISC B-MISC -supply O O -. O O -Return O O -on O O -T B-ORG B-MISC -were O O -also O O -in O O -negative O O -territory O O -at O O -minus O O -0 O O -percent O O -, O O -the O O -poor O O -result O O -after O O -Canada B-LOC B-LOC -and O O -British B-MISC B-MISC -g O O -which O O -lost O O -0 O O -percent O O -. O O -Australia B-LOC B-LOC -was O O -the O O -only O O -dollar O O -country O O -in O O -the O O -table O O -to O O -e O O -out O O -a O O -positive O O -return O O -, O O -albeit O O -a O O -p O O -0 O O -percent O O -. O O -German B-MISC B-MISC -B I-MISC O -were O O -not O O -much O O -better O O -, O O -offering O O -returns O O -of O O -0 O O -percent O O -, O O -while O O -Japanese B-MISC B-MISC -government O O -bonds O O -managed O O -a O O -0 O O -percent O O -gain O O -. O O -Spanish B-MISC B-MISC -bonds O O -, O O -which O O -had O O -been O O -top O O -performers O O -in O O -Sal B-ORG B-ORG -Brothers I-ORG I-ORG -' O O -league O O -table O O -for O O -November O O -as O O -a O O -whole O O -, O O -turned O O -in O O -a O O -more O O -subdued O O -weekly O O -performance O O -with O O -a O O -return O O -of O O -only O O -0 O O -percent O O -. O O -In O O -U B-LOC B-LOC -dollar O O -terms O O -, O O -Japan B-LOC B-LOC -was O O -the O O -only O O -country O O -to O O -give O O -positive O O -returns O O -at O O -1 O O -percent O O -. O O -France B-LOC B-LOC -lost O O -0 O O -percent O O -, O O -followed O O -by O O -Italy B-LOC B-LOC -on O O -minus O O -0 O O -percent O O -. O O -The O O -biggest O O -loser O O -in O O -dollar O O -terms O O -were O O -British B-MISC B-MISC -g O O -, O O -which O O -shed O O -3 O O -percent O O -, O O -Canada B-LOC B-LOC -with O O -minus O O -3 O O -percent O O -and O O -Australia B-LOC B-LOC -at O O -minus O O -1 O O -percent O O -. O O -Sal B-ORG B-ORG -' O O -bond O O -index O O -is O O -calculated O O -using O O -all O O -government O O -bonds O O -with O O -over O O -one O O -year O O -to O O -maturity O O -, O O -weighted O O -for O O -market O O -capital O O -. O O -Only O O -bonds O O -freely O O -available O O -to O O -institutional O O -investors O O -and O O -with O O -a O O -certain O O -minimum O O -amount O O -outstanding O O -are O O -included O O -. O O -Return O O -take O O -account O O -of O O -price O O -moves O O -and O O -a O O -interest O O -. O O -- O O -Stephen B-PER B-PER -Ni I-PER I-PER -, O O -International B-ORG B-ORG -Bond I-ORG I-ORG -+ O O -171 O O -63 O O -O B-ORG B-ORG -basket O O -price O O -$ O O -24 O O -on O O -Thursday O O -. O O -L B-LOC B-LOC -1996 O O -The O O -price O O -of O O -the O O -O B-ORG B-ORG -basket O O -of O O -seven O O -crude O O -stood O O -at O O -$ O O -24 O O -a O O -barrel O O -on O O -Thursday O O -, O O -against O O -$ O O -23 O O -on O O -Wednesday O O -, O O -the O O -O B-ORG B-ORG -news O O -agency O O -said O O -, O O -q O O -the O O -O B-ORG B-ORG -secret O O -. O O -The O O -basket O O -comprises O O -Algeria B-LOC B-LOC -' O O -Saharan B-MISC B-MISC -B I-MISC I-MISC -, O O -Indonesia B-LOC B-LOC -' O O -Minas B-LOC B-MISC -, O O -Nigeria B-LOC B-LOC -' O O -Bonn B-ORG B-MISC -Light I-ORG I-MISC -, O O -Saudi B-LOC B-LOC -Arabia I-LOC I-LOC -' O O -Arabian B-ORG B-MISC -Light I-ORG I-MISC -, O O -Dubai B-LOC B-MISC -of O O -the O O -UAE B-LOC B-LOC -, O O -Venezuela B-LOC B-LOC -' O O -T B-PER B-MISC -Juan I-PER I-MISC -and O O -Mexico B-LOC B-LOC -' O O -Is B-LOC B-MISC -. O O -- O O -London B-ORG B-ORG -News I-ORG I-ORG -+ O O -171 O O -54 O O -76 O O -Relations O O -between O O -Clarke B-PER B-PER -, O O -Major O B-PER -good O O -- O O -spokesman O O -. O O -L B-LOC B-LOC -1996 O O -Relations O O -between O O -Chancellor O O -of O O -the O O -Ex B-ORG O -Kenneth B-PER B-PER -Clarke I-PER I-PER -and O O -Prime O O -Minister O O -John B-PER B-PER -Major I-PER I-PER -are O O -good O O -despite O O -media O O -reports O O -of O O -a O O -rift O O -over O O -European B-MISC B-MISC -policy O O -, O O -a O O -spokesman O O -for O O -Major B-PER B-PER -' O O -office O O -said O O -on O O -Friday O O -. O O -Ask O O -about O O -the O O -reports O O -, O O -the O O -spokesman O O -said O O -: O O -" O O -Relations O O -are O O -good O O -. O O -" O O -Ask O O -about O O -Major B-PER B-PER -' O O -mood O O -after O O -a O O -day O O -of O O -media O O -speculation O O -about O O -his O O -political O O -fortunes O O -, O O -the O O -spokesman O O -said O O -: O O -" O O -He O O -is O O -re O O -. O O -He O O -is O O -getting O O -on O O -with O O -the O O -job O O -. O O -" O O -The O O -spokesman O O -said O O -he O O -was O O -not O O -aware O O -of O O -any O O -meetings O O -overnight O O -between O O -Clarke B-PER B-PER -and O O -Major B-PER B-PER -, O O -nor O O -of O O -any O O -talks O O -between O O -the O O -prime O O -minister O O -and O O -parliamentary O O -business O O -managers O O -. O O -Both O O -Major B-PER B-PER -and O O -Clarke B-PER B-PER -were O O -in O O -their O O -constituencies O O -on O O -Friday O O -. O O -Two O O -dead O O -after O O -executive O O -jet O O -crashes O O -in O O -Newfoundland B-LOC B-LOC -. O O -ST B-LOC B-LOC -, O O -Newfoundland B-LOC B-LOC -1996 O O -Two O O -people O O -were O O -killed O O -when O O -an O O -executive O O -jet O O -en O O -route O O -to O O -Ireland B-LOC B-LOC -from O O -Michigan B-LOC B-LOC -crashed O O -on O O -approach O O -to O O -an O O -airport O O -in O O -Stephen B-LOC B-LOC -, O O -Newfoundland B-LOC B-LOC -, O O -on O O -Friday O O -, O O -authorities O O -said O O -. O O -The O O -pilot O O -and O O -co O O -, O O -the O O -only O O -two O O -aboard O O -, O O -were O O -killed O O -in O O -the O O -crash O O -of O O -the O O -Lea B-MISC B-MISC -36 I-MISC I-MISC -, O O -airport O O -manager O O -David B-PER B-PER -Snow I-PER I-PER -said O O -in O O -a O O -telephone O O -interview O O -. O O -Snow O B-PER -said O O -the O O -plane O O -last O O -reported O O -to O O -air O O -traffic O O -control O O -at O O -about O O -3 O O -A B-MISC O -local O O -time O O -/ O O -1 O O -A B-MISC O -E O O -( O O -06 O O -GM B-MISC B-MISC -) O O -when O O -it O O -began O O -its O O -final O O -approach O O -about O O -10 O O -miles O O -( O O -16 O O -km O O -) O O -from O O -the O O -airport O O -in O O -this O O -east O O -coast O O -Canadian B-MISC B-MISC -province O O -. O O -That O O -was O O -the O O -last O O -communication O O -the O O -aircraft O O -made O O -with O O -the O O -airport O O -, O O -he O O -added O O -. O O -" O O -We O O -considered O O -it O O -as O O -being O O -missing O O -until O O -about O O -06 O O -( O O -4 O O -A B-MISC O -E O O -) O O -( O O -09 O O -GM B-MISC B-MISC -) O O -. O O -That O O -' O O -when O O -the O O -wreckage O O -was O O -discovered O O -, O O -" O O -Snow B-PER B-PER -said O O -. O O -He O O -said O O -the O O -cargo O O -flight O O -originated O O -in O O -Grand B-LOC B-LOC -Rapids I-LOC I-LOC -, O O -Michigan B-LOC B-LOC -, O O -and O O -was O O -due O O -to O O -stop O O -at O O -Stephen B-LOC B-LOC -for O O -re O O -before O O -going O O -to O O -Shannon B-LOC B-LOC -, O O -Ireland B-LOC B-LOC -. O O -The O O -cause O O -of O O -the O O -crash O O -was O O -not O O -yet O O -known O O -. O O -In O O -were O O -due O O -to O O -fly O O -to O O -Stephen B-LOC B-LOC -later O O -on O O -Friday O O -. O O -P B-ORG B-ORG -says O O -Ara B-PER B-PER -, O O -Net B-PER B-PER -could O O -meet O O -Saturday O O -. O O -J B-LOC B-LOC -1996 O O -P B-ORG B-ORG -ne O O -said O O -on O O -Friday O O -Palestinian B-MISC B-MISC -President O O -Ya B-PER B-PER -Ara I-PER I-PER -, O O -Israeli B-MISC B-MISC -Prime O O -Minister O O -Benjamin B-PER B-PER -Net I-PER I-PER -and O O -Egyptian B-MISC B-MISC -President O O -Ho B-PER B-PER -Mu I-PER I-PER -might O O -all O O -meet O O -on O O -Saturday O O -to O O -try O O -to O O -c O O -a O O -deal O O -on O O -Israel B-LOC B-LOC -' O O -hand O O -of O O -He B-LOC B-LOC -to O O -the O O -P B-ORG B-ORG -. O O -" O O -It O O -is O O -very O O -possible O O -that O O -Ara B-PER B-PER -and O O -Net B-PER B-PER -will O O -meet O O -in O O -Cairo B-LOC B-LOC -on O O -Saturday O O -. O O -There O O -is O O -work O O -on O O -arranging O O -such O O -a O O -meeting O O -hosted O O -by O O -President O O -Mu B-PER B-PER -, O O -" O O -one O O -P B-ORG B-ORG -official O O -, O O -who O O -requested O O -an O O -, O O -told O O -Re B-ORG B-ORG -. O O -Israeli B-MISC B-MISC -officials O O -said O O -no O O -meeting O O -had O O -yet O O -been O O -set O O -. O O -Ara B-PER B-PER -' O O -adviser O O -Na B-PER B-PER -Abu I-PER I-PER -Rd I-PER I-PER -said O O -: O O -" O O -President O O -Ara B-PER B-PER -is O O -ready O O -to O O -meet O O -Prime O O -Minister O O -Net B-PER B-PER -but O O -no O O -time O O -or O O -date O O -has O O -been O O -set O O -for O O -such O O -a O O -meeting O O -yet O O -. O O -" O O -President O O -Ara B-PER B-PER -' O O -position O O -is O O -clear O O -that O O -such O O -a O O -meeting O O -should O O -come O O -after O O -successful O O -negotiations O O -so O O -that O O -the O O -meeting O O -would O O -have O O -positive O O -results O O -. O O -Especially O O -since O O -the O O -He B-LOC B-LOC -issue O O -has O O -not O O -been O O -agreed O O -yet O O -and O O -the O O -crucial O O -disputed O O -issues O O -have O O -not O O -been O O -resolved O O -. O O -" O O -But O O -Rd B-PER B-PER -said O O -Ara B-PER B-PER -would O O -go O O -to O O -Cairo B-LOC B-LOC -on O O -Saturday O O -for O O -talks O O -with O O -Mu B-PER B-PER -. O O -Both O O -Ara B-PER B-PER -and O O -Net B-PER B-PER -have O O -expressed O O -willingness O O -to O O -meet O O -. O O -They O O -last O O -met O O -in O O -Washington B-LOC B-LOC -after O O -clashes O O -in O O -September O O -that O O -killed O O -60 O O -Palestinians B-MISC B-MISC -and O O -15 O O -Israeli B-MISC B-MISC -. O O -The O O -violence O O -was O O -spurred O O -by O O -Israel B-LOC B-LOC -' O O -opening O O -an O O -entrance O O -to O O -a O O -tunnel O O -near O O -Mo B-MISC B-MISC -sites O O -in O O -Jerusalem B-LOC B-LOC -. O O -The O O -Palestine B-ORG B-ORG -Liberation I-ORG I-ORG -Organisation I-ORG I-ORG -( O O -P B-ORG B-ORG -) O O -ne O O -said O O -the O O -last O O -two O O -weeks O O -of O O -talks O O -with O O -Israel B-LOC B-LOC -on O O -implementing O O -the O O -long O O -hand O O -of O O -most O O -of O O -He B-LOC B-LOC -to O O -P B-ORG B-ORG -rule O O -had O O -been O O -" O O -meaning O O -" O O -, O O -ne O O -an O O -Ara B-PER O -meeting O O -. O O -Mu B-PER B-PER -' O O -adviser O O -O B-PER B-PER -el I-PER I-PER -said O O -on O O -Thursday O O -there O O -were O O -efforts O O -to O O -arrange O O -a O O -meeting O O -between O O -the O O -Israeli B-MISC B-MISC -and O O -Palestinian B-MISC B-MISC -leaders O O -. O O -Palestinian B-ORG B-ORG -Authority I-ORG I-ORG -Secretary O O -General O O -Ahmed B-PER B-PER -Abd I-PER I-PER -said O O -on O O -Thursday O O -he O O -understood O O -it O O -could O O -be O O -held O O -in O O -Cairo B-LOC B-LOC -either O O -on O O -Friday O O -or O O -Sunday O O -. O O -Abd B-PER B-PER -had O O -said O O -on O O -Thursday O O -he O O -did O O -not O O -think O O -Saturday O O -would O O -be O O -the O O -date O O -because O O -it O O -is O O -the O O -Jewish B-MISC B-MISC -sa O O -. O O -But O O -the O O -Jewish B-MISC B-MISC -sa O O -ends O O -at O O -sun O O -, O O -so O O -a O O -night O O -meeting O O -would O O -not O O -interfere O O -with O O -the O O -religious O O -o O O -. O O -Turkey B-LOC B-LOC -hind O O -by O O -own O O -land O O -on O O -Syrian B-MISC B-MISC -border O O -. O O -AN B-LOC B-LOC -1996 O O -Turkey B-LOC B-LOC -' O O -efforts O O -to O O -prevent O O -Kurdish B-MISC B-MISC -rebels O O -and O O -smug O O -in O O -from O O -Syria B-LOC B-LOC -are O O -being O O -badly O O -hind O O -because O O -the O O -military O O -does O O -not O O -have O O -a O O -map O O -of O O -its O O -own O O -mine O O -on O O -the O O -border O O -, O O -a O O -commission O O -of O O -parliament O O -said O O -. O O -" O O -It O O -is O O -not O O -known O O -exactly O O -where O O -the O O -mines O O -have O O -been O O -so O O -because O O -a O O -mine O O -chart O O -cannot O O -be O O -found O O -, O O -" O O -the O O -commission O O -said O O -in O O -a O O -report O O -on O O -border O O -protection O O -. O O -The O O -report O O -, O O -to O O -be O O -debated O O -in O O -parliament O O -in O O -coming O O -weeks O O -, O O -was O O -seen O O -by O O -Re B-ORG B-ORG -on O O -Friday O O -. O O -" O O -Official O O -say O O -the O O -mine O O -present O O -an O O -obstacle O O -to O O -the O O -security O O -forces O O -, O O -" O O -it O O -said O O -. O O -It O O -said O O -Kurdistan B-ORG B-ORG -Workers I-ORG I-ORG -Party I-ORG I-ORG -( O O -P B-ORG B-ORG -) O O -guerrilla O O -sometimes O O -know O O -the O O -layout O O -of O O -mined O O -areas O O -along O O -the O O -border O O -better O O -than O O -the O O -security O O -forces O O -. O O -" O O -Terror O O -and O O -smug O O -have O O -dug O O -up O O -the O O -mines O O -, O O -def O O -them O O -and O O -opened O O -up O O -wide O O -paths O O -in O O -some O O -areas O O -. O O -They O O -can O O -come O O -in O O -and O O -out O O -easily O O -as O O -the O O -mine O O -are O O -not O O -an O O -obstacle O O -, O O -" O O -it O O -said O O -. O O -An O O -armed O O -forces I-ORG O -spokesman O O -was O O -not O O -available O O -for O O -comment O O -. O O -Turkey B-LOC B-LOC -says O O -Syria B-LOC B-LOC -sponsors O O -the O O -P B-ORG B-ORG -, O O -fighting O O -for O O -Kurdish B-MISC B-MISC -self O O -in O O -southeast O O -Turkey B-LOC B-LOC -. O O -Damascus B-LOC B-LOC -denies O O -aid O O -the O O -rebels O O -. O O -The O O -P B-ORG B-ORG -also O O -crosses O O -into O O -Turkey B-LOC B-LOC -from O O -bases O O -in O O -the O O -mountains O O -of O O -northern O O -Iraq B-LOC B-LOC -. O O -More O O -than O O -21 O O -people O O -have O O -died O O -in O O -the O O -12 O O -conflict O O -. O O -Three O O -dead O O -in O O -Ku B-MISC B-MISC -militia O O -blood O O -feud O O -in O O -Turkey B-LOC B-LOC -. O O -D B-LOC B-LOC -, O O -Turkey B-LOC B-LOC -1996 O O -Three O O -people O O -were O O -killed O O -on O O -Friday O O -in O O -a O O -gun O O -battle O O -between O O -rival O O -groups O O -of O O -anti O O -militia O O -on O O -the O O -streets O O -of O O -this O O -southeastern O O -Turkish B-MISC B-MISC -city O O -, O O -police O O -said O O -. O O -Four O O -others O O -were O O -wounded O O -in O O -the O O -clash O O -, O O -caused O O -by O O -a O O -blood O O -feud O O -between O O -two O O -families O O -, O O -the O O -Ke B-PER B-PER -and O O -Kara B-PER B-PER -, O O -serving O O -as O O -state O O -village O O -guards O O -against O O -Kurdish B-MISC B-MISC -rebels O O -. O O -Police O O -said O O -the O O -guards O O -fired O O -automatic O O -weapons O O -at O O -each O O -other O O -. O O -One O O -of O O -the O O -dead O O -was O O -a O O -civilian O O -pass O O -. O O -The O O -role O O -of O O -the O O -70 O O -mainly O O -Kurdish B-MISC B-MISC -village O O -guards O O -who O O -fight O O -Kurdistan B-ORG B-ORG -Workers I-ORG I-ORG -Party I-ORG I-ORG -( O O -P B-ORG B-ORG -) O O -guerrilla O O -in O O -the O O -southeast O O -has O O -been O O -questioned O O -recently O O -after O O -media O O -allegations O O -that O O -many O O -of O O -them O O -are O O -involved O O -in O O -common O O -crime O O -. O O -The O O -head O O -of O O -the O O -region O O -' O O -main O O -pro O O -militia O O -is O O -at O O -the O O -centre O O -of O O -a O O -security O O -scandal O O -that O O -has O O -shaken O O -the O O -government O O -. O O -More O O -than O O -21 O O -people O O -have O O -been O O -killed O O -in O O -the O O -12 O O -conflict O O -between O O -Turkish B-MISC B-MISC -security O O -forces O O -and O O -the O O -P B-ORG B-ORG -, O O -fighting O O -for O O -Kurdish B-MISC B-MISC -autonomy O O -or O O -independence O O -. O O -Texas B-LOC B-LOC -/ O O -w O O -Ok B-LOC B-LOC -fed O O -cattle O O -round O O -- O O -USD B-ORG B-ORG -. O O -AM B-LOC B-LOC -1996 O O -Trade O O -very O O -slow O O -in O O -the O O -Pan B-LOC B-LOC -area O O -Thursday O O -. O O -S O O -steer O O -and O O -he O O -not O O -well O O -tested O O -. O O -Fe O O -reporting O O -light O O -inquiry O O -from O O -buyers O O -. O O -- O O -USD B-ORG B-ORG -Thursday O O -200 O O -Week O O -A O O -Holiday O O -Year O O -A O O -10 O O -W O O -to O O -Date O O -69 O O -Week O O -A O O -58 O O -Year O O -A O O -30 O O -Sales O O -reported O O -on O O -200 O O -head O O -steer O O -; O O -69 O O -head O O -confirmed O O -for O O -week O O -to O O -date O O -which O O -includes O O -14 O O -formulated O O -and O O -3 O O -contracted O O -cattle O O -to O O -be O O -shipped O O -this O O -week O O -. O O -S B-ORG O -St I-ORG O -: O O -Pen B-MISC O -Select O O -and O O -Choice O O -2 O O -, O O -115 O O -lbs O O -67 O O -. O O -Pen O O -Select O O -, O O -few O O -choice O O -2 O O -115 O O -lbs O O -66 O O -. O O -Kansas B-LOC B-LOC -feed O O -cattle O O -round O O -- O O -USD B-ORG B-ORG -. O O -D B-LOC B-LOC -C I-LOC I-LOC -1996 O O -Trade O O -slow O O -. O O -Not O O -enough O O -slaughter O O -steer O O -or O O -he O O -sales O O -confirmed O O -for O O -an O O -adequate O O -market O O -test O O -. O O -- O O -USD B-ORG B-ORG -Thursday O O -600 O O -week O O -ago O O -holiday O O -year O O -ago O O -14 O O -week O O -to O O -date O O -89 O O -week O O -ago O O -71 O O -year O O -ago O O -47 O O -Inquiry O O -good O O -, O O -demand O O -light O O -. O O -Sales O O -confirmed O O -on O O -500 O O -slaughter O O -steer O O -and O O -100 O O -slaughter O O -he O O -Thursday O O -. O O -For O O -the O O -week O O -to O O -date O O -89 O O -head O O -confirmed O O -including O O -30 O O -head O O -of O O -contracted O O -or O O -formulated O O -cattle O O -. O O -St B-ORG O -: O O -Select O O -and O O -Choice O O -2 O O -, O O -1200 O O -lbs O O -67 O O -. O O -He O O -: O O -Select O O -and O O -Choice O O -2 O O -, O O -115 O O -lbs O O -67 O O -. O O -Del B-ORG B-ORG -Hanover I-ORG I-ORG -weekly O O -municipal O O -bond O O -yields O O -. O O -Del B-ORG B-ORG -Hanover I-ORG I-ORG -weekly O O -m O O -bond O O -yields O O -calculated O O -Dec O O -5 O O -A B-ORG O -A I-ORG O -A I-ORG O -Ba I-ORG O -1997 O O -3 O O -3 O O -3 O O -3 O O -4 O O -4 O O -4 O O -4 O O -2001 O O -4 O O -4 O O -4 O O -4 O O -4 O O -4 O O -5 O O -5 O O -2006 O O -4 O O -4 O O -4 O O -4 O O -5 O O -5 O O -5 O O -5 O O -2011 O O -5 O O -5 O O -5 O O -5 O O -5 O O -5 O O -5 O O -5 O O -2016 O O -5 O O -5 O O -L B-LOC B-ORG -AN I-LOC I-ORG -1996 O O -U B-LOC B-LOC -energy O O -future O O -added O O -to O O -floor O O -session O O -gains O O -in O O -light O O -NY B-MISC B-MISC -ACC I-MISC I-MISC -trade O O -Thursday O O -, O O -as O O -forecast O O -for O O -colder O O -temperatures O O -in O O -di O O -Northeastern B-LOC O -markets O O -raised O O -supply O O -concerns O O -. O O -" O O -The O O -cold O O -weather O O -forecast O O -are O O -helping O O -right O O -now O O -, O O -" O O -a O O -trader O O -said O O -. O O -Earlier O O -, O O -NY B-ORG B-ORG -crude O O -ended O O -daytime O O -trade O O -78 O O -cents O O -higher O O -at O O -$ O O -25 O O -a O O -barrel O O -, O O -following O O -breakthrough O O -of O O -key O O -technical O O -levels O O -and O O -reports O O -of O O -tighter O O -supplies O O -. O O -Front O O -heating O O -oil O O -firm O O -0 O O -cents O O -a O O -gal O O -to O O -75 O O -cents O O -as O O -roughly O O -100 O O -lots O O -changed O O -hands O O -within O O -the O O -first O O -few O O -hours O O -of O O -ACC O B-MISC -. O O -About O O -112 O O -lots O O -were O O -exchanged O O -overall O O -, O O -traders O O -said O O -. O O -NY B-ORG B-ORG -gasoline O O -for O O -January O O -delivery O O -climbed O O -0 O O -cents O O -a O O -gal O O -to O O -69 O O -cents O O -as O O -a O O -light O O -33 O O -lots O O -traded O O -in O O -the O O -nearby O O -month O O -and O O -35 O O -moved O O -overall O O -. O O -January O O -crude O O -was O O -barely O O -changed O O -from O O -its O O -settlement O O -, O O -ed O O -up O O -one O O -cent O O -to O O -$ O O -25 O O -a O O -barrel O O -. O O -About O O -350 O O -lots O O -were O O -traded O O -for O O -January O O -and O O -87 O O -in O O -all O O -months O O -. O O -- O O -David B-PER B-PER -B I-PER I-PER -, O O -Los B-LOC B-LOC -Angeles I-LOC I-LOC -bureau O O -+ O O -213 O O -380 O O -2014 O O -U B-LOC B-LOC -blast O O -release O O -of O O -convicted O O -bomber O O -. O O -WA B-LOC B-LOC -1996 O O -The O O -United B-LOC B-LOC -States I-LOC I-LOC -Thursday O O -blasted O O -the O O -release O O -from O O -a O O -Greek B-MISC B-MISC -prison O O -of O O -a O O -Palestinian B-MISC B-MISC -guerrilla O O -convicted O O -of O O -bombing O O -an O O -airline O O -and O O -killing O O -a O O -teenager O O -in O O -1982 O O -, O O -saying O O -the O O -move O O -" O O -does O O -not O O -make O O -sense O O -. O O -" O O -" O O -All O O -of O O -us O O -who O O -have O O -been O O -victim O O -by O O -terrorists O O -. O O -need O O -to O O -stand O O -together O O -against O O -terrorists O O -. O O -We O O -ca O O -n O O -let O O -terrorists O O -out O O -of O O -jail O O -when O O -they O O -are O O -a O O -danger O O -to O O -civilians O O -all O O -around O O -the O O -world O O -, O O -" O O -State B-ORG B-ORG -Department I-ORG I-ORG -spokesman O O -Nicholas B-PER B-PER -Burns I-PER I-PER -said O O -. O O -' O O -Mohammed B-PER B-PER -Rashid I-PER I-PER -" O O -is O O -a O O -terrorist O O -who O O -deserves O O -to O O -be O O -behind O O -bars O O -. O O -It O O -is O O -in O O -to O O -us O O -why O O -he O O -would O O -have O O -been O O -allowed O O -to O O -leave O O -Greece B-LOC B-LOC -before O O -serving O O -his O O -just O O -sentence O O -. O O -This O O -is O O -an O O -in O O -move O O -. O O -It O O -does O O -not O O -make O O -sense O O -, O O -" O O -Burns B-PER B-PER -told O O -a O O -news O O -brief O O -. O O -He O O -spoke O O -after O O -Rashid B-PER B-PER -left O O -Greece B-LOC B-LOC -Thursday O O -on O O -being O O -freed O O -from O O -prison O O -early O O -for O O -good O O -behaviour O O -after O O -serving O O -8 O O -years O O -. O O -The O O -Clinton B-PER B-PER -administration O O -' O O -strong O O -views O O -on O O -this O O -subject O O -have O O -been O O -conveyed O O -to O O -the O O -Greek B-MISC B-MISC -government O O -, O O -Burns B-PER B-PER -said O O -. O O -Ma B-PER B-PER -Rashid I-PER I-PER -was O O -w O O -from O O -Ko B-LOC B-LOC -maximum O O -security O O -prison O O -just O O -outside O O -Athens B-LOC B-LOC -to O O -the O O -airport O O -where O O -he O O -boarded O O -a O O -regular O O -Olympic B-ORG B-ORG -Airways I-ORG I-ORG -flight O O -to O O -Cairo B-LOC B-LOC -where O O -he O O -would O O -transit O O -to O O -Tu B-LOC B-LOC -and O O -the O O -former O O -Palestine B-ORG B-ORG -Liberation I-ORG I-ORG -Organisation I-ORG I-ORG -headquarters O O -. O O -Rashid B-PER B-PER -, O O -46 O O -, O O -was O O -sentenced O O -to O O -18 O O -years O O -in O O -prison O O -by O O -a O O -Greek B-MISC B-MISC -court O O -in O O -1992 O O -after O O -being O O -convicted O O -of O O -pre O O -murder O O -in O O -the O O -mid O O -bombing O O -of O O -a O O -Pan B-MISC B-MISC -American I-MISC I-MISC -airline O O -in O O -1982 O O -. O O -His O O -sentence O O -had O O -been O O -reduced O O -to O O -15 O O -years O O -in O O -1993 O O -. O O -A O O -parole O O -court O O -ruled O O -recently O O -that O O -Rashid B-PER B-PER -could O O -be O O -freed O O -after O O -serving O O -8 O O -years O O -, O O -with O O -time O O -in O O -pre O O -detention O O -counted O O -towards O O -his O O -term O O -, O O -but O O -said O O -he O O -must O O -be O O -expelled O O -immediately O O -from O O -Greece B-LOC B-LOC -. O O -The O O -United B-LOC B-LOC -States I-LOC I-LOC -a O O -Rashid B-PER B-PER -of O O -belonging O O -to O O -the O O -May O O -15 O O -Palestinian B-MISC B-MISC -guerrilla O O -group O O -and O O -being O O -an O O -accomplished O O -student O O -of O O -master O O -Palestinian B-MISC B-MISC -bomb O O -Abu B-PER B-PER -Ibrahim I-PER I-PER -. O O -Three O O -FBI B-ORG B-ORG -agents O O -who O O -testified O O -against O O -Rashid B-PER B-PER -during O O -the O O -trial O O -, O O -held O O -at O O -Ko B-LOC B-LOC -prison O O -, O O -said O O -they O O -had O O -ample O O -evidence O O -against O O -Rashid B-PER B-PER -for O O -a O O -bomb O O -planted O O -on O O -a O O -Pan B-MISC B-MISC -American I-MISC I-MISC -plane O O -in O O -Brazil B-LOC B-LOC -in O O -1982 O O -and O O -a O O -mid O O -bomb O O -blast O O -on O O -a O O -T B-ORG B-ORG -airline O O -approaching O O -Athens B-LOC B-LOC -in O O -1986 O O -which O O -killed O O -four O O -U B-LOC B-LOC -citizens O O -. O O -School O O -football O O -player O O -banned O O -for O O -slash O O -opponents O O -. O O -AL B-LOC B-LOC -, O O -N B-LOC B-LOC -1996 O O -A O O -New B-LOC B-LOC -Mexico I-LOC I-LOC -high O O -school O O -football O O -player O O -who O O -used O O -razor O O -helmet O O -b O O -to O O -slash O O -opponents O O -and O O -a O O -referee O O -was O O -expelled O O -from O O -high O O -school O O -banned O O -Thursday O O -from O O -competition O O -for O O -one O O -year O O -. O O -Mike B-PER B-PER -C I-PER I-PER -, O O -17 O O -, O O -was O O -expelled O O -from O O -St B-LOC B-ORG -Pius I-LOC I-ORG -X I-LOC I-ORG -High I-LOC I-ORG -School I-LOC I-ORG -in O O -Albuquerque B-LOC B-LOC -after O O -an O O -October O O -game O O -in O O -which O O -he O O -used O O -the O O -sharp O O -chin O O -strap O O -b O O -to O O -in O O -two O O -opposing O O -players O O -and O O -the O O -referee O O -. O O -One O O -of O O -the O O -players O O -need O O -10 O O -s O O -to O O -a O O -cut O O -on O O -his O O -forearm O O -. O O -Official O O -said O O -the O O -New B-ORG B-ORG -Mexico I-ORG I-ORG -Activities I-ORG I-ORG -Association I-ORG I-ORG -decided O O -to O O -bar O O -C B-PER B-PER -from O O -any O O -inter O O -competition O O -until O O -next O O -October O O -, O O -regardless O O -of O O -the O O -school O O -he O O -attends O O -. O O -C B-PER B-PER -' O O -father O O -, O O -Stephen B-PER B-PER -C I-PER I-PER -, O O -had O O -admitted O O -filing O O -the O O -metal O O -b O O -to O O -a O O -fine O O -edge O O -, O O -saying O O -he O O -did O O -it O O -to O O -get O O -even O O -with O O -the O O -referee O O -and O O -with O O -players O O -who O O -had O O -rough O O -up O O -his O O -son O O -in O O -a O O -previous O O -game O O -. O O -Cy B-ORG O -sq O O -overs O O -copyright O O -talks O O -. O O -Eli B-PER B-PER -Ka I-PER I-PER -GE B-LOC B-LOC -1996 O O -In O O -a O O -g O O -Geneva B-LOC B-LOC -conference O O -centre O O -built O O -before O O -the O O -dawn O O -of O O -the O O -Internet B-MISC B-MISC -, O O -groups O O -of O O -s O O -officials O O -made O O -a O O -first O O -stab O O -on O O -Friday O O -at O O -re O O -copyright O O -laws O O -for O O -the O O -digital O O -age O O -. O O -But O O -critics O O -at O O -the O O -first O O -government O O -meeting O O -to O O -re O O -copyright O O -laws O O -in O O -25 O O -years O O -said O O -the O O -officials O O -and O O -legislators O O -might O O -as O O -well O O -be O O -trying O O -to O O -police O O -the O O -et O O -. O O -After O O -four O O -days O O -of O O -diplomatic O O -w O O -over O O -procedures O O -, O O -some O O -600 O O -delegates O O -from O O -nations O O -small O O -and O O -large O O -got O O -down O O -to O O -the O O -ni O O -of O O -setting O O -the O O -digital O O -agenda O O -for O O -the O O -first O O -time O O -. O O -Cy B-ORG O -sq O O -overs O O -the O O -debate O O -on O O -a O O -stack O O -of O O -proposals O O -covering O O -literary O O -and O O -artistic O O -works O O -, O O -the O O -rights O O -of O O -performers O O -and O O -producers O O -of O O -music O O -and O O -producers O O -of O O -databases O O -. O O -" O O -If O O -it O O -goes O O -on O O -like O O -this O O -, O O -we O O -w O O -n O O -have O O -enough O O -time O O -to O O -finish O O -all O O -the O O -discussions O O -, O O -" O O -a O O -frustrated O O -Western B-MISC O -delegate O O -said O O -. O O -" O O -They O O -announced O O -they O O -will O O -start O O -evening O O -sessions O O -next O O -week O O -. O O -" O O -At O O -by O O -copyright O O -industries O O -to O O -ensure O O -they O O -get O O -a O O -cut O O -from O O -online O O -works O O -led O O -to O O -a O O -storm O O -of O O -protests O O -by O O -Internet B-ORG B-MISC -companies O O -and O O -critics O O -who O O -say O O -the O O -pact O O -would O O -curb O O -public O O -access O O -to O O -online O O -information O O -from O O -soccer O O -results O O -to O O -stock O O -prices O O -. O O -" O O -It O O -' O O -not O O -illegal O O -to O O -make O O -photo O O -of O O -newspaper O O -articles O O -. O O -It O O -' O O -fair O O -use O O -. O O -We O O -can O O -read O O -sports O O -statistics O O -or O O -stock O O -prices O O -. O O -But O O -with O O -the O O -treaty O O -, O O -this O O -kind O O -of O O -fact O O -will O O -be O O -owned O O -and O O -subject O O -to O O -licensing O O -, O O -" O O -said O O -James B-PER B-PER -Love I-PER I-PER -, O O -a O O -consumer O O -lobby O O -heading O O -the O O -Washington B-MISC B-MISC -Consumer B-ORG B-PER -Project I-ORG I-PER -on I-ORG O -Technology I-ORG O -. O O -" O O -None O O -of O O -the O O -treaties O O -are O O -ready O O -to O O -move O O -. O O -These O O -people O O -do O O -n O O -understand O O -what O O -they O O -' O O -doing O O -. O O -" O O -At O O -stake O O -are O O -billion O O -of O O -dollars O O -and O O -the O O -future O O -of O O -the O O -electronic O O -information O O -industry O O -- O O -the O O -coming O O -medium O O -for O O -the O O -distribution O O -of O O -music O O -, O O -films O O -, O O -literature O O -, O O -software O O -and O O -commerce O O -. O O -Support O O -of O O -the O O -three O O -pact O O -say O O -they O O -are O O -only O O -an O O -extension O O -of O O -existing O O -intellectual O O -property O O -rights O O -, O O -covered O O -by O O -the O O -century O O -Bern B-MISC B-MISC -Convention I-MISC I-MISC -. O O -But O O -an O O -array O O -of O O -opponents O O -from O O -the O O -network O O -industry O O -to O O -consumer O O -, O O -scientific O O -and O O -academic O O -groups O O -say O O -the O O -pact O O -will O O -give O O -sweeping O O -powers O O -to O O -entertainment O O -and O O -copyright O O -industries O O -. O O -A O O -quick O O -survey O O -at O O -the O O -conference O O -centre O O -found O O -few O O -officials O O -who O O -had O O -actually O O -surf O O -the O O -Internet B-MISC B-MISC -. O O -Mongolia B-LOC B-LOC -' O O -state O O -copyright O O -official O O -, O O -Gun B-PER B-PER -J I-PER I-PER -, O O -said O O -a O O -that O O -he O O -had O O -just O O -arrived O O -from O O -U B-LOC B-LOC -Bat I-LOC I-LOC -and O O -was O O -not O O -aware O O -of O O -the O O -details O O -of O O -the O O -digital O O -agenda O O -. O O -" O O -We O O -do O O -n O O -have O O -money O O -for O O -Internet B-ORG B-MISC -in O O -Mongolia B-LOC B-LOC -, O O -" O O -he O O -added O O -. O O -Alexander B-PER B-PER -Ba I-PER I-PER -, O O -deputy O O -legal O O -chief O O -at O O -Russia B-LOC B-LOC -' O O -foreign O O -ministry O O -, O O -said O O -Moscow B-LOC B-LOC -had O O -yet O O -to O O -formula O O -a O O -policy O O -on O O -copyright O O -in O O -c O O -. O O -He O O -too O O -had O O -never O O -brows O O -the O O -Net B-MISC O -. O O -" O O -I O O -' O O -never O O -tried O O -it O O -and O O -why O O -should O O -I O O -? O O -There O O -are O O -lots O O -of O O -other O O -things O O -in O O -this O O -life O O -I O O -have O O -n O O -tried O O -either O O -, O O -" O O -he O O -said O O -. O O -A O O -visit O O -to O O -the O O -computer O O -centre O O -offering O O -Internet B-MISC B-MISC -services O O -found O O -a O O -lone O O -European B-MISC B-MISC -official O O -clicking O O -away O O -on O O -his O O -mouse O O -. O O -" O O -Internet B-MISC B-MISC -is O O -a O O -potential O O -cash O O -cow O O -for O O -copyright O O -industries O O -and O O -we O O -need O O -road O O -on O O -the O O -information O O -super O O -, O O -" O O -said O O -Marc B-PER B-PER -Pearl I-PER I-PER -, O O -vice O O -of O O -the O O -Information B-ORG B-ORG -Technology I-ORG I-ORG -Association I-ORG I-ORG -of I-ORG I-ORG -America I-ORG I-ORG -, O O -a O O -trade O O -association O O -of O O -U B-LOC B-LOC -network O O -companies O O -opposing O O -the O O -treaties O O -. O O -" O O -But O O -there O O -are O O -a O O -lot O O -of O O -dinosaurs O O -here O O -. O O -People O O -here O O -do O O -n O O -understand O O -Internet B-MISC B-MISC -technology O O -. O O -Because O O -they O O -do O O -n O O -understand O O -technology O O -, O O -they O O -fear O O -the O O -unknown O O -. O O -" O O -Before O O -the O O -Internet B-MISC B-MISC -, O O -those O O -whose O O -business O O -was O O -to O O -protect O O -copyright O O -knew O O -where O O -they O O -stood O O -. O O -Their O O -enemies O O -were O O -tan O O -if O O -el O O -, O O -such O O -as O O -the O O -people O O -who O O -pirate O O -music O O -cassette O O -. O O -But O O -the O O -Internet B-ORG B-MISC -, O O -a O O -global O O -computer O O -network O O -where O O -anything O O -from O O -music O O -to O O -software O O -can O O -be O O -du O O -and O O -distributed O O -at O O -the O O -click O O -of O O -a O O -computer O O -mouse O O -, O O -has O O -ripped O O -up O O -the O O -rule O O -. O O -Network O B-MISC -operators O O -said O O -the O O -draft O O -laws O O -would O O -hold O O -them O O -responsible O O -for O O -copyright O O -infringement O O -in O O -the O O -system O O -and O O -expose O O -them O O -to O O -multi O O -l O O -. O O -" O O -There O O -are O O -500 O O -million O O -messages O O -transmitted O O -through O O -the O O -Internet B-MISC B-MISC -everyday O O -, O O -" O O -said O O -Tim B-PER B-PER -Casey I-PER I-PER -of O O -the O O -U B-MISC B-MISC -MC B-ORG B-ORG -Communications I-ORG I-ORG -Corporation I-ORG I-ORG -. O O -" O O -How O O -can O O -we O O -control O O -them O O -all O O -? O O -" O O -Italy B-LOC B-LOC -evacuate O O -17 O O -nuns O O -and O O -priests O O -from O O -Z B-LOC B-LOC -. O O -ROM B-LOC B-LOC -1996 O O -Italy B-LOC B-LOC -said O O -on O O -Friday O O -it O O -had O O -evacuated O O -17 O O -Roman B-MISC B-MISC -Catholic I-MISC I-MISC -nuns O O -and O O -priests O O -from O O -Z B-LOC B-LOC -where O O -they O O -had O O -been O O -at O O -risk O O -from O O -fighting O O -between O O -government O O -troops O O -and O O -ethnic O O -Tu B-MISC B-MISC -rebels O O -. O O -The O O -Foreign B-ORG B-ORG -Ministry I-ORG I-ORG -said O O -the O O -10 O O -Europeans B-MISC B-MISC -and O O -seven O O -Africans B-MISC B-MISC -took O O -a O O -special O O -flight O O -from O O -the O O -G B-LOC B-LOC -national O O -park O O -in O O -northern O O -Z B-LOC B-LOC -to O O -the O O -Uganda B-MISC B-MISC -capital O O -Ka B-LOC B-LOC -where O O -they O O -were O O -being O O -looked O O -after O O -at O O -the O O -Italian B-MISC B-MISC -embassy O O -. O O -The O O -group O O -had O O -travelled O O -from O O -their O O -mission O O -on O O -the O O -edge O O -of O O -the O O -park O O -to O O -a O O -landing O O -strip O O -to O O -make O O -the O O -re O O -, O O -a O O -ministry O O -official O O -said O O -. O O -The O O -ministry O O -said O O -the O O -group O O -consisted O O -of O O -13 O O -nuns O O -, O O -seven O O -Italians B-MISC B-MISC -and O O -six O O -Z B-MISC B-MISC -, O O -and O O -four O O -priests O O -, O O -two O O -from O O -Belgium B-LOC B-LOC -, O O -one O O -from O O -Spain B-LOC B-LOC -and O O -one O O -from O O -Zambia B-LOC B-LOC -. O O -Third O O -Paris B-LOC B-LOC -blast O O -victim O O -was O O -Moroccan B-MISC B-MISC -student O O -. O O -PA B-LOC B-LOC -1996 O O -Moroccan B-MISC B-MISC -Mohamed B-PER B-PER -Ben I-PER I-PER -, O O -the O O -third O O -person O O -to O O -die O O -after O O -a O O -bombing O O -on O O -a O O -Paris B-LOC B-LOC -train O O -, O O -was O O -a O O -25 O O -student O O -about O O -to O O -submit O O -a O O -mathematics O O -doctorate O O -, O O -the O O -Moroccan B-MISC B-MISC -embassy O O -said O O -on O O -Friday O O -. O O -Ben B-PER B-PER -died O O -of O O -his O O -injuries O O -on O O -Thursday O O -night O O -, O O -two O O -days O O -after O O -the O O -blast O O -. O O -A O O -newly O O -Canadian B-MISC B-MISC -woman O O -and O O -a O O -man O O -from O O -New B-LOC B-LOC -Caledonia I-LOC I-LOC -died O O -instantly O O -in O O -the O O -bomb O O -that O O -injured O O -90 O O -others O O -in O O -the O O -rush O O -train O O -. O O -An O O -embassy O O -spokesman O O -said O O -Ben B-PER B-PER -, O O -the O O -son O O -of O O -a O O -Moroccan B-MISC B-MISC -army O O -colonel O O -, O O -had O O -been O O -due O O -to O O -take O O -his O O -doctorate O O -in O O -March O O -and O O -hoped O O -to O O -become O O -a O O -teacher O O -. O O -In O O -have O O -said O O -the O O -explosion O O -bore O O -the O O -hall O O -of O O -Algerian B-MISC B-MISC -Mo I-MISC B-MISC -fundamental O O -who O O -staged O O -a O O -series O O -of O O -bombings O O -last O O -year O O -which O O -killed O O -eight O O -people O O -and O O -injured O O -more O O -than O O -160 O O -. O O -Italian B-MISC B-MISC -President O O -urges O O -se O O -to O O -turn O O -back O O -. O O -MA B-LOC O -, O O -Italy B-LOC B-LOC -1996 O O -Italian B-MISC B-MISC -President O O -Oscar B-PER B-PER -Luigi I-PER I-PER -Sc I-PER I-PER -visited O O -the O O -symbolic O O -heart O O -of O O -the O O -se O O -Northern B-ORG B-ORG -League I-ORG I-ORG -on O O -Friday O O -and O O -appealed O O -to O O -its O O -supporters O O -to O O -drop O O -their O O -campaign O O -for O O -a O O -break O O -state O O -. O O -Ad O O -a O O -convention O O -on O O -Italian B-MISC B-MISC -unity O O -in O O -Man B-LOC O -, O O -where O O -the O O -party O O -has O O -set O O -up O O -its O O -own O O -" O O -parliament O O -of O O -the O O -north O O -" O O -, O O -Sc B-PER B-PER -made O O -a O O -direct O O -appeal O O -to O O -what O O -he O O -called O O -" O O -my O O -friends O O -from O O -the O O -League B-ORG B-ORG -" O O -to O O -work O O -instead O O -for O O -federal O O -reform O O -. O O -" O O -It O O -is O O -an O O -invitation O O -, O O -a O O -commitment O O -, O O -a O O -promise O O -. O O -Let O B-LOC -' O O -march O O -together O O -, O O -" O O -Sc B-PER B-PER -, O O -a O O -northern O O -himself O O -, O O -said O O -. O O -" O O -Help O O -Italy B-LOC B-LOC -to O O -teach O O -, O O -to O O -propose O O -a O O -capacity O O -for O O -strong O O -local O O -autonomy O O -, O O -for O O -the O O -federal O O -which O O -can O O -give O O -new O O -v O O -to O O -our O O -blood O O -. O O -But O O -turn O O -back O O -from O O -the O O -line O O -you O O -are O O -taking O O -now O O -, O O -" O O -he O O -said O O -. O O -Sc B-PER B-PER -was O O -in O O -Man B-LOC B-LOC -to O O -attend O O -a O O -ceremony O O -commemorating O O -the O O -executions O O -there O O -by O O -Austrian B-MISC B-MISC -rulers O O -in O O -1852 O O -and O O -1853 O O -of O O -a O O -group O O -of O O -Italians B-MISC B-MISC -who O O -had O O -campaigned O O -for O O -national O O -unity O O -. O O -He O O -was O O -j O O -and O O -whistle O O -at O O -by O O -a O O -small O O -group O O -of O O -League B-ORG B-LOC -supporters O O -when O O -he O O -arrived O O -for O O -a O O -visit O O -marked O O -by O O -heavy O O -security O O -. O O -Witness O O -said O O -the O O -protesters O O -were O O -outnumbered O O -by O O -other O O -Italians B-MISC B-MISC -who O O -waved O O -t O O -flags O O -in O O -the O O -national O O -red O O -, O O -white O O -and O O -green O O -or O O -shouted O O -" O O -Viva B-MISC O -Italia I-MISC B-LOC -" O O -. O O -The O O -League B-ORG B-ORG -won O O -more O O -than O O -eight O O -percent O O -of O O -votes O O -at O O -the O O -last O O -general O O -election O O -in O O -April O O -on O O -a O O -federal O O -platform O O -but O O -its O O -leader O O -Um B-PER B-PER -Boss I-PER I-PER -later O O -switched O O -to O O -a O O -se O O -agenda O O -. O O -A O O -three O O -" O O -independence O O -" O O -march O O -along O O -the O O -Po B-LOC B-LOC -River I-LOC O -in O O -September O O -, O O -culminating O O -in O O -a O O -declaration O O -in O O -Venice B-LOC B-LOC -of O O -a O O -self O O -" O O -Republic B-ORG B-LOC -of I-ORG I-LOC -Pa I-LOC I-LOC -" O O -, O O -flopped O O -badly O O -. O O -Denmark B-LOC B-LOC -' O O -Radio B-MISC O -H I-MISC O -result O O -seen O O -flat O O -. O O -CO B-LOC B-LOC -1996 O O -A O O -Re B-ORG B-ORG -consensus O O -survey O O -sees O O -medical O O -equipment O O -group O O -Radio B-ORG B-ORG -reporting O O -largely O O -unchanged O O -earnings O O -when O O -it O O -publishes O O -first O O -half O O -1999 O O -results O O -next O O -Wednesday O B-ORG -. O O -An O O -average O O -of O O -four O O -analysts O O -' O O -forecast O O -predicted O O -pre O O -profit O O -of O O -147 O O -million O O -crown O O -compared O O -to O O -144 O O -million O O -in O O -the O O -first O O -six O O -months O O -of O O -1995 O O -. O O -They O O -said O O -that O O -the O O -group O O -' O O -failure O O -to O O -introduce O O -new O O -products O O -was O O -behind O O -the O O -share O O -' O O -weak O O -performance O O -in O O -1996 O O -, O O -during O O -which O O -it O O -has O O -lost O O -seven O O -percent O O -so O O -far O O -. O O -- O O -So B-PER B-PER -Lin I-PER I-PER -Jakob I-PER I-PER -, O O -Copenhagen B-LOC B-LOC -news O O -+ O O -33 O O -Mo B-MISC B-MISC -fundamental O O -kill O O -19 O O -Algerian B-MISC B-MISC -- O O -agency O O -. O O -PA B-LOC B-LOC -1996 O O -Mo B-MISC B-MISC -fundamental O O -killed O O -19 O O -civilians O O -overnight O O -in O O -B B-LOC B-LOC -province O O -south O O -of O O -Algiers B-LOC B-LOC -, O O -Algerian B-MISC B-MISC -security O O -forces O O -said O O -on O O -Friday O O -. O O -In O O -a O O -statement O O -carried O O -on O O -the O O -official O O -Algerian B-MISC B-MISC -news O O -agency O O -AP B-ORG B-ORG -, O O -the O O -security O O -forces O O -said O O -the O O -19 O O -had O O -been O O -killed O O -by O O -" O O -a O O -group O O -of O O -terrorists O O -" O O -. O O -Belgian B-MISC B-MISC -police O O -smash O O -major O O -drugs O O -rings O O -, O O -30 O O -arrested O O -. O O -BR B-LOC B-LOC -1996 O O -Police O O -smashed O O -two O O -drugs O O -smuggling O O -rings O O -and O O -arrested O O -30 O O -people O O -after O O -a O O -taxi O O -in O O -Spain B-LOC B-LOC -alerted O O -them O O -to O O -a O O -suitcase O O -of O O -heroin O O -left O O -in O O -his O O -cab O O -, O O -Belgian B-MISC B-MISC -police O O -said O O -on O O -Friday O O -. O O -Police O O -seized O O -dozens O O -of O O -k O O -of O O -heroin O O -with O O -a O O -street O O -value O O -of O O -hundreds O O -of O O -millions O O -of O O -Belgian B-MISC B-MISC -f O O -, O O -a O O -public O O -prosecutor O O -' O O -office O O -spokesman O O -in O O -the O O -port O O -city O O -of O O -Antwerp B-LOC B-ORG -said O O -. O O -He O O -said O O -a O O -24 O O -Belgian B-MISC B-MISC -woman O O -left O O -a O O -suitcase O O -containing O O -13 O O -kg O O -( O O -29 O O -lb O O -) O O -of O O -heroin O O -in O O -a O O -taxi O O -in O O -Barcelona B-LOC B-LOC -. O O -The O O -taxi O O -alerted O O -police O O -who O O -arrested O O -a O O -33 O O -Turkish B-MISC B-MISC -man O O -when O O -he O O -came O O -to O O -pick O O -up O O -the O O -suitcase O O -at O O -a O O -lost O O -luggage O O -office O O -. O O -The O O -woman O O -was O O -later O O -arrested O O -in O O -Belgium B-LOC B-LOC -. O O -She O O -and O O -the O O -Turkish B-MISC B-MISC -man O O -smug O O -heroin O O -from O O -Turkey B-LOC B-LOC -to O O -Antwerp B-LOC B-ORG -from O O -where O O -it O O -was O O -taken O O -to O O -Spain B-LOC B-LOC -, O O -France B-LOC B-LOC -and O O -Germany B-LOC B-LOC -by O O -others O O -, O O -the O O -spokesman O O -said O O -. O O -He O O -said O O -14 O O -people O O -were O O -arrested O O -in O O -Belgium B-LOC B-LOC -and O O -16 O O -others O O -in O O -other O O -European B-MISC B-MISC -nations O O -after O O -an O O -investigation O O -lasting O O -nearly O O -a O O -year O O -. O O -( O O -$ O O -1 O O -Belgian B-MISC B-MISC -Fr I-ORG O -) O O -Port O O -conditions O O -update O O -- O O -Lloyd B-ORG B-ORG -Shipping I-ORG I-ORG -. O O -G B-LOC B-LOC -, O O -Dec O O -5 O O -- O O -Greek B-MISC B-MISC -port O O -workers O O -called O O -off O O -a O O -strike O O -which O O -had O O -kept O O -the O O -country O O -' O O -ports O O -closed O O -, O O -giving O O -the O O -government O O -until O O -Feb O O -1 O O -to O O -introduce O O -a O O -promised O O -bonus O O -scheme O O -. O O -German B-MISC B-MISC -Jan O O -coffee O O -imports O O -detailed O O -. O O -H B-LOC B-LOC -1996 O O -German B-MISC B-MISC -net O O -green O O -coffee O O -imports O O -from O O -outside O O -the O O -EU B-LOC B-ORG -total O O -7 O O -million O O -bags O O -in O O -January O O -compared O O -with O O -7 O O -million O O -in O O -the O O -year O O -period O O -, O O -the O O -D B-ORG B-ORG -coffee O O -association O O -said O O -. O O -I O O -of O O -1 O O -million O O -bags O O -in O O -August O O -were O O -down O O -from O O -1 O O -million O O -in O O -August O O -1995 O O -but O O -up O O -from O O -99 O O -bags O O -in O O -July O O -1996 O O -. O O -Colombia B-LOC B-LOC -shipped O O -198 O O -bags O O -in O O -August O O -after O O -164 O O -in O O -July O O -, O O -El B-LOC B-LOC -Salvador I-LOC I-LOC -160 O O -( O O -129 O O -) O O -, O O -Indonesia B-LOC B-LOC -72 O O -( O O -78 O O -) O O -, O O -Ethiopia B-LOC B-LOC -69 O O -( O O -60 O O -) O O -and O O -Kenya B-LOC B-LOC -63 O O -( O O -60 O O -) O O -. O O -Brazil B-LOC B-LOC -was O O -in O O -seventh O O -position O O -with O O -54 O O -bags O O -( O O -29 O O -) O O -. O O -- O O -Hamburg B-LOC B-LOC -news O O -+ O O -Munich B-ORG B-ORG -Re I-ORG I-ORG -says O O -to O O -split O O -stock O O -. O O -M B-LOC B-LOC -, O O -Germany B-LOC B-LOC -1996 O O -Mu B-ORG B-MISC -Rue I-ORG B-ORG -AG I-ORG I-ORG -, O O -the O O -world O O -' O O -largest O O -reins O O -, O O -said O O -on O O -Friday O O -it O O -expected O O -to O O -switch O O -its O O -shares O O -to O O -a O O -lower O O -par O O -value O O -by O O -September O O -1997 O O -at O O -the O O -earliest O O -. O O -The O O -group O O -, O O -known O O -as O O -Munich B-ORG B-ORG -Re I-ORG I-ORG -, O O -plans O O -to O O -seek O O -approval O O -for O O -the O O -move O O -at O O -its O O -shareholders O O -' O O -meeting O O -today O O -. O O -The O O -company O O -said O O -the O O -switch O O -would O O -probably O O -become O O -effective O O -in O O -September O O -. O O -The O O -planned O O -10 O O -stock O O -split O O -would O O -reduce O O -the O O -par O O -value O O -of O O -Munich B-ORG B-ORG -Re I-ORG I-ORG -' O O -shares O O -to O O -five O O -marks O O -from O O -50 O O -, O O -causing O O -their O O -price O O -to O O -drop O O -to O O -around O O -one O O -tenth O O -of O O -the O O -present O O -value O O -. O O -Munich B-ORG B-ORG -Re I-ORG I-ORG -' O O -registered O O -shares O O -, O O -part O O -of O O -the O O -blue O O -D B-MISC B-MISC -index O O -, O O -were O O -trading O O -at O O -3 O O -marks O O -on O O -Friday O O -. O O -- O O -Frankfurt B-ORG B-ORG -News I-ORG I-ORG -, O O -+ O O -69 O O -75 O O -EU B-ORG B-ORG -experts O O -post O O -talks O O -on O O -rice O O -area O O -aid O O -. O O -BR B-LOC B-LOC -1996 O O -European B-ORG B-ORG -Union I-ORG I-ORG -rice O O -experts O O -on O O -Thursday O O -postponed O O -discussion O O -on O O -area O O -aid O O -payments O O -to O O -rice O O -producers O O -because O O -the O O -documents O O -were O O -not O O -available O O -in O O -all O O -the O O -EU B-ORG B-ORG -languages O O -, O O -an O O -EU B-ORG B-ORG -off O O -said O O -on O O -Friday O O -. O O -" O O -The O O -discussion O O -in O O -the O O -experts O O -group O O -had O O -to O O -be O O -postponed O O -because O O -the O O -documents O O -needed O O -to O O -be O O -translated O O -into O O -the O O -official O O -languages O O -and O O -the O O -item O O -will O O -be O O -on O O -next O O -week O O -' O O -agenda O O -, O O -" O O -the O O -off O O -said O O -. O O -European B-MISC B-MISC -rice O O -producers O O -are O O -due O O -to O O -get O O -com O O -area O O -aid O O -payments O O -similar O O -to O O -those O O -paid O O -to O O -cereal O O -producers O O -because O O -of O O -cuts O O -in O O -intervention O O -prices O O -. O O -- O O -Brussels B-ORG B-ORG -News I-ORG I-ORG -32 O O -2 O O -287 O O -680 O O -Frankfurt B-LOC B-LOC -dollar O O -fix O O -1 O O -marks O O -. O O -F B-LOC B-LOC -1996 O O -The O O -dollar O O -was O O -fixed O O -at O O -1 O O -marks O O -in O O -Frankfurt B-LOC B-LOC -on O O -Friday O O -, O O -after O O -1 O O -marks O O -on O O -Thursday O O -. O O -There O O -was O O -no O O -B B-ORG B-ORG -intervention O O -. O O -John B-PER B-ORG -Lewis I-PER I-ORG -UK B-LOC I-ORG -store O O -sales O O -up O O -4 O O -% O O -in O O -week O O -. O O -L B-LOC B-LOC -1996 O O -The O O -John B-ORG B-PER -Lewis I-ORG I-PER -Partnership I-ORG O -said O O -its O O -UK B-LOC B-LOC -department O O -store O O -sales O O -rose O O -4 O O -percent O O -in O O -the O O -week O O -to O O -November O O -30 O O -compared O O -with O O -the O O -same O O -week O O -a O O -year O O -earlier O O -. O O -In O O -the O O -18 O O -weeks O O -to O O -November O O -30 O O -, O O -sales O O -were O O -up O O -13 O O -percent O O -year O O -. O O -Total O O -sales O O -, O O -including O O -the O O -Wait B-ORG B-ORG -supermarket O O -chain O O -, O O -rose O O -5 O O -percent O O -in O O -the O O -week O O -and O O -were O O -up O O -11 O O -percent O O -in O O -the O O -18 O O -period O O -. O O -- O O -Rosemary B-PER B-PER -Bennett I-PER I-PER -, O O -London B-ORG B-ORG -News I-ORG I-ORG -44 O O -171 O O -54 O O -27 O O -Tim B-PER B-ORG -at O O -15 O O -in O O -London B-LOC B-LOC -at O O -09 O O -GM B-MISC B-MISC -. O O -L B-LOC B-LOC -1996 O O -PT B-ORG B-ORG -Tam I-ORG I-ORG -Tim I-ORG I-ORG -was O O -traded O O -at O O -$ O O -15 O O -per O O -G B-MISC O -in O O -London B-LOC B-LOC -on O O -Friday O O -at O O -around O O -09 O O -GM B-MISC B-MISC -. O O -It O O -recorded O O -a O O -low O O -of O O -$ O O -15 O O -and O O -a O O -high O O -of O O -$ O O -15 O O -. O O -Its O O -previous O O -close O O -on O O -Thursday O O -was O O -$ O O -15 O O -. O O -One O O -Global B-ORG O -De I-ORG O -Re I-ORG O -represents O O -10 O O -common O O -shares O O -. O O -- O O -Jakarta B-LOC B-LOC -news O O -+ O O -38 O O -British B-MISC B-MISC -" O O -Euro B-MISC B-MISC -" O O -says O O -Clarke B-PER B-PER -should O O -resign O O -. O O -L B-LOC B-LOC -1996 O O -A O O -" O O -Euro B-MISC B-MISC -" O O -member O O -of O O -the O O -ruling O O -Conservative B-ORG B-MISC -party O O -said O O -on O O -Thursday O O -British B-MISC B-MISC -finance O O -minister O O -Kenneth B-PER B-PER -Clarke I-PER I-PER -had O O -to O O -resign O O -to O O -prevent O O -the O O -party O O -di O O -over O O -the O O -issue O O -of O O -a O O -single O O -European B-MISC B-MISC -currency O O -. O O -Member O O -of O O -Parliament O O -Tony B-PER B-PER -Mar I-PER I-PER -said O O -the O O -resignation O O -of O O -the O O -chancellor O O -of O O -the O O -ex O O -was O O -the O O -only O O -way O O -to O O -make O O -the O O -Conservatives B-MISC O -elect O O -in O O -a O O -general O O -election O O -which O O -must O O -take O O -place O O -by O O -May O O -next O O -year O O -. O O -" O O -We O O -have O O -a O O -divided O O -and O O -split O O -Cabinet B-ORG B-ORG -. O O -This O O -cannot O O -endure O O -, O O -" O O -Mar B-PER B-PER -told O O -BBC B-ORG B-ORG -television O O -' O O -News B-ORG B-MISC -programme O O -on O O -Thursday O O -. O O -" O O -It O O -is O O -not O O -sustainable O O -. O O -Kenneth B-PER B-PER -Clarke I-PER I-PER -has O O -to O O -go O O -. O O -If O O -he O O -does O O -n O O -resign O O -, O O -the O O -prime O O -minister O O -has O O -got O O -to O O -fire O O -him O O -. O O -" O O -Mar B-PER B-PER -' O O -comment O O -come O O -on O O -the O O -heels O O -of O O -speculation O O -that O O -Clarke B-PER B-PER -had O O -threatened O O -to O O -resign O O -if O O -the O O -government O O -changed O O -its O O -" O O -wait O O -and O O -see O O -" O O -policy O O -on O O -a O O -single O O -currency O O -and O O -declared O O -it O O -would O O -not O O -sign O O -up O O -for O O -the O O -currency O O -in O O -the O O -next O O -Parliament B-ORG O -. O O -Clarke B-PER B-PER -denied O O -on O O -Thursday O O -he O O -had O O -threatened O O -to O O -resign O O -and O O -said O O -his O O -position O O -on O O -the O O -single O O -currency O O -was O O -in O O -tune O O -with O O -that O O -of O O -Prime O O -Minister O O -John B-PER B-PER -Major I-PER I-PER -. O O -Major B-PER B-PER -told O O -parliament O O -on O O -Thursday O O -he O O -would O O -keep O O -his O O -options O O -open O O -on O O -single O O -membership O O -. O O -His O O -statement O O -was O O -interpreted O O -as O O -a O O -significant O O -victory O O -for O O -Clarke B-PER B-PER -and O O -fellow O O -pro B-MISC B-MISC -Michael B-PER B-PER -He I-PER I-PER -, O O -deputy O O -prime O O -minister O O -. O O -Pro B-MISC B-MISC -Conservative B-MISC B-MISC -MP O O -Edwin B-PER B-PER -Currie I-PER I-PER -told O O -the O O -BBC B-ORG B-ORG -that O O -if O O -Clarke B-PER B-PER -resigned O O -, O O -other O O -ministers O O -would O O -go O O -with O O -him O O -. O O -Court B-ORG O -e O O -head O O -of O O -Australian B-MISC B-MISC -child O O -inquiry O O -. O O -CA B-LOC B-LOC -1996 O O -The O O -Australian B-MISC B-MISC -opposition O O -on O O -Friday O O -demanded O O -a O O -high O O -investigation O O -into O O -p O O -in O O -the O O -Australian B-MISC B-MISC -diplomatic O O -service O O -after O O -the O O -federal O O -court O O -forced O O -the O O -head O O -of O O -the O O -existing O O -inquiry O O -to O O -stand O O -aside O O -. O O -The O O -court O O -said O O -inquiry O O -head O O -Chris B-PER B-PER -Hunt I-PER I-PER -might O O -be O O -bias O O -, O O -since O O -he O O -privately O O -told O O -a O O -newspaper O O -he O O -had O O -turned O O -up O O -no O O -major O O -evidence O O -of O O -p O O -activity O O -, O O -even O O -though O O -he O O -still O O -had O O -months O O -' O O -of O O -investigation O O -before O O -him O O -. O O -" O O -Today O O -we O O -are O O -left O O -with O O -a O O -ruin O O -wreck O O -beyond O O -salvage O O -and O O -a O O -continuing O O -p O O -of O O -doubt O O -and O O -suspicion O O -hanging O O -over O O -our O O -diplomatic O O -service O O -, O O -" O O -opposition O O -foreign O O -affairs O O -spokesman O O -Laurie B-PER B-PER -B I-PER I-PER -said O O -. O O -But O O -the O O -government O O -responded O O -by O O -pressing O O -ahead O O -with O O -the O O -original O O -inquiry O O -, O O -established O O -in O O -May O O -, O O -appoint O O -a O O -new O O -head O O -to O O -lead O O -it O O -. O O -Critics O O -say O O -that O O -if O O -there O O -were O O -many O O -p O O -in O O -senior O O -posts O O -in O O -the O O -Foreign B-ORG B-ORG -Affairs I-ORG I-ORG -Department I-ORG I-ORG -then O O -a O O -secret O O -inquiry O O -would O O -be O O -open O O -to O O -internal O O -influence O O -and O O -would O O -become O O -a O O -public O O -service O O -white O O -. O O -Accordingly O O -, O O -they O O -demand O O -an O O -open O O -investigation O O -. O O -A O O -spokesman O O -for O O -Foreign B-ORG B-ORG -Affairs O I-ORG -Minister O O -Alexander B-PER B-PER -Down I-PER I-PER -said O O -the O O -appointment O O -of O O -a O O -new O O -inquiry O O -head O O -, O O -administrative O O -law O O -expert O O -Pamela B-PER B-PER -O I-PER I-PER -, O O -showed O O -the O O -government O O -' O O -commitment O O -to O O -pursue O O -the O O -matter O O -. O O -A O O -report O O -is O O -due O O -in O O -May O O -next O O -year O O -. O O -One O O -Australian B-MISC B-MISC -diplomat O O -has O O -been O O -prosecuted O O -this O O -year O O -for O O -having O O -sex O O -with O O -a O O -Cambodian B-MISC B-MISC -boy O O -under O O -16 O O -but O O -was O O -acquitted O O -. O O -Police O O -have O O -investigated O O -others O O -. O O -A O O -newspaper O O -reported O O -allegations O O -in O O -April O O -that O O -diplomat O O -had O O -directed O O -Australian B-MISC B-MISC -government O O -aid O O -to O O -certain O O -foreign O O -orphanage O O -to O O -secure O O -sex O O -with O O -children O O -. O O -Australian B-MISC B-MISC -hit O O -killed O O -wrong O O -victim O O -. O O -S B-LOC B-LOC -1996 O O -An O O -Australian B-MISC B-MISC -hit O O -who O O -went O O -to O O -the O O -wrong O O -house O O -and O O -killed O O -the O O -wrong O O -man O O -was O O -sentenced O O -to O O -20 O O -years O O -jail O O -on O O -Friday O O -. O O -Paul B-PER B-PER -C I-PER I-PER -, O O -33 O O -, O O -and O O -an O O -a O O -were O O -contracted O O -to O O -shoot O O -a O O -man O O -, O O -identified O O -only O O -as O O -Tony B-PER B-PER -, O O -in O O -the O O -leg O O -to O O -punish O O -him O O -for O O -his O O -misconduct O O -with O O -a O O -female O O -friend O O -of O O -the O O -contractor O O -, O O -the O O -New B-ORG B-ORG -South I-ORG I-ORG -Wales I-ORG I-ORG -Supreme I-ORG I-ORG -Court I-ORG I-ORG -was O O -told O O -. O O -But O O -in O O -February O O -1993 O O -Les B-PER B-PER -Bet I-PER I-PER -, O O -was O O -shot O O -and O O -killed O O -after O O -answering O O -a O O -knock O O -at O O -the O O -door O O -of O O -his O O -Sydney B-LOC B-LOC -home O O -. O O -" O O -The O O -in O O -from O O -all O O -the O O -material O O -is O O -that O O -the O O -ma O O -had O O -come O O -to O O -the O O -wrong O O -house O O -, O O -" O O -Judge O O -Michael B-PER B-PER -Grove I-PER I-PER -said O O -. O O -In O O -sent O O -C B-PER B-PER -, O O -who O O -pleaded O O -guilty O O -, O O -Grove B-PER B-PER -took O O -into O O -account O O -his O O -" O O -mildly O O -re O O -" O O -intellectual O O -state O O -, O O -which O O -placed O O -him O O -in O O -the O O -lowest O O -two O O -percent O O -of O O -the O O -population O O -. O O -Grove B-PER B-PER -said O O -Bet B-PER B-PER -was O O -" O O -not O O -only O O -the O O -victim O O -of O O -a O O -ho O O -crime O O -, O O -but O O -his O O -death O O -was O O -brought O O -about O O -in O O -circumstances O O -of O O -an O O -equally O O -g O O -error O O -on O O -the O O -part O O -of O O -the O O -prisoner O O -and O O -his O O -a O O -" O O -. O O -The O O -unnamed O O -a O O -was O O -earlier O O -sentenced O O -to O O -20 O O -years O O -in O O -prison O O -. O O -NZ B-LOC B-LOC -' O O -Bo B-PER B-PER -says O O -Nat B-PER B-PER -to O O -meet O O -NZ B-ORG B-LOC -First I-ORG O -on O O -Sunday O O -. O O -W B-LOC B-LOC -1996 O O -New B-LOC B-LOC -Zealand I-LOC I-LOC -Prime O O -Minister O O -Jim B-PER B-PER -Bo I-PER I-PER -, O O -emerging O O -from O O -coalition O O -talks O O -with O O -the O O -nationalist O O -New B-ORG B-ORG -Zealand I-ORG I-ORG -First I-ORG I-ORG -party O O -on O O -Friday O O -afternoon O O -, O O -said O O -National B-ORG B-ORG -and O O -NZ B-ORG B-ORG -First I-ORG I-ORG -would O O -meet O O -again O O -on O O -Sunday O O -. O O -Bo B-PER B-PER -said O O -he O O -expected O O -a O O -government O O -to O O -be O O -formed O O -by O O -Thursday O O -. O O -NZ B-LOC B-LOC -' O O -Peters B-PER B-PER -says O O -Nat B-PER B-PER -, O O -Lab B-ORG B-PER -talks O O -at O O -similar O O -stage O O -. O O -W B-LOC B-LOC -1996 O O -New B-ORG B-ORG -Zealand I-ORG I-ORG -First I-ORG I-ORG -leader O O -Winston B-PER B-PER -Peters I-PER I-PER -on O O -Friday O O -said O O -coalition O O -talks O O -with O O -the O O -National B-ORG B-ORG -and O O -Labour B-ORG B-ORG -parties O O -were O O -at O O -a O O -similar O O -level O O -of O O -completion O O -. O O -Peters B-PER B-PER -left O O -a O O -meeting O O -between O O -NZ B-ORG B-ORG -First I-ORG I-ORG -and O O -National B-ORG B-ORG -ne O O -to O O -spend O O -20 O O -minutes O O -speaking O O -to O O -Labour B-ORG B-ORG -leader O O -Helen B-PER B-PER -Clark I-PER I-PER -. O O -He O O -told O O -Re B-ORG B-ORG -he O O -had O O -needed O O -to O O -speak O O -to O O -her O O -before O O -she O O -left O O -Wellington B-LOC B-LOC -later O O -on O O -Friday O O -. O O -Peters B-PER B-PER -said O O -the O O -talks O O -with O O -Labour B-ORG B-ORG -and O O -National B-ORG B-ORG -had O O -reached O O -" O O -about O O -the O O -same O O -level O O -of O O -completion O O -, O O -and O O -that O O -' O O -good O O -" O O -. O O -R B-ORG B-ORG -- O O -Australian B-MISC B-MISC -MP O O -John B-PER B-PER -Lang I-PER I-PER -formally O O -resign O O -. O O -CA B-LOC B-LOC -1996 O O -Australian B-MISC B-MISC -parliament O O -John B-PER B-PER -Lang I-PER I-PER -has O O -formally O O -resigned O O -from O O -his O O -lower O O -house O O -seat O O -, O O -the O O -office O O -of O O -House B-ORG B-ORG -of I-ORG I-ORG -Representatives I-ORG I-ORG -speaker O O -Bob B-PER B-PER -Hal I-PER I-PER -said O O -on O O -Friday O O -. O O -" O O -Hal B-PER B-PER -announced O O -that O O -he O O -had O O -received O O -today O O -from O O -Mr O O -John B-PER B-PER -Vance I-PER I-PER -Lang I-PER I-PER -, O O -a O O -letter O O -resign O O -his O O -place O O -as O O -member O O -of O O -the O O -House B-ORG B-ORG -of I-ORG I-ORG -Representatives I-ORG I-ORG -for O O -the O O -electoral O O -division O O -of O O -Fraser B-LOC B-PER -in O O -the O O -Australian B-LOC B-MISC -Capital I-LOC I-MISC -Territory I-LOC I-MISC -, O O -" O O -his O O -office O O -said O O -in O O -a O O -statement O O -. O O -Hal B-PER B-PER -was O O -considering O O -possible O O -dates O O -for O O -the O O -by O O -, O O -his O O -office O O -said O O -. O O -Lang B-PER B-PER -, O O -57 O O -, O O -announced O O -in O O -November O O -that O O -he O O -intended O O -to O O -resign O O -from O O -parliament O O -to O O -take O O -up O O -a O O -position O O -as O O -Australia B-LOC B-LOC -' O O -senior O O -representative O O -at O O -the O O -United B-ORG B-ORG -Nations I-ORG I-ORG -headquarters O O -in O O -New B-LOC B-LOC -York I-LOC I-LOC -. O O -He O O -played O O -an O O -active O O -role O O -at O O -the O O -U B-ORG B-ORG -social O O -development O O -conference O O -in O O -Copenhagen B-LOC B-LOC -last O O -year O O -and O O -has O O -co O O -articles O O -with O O -U B-ORG B-ORG -development O O -programme O O -officer O O -In B-PER B-PER -Ka I-PER I-PER -. O O -Lang B-PER B-PER -, O O -a O O -persistent O O -campaign O O -for O O -intervention O O -economic O O -policy O O -, O O -has O O -been O O -Labor B-ORG O -member O O -for O O -Fraser B-LOC B-PER -since O O -1984 O O -. O O -He O O -was O O -senior O O -private O O -secretary O O -to O O -the O O -employment O O -and O O -industrial O O -relations O O -minister O O -from O O -1983 O O -to O O -1984 O O -and O O -was O O -economic O O -advisor O O -to O O -then O O -treasurer O O -Paul B-PER B-PER -Ke I-PER I-PER -in O O -1983 O O -. O O -His O O -previous O O -posts O O -include O O -assistant O O -director O O -of O O -the O O -national O O -planning O O -office O O -of O O -Papua B-LOC B-LOC -New I-LOC I-LOC -Guinea I-LOC I-LOC -from O O -1969 O O -to O O -1973 O O -. O O -- O O -Canberra B-ORG B-LOC -Bureau I-ORG O -61 O O -27 O O -Burmese B-MISC B-MISC -students O O -march O O -out O O -of O O -campus O O -again O O -. O O -RA B-LOC B-LOC -1996 O O -A O O -group O O -of O O -Burmese B-MISC B-MISC -students O O -on O O -Friday O O -marched O O -out O O -of O O -the O O -Yang B-ORG B-ORG -Institute I-ORG I-ORG -of I-ORG I-ORG -Technology I-ORG I-ORG -( O O -Y B-ORG B-ORG -) O O -in O O -the O O -northern O O -outskirts O O -of O O -Ra B-LOC B-LOC -and O O -moved O O -toward O O -the O O -University B-ORG B-ORG -of I-ORG I-ORG -Yang I-ORG I-ORG -about O O -six O O -km O O -( O O -four O O -miles O O -) O O -away O O -, O O -witnesses O O -said O O -. O O -The O O -witnesses O O -could O O -not O O -give O O -exact O O -numbers O O -of O O -those O O -taking O O -part O O -in O O -the O O -march O O -or O O -any O O -other O O -details O O -immediately O O -. O O -On O O -Monday O O -and O O -Tuesday O O -, O O -students O O -from O O -the O O -Y B-ORG B-ORG -and O O -the O O -university O O -launched O O -street O O -protests O O -against O O -what O O -they O O -called O O -unfair O O -handling O O -by O O -police O O -of O O -a O O -bra O O -between O O -some O O -of O O -their O O -colleagues O O -and O O -restaurant O O -owners O O -in O O -October O O -. O O -The O O -protests O O -culminated O O -at O O -dawn O O -on O O -Tuesday O O -with O O -several O O -hundred O O -of O O -the O O -student O O -protesters O O -being O O -detained O O -briefly O O -by O O -police O O -near O O -the O O -central O O -S B-LOC B-LOC -Da I-LOC I-LOC -p O O -in O O -Ra B-LOC B-LOC -. O O -They O O -were O O -later O O -released O O -. O O -On O O -Friday O O -, O O -some O O -students O O -told O O -Re B-ORG B-ORG -that O O -they O O -were O O -still O O -di O O -with O O -the O O -ruling O O -State B-ORG B-ORG -Law I-ORG I-ORG -and I-ORG I-ORG -Order I-ORG I-ORG -Restoration I-ORG I-ORG -Council I-ORG I-ORG -' O O -( O O -SL B-ORG B-ORG -) O O -handling O O -of O O -their O O -demands O O -. O O -They O O -said O O -they O O -wanted O O -to O O -organise O O -independent O O -unions O O -on O O -university O O -campuses O O -and O O -demanded O O -that O O -details O O -of O O -the O O -punishment O O -of O O -policemen O O -who O O -allegedly O O -man O O -some O O -students O O -at O O -the O O -October O O -bra O O -be O O -published O O -in O O -newspapers O O -. O O -Thai B-MISC B-MISC -rice O O -vessels O O -loading O O -and O O -movements O O -at O O -Dec O O -06 O O -. O O -BA B-LOC B-LOC -1996 O O -The O O -Thai B-MISC B-MISC -Commerce I-ORG B-ORG -Ministry I-ORG I-ORG -detailed O O -rice O O -loading O O -at O O -Thai B-MISC B-MISC -ports O O -as O O -follows O O -( O O -in O O -tonnes O O -) O O -: O O -V O O -Date O O -of O O -A O O -Q O O -Des O O -Iran B-LOC B-MISC -Sa O I-MISC -19 O O -9 O O -Iran B-LOC B-LOC -Princess O B-MISC -of O I-MISC -Lo B-LOC I-MISC -19 O O -10 O O -Philippines B-LOC B-LOC -Del O B-MISC -20 O O -5 O O -Indonesia B-LOC B-LOC -Sea B-ORG B-MISC -ace O O -20 O O -5 O O -Japan B-LOC B-LOC -Lucky B-ORG B-MISC -Em I-ORG I-MISC -20 O O -5 O O -Japan B-LOC B-LOC -Al B-LOC B-MISC -Day O I-MISC -21 O O -6 O O -Africa B-LOC B-LOC -Sang B-ORG B-MISC -Glory I-ORG I-MISC -22 O O -SH B-LOC B-LOC -1996 O O -A O O -five O O -girl O O -in O O -the O O -east O O -China B-LOC B-LOC -city O O -of O O -T B-LOC B-LOC -choked O O -and O O -almost O O -died O O -from O O -cigarette O O -smoke O O -at O O -her O O -grandfather O O -' O O -birthday O O -with O O -relatives O O -smoking O O -for O O -hours O O -in O O -a O O -small O O -room O O -, O O -the O O -Wen B-ORG B-ORG -Hui I-ORG I-ORG -Ba I-ORG I-ORG -newspaper O O -said O O -on O O -Friday O O -. O O -The O O -newspaper O O -said O O -the O O -girl O O -was O O -rushed O O -to O O -hospital O O -and O O -found O O -to O O -be O O -having O O -extreme O O -difficulty O O -breathing O O -. O O -It O O -said O O -eight O O -of O O -the O O -people O O -at O O -the O O -party O O -, O O -including O O -the O O -girl O O -' O O -father O O -, O O -immediately O O -announced O O -they O O -would O O -give O O -up O O -smoking O O -. O O -South B-MISC B-MISC -Korean I-MISC I-MISC -won O O -closes O O -down O O -on O O -import O O -settlements O O -. O O -SE B-LOC B-LOC -1996 O O -The O O -won O O -slid O O -against O O -the O O -U B-LOC B-LOC -unit O O -on O O -Friday O O -as O O -players O O -prepared O O -for O O -Monday O O -' O O -import O O -settlement O O -needs O O -, O O -traders O O -said O O -. O O -The O O -won O O -ended O O -at O O -83 O O -, O O -slightly O O -down O O -from O O -an O O -opening O O -of O O -83 O O -. O O -It O O -ranged O O -between O O -83 O O -and O O -83 O O -. O O -" O O -A O O -sale O O -of O O -about O O -$ O O -60 O O -million O O -by O O -H B-ORG B-ORG -Heavy I-ORG I-ORG -pushed O O -the O O -dollar O O -down O O -earlier O O -in O O -the O O -day O O -, O O -but O O -Monday O O -' O O -import O O -needs O O -helped O O -it O O -recover O O -, O O -" O O -said O O -a O O -Ko B-ORG B-ORG -Bank I-ORG I-ORG -dealer O O -. O O -Deal O O -said O O -the O O -dollar O O -/ O O -ye O O -' O O -movement O O -on O O -the O O -world O O -market O O -would O O -continue O O -to O O -set O O -the O O -trend O O -for O O -the O O -dollar O O -/ O O -won O O -next O O -week O O -. O O -Foreign O O -planes O O -to O O -land O O -in O O -China B-LOC B-LOC -' O O -popular O O -G B-LOC B-LOC -. O O -B B-LOC B-LOC -1996 O O -China B-LOC B-LOC -' O O -tourist O O -spot O O -of O O -G B-LOC B-LOC -in O O -the O O -southern O O -region O O -of O O -G B-LOC B-LOC -will O O -open O O -its O O -airport O O -to O O -foreign O O -aircraft O O -, O O -the O O -Xi B-ORG B-ORG -news O O -agency O O -said O O -on O O -Friday O O -. O O -An O O -assessment O O -group O O -made O O -up O O -of O O -the O O -State B-ORG B-ORG -Council I-ORG I-ORG -' O O -Port B-ORG O -Office I-ORG O -, O O -the O O -Civil B-ORG B-ORG -Aviation I-ORG I-ORG -Administration I-ORG I-ORG -of I-ORG I-ORG -China I-ORG I-ORG -, O O -the O O -General B-ORG B-ORG -Administration I-ORG I-ORG -of I-ORG I-ORG -Customs I-ORG I-ORG -and O O -other O O -authorities O O -had O O -granted O O -the O O -airport O O -permission O O -to O O -handle O O -foreign O O -aircraft O O -, O O -Xi B-PER B-ORG -said O O -. O O -" O O -The O O -move O O -is O O -expected O O -to O O -give O O -a O O -shot O O -in O O -the O O -arm O O -to O O -the O O -economic O O -expansion O O -of O O -G B-LOC B-LOC -and O O -southwest O O -China B-LOC B-LOC -as O O -a O O -whole O O -, O O -" O O -the O O -agency O O -said O O -but O O -gave O O -no O O -further O O -details O O -. O O -G B-LOC B-LOC -is O O -well O O -known O O -for O O -its O O -mountain O O -and O O -river O O -scenery O O -and O O -is O O -one O O -of O O -China B-LOC B-LOC -' O O -most O O -popular O O -tourist O O -destinations O O -. O O -EPA B-ORG B-ORG -says O O -economic O O -assessment O O -unchanged O O -by O O -GDP O O -data O O -. O O -TO B-LOC B-LOC -1996 O O -Japan B-LOC B-LOC -' O O -Economic B-ORG B-ORG -Planning I-ORG I-ORG -Agency I-ORG I-ORG -has O O -not O O -changed O O -its O O -view O O -that O O -the O O -economy O O -is O O -gradually O O -recovering O O -, O O -despite O O -relatively O O -weak O O -gross O O -domestic O O -product O O -figures O O -released O O -on O O -Tuesday O O -, O O -EPA B-ORG B-ORG -Vice O O -Minister O O -Shi B-PER B-PER -N I-PER I-PER -told O O -reporters O O -on O O -Friday O O -. O O -He O O -said O O -the O O -GDP O O -growth O O -was O O -weak O O -but O O -that O O -this O O -reflected O O -the O O -economy O O -between O O -July O O -and O O -September O O -and O O -did O O -not O O -take O O -into O O -account O O -more O O -recent O O -data O O -. O O -When O O -asked O O -about O O -the O O -outlook O O -for O O -the O O -fiscal O O -year O O -beginning O O -in O O -April O O -, O O -N B-ORG B-PER -said O O -the O O -economy O O -may O O -slow O O -down O O -in O O -the O O -early O O -part O O -of O O -the O O -fiscal O O -year O O -due O O -to O O -a O O -planned O O -consumption O O -tax O O -hike O O -, O O -but O O -that O O -would O O -be O O -only O O -temporary O O -. O O -The O O -consumption O O -tax O O -will O O -be O O -raised O O -to O O -five O O -percent O O -from O O -three O O -percent O O -from O O -April O O -1 O O -. O O -Sang B-ORG B-ORG -- O O -96 O O -parent O O -forecast O O -. O O -TO B-LOC B-LOC -1996 O O -Year O O -to O O -March O O -31 O O -, O O -1997 O O -( O O -in O O -billion O O -of O O -ye O O -unless O O -specified O O -) O O -LA O O -ACT O O -( O O -Pa O O -) O O -F O O -Y O O -Sales O O -128 O O -117 O O -Current O O -12 O O -9 O O -Net O O -6 O O -5 O O -EP O O -143 O O -ye O O -127 O O -ye O O -Or B-MISC O -di O O -30 O O -ye O O -30 O O -ye O O -NO O O -- O O -Sang B-ORG B-ORG -Co I-ORG I-ORG -Ltd I-ORG I-ORG -is O O -a O O -trader O O -specialising O O -in O O -interiors O O -. O O -B B-ORG B-ORG -, O O -Barr B-PER B-ORG -said O O -to O O -continue O O -Bus B-LOC B-LOC -talks O O -. O O -K B-LOC B-LOC -Ara I-ORG I-LOC -J B-LOC B-LOC -1996 O O -Canada B-LOC B-LOC -' O O -B B-ORG B-ORG -Mine I-ORG I-ORG -Ltd I-ORG I-ORG -and O O -Barr B-ORG B-ORG -Gold I-ORG I-ORG -Corp I-ORG I-ORG -are O O -to O O -continue O O -negotiations O O -to O O -hammer O O -out O O -a O O -partnership O O -agreement O O -to O O -develop O O -the O O -spectacular O O -Bus B-LOC B-LOC -gold O O -find O O -in O O -Indonesia B-LOC B-LOC -, O O -sources O O -close O O -to O O -the O O -talks O O -said O O -on O O -Friday O O -. O O -" O O -The O O -negotiations O O -will O O -be O O -held O O -both O O -in O O -Toronto B-LOC B-LOC -and O O -in O O -Jakarta B-LOC B-LOC -, O O -" O O -one O O -source O O -, O O -speaking O O -on O O -condition O O -of O O -an O O -, O O -told O O -Re B-ORG B-ORG -. O O -Another O O -source O O -said O O -most O O -of O O -the O O -key O O -ne O O -from O O -both O O -B B-ORG B-ORG -and O O -Barr B-ORG B-ORG -had O O -returned O O -to O O -Toronto B-LOC B-LOC -, O O -but O O -declined O O -to O O -say O O -if O O -there O O -had O O -been O O -any O O -progress O O -in O O -their O O -negotiations O O -. O O -Both O O -sources O O -said O O -B B-ORG B-ORG -and O O -Barr B-PER B-ORG -did O O -not O O -hold O O -talks O O -on O O -Thursday O O -with O O -Mines B-ORG B-ORG -and I-ORG I-ORG -Energy I-ORG I-ORG -Ministry I-ORG I-ORG -Secretary O O -Um B-PER B-PER -Said I-PER I-PER -, O O -who O O -is O O -coordinating O O -the O O -negotiations O O -over O O -the O O -Bus B-MISC B-LOC -find O O -in O O -East B-LOC B-LOC -Kali I-LOC I-LOC -. O O -The O O -first O O -source O O -also O O -said O O -B B-ORG B-ORG -had O O -until O O -December O O -21 O O -to O O -submit O O -to O O -the O O -Indonesian B-ORG B-ORG -Mines I-ORG I-ORG -and I-ORG I-ORG -Energy I-ORG I-ORG -Ministry I-ORG I-ORG -a O O -f O O -study O O -on O O -the O O -central O O -region O O -of O O -the O O -Bus B-LOC B-LOC -property O O -, O O -estimated O O -to O O -contain O O -2 O O -million O O -ounce O O -of O O -gold O O -. O O -The O O -richest O O -parts O O -of O O -the O O -property O O -to O O -the O O -north O O -and O O -south O O -of O O -the O O -central O O -region O O -have O O -been O O -estimated O O -by O O -B B-ORG B-ORG -to O O -contain O O -57 O O -million O O -ounce O O -of O O -gold O O -. O O -" O O -B B-ORG B-ORG -is O O -expected O O -to O O -complete O O -the O O -f O O -report O O -by O O -December O O -16 O O -and O O -submit O O -it O O -to O O -the O O -government O O -before O O -the O O -December O O -21 O O -deadline O O -, O O -" O O -the O O -source O O -said O O -. O O -He O O -said O O -B B-ORG B-ORG -would O O -then O O -formally O O -seek O O -the O O -permission O O -of O O -the O O -Indonesian B-MISC B-MISC -government O O -to O O -begin O O -construction O O -to O O -develop O O -Bus B-LOC B-LOC -' O O -central O O -region O O -, O O -which O O -might O O -take O O -up O O -to O O -two O O -years O O -. O O -The O O -source O O -declined O O -to O O -say O O -if O O -there O O -had O O -been O O -any O O -progress O O -in O O -the O O -talks O O -between O O -B B-ORG B-ORG -and O O -Barr B-ORG B-ORG -. O O -" O O -This O O -is O O -a O O -huge O O -project O O -. O O -we O O -are O O -not O O -selling O O -furniture O O -, O O -and O O -B B-ORG B-ORG -has O O -13 O O -shareholders O O -to O O -answer O O -to O O -, O O -" O O -the O O -source O O -said O O -. O O -" O O -While O O -there O O -has O O -been O O -some O O -agreement O O -in O O -principle O O -on O O -some O O -issues O O -, O O -there O O -are O O -still O O -others O O -such O O -as O O -procedures O O -and O O -mechanisms O O -that O O -needed O O -to O O -be O O -sorted O O -out O O -, O O -" O O -he O O -added O O -. O O -The O O -source O O -said O O -no O O -new O O -deadline O O -had O O -been O O -set O O -by O O -the O O -Mines B-ORG B-ORG -and I-ORG I-ORG -Energy I-ORG I-ORG -Ministry I-ORG I-ORG -for O O -B B-ORG B-ORG -and O O -Barr B-ORG B-ORG -to O O -strike O O -a O O -deal O O -. O O -The O O -Ministry B-ORG O -had O O -given O O -the O O -companies O O -until O O -December O O -4 O O -to O O -complete O O -a O O -partnership O O -deal O O -, O O -and O O -advised O O -B B-ORG B-ORG -to O O -take O O -a O O -25 O O -percent O O -stake O O -and O O -Barr B-ORG B-ORG -75 O O -percent O O -to O O -develop O O -the O O -property O O -. O O -" O O -As O O -far O O -as O O -I O O -am O O -aware O O -, O O -there O O -' O O -been O O -no O O -new O O -deadline O O -, O O -" O O -the O O -source O O -said O O -. O O -The O O -Ministry B-ORG O -' O O -Um B-PER B-PER -said O O -on O O -Thursday O O -that O O -both O O -B B-ORG B-ORG -and O O -Barr B-ORG B-ORG -had O O -responded O O -positively O O -to O O -a O O -government O O -letter O O -recommend O O -a O O -25 O O -split O O -in O O -the O O -Bus B-LOC B-ORG -gold O O -property O O -. O O -The O O -government O O -also O O -wants O O -10 O O -percent O O -of O O -the O O -property O O -. O O -Um B-PER B-PER -said O O -the O O -government O O -had O O -yet O O -to O O -receive O O -a O O -formal O O -reply O O -from O O -the O O -companies O O -. O O -He O O -had O O -said O O -earlier O O -that O O -if O O -the O O -two O O -companies O O -failed O O -to O O -reach O O -a O O -partnership O O -agreement O O -, O O -the O O -government O O -would O O -explore O O -other O O -ways O O -to O O -ex O O -development O O -of O O -the O O -Bus B-LOC B-ORG -find O O -. O O -B B-ORG B-ORG -has O O -a O O -partnership O O -deal O O -with O O -PT B-ORG B-PER -Pan I-ORG I-PER -Du I-ORG I-PER -of O O -the O O -Pan B-ORG B-ORG -Group I-ORG I-ORG -run O O -by O O -President O O -Su B-PER B-PER -' O O -eldest O O -son O O -, O O -Si B-PER B-PER -Ha I-PER I-PER -, O O -under O O -which O O -Pan B-ORG B-PER -would O O -receive O O -$ O O -40 O O -million O O -over O O -40 O O -months O O -plus O O -a O O -10 O O -percent O O -stake O O -Bus B-ORG B-ORG -' O O -richest O O -parts O O -. O O -Barr B-ORG B-ORG -has O O -teamed O O -up O O -with O O -a O O -construction O O -company O O -in O O -the O O -C B-ORG B-ORG -Group I-ORG I-ORG -of O O -Su B-LOC B-PER -' O O -eldest O O -daughter O O -, O O -Sit B-PER B-PER -Hard I-PER I-PER -R I-PER I-PER -, O O -in O O -what O O -Barr B-PER B-ORG -had O O -said O O -was O O -a O O -partnership O O -" O O -to O O -prepare O O -us O O -for O O -a O O -potential O O -mining O O -development O O -project O O -" O O -. O O -Honda B-ORG B-MISC -R I-ORG I-MISC -exceeds O O -sales O O -target O O -. O O -TO B-LOC B-LOC -1996 O O -Honda B-ORG B-ORG -Motor I-ORG I-ORG -Co I-ORG I-ORG -Ltd I-ORG I-ORG -said O O -on O O -Friday O O -that O O -it O O -had O O -received O O -15 O O -domestic O O -orders O O -for O O -its O O -S B-MISC B-MISC -recreational O O -vehicle O O -in O O -the O O -first O O -two O O -weeks O O -after O O -its O O -launch O O -. O O -Honda B-ORG B-ORG -launched O O -the O O -S B-MISC B-MISC -light O O -mini O O -, O O -featuring O O -cubic O O -body O O -styling O O -, O O -on O O -November O O -22 O O -with O O -a O O -monthly O O -sales O O -target O O -of O O -5 O O -units O O -. O O -A O O -version O O -with O O -lower O O -road O O -clearance O O -and O O -front O O -and O O -rear O O -s O O -accounted O O -for O O -two O O -of O O -the O O -sales O O -. O O -F O O -- O O -Singapore B-LOC B-LOC -sees O O -prestige O O -in O O -hosting O O -W B-ORG B-ORG -. O O -Ram B-PER B-PER -Hussain I-PER I-PER -S B-LOC B-LOC -1996 O O -Singapore B-LOC B-LOC -' O O -winning O O -campaign O O -to O O -host O O -the O O -World B-ORG B-ORG -Trade I-ORG I-ORG -Organisation I-ORG I-ORG -( O O -W B-ORG B-ORG -) O O -' O O -first O O -ministerial O O -meeting O O -reflected O O -its O O -ambition O O -to O O -play O O -a O O -key O O -role O O -in O O -shaping O O -global O O -free O O -trade O O -, O O -the O O -life O O -of O O -its O O -economy O O -, O O -analysts O O -said O O -. O O -" O O -As O O -one O O -of O O -the O O -world O O -' O O -most O O -external O O -oriented O O -economies O O -, O O -Singapore B-LOC B-LOC -has O O -a O O -di O O -large O O -stake O O -in O O -the O O -W B-ORG B-ORG -, O O -" O O -said O O -Desmond B-PER B-PER -Su I-PER I-PER -, O O -economist O O -at O O -research O O -house O O -I B-ORG B-ORG -. O O -" O O -Singapore B-LOC B-LOC -stands O O -to O O -benefit O O -more O O -than O O -most O O -from O O -continued O O -global O O -trade O O -liberal O O -as O O -trade O O -is O O -the O O -engine O O -of O O -its O O -growth O O -, O O -accounting O O -for O O -nearly O O -three O O -times O O -its O O -gross O O -domestic O O -product O O -. O O -" O O -The O O -city O O -met O O -U B-LOC B-LOC -opposition O O -two O O -years O O -ago O O -in O O -its O O -bid O O -to O O -host O O -the O O -meeting O O -, O O -expected O O -to O O -gather O O -4 O O -officials O O -from O O -160 O O -countries O O -from O O -December O O -9 O O -to O O -13 O O -. O O -In O O -a O O -stand O O -some O O -analysts O O -linked O O -to O O -controversy O O -over O O -Singapore B-LOC B-LOC -' O O -can O O -of O O -an O O -American B-MISC B-MISC -teenager O O -for O O -van O O -, O O -then O B-MISC -Trade O O -Representative O O -Mickey B-PER B-PER -Ka I-PER I-PER -had O O -said O O -the O O -meeting O O -ought O O -to O O -be O O -held O O -where O O -the O O -W B-ORG B-ORG -was O O -going O O -to O O -be O O -headquartered O O -. O O -That O O -would O O -have O O -meant O O -Geneva B-LOC B-LOC -. O O -But O O -Singapore B-LOC B-LOC -had O O -the O O -support O O -of O O -other O O -W B-ORG B-ORG -members O O -. O O -Derek B-PER B-PER -da I-PER I-PER -C I-PER I-PER -, O O -senior O O -fellow O O -at O O -the O O -Institute B-ORG B-ORG -of I-ORG I-ORG -Policy I-ORG I-ORG -Studies I-ORG I-ORG -( O O -IS B-ORG B-ORG -) O O -, O O -said O O -Singapore B-LOC B-LOC -' O O -hosting O O -of O O -the O O -conference O O -" O O -carries O O -a O O -great O O -deal O O -of O O -symbol O O -for O O -the O O -city O O -, O O -under O O -its O O -commitment O O -to O O -free O O -trade O O -and O O -its O O -trading O O -links O O -across O O -the O O -globe O O -. O O -" O O -There O O -is O O -the O O -international O O -prestige O O -Singapore B-LOC B-LOC -would O O -enjoy O O -, O O -but O O -" O O -more O O -importantly O O -there O O -is O O -a O O -genuine O O -national O O -interest O O -in O O -foster O O -better O O -global O O -free O O -trade O O -and O O -an O O -open O O -market O O -" O O -, O O -said O O -Tan B-PER B-PER -Kong I-PER I-PER -Ya I-PER I-PER -, O O -head O O -of O O -Business B-ORG O -Policy I-ORG O -at O O -the O O -National B-ORG B-ORG -University I-ORG I-ORG -of I-ORG I-ORG -Singapore I-ORG I-ORG -. O O -At O O -the O O -ministerial O O -meeting O O -, O O -trade O O -ministers O O -will O O -review O O -the O O -work O O -of O O -the O O -W B-ORG B-ORG -and O O -the O O -implementation O O -of O O -the O O -Uruguay B-MISC B-LOC -Round I-MISC O -free O O -trade O O -commitments O O -under O O -its O O -predecessor O O -the O O -General B-MISC B-MISC -Agreement I-MISC I-MISC -on I-MISC I-MISC -Ta I-ORG I-MISC -and I-ORG I-MISC -Trade I-ORG I-MISC -( O O -GA B-ORG B-MISC -) O O -. O O -In O O -June O O -, O O -the O O -W B-ORG B-ORG -hailed O O -Singapore B-LOC B-LOC -for O O -its O O -open O O -market O O -policies O O -but O O -the O O -European B-ORG B-ORG -Union I-ORG I-ORG -and O O -other O O -trading O O -powers O O -called O O -on O O -Singapore B-LOC B-LOC -to O O -speed O O -up O O -the O O -opening O O -of O O -its O O -services O O -sector O O -. O O -Su B-PER B-PER -said O O -the O O -struggle O O -that O O -Singapore B-LOC B-LOC -had O O -to O O -wage O O -in O O -v O O -to O O -host O O -the O O -meeting O O -would O O -be O O -repeated O O -during O O -the O O -talks O O -. O O -" O O -There O O -is O O -tension O O -at O O -every O O -step O O -of O O -the O O -way O O -, O O -" O O -since O O -a O O -battle O O -line O O -between O O -the O O -West B-MISC O -and O O -developing O O -countries O O -has O O -been O O -drawn O O -over O O -the O O -issue O O -of O O -linking O O -trade O O -liberal O O -with O O -labour O O -rights O O -, O O -he O O -said O O -. O O -Su B-PER B-PER -said O O -hosting O O -the O O -meeting O O -carried O O -prestige O O -for O O -Singapore B-LOC B-LOC -, O O -" O O -however O O -, O O -this O O -is O O -quite O O -in O O -as O O -the O O -prestige O O -factor O O -may O O -not O O -necessarily O O -lead O O -to O O -any O O -additional O O -investment O O -and O O -trade O O -flows O O -to O O -this O O -region O O -. O O -" O O -From O O -a O O -commercial O O -point O O -of O O -view O O -, O O -the O O -meeting O O -would O O -be O O -good O O -for O O -Singapore B-LOC B-LOC -' O O -tourism O O -industry O O -, O O -Tan B-PER B-PER -said O O -. O O -A O O -large O O -part O O -of O O -Singapore B-LOC B-LOC -' O O -workforce O O -would O O -be O O -mob O O -to O O -ensure O O -the O O -meeting O O -would O O -run O O -without O O -a O O -g O O -but O O -the O O -average O O -Singapore B-MISC B-MISC -" O O -would O O -probably O O -not O O -be O O -too O O -concerned O O -about O O -some O O -of O O -the O O -issues O O -, O O -" O O -Tan B-PER B-PER -said O O -. O O -" O O -But O O -the O O -more O O -educated O O -public O O -will O O -realise O O -that O O -these O O -kind O O -of O O -things O O -are O O -important O O -for O O -Singapore B-LOC B-LOC -as O O -a O O -small O O -economy O O -. O O -" O O -Su B-PER B-PER -said O O -any O O -political O O -gains O O -the O O -Singapore B-LOC B-LOC -government O O -would O O -get O O -from O O -the O O -W B-ORG B-ORG -meeting O O -- O O -ahead O O -of O O -a O O -general O O -election O O -due O O -by O O -April O O -1997 O O -- O O -would O O -depend O O -on O O -how O O -successful O O -it O O -was O O -in O O -pushing O O -its O O -economic O O -agenda O O -. O O -" O O -If O O -there O O -are O O -any O O -movements O O -toward O O -free O O -trade O O -, O O -then O O -Singapore B-LOC B-LOC -' O O -economy O O -and O O -the O O -electorate O O -would O O -gain O O -, O O -" O O -he O O -said O O -. O O -" O O -But O O -I O O -do O O -n O O -think O O -it O O -would O O -be O O -wise O O -to O O -play O O -up O O -the O O -political O O -aspect O O -of O O -this O O -. O O -I O O -think O O -political O O -issues O O -will O O -take O O -secondary O O -importance O O -to O O -all O O -these O O -economic O O -issues O O -that O O -will O O -be O O -displayed O O -. O O -" O O -Japan B-LOC B-LOC -N B-ORG B-ORG -says O O -hopes O O -to O O -start O O -in O O -business O O -soon O O -. O O -TO B-LOC B-LOC -1996 O O -Nippon B-ORG B-ORG -Telegraph I-ORG I-ORG -and I-ORG I-ORG -Telephone I-ORG I-ORG -Corp I-ORG I-ORG -( O O -N B-ORG B-ORG -) O O -said O O -on O O -Friday O O -that O O -it O O -hopes O O -to O O -move O O -into O O -the O O -international O O -telecommunications O O -business O O -as O O -soon O O -as O O -possible O O -following O O -the O O -government O O -' O O -decision O O -to O O -split O O -N B-ORG B-ORG -into O O -three O O -firms O O -under O O -a O O -holding O O -company O O -. O O -" O O -We O O -hope O O -to O O -start O O -international O O -telephone O O -business O O -as O O -soon O O -as O O -possible O O -, O O -" O O -a O O -company O O -official O O -told O O -Re B-ORG B-ORG -. O O -The O O -official O O -said O O -the O O -latest O O -government O O -decision O O -to O O -split O O -the O O -company O O -under O O -a O O -holding O O -company O O -would O O -allow O O -flexibility O O -in O O -N B-ORG B-ORG -' O O -international O O -phone O O -business O O -. O O -Earlier O O -, O O -Post B-ORG O -and I-ORG O -Telecommunications I-ORG O -Minister O O -His B-PER B-PER -Ho I-PER I-PER -told O O -a O O -news O O -conference O O -the O O -government O O -plans O O -to O O -split O O -N B-ORG B-ORG -into O O -three O O -firms O O -under O O -a O O -holding O O -company O O -, O O -but O O -did O O -not O O -specify O O -when O O -the O O -restructuring O O -would O O -likely O O -take O O -effect O O -. O O -One O O -of O O -the O O -three O O -new O O -companies O O -will O O -be O O -a O O -long O O -operator O O -and O O -the O O -other O O -two O O -will O O -be O O -local O O -operators O O -, O O -Ho B-ORG B-PER -said O O -. O O -One O O -of O O -the O O -local O O -firms O O -will O O -operate O O -in O O -west O O -Japan B-LOC B-LOC -and O O -the O O -other O O -in O O -east O O -Japan B-LOC B-LOC -, O O -he O O -added O O -. O O -The O O -long O O -operator O O -will O O -offer O O -international O O -services O O -, O O -Ho B-ORG B-PER -said O O -. O O -The O O -N B-ORG B-ORG -official O O -said O O -the O O -timing O O -of O O -the O O -planned O O -split O O -was O O -uncertain O O -because O O -more O O -discussions O O -by O O -government O O -officials O O -were O O -required O O -. O O -Ah B-ORG B-ORG -launches O O -Asian B-MISC B-MISC -food O O -discount O O -stores O O -. O O -Z B-LOC B-LOC -, O O -Netherlands B-LOC B-LOC -1996 O O -Dutch B-MISC B-MISC -supermarket O O -group O O -Ah B-ORG B-ORG -N I-ORG I-ORG -said O O -on O O -Friday O O -it O O -had O O -launched O O -a O O -second O O -food O O -store O O -format O O -for O O -Asian B-MISC B-MISC -consumers O O -today O O -, O O -opening O O -16 O O -B B-MISC B-MISC -food O O -discount O O -stores O O -in O O -Malaysia B-LOC B-LOC -. O O -The O O -B B-ORG B-MISC -stores O O -are O O -located O O -in O O -Mal B-ORG B-LOC -' O O -capital O O -Kuala B-LOC B-LOC -Lumpur O I-LOC -and O O -in O O -the O O -country O O -' O O -second O O -city O O -Jo B-LOC B-LOC -Ba I-LOC I-LOC -. O O -The O O -discount O O -price O O -format O O -store O O -B B-ORG B-MISC -is O O -to O O -complement O O -Ah B-ORG B-ORG -' O O -full O O -service O O -supermarket O O -TO B-ORG B-MISC -, O O -recently O O -launched O O -in O O -Asia B-LOC B-LOC -. O O -" O O -In O O -the O O -coming O O -five O O -to O O -ten O O -years O O -, O O -Ah B-ORG B-ORG -plans O O -to O O -open O O -many O O -more O O -stores O O -of O O -both O O -formats O O -, O O -making O O -TO B-MISC B-MISC -and O O -B B-MISC B-MISC -household O O -names O O -in O O -the O O -region O O -, O O -" O O -Ah B-ORG B-ORG -said O O -in O O -a O O -statement O O -. O O -As O O -well O O -as O O -its O O -activities O O -in O O -Asia B-LOC B-LOC -, O O -Dutch B-MISC B-MISC -retail O O -group O O -Ah B-ORG B-ORG -has O O -a O O -strong O O -presence O O -in O O -Europe B-LOC B-LOC -, O O -in O O -the O O -U B-LOC B-LOC -and O O -the O O -company O O -recently O O -announced O O -a O O -joint O O -venture O O -agreement O O -in O O -Brazil B-LOC B-LOC -. O O -Ah B-ORG B-ORG -has O O -annual O O -sales O O -of O O -approximately O O -US B-MISC B-MISC -24 O O -billion O O -, O O -and O O -employs O O -180 O O -people O O -worldwide O O -. O O -- O O -Amsterdam B-LOC B-LOC -news O O -+ O O -20 O O -50 O O -5000 O O -, O O -F O O -+ O O -20 O O -50 O O -50 O O -AL B-ORG O -SK O O -' O O -W O B-MISC -C I-MISC I-MISC -S O O -G O O -W O O -PR O O -. O O -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Profile O O -of O O -the O O -winner O O -of O O -Saturday O O -' O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -super O O -G O O -race O O -: O O -Name O O -: O O -S B-PER B-PER -Glad I-PER I-PER -Age O O -: O O -25 O O -Nation O O -: O O -Russia B-LOC B-LOC -Previous O O -World B-MISC B-MISC -Cup I-MISC I-MISC -victories O O -: O O -None O O -Other O O -F O O -: O O -Glad B-PER B-PER -won O O -a O O -silver O O -medal O O -in O O -super O O -G I-MISC O -at O O -the O O -1994 O O -Lille B-MISC B-LOC -Winter I-MISC B-MISC -Olympics I-MISC I-MISC -and O O -a O O -bronze O O -medal O O -in O O -downhill O O -at O O -the O O -1991 O O -World B-MISC B-MISC -Championships I-MISC I-MISC -. O O -AL B-MISC O -SK O O -' O O -W B-MISC B-MISC -C I-MISC I-MISC -S O O -G O O -R O O -. O O -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Provisional O O -results O O -from O O -Saturday O O -' O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -super O O -G O O -race O O -: O O -1 O O -S B-PER B-PER -Glad I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -one O O -minute O O -17 O O -seconds O O -2 O O -Per B-PER B-PER -W I-PER I-PER -( O O -Sweden B-LOC B-LOC -) O O -1 O O -3 O O -Carole B-PER B-PER -Mont I-PER I-PER -( O O -France B-LOC B-LOC -) O O -1 O O -4 O O -Hi B-PER B-PER -G I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -1 O O -5 O O -Is B-PER B-PER -Ko I-PER I-PER -( O O -Italy B-LOC B-LOC -) O O -1 O O -6 O O -War B-PER B-PER -Z I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -1 O O -7 O O -Mad B-PER B-PER -B I-PER I-PER -AL B-ORG O -SK O B-MISC -W O O -W O B-MISC -C I-MISC I-MISC -S O O -G O O -. O O -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -S B-PER B-PER -Glad I-PER I-PER -of O O -Russia B-LOC B-LOC -won O O -the O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -Super B-MISC O -G I-MISC O -race O O -on O O -Saturday O O -. O O -Per B-PER B-PER -W I-PER I-PER -of O O -Sweden B-LOC B-LOC -finished O O -second O O -and O O -Carole B-PER B-PER -Mont I-PER I-PER -of O O -France B-LOC B-LOC -came O O -in O O -third O O -, O O -according O O -to O O -provisional O O -results O O -. O O -GO O O -- O O -T O O -R O O -OF O O -J B-MISC B-MISC -C I-MISC I-MISC -WA O O -O O O -. O O -T B-LOC B-LOC -SP I-LOC I-LOC -, O O -Florida B-LOC B-LOC -1996 O O -Heavy O O -rains O O -on O O -Saturday O O -washed O O -out O O -the O O -third O O -round O O -of O O -the O O -$ O O -1 O O -million O O -J B-MISC B-MISC -Classic I-MISC I-MISC -at O O -the O O -Inn B-LOC B-LOC -Hilton I-LOC I-LOC -Resort I-LOC I-LOC -. O O -Official O O -said O O -the O O -tournament O O -would O O -be O O -reduced O O -to O O -54 O O -holes O O -for O O -the O O -first O O -time O O -in O O -its O O -37 O O -history O O -. O O -The O O -final O O -round O O -of O O -the O O -special O O -event O O -, O O -which O O -pairs O O -players O O -from O O -the O O -PGA B-MISC B-ORG -and O O -LP B-MISC B-ORG -Tours I-MISC O -, O O -will O O -be O O -played O O -in O O -the O O -alternate O O -shot O O -format O O -on O O -Sunday O O -. O O -The O O -duo O O -of O O -Pat B-PER B-PER -Hu I-PER I-PER -and O O -Scott B-PER B-PER -M I-PER I-PER -were O O -tied O O -for O O -the O O -lead O O -with O O -the O O -team O O -of O O -Donna B-PER B-PER -Andrews I-PER I-PER -and O O -Mike B-PER B-PER -Hu I-PER I-PER -at O O -13 O O -129 O O -through O O -36 O O -holes O O -. O O -The O O -tandem O O -of O O -reigning O O -U B-MISC B-LOC -Amateur I-MISC O -champions O O -Ke B-PER B-PER -Ku I-PER I-PER -and O O -Tiger B-PER B-PER -Woods I-PER I-PER -were O O -another O O -shot O O -back O O -at O O -12 O O -130 O O -. O O -De O O -champions O O -Beth B-PER B-PER -Daniel I-PER I-PER -and O O -Davis B-PER B-PER -Love I-PER I-PER -will O O -start O O -the O O -final O O -round O O -six O O -shots O O -off O O -the O O -pace O O -. O O -AL B-ORG O -SK O O -' O O -D O O -W O O -PR O O -. O O -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Profile O O -of O O -the O O -winner O O -of O O -Saturday O O -' O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -downhill O O -race O O -: O O -Name O O -: O O -Ren B-PER B-PER -Go I-PER I-PER -Age O O -: O O -20 O O -Nation O O -: O O -Austria B-LOC B-LOC -Previous O O -victories O O -( O O -two O O -) O O -: O O -slalom O O -, O O -Lille B-LOC B-LOC -Norway I-LOC B-LOC -, O O -1993 O O -; O O -super O O -G O O -, O O -F B-LOC B-LOC -, O O -Austria B-LOC B-LOC -, O O -1995 O O -. O O -Other O O -facts O O -: O O -As O O -a O O -qualifier O O -for O O -the O O -1993 O B-MISC -World B-MISC I-MISC -Cup I-MISC I-MISC -finals O O -through O O -Europa B-MISC B-MISC -Cup I-MISC I-MISC -results O O -, O O -16 O O -Go B-PER B-PER -won O O -the O O -slalom O O -to O O -become O O -history O O -' O O -youngest O O -World B-MISC B-MISC -Cup I-MISC I-MISC -v O O -. O O -AL B-MISC O -SK O O -' O O -W B-MISC B-MISC -C I-MISC I-MISC -ST O O -. O O -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -standings O O -after O O -Saturday O O -' O O -downhill O O -race O O -: O O -Down O O -Standing O O -1 O O -Kat B-PER B-PER -Se I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -180 O O -points O O -2 O O -Ren B-PER B-PER -Go I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -132 O O -3 O O -Carole B-PER B-PER -Mont I-PER I-PER -( O O -France B-LOC B-LOC -) O O -86 O O -4 O O -Per B-PER B-PER -W I-PER I-PER -( O O -Sweden B-LOC B-LOC -) O O -75 O O -5 O O -Heidi B-PER B-PER -Z I-PER I-PER -( O O -Switzerland B-LOC B-LOC -) O O -69 O O -6 O O -Regina B-PER B-PER -Ha I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -66 O O -7 O O -Alexandra B-PER B-PER -Mei I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -65 O O -8 O O -Is B-PER B-PER -Ko I-PER I-PER -( O O -Italy B-LOC B-LOC -) O O -60 O O -9 O O -In B-PER B-PER -Helen I-PER I-PER -Mark I-PER O -( O O -Norway B-LOC B-LOC -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Provisional O O -results O O -from O O -Saturday O O -' O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -downhill O O -race O O -: O O -1 O O -Ren B-PER B-PER -Go I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -one O O -minute O O -47 O O -seconds O O -2 O O -Kat B-PER B-PER -Se I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -1 O O -3 O O -Is B-PER B-PER -Ko I-PER I-PER -( O O -Italy B-LOC B-LOC -) O O -1 O O -4 O O -Alexandra B-PER B-PER -Mei I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -1 O O -5 O O -Megan B-PER B-PER -G I-PER I-PER -( O O -U B-LOC B-LOC -) O O -1 O O -6 O O -Miriam B-PER B-PER -V I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -1 O O -7 O O -Stefan B-PER B-PER -Schuster I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -1 O O -NO B-MISC O -SK O B-MISC -C I-MISC I-MISC -B O O -R O O -. O O -O B-LOC B-LOC -, O O -Sweden B-LOC B-LOC -1996 O O -Results O O -of O O -Saturday O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -bi O O -races O O -: O O -Men O O -' O O -10 O O -km O O -1 O O -V B-PER B-PER -Sa I-PER I-PER -( O O -Belarus B-LOC B-LOC -) O O -26 O O -minutes O O -17 O O -seconds O O -( O O -no O O -penalty O O -rounds O O -) O O -2 O O -Fr B-PER B-PER -Andre I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -26 O O -( O O -2 O O -) O O -3 O O -Ole B-PER B-PER -Ein I-PER I-PER -B I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -26 O O -( O O -2 O O -) O O -4 O O -Sven B-PER B-PER -Fischer I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -26 O O -( O O -1 O O -) O O -5 O O -R B-PER B-PER -Gross I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -26 O O -( O O -1 O O -) O O -World B-MISC B-MISC -Cup I-MISC I-MISC -standings O O -1 O O -Women O O -' O O -7 O O -km O O -1 O O -Olga B-PER B-PER -Mel I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -23 O O -( O O -0 O O -) O O -2 O O -S B-PER B-PER -Para I-PER I-PER -( O O -Bel B-LOC B-LOC -) O O -23 O O -( O O -0 O O -) O O -3 O O -Gunn B-PER B-PER -Mar I-PER I-PER -Andreas I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -24 O O -( O O -0 O O -) O O -4 O O -Simone B-PER B-PER -G I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -24 O O -( O O -1 O O -) O O -5 O O -Petra B-PER B-PER -Be I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -24 O O -( O O -2 O O -) O O -World B-MISC B-MISC -Cup I-MISC I-MISC -standings O O -1 O O -Be B-PER B-PER -89 O O -2 O O -Para B-PER B-PER -79 O O -3 O O -G B-PER B-PER -VA B-LOC B-LOC -, O O -Colorado B-LOC B-LOC -1996 O O -Ren B-PER B-PER -Go I-PER I-PER -of O O -Austria B-LOC B-LOC -won O O -the O O -women O O -' O O -World B-MISC B-MISC -Cup I-MISC I-MISC -downhill O O -race O O -on O O -Saturday O O -, O O -according O O -to O O -provisional O O -results O O -. O O -Kat B-PER B-PER -Se I-PER I-PER -of O O -Germany B-LOC B-LOC -finished O O -second O O -and O O -Is B-PER B-PER -Ko I-PER I-PER -of O O -Italy B-LOC B-LOC -took O O -third O O -. O O -B O B-MISC -P O O -USA B-LOC B-ORG -III O I-ORG -TO O O -S O O -W O O -. O O -I B-LOC B-LOC -, O O -Austria B-LOC B-LOC -1996 O O -Brian B-PER B-PER -Shi I-PER I-PER -pilot O O -USA B-LOC B-ORG -III I-MISC I-ORG -to O O -a O O -surprise O O -victory O O -in O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -two O O -b O O -race O O -on O O -Saturday O O -. O O -L O O -fifth O O -after O O -the O O -first O O -run O O -, O O -Shi B-PER B-PER -and O O -break O O -Randy B-PER B-PER -Jones I-PER I-PER -delivered O O -a O O -near O O -second O O -trip O O -down O O -the O O -1976 B-MISC O -Olympic B-MISC B-MISC -course O O -for O O -an O O -aggregate O O -time O O -of O O -one O O -minute O O -45 O O -seconds O O -. O O -First O O -run O O -leaders O O -G B-PER B-PER -Hu I-PER I-PER -and O O -break O O -Antonio B-PER B-PER -Ta I-PER I-PER -in O O -the O O -Italy B-LOC B-LOC -I O O -s O O -finished O O -second O O -two O O -of O O -a O O -second O O -behind O O -the O O -Americans B-MISC B-MISC -. O O -Canada B-LOC B-ORG -I O I-ORG -, O O -represented O O -by O O -Pierre B-PER B-PER -Lu I-PER I-PER -and O O -break O O -Dave B-PER B-PER -Mac I-PER I-PER -, O O -completed O O -the O O -third O O -World B-MISC B-MISC -cup O I-MISC -event O O -of O O -the O O -winter O O -a O O -further O O -one O O -of O O -a O O -second O O -behind O O -the O O -Italians B-MISC B-MISC -. O O -The O O -Canadians B-MISC B-MISC -, O O -winners O O -of O O -the O O -opening O O -two O O -events O O -in O O -Alt B-LOC B-LOC -, O O -Germany B-LOC B-LOC -, O O -and O O -La B-LOC B-LOC -P I-LOC I-LOC -, O O -France B-LOC B-LOC -, O O -increased O O -their O O -lead O O -in O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -standings O O -. O O -They O O -have O O -104 O O -points O O -, O O -15 O O -ahead O O -of O O -USA B-LOC B-ORG -I I-MISC I-ORG -' O O -Jim B-PER B-PER -Herb I-PER I-PER -and O O -break O O -Garrett B-PER B-PER -Hi I-PER I-PER -who O O -managed O O -only O O -10th O O -place O O -on O O -Saturday O O -. O O -SK O B-MISC -MA O O -PR O O -F O O -SK O O -DE O O -. O O -T B-LOC B-LOC -, O O -France B-LOC B-LOC -1996 O O -China B-LOC B-LOC -made O O -a O O -promising O O -debut O O -on O O -the O O -freestyle O O -skiing O O -world O O -cup O O -circuit O O -in O O -an O O -aerial O O -event O O -in O O -the O O -French B-MISC B-MISC -resort O O -of O O -T B-LOC B-LOC -on O O -Saturday O O -. O O -While O O -the O O -Chinese B-MISC B-MISC -failed O O -to O O -gain O O -a O O -place O O -in O O -the O O -men O O -' O O -final O O -, O O -they O O -had O O -two O O -in O O -the O O -top O O -10 O O -of O O -the O O -women O O -' O O -competition O O -, O O -C B-PER B-PER -Dan I-PER I-PER -finishing O O -a O O -respectable O O -seventh O O -and O O -Xu B-PER B-PER -Nan I-PER I-PER -ninth O O -. O O -But O O -overall O O -, O O -it O O -was O O -France B-LOC B-LOC -and O O -Canada B-LOC B-LOC -who O O -dominated O O -the O O -day O O -. O O -Alexis B-PER B-PER -Blanc I-PER I-PER -and O O -Se B-PER B-PER -F I-PER I-PER -gave O O -France B-LOC B-LOC -a O O -one O O -finish O O -in O O -the O O -first O O -aerial O O -competition O O -of O O -the O O -season O O -. O O -Blanc B-PER B-PER -collected O O -his O O -seventh O O -career O O -World B-MISC B-MISC -Cup I-MISC I-MISC -win O O -with O O -a O O -two O O -jump O O -combined O O -score O O -of O O -238 O O -points O O -, O O -easily O O -beating O O -F B-PER B-PER -, O O -the O O -overall O O -World B-MISC B-MISC -Cup I-MISC I-MISC -aerial O O -champion O O -, O O -who O O -was O O -a O O -distant O O -second O O -with O O -223 O O -. O O -Canada B-LOC B-LOC -' O O -Jeff B-PER B-PER -Bean I-PER I-PER -, O O -who O O -had O O -never O O -finished O O -higher O O -than O O -ninth O O -in O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -event O O -, O O -made O O -his O O -first O O -trip O O -to O O -the O O -podium O O -taking O O -third O O -place O O -with O O -a O O -mark O O -of O O -209 O O -. O O -Veronica B-PER B-PER -B I-PER I-PER -of O O -Canada B-LOC B-LOC -, O O -who O O -picked O O -up O O -her O O -first O O -career O O -victory O O -at O O -T B-LOC B-LOC -last O O -year O O -, O O -made O O -it O O -two O O -wins O O -in O O -a O O -row O O -at O O -the O O -French B-MISC B-MISC -resort O O -taking O O -first O O -in O O -the O O -women O O -' O O -competition O O -with O O -a O O -score O O -of O O -170 O O -. O O -Swiss B-MISC B-MISC -skier O O -occupied O O -the O O -other O O -two O O -places O O -on O O -the O O -podium O O -, O O -Karin B-PER B-PER -Ku I-PER I-PER -taking O O -second O O -with O O -160 O O -narrowly O O -ahead O O -of O O -Evelyn B-PER B-PER -Le I-PER I-PER -with O O -160 O O -. O O -B O B-MISC -C I-MISC I-MISC -T O O -R O O -. O O -I B-LOC B-LOC -, O O -Austria B-LOC B-LOC -1996 O O -Results O O -of O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -two O O -b O O -event O O -on O O -Saturday O O -: O O -1 O O -United B-LOC B-ORG -States I-LOC I-ORG -III O I-ORG -( O O -Brian B-PER B-PER -Shi I-PER I-PER -, O O -Randy B-PER B-PER -Jones I-PER I-PER -) O O -one O O -minute O O -45 O O -seconds O O -( O O -52 O O -/ O O -53 O O -) O O -2 O O -Italy B-LOC B-ORG -I O I-ORG -( O O -G B-PER B-PER -Hu I-PER I-PER -, O O -Antonio B-PER B-PER -Ta I-PER I-PER -) O O -1 O O -( O O -52 O O -/ O O -53 O O -) O O -3 O O -Canada B-LOC B-ORG -I O I-ORG -( O O -Pierre B-PER B-PER -Lu I-PER I-PER -, O O -Dave B-PER B-PER -Mac I-PER I-PER -) O O -1 O O -( O O -52 O O -/ O O -53 O O -) O O -4 O O -German B-MISC B-ORG -I O I-ORG -( O O -Sep B-PER B-PER -Do I-PER I-PER -, O O -Thomas B-PER B-PER -United B-LOC B-ORG -States I-LOC I-ORG -I O I-ORG -( O O -Jim B-PER B-PER -Herb I-PER I-PER -, O O -Garrett B-PER B-PER -Hi I-PER B-PER -) O O -1 O O -( O O -53 O O -/ O O -53 O O -) O O -and O O -Austria B-LOC B-ORG -III O I-ORG -( O O -Han B-PER B-PER -Con I-PER I-PER -, O O -Georg B-PER B-PER -Ku I-PER I-PER -) O O -1 O O -( O O -53 O O -53 O O -CR O O -- O O -W O B-PER -MA O O -SE O O -R O O -TO O O -K B-LOC B-LOC -. O O -K B-LOC B-LOC -, O O -India B-LOC B-LOC -1996 O O -South B-LOC B-LOC -Africa I-LOC I-LOC -' O O -trip O O -to O O -Ka B-LOC B-LOC -for O O -the O O -third O O -test O O -against O O -India B-LOC B-LOC -has O O -given O O -former O O -England B-LOC B-LOC -test O O -cricketer O O -Bob B-PER B-PER -W I-PER I-PER -the O O -chance O O -of O O -a O O -sentiment O O -return O O -to O O -his O O -birthplace O O -. O O -W B-PER B-PER -was O O -born O O -in O O -the O O -northern O O -city O O -of O O -Ka B-LOC B-LOC -when O O -his O O -father O O -worked O O -there O O -for O O -an O O -insurance O O -com O O -and O O -was O O -himself O O -an O O -active O O -cricketer O O -. O O -" O O -It O O -' O O -been O O -a O O -sentiment O O -journey O O -. O O -A O O -visit O O -to O O -India B-LOC B-LOC -is O O -always O O -an O O -intriguing O O -experience O O -, O O -" O O -W B-PER B-PER -, O O -now O O -the O O -South B-MISC B-MISC -African I-MISC I-MISC -coach O O -, O O -said O O -on O O -Saturday O O -. O O -W B-PER B-PER -, O O -48 O O -, O O -played O O -19 O O -tests O O -for O O -England B-LOC B-LOC -between O O -1975 O O -and O O -1981 O O -. O O -His O O -first O O -cricket O O -so O O -to O O -India B-LOC B-LOC -was O O -as O O -a O O -member O O -of O O -Tony B-PER B-PER -G I-PER I-PER -' O O -England B-LOC B-LOC -side O O -in O O -1976 O O -. O O -His O O -father O O -Clarence B-PER B-PER -W I-PER I-PER -represented O O -United B-ORG B-LOC -Province I-ORG I-LOC -, O O -now O O -renamed O O -Uttar B-LOC B-LOC -Pradesh I-LOC I-LOC -, O O -in O O -India B-LOC B-LOC -' O O -Ra B-MISC B-MISC -Trophy I-MISC I-MISC -national O O -championship O O -and O O -captained O O -the O O -state O O -during O O -1949 O O -. O O -Now O O -aged O O -86 O O -, O O -W B-PER B-PER -senior O O -lives O O -with O O -his O O -son O O -in O O -Cape B-LOC B-LOC -Town I-LOC I-LOC -. O O -W B-PER B-PER -' O O -memories O O -of O O -Ka B-LOC B-LOC -are O O -few O O -and O O -blurred O O -. O O -" O O -I O O -do O O -n O O -remember O O -much O O -of O O -the O O -place O O -, O O -" O O -he O O -said O O -. O O -" O O -I O O -came O O -here O O -on O O -zero O O -and O O -left O O -at O O -three O O -( O O -aged O O -three O O -) O O -when O O -my O O -father O O -was O O -transferred O O -to O O -Calcutta B-LOC B-LOC -where O O -I O O -spent O O -another O O -four O O -and O O -half O O -years O O -. O O -" O O -But O O -I O O -do O O -remember O O -we O O -had O O -a O O -co O O -snake O O -in O O -the O O -basement O O -of O O -our O O -house O O -. O O -Also O O -that O O -my O O -father O O -bought O O -a O O -bicycle O O -and O O -when O O -we O O -rode O O -over O O -a O O -hose O O -pipe O O -it O O -broke O O -into O O -two O O -. O O -" O O -W B-PER B-PER -said O O -the O O -hospital O O -where O O -he O O -was O O -born O O -is O O -close O O -to O O -the O O -stadium O O -where O O -the O O -India B-LOC B-MISC -Africa I-MISC I-MISC -test O O -will O O -be O O -played O O -. O O -F O O -SK O B-MISC -C I-MISC I-MISC -A I-MISC O -R O O -. O O -T B-LOC B-LOC -, O O -France B-LOC B-LOC -1996 O O -Results O O -of O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -freestyle O O -skiing O O -aerial O O -competition O O -on O O -Saturday O O -: O O -Men O O -: O O -1 O O -Alexis B-PER B-PER -Blanc I-PER I-PER -( O O -France B-LOC B-LOC -) O O -238 O O -points O O -2 O O -Se B-PER B-PER -F I-PER I-PER -( O O -France B-LOC B-LOC -) O O -223 O O -3 O O -Jeff B-PER B-PER -Bean I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -209 O O -4 O O -Eric B-PER B-PER -Berg I-PER I-PER -( O O -U B-LOC B-LOC -) O O -207 O O -5 O O -Christian B-PER B-PER -R I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -204 O O -6 O O -Alexandre B-PER B-PER -Mikhail I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -202 O O -7 O O -Al B-PER B-PER -Vale I-PER I-PER -( O O -Czech B-LOC B-LOC -Republic I-LOC I-LOC -) O O -194 O O -8 O O -Andy B-PER B-PER -Cap I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -193 O O -9 O O -K B-LOC B-LOC -, O O -Finland B-LOC B-LOC -1996 O O -Leading O O -results O O -in O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -high O O -hill O O -( O O -120 O O -) O O -ski O O -jumping O O -event O O -on O O -Saturday O O -: O O -1 O O -Ta B-PER B-PER -Ok I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -303 O O -points O O -( O O -first O O -jump O O -145 O O -/ O O -second O O -jump O O -158 O O -) O O -2 O O -Ka B-PER B-PER -Fun I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -295 O O -( O O -151 O O -/ O O -143 O O -) O O -3 O O -Andreas B-PER B-PER -Goldberg I-PER I-PER -( O O -Austria B-LOC B-LOC -) O O -27 O O -( O O -144 O O -/ O O -130 O O -) O O -4 O O -Diet B-PER B-PER -Thom I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -26 O O -( O O -141 O O -/ O O -124 O O -) O O -5 O O -Ari B-PER B-PER -Nik I-PER I-PER -T B-LOC B-LOC -DE I-LOC I-LOC -, O O -Bali B-LOC B-LOC -1996 O O -Results O O -of O O -semifinals O O -at O O -the O O -World B-MISC B-MISC -Grand B-MISC I-MISC -Prix I-MISC I-MISC -finals O O -on O O -Saturday O O -: O O -Men O O -' O O -singles O O -Fun B-PER B-PER -Per I-PER I-PER -( O O -Taiwan B-LOC B-LOC -) O O -beat O O -In B-PER B-PER -W I-PER I-PER -( O O -Indonesia B-LOC B-LOC -) O O -15 O O -15 O O -Sun B-PER B-PER -Jun I-PER I-PER -( O O -China B-LOC B-LOC -) O O -beat O O -Allan B-PER B-PER -Bud I-PER I-PER -Ku I-PER I-PER -( O O -Indonesia B-LOC B-LOC -) O O -15 O O -15 O O -Women O O -' O O -singles O O -Su B-PER B-PER -Susan I-PER I-PER -( O O -Indonesia B-LOC B-LOC -) O O -beat O O -Cam B-PER B-PER -Martin I-PER I-PER -( O O -Denmark B-LOC B-LOC -) O O -11 O O -11 O O -Ye B-PER B-PER -Zhao I-PER I-PER -( O O -China B-LOC B-LOC -) O O -beat O O -Gong B-PER B-PER -Z I-PER I-PER -( O O -China B-LOC B-LOC -) O O -11 O O -11 O O -SP O O -SK O O -CH B-LOC B-LOC -, O O -South B-LOC B-LOC -Korea I-LOC I-LOC -1996 O O -Results O O -on O O -the O O -first O O -day O O -of O O -the O O -World B-MISC B-MISC -Cup I-MISC I-MISC -speed O O -skating O O -races O O -here O O -on O O -Saturday O O -. O O -Men O O -' O O -500 O O -metres O O -first O O -round O O -: O O -1 O O -. O O -Ho B-PER B-PER -Man I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -37 O O -seconds O O -; O O -2 O O -. O O -J B-PER B-PER -Sung I-PER I-PER -( O O -South B-LOC B-LOC -Korea I-LOC I-LOC -) O O -37 O O -; O O -3 O O -. O O -G B-PER B-PER -N I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -37 O O -; O O -4 O O -. O O -Shi B-PER B-PER -Hi I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -37 O O -; O O -5 O O -. O O -Sergey B-PER B-PER -K I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -37 O O -; O O -6 O O -. O O -Ya B-PER B-PER -Hi I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -37 O O -; O O -7 O O -. O O -Casey B-PER B-PER -Fi I-PER I-PER -( O O -US B-LOC B-LOC -) O O -37 O O -; O O -8 O O -. O O -S B-PER B-PER -Bo I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -38 O O -; O O -9 O O -. O O -Kim B-PER B-PER -Yo I-PER I-PER -( O O -South B-LOC B-LOC -Korea I-LOC I-LOC -) O O -38 O O -; O O -10 O O -. O O -In B-PER B-PER -Jun I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -38 O O -. O O -Women O O -' O O -500 O O -metres O O -first O O -round O O -: O O -1 O O -. O O -Xu B-PER B-PER -R I-PER I-PER -( O O -China B-LOC B-LOC -) O O -40 O O -; O O -2 O O -. O O -S B-PER B-PER -J I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -41 O O -; O O -3 O O -. O O -Franz B-PER B-PER -Sc I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -41 O O -; O O -4 O O -. O O -Ok B-PER B-PER -Tom I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -41 O O -; O O -5 O O -. O O -Shi B-PER B-PER -K B-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -41 O O -; O O -6 O O -. O O -Marianne B-PER B-PER -Tim I-PER I-PER -( O O -Netherlands B-LOC B-LOC -) O O -41 O O -; O O -7 O O -. O O -Jin B-PER B-PER -Hu I-PER I-PER -( O O -China B-LOC B-LOC -) O O -41 O O -; O O -8 O O -. O O -Al B-PER B-PER -Ko I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -41 O O -; O O -9 O O -. O O -Chris B-PER B-PER -W I-PER I-PER -( O O -US B-LOC B-LOC -) O O -41 O O -; O O -10 O O -. O O -An B-PER B-PER -Ba I-PER I-PER -( O O -Germany B-LOC B-LOC -) O O -41 O O -. O O -Men O O -' O O -1 O O -metres O O -first O O -round O O -: O O -1 O O -S B-PER B-PER -Bo I-PER I-PER -( O O -Canada B-LOC B-LOC -) O O -1 O O -minute O O -16 O O -seconds O O -2 O O -Sergey B-PER B-PER -K I-PER I-PER -( O O -Russia B-LOC B-LOC -) O O -1 O O -3 O O -Jan B-PER B-PER -Bo I-PER I-PER -( O O -Netherlands B-LOC B-LOC -) O O -1 O O -4 O O -G B-PER B-PER -N I-PER I-PER -( O O -Norway B-LOC B-LOC -) O O -1 O O -5 O O -Lee B-PER B-PER -K I-PER I-PER -( O O -South B-LOC B-LOC -Korea I-LOC I-LOC -) O O -1 O O -6 O O -In B-PER B-PER -Jun I-PER I-PER -( O O -Japan B-LOC B-LOC -) O O -1 O O -7 O O -Gerard B-PER B-PER -Van I-PER I-PER -V I-PER I-PER -( O O -Netherlands B-LOC B-LOC -) O O -1 O O -8 O O -Kim B-PER B-PER -Yo I-PER I-PER -W B-LOC B-LOC -, O O -British B-LOC B-LOC -Columbia I-LOC I-LOC -1996 O O -World B-MISC B-MISC -Cup I-MISC I-MISC -ski O O -officials O O -hope O O -to O O -be O O -able O O -to O O -get O O -in O O -at O O -least O O -one O O -men O O -' O O -downhill O O -training O O -run O O -on O O -Saturday O O -in O O -an O O -effort O O -to O O -salvage O O -the O O -weekend O O -racing O O -programme O O -. O O -For O O -the O O -third O O -consecutive O O -day O O -, O O -Friday O O -' O O -scheduled O O -training O O -runs O O -were O O -cancelled O O -due O O -to O O -heavy O O -wet O O -snow O O -and O O -fog O O -on O O -W B-LOC B-LOC -Mountain I-LOC I-LOC -, O O -leaving O O -the O O -scheduled O O -World B-MISC B-MISC -Cup I-MISC I-MISC -events O O -in O O -j O O -. O O -Rules O O -call O O -for O O -at O O -least O O -one O O -training O O -run O O -to O O -be O O -completed O O -before O O -a O O -World B-MISC B-MISC -Cup I-MISC I-MISC -downhill O O -race O O -can O O -be O O -staged O O -. O O -Organ O O -hope O O -to O O -get O O -that O O -run O O -in O O -on O O -Saturday O O -morning O O -, O O -conditions O O -permitting O O -, O O -and O O -stage O O -the O O -race O O -later O O -in O O -the O O -day O O -or O O -on O O -Sunday O O -. O O -" O O -There O O -was O O -no O O -possibility O O -today O O -to O O -make O O -a O O -training O O -run O O -, O O -" O O -said O O -Bern B-PER B-PER -Z I-PER I-PER -, O O -the O O -Canadian B-MISC B-MISC -men O O -' O O -national O O -coach O O -, O O -citing O O -too O O -much O O -new O O -snow O O -and O O -poor O O -visibility O O -. O O -If O O -organise O O -are O O -forced O O -to O O -run O O -the O O -downhill O O -on O O -Sunday O O -, O O -the O O -super O O -slalom O O -originally O O -scheduled O O -for O O -Sunday O O -would O O -likely O O -be O O -abandoned O O -. O O -S O O -- O O -L O O -SC B-MISC B-MISC -PR O I-MISC -D O I-MISC -SC O O -. O O -G B-LOC B-LOC -1996 O O -Leading O O -goals O O -in O O -the O O -Scottish B-MISC B-MISC -premier O I-MISC -division O O -after O O -Saturday O O -' O O -matches O O -: O O -10 O O -- O O -Billy B-PER B-PER -Dodd I-PER I-PER -( O O -Aberdeen B-ORG B-ORG -) O O -, O O -Pierre B-PER B-PER -Van I-PER I-PER -Ho I-PER I-PER -( O O -Celtic B-ORG B-ORG -) O O -9 O O -- O O -Paul B-PER B-PER -Gas I-PER I-PER -( O O -Rangers B-ORG B-ORG -) O O -7 O O -- O O -Paul B-PER B-PER -Wright I-PER I-PER -( O O -Ki B-ORG B-ORG -) O O -, O O -Ally B-PER B-PER -M I-PER I-PER -( O O -Rangers B-ORG B-ORG -) O O -6 O O -- O O -Andreas B-PER B-PER -Thom I-PER I-PER -( O O -Celtic B-ORG B-ORG -) O O -, O O -Dean B-PER B-PER -Wind I-PER I-PER -( O O -Aberdeen B-ORG B-ORG -) O O -, O O -Brian B-PER B-PER -Lau I-PER I-PER -( O O -Rangers B-ORG B-ORG -) O O -, O O -Darren B-PER B-PER -Jackson I-PER I-PER -( O O -Hi B-ORG B-ORG -) O O -5 O O -- O O -Peter B-PER B-PER -van I-PER I-PER -V I-PER I-PER -( O O -Rangers B-ORG B-ORG -) O O -, O O -Gerry B-PER B-PER -B I-PER I-PER -( O O -Du B-ORG B-ORG -) O O -, O O -Colin B-PER B-PER -S O O -- O O -L O O -E B-MISC B-MISC -GO O O -. O O -L B-LOC B-LOC -1996 O O -Leading O O -goals O O -in O O -the O O -English B-MISC B-MISC -premier O O -league O O -after O O -Saturday O O -' O O -matches O O -: O O -13 O O -- O O -Ian B-PER B-PER -Wright I-PER I-PER -( O O -Arsenal B-ORG B-ORG -) O O -9 O O -- O O -F B-PER B-PER -Ra I-PER I-PER -( O O -Middlesbrough B-ORG B-ORG -) O O -, O O -Alan B-PER B-PER -Shea I-PER I-PER -( O O -Newcastle B-ORG B-ORG -) O O -8 O O -- O O -Matthew B-PER B-PER -Le I-PER I-PER -T I-PER I-PER -( O O -Southampton B-ORG B-ORG -) O O -, O O -Dwight B-PER B-PER -York I-PER I-PER -( O O -Aston B-ORG B-ORG -Villa I-ORG B-ORG -) O O -, O O -Les B-PER B-PER -Ferdinand I-PER I-PER -( O O -Newcastle B-ORG B-ORG -) O O -, O O -E B-PER B-PER -E I-PER I-PER -( O O -Wimbledon B-ORG B-LOC -) O O -, O O -G B-PER B-PER -Via I-PER I-PER -( O O -Chelsea B-ORG B-ORG -) O O -7 O O -- O O -Robbie B-PER B-PER -Earle I-PER I-PER -( O O -Wimbledon B-ORG B-LOC -) O O -, O O -Les B-PER B-PER -Ferdinand I-PER I-PER -( O O -Newcastle B-ORG B-ORG -) O O -6 O O -- O O -Marcus B-PER B-PER -Gay I-PER I-PER -( O O -Wimbledon B-ORG B-LOC -) O O -, O O -Gary B-PER B-PER -Speed I-PER I-PER -( O O -Everton B-ORG B-ORG -) O O -, O O -Chris B-PER B-PER -Sutton I-PER B-PER -( O O -Blackburn B-ORG B-ORG -) O O -5 O O -S O O -- O O -NO B-MISC B-LOC -I B-MISC I-LOC -PR O O -D O O -R O O -/ O O -ST O O -. O O -L B-LOC B-LOC -1996 O O -Results O O -of O O -Northern B-LOC B-LOC -Ireland I-LOC I-LOC -premier O O -division O O -matches O O -on O O -Saturday O O -: O O -A B-ORG B-ORG -0 O O -Crusaders B-ORG B-ORG -0 O O -Clifton B-ORG B-ORG -1 O O -Port B-ORG B-ORG -1 O O -Glen B-ORG B-ORG -2 O O -Lin B-ORG B-ORG -1 O O -Glen B-ORG B-ORG -1 O O -Cole B-ORG B-ORG -0 O O -Standing O O -( O O -ta O O -- O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -Cole B-ORG B-ORG -10 O O -7 O O -1 O O -2 O O -18 O O -11 O O -22 O O -Lin B-ORG B-ORG -10 O O -4 O O -3 O O -3 O O -13 O O -10 O O -15 O O -Crusaders B-ORG B-ORG -10 O O -3 O O -4 O O -3 O O -11 O O -9 O O -13 O O -Glen B-ORG B-ORG -10 O O -3 O O -4 O O -3 O O -15 O O -14 O O -13 O O -Glen B-ORG B-ORG -10 O O -3 O O -3 O O -4 O O -18 O O -18 O O -12 O O -Port B-ORG B-ORG -9 O O -3 O O -3 O O -3 O O -11 O O -12 O O -12 O O -L B-LOC B-LOC -1996 O O -Results O O -of O O -British B-MISC B-MISC -rugby O O -union O O -matches O O -on O O -Saturday O O -: O O -Pi B-MISC B-MISC -Cup I-MISC I-MISC -fourth O O -round O O -Reading B-ORG B-ORG -50 O O -W B-ORG B-ORG -3 O O -English B-MISC B-MISC -division O O -one O O -Bath B-ORG B-ORG -35 O O -Ha B-ORG B-ORG -20 O O -Gloucester B-ORG B-ORG -29 O O -London B-ORG B-ORG -Irish I-ORG I-ORG -19 O O -Or B-ORG B-ORG -22 O O -West B-ORG B-ORG -Hart I-ORG I-ORG -15 O O -Was B-ORG B-ORG -15 O O -Bristol B-ORG B-ORG -13 O O -Welsh B-MISC B-MISC -division O O -one O O -C B-ORG B-ORG -20 O O -Cardiff B-ORG B-ORG -34 O O -L B-ORG B-ORG -97 O O -New B-ORG B-ORG -10 O O -Newport B-ORG B-ORG -45 O O -Du B-ORG B-ORG -22 O O -Pont B-ORG B-ORG -53 O O -Bridge B-ORG B-ORG -9 O O -Swansea B-ORG B-ORG -49 O O -N B-ORG B-ORG -10 O O -T B-ORG B-ORG -13 O O -E B-ORG B-ORG -Vale I-ORG I-ORG -17 O O -Scottish B-MISC B-MISC -division O O -one O O -Borough B-ORG B-ORG -31 O O -Watson B-ORG B-ORG -35 O O -S O O -- O O -SC B-ORG B-MISC -G B-LOC B-LOC -1996 O O -Su O O -of O O -Scottish B-MISC B-MISC -premier O O -division O O -matches O O -played O O -on O O -Saturday O O -: O O -Du B-ORG B-ORG -2 O O -( O O -Mill B-PER B-PER -43 O O -, O O -46 O O -penalty O O -) O O -Aberdeen B-ORG B-ORG -3 O O -( O O -Miller B-PER B-PER -10 O O -, O O -Row B-PER B-PER -55 O O -, O O -Wind B-PER O -78 O O -) O O -. O O -Half O O -1 O O -. O O -Attendance O O -: O O -5 O O -Hearts B-ORG B-ORG -0 O O -Rai B-ORG B-ORG -0 O O -. O O -10 O O -Ki B-ORG B-ORG -0 O O -Dundee B-ORG B-ORG -United I-ORG I-ORG -2 O O -( O O -Olaf B-PER B-PER -22 O O -, O O -51 O O -) O O -. O O -0 O O -. O O -5 O O -Mother B-ORG B-ORG -2 O O -( O O -Davies B-PER B-PER -39 O O -, O O -Ross B-PER B-PER -89 O O -) O O -Celtic B-ORG B-ORG -1 O O -( O O -Hay B-PER B-PER -83 O O -) O O -. O O -1 O O -. O O -11 O O -Rangers B-ORG B-ORG -4 O O -( O O -Ferguson B-PER B-PER -34 O O -, O O -M B-PER B-PER -71 O O -74 O O -, O O -Lau B-PER B-PER -83 O O -) O O -Hi B-ORG B-ORG -3 O O -( O O -Wright B-PER B-PER -21 O O -, O O -Jackson B-PER B-PER -41 O O -, O O -M B-PER B-PER -86 O O -) O O -. O O -1 O O -. O O -48 O O -. O O -R B-ORG B-ORG -UN I-ORG I-ORG -- O O -R B-PER O -CA O B-PER -W O O -UP O O -O O O -. O O -L B-LOC B-LOC -1996 O O -David B-PER B-PER -Camp I-PER I-PER -will O O -consider O O -offers O O -to O O -play O O -club O O -rugby O O -in O O -England B-LOC B-LOC -but O O -looks O O -more O O -likely O O -to O O -spend O O -the O O -next O O -year O O -chasing O O -business O O -opportunities O O -in O O -Australia B-LOC B-LOC -. O O -The O O -34 O O -winger O O -played O O -his O O -final O O -game O O -in O O -a O O -Wall B-MISC B-ORG -jersey O O -on O O -Saturday O O -but O O -is O O -currently O O -a O O -target O O -for O O -clubs O O -eager O O -to O O -match O O -London B-LOC B-LOC -side O O -Sara B-ORG B-ORG -who O O -have O O -already O O -snapped O O -up O O -Franco B-PER B-PER -Pie I-PER I-PER -, O O -Michael B-PER B-PER -L I-PER I-PER -and O O -Philippe B-PER B-PER -Se I-PER I-PER -. O O -" O O -If O O -the O O -opportunity O O -is O O -there O O -I O O -' O O -obviously O O -think O O -about O O -it O O -but O O -the O O -thing O O -that O O -holds O O -me O O -back O O -is O O -business O O -, O O -" O O -said O O -Camp B-PER B-PER -. O O -" O O -I O O -' O O -like O O -to O O -come O O -over O O -but O O -there O O -are O O -a O O -lot O O -of O O -things O O -happening O O -at O O -home O O -. O O -I O O -' O O -also O O -got O O -a O O -contract O O -to O O -play O O -for O O -New B-LOC B-ORG -South I-LOC I-ORG -Wales I-LOC I-ORG -in O O -the O O -Super B-MISC O -12 I-MISC O -next O O -year O O -. O O -" O O -Former O O -Wall B-MISC B-ORG -captain O O -Nick B-PER B-PER -Far I-PER I-PER -believes O O -Camp B-PER B-PER -may O O -yet O O -be O O -tempted O O -to O O -England B-LOC B-LOC -. O O -" O O -I O O -' O O -sure O O -there O O -are O O -a O O -few O O -people O O -in O O -England B-LOC B-LOC -who O O -' O O -be O O -delighted O O -to O O -have O O -David B-PER B-PER -Camp I-PER I-PER -in O O -their O O -club O O -' O O -jersey O O -, O O -" O O -he O O -said O O -. O O -S O O -- O O -E B-MISC B-MISC -PR O O -L O O -S O O -. O O -L B-LOC B-LOC -1996 O O -Su O O -of O O -English B-MISC B-MISC -premier O O -le O O -matches O O -on O O -Saturday O O -: O O -Arsenal B-ORG B-ORG -2 O O -( O O -Adams B-PER B-PER -45 O O -, O O -V B-PER B-PER -90 O O -) O O -Derby B-ORG B-ORG -2 O O -( O O -St B-PER B-PER -62 O O -, O O -Powell B-PER B-PER -71 O O -) O O -. O O -Half O O -1 O O -. O O -Attendance O O -: O O -38 O O -Chelsea B-ORG B-ORG -2 O O -( O O -Z B-PER B-PER -12 O O -, O O -Via B-PER B-PER -55 O O -) O O -Everton B-ORG B-ORG -2 O O -( O O -Branch B-PER B-PER -17 O O -, O O -Ka B-PER B-PER -28 O O -) O O -. O O -1 O O -. O O -28 O O -Coventry B-ORG B-ORG -1 O O -( O O -W B-PER B-PER -60 O O -) O O -Tottenham B-ORG B-ORG -2 O O -( O O -She B-PER B-PER -27 O O -, O O -Sin B-ORG B-PER -75 O O -) O O -. O O -0 O O -. O O -19 O O -Leicester B-ORG B-ORG -1 O O -( O O -Marshall B-PER B-PER -78 O O -) O O -Blackburn B-ORG B-ORG -1 O O -( O O -Sutton B-PER B-PER -34 O O -) O O -. O O -0 O O -. O O -19 O O -Liverpool B-ORG B-ORG -0 O O -Sheffield B-ORG B-ORG -Wednesday I-ORG I-ORG -1 O O -( O O -W B-PER B-PER -22 O O -) O O -. O O -0 O O -. O O -39 O O -Middlesbrough B-ORG B-ORG -0 O O -Leeds B-ORG B-ORG -0 O O -. O O -30 O O -Southampton B-ORG B-ORG -0 O O -Aston B-ORG B-ORG -Villa I-ORG I-ORG -1 O O -( O O -Townsend B-PER B-PER -34 O O -) O O -. O O -0 O O -. O O -15 O O -Sunderland B-ORG B-ORG -1 O O -( O O -Melville B-PER B-PER -83 O O -) O O -Wimbledon B-ORG B-LOC -3 O O -( O O -E B-PER B-PER -8 O O -, O O -29 O O -, O O -Hold B-PER B-PER -89 O O -) O O -. O O -0 O O -. O O -19 O O -. O O -S O O -- O O -SC B-MISC B-MISC -L O O -ST O O -. O O -G B-LOC B-LOC -1996 O O -Scottish B-MISC B-MISC -league O O -standings O O -after O O -Saturday O O -' O O -matches O O -( O O -ta O O -- O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -Premier O O -division O O -Rangers B-ORG B-ORG -14 O O -11 O O -2 O O -1 O O -35 O O -12 O O -35 O O -Celtic B-ORG B-ORG -14 O O -8 O O -3 O O -3 O O -32 O O -15 O O -27 O O -Aberdeen B-ORG B-ORG -15 O O -7 O O -4 O O -4 O O -28 O O -19 O O -25 O O -Hearts B-ORG B-ORG -15 O O -5 O O -6 O O -4 O O -18 O O -19 O O -21 O O -Hi B-ORG B-ORG -15 O O -5 O O -3 O O -7 O O -16 O O -25 O O -18 O O -Dundee B-ORG B-ORG -United I-ORG I-ORG -15 O O -4 O O -5 O O -6 O O -17 O O -17 O O -17 O O -Mother B-ORG B-ORG -15 O O -4 O O -5 O O -6 O O -17 O O -23 O O -17 O O -Du B-ORG B-ORG -14 O O -4 O O -5 O O -5 O O -19 O O -27 O O -17 O O -Rai B-ORG B-ORG -15 O O -3 O O -3 O O -9 O O -14 O O -27 O O -12 O O -Ki B-ORG B-ORG -L B-LOC B-LOC -1996 O O -Standing O O -in O O -English B-MISC B-MISC -league O O -soccer O O -after O O -Saturday O O -' O O -matches O O -( O O -ta O O -- O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -Premier O B-MISC -league O I-MISC -Arsenal B-ORG B-ORG -17 O O -10 O O -5 O O -2 O O -34 O O -16 O O -35 O O -Wimbledon B-ORG B-ORG -16 O O -9 O O -4 O O -3 O O -29 O O -17 O O -31 O O -Liverpool B-ORG B-ORG -16 O O -9 O O -4 O O -3 O O -26 O O -14 O O -31 O O -Aston B-ORG B-ORG -Villa I-ORG I-ORG -17 O O -9 O O -3 O O -5 O O -22 O O -15 O O -30 O O -Newcastle B-ORG B-ORG -15 O O -9 O O -2 O O -4 O O -26 O O -17 O O -29 O O -Manchester B-ORG B-ORG -United I-ORG I-ORG -15 O O -7 O O -5 O O -3 O O -29 O O -22 O O -26 O O -Chelsea B-ORG B-ORG -16 O O -6 O O -7 O O -3 O O -25 O O -23 O O -25 O O -Everton B-ORG B-ORG -16 O O -6 O O -6 O O -4 O O -25 O O -20 O O -24 O O -Sheffield B-ORG B-ORG -Wednesday I-ORG I-ORG -16 O O -6 O O -6 O O -4 O O -17 O O -18 O O -24 O O -Tottenham B-ORG B-ORG -16 O O -7 O O -2 O O -7 O O -17 O O -17 O O -23 O O -L B-LOC B-LOC -1996 O O -French B-MISC B-MISC -Patrick B-PER B-PER -V I-PER I-PER -blasted O O -a O O -last O O -goal O O -to O O -salvage O O -a O O -2 O O -draw O O -for O O -English B-MISC B-MISC -premier O O -league O O -leaders O O -Arsenal B-ORG B-ORG -at O O -home O O -to O O -Derby B-ORG B-ORG -on O O -Saturday O O -. O O -The O O -London B-LOC B-LOC -club O O -had O O -been O O -rocked O O -by O O -a O O -two O O -burst O O -from O O -forwards O O -Dean B-PER B-PER -St I-PER I-PER -and O O -Darryl B-PER B-PER -Powell I-PER I-PER -in O O -the O O -62 O O -and O O -71 O O -minutes O O -which O O -overturned O O -Arsenal B-ORG B-ORG -' O O -1 O O -lead O O -from O O -a O O -diving O O -header O O -by O O -captain O O -Tony B-PER B-PER -Adams I-PER I-PER -on O O -the O O -stroke O O -of O O -halftime O O -. O O -Liverpool B-ORG B-ORG -suffered O O -an O O -upset O O -first O O -home O O -league O O -defeat O O -of O O -the O O -season O O -, O O -beaten O O -1 O O -by O O -a O O -Guy B-PER B-PER -W I-PER I-PER -goal O O -for O O -Sheffield B-ORG B-ORG -Wednesday I-ORG I-ORG -. O O -Wimbledon B-ORG B-ORG -leap O O -over O O -Liverpool B-ORG B-ORG -into O O -second O O -place O O -by O O -winning O O -3 O O -at O O -Sunderland B-ORG B-ORG -to O O -extend O O -their O O -unbeaten O O -league O O -and O O -cup O O -run O O -to O O -18 O O -games O O -. O O -Two O O -strikes O O -by O O -E B-PER B-PER -E I-PER I-PER -in O O -the O O -first O O -half O O -and O O -a O O -late O O -goal O O -from O O -fellow O O -forward O O -Dean B-PER B-PER -Hold I-PER I-PER -secured O O -victory O O -for O O -Wimbledon B-ORG B-ORG -, O O -who O O -trail O O -pace O O -Arsenal B-ORG B-ORG -by O O -four O O -points O O -. O O -S O O -- O O -SC B-MISC B-MISC -L O O -AND O O -C B-MISC O -R O O -. O O -G B-LOC B-LOC -1996 O O -Results O O -of O O -Scottish B-MISC B-MISC -league O O -and O O -cup O O -matches O O -played O O -on O O -Saturday O O -: O O -Premier O O -division O O -Du B-ORG B-ORG -2 O O -Aberdeen B-ORG B-ORG -3 O O -Hearts B-ORG B-ORG -0 O O -Rai B-ORG B-ORG -0 O O -Ki B-ORG B-ORG -0 O O -Dundee B-ORG B-ORG -United I-ORG I-ORG -2 O O -Mother B-ORG B-ORG -2 O O -Celtic B-ORG B-ORG -1 O O -Rangers B-ORG B-ORG -4 O O -Hi B-ORG B-ORG -3 O O -Division O O -one O O -Dundee B-ORG B-ORG -2 O O -F B-ORG B-ORG -0 O O -Green B-ORG B-ORG -Morton I-ORG I-ORG -0 O O -St B-ORG B-ORG -Johnstone I-ORG I-ORG -2 O O -Post O O -: O O -Air B-ORG B-ORG -v O O -Clyde B-ORG B-ORG -( O O -to O O -Wednesday O O -) O O -, O O -East B-ORG B-ORG -Fife I-ORG B-ORG -v O O -Part B-ORG B-ORG -, O O -Stirling B-ORG B-ORG -v O O -St B-ORG B-ORG -Mir I-ORG I-ORG -( O O -to O O -Tuesday O O -) O O -Division O O -two O O -Livingston B-ORG B-ORG -2 O O -St B-ORG B-ORG -1 O O -St B-ORG B-ORG -0 O O -B B-ORG B-ORG -1 O O -Division O O -three O O -Ross B-ORG B-ORG -County I-ORG I-ORG -4 O O -Mont B-ORG B-ORG -L B-LOC B-LOC -1996 O O -Results O O -of O O -English B-MISC B-MISC -league O O -and O O -cup O O -matches O O -on O O -Saturday O O -: O O -Premier O B-MISC -league O I-MISC -Arsenal B-ORG B-ORG -2 O O -Derby B-ORG B-ORG -2 O O -Chelsea B-ORG B-ORG -2 O O -Everton B-ORG B-ORG -2 O O -Coventry B-ORG B-ORG -1 O O -Tottenham B-ORG B-ORG -2 O O -Leicester B-ORG B-ORG -1 O O -Blackburn B-ORG B-ORG -1 O O -Liverpool B-ORG B-ORG -0 O O -Sheffield B-ORG B-ORG -Wednesday I-ORG I-ORG -1 O O -Middlesbrough B-ORG B-ORG -0 O O -Leeds B-ORG B-ORG -0 O O -Southampton B-ORG B-ORG -0 O O -Aston B-ORG B-ORG -Villa I-ORG I-ORG -1 O O -Sunderland B-ORG B-ORG -1 O O -Wimbledon B-ORG B-ORG -3 O O -Division O O -one O O -Bar B-ORG B-ORG -3 O O -South B-ORG B-ORG -0 O O -Birmingham B-ORG B-ORG -0 O O -Grimsby B-ORG B-ORG -0 O O -Charlton B-ORG B-ORG -2 O O -Swindon B-ORG B-ORG -0 O O -Crystal B-ORG B-ORG -Palace I-ORG I-ORG -2 O O -Oxford B-ORG B-ORG -2 O O -Huddersfield B-ORG B-ORG -2 O O -Norwich B-ORG B-ORG -0 O O -Ipswich B-ORG B-ORG -0 O O -Wolverhampton B-ORG B-ORG -0 O O -Manchester B-ORG B-ORG -City I-ORG I-ORG -3 O O -Bradford B-ORG B-ORG -2 O O -Oldham B-ORG B-ORG -0 O O -Queens B-ORG B-ORG -Park I-ORG I-ORG -Rangers I-ORG I-ORG -2 O O -Reading B-ORG B-ORG -0 O O -Port B-ORG B-ORG -Vale I-ORG I-ORG -1 O O -Sheffield B-ORG B-ORG -United I-ORG I-ORG -1 O O -Portsmouth B-ORG B-ORG -0 O O -Stoke B-ORG B-ORG -2 O O -T B-ORG B-ORG -0 O O -Playing O O -Sunday O O -: O O -West B-ORG B-ORG -B I-ORG I-ORG -v O O -Bolton B-ORG B-ORG -F B-ORG B-MISC -Challenge B-MISC I-MISC -Cup I-MISC I-MISC -second O O -L B-LOC B-LOC -1996 O O -Australia B-LOC B-LOC -bad O O -farewell O O -to O O -David B-PER B-PER -Camp I-PER I-PER -in O O -spectacular O O -fashion O O -by O O -overwhelming O O -the O O -Bar B-ORG B-ORG -39 O O -in O O -the O O -final O O -match O O -of O O -their O O -European B-MISC B-MISC -tour O O -at O O -T B-LOC B-LOC -on O O -Saturday O O -. O O -The O O -Wall B-ORG B-ORG -ran O O -in O O -five O O -tries O O -with O O -Camp B-PER B-PER -, O O -who O O -has O O -retired O O -from O O -test O O -rugby O O -after O O -collecting O O -101 O O -caps O O -and O O -a O O -world O O -record O O -64 O O -tries O O -, O O -adding O O -one O O -last O O -touchdown O O -in O O -a O O -Wall B-MISC B-ORG -jersey O O -before O O -departing O O -the O O -international O O -game O O -. O O -The O O -Bar B-ORG B-ORG -included O O -14 O O -international O O -but O O -, O O -with O O -only O O -two O O -pre O O -practice O O -sessions O O -behind O O -them O O -, O O -proved O O -no O O -real O O -match O O -for O O -a O O -Wall B-MISC B-ORG -side O O -determined O O -to O O -finish O O -their O O -12 O O -tour O O -unbeaten O O -. O O -The O O -touring O O -team O O -were O O -27 O O -ahead O O -by O O -half O O -before O O -e O O -up O O -in O O -the O O -second O O -. O O -Full O O -Matthew B-PER B-PER -Burke I-PER I-PER -finished O O -with O O -a O O -personal O O -haul O O -of O O -24 O O -points O O -to O O -take O O -his O O -tour O O -aggregate O O -to O O -136 O O -. O O -R B-ORG B-ORG -UN I-ORG I-ORG -- O O -AU B-ORG B-LOC -B O O -BA B-LOC B-ORG -39 O O -. O O -L B-LOC B-LOC -1996 O O -Australia B-LOC B-LOC -beat O O -the O O -Bar B-ORG B-ORG -39 O O -( O O -halftime O O -27 O O -) O O -in O O -the O O -final O O -match O O -of O O -their O O -European B-MISC B-MISC -tour O O -on O O -Saturday O O -: O O -Score O O -: O O -Australia B-LOC B-LOC -- O O -Tri O O -: O O -Matthew B-PER B-PER -Burke I-PER I-PER -( O O -2 O O -) O O -, O O -Joe B-PER B-PER -R I-PER I-PER -, O O -David B-PER B-PER -Camp I-PER I-PER -, O O -Tim B-PER B-PER -Ho I-PER I-PER -. O O -Pen O O -: O O -Burke B-PER B-PER -( O O -2 O O -) O O -. O O -Con O O -: O O -Burke B-PER B-PER -( O O -4 O O -) O O -. O O -Bar O B-ORG -- O O -Tri O O -: O O -Alan B-PER B-PER -Bat I-PER I-PER -, O O -Scott B-PER B-PER -Quinn I-PER I-PER -. O O -Con O O -: O O -Rob B-PER B-PER -Andrew I-PER I-PER -. O O -GO O O -- O O -Z B-LOC B-MISC -O I-MISC I-MISC -T O O -R O O -SC O O -. O O -H B-LOC B-LOC -1996 O O -Leading O O -third O O -round O O -scores O O -in O O -the O O -Zimbabwe B-MISC B-MISC -Open I-MISC I-MISC -on O O -Saturday O O -( O O -South B-MISC B-MISC -African I-MISC I-MISC -unless O O -stated O O -) O O -: O O -201 O O -Mark B-PER B-PER -M I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -72 O O -61 O O -68 O O -205 O O -Des B-PER B-PER -Te I-PER I-PER -65 O O -67 O O -73 O O -206 O O -Nick B-PER B-PER -Price I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -68 O O -68 O O -70 O O -207 O O -Clinton B-PER B-PER -White I-PER I-PER -70 O O -70 O O -67 O O -, O O -Mark B-PER B-PER -C I-PER I-PER -( O O -Zimbabwe B-LOC B-LOC -) O O -69 O O -69 O O -69 O O -, O O -Justin B-PER B-PER -Ho I-PER I-PER -71 O O -65 O O -71 O O -209 O O -Steve B-PER B-PER -van I-PER I-PER -V I-PER I-PER -65 O O -69 O O -75 O O -210 O O -Brett B-PER B-PER -Li I-PER I-PER -75 O O -65 O O -70 O O -211 O O -Hugh B-PER B-PER -Bai I-PER I-PER -73 O O -67 O O -71 O O -, O O -Greg B-PER B-PER -Reid I-PER I-PER -72 O O -68 O O -71 O O -, O O -Mark B-PER B-PER -Mu I-PER B-PER -71 O O -67 O O -73 O O -212 O O -Trevor B-PER B-PER -Dodd I-PER I-PER -( O O -Namibia B-LOC B-LOC -) O O -S O O -- O O -R B-MISC O -AL B-MISC B-LOC -N O O -S O O -TO O O -P O O -N B-ORG B-LOC -. O O -T B-LOC B-LOC -1996 O O -Albanian B-MISC B-MISC -coach O O -As B-PER B-PER -Ha I-PER I-PER -said O O -on O O -Saturday O O -it O O -was O O -important O O -that O O -his O O -players O O -brush O O -aside O O -the O O -country O O -' O O -short O O -ban O O -by O O -FIFA B-ORG B-ORG -in O O -order O O -to O O -concentrate O O -on O O -next O O -Saturday O B-MISC -Cup I-MISC I-MISC -group O O -nine O O -qualifier O O -against O O -Northern B-LOC B-LOC -Ireland I-LOC I-LOC -. O O -World O O -soccer O O -' O O -governing O O -body O O -reinstated O O -Albania B-LOC B-LOC -last O O -Tuesday O O -after O O -the O O -Balkan B-LOC B-LOC -country O O -' O O -government O O -lifted O O -suspension O O -on O O -various O O -soccer O O -officials O O -. O O -FIFA B-ORG B-ORG -had O O -banned O O -Albania B-LOC B-LOC -indefinitely O O -after O O -its O O -sports O O -ministry O O -had O O -ordered O O -the O O -suspension O O -of O O -Albanian B-ORG B-ORG -Football I-ORG I-ORG -Association I-ORG I-ORG -general O O -secretary O O -Eduard B-PER B-PER -Der I-PER I-PER -and O O -dissolved O O -the O O -executive O O -committee O O -. O O -" O O -We O O -would O O -be O O -very O O -happy O O -with O O -a O O -draw O O -in O O -Belfast B-LOC B-LOC -, O O -" O O -said O O -Ha B-PER B-PER -. O O -" O O -Especially O O -if O O -one O O -takes O O -into O O -consideration O O -our O O -difficult O O -post O O -situation O O -and O O -the O O -fact O O -Northern B-LOC B-LOC -Ireland I-LOC I-LOC -is O O -very O O -keen O O -to O O -win O O -. O O -" O O -Regular O O -defender O O -Art B-PER B-PER -Le I-PER I-PER -, O O -who O O -is O O -injured O O -, O O -was O O -missing O O -from O O -Ha B-PER B-PER -' O O -squad O O -named O O -on O O -Saturday O O -for O O -the O O -Belfast B-LOC B-LOC -match O O -. O O -Squad O O -: O O -Goal O O -- O O -B B-PER B-PER -Na I-PER I-PER -, O O -Arm B-PER B-PER -G I-PER I-PER -De O O -- O O -R B-PER B-PER -V I-PER I-PER -, O O -Sai B-PER B-PER -Mal I-PER I-PER -, O O -A B-PER B-PER -X I-PER I-PER -, O O -Il B-PER B-PER -Shu I-PER I-PER -, O O -A B-PER B-PER -To I-PER I-PER -, O O -N B-PER B-PER -De I-PER I-PER -, O O -A B-PER B-PER -Bella I-PER I-PER -Mid O O -- O O -B B-PER B-PER -Ko I-PER I-PER -, O O -Alt B-PER B-PER -Ha I-PER I-PER -, O O -So B-PER B-PER -Pre I-PER I-PER -, O O -E B-PER B-PER -F I-PER I-PER -Forward O O -- O O -Alt B-PER B-PER -R I-PER I-PER -, O O -Viktor B-PER B-PER -Pac I-PER I-PER -, O O -Fat B-PER B-PER -V I-PER I-PER -, O O -E B-PER B-PER -Bo I-PER I-PER -. O O -CR O O -- O O -J B-PER B-PER -H O O -CE O O -AS O O -VI B-MISC B-ORG -F O O -BA O O -. O O -H B-LOC B-LOC -, O O -Australia B-LOC B-LOC -1996 O O -Former O O -Australia B-LOC B-LOC -test O O -batsman O O -Dean B-PER B-PER -Jones I-PER I-PER -hit O O -an O O -unbeaten O O -130 O O -to O O -lead O O -Victoria B-LOC B-LOC -' O O -fight O O -in O O -their O O -Sheffield B-MISC B-MISC -Shield I-MISC I-MISC -match O O -against O O -Tasmania B-LOC B-ORG -on O O -Saturday O O -. O O -Rep O O -to O O -the O O -home O O -side O O -' O O -first O O -innings O O -48 O O -for O O -eight O O -declared O O -, O O -Victoria B-ORG B-LOC -reached O O -220 O O -for O O -three O O -at O O -close O O -of O O -play O O -on O O -the O O -second O O -day O O -of O O -the O O -four O O -match O O -at O O -Hobart B-LOC B-LOC -' O O -Belle B-LOC B-LOC -Oval I-LOC I-LOC -. O O -Jones B-PER B-PER -became O O -the O O -fourth O O -century O O -of O O -the O O -match O O -, O O -equal O O -the O O -feat O O -of O O -Tasmanian B-MISC B-MISC -trio O O -David B-PER B-PER -Bo I-PER I-PER -, O O -Shaun B-PER B-PER -Young I-PER I-PER -and O O -Michael B-PER B-PER -Di I-PER I-PER -. O O -Jones B-PER B-PER -, O O -who O O -took O O -over O O -as O O -captain O O -for O O -the O O -match O O -in O O -the O O -absence O O -of O O -Australia B-LOC B-LOC -test O O -leg O O -Shane B-PER B-PER -War I-PER I-PER -, O O -added O O -195 O O -runs O O -for O O -the O O -third O O -wicket O O -with O O -left O O -Laurie B-PER B-PER -Harper I-PER I-PER -. O O -Harper B-PER B-PER -was O O -eventually O O -dismissed O O -for O O -77 O O -after O O -the O O -pair O O -joined O O -forces O O -with O O -their O O -side O O -reel O O -on O O -nine O O -for O O -two O O -. O O -Earlier O O -, O O -former O O -Australia B-LOC B-LOC -test O O -batsman O O -David B-PER B-PER -Bo I-PER I-PER -scored O O -118 O O -and O O -all O O -Shaun B-PER B-PER -Young I-PER I-PER -hit O O -113 O O -. O O -The O O -pair O O -hammer O O -36 O O -boundaries O O -between O O -them O O -. O O -Pace O B-PER -bowler O O -Ian B-PER B-PER -Harvey I-PER I-PER -claimed O O -three O O -for O O -81 O O -for O O -Victoria B-LOC B-LOC -. O O -CR O O -- O O -SH B-PER B-MISC -SH O I-MISC -SC O O -. O O -H B-LOC B-LOC -, O O -Australia B-LOC B-LOC -1996 O O -Close O O -of O O -play O O -score O O -on O O -the O O -second O O -day O O -of O O -the O O -four O O -Sheffield B-MISC B-MISC -Shield I-MISC I-MISC -cricket O O -match O O -between O O -Tasmania B-LOC B-ORG -and O O -Victoria B-LOC B-ORG -at O O -Belle B-LOC B-LOC -Oval I-LOC I-LOC -on O O -Saturday O O -: O O -Tasmania B-LOC B-ORG -48 O O -for O O -eight O O -declared O O -( O O -Michael B-PER B-PER -Di I-PER I-PER -119 O O -, O O -David B-PER B-PER -Bo I-PER I-PER -118 O O -, O O -Shaun B-PER B-PER -Young I-PER I-PER -113 O O -) O O -; O O -Victoria B-ORG B-ORG -220 O O -for O O -three O O -( O O -Dean B-PER B-PER -Jones I-PER I-PER -130 O O -not O O -out O O -) O O -. O O -S O O -- O O -S B-PER B-LOC -K B-PER I-LOC -M O O -C O O -TO O O -Q O O -B O O -. O O -AB B-LOC B-LOC -D I-LOC I-LOC -1996 O O -South B-LOC B-LOC -Korea I-LOC I-LOC -made O O -virtually O O -certain O O -of O O -an O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -quarter O O -spot O O -with O O -a O O -4 O O -win O O -over O O -Indonesia B-LOC B-LOC -in O O -a O O -Group O O -A O O -match O O -on O O -Saturday O O -. O O -After O O -going O O -four O O -up O O -in O O -the O O -first O O -55 O O -minutes O O -South B-LOC B-LOC -Korea I-LOC I-LOC -allowed O O -Indonesia B-LOC B-LOC -, O O -newcomer O O -to O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -finals O O -, O O -back O O -into O O -the O O -match O O -, O O -con O O -two O O -goals O O -from O O -rare O O -counter O O -attacks O O -. O O -Kim B-PER B-PER -Do I-PER I-PER -Ho I-PER I-PER -opened O O -the O O -scoring O O -for O O -South B-LOC B-LOC -Korea I-LOC I-LOC -in O O -only O O -the O O -fifth O O -minute O O -, O O -turning O O -un O O -on O O -the O O -penalty O O -spot O O -to O O -fire O O -a O O -shot O O -into O O -the O O -top O O -corner O O -. O O -It O O -looked O O -like O O -turning O O -into O O -a O O -r O O -as O O -H B-PER B-PER -Sun I-PER I-PER -Hong I-PER I-PER -rapidly O O -added O O -two O O -more O O -in O O -the O O -seventh O O -and O O -15th O O -minutes O O -but O O -although O O -the O O -Koreans B-MISC B-MISC -continued O O -to O O -dominate O O -they O O -failed O O -to O O -add O O -to O O -the O O -score O O -before O O -the O O -interval O O -. O O -But O O -they O O -started O O -the O O -second O O -half O O -where O O -they O O -had O O -left O O -off O O -and O O -it O O -was O O -not O O -long O O -before O O -they O O -went O O -four O O -up O O -, O O -Ko B-PER B-PER -Je I-PER I-PER -W I-PER I-PER -heading O O -in O O -from O O -a O O -free O O -kick O O -in O O -the O O -55 O O -minute O O -. O O -The O O -Koreans B-MISC B-MISC -then O O -appeared O O -to O O -relax O O -, O O -allowing O O -the O O -Indonesian B-MISC B-MISC -to O O -get O O -back O O -into O O -the O O -match O O -. O O -Ron B-PER B-PER -W I-PER I-PER -scored O O -for O O -Indonesia B-LOC B-LOC -three O O -minutes O O -later O O -direct O O -from O O -a O O -a O O -corner O O -kick O O -that O O -Korean B-MISC B-MISC -goalkeeper O O -Kim B-PER B-PER -By I-PER I-PER -reached O O -with O O -one O O -hand O O -but O O -failed O O -to O O -keep O O -out O O -. O O -With O O -65 O O -minutes O O -gone O O -Indonesia B-LOC B-LOC -' O O -W B-PER B-PER -Put I-PER I-PER -, O O -who O O -scored O O -a O O -spectacular O O -goal O O -against O O -Kuwait B-LOC B-LOC -on O O -Wednesday O O -, O O -was O O -again O O -on O O -target O O -, O O -breaking O O -through O O -the O O -Korean B-MISC B-MISC -defence O O -to O O -beat O O -the O O -keeper O O -with O O -a O O -low O O -shot O O -. O O -Indonesian B-MISC B-MISC -keeper O O -He B-PER B-PER -Ka I-PER I-PER -produced O O -a O O -string O O -of O O -fine O O -saves O O -to O O -prevent O O -the O O -Koreans B-MISC B-MISC -increasing O O -their O O -lead O O -. O O -Teams O O -: O O -Indonesia B-LOC B-LOC -: O O -20 O O -- O O -He B-PER B-PER -Ka I-PER I-PER -; O O -2 O O -- O O -A B-PER B-PER -Set I-PER I-PER -; O O -3 O O -- O O -Su B-PER B-PER -Si I-PER I-PER -; O O -4 O O -- O O -Ye B-PER B-PER -Tu I-PER I-PER -; O O -5 O O -- O O -A B-PER B-PER -Te I-PER I-PER -; O O -6 O O -- O O -Su B-PER B-PER -; O O -7 O O -- O O -W B-PER B-PER -G I-PER I-PER -P I-PER I-PER -; O O -8 O O -- O O -Ron B-PER B-PER -W I-PER I-PER -; O O -11 O O -- O O -B B-PER B-PER -Sa I-PER I-PER -; O O -12 O O -- O O -Chris B-PER B-PER -Ya I-PER I-PER -( O O -15 O O -- O O -Francis B-PER B-PER -We I-PER I-PER -36 O O -) O O -; O O -16 O O -- O O -Mar B-PER B-PER -Bad I-PER I-PER -. O O -South B-LOC B-LOC -Korea I-LOC I-LOC -: O O -1 O O -- O O -Kim B-PER B-PER -By I-PER I-PER -Ji I-PER I-PER -; O O -2 O O -- O O -Kim B-PER B-PER -Pan I-PER I-PER -Ke I-PER I-PER -; O O -5 O O -- O O -Huh B-PER B-PER -Ki I-PER I-PER -Ta I-PER I-PER -; O O -8 O O -- O O -R B-PER B-PER -Sang I-PER I-PER -Rae I-PER I-PER -( O O -7 O O -- O O -Sin B-PER B-PER -Ta I-PER I-PER -Yong I-PER I-PER -33 O O -) O O -; O O -9 O O -- O O -Kim B-PER B-PER -Do I-PER I-PER -Ho I-PER I-PER -; O O -11 O O -- O O -Ko B-PER B-PER -Je I-PER I-PER -W I-PER I-PER -; O O -17 O O -- O O -Ha B-PER B-PER -Se I-PER I-PER -Ju I-PER I-PER -; O O -18 O O -- O O -H B-PER B-PER -Sun I-PER I-PER -Hong I-PER I-PER -; O O -22 O O -- O O -Lee B-PER B-PER -Young I-PER I-PER -Jin I-PER I-PER -; O O -23 O O -- O O -Yo B-PER B-PER -Sang I-PER I-PER -Chu I-PER I-PER -; O O -24 O O -- O O -Kim B-PER B-PER -Jo I-PER I-PER -Sung I-PER I-PER -. O O -S O O -- O O -IS B-MISC B-MISC -F O O -D O O -R O O -/ O O -ST O O -. O O -J B-LOC B-LOC -1996 O O -Results O O -of O O -first O O -division O O -soccer O O -matches O O -played O O -over O O -the O O -weekend O O -: O O -Z B-ORG B-ORG -Ho I-ORG I-ORG -1 O O -Hapoel B-ORG B-ORG -Pet I-ORG I-ORG -T I-ORG I-ORG -1 O O -Maccabi B-ORG B-ORG -Haifa I-ORG I-ORG -1 O O -Hapoel B-ORG B-ORG -Tai I-ORG I-ORG -1 O O -Hapoel B-ORG B-ORG -K I-ORG I-ORG -Sa I-ORG I-ORG -1 O O -B B-ORG B-ORG -Ye I-ORG I-ORG -0 O O -Hapoel B-ORG B-ORG -Tel I-ORG I-ORG -Aviv I-ORG I-ORG -1 O O -Beta B-ORG B-ORG -Jerusalem I-ORG I-ORG -4 O O -Hapoel B-ORG B-ORG -Jerusalem I-ORG I-ORG -0 O O -Maccabi B-ORG B-ORG -Tel I-ORG I-ORG -Aviv I-ORG I-ORG -4 O O -Iron B-ORG B-ORG -R I-ORG I-ORG -Le I-ORG I-ORG -1 O O -Maccabi B-ORG B-ORG -Her I-ORG I-ORG -0 O O -Hapoel B-ORG B-ORG -Be I-ORG I-ORG -She I-ORG I-ORG -2 O O -Hapoel B-ORG B-ORG -Beer I-ORG I-ORG -1 O O -Maccabi B-ORG B-ORG -Pet I-ORG I-ORG -T I-ORG I-ORG -0 O O -Hapoel B-ORG B-ORG -Haifa I-ORG I-ORG -2 O O -Standing O O -( O O -ta O O -under O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -against O O -, O O -AB B-LOC B-LOC -D I-LOC I-LOC -1996 O O -Results O O -of O O -Asian B-MISC B-MISC -Cup I-MISC I-MISC -group O O -A O O -matches O O -on O O -Saturday O O -: O O -United B-ORG B-LOC -Arab I-ORG I-LOC -Emirates I-ORG I-LOC -3 O O -Kuwait B-LOC B-LOC -2 O O -( O O -halftime O O -0 O O -) O O -Score O O -: O O -UAE B-LOC B-LOC -- O O -Hassan B-PER B-PER -Ahmed I-PER I-PER -53 O O -, O O -Ad B-PER B-PER -Al I-PER I-PER -Ta I-PER I-PER -55 O O -, O O -Ba B-PER B-PER -Sa I-PER I-PER -80 O O -Kuwait B-LOC B-LOC -- O O -J B-PER B-PER -Al I-PER I-PER -9 O O -, O O -44 O O -Attendance O O -: O O -15 O O -South B-LOC B-LOC -Korea I-LOC I-LOC -4 O O -Indonesia B-LOC B-LOC -2 O O -( O O -3 O O -) O O -Score O O -: O O -South B-LOC B-LOC -Korea I-LOC I-LOC -- O O -Kim B-PER B-PER -Do I-PER I-PER -Ho I-PER I-PER -5 O O -, O O -H B-PER B-PER -Sun I-PER I-PER -Hong I-PER I-PER -7 O O -and O O -15 O O -, O O -Ko B-PER B-PER -Je I-PER B-PER -W I-PER I-PER -55 O O -Indonesia B-LOC B-LOC -- O O -Ron B-PER B-PER -W I-PER I-PER -58 O O -, O O -W B-PER B-PER -Put I-ORG I-PER -65 O O -Attendance O O -: O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Standing O O -of O O -National B-MISC B-ORG -Basketball I-MISC B-ORG -Association I-MISC I-ORG -teams O O -after O O -games O O -played O O -on O O -Friday O O -( O O -ta O O -under O O -won O O -, O O -lost O O -, O O -percentage O O -, O O -games O O -behind O O -) O O -: O O -EA B-MISC O -CO O O -AT B-ORG B-LOC -D O O -W O O -L O O -PC O O -GB O O -MI B-ORG B-ORG -14 O O -5 O O -. O O -- O O -NE B-ORG B-ORG -Y I-ORG I-ORG -11 O O -6 O O -. O O -2 O O -OR B-ORG B-ORG -8 O O -7 O O -. O O -4 O O -WA B-ORG B-ORG -7 O O -9 O O -. O O -5 O O -1 O O -P B-ORG B-ORG -7 O O -10 O O -. O O -6 O O -NE B-ORG B-ORG -J I-ORG I-ORG -4 O O -10 O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Results O O -of O O -National B-MISC B-ORG -Basketball I-MISC I-ORG -Association I-MISC B-ORG -games O O -on O O -Friday O O -( O O -home O O -team O O -in O O -CA O O -) O O -: O O -New B-ORG B-ORG -Jersey I-ORG I-ORG -110 O O -B B-ORG B-ORG -108 O O -( O O -O O O -) O O -DE B-ORG B-ORG -93 O O -Cleveland B-ORG B-ORG -81 O O -New B-ORG B-ORG -York I-ORG I-ORG -103 O O -MI B-ORG B-ORG -85 O O -Phoenix B-ORG B-ORG -101 O O -SA B-ORG B-ORG -95 O O -Vancouver B-ORG B-ORG -105 O O -SA B-ORG B-ORG -AN I-ORG I-ORG -89 O O -U B-ORG B-ORG -106 O O -Minnesota B-ORG B-ORG -95 O O -P B-ORG B-ORG -97 O O -Charlotte B-ORG B-ORG -93 O O -Indiana B-ORG B-ORG -86 O O -GO B-ORG B-ORG -ST I-ORG I-ORG -71 O O -LA B-ORG B-ORG -LA I-ORG I-ORG -92 O O -Orlando B-ORG B-ORG -81 O O -NHL B-ORG B-ORG -I I-MISC O -H O O -- O O -ST O O -A O O -F O O -' O O -GA O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Standing O O -of O O -National B-MISC B-ORG -Hockey I-MISC I-ORG -League I-MISC B-ORG -teams O O -after O O -games O O -played O O -on O O -Friday O O -( O O -ta O O -under O O -won O O -, O O -lost O O -, O O -tied O O -, O O -goals O O -for O O -, O O -goals O O -against O O -, O O -points O O -) O O -: O O -EA B-MISC O -CO O O -NO O O -D O O -W O O -L O O -T O O -G O O -GA B-ORG O -PT I-ORG O -H I-ORG B-ORG -12 O O -7 O O -6 O O -77 O O -76 O O -30 O O -B B-ORG B-ORG -13 O O -12 O O -2 O O -78 O O -77 O O -28 O O -M B-ORG B-ORG -11 O O -14 O O -4 O O -99 O O -104 O O -26 O O -B B-ORG B-ORG -10 O O -11 O O -4 O O -74 O O -84 O O -24 O O -P B-ORG B-ORG -10 O O -13 O O -3 O O -86 O O -94 O O -23 O O -O B-ORG B-ORG -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Results O O -of O O -National B-MISC B-ORG -Hockey I-MISC I-ORG -League I-MISC B-ORG -games O O -on O O -Friday O O -( O O -home O O -team O O -in O O -CA O O -) O O -: O O -NY B-ORG B-ORG -RA I-ORG I-ORG -6 O O -Toronto B-ORG B-ORG -5 O O -B B-ORG B-ORG -1 O O -Anaheim B-ORG B-ORG -1 O O -( O O -O O O -) O O -Pittsburgh B-ORG B-ORG -5 O O -WA B-ORG B-ORG -3 O O -Montreal B-ORG B-ORG -3 O O -CH B-ORG B-ORG -1 O O -Philadelphia B-ORG B-LOC -6 O O -D B-ORG B-LOC -3 O O -St B-ORG B-LOC -Louis I-ORG I-LOC -4 O O -CO B-ORG B-LOC -3 O O -E B-ORG B-LOC -5 O O -Ottawa B-ORG B-LOC -2 O O -NHL B-ORG B-ORG -I O O -H O O -- O O -CA B-LOC B-ORG -R B-LOC O -B O B-PER -S O O -F O O -ONE O O -GA O O -. O O -NE B-LOC B-LOC -Y I-LOC I-LOC -1996 O O -Vancouver B-ORG B-ORG -Can I-ORG I-ORG -star O O -right O O -wing O O -Pavel B-PER B-PER -B I-PER I-PER -was O O -suspended O O -for O O -one O O -game O O -by O O -the O O -National B-MISC B-ORG -Hockey I-MISC I-ORG -League I-MISC I-ORG -and O O -fined O O -$ O O -1 O O -Friday O O -for O O -his O O -hit O O -on O O -Buffalo B-ORG B-ORG -Sa I-ORG I-ORG -defence O O -Garry B-PER B-PER -G I-PER I-PER -on O O -Wednesday O O -. O O -B B-PER B-PER -received O O -a O O -double O O -penalty O O -for O O -high O O -with O O -2 O O -left O O -in O O -the O O -first O O -period O O -of O O -Wednesday O O -' O O -7 O O -overtime O O -win O O -by O O -Vancouver B-ORG B-ORG -after O O -co O O -with O O -G B-PER B-PER -in O O -Buffalo B-LOC B-ORG -zone O O -. O O -G B-PER B-PER -suffered O O -a O O -concussion O O -and O O -did O O -not O O -return O O -to O O -the O O -game O O -. O O -" O O -Mr O O -B B-PER B-PER -left O O -his O O -feet O O -to O O -deliver O O -a O O -forearm O O -blow O O -to O O -Mr O O -G B-PER B-PER -as O O -he O O -was O O -about O O -to O O -be O O -checked O O -legally O O -by O O -his O O -opponent O O -, O O -" O O -said O O -NHL B-ORG B-ORG -discipline O O -chief O O -Brian B-PER B-PER -Burke I-PER I-PER -in O O -handing O O -out O O -the O O -suspension O O -. O O -" O O -Although O O -it O O -is O O -clear O O -from O O -the O O -video O O -that O O -Mr O O -B B-PER B-PER -' O O -actions O O -were O O -a O O -reaction O O -to O O -the O O -impending O O -hit O O -and O O -there O O -was O O -no O O -intent O O -to O O -in O O -his O O -opponent O O -, O O -there O O -can O O -be O O -no O O -excuse O O -for O O -this O O -type O O -of O O -conduct O O -, O O -" O O -Burke B-PER B-PER -said O O -. O O -B B-PER B-PER -, O O -who O O -is O O -struggling O O -with O O -only O O -nine O O -goals O O -and O O -12 O O -assists O O -in O O -26 O O -games O O -, O O -will O O -miss O O -Saturday O O -' O O -home O O -game O O -against O O -Ottawa B-ORG B-ORG -. O O -B O O -- O O -SC B-PER B-PER -DE O O -R B-LOC B-PER -IN O O -I B-ORG B-ORG -H O O -F O O -. O O -VI B-LOC B-LOC -1996 O O -German B-MISC B-MISC -Axel B-PER B-PER -Sc I-PER I-PER -out O O -Cuba B-LOC B-LOC -' O O -Jose B-PER B-PER -R I-PER I-PER -in O O -their O O -International B-ORG B-ORG -Boxing I-ORG I-ORG -Federation I-ORG I-ORG -non O O -10 O O -heavyweight O O -fight O O -on O O -Saturday O O -. O O -S O O -- O O -SP B-MISC B-MISC -F O O -D O O -S O O -. O O -MA B-LOC B-LOC -1996 O O -Su O O -of O O -Saturday O O -' O O -Spanish B-MISC B-MISC -first O O -division O O -match O O -: O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -2 O O -( O O -Da B-PER B-PER -Su I-PER I-PER -24 O O -, O O -Pre B-PER B-PER -Mi I-PER I-PER -48 O O -) O O -Barcelona B-ORG B-ORG -0 O O -. O O -Half O O -1 O O -. O O -Attendance O O -106 O O -. O O -S O O -- O O -BA B-PER B-LOC -ST O O -F O O -W O O -O O O -F O O -GA O O -F O O -R O B-ORG -. O O -MA B-LOC B-LOC -1996 O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -' O O -Balkan B-LOC B-LOC -strike O O -force O O -of O O -Da B-PER B-PER -Su I-PER I-PER -and O O -Pre B-PER B-PER -Mi I-PER I-PER -shot O O -their O O -side O O -to O O -a O O -2 O O -win O O -over O O -Barcelona B-ORG B-ORG -in O O -Spain B-LOC B-LOC -' O O -old O O -firm O O -game O O -on O O -Saturday O O -. O O -The O O -result O O -leaves O O -Real B-ORG B-ORG -on O O -38 O O -points O O -after O O -16 O O -games O O -, O O -four O O -ahead O O -of O O -Barcelona B-ORG B-ORG -. O O -With O O -just O O -one O O -league O O -match O O -scheduled O O -before O O -the O O -New O O -Year I-MISC O -break O O -, O O -Real B-ORG B-ORG -are O O -also O O -assured O O -of O O -spending O O -Christmas O O -ahead O O -of O O -their O O -arch O O -. O O -A O O -mix O O -in O O -the O O -Barcelona B-ORG B-ORG -defence O O -let O O -Croatian B-MISC B-MISC -international O O -Su B-PER B-PER -in O O -midway O O -through O O -the O O -first O O -half O O -, O O -and O O -Montenegrin B-MISC B-MISC -striker O O -Mi B-PER B-PER -made O O -it O O -2 O O -after O O -fine O O -work O O -by O O -Clarence B-PER B-PER -See I-PER I-PER -just O O -after O O -the O O -break O O -. O O -Barcelona B-ORG B-ORG -fought O O -back O O -strongly O O -but O O -were O O -twice O O -denied O O -by O O -the O O -wood O O -on O O -an O O -unusually O O -quiet O O -night O O -for O O -Brazilian B-MISC B-MISC -striker O O -Ronald B-PER B-PER -. O O -S O O -- O O -PS B-PER B-ORG -H O O -V O B-ORG -F O O -S O O -. O O -AM B-LOC B-LOC -1996 O O -Brazilian B-MISC B-MISC -striker O O -Marcel B-PER B-PER -and O O -Yugoslav B-MISC B-MISC -midfielder O O -Z B-PER B-PER -Pet I-PER I-PER -each O O -scored O O -twice O O -as O O -Dutch B-MISC B-MISC -first O O -division O O -leaders O O -PS B-ORG B-ORG -Ein I-ORG I-ORG -r O O -to O O -a O O -6 O O -win O O -over O O -Vol B-ORG B-ORG -on O O -Saturday O O -. O O -Their O O -other O O -marks O O -were O O -Brazilian B-MISC B-MISC -defender O O -V B-PER B-PER -and O O -Belgian B-MISC B-MISC -striker O O -Luc B-PER B-PER -Ni I-PER I-PER -, O O -his O O -14th O O -of O O -the O O -season O O -. O O -PS B-ORG B-ORG -, O O -well O O -on O O -the O O -way O O -to O O -their O O -14th O O -league O O -title O O -, O O -out O O -Vol B-ORG B-ORG -in O O -every O O -department O O -of O O -the O O -game O O -. O O -They O O -b O O -a O O -nine O O -lead O O -over O O -Fe B-ORG B-ORG -, O O -who O O -have O O -two O O -games O O -in O O -hand O O -, O O -and O O -are O O -16 O O -points O O -clear O O -of O O -champions O O -Ajax B-ORG B-ORG -Amsterdam I-ORG I-ORG -, O O -who O O -have O O -played O O -18 O O -matches O O -compared O O -to O O -PS B-ORG B-ORG -' O O -19 O O -. O O -Ajax B-ORG B-ORG -face O O -A B-ORG B-ORG -Al I-ORG I-ORG -away O O -on O O -Sunday O O -and O O -Fe B-ORG B-ORG -, O O -eliminated O O -from O O -the O O -UEFA B-MISC B-MISC -Cup I-MISC I-MISC -after O O -losing O O -4 O O -on O O -aggregate O O -to O O -Ten B-ORG B-ORG -on O O -Tuesday O O -, O O -travel O O -to O O -De B-ORG B-ORG -G I-ORG I-ORG -Do I-ORG I-ORG -. O O -The O O -Do B-ORG B-ORG -side O O -, O O -dubbed O O -" O O -The B-ORG B-ORG -Super B-ORG I-ORG -P I-ORG I-ORG -" O O -, O O -are O O -one O O -of O O -the O O -surprise O O -packages O O -of O O -the O O -season O O -. O O -They O O -are O O -fourth O O -in O O -the O O -table O O -. O O -S O O -- O O -SP B-MISC B-MISC -F O O -D O O -R O O -/ O O -ST O O -. O O -MA B-LOC B-LOC -1996 O O -Re O O -of O O -Saturday O O -' O O -only O O -Spanish B-MISC B-MISC -first O O -division O O -match O O -: O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -2 O O -Barcelona B-ORG B-ORG -0 O O -Standing O O -( O O -ta O O -under O O -games O O -played O O -, O O -won O O -, O O -drawn O O -, O O -lost O O -, O O -goals O O -for O O -, O O -against O O -, O O -points O O -) O O -: O O -Real B-ORG B-ORG -Madrid I-ORG I-ORG -16 O O -11 O O -5 O O -0 O O -32 O O -12 O O -38 O O -Barcelona B-ORG B-ORG -16 O O -10 O O -4 O O -2 O O -46 O O -21 O O -34 O O -Deportivo B-ORG B-ORG -Co I-ORG I-ORG -15 O O -9 O O -6 O O -0 O O -23 O O -7 O O -33 O O -Real B-ORG B-ORG -Bet I-ORG I-ORG -15 O O -8 O O -5 O O -2 O O -28 O O -13 O O -29 O O -At B-ORG B-ORG -Madrid I-ORG I-ORG -15 O O -8 O O -3 O O -4 O O -26 O O -17 O O -27 O O -Athletic B-ORG B-ORG -B I-ORG I-ORG -15 O O -7 O O -4 O O -4 O O -28 O O -22 O O -25 O O -Real B-ORG B-ORG -So I-ORG I-ORG -15 O O -7 O O -3 O O -5 O O -20 O O -18 O O -24 O O -Val B-ORG B-ORG -15 O O -7 O O -D B-LOC B-LOC -1996 O O -Jack B-PER B-PER -Charlton I-PER I-PER -' O O -relationship O O -with O O -the O O -people O O -of O O -Ireland B-LOC B-LOC -was O O -cement O O -on O O -Saturday O O -when O O -the O O -English B-MISC B-MISC -was O O -officially O O -declared O O -one O O -of O O -their O O -own O O -. O O -Charlton B-PER B-PER -, O O -61 O O -, O O -and O O -his O O -wife O O -, O O -Peggy B-PER B-PER -, O O -became O O -citizens O O -of O O -Ireland B-LOC B-LOC -when O O -they O O -formally O O -received O O -Irish B-MISC B-MISC -passports O O -from O O -deputy O O -Prime O O -Minister O O -Dick B-PER B-PER -Spring I-PER I-PER -who O O -said O O -the O O -honour O O -had O O -been O O -made O O -in O O -recognition O O -of O O -Charlton B-PER B-PER -' O O -achievements O O -as O O -the O O -national O O -soccer O O -manager O O -. O O -" O O -The O O -years O O -I O O -spent O O -as O O -manager O O -of O O -the O O -Republic B-LOC B-LOC -of I-LOC I-LOC -Ireland I-LOC I-LOC -were O O -the O O -best O O -years O O -of O O -my O O -life O O -. O O -It O O -all O O -culminated O O -in O O -the O O -fact O O -that O O -I O O -now O O -have O O -lots O O -of O O -great O O -, O O -great O O -friends O O -in O O -Ireland B-LOC B-LOC -. O O -That O O -is O O -why O O -this O O -is O O -so O O -emotional O O -a O O -night O O -for O O -me O O -, O O -" O O -Charlton B-PER B-PER -said O O -. O O -" O O -It O O -was O O -the O O -joy O O -that O O -we O O -all O O -had O O -over O O -the O O -period O O -, O O -that O O -I O O -shared O O -with O O -people O O -that O O -I O O -grew O O -to O O -love O O -, O O -that O O -I O O -treasure O O -most O O -, O O -" O O -he O O -added O O -. O O -Charlton B-PER B-PER -managed O O -Ireland B-LOC B-LOC -for O O -93 O O -matches O O -, O O -during O O -which O O -time O O -they O O -lost O O -only O O -17 O O -times O O -in O O -almost O O -10 O O -years O O -until O O -he O O -resigned O O -in O O -December O O -1995 O O -. 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O O diff --git a/TensorFlow/built-in/nlp/Bert-base_ID0060_for_TensorFlow/configs/bert_base_vocab.txt b/TensorFlow/built-in/nlp/Bert-base_ID0060_for_TensorFlow/configs/bert_base_vocab.txt deleted file mode 100644 index ca4f9781030019ab9b253c6dcb8c7878b6dc87a5..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/nlp/Bert-base_ID0060_for_TensorFlow/configs/bert_base_vocab.txt +++ /dev/null @@ -1,21128 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] -[unused74] -[unused75] -[unused76] -[unused77] -[unused78] -[unused79] -[unused80] -[unused81] -[unused82] -[unused83] -[unused84] -[unused85] -[unused86] -[unused87] -[unused88] -[unused89] -[unused90] -[unused91] -[unused92] -[unused93] -[unused94] -[unused95] -[unused96] -[unused97] -[unused98] -[unused99] -[UNK] -[CLS] -[SEP] -[MASK] - - -! -" -# -$ -% -& -' -( -) -* -+ -, -- -. -/ -0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -: -; -< -= -> -? -@ -[ -\ -] -^ -_ -a -b -c -d -e -f -g -h -i -j -k -l -m -n -o -p -q -r -s -t -u -v -w -x -y -z -{ -| -} -~ -£ -¤ -¥ -§ -© -« -® -° -± -² -³ -µ -· -¹ -º -» -¼ -× -ß -æ -÷ -ø -đ -ŋ -ɔ -ə -ɡ -ʰ -ˇ -ˈ -ˊ -ˋ -ˍ -ː -˙ -˚ -ˢ -α -β -γ -δ -ε -η -θ -ι -κ -λ -μ -ν -ο -π -ρ -ς -σ -τ -υ -φ -χ -ψ -ω -а -б -в -г -д -е -ж -з -и -к -л -м -н -о -п -р -с -т -у -ф -х -ц -ч -ш -ы -ь -я -і -ا -ب -ة -ت -د -ر -س -ع -ل -م -ن -ه -و -ي -۩ -ก -ง -น -ม -ย -ร -อ -า -เ -๑ -་ -ღ -ᄀ -ᄁ -ᄂ -ᄃ -ᄅ -ᄆ -ᄇ -ᄈ -ᄉ -ᄋ -ᄌ -ᄎ -ᄏ -ᄐ -ᄑ -ᄒ -ᅡ -ᅢ -ᅣ -ᅥ -ᅦ -ᅧ -ᅨ -ᅩ -ᅪ -ᅬ -ᅭ -ᅮ -ᅯ -ᅲ -ᅳ -ᅴ -ᅵ -ᆨ -ᆫ -ᆯ -ᆷ -ᆸ -ᆺ -ᆻ -ᆼ -ᗜ -ᵃ -ᵉ -ᵍ -ᵏ -ᵐ -ᵒ -ᵘ -‖ -„ -† -• -‥ -‧ -
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-##鳍 -##鳕 -##鳖 -##鳗 -##鳝 -##鳞 -##鳥 -##鳩 -##鳳 -##鳴 -##鳶 -##鴉 -##鴕 -##鴛 -##鴦 -##鴨 -##鴻 -##鴿 -##鵑 -##鵜 -##鵝 -##鵡 -##鵬 -##鵰 -##鵲 -##鶘 -##鶩 -##鶯 -##鶴 -##鷗 -##鷲 -##鷹 -##鷺 -##鸚 -##鸞 -##鸟 -##鸠 -##鸡 -##鸢 -##鸣 -##鸥 -##鸦 -##鸨 -##鸪 -##鸭 -##鸯 -##鸳 -##鸵 -##鸽 -##鸾 -##鸿 -##鹂 -##鹃 -##鹄 -##鹅 -##鹈 -##鹉 -##鹊 -##鹌 -##鹏 -##鹑 -##鹕 -##鹘 -##鹜 -##鹞 -##鹤 -##鹦 -##鹧 -##鹫 -##鹭 -##鹰 -##鹳 -##鹵 -##鹹 -##鹼 -##鹽 -##鹿 -##麂 -##麋 -##麒 -##麓 -##麗 -##麝 -##麟 -##麥 -##麦 -##麩 -##麴 -##麵 -##麸 -##麺 -##麻 -##麼 -##麽 -##麾 -##黃 -##黄 -##黍 -##黎 -##黏 -##黑 -##黒 -##黔 -##默 -##黛 -##黜 -##黝 -##點 -##黠 -##黨 -##黯 -##黴 -##鼋 -##鼎 -##鼐 -##鼓 -##鼠 -##鼬 -##鼹 -##鼻 -##鼾 -##齁 -##齊 -##齋 -##齐 -##齒 -##齡 -##齢 -##齣 -##齦 -##齿 -##龄 -##龅 -##龈 -##龊 -##龋 -##龌 -##龍 -##龐 -##龔 -##龕 -##龙 -##龚 -##龛 -##龜 -##龟 -##︰ -##︱ -##︶ -##︿ -##﹁ -##﹂ -##﹍ -##﹏ -##﹐ -##﹑ -##﹒ -##﹔ -##﹕ -##﹖ -##﹗ -##﹙ -##﹚ -##﹝ -##﹞ -##﹡ -##﹣ -##！ -##＂ -##＃ -##＄ -##％ -##＆ -##＇ -##（ -##） -##＊ -##， -##－ -##． -##／ -##： -##； -##＜ -##？ -##＠ -##［ -##＼ -##］ -##＾ -##＿ -##｀ -##ｆ -##ｈ -##ｊ -##ｕ -##ｗ -##ｚ -##｛ -##｝ -##｡ -##｢ -##｣ -##､ -##･ -##ｯ -##ｰ -##ｲ -##ｸ -##ｼ -##ｽ -##ﾄ -##ﾉ -##ﾌ -##ﾗ -##ﾙ -##ﾝ -##ﾞ -##ﾟ -##￣ -##￥ -##👍 -##🔥 -##😂 -##😎 diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_1p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_1p.sh index ff70a546a5c55fdee7ed394da559b91cfa09b37a..771488ea15bc6699199b550c8a6f682f8c4fe7c8 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_1p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_1p.sh @@ -105,8 +105,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=32 \ - --num_train_epochs=2 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --learning_rate=3e-5 \ --max_seq_length=384 \ --doc_stride=128 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_8p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_8p.sh index 40a41dc7c2cc1f04971cea46a06458a061c29e34..a12eeef6d5f5d1c2f593e65235c7de3686d35d10 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_full_8p.sh @@ -107,8 +107,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=32 \ - --num_train_epochs=2 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --learning_rate=3e-5 \ --max_seq_length=384 \ --doc_stride=128 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh index b11e96d6a07b00abf03721f947ecd89cd256d4ef..0d67fba344620ab05afedff404e4acbe7f2869e6 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_1p.sh @@ -105,8 +105,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=32 \ - --num_train_epochs=1 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --num_train_steps=1000 \ --learning_rate=3e-5 \ --max_seq_length=384 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_8p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_8p.sh index 55f784e2491f1563e089ad33796b6a2cfc346ebf..c13ecf75593eb3f23e3c3ac5523ba4dca3226426 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID0495_Bert-Squad_performance_8p.sh @@ -107,8 +107,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=32 \ - --num_train_epochs=1 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --num_train_steps=1000 \ --learning_rate=3e-5 \ --max_seq_length=384 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_1p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_1p.sh index a41c04a3c36ab0824140f851d6f931836aaa4f1c..0fc7048a3bacbe715583d1d2c1e6615cd280d035 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_1p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_1p.sh @@ -15,7 +15,7 @@ data_path="" #基础参数 需要模型审视修改 #网络名称，同目录名称 Network="BertLarge-Squad_ID3082_for_TensorFlow" -batch_size=24 +batch_size=32 epoch=2 #维持参数，不需要修改 @@ -104,8 +104,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=24 \ - --num_train_epochs=2 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --learning_rate=3e-5 \ --max_seq_length=384 \ --doc_stride=128 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_8p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_8p.sh index 943c52c52dacbf40f1dcb7d9ae5287514e1097bd..4e53b8c0c86c5c45f30c8bc26965f2c3cd04308e 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_full_8p.sh @@ -16,7 +16,7 @@ data_path="" #基础参数 需要模型审视修改 #网络名称，同目录名称 Network="BertLarge-Squad_ID3082_for_TensorFlow" -batch_size=24 +batch_size=32 epoch=2 #维持参数，不需要修改 @@ -106,8 +106,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=24 \ - --num_train_epochs=2 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --learning_rate=3e-5 \ --max_seq_length=384 \ --doc_stride=128 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_1p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_1p.sh index ef213866b21021eaec6769f97d28e8d9effaecd6..25c14fab37aa242a15ba09fb3878389539eb9ac1 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_1p.sh @@ -15,7 +15,7 @@ data_path="" #基础参数 需要模型审视修改 #网络名称，同目录名称 Network="BertLarge-Squad_ID3082_for_TensorFlow" -batch_size=24 +batch_size=32 epoch=1 @@ -105,8 +105,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=24 \ - --num_train_epochs=1 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --num_train_steps=1000 \ --learning_rate=3e-5 \ --max_seq_length=384 \ diff --git a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_8p.sh b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_8p.sh index 28c3e9bf564244e3c3c682e2de59d579bd269b60..7634bcad70fc146c04c165a8682f8638f3f17f99 100644 --- a/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertGoogle_Series_for_TensorFlow/test/train_ID3082_BertLarge-Squad_performance_8p.sh @@ -16,7 +16,7 @@ data_path="" #基础参数 需要模型审视修改 #网络名称，同目录名称 Network="BertLarge-Squad_ID3082_for_TensorFlow" -batch_size=24 +batch_size=32 epoch=1 #维持参数，不需要修改 @@ -106,8 +106,8 @@ do --do_predict=True \ --do_train=True \ --predict_file=$predict_file \ - --train_batch_size=24 \ - --num_train_epochs=1 \ + --train_batch_size=${batch_size} \ + --num_train_epochs=${epoch} \ --num_train_steps=1000 \ --learning_rate=3e-5 \ --max_seq_length=384 \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/create_pretraining_data.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/create_pretraining_data.py deleted file mode 100644 index ec94c765bde139fe6826f1b37236aa7950c05544..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/create_pretraining_data.py +++ /dev/null @@ -1,457 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -============================================================================== -# -# Copyright 2020 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Create masked LM/next sentence masked_lm TF examples for BERT.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import collections -import random -import tokenization -import tensorflow as tf - -flags = tf.flags - -FLAGS = flags.FLAGS - -flags.DEFINE_string("input_file", None, - "Input raw text file (or comma-separated list of files).") - -flags.DEFINE_string( - "output_file", None, - "Output TF example file (or comma-separated list of files).") - -flags.DEFINE_string("vocab_file", None, - "The vocabulary file that the BERT model was trained on.") - -flags.DEFINE_bool( - "do_lower_case", True, - "Whether to lower case the input text. Should be True for uncased " - "models and False for cased models.") - -flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.") - -flags.DEFINE_integer("max_predictions_per_seq", 20, - "Maximum number of masked LM predictions per sequence.") - -flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.") - -flags.DEFINE_integer( - "dupe_factor", 10, - "Number of times to duplicate the input data (with different masks).") - -flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.") - -flags.DEFINE_float( - "short_seq_prob", 0.1, - "Probability of creating sequences which are shorter than the " - "maximum length.") - - -class TrainingInstance(object): - """A single training instance (sentence pair).""" - - def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels, - is_random_next): - self.tokens = tokens - self.segment_ids = segment_ids - self.is_random_next = is_random_next - self.masked_lm_positions = masked_lm_positions - self.masked_lm_labels = masked_lm_labels - - def __str__(self): - s = "" - s += "tokens: %s\n" % (" ".join( - [tokenization.printable_text(x) for x in self.tokens])) - s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids])) - s += "is_random_next: %s\n" % self.is_random_next - s += "masked_lm_positions: %s\n" % (" ".join( - [str(x) for x in self.masked_lm_positions])) - s += "masked_lm_labels: %s\n" % (" ".join( - [tokenization.printable_text(x) for x in self.masked_lm_labels])) - s += "\n" - return s - - def __repr__(self): - return self.__str__() - - -def write_instance_to_example_files(instances, tokenizer, max_seq_length, - max_predictions_per_seq, output_files): - """Create TF example files from `TrainingInstance`s.""" - writers = [] - for output_file in output_files: - writers.append(tf.python_io.TFRecordWriter(output_file)) - - writer_index = 0 - - total_written = 0 - for (inst_index, instance) in enumerate(instances): - input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) - input_mask = [1] * len(input_ids) - segment_ids = list(instance.segment_ids) - assert len(input_ids) <= max_seq_length - - while len(input_ids) < max_seq_length: - input_ids.append(0) - input_mask.append(0) - segment_ids.append(0) - - assert len(input_ids) == max_seq_length - assert len(input_mask) == max_seq_length - assert len(segment_ids) == max_seq_length - - masked_lm_positions = list(instance.masked_lm_positions) - masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels) - masked_lm_weights = [1.0] * len(masked_lm_ids) - - while len(masked_lm_positions) < max_predictions_per_seq: - masked_lm_positions.append(0) - masked_lm_ids.append(0) - masked_lm_weights.append(0.0) - - next_sentence_label = 1 if instance.is_random_next else 0 - - features = collections.OrderedDict() - features["input_ids"] = create_int_feature(input_ids) - features["input_mask"] = create_int_feature(input_mask) - features["segment_ids"] = create_int_feature(segment_ids) - features["masked_lm_positions"] = create_int_feature(masked_lm_positions) - features["masked_lm_ids"] = create_int_feature(masked_lm_ids) - features["masked_lm_weights"] = create_float_feature(masked_lm_weights) - features["next_sentence_labels"] = create_int_feature([next_sentence_label]) - - tf_example = tf.train.Example(features=tf.train.Features(feature=features)) - - writers[writer_index].write(tf_example.SerializeToString()) - writer_index = (writer_index + 1) % len(writers) - - total_written += 1 - - if inst_index < 20: - tf.logging.info("*** Example ***") - tf.logging.info("tokens: %s" % " ".join( - [tokenization.printable_text(x) for x in instance.tokens])) - - for feature_name in features.keys(): - feature = features[feature_name] - values = [] - if feature.int64_list.value: - values = feature.int64_list.value - elif feature.float_list.value: - values = feature.float_list.value - tf.logging.info( - "%s: %s" % (feature_name, " ".join([str(x) for x in values]))) - - for writer in writers: - writer.close() - - tf.logging.info("Wrote %d total instances", total_written) - - -def create_int_feature(values): - feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) - return feature - - -def create_float_feature(values): - feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) - return feature - - -def create_training_instances(input_files, tokenizer, max_seq_length, - dupe_factor, short_seq_prob, masked_lm_prob, - max_predictions_per_seq, rng): - """Create `TrainingInstance`s from raw text.""" - all_documents = [[]] - - # Input file format: - # (1) One sentence per line. These should ideally be actual sentences, not - # entire paragraphs or arbitrary spans of text. (Because we use the - # sentence boundaries for the "next sentence prediction" task). - # (2) Blank lines between documents. Document boundaries are needed so - # that the "next sentence prediction" task doesn't span between documents. - for input_file in input_files: - with tf.gfile.GFile(input_file, "r") as reader: - while True: - line = tokenization.convert_to_unicode(reader.readline()) - if not line: - break - line = line.strip() - - # Empty lines are used as document delimiters - if not line: - all_documents.append([]) - tokens = tokenizer.tokenize(line) - if tokens: - all_documents[-1].append(tokens) - - # Remove empty documents - all_documents = [x for x in all_documents if x] - rng.shuffle(all_documents) - - vocab_words = list(tokenizer.vocab.keys()) - instances = [] - for _ in range(dupe_factor): - for document_index in range(len(all_documents)): - instances.extend( - create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) - - rng.shuffle(instances) - return instances - - -def create_instances_from_document( - all_documents, document_index, max_seq_length, short_seq_prob, - masked_lm_prob, max_predictions_per_seq, vocab_words, rng): - """Creates `TrainingInstance`s for a single document.""" - document = all_documents[document_index] - - # Account for [CLS], [SEP], [SEP] - max_num_tokens = max_seq_length - 3 - - # We *usually* want to fill up the entire sequence since we are padding - # to `max_seq_length` anyways, so short sequences are generally wasted - # computation. However, we *sometimes* - # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter - # sequences to minimize the mismatch between pre-training and fine-tuning. - # The `target_seq_length` is just a rough target however, whereas - # `max_seq_length` is a hard limit. - target_seq_length = max_num_tokens - if rng.random() < short_seq_prob: - target_seq_length = rng.randint(2, max_num_tokens) - - # We DON'T just concatenate all of the tokens from a document into a long - # sequence and choose an arbitrary split point because this would make the - # next sentence prediction task too easy. Instead, we split the input into - # segments "A" and "B" based on the actual "sentences" provided by the user - # input. - instances = [] - current_chunk = [] - current_length = 0 - i = 0 - while i < len(document): - segment = document[i] - current_chunk.append(segment) - current_length += len(segment) - if i == len(document) - 1 or current_length >= target_seq_length: - if current_chunk: - # `a_end` is how many segments from `current_chunk` go into the `A` - # (first) sentence. - a_end = 1 - if len(current_chunk) >= 2: - a_end = rng.randint(1, len(current_chunk) - 1) - - tokens_a = [] - for j in range(a_end): - tokens_a.extend(current_chunk[j]) - - tokens_b = [] - # Random next - is_random_next = False - if len(current_chunk) == 1 or rng.random() < 0.5: - is_random_next = True - target_b_length = target_seq_length - len(tokens_a) - - # This should rarely go for more than one iteration for large - # corpora. However, just to be careful, we try to make sure that - # the random document is not the same as the document - # we're processing. - for _ in range(10): - random_document_index = rng.randint(0, len(all_documents) - 1) - if random_document_index != document_index: - break - - random_document = all_documents[random_document_index] - random_start = rng.randint(0, len(random_document) - 1) - for j in range(random_start, len(random_document)): - tokens_b.extend(random_document[j]) - if len(tokens_b) >= target_b_length: - break - # We didn't actually use these segments so we "put them back" so - # they don't go to waste. - num_unused_segments = len(current_chunk) - a_end - i -= num_unused_segments - # Actual next - else: - is_random_next = False - for j in range(a_end, len(current_chunk)): - tokens_b.extend(current_chunk[j]) - truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) - - assert len(tokens_a) >= 1 - assert len(tokens_b) >= 1 - - tokens = [] - segment_ids = [] - tokens.append("[CLS]") - segment_ids.append(0) - for token in tokens_a: - tokens.append(token) - segment_ids.append(0) - - tokens.append("[SEP]") - segment_ids.append(0) - - for token in tokens_b: - tokens.append(token) - segment_ids.append(1) - tokens.append("[SEP]") - segment_ids.append(1) - - (tokens, masked_lm_positions, - masked_lm_labels) = create_masked_lm_predictions( - tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) - instance = TrainingInstance( - tokens=tokens, - segment_ids=segment_ids, - is_random_next=is_random_next, - masked_lm_positions=masked_lm_positions, - masked_lm_labels=masked_lm_labels) - instances.append(instance) - current_chunk = [] - current_length = 0 - i += 1 - - return instances - - -MaskedLmInstance = collections.namedtuple("MaskedLmInstance", - ["index", "label"]) - - -def create_masked_lm_predictions(tokens, masked_lm_prob, - max_predictions_per_seq, vocab_words, rng): - """Creates the predictions for the masked LM objective.""" - - cand_indexes = [] - for (i, token) in enumerate(tokens): - if token == "[CLS]" or token == "[SEP]": - continue - cand_indexes.append(i) - - rng.shuffle(cand_indexes) - - output_tokens = list(tokens) - - num_to_predict = min(max_predictions_per_seq, - max(1, int(round(len(tokens) * masked_lm_prob)))) - - masked_lms = [] - covered_indexes = set() - for index in cand_indexes: - if len(masked_lms) >= num_to_predict: - break - if index in covered_indexes: - continue - covered_indexes.add(index) - - masked_token = None - # 80% of the time, replace with [MASK] - if rng.random() < 0.8: - masked_token = "[MASK]" - else: - # 10% of the time, keep original - if rng.random() < 0.5: - masked_token = tokens[index] - # 10% of the time, replace with random word - else: - masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] - - output_tokens[index] = masked_token - - masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) - - masked_lms = sorted(masked_lms, key=lambda x: x.index) - - masked_lm_positions = [] - masked_lm_labels = [] - for p in masked_lms: - masked_lm_positions.append(p.index) - masked_lm_labels.append(p.label) - - return (output_tokens, masked_lm_positions, masked_lm_labels) - - -def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): - """Truncates a pair of sequences to a maximum sequence length.""" - while True: - total_length = len(tokens_a) + len(tokens_b) - if total_length <= max_num_tokens: - break - - trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b - assert len(trunc_tokens) >= 1 - - # We want to sometimes truncate from the front and sometimes from the - # back to add more randomness and avoid biases. - if rng.random() < 0.5: - del trunc_tokens[0] - else: - trunc_tokens.pop() - - -def main(_): - tf.logging.set_verbosity(tf.logging.INFO) - - tokenizer = tokenization.FullTokenizer( - vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) - - input_files = [] - for input_pattern in FLAGS.input_file.split(","): - input_files.extend(tf.gfile.Glob(input_pattern)) - - tf.logging.info("*** Reading from input files ***") - for input_file in input_files: - tf.logging.info(" %s", input_file) - - rng = random.Random(FLAGS.random_seed) - instances = create_training_instances( - input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor, - FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq, - rng) - - output_files = FLAGS.output_file.split(",") - tf.logging.info("*** Writing to output files ***") - for output_file in output_files: - tf.logging.info(" %s", output_file) - - write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length, - FLAGS.max_predictions_per_seq, output_files) - - -if __name__ == "__main__": - flags.mark_flag_as_required("input_file") - flags.mark_flag_as_required("output_file") - flags.mark_flag_as_required("vocab_file") - tf.app.run() diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_pretraining.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_pretraining.py index fdb3eda48c3e6c5c8d8f1153766fdb0ed7d02bd8..b6f357138343571869e6ac2d5fb3b5f56b2d8c88 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_pretraining.py +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_pretraining.py @@ -26,7 +26,7 @@ import modeling import optimization import tensorflow as tf import glob -from utils import LogEvalRunHook +from utils.utils import LogEvalRunHook from tensorflow.core.protobuf import rewriter_config_pb2 from gpu_environment import get_custom_getter @@ -37,6 +37,9 @@ from npu_bridge.estimator.npu.npu_estimator import NPUEstimator os.environ['GE_USE_STATIC_MEMORY'] = '1' +rank_size = int(os.getenv('RANK_SIZE')) +rank_id = int(os.getenv('RANK_ID')) + flags = tf.flags FLAGS = flags.FLAGS @@ -290,7 +293,7 @@ def model_fn_builder(bert_config, init_checkpoint, learning_rate, tvars = tf.trainable_variables() initialized_variable_names = {} - if init_checkpoint and (hvd is None or hvd.rank() == 0): + if init_checkpoint and (rank_id == 0): print("Loading checkpoint", init_checkpoint) (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) @@ -500,9 +503,7 @@ def input_fn_builder(input_files, # For eval, we want no shuffling and parallel reading doesn't matter. if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) - if FLAGS.distributed: - rank_size = int(os.getenv('RANK_SIZE')) - rank_id = int(os.getenv('RANK_ID')) + if FLAGS.distributed: print('RANK_SIZE=', rank_size, ' RANK_ID=', rank_id) d = d.shard(rank_size, rank_id) d = d.repeat() @@ -633,7 +634,7 @@ def main(_): model_dir=FLAGS.output_dir, save_summary_steps=0, session_config=config, - save_checkpoints_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None, + save_checkpoints_steps=FLAGS.save_checkpoints_steps if rank_id == 0 else 0, # This variable controls how often estimator reports examples/sec. # Default value is every 100 steps. # When --report_loss is True, we set to very large value to prevent @@ -645,12 +646,10 @@ def main(_): is_tailing_optimization=FLAGS.npu_bert_tail_optimize, hcom_parallel=FLAGS.hcom_parallel) - if FLAGS.distributed: - rank_size = int(os.getenv('RANK_SIZE')) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, - learning_rate=FLAGS.learning_rate * rank_size if FLAGS.distributed else FLAGS.learning_rate, + learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_one_hot_embeddings=False, @@ -688,7 +687,7 @@ def main(_): estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=FLAGS.num_train_steps) - if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0): + if FLAGS.do_eval and (rank_id == 0): tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) @@ -710,9 +709,10 @@ def main(_): input_fn=eval_input_fn, steps=FLAGS.max_eval_steps, hooks=eval_hooks) eval_time_elapsed = time.time() - eval_start_time - eval_time_wo_overhead = eval_hooks[-1].total_time - - num_sentences = (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.eval_batch_size + time_list = eval_hooks[-1].time_list + time_list.sort() + eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.99)]) + num_sentences = (int(len(time_list) * 0.99)) * FLAGS.eval_batch_size ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead @@ -720,7 +720,7 @@ def main(_): tf.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed, eval_hooks[-1].count * FLAGS.eval_batch_size) tf.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead, - (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.eval_batch_size) + num_sentences) tf.logging.info("Summary Inference Statistics on EVAL set") tf.logging.info("Batch size = %d", FLAGS.eval_batch_size) tf.logging.info("Sequence Length = %d", FLAGS.max_seq_length) diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_squad.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_squad.py new file mode 100644 index 0000000000000000000000000000000000000000..56815803e955ee20039632a1b328797c951b1890 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/run_squad.py @@ -0,0 +1,1327 @@ +# coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""Run BERT on SQuAD 1.1 and SQuAD 2.0.""" + +from __future__ import absolute_import, division, print_function +from npu_bridge.npu_init import * + +import collections +import json +import math +import os +import random +import shutil +import time + +#import horovod.tensorflow as hvd +import numpy as np +import six +import tensorflow as tf +from tensorflow.python.client import device_lib + +import modeling +import optimization +import tokenization +from utils.create_squad_data import * +from utils.utils import LogEvalRunHook, LogTrainRunHook, ExamplesPerSecondHook +#from utils.gpu_affinity import set_affinity +# import utils.dllogger_class +# from dllogger import Verbosity + +flags = tf.flags +FLAGS = None + +rank_size = int(os.getenv('RANK_SIZE')) +rank_id = int(os.getenv('RANK_ID')) + +def extract_run_squad_flags(): + + ## Required parameters + flags.DEFINE_string( + "bert_config_file", None, + "The config json file corresponding to the pre-trained BERT model. " + "This specifies the model architecture.") + + flags.DEFINE_string("vocab_file", None, + "The vocabulary file that the BERT model was trained on.") + + flags.DEFINE_string( + "output_dir", None, + "The output directory where the model checkpoints will be written.") + + ## Other parameters + + flags.DEFINE_string( + "dllog_path", "/results/bert_dllog.json", + "filename where dllogger writes to") + + flags.DEFINE_string("train_file", None, + "SQuAD json for training. E.g., train-v1.1.json") + + flags.DEFINE_string( + "predict_file", None, + "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") + flags.DEFINE_string( + "eval_script", None, + "SQuAD evaluate.py file to compute f1 and exact_match E.g., evaluate-v1.1.py") + + flags.DEFINE_string( + "init_checkpoint", None, + "Initial checkpoint (usually from a pre-trained BERT model).") + + flags.DEFINE_bool( + "do_lower_case", True, + "Whether to lower case the input text. Should be True for uncased " + "models and False for cased models.") + + flags.DEFINE_integer( + "max_seq_length", 384, + "The maximum total input sequence length after WordPiece tokenization. " + "Sequences longer than this will be truncated, and sequences shorter " + "than this will be padded.") + + flags.DEFINE_integer( + "doc_stride", 128, + "When splitting up a long document into chunks, how much stride to " + "take between chunks.") + + flags.DEFINE_integer( + "max_query_length", 64, + "The maximum number of tokens for the question. Questions longer than " + "this will be truncated to this length.") + + flags.DEFINE_bool("do_train", False, "Whether to run training.") + + flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.") + + flags.DEFINE_integer("train_batch_size", 8, "Total batch size for training.") + + flags.DEFINE_integer("predict_batch_size", 8, + "Total batch size for predictions.") + + flags.DEFINE_float("learning_rate", 5e-6, "The initial learning rate for Adam.") + + flags.DEFINE_bool("use_trt", False, "Whether to use TF-TRT") + + flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs") + + flags.DEFINE_float("num_train_epochs", 3.0, + "Total number of training epochs to perform.") + + flags.DEFINE_integer("num_train_steps", 0, + "How many steps to train.") + + flags.DEFINE_float( + "warmup_proportion", 0.1, + "Proportion of training to perform linear learning rate warmup for. " + "E.g., 0.1 = 10% of training.") + + flags.DEFINE_integer("save_checkpoints_steps", 5000, + "How often to save the model checkpoint.") + flags.DEFINE_integer("display_loss_steps", 100, + "How often to print loss from estimator") + + flags.DEFINE_integer("num_accumulation_steps", 1, + "Number of accumulation steps before gradient update" + "Global batch size = num_accumulation_steps * train_batch_size") + + flags.DEFINE_integer( + "n_best_size", 20, + "The total number of n-best predictions to generate in the " + "nbest_predictions.json output file.") + + flags.DEFINE_integer( + "max_answer_length", 30, + "The maximum length of an answer that can be generated. This is needed " + "because the start and end predictions are not conditioned on one another.") + + + flags.DEFINE_bool( + "verbose_logging", False, + "If true, all of the warnings related to data processing will be printed. " + "A number of warnings are expected for a normal SQuAD evaluation.") + + flags.DEFINE_bool( + "version_2_with_negative", False, + "If true, the SQuAD examples contain some that do not have an answer.") + + flags.DEFINE_float( + "null_score_diff_threshold", 0.0, + "If null_score - best_non_null is greater than the threshold predict null.") + + flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.") + flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.") + flags.DEFINE_integer("num_eval_iterations", None, + "How many eval iterations to run - performs inference on subset") + # npu parameter + flags.DEFINE_bool('npu_bert_debug', False, 'If True, dropout and shuffle is disabled.') + flags.DEFINE_integer('init_loss_scale_value', 2 ** 32, 'Initial loss scale value for loss scale optimizer') + flags.DEFINE_integer("iterations_per_loop", 100, "How many steps to make in each estimator call.") + flags.DEFINE_bool("use_fp16_cls", False, "Whether to use fp16 in cls and pooler.") + flags.DEFINE_bool('npu_bert_fused_gelu', True, 'Whether to use npu defined gelu op') + flags.DEFINE_integer("npu_bert_loss_scale", 0, + "Whether to use loss scale, -1 is disable, 0 is dynamic loss scale, >=1 is static loss scale") + flags.DEFINE_bool("npu_bert_clip_by_global_norm", False, + "Use clip_by_global_norm if True, or use clip_by_norm for each gradient") + + flags.DEFINE_bool('npu_bert_npu_dropout', True, 'Whether to use npu defined dropout op') + + flags.DEFINE_bool('npu_bert_npu_dropout_v3', False, 'Whether to use npu defined dropout_v3 op') + + flags.DEFINE_bool('npu_bert_tail_optimize', False, 'Whether to use npu allreduce tail optimization') + + flags.DEFINE_bool('npu_gather', True, 'Whether to use gather_npu whose backward propagation avoids IndexedSlices') + flags.DEFINE_bool("distributed", False, "Whether to train for multi-npu runs") + flags.DEFINE_bool('hcom_parallel', True, 'Whether to use parallel allreduce') + + flags.DEFINE_bool('use_fast_gelu', True, 'use fast gelu instead gelu') + + flags.DEFINE_bool('npu_bert_use_fused_adam_momentum', False, 'Whether to use fused apply and assign in adam') + + flags.DEFINE_bool('npu_bert_use_fused_lamb_momentum', False, 'Whether to use fused apply and assign in lamb') + flags.DEFINE_string("precision_mode", "allow_mix_precision", "Npu Precision Mode") + flags.DEFINE_bool("enable_exception_dump", False, "Whether to enable excepttion dump.") + flags.DEFINE_bool("data_dump_flag", False, "Whether to dump data.") + flags.DEFINE_string("data_dump_step", "0", "How many steps to dump data.") + flags.DEFINE_string("data_dump_path", "./output/data_dump", "path to dump data.") + flags.DEFINE_bool("over_dump", False, "Whether to over_dump.") + flags.DEFINE_string("over_dump_path", "./output/doverflow_dump", "path to dump overflow data.") + # Triton Specific flags + flags.DEFINE_bool("export_triton", False, "Whether to export saved model or run inference with Triton") + flags.DEFINE_string("triton_model_name", "bert", "exports to appropriate directory for Triton") + flags.DEFINE_integer("triton_model_version", 1, "exports to appropriate directory for Triton") + flags.DEFINE_string("triton_server_url", "localhost:8001", "exports to appropriate directory for Triton") + flags.DEFINE_bool("triton_model_overwrite", False, "If True, will overwrite an existing directory with the specified 'model_name' and 'version_name'") + flags.DEFINE_integer("triton_max_batch_size", 8, "Specifies the 'max_batch_size' in the Triton model config. See the Triton documentation for more info.") + flags.DEFINE_float("triton_dyn_batching_delay", 0, "Determines the dynamic_batching queue delay in milliseconds(ms) for the Triton model config. Use '0' or '-1' to specify static batching. See the Triton documentation for more info.") + flags.DEFINE_integer("triton_engine_count", 1, "Specifies the 'instance_group' count value in the Triton model config. See the Triton documentation for more info.") + flags.mark_flag_as_required("vocab_file") + flags.mark_flag_as_required("bert_config_file") + flags.mark_flag_as_required("output_dir") + + return flags.FLAGS + +def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, + use_one_hot_embeddings): + """Creates a classification model.""" + model = modeling.BertModel( + config=bert_config, + is_training=is_training, + input_ids=input_ids, + input_mask=input_mask, + token_type_ids=segment_ids, + use_one_hot_embeddings=use_one_hot_embeddings, + compute_type=tf.float32) + + final_hidden = model.get_sequence_output() + + final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3) + batch_size = final_hidden_shape[0] + seq_length = final_hidden_shape[1] + hidden_size = final_hidden_shape[2] + + output_weights = tf.get_variable( + "cls/squad/output_weights", [2, hidden_size], + initializer=tf.truncated_normal_initializer(stddev=0.02)) + + output_bias = tf.get_variable( + "cls/squad/output_bias", [2], initializer=tf.zeros_initializer()) + + final_hidden_matrix = tf.reshape(final_hidden, + [batch_size * seq_length, hidden_size]) + logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True) + logits = tf.nn.bias_add(logits, output_bias) + + logits = tf.reshape(logits, [batch_size, seq_length, 2]) + logits = tf.transpose(logits, [2, 0, 1]) + + unstacked_logits = tf.unstack(logits, axis=0, name='unstack') + + (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1]) + + return (start_logits, end_logits) + +def get_frozen_tftrt_model(bert_config, shape, use_one_hot_embeddings, init_checkpoint): + tf_config = tf.compat.v1.ConfigProto() + tf_config.gpu_options.allow_growth = True + output_node_names = ['unstack'] + + with tf.Session(config=tf_config) as tf_sess: + input_ids = tf.placeholder(tf.int32, shape, 'input_ids') + input_mask = tf.placeholder(tf.int32, shape, 'input_mask') + segment_ids = tf.placeholder(tf.int32, shape, 'segment_ids') + + (start_logits, end_logits) = create_model(bert_config=bert_config, + is_training=False, + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + use_one_hot_embeddings=use_one_hot_embeddings) + + + tvars = tf.trainable_variables() + (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) + tf.train.init_from_checkpoint(init_checkpoint, assignment_map) + tf_sess.run(tf.global_variables_initializer()) + print("LOADED!") + tf.compat.v1.logging.info("**** Trainable Variables ****") + for var in tvars: + init_string = "" + if var.name in initialized_variable_names: + init_string = ", *INIT_FROM_CKPT*" + else: + init_string = ", *NOTTTTTTTTTTTTTTTTTTTTT" + tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) + + frozen_graph = tf.graph_util.convert_variables_to_constants(tf_sess, + tf_sess.graph.as_graph_def(), output_node_names) + + num_nodes = len(frozen_graph.node) + print('Converting graph using TensorFlow-TensorRT...') + from tensorflow.python.compiler.tensorrt import trt_convert as trt + converter = trt.TrtGraphConverter( + input_graph_def=frozen_graph, + nodes_blacklist=output_node_names, + max_workspace_size_bytes=(4096 << 20) - 1000, + precision_mode="FP16" if FLAGS.amp else "FP32", + minimum_segment_size=4, + is_dynamic_op=True, + maximum_cached_engines=1000 + ) + frozen_graph = converter.convert() + + print('Total node count before and after TF-TRT conversion:', + num_nodes, '->', len(frozen_graph.node)) + print('TRT node count:', + len([1 for n in frozen_graph.node if str(n.op) == 'TRTEngineOp'])) + + with tf.io.gfile.GFile("frozen_modelTRT.pb", "wb") as f: + f.write(frozen_graph.SerializeToString()) + + return frozen_graph + + +def model_fn_builder(bert_config, init_checkpoint, learning_rate, + num_train_steps, num_warmup_steps, + hvd=None, amp=False, use_one_hot_embeddings=False): + """Returns `model_fn` closure for Estimator.""" + + def model_fn(features, labels, mode, params): # pylint: disable=unused-argument + """The `model_fn` for Estimator.""" + if FLAGS.verbose_logging: + tf.compat.v1.logging.info("*** Features ***") + for name in sorted(features.keys()): + tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) + + unique_ids = features["unique_ids"] + input_ids = features["input_ids"] + input_mask = features["input_mask"] + segment_ids = features["segment_ids"] + + is_training = (mode == tf.estimator.ModeKeys.TRAIN) + + if not is_training and FLAGS.use_trt: + trt_graph = get_frozen_tftrt_model(bert_config, input_ids.shape, use_one_hot_embeddings, init_checkpoint) + (start_logits, end_logits) = tf.import_graph_def(trt_graph, + input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids}, + return_elements=['unstack:0', 'unstack:1'], + name='') + predictions = { + "unique_ids": unique_ids, + "start_logits": start_logits, + "end_logits": end_logits, + } + output_spec = tf.estimator.EstimatorSpec( + mode=mode, predictions=predictions) + return output_spec + + (start_logits, end_logits) = create_model( + bert_config=bert_config, + is_training=is_training, + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + use_one_hot_embeddings=use_one_hot_embeddings) + + tvars = tf.trainable_variables() + + initialized_variable_names = {} + if init_checkpoint and (rank_id == 0): + (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) + + tf.train.init_from_checkpoint(init_checkpoint, assignment_map) + + if FLAGS.verbose_logging: + tf.compat.v1.logging.info("**** Trainable Variables ****") + for var in tvars: + init_string = "" + if var.name in initialized_variable_names: + init_string = ", *INIT_FROM_CKPT*" + tf.compat.v1.logging.info(" %d name = %s, shape = %s%s", rank_id, var.name, var.shape, + init_string) + + if mode == tf.estimator.ModeKeys.TRAIN: + seq_length = modeling.get_shape_list(input_ids)[1] + + def compute_loss(logits, positions): + one_hot_positions = tf.one_hot( + positions, depth=seq_length, dtype=tf.float32) + log_probs = tf.nn.log_softmax(logits, axis=-1) + loss = -tf.reduce_mean( + tf.reduce_sum(one_hot_positions * log_probs, axis=-1)) + return loss + + start_positions = features["start_positions"] + end_positions = features["end_positions"] + + start_loss = compute_loss(start_logits, start_positions) + end_loss = compute_loss(end_logits, end_positions) + + total_loss = (start_loss + end_loss) / 2.0 + + total_loss = tf.identity(total_loss, name='total_loss') + + train_op = optimization.create_optimizer( + total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, False, amp, FLAGS.num_accumulation_steps) + + output_spec = NPUEstimatorSpec( + mode=mode, + loss=total_loss, + train_op=train_op) + # output_spec = tf.estimator.EstimatorSpec( + # mode=mode, + # loss=total_loss, + # train_op=train_op) + elif mode == tf.estimator.ModeKeys.PREDICT: + + # dummy_op = tf.no_op() + # # Need to call mixed precision graph rewrite if fp16 to enable graph rewrite + # if amp: + # loss_scaler = FixedLossScaleManager(1) + # dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite( + # optimization.LAMBOptimizer(learning_rate=0.0), loss_scaler) + + predictions = { + "unique_ids": tf.identity(unique_ids), + "start_logits": start_logits, + "end_logits": end_logits, + } + output_spec = NPUEstimatorSpec( + mode=mode, predictions=predictions) + # output_spec = tf.estimator.EstimatorSpec( + # mode=mode, predictions=predictions) + else: + raise ValueError( + "Only TRAIN and PREDICT modes are supported: %s" % (mode)) + + return output_spec + + return model_fn + + +def input_fn_builder(input_file, batch_size, seq_length, is_training, drop_remainder, hvd=None): + """Creates an `input_fn` closure to be passed to Estimator.""" + + name_to_features = { + "unique_ids": tf.io.FixedLenFeature([], tf.int64), + "input_ids": tf.io.FixedLenFeature([seq_length], tf.int64), + "input_mask": tf.io.FixedLenFeature([seq_length], tf.int64), + "segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64), + } + + if is_training: + name_to_features["start_positions"] = tf.io.FixedLenFeature([], tf.int64) + name_to_features["end_positions"] = tf.io.FixedLenFeature([], tf.int64) + + def _decode_record(record, name_to_features): + """Decodes a record to a TensorFlow example.""" + example = tf.parse_single_example(record, name_to_features) + + # tf.Example only supports tf.int64, but the TPU only supports tf.int32. + # So cast all int64 to int32. + for name in list(example.keys()): + t = example[name] + if t.dtype == tf.int64: + t = tf.to_int32(t) + example[name] = t + + return example + + def input_fn(): + """The actual input function.""" + + # For training, we want a lot of parallel reading and shuffling. + # For eval, we want no shuffling and parallel reading doesn't matter. + if is_training: + d = tf.data.TFRecordDataset(input_file, num_parallel_reads=4) + if rank_size > 1: + d = d.shard(rank_size, rank_id) + d = d.apply(tf.data.experimental.ignore_errors()) + if not FLAGS.npu_bert_debug: + d = d.shuffle(buffer_size=100) + d = d.repeat() + else: + d = tf.data.TFRecordDataset(input_file) + + d = d.apply( + tf.contrib.data.map_and_batch( + lambda record: _decode_record(record, name_to_features), + batch_size=batch_size, + drop_remainder=True)) + + return d + + return input_fn + + + +RawResult = collections.namedtuple("RawResult", + ["unique_id", "start_logits", "end_logits"]) + + +def get_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length, + do_lower_case, version_2_with_negative, verbose_logging): + """Get final predictions""" + + example_index_to_features = collections.defaultdict(list) + for feature in all_features: + example_index_to_features[feature.example_index].append(feature) + + unique_id_to_result = {} + for result in all_results: + unique_id_to_result[result.unique_id] = result + + # process unique id issue + max_unique_id = all_results[-1].unique_id + print("max_unique_id=%d" % max_unique_id) + + _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name + "PrelimPrediction", + ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]) + + all_predictions = collections.OrderedDict() + all_nbest_json = collections.OrderedDict() + scores_diff_json = collections.OrderedDict() + + for (example_index, example) in enumerate(all_examples): + features = example_index_to_features[example_index] + + prelim_predictions = [] + # keep track of the minimum score of null start+end of position 0 + score_null = 1000000 # large and positive + min_null_feature_index = 0 # the paragraph slice with min mull score + null_start_logit = 0 # the start logit at the slice with min null score + null_end_logit = 0 # the end logit at the slice with min null score + for (feature_index, feature) in enumerate(features): + if feature.unique_id > max_unique_id: + continue + result = unique_id_to_result[feature.unique_id] + start_indexes = _get_best_indexes(result.start_logits, n_best_size) + end_indexes = _get_best_indexes(result.end_logits, n_best_size) + # if we could have irrelevant answers, get the min score of irrelevant + if version_2_with_negative: + feature_null_score = result.start_logits[0] + result.end_logits[0] + if feature_null_score < score_null: + score_null = feature_null_score + min_null_feature_index = feature_index + null_start_logit = result.start_logits[0] + null_end_logit = result.end_logits[0] + for start_index in start_indexes: + for end_index in end_indexes: + # We could hypothetically create invalid predictions, e.g., predict + # that the start of the span is in the question. We throw out all + # invalid predictions. + if start_index >= len(feature.tokens): + continue + if end_index >= len(feature.tokens): + continue + if start_index not in feature.token_to_orig_map: + continue + if end_index not in feature.token_to_orig_map: + continue + if not feature.token_is_max_context.get(start_index, False): + continue + if end_index < start_index: + continue + length = end_index - start_index + 1 + if length > max_answer_length: + continue + prelim_predictions.append( + _PrelimPrediction( + feature_index=feature_index, + start_index=start_index, + end_index=end_index, + start_logit=result.start_logits[start_index], + end_logit=result.end_logits[end_index])) + + if version_2_with_negative: + prelim_predictions.append( + _PrelimPrediction( + feature_index=min_null_feature_index, + start_index=0, + end_index=0, + start_logit=null_start_logit, + end_logit=null_end_logit)) + prelim_predictions = sorted( + prelim_predictions, + key=lambda x: (x.start_logit + x.end_logit), + reverse=True) + + _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name + "NbestPrediction", ["text", "start_logit", "end_logit"]) + + seen_predictions = {} + nbest = [] + for pred in prelim_predictions: + if len(nbest) >= n_best_size: + break + feature = features[pred.feature_index] + if pred.start_index > 0: # this is a non-null prediction + tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)] + orig_doc_start = feature.token_to_orig_map[pred.start_index] + orig_doc_end = feature.token_to_orig_map[pred.end_index] + orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)] + tok_text = " ".join(tok_tokens) + + # De-tokenize WordPieces that have been split off. + tok_text = tok_text.replace(" ##", "") + tok_text = tok_text.replace("##", "") + + # Clean whitespace + tok_text = tok_text.strip() + tok_text = " ".join(tok_text.split()) + orig_text = " ".join(orig_tokens) + + final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging) + if final_text in seen_predictions: + continue + + seen_predictions[final_text] = True + else: + final_text = "" + seen_predictions[final_text] = True + nbest.append( + _NbestPrediction( + text=final_text, + start_logit=pred.start_logit, + end_logit=pred.end_logit)) + + # if we didn't inlude the empty option in the n-best, inlcude it + if version_2_with_negative: + if "" not in seen_predictions: + nbest.append( + _NbestPrediction( + text="", start_logit=null_start_logit, + end_logit=null_end_logit)) + # In very rare edge cases we could have no valid predictions. So we + # just create a nonce prediction in this case to avoid failure. + if not nbest: + nbest.append( + _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) + + assert len(nbest) >= 1 + + total_scores = [] + best_non_null_entry = None + for entry in nbest: + total_scores.append(entry.start_logit + entry.end_logit) + if not best_non_null_entry: + if entry.text: + best_non_null_entry = entry + + probs = _compute_softmax(total_scores) + + nbest_json = [] + for (i, entry) in enumerate(nbest): + output = collections.OrderedDict() + output["text"] = entry.text + output["probability"] = probs[i] + output["start_logit"] = entry.start_logit + output["end_logit"] = entry.end_logit + nbest_json.append(output) + + assert len(nbest_json) >= 1 + + if not version_2_with_negative: + all_predictions[example.qas_id] = nbest_json[0]["text"] + else: + # predict "" iff the null score - the score of best non-null > threshold + score_diff = score_null - best_non_null_entry.start_logit - ( + best_non_null_entry.end_logit) + scores_diff_json[example.qas_id] = score_diff + + try: + null_score_diff_threshold = FLAGS.null_score_diff_threshold + except: + null_score_diff_threshold = 0.0 + if score_diff > null_score_diff_threshold: + all_predictions[example.qas_id] = "" + else: + all_predictions[example.qas_id] = best_non_null_entry.text + + all_nbest_json[example.qas_id] = nbest_json + return all_predictions, all_nbest_json, scores_diff_json + +def write_predictions(all_examples, all_features, all_results, n_best_size, + max_answer_length, do_lower_case, output_prediction_file, + output_nbest_file, output_null_log_odds_file, + version_2_with_negative, verbose_logging): + """Write final predictions to the json file and log-odds of null if needed.""" + + tf.compat.v1.logging.info("Writing predictions to: %s" % (output_prediction_file)) + tf.compat.v1.logging.info("Writing nbest to: %s" % (output_nbest_file)) + + all_predictions, all_nbest_json, scores_diff_json = get_predictions(all_examples, all_features, + all_results, n_best_size, max_answer_length, do_lower_case, version_2_with_negative, verbose_logging) + + with tf.io.gfile.GFile(output_prediction_file, "w") as writer: + writer.write(json.dumps(all_predictions, indent=4) + "\n") + + with tf.io.gfile.GFile(output_nbest_file, "w") as writer: + writer.write(json.dumps(all_nbest_json, indent=4) + "\n") + + if version_2_with_negative: + with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer: + writer.write(json.dumps(scores_diff_json, indent=4) + "\n") + + +def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging): + """Project the tokenized prediction back to the original text.""" + + # When we created the data, we kept track of the alignment between original + # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So + # now `orig_text` contains the span of our original text corresponding to the + # span that we predicted. + # + # However, `orig_text` may contain extra characters that we don't want in + # our prediction. + # + # For example, let's say: + # pred_text = steve smith + # orig_text = Steve Smith's + # + # We don't want to return `orig_text` because it contains the extra "'s". + # + # We don't want to return `pred_text` because it's already been normalized + # (the SQuAD eval script also does punctuation stripping/lower casing but + # our tokenizer does additional normalization like stripping accent + # characters). + # + # What we really want to return is "Steve Smith". + # + # Therefore, we have to apply a semi-complicated alignment heruistic between + # `pred_text` and `orig_text` to get a character-to-charcter alignment. This + # can fail in certain cases in which case we just return `orig_text`. + + def _strip_spaces(text): + ns_chars = [] + ns_to_s_map = collections.OrderedDict() + for (i, c) in enumerate(text): + if c == " ": + continue + ns_to_s_map[len(ns_chars)] = i + ns_chars.append(c) + ns_text = "".join(ns_chars) + return (ns_text, ns_to_s_map) + + # We first tokenize `orig_text`, strip whitespace from the result + # and `pred_text`, and check if they are the same length. If they are + # NOT the same length, the heuristic has failed. If they are the same + # length, we assume the characters are one-to-one aligned. + tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case) + + tok_text = " ".join(tokenizer.tokenize(orig_text)) + + start_position = tok_text.find(pred_text) + if start_position == -1: + if verbose_logging: + tf.compat.v1.logging.info( + "Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) + return orig_text + end_position = start_position + len(pred_text) - 1 + + (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) + (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) + + if len(orig_ns_text) != len(tok_ns_text): + if verbose_logging: + tf.compat.v1.logging.info("Length not equal after stripping spaces: '%s' vs '%s'", + orig_ns_text, tok_ns_text) + return orig_text + + # We then project the characters in `pred_text` back to `orig_text` using + # the character-to-character alignment. + tok_s_to_ns_map = {} + for (i, tok_index) in six.iteritems(tok_ns_to_s_map): + tok_s_to_ns_map[tok_index] = i + + orig_start_position = None + if start_position in tok_s_to_ns_map: + ns_start_position = tok_s_to_ns_map[start_position] + if ns_start_position in orig_ns_to_s_map: + orig_start_position = orig_ns_to_s_map[ns_start_position] + + if orig_start_position is None: + if verbose_logging: + tf.compat.v1.logging.info("Couldn't map start position") + return orig_text + + orig_end_position = None + if end_position in tok_s_to_ns_map: + ns_end_position = tok_s_to_ns_map[end_position] + if ns_end_position in orig_ns_to_s_map: + orig_end_position = orig_ns_to_s_map[ns_end_position] + + if orig_end_position is None: + if verbose_logging: + tf.compat.v1.logging.info("Couldn't map end position") + return orig_text + + output_text = orig_text[orig_start_position:(orig_end_position + 1)] + return output_text + + +def _get_best_indexes(logits, n_best_size): + """Get the n-best logits from a list.""" + index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) + + best_indexes = [] + for i in range(len(index_and_score)): + if i >= n_best_size: + break + best_indexes.append(index_and_score[i][0]) + return best_indexes + + +def _compute_softmax(scores): + """Compute softmax probability over raw logits.""" + if not scores: + return [] + + max_score = None + for score in scores: + if max_score is None or score > max_score: + max_score = score + + exp_scores = [] + total_sum = 0.0 + for score in scores: + x = math.exp(score - max_score) + exp_scores.append(x) + total_sum += x + + probs = [] + for score in exp_scores: + probs.append(score / total_sum) + return probs + + + +def validate_flags_or_throw(bert_config): + """Validate the input FLAGS or throw an exception.""" + tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, + FLAGS.init_checkpoint) + + if not FLAGS.do_train and not FLAGS.do_predict and not FLAGS.export_triton: + raise ValueError("At least one of `do_train` or `do_predict` or `export_SavedModel` must be True.") + + if FLAGS.do_train: + if not FLAGS.train_file: + raise ValueError( + "If `do_train` is True, then `train_file` must be specified.") + if FLAGS.do_predict: + if not FLAGS.predict_file: + raise ValueError( + "If `do_predict` is True, then `predict_file` must be specified.") + + if FLAGS.max_seq_length > bert_config.max_position_embeddings: + raise ValueError( + "Cannot use sequence length %d because the BERT model " + "was only trained up to sequence length %d" % + (FLAGS.max_seq_length, bert_config.max_position_embeddings)) + + if FLAGS.max_seq_length <= FLAGS.max_query_length + 3: + raise ValueError( + "The max_seq_length (%d) must be greater than max_query_length " + "(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length)) + + +def export_model(estimator, export_dir, init_checkpoint): + """Exports a checkpoint in SavedModel format in a directory structure compatible with Triton.""" + def serving_input_fn(): + label_ids = tf.placeholder(tf.int32, [None,], name='unique_ids') + input_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids') + input_mask = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask') + segment_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids') + input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ + 'unique_ids': label_ids, + 'input_ids': input_ids, + 'input_mask': input_mask, + 'segment_ids': segment_ids, + })() + return input_fn + + saved_dir = estimator.export_savedmodel( + export_dir, + serving_input_fn, + assets_extra=None, + as_text=False, + checkpoint_path=init_checkpoint, + strip_default_attrs=False) + + model_name = FLAGS.triton_model_name + + model_folder = export_dir + "/triton_models/" + model_name + version_folder = model_folder + "/" + str(FLAGS.triton_model_version) + final_model_folder = version_folder + "/model.savedmodel" + + if not os.path.exists(version_folder): + os.makedirs(version_folder) + + if (not os.path.exists(final_model_folder)): + os.rename(saved_dir, final_model_folder) + print("Model saved to dir", final_model_folder) + else: + if (FLAGS.triton_model_overwrite): + shutil.rmtree(final_model_folder) + os.rename(saved_dir, final_model_folder) + print("WARNING: Existing model was overwritten. Model dir: {}".format(final_model_folder)) + else: + print("ERROR: Could not save Triton model. Folder already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model. Model dir: {}".format(final_model_folder)) + return + + # Now build the config for Triton. Check to make sure we can overwrite it, if it exists + config_filename = os.path.join(model_folder, "config.pbtxt") + + optimization_str = "" + if FLAGS.amp: + optimization_str = r""" +optimization { + execution_accelerators + { + gpu_execution_accelerator : + [ { + name : "auto_mixed_precision" + } ] + } +}""" + + if (os.path.exists(config_filename) and not FLAGS.triton_model_overwrite): + print("ERROR: Could not save Triton model config. Config file already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model config. Model config: {}".format(config_filename)) + return + + config_template = r""" +name: "{model_name}" +platform: "tensorflow_savedmodel" +max_batch_size: {max_batch_size} +{optimization_str} +input [ + {{ + name: "unique_ids" + data_type: TYPE_INT32 + dims: [ 1 ] + reshape: {{ shape: [ ] }} + }}, + {{ + name: "segment_ids" + data_type: TYPE_INT32 + dims: {seq_length} + }}, + {{ + name: "input_ids" + data_type: TYPE_INT32 + dims: {seq_length} + }}, + {{ + name: "input_mask" + data_type: TYPE_INT32 + dims: {seq_length} + }} + ] + output [ + {{ + name: "end_logits" + data_type: TYPE_FP32 + dims: {seq_length} + }}, + {{ + name: "start_logits" + data_type: TYPE_FP32 + dims: {seq_length} + }} +] +{dynamic_batching} +instance_group [ + {{ + count: {engine_count} + }} +]""" + + batching_str = "" + max_batch_size = FLAGS.triton_max_batch_size + + if (FLAGS.triton_dyn_batching_delay > 0): + + # Use only full and half full batches + pref_batch_size = [int(max_batch_size / 2.0), max_batch_size] + + batching_str = r""" +dynamic_batching {{ + preferred_batch_size: [{0}] + max_queue_delay_microseconds: {1} +}}""".format(", ".join([str(x) for x in pref_batch_size]), int(FLAGS.triton_dyn_batching_delay * 1000.0)) + + config_values = { + "model_name": model_name, + "max_batch_size": max_batch_size, + "seq_length": FLAGS.max_seq_length, + "dynamic_batching": batching_str, + "engine_count": FLAGS.triton_engine_count, + "optimization_str":optimization_str, + } + + with open(model_folder + "/config.pbtxt", "w") as file: + + final_config_str = config_template.format_map(config_values) + file.write(final_config_str) + +def main(_): + # setup_xla_flags() + + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) + # dllogging = utils.dllogger_class.dllogger_class(FLAGS.dllog_path) + + # if FLAGS.horovod: + # hvd.init() + + bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) + + validate_flags_or_throw(bert_config) + + tf.io.gfile.makedirs(FLAGS.output_dir) + + tokenizer = tokenization.FullTokenizer( + vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) + + master_process = True + training_hooks = [] + global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps + # hvd_rank = 0 + + config = tf.ConfigProto( + inter_op_parallelism_threads=0, + intra_op_parallelism_threads=0, + allow_soft_placement=True) + + learning_rate = FLAGS.learning_rate + # if FLAGS.horovod: + if FLAGS.distributed: + tf.compat.v1.logging.info("Multi-NPU training...") + tf.compat.v1.logging.info("rank_size = %d rank_id = %d", rank_size, rank_id) + global_batch_size = FLAGS.train_batch_size * rank_size * FLAGS.num_accumulation_steps + learning_rate = learning_rate * rank_size + master_process = (rank_id == 0) + # hvd_rank = rank_id + # config.gpu_options.visible_device_list = str(hvd.local_rank()) + #set_affinity(hvd.local_rank()) + # if rank_size > 1: + # training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) + if FLAGS.use_xla: + config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1 + if FLAGS.amp: + tf.enable_resource_variables() + # run_config = tf.estimator.RunConfig( + # model_dir=FLAGS.output_dir if master_process else None, + # session_config=config, + # save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else 0, + # save_summary_steps=FLAGS.save_checkpoints_steps if master_process else 0, + # log_step_count_steps=FLAGS.display_loss_steps, + # keep_checkpoint_max=1) + + run_config = NPURunConfig( + model_dir=FLAGS.output_dir if master_process else None, + save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else 0, + save_summary_steps=0, + iterations_per_loop=FLAGS.iterations_per_loop, + session_config=config, + precision_mode=FLAGS.precision_mode, + log_step_count_steps=None, + is_tailing_optimization=FLAGS.npu_bert_tail_optimize, + hcom_parallel=FLAGS.hcom_parallel) + + if master_process: + tf.compat.v1.logging.info("***** Configuaration *****") + for key in FLAGS.__flags.keys(): + tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key))) + tf.compat.v1.logging.info("**************************") + + # train_examples = None + num_train_steps = FLAGS.num_train_steps + num_warmup_steps = None + training_hooks.append(ExamplesPerSecondHook(global_batch_size, FLAGS.iterations_per_loop)) + + # Prepare Training Data + if FLAGS.do_train: + # train_examples = read_squad_examples( + # input_file=FLAGS.train_file, is_training=True, + # version_2_with_negative=FLAGS.version_2_with_negative) + # Squad_V1.1 train_examples = 87599 + # Squad_V2.0 train_examples = 130319 + if num_train_steps == 0: + if FLAGS.version_2_with_negative: + num_train_steps = int(130319 / global_batch_size * FLAGS.num_train_epochs) + else: + num_train_steps = int(87599 / global_batch_size * FLAGS.num_train_epochs) + num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) + + # Pre-shuffle the input to avoid having to make a very large shuffle + # buffer in in the `input_fn`. + # rng = random.Random(12345) + # rng.shuffle(train_examples) + # + # start_index = 0 + # end_index = len(train_examples) + # tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")] + + # if FLAGS.horovod: + # if FLAGS.distributed: + # tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(rank_size)] + # num_examples_per_rank = len(train_examples) // rank_size + # remainder = len(train_examples) % rank_size + # if rank_id < remainder: + # start_index = rank_id * (num_examples_per_rank + 1) + # end_index = start_index + num_examples_per_rank + 1 + # else: + # start_index = rank_id * num_examples_per_rank + remainder + # end_index = start_index + num_examples_per_rank + + + model_fn = model_fn_builder( + bert_config=bert_config, + init_checkpoint=FLAGS.init_checkpoint, + learning_rate=learning_rate, + num_train_steps=num_train_steps, + num_warmup_steps=num_warmup_steps, + hvd=None if not FLAGS.horovod else hvd, + amp=FLAGS.amp) + + # estimator = tf.estimator.Estimator( + # model_fn=model_fn, + # config=npu_run_config_init(run_config=run_config)) + + estimator = NPUEstimator( + model_fn=model_fn, + config=run_config, + model_dir=FLAGS.output_dir, + params={"batch_size": FLAGS.train_batch_size, "predict_batch_size": FLAGS.predict_batch_size}) + + if FLAGS.do_train: + + # We write to a temporary file to avoid storing very large constant tensors + # in memory. + # train_writer = FeatureWriter( + # filename=tmp_filenames[hvd_rank], + # is_training=True) + # convert_examples_to_features( + # examples=train_examples[start_index:end_index], + # tokenizer=tokenizer, + # max_seq_length=FLAGS.max_seq_length, + # doc_stride=FLAGS.doc_stride, + # max_query_length=FLAGS.max_query_length, + # is_training=True, + # output_fn=train_writer.process_feature, + # verbose_logging=FLAGS.verbose_logging) + # train_writer.close() + + tf.compat.v1.logging.info("***** Running training *****") + # tf.compat.v1.logging.info(" Num orig examples = %d", end_index - start_index) + # tf.compat.v1.logging.info(" Num split examples = %d", train_writer.num_features) + tf.compat.v1.logging.info(" Batch size = %d", FLAGS.train_batch_size) + tf.compat.v1.logging.info(" Num steps = %d", num_train_steps) + tf.compat.v1.logging.info(" LR = %f", learning_rate) + # del train_examples + + train_input_fn = input_fn_builder( + # input_file=tmp_filenames, + input_file=FLAGS.train_file, + batch_size=FLAGS.train_batch_size, + seq_length=FLAGS.max_seq_length, + is_training=True, + drop_remainder=True, + hvd=None if not FLAGS.horovod else hvd) + + # train_start_time = time.time() + estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=num_train_steps) + # train_time_elapsed = time.time() - train_start_time + # train_time_wo_overhead = training_hooks[-1].total_time + # avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_elapsed + # ss_sentences_per_second = (num_train_steps - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead + + # if master_process: + # tf.compat.v1.logging.info("-----------------------------") + # tf.compat.v1.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed, + # num_train_steps * global_batch_size) + # tf.compat.v1.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead, + # (num_train_steps - training_hooks[-1].skipped) * global_batch_size) + # tf.compat.v1.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second) + # tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) + # # dllogging.logger.log(step=(), data={"throughput_train": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT) + # tf.compat.v1.logging.info("-----------------------------") + + + if FLAGS.export_triton and master_process: + export_model(estimator, FLAGS.output_dir, FLAGS.init_checkpoint) + + if FLAGS.do_predict and master_process: + eval_examples = read_squad_examples( + input_file=FLAGS.predict_file, is_training=False, + version_2_with_negative=FLAGS.version_2_with_negative) + + # Perform evaluation on subset, useful for profiling + if FLAGS.num_eval_iterations is not None: + eval_examples = eval_examples[:FLAGS.num_eval_iterations*FLAGS.predict_batch_size] + + eval_writer = FeatureWriter( + filename=os.path.join(FLAGS.output_dir, "eval.tf_record"), + is_training=False) + eval_features = [] + + def append_feature(feature): + eval_features.append(feature) + eval_writer.process_feature(feature) + + convert_examples_to_features( + examples=eval_examples, + tokenizer=tokenizer, + max_seq_length=FLAGS.max_seq_length, + doc_stride=FLAGS.doc_stride, + max_query_length=FLAGS.max_query_length, + is_training=False, + output_fn=append_feature, + verbose_logging=FLAGS.verbose_logging) + eval_writer.close() + + tf.compat.v1.logging.info("***** Running predictions *****") + tf.compat.v1.logging.info(" Num orig examples = %d", len(eval_examples)) + tf.compat.v1.logging.info(" Num split examples = %d", len(eval_features)) + tf.compat.v1.logging.info(" Batch size = %d", FLAGS.predict_batch_size) + + predict_input_fn = input_fn_builder( + input_file=eval_writer.filename, + batch_size=FLAGS.predict_batch_size, + seq_length=FLAGS.max_seq_length, + is_training=False, + drop_remainder=False) + + all_results = [] + eval_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)] + eval_start_time = time.time() + for result in estimator.predict( + predict_input_fn, yield_single_examples=True, hooks=eval_hooks): + if len(all_results) % 1000 == 0: + tf.compat.v1.logging.info("Processing example: %d" % (len(all_results))) + unique_id = int(result["unique_ids"]) + start_logits = [float(x) for x in result["start_logits"].flat] + end_logits = [float(x) for x in result["end_logits"].flat] + all_results.append( + RawResult( + unique_id=unique_id, + start_logits=start_logits, + end_logits=end_logits)) + + eval_time_elapsed = time.time() - eval_start_time + + time_list = eval_hooks[-1].time_list + time_list.sort() + # Removing outliers (init/warmup) in throughput computation. + eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.99)]) + num_sentences = (int(len(time_list) * 0.99)) * FLAGS.predict_batch_size + + avg = np.mean(time_list) + cf_50 = max(time_list[:int(len(time_list) * 0.50)]) + cf_90 = max(time_list[:int(len(time_list) * 0.90)]) + cf_95 = max(time_list[:int(len(time_list) * 0.95)]) + cf_99 = max(time_list[:int(len(time_list) * 0.99)]) + cf_100 = max(time_list[:int(len(time_list) * 1)]) + ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead + + tf.compat.v1.logging.info("-----------------------------") + tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed, + eval_hooks[-1].count * FLAGS.predict_batch_size) + tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead, + num_sentences) + tf.compat.v1.logging.info("Summary Inference Statistics") + tf.compat.v1.logging.info("Batch size = %d", FLAGS.predict_batch_size) + tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length) + tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32") + tf.compat.v1.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000) + tf.compat.v1.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000) + tf.compat.v1.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000) + tf.compat.v1.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000) + tf.compat.v1.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000) + tf.compat.v1.logging.info("Latency Average (ms) = %0.2f", avg * 1000) + tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) + # dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT) + tf.compat.v1.logging.info("-----------------------------") + + output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json") + output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json") + output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json") + + write_predictions(eval_examples, eval_features, all_results, + FLAGS.n_best_size, FLAGS.max_answer_length, + FLAGS.do_lower_case, output_prediction_file, + output_nbest_file, output_null_log_odds_file, + FLAGS.version_2_with_negative, FLAGS.verbose_logging) + + if FLAGS.eval_script: + import sys + import subprocess + eval_out = subprocess.check_output([sys.executable, FLAGS.eval_script, + FLAGS.predict_file, output_prediction_file]) + scores = str(eval_out).strip() + print(str(eval_out)) + exact_match = float(scores.split(":")[1].split(",")[0]) + if FLAGS.version_2_with_negative: + f1 = float(scores.split(":")[2].split(",")[0]) + else: + f1 = float(scores.split(":")[2].split("}")[0]) + tf.compat.v1.logging.info("f1 = %2.7f", f1) + tf.compat.v1.logging.info("exact_match = %2.7f", exact_match) + # dllogging.logger.log(step=(), data={"f1": f1}, verbosity=Verbosity.DEFAULT) + # dllogging.logger.log(step=(), data={"exact_match": exact_match}, verbosity=Verbosity.DEFAULT) + + +if __name__ == "__main__": + FLAGS = extract_run_squad_flags() + tf.app.run() diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/__init__.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..effb57b1e893fc03b3782961deb060749083c696 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/__init__.py @@ -0,0 +1,15 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_glue_data.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_glue_data.py new file mode 100644 index 0000000000000000000000000000000000000000..99b70f2a7d7a0878676dd1e06043693d08f278b2 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_glue_data.py @@ -0,0 +1,531 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import json +import math +import os +import random +import modeling +import optimization +import tokenization +import six +import tensorflow as tf +#import horovod.tensorflow as hvd +import time +import csv + +flags = tf.flags +FLAGS = None + +def extract_flags(): + + ## Required parameters + flags.DEFINE_string( + "data_dir", None, + "The input data dir. Should contain the .tsv files (or other data files) " + "for the task.") + + flags.DEFINE_string("task_name", None, "The name of the task to train.") + + flags.DEFINE_string("vocab_file", None, + "The vocabulary file that the BERT model was trained on.") + + flags.DEFINE_bool( + "do_lower_case", True, + "Whether to lower case the input text. Should be True for uncased " + "models and False for cased models.") + + flags.DEFINE_integer( + "max_seq_length", 128, + "The maximum total input sequence length after WordPiece tokenization. " + "Sequences longer than this will be truncated, and sequences shorter " + "than this will be padded.") + + flags.DEFINE_bool( + "verbose_logging", False, + "If true, all of the warnings related to data processing will be printed. " + "A number of warnings are expected for a normal SQuAD evaluation.") + flags.mark_flag_as_required("data_dir") + flags.mark_flag_as_required("task_name") + flags.mark_flag_as_required("vocab_file") + return flags.FLAGS + + +class InputExample(object): + """A single training/test example for simple sequence classification.""" + + def __init__(self, guid, text_a, text_b=None, label=None): + """Constructs a InputExample. + Args: + guid: Unique id for the example. + text_a: string. The untokenized text of the first sequence. For single + sequence tasks, only this sequence must be specified. + text_b: (Optional) string. The untokenized text of the second sequence. + Only must be specified for sequence pair tasks. + label: (Optional) string. The label of the example. This should be + specified for train and dev examples, but not for test examples. + """ + self.guid = guid + self.text_a = text_a + self.text_b = text_b + self.label = label + +class PaddingInputExample(object): + """Fake example so the num input examples is a multiple of the batch size. + + When running eval/predict on the TPU, we need to pad the number of examples + to be a multiple of the batch size, because the TPU requires a fixed batch + size. The alternative is to drop the last batch, which is bad because it means + the entire output data won't be generated. + + We use this class instead of `None` because treating `None` as padding + battches could cause silent errors. + """ + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__(self, + input_ids, + input_mask, + segment_ids, + label_id, + is_real_example=True): + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.label_id = label_id + self.is_real_example = is_real_example + + +class DataProcessor(object): + """Base class for data converters for sequence classification data sets.""" + + def get_train_examples(self, data_dir): + """Gets a collection of `InputExample`s for the train set.""" + raise NotImplementedError() + + def get_dev_examples(self, data_dir): + """Gets a collection of `InputExample`s for the dev set.""" + raise NotImplementedError() + + def get_test_examples(self, data_dir): + """Gets a collection of `InputExample`s for prediction.""" + raise NotImplementedError() + + def get_labels(self): + """Gets the list of labels for this data set.""" + raise NotImplementedError() + + @classmethod + def _read_tsv(cls, input_file, quotechar=None): + """Reads a tab separated value file.""" + with tf.gfile.Open(input_file, "r") as f: + reader = csv.reader(f, delimiter="\t", quotechar=quotechar) + lines = [] + for line in reader: + lines.append(line) + return lines + + +class XnliProcessor(DataProcessor): + """Processor for the XNLI data set.""" + + def __init__(self): + self.language = "zh" + + def get_train_examples(self, data_dir): + """See base class.""" + lines = self._read_tsv( + os.path.join(data_dir, "multinli", + "multinli.train.%s.tsv" % self.language)) + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "train-%d" % (i) + text_a = tokenization.convert_to_unicode(line[0]) + text_b = tokenization.convert_to_unicode(line[1]) + label = tokenization.convert_to_unicode(line[2]) + if label == tokenization.convert_to_unicode("contradictory"): + label = tokenization.convert_to_unicode("contradiction") + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_dev_examples(self, data_dir): + """See base class.""" + lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv")) + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "dev-%d" % (i) + language = tokenization.convert_to_unicode(line[0]) + if language != tokenization.convert_to_unicode(self.language): + continue + text_a = tokenization.convert_to_unicode(line[6]) + text_b = tokenization.convert_to_unicode(line[7]) + label = tokenization.convert_to_unicode(line[1]) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + def get_labels(self): + """See base class.""" + return ["contradiction", "entailment", "neutral"] + + +class MnliProcessor(DataProcessor): + """Processor for the MultiNLI data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), + "dev_matched") + + def get_test_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test") + + def get_labels(self): + """See base class.""" + return ["contradiction", "entailment", "neutral"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0])) + text_a = tokenization.convert_to_unicode(line[8]) + text_b = tokenization.convert_to_unicode(line[9]) + if set_type == "test": + label = "contradiction" + else: + label = tokenization.convert_to_unicode(line[-1]) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + +class MrpcProcessor(DataProcessor): + """Processor for the MRPC data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_test_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + if i == 0: + continue + guid = "%s-%s" % (set_type, i) + text_a = tokenization.convert_to_unicode(line[3]) + text_b = tokenization.convert_to_unicode(line[4]) + if set_type == "test": + label = "0" + else: + label = tokenization.convert_to_unicode(line[0]) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) + return examples + + +class ColaProcessor(DataProcessor): + """Processor for the CoLA data set (GLUE version).""" + + def get_train_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") + + def get_dev_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") + + def get_test_examples(self, data_dir): + """See base class.""" + return self._create_examples( + self._read_tsv(os.path.join(data_dir, "test.tsv")), "test") + + def get_labels(self): + """See base class.""" + return ["0", "1"] + + def _create_examples(self, lines, set_type): + """Creates examples for the training and dev sets.""" + examples = [] + for (i, line) in enumerate(lines): + # Only the test set has a header + if set_type == "test" and i == 0: + continue + guid = "%s-%s" % (set_type, i) + if set_type == "test": + text_a = tokenization.convert_to_unicode(line[1]) + label = "0" + else: + text_a = tokenization.convert_to_unicode(line[3]) + label = tokenization.convert_to_unicode(line[1]) + examples.append( + InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) + return examples + + +def _truncate_seq_pair(tokens_a, tokens_b, max_length): + """Truncates a sequence pair in place to the maximum length.""" + + # This is a simple heuristic which will always truncate the longer sequence + # one token at a time. This makes more sense than truncating an equal percent + # of tokens from each, since if one sequence is very short then each token + # that's truncated likely contains more information than a longer sequence. + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_length: + break + if len(tokens_a) > len(tokens_b): + tokens_a.pop() + else: + tokens_b.pop() + +def convert_single_example(ex_index, example, label_list, max_seq_length, + tokenizer, verbose_logging=False): + """Converts a single `InputExample` into a single `InputFeatures`.""" + + if isinstance(example, PaddingInputExample): + return InputFeatures( + input_ids=[0] * max_seq_length, + input_mask=[0] * max_seq_length, + segment_ids=[0] * max_seq_length, + label_id=0, + is_real_example=False) + + label_map = {} + for (i, label) in enumerate(label_list): + label_map[label] = i + + tokens_a = tokenizer.tokenize(example.text_a) + tokens_b = None + if example.text_b: + tokens_b = tokenizer.tokenize(example.text_b) + + if tokens_b: + # Modifies `tokens_a` and `tokens_b` in place so that the total + # length is less than the specified length. + # Account for [CLS], [SEP], [SEP] with "- 3" + _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) + else: + # Account for [CLS] and [SEP] with "- 2" + if len(tokens_a) > max_seq_length - 2: + tokens_a = tokens_a[0:(max_seq_length - 2)] + + # The convention in BERT is: + # (a) For sequence pairs: + # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] + # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 + # (b) For single sequences: + # tokens: [CLS] the dog is hairy . [SEP] + # type_ids: 0 0 0 0 0 0 0 + # + # Where "type_ids" are used to indicate whether this is the first + # sequence or the second sequence. The embedding vectors for `type=0` and + # `type=1` were learned during pre-training and are added to the wordpiece + # embedding vector (and position vector). This is not *strictly* necessary + # since the [SEP] token unambiguously separates the sequences, but it makes + # it easier for the model to learn the concept of sequences. + # + # For classification tasks, the first vector (corresponding to [CLS]) is + # used as the "sentence vector". Note that this only makes sense because + # the entire model is fine-tuned. + tokens = [] + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in tokens_a: + tokens.append(token) + segment_ids.append(0) + tokens.append("[SEP]") + segment_ids.append(0) + + if tokens_b: + for token in tokens_b: + tokens.append(token) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1] * len(input_ids) + + # Zero-pad up to the sequence length. + while len(input_ids) < max_seq_length: + input_ids.append(0) + input_mask.append(0) + segment_ids.append(0) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + label_id = label_map[example.label] + if ex_index < 5 and verbose_logging: + tf.compat.v1.logging.info("*** Example ***") + tf.compat.v1.logging.info("guid: %s" % (example.guid)) + tf.compat.v1.logging.info("tokens: %s" % " ".join( + [tokenization.printable_text(x) for x in tokens])) + tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) + tf.compat.v1.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) + tf.compat.v1.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) + tf.compat.v1.logging.info("label: %s (id = %d)" % (example.label, label_id)) + + feature = InputFeatures( + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + label_id=label_id, + is_real_example=True) + return feature + +# This function is not used by this file but is still used by the Colab and +# people who depend on it. +def convert_examples_to_features(examples, label_list, max_seq_length, + tokenizer): + """Convert a set of `InputExample`s to a list of `InputFeatures`.""" + + features = [] + for (ex_index, example) in enumerate(examples): + if ex_index % 10000 == 0: + tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples))) + + feature = convert_single_example(ex_index, example, label_list, + max_seq_length, tokenizer, FLAGS.verbose_logging) + + features.append(feature) + return features + +def file_based_convert_examples_to_features( + examples, label_list, max_seq_length, tokenizer, output_file): + """Convert a set of `InputExample`s to a TFRecord file.""" + + writer = tf.python_io.TFRecordWriter(output_file) + + for (ex_index, example) in enumerate(examples): + if ex_index % 10000 == 0: + tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples))) + + feature = convert_single_example(ex_index, example, label_list, + max_seq_length, tokenizer) + + def create_int_feature(values): + f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) + return f + + features = collections.OrderedDict() + features["input_ids"] = create_int_feature(feature.input_ids) + features["input_mask"] = create_int_feature(feature.input_mask) + features["segment_ids"] = create_int_feature(feature.segment_ids) + features["label_ids"] = create_int_feature([feature.label_id]) + features["is_real_example"] = create_int_feature( + [int(feature.is_real_example)]) + + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + writer.write(tf_example.SerializeToString()) + writer.close() + +def main(): + processors = { + "cola": ColaProcessor, + "mnli": MnliProcessor, + "mrpc": MrpcProcessor, + "xnli": XnliProcessor, + } + task_name = FLAGS.task_name.lower() + if task_name not in processors: + raise ValueError("Task not found: %s" % (task_name)) + processor = processors[task_name]() + label_list = processor.get_labels() + + tokenizer = tokenization.FullTokenizer( + vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) + tf.gfile.MakeDirs(FLAGS.data_dir + "final_tfrecords_sharded") + train_examples = processor.get_train_examples(FLAGS.data_dir) + train_file = os.path.join(FLAGS.data_dir, "final_tfrecords_sharded/" + task_name + "train.tf_record") + file_based_convert_examples_to_features( + train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file) + + eval_examples = processor.get_dev_examples(FLAGS.data_dir) + eval_file = os.path.join(FLAGS.data_dir, "final_tfrecords_sharded/" + task_name + "eval.tf_record") + file_based_convert_examples_to_features( + eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) + + predict_examples = processor.get_test_examples(FLAGS.data_dir) + predict_file = os.path.join(FLAGS.data_dir, "final_tfrecords_sharded/" + task_name + "predict.tf_record") + file_based_convert_examples_to_features(predict_examples, label_list, + FLAGS.max_seq_length, tokenizer, + predict_file) + +if __name__ == "__main__": + (npu_sess, npu_shutdown) = init_resource() + main() + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_pretraining_data.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_pretraining_data.py new file mode 100644 index 0000000000000000000000000000000000000000..79bfc5940e3e253a664472f243ad015a1c6aee38 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_pretraining_data.py @@ -0,0 +1,522 @@ +# coding=utf-8 +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Copyright 2018 The Google AI Language Team Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""Create masked LM/next sentence masked_lm TF examples for BERT.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import argparse +import logging +import os +import random +from io import open +import h5py +import tensorflow as tf +import numpy as np +from tqdm import tqdm, trange + +from tokenization import BertTokenizer +import tokenization as tokenization + +import random +import collections + +class TrainingInstance(object): + """A single training instance (sentence pair).""" + + def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels, + is_random_next): + self.tokens = tokens + self.segment_ids = segment_ids + self.is_random_next = is_random_next + self.masked_lm_positions = masked_lm_positions + self.masked_lm_labels = masked_lm_labels + + def __str__(self): + s = "" + s += "tokens: %s\n" % (" ".join( + [tokenization.printable_text(x) for x in self.tokens])) + s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids])) + s += "is_random_next: %s\n" % self.is_random_next + s += "masked_lm_positions: %s\n" % (" ".join( + [str(x) for x in self.masked_lm_positions])) + s += "masked_lm_labels: %s\n" % (" ".join( + [tokenization.printable_text(x) for x in self.masked_lm_labels])) + s += "\n" + return s + + def __repr__(self): + return self.__str__() + + +def write_instance_to_example_files(instances, tokenizer, max_seq_length, + max_predictions_per_seq, output_files, output_formats="tfrecord"): + """Create TF example files from `TrainingInstance`s.""" + writers = [] + for output_file in output_files: + writers.append(tf.python_io.TFRecordWriter(output_file)) + + writer_index = 0 + + total_written = 0 + if 'hdf5' in output_formats: + features_hdf5 = collections.OrderedDict() + num_instances = len(instances) + features_hdf5["input_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["input_mask"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["segment_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") + features_hdf5["masked_lm_positions"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") + features_hdf5["masked_lm_ids"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") + features_hdf5["next_sentence_labels"] = np.zeros(num_instances, dtype="int32") + + for (inst_index, instance) in enumerate(instances): + input_ids = tokenizer.convert_tokens_to_ids(instance.tokens) + input_mask = [1] * len(input_ids) + segment_ids = list(instance.segment_ids) + assert len(input_ids) <= max_seq_length + + while len(input_ids) < max_seq_length: + input_ids.append(0) + input_mask.append(0) + segment_ids.append(0) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + masked_lm_positions = list(instance.masked_lm_positions) + masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels) + masked_lm_weights = [1.0] * len(masked_lm_ids) + + while len(masked_lm_positions) < max_predictions_per_seq: + masked_lm_positions.append(0) + masked_lm_ids.append(0) + masked_lm_weights.append(0.0) + + next_sentence_label = 1 if instance.is_random_next else 0 + + features = collections.OrderedDict() + features["input_ids"] = create_int_feature(input_ids) + features["input_mask"] = create_int_feature(input_mask) + features["segment_ids"] = create_int_feature(segment_ids) + features["masked_lm_positions"] = create_int_feature(masked_lm_positions) + features["masked_lm_ids"] = create_int_feature(masked_lm_ids) + features["masked_lm_weights"] = create_float_feature(masked_lm_weights) + features["next_sentence_labels"] = create_int_feature([next_sentence_label]) + + if 'tfrecord' in output_formats: + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + writers[writer_index].write(tf_example.SerializeToString()) + if 'hdf5' in output_formats: + features_hdf5["input_ids"][inst_index] = input_ids + features_hdf5["input_mask"][inst_index] = input_mask + features_hdf5["segment_ids"][inst_index] = segment_ids + features_hdf5["masked_lm_positions"][inst_index] = masked_lm_positions + features_hdf5["masked_lm_ids"][inst_index] = masked_lm_ids + features_hdf5["next_sentence_labels"][inst_index] = next_sentence_label + if 'tfrecord' not in output_formats and 'hdf5' not in output_formats: + assert False, 'Either empty output_formats list or unsupported type specified. Try: tfrecord or hdf5' + + writer_index = (writer_index + 1) % len(writers) + + total_written += 1 + + if inst_index < 20: + tf.compat.v1.logging.info("*** Example ***") + tf.compat.v1.logging.info("tokens: %s" % " ".join( + [tokenization.printable_text(x) for x in instance.tokens])) + + for feature_name in features.keys(): + feature = features[feature_name] + values = [] + if feature.int64_list.value: + values = feature.int64_list.value + elif feature.float_list.value: + values = feature.float_list.value + tf.compat.v1.logging.info( + "%s: %s" % (feature_name, " ".join([str(x) for x in values]))) + + for writer in writers: + writer.close() + + if 'hdf5' in output_formats: + f = h5py.File(output_file, 'w') + f.create_dataset("input_ids", data=features_hdf5["input_ids"], dtype='i4', compression='gzip') + f.create_dataset("input_mask", data=features_hdf5["input_mask"], dtype='i1', compression='gzip') + f.create_dataset("segment_ids", data=features_hdf5["segment_ids"], dtype='i1', compression='gzip') + f.create_dataset("masked_lm_positions", data=features_hdf5["masked_lm_positions"], dtype='i4', compression='gzip') + f.create_dataset("masked_lm_ids", data=features_hdf5["masked_lm_ids"], dtype='i4', compression='gzip') + f.create_dataset("next_sentence_labels", data=features_hdf5["next_sentence_labels"], dtype='i1', compression='gzip') + f.flush() + f.close() + + tf.compat.v1.logging.info("Wrote %d total instances", total_written) + + +def create_int_feature(values): + feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) + return feature + + +def create_float_feature(values): + feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) + return feature + + +def create_training_instances(input_files, tokenizer, max_seq_length, + dupe_factor, short_seq_prob, masked_lm_prob, + max_predictions_per_seq, rng): + """Create `TrainingInstance`s from raw text.""" + all_documents = [[]] + + # Input file format: + # (1) One sentence per line. These should ideally be actual sentences, not + # entire paragraphs or arbitrary spans of text. (Because we use the + # sentence boundaries for the "next sentence prediction" task). + # (2) Blank lines between documents. Document boundaries are needed so + # that the "next sentence prediction" task doesn't span between documents. + for input_file in input_files: + print("creating instance from {}".format(input_file)) + with open(input_file, "r") as reader: + while True: + line = tokenization.convert_to_unicode(reader.readline()) + if not line: + break + line = line.strip() + + # Empty lines are used as document delimiters + if not line: + all_documents.append([]) + tokens = tokenizer.tokenize(line) + if tokens: + all_documents[-1].append(tokens) + + # Remove empty documents + all_documents = [x for x in all_documents if x] + rng.shuffle(all_documents) + + vocab_words = list(tokenizer.vocab.keys()) + instances = [] + for _ in range(dupe_factor): + for document_index in range(len(all_documents)): + instances.extend( + create_instances_from_document( + all_documents, document_index, max_seq_length, short_seq_prob, + masked_lm_prob, max_predictions_per_seq, vocab_words, rng)) + + rng.shuffle(instances) + return instances + + +def create_instances_from_document( + all_documents, document_index, max_seq_length, short_seq_prob, + masked_lm_prob, max_predictions_per_seq, vocab_words, rng): + """Creates `TrainingInstance`s for a single document.""" + document = all_documents[document_index] + + # Account for [CLS], [SEP], [SEP] + max_num_tokens = max_seq_length - 3 + + # We *usually* want to fill up the entire sequence since we are padding + # to `max_seq_length` anyways, so short sequences are generally wasted + # computation. However, we *sometimes* + # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter + # sequences to minimize the mismatch between pre-training and fine-tuning. + # The `target_seq_length` is just a rough target however, whereas + # `max_seq_length` is a hard limit. + target_seq_length = max_num_tokens + if rng.random() < short_seq_prob: + target_seq_length = rng.randint(2, max_num_tokens) + + # We DON'T just concatenate all of the tokens from a document into a long + # sequence and choose an arbitrary split point because this would make the + # next sentence prediction task too easy. Instead, we split the input into + # segments "A" and "B" based on the actual "sentences" provided by the user + # input. + instances = [] + current_chunk = [] + current_length = 0 + i = 0 + while i < len(document): + segment = document[i] + current_chunk.append(segment) + current_length += len(segment) + if i == len(document) - 1 or current_length >= target_seq_length: + if current_chunk: + # `a_end` is how many segments from `current_chunk` go into the `A` + # (first) sentence. + a_end = 1 + if len(current_chunk) >= 2: + a_end = rng.randint(1, len(current_chunk) - 1) + + tokens_a = [] + for j in range(a_end): + tokens_a.extend(current_chunk[j]) + + tokens_b = [] + # Random next + is_random_next = False + if len(current_chunk) == 1 or rng.random() < 0.5: + is_random_next = True + target_b_length = target_seq_length - len(tokens_a) + + # This should rarely go for more than one iteration for large + # corpora. However, just to be careful, we try to make sure that + # the random document is not the same as the document + # we're processing. + for _ in range(10): + random_document_index = rng.randint(0, len(all_documents) - 1) + if random_document_index != document_index: + break + + #If picked random document is the same as the current document + if random_document_index == document_index: + is_random_next = False + + random_document = all_documents[random_document_index] + random_start = rng.randint(0, len(random_document) - 1) + for j in range(random_start, len(random_document)): + tokens_b.extend(random_document[j]) + if len(tokens_b) >= target_b_length: + break + # We didn't actually use these segments so we "put them back" so + # they don't go to waste. + num_unused_segments = len(current_chunk) - a_end + i -= num_unused_segments + # Actual next + else: + is_random_next = False + for j in range(a_end, len(current_chunk)): + tokens_b.extend(current_chunk[j]) + truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng) + + assert len(tokens_a) >= 1 + assert len(tokens_b) >= 1 + + tokens = [] + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in tokens_a: + tokens.append(token) + segment_ids.append(0) + + tokens.append("[SEP]") + segment_ids.append(0) + + for token in tokens_b: + tokens.append(token) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) + + (tokens, masked_lm_positions, + masked_lm_labels) = create_masked_lm_predictions( + tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng) + instance = TrainingInstance( + tokens=tokens, + segment_ids=segment_ids, + is_random_next=is_random_next, + masked_lm_positions=masked_lm_positions, + masked_lm_labels=masked_lm_labels) + instances.append(instance) + current_chunk = [] + current_length = 0 + i += 1 + + return instances + + +MaskedLmInstance = collections.namedtuple("MaskedLmInstance", + ["index", "label"]) + + +def create_masked_lm_predictions(tokens, masked_lm_prob, + max_predictions_per_seq, vocab_words, rng): + """Creates the predictions for the masked LM objective.""" + + cand_indexes = [] + for (i, token) in enumerate(tokens): + if token == "[CLS]" or token == "[SEP]": + continue + cand_indexes.append(i) + + rng.shuffle(cand_indexes) + + output_tokens = list(tokens) + + num_to_predict = min(max_predictions_per_seq, + max(1, int(round(len(tokens) * masked_lm_prob)))) + + masked_lms = [] + covered_indexes = set() + for index in cand_indexes: + if len(masked_lms) >= num_to_predict: + break + if index in covered_indexes: + continue + covered_indexes.add(index) + + masked_token = None + # 80% of the time, replace with [MASK] + if rng.random() < 0.8: + masked_token = "[MASK]" + else: + # 10% of the time, keep original + if rng.random() < 0.5: + masked_token = tokens[index] + # 10% of the time, replace with random word + else: + masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)] + + output_tokens[index] = masked_token + + masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) + + masked_lms = sorted(masked_lms, key=lambda x: x.index) + + masked_lm_positions = [] + masked_lm_labels = [] + for p in masked_lms: + masked_lm_positions.append(p.index) + masked_lm_labels.append(p.label) + + return (output_tokens, masked_lm_positions, masked_lm_labels) + + +def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng): + """Truncates a pair of sequences to a maximum sequence length.""" + while True: + total_length = len(tokens_a) + len(tokens_b) + if total_length <= max_num_tokens: + break + + trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b + assert len(trunc_tokens) >= 1 + + # We want to sometimes truncate from the front and sometimes from the + # back to add more randomness and avoid biases. + if rng.random() < 0.5: + del trunc_tokens[0] + else: + trunc_tokens.pop() + + +def main(): + parser = argparse.ArgumentParser() + ## Required parameters + parser.add_argument("--vocab_file", + default=None, + type=str, + required=True, + help="The vocabulary the BERT model will train on.") + parser.add_argument("--input_file", + default=None, + type=str, + required=True, + help="The input train corpus. can be directory with .txt files or a path to a single file") + parser.add_argument("--output_file", + default=None, + type=str, + required=True, + help="The output file where the model checkpoints will be written.") + + ## Other parameters + # int + parser.add_argument("--max_seq_length", + default=128, + type=int, + help="The maximum total input sequence length after WordPiece tokenization. \n" + "Sequences longer than this will be truncated, and sequences shorter \n" + "than this will be padded.") + parser.add_argument("--dupe_factor", + default=10, + type=int, + help="Number of times to duplicate the input data (with different masks).") + parser.add_argument("--max_predictions_per_seq", + default=20, + type=int, + help="Maximum sequence length.") + + # floats + + parser.add_argument("--masked_lm_prob", + default=0.15, + type=float, + help="Masked LM probability.") + + parser.add_argument("--short_seq_prob", + default=0.1, + type=float, + help="Probability to create a sequence shorter than maximum sequence length") + + parser.add_argument("--do_lower_case", + action='store_true', + default=True, + help="Whether to lower case the input text. True for uncased models, False for cased models.") + parser.add_argument('--random_seed', + type=int, + default=12345, + help="random seed for initialization") + + args = parser.parse_args() + + tokenizer = BertTokenizer(args.vocab_file, do_lower_case=args.do_lower_case) + + input_files = [] + if os.path.isfile(args.input_file): + input_files.append(args.input_file) + elif os.path.isdir(args.input_file): + input_files = [os.path.join(args.input_file, f) for f in os.listdir(args.input_file) if + (os.path.isfile(os.path.join(args.input_file, f)) and f.endswith('.txt'))] + else: + raise ValueError("{} is not a valid path".format(args.input_file)) + + rng = random.Random(args.random_seed) + instances = create_training_instances( + input_files, tokenizer, args.max_seq_length, args.dupe_factor, + args.short_seq_prob, args.masked_lm_prob, args.max_predictions_per_seq, + rng) + + output_files = args.output_file.split(",") + print("*** Writing to output files ***") + for output_file in output_files: + print(output_file) + + + write_instance_to_example_files(instances, tokenizer, args.max_seq_length, + args.max_predictions_per_seq, output_files) + + +if __name__ == "__main__": + main() + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_squad_data.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_squad_data.py new file mode 100644 index 0000000000000000000000000000000000000000..a65748c0ebaca5dea6ca79acb038dc70288d37ab --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/create_squad_data.py @@ -0,0 +1,581 @@ +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import json +import math +import os +import random +import modeling +import optimization +import tokenization +import six +import tensorflow as tf +#import horovod.tensorflow as hvd +import time + +flags = tf.flags +FLAGS = None + +def extract_flags(): + flags.DEFINE_integer( + "max_seq_length", 384, + "The maximum total input sequence length after WordPiece tokenization. " + "Sequences longer than this will be truncated, and sequences shorter " + "than this will be padded.") + + flags.DEFINE_integer( + "doc_stride", 128, + "When splitting up a long document into chunks, how much stride to " + "take between chunks.") + + flags.DEFINE_integer( + "max_query_length", 64, + "The maximum number of tokens for the question. Questions longer than " + "this will be truncated to this length.") + + flags.DEFINE_bool( + "version_2_with_negative", False, + "If true, the SQuAD examples contain some that do not have an answer.") + + flags.DEFINE_string("train_file", None, + "SQuAD json for training. E.g., train-v1.1.json") + + flags.DEFINE_string( + "predict_file", None, + "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") + + flags.DEFINE_string( + "squad_dir", None, + "The output directory where the model checkpoints will be written.") + + flags.DEFINE_string("vocab_file", None, + "The vocabulary file that the BERT model was trained on.") + + flags.DEFINE_bool( + "do_lower_case", True, + "Whether to lower case the input text. Should be True for uncased " + "models and False for cased models.") + + flags.DEFINE_bool( + "verbose_logging", False, + "If true, all of the warnings related to data processing will be printed. " + "A number of warnings are expected for a normal SQuAD evaluation.") + flags.mark_flag_as_required("train_file") + flags.mark_flag_as_required("predict_file") + flags.mark_flag_as_required("squad_dir") + flags.mark_flag_as_required("vocab_file") + return flags.FLAGS + +class SquadExample(object): + """A single training/test example for simple sequence classification. + + For examples without an answer, the start and end position are -1. + """ + + def __init__(self, + qas_id, + question_text, + doc_tokens, + orig_answer_text=None, + start_position=None, + end_position=None, + is_impossible=False): + self.qas_id = qas_id + self.question_text = question_text + self.doc_tokens = doc_tokens + self.orig_answer_text = orig_answer_text + self.start_position = start_position + self.end_position = end_position + self.is_impossible = is_impossible + + def __str__(self): + return self.__repr__() + + def __repr__(self): + s = "" + s += "qas_id: %s" % (tokenization.printable_text(self.qas_id)) + s += ", question_text: %s" % ( + tokenization.printable_text(self.question_text)) + s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens)) + if self.start_position: + s += ", start_position: %d" % (self.start_position) + if self.start_position: + s += ", end_position: %d" % (self.end_position) + if self.start_position: + s += ", is_impossible: %r" % (self.is_impossible) + return s + +class InputFeatures(object): + """A single set of features of data.""" + + def __init__(self, + unique_id, + example_index, + doc_span_index, + tokens, + token_to_orig_map, + token_is_max_context, + input_ids, + input_mask, + segment_ids, + start_position=None, + end_position=None, + is_impossible=None): + self.unique_id = unique_id + self.example_index = example_index + self.doc_span_index = doc_span_index + self.tokens = tokens + self.token_to_orig_map = token_to_orig_map + self.token_is_max_context = token_is_max_context + self.input_ids = input_ids + self.input_mask = input_mask + self.segment_ids = segment_ids + self.start_position = start_position + self.end_position = end_position + self.is_impossible = is_impossible + +def read_squad_examples(input_file, is_training, version_2_with_negative=False, input_data=None): + """Return list of SquadExample from input_data or input_file (SQuAD json file)""" + if input_data is None: + with tf.gfile.Open(input_file, "r") as reader: + input_data = json.load(reader)["data"] + + def is_whitespace(c): + if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: + return True + return False + + examples = [] + for entry in input_data: + for paragraph in entry["paragraphs"]: + paragraph_text = paragraph["context"] + doc_tokens = [] + char_to_word_offset = [] + prev_is_whitespace = True + for c in paragraph_text: + if is_whitespace(c): + prev_is_whitespace = True + else: + if prev_is_whitespace: + doc_tokens.append(c) + else: + doc_tokens[-1] += c + prev_is_whitespace = False + char_to_word_offset.append(len(doc_tokens) - 1) + + for qa in paragraph["qas"]: + qas_id = qa["id"] + question_text = qa["question"] + start_position = None + end_position = None + orig_answer_text = None + is_impossible = False + if is_training: + + if version_2_with_negative: + is_impossible = qa["is_impossible"] + if (len(qa["answers"]) != 1) and (not is_impossible): + raise ValueError( + "For training, each question should have exactly 1 answer.") + if not is_impossible: + answer = qa["answers"][0] + orig_answer_text = answer["text"] + answer_offset = answer["answer_start"] + answer_length = len(orig_answer_text) + start_position = char_to_word_offset[answer_offset] + end_position = char_to_word_offset[answer_offset + answer_length - + 1] + # Only add answers where the text can be exactly recovered from the + # document. If this CAN'T happen it's likely due to weird Unicode + # stuff so we will just skip the example. + # + # Note that this means for training mode, every example is NOT + # guaranteed to be preserved. + actual_text = " ".join( + doc_tokens[start_position:(end_position + 1)]) + cleaned_answer_text = " ".join( + tokenization.whitespace_tokenize(orig_answer_text)) + if actual_text.find(cleaned_answer_text) == -1: + tf.logging.warning("Could not find answer: '%s' vs. '%s'", + actual_text, cleaned_answer_text) + continue + else: + start_position = -1 + end_position = -1 + orig_answer_text = "" + + example = SquadExample( + qas_id=qas_id, + question_text=question_text, + doc_tokens=doc_tokens, + orig_answer_text=orig_answer_text, + start_position=start_position, + end_position=end_position, + is_impossible=is_impossible) + examples.append(example) + + return examples + +def _check_is_max_context(doc_spans, cur_span_index, position): + """Check if this is the 'max context' doc span for the token.""" + + # Because of the sliding window approach taken to scoring documents, a single + # token can appear in multiple documents. E.g. + # Doc: the man went to the store and bought a gallon of milk + # Span A: the man went to the + # Span B: to the store and bought + # Span C: and bought a gallon of + # ... + # + # Now the word 'bought' will have two scores from spans B and C. We only + # want to consider the score with "maximum context", which we define as + # the *minimum* of its left and right context (the *sum* of left and + # right context will always be the same, of course). + # + # In the example the maximum context for 'bought' would be span C since + # it has 1 left context and 3 right context, while span B has 4 left context + # and 0 right context. + best_score = None + best_span_index = None + for (span_index, doc_span) in enumerate(doc_spans): + end = doc_span.start + doc_span.length - 1 + if position < doc_span.start: + continue + if position > end: + continue + num_left_context = position - doc_span.start + num_right_context = end - position + score = min(num_left_context, num_right_context) + 0.01 * doc_span.length + if best_score is None or score > best_score: + best_score = score + best_span_index = span_index + + return cur_span_index == best_span_index + +def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, + orig_answer_text): + """Returns tokenized answer spans that better match the annotated answer.""" + + # The SQuAD annotations are character based. We first project them to + # whitespace-tokenized words. But then after WordPiece tokenization, we can + # often find a "better match". For example: + # + # Question: What year was John Smith born? + # Context: The leader was John Smith (1895-1943). + # Answer: 1895 + # + # The original whitespace-tokenized answer will be "(1895-1943).". However + # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match + # the exact answer, 1895. + # + # However, this is not always possible. Consider the following: + # + # Question: What country is the top exporter of electornics? + # Context: The Japanese electronics industry is the lagest in the world. + # Answer: Japan + # + # In this case, the annotator chose "Japan" as a character sub-span of + # the word "Japanese". Since our WordPiece tokenizer does not split + # "Japanese", we just use "Japanese" as the annotation. This is fairly rare + # in SQuAD, but does happen. + tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) + + for new_start in range(input_start, input_end + 1): + for new_end in range(input_end, new_start - 1, -1): + text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) + if text_span == tok_answer_text: + return (new_start, new_end) + + return (input_start, input_end) + + +def convert_examples_to_features(examples, tokenizer, max_seq_length, + doc_stride, max_query_length, is_training, + output_fn, verbose_logging=False): + """Loads a data file into a list of `InputBatch`s.""" + + unique_id = 1000000000 + + for (example_index, example) in enumerate(examples): + query_tokens = tokenizer.tokenize(example.question_text) + + if len(query_tokens) > max_query_length: + query_tokens = query_tokens[0:max_query_length] + + tok_to_orig_index = [] + orig_to_tok_index = [] + all_doc_tokens = [] + for (i, token) in enumerate(example.doc_tokens): + orig_to_tok_index.append(len(all_doc_tokens)) + sub_tokens = tokenizer.tokenize(token) + for sub_token in sub_tokens: + tok_to_orig_index.append(i) + all_doc_tokens.append(sub_token) + + tok_start_position = None + tok_end_position = None + if is_training and example.is_impossible: + tok_start_position = -1 + tok_end_position = -1 + if is_training and not example.is_impossible: + tok_start_position = orig_to_tok_index[example.start_position] + if example.end_position < len(example.doc_tokens) - 1: + tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 + else: + tok_end_position = len(all_doc_tokens) - 1 + (tok_start_position, tok_end_position) = _improve_answer_span( + all_doc_tokens, tok_start_position, tok_end_position, tokenizer, + example.orig_answer_text) + + # The -3 accounts for [CLS], [SEP] and [SEP] + max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 + + # We can have documents that are longer than the maximum sequence length. + # To deal with this we do a sliding window approach, where we take chunks + # of the up to our max length with a stride of `doc_stride`. + _DocSpan = collections.namedtuple( # pylint: disable=invalid-name + "DocSpan", ["start", "length"]) + doc_spans = [] + start_offset = 0 + while start_offset < len(all_doc_tokens): + length = len(all_doc_tokens) - start_offset + if length > max_tokens_for_doc: + length = max_tokens_for_doc + doc_spans.append(_DocSpan(start=start_offset, length=length)) + if start_offset + length == len(all_doc_tokens): + break + start_offset += min(length, doc_stride) + + for (doc_span_index, doc_span) in enumerate(doc_spans): + tokens = [] + token_to_orig_map = {} + token_is_max_context = {} + segment_ids = [] + tokens.append("[CLS]") + segment_ids.append(0) + for token in query_tokens: + tokens.append(token) + segment_ids.append(0) + tokens.append("[SEP]") + segment_ids.append(0) + + for i in range(doc_span.length): + split_token_index = doc_span.start + i + token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index] + + is_max_context = _check_is_max_context(doc_spans, doc_span_index, + split_token_index) + token_is_max_context[len(tokens)] = is_max_context + tokens.append(all_doc_tokens[split_token_index]) + segment_ids.append(1) + tokens.append("[SEP]") + segment_ids.append(1) + + input_ids = tokenizer.convert_tokens_to_ids(tokens) + + # The mask has 1 for real tokens and 0 for padding tokens. Only real + # tokens are attended to. + input_mask = [1] * len(input_ids) + + # Zero-pad up to the sequence length. + while len(input_ids) < max_seq_length: + input_ids.append(0) + input_mask.append(0) + segment_ids.append(0) + + assert len(input_ids) == max_seq_length + assert len(input_mask) == max_seq_length + assert len(segment_ids) == max_seq_length + + start_position = None + end_position = None + if is_training and not example.is_impossible: + # For training, if our document chunk does not contain an annotation + # we throw it out, since there is nothing to predict. + doc_start = doc_span.start + doc_end = doc_span.start + doc_span.length - 1 + out_of_span = False + if not (tok_start_position >= doc_start and + tok_end_position <= doc_end): + out_of_span = True + if out_of_span: + start_position = 0 + end_position = 0 + else: + doc_offset = len(query_tokens) + 2 + start_position = tok_start_position - doc_start + doc_offset + end_position = tok_end_position - doc_start + doc_offset + + if is_training and example.is_impossible: + start_position = 0 + end_position = 0 + + if verbose_logging and example_index < 20: + tf.compat.v1.logging.info("*** Example ***") + tf.compat.v1.logging.info("unique_id: %s" % (unique_id)) + tf.compat.v1.logging.info("example_index: %s" % (example_index)) + tf.compat.v1.logging.info("doc_span_index: %s" % (doc_span_index)) + tf.compat.v1.logging.info("tokens: %s" % " ".join( + [tokenization.printable_text(x) for x in tokens])) + tf.compat.v1.logging.info("token_to_orig_map: %s" % " ".join( + ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)])) + tf.compat.v1.logging.info("token_is_max_context: %s" % " ".join([ + "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context) + ])) + tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) + tf.compat.v1.logging.info( + "input_mask: %s" % " ".join([str(x) for x in input_mask])) + tf.compat.v1.logging.info( + "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) + if is_training and example.is_impossible: + tf.compat.v1.logging.info("impossible example") + if is_training and not example.is_impossible: + answer_text = " ".join(tokens[start_position:(end_position + 1)]) + tf.compat.v1.logging.info("start_position: %d" % (start_position)) + tf.compat.v1.logging.info("end_position: %d" % (end_position)) + tf.compat.v1.logging.info( + "answer: %s" % (tokenization.printable_text(answer_text))) + + feature = InputFeatures( + unique_id=unique_id, + example_index=example_index, + doc_span_index=doc_span_index, + tokens=tokens, + token_to_orig_map=token_to_orig_map, + token_is_max_context=token_is_max_context, + input_ids=input_ids, + input_mask=input_mask, + segment_ids=segment_ids, + start_position=start_position, + end_position=end_position, + is_impossible=example.is_impossible) + + # Run callback + output_fn(feature) + + unique_id += 1 + +class FeatureWriter(object): + """Writes InputFeature to TF example file.""" + + def __init__(self, filename, is_training): + self.filename = filename + self.is_training = is_training + self.num_features = 0 + self._writer = tf.python_io.TFRecordWriter(filename) + + def process_feature(self, feature): + """Write a InputFeature to the TFRecordWriter as a tf.train.Example.""" + self.num_features += 1 + + def create_int_feature(values): + feature = tf.train.Feature( + int64_list=tf.train.Int64List(value=list(values))) + return feature + + features = collections.OrderedDict() + features["unique_ids"] = create_int_feature([feature.unique_id]) + features["input_ids"] = create_int_feature(feature.input_ids) + features["input_mask"] = create_int_feature(feature.input_mask) + features["segment_ids"] = create_int_feature(feature.segment_ids) + + if self.is_training: + features["start_positions"] = create_int_feature([feature.start_position]) + features["end_positions"] = create_int_feature([feature.end_position]) + impossible = 0 + if feature.is_impossible: + impossible = 1 + features["is_impossible"] = create_int_feature([impossible]) + + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + self._writer.write(tf_example.SerializeToString()) + + def close(self): + self._writer.close() + +def main(): + + FLAGS = extract_flags() + tokenizer = tokenization.FullTokenizer( + vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) + tf.gfile.MakeDirs(FLAGS.squad_dir + "/final_tfrecords_sharded") + # We write to a temporary file to avoid storing very large constant tensors + # in memory. + train_examples = read_squad_examples( + input_file=FLAGS.train_file, is_training=True, + version_2_with_negative=FLAGS.version_2_with_negative) + train_writer = FeatureWriter( + filename=os.path.join(FLAGS.squad_dir, "final_tfrecords_sharded/train.tf_record"), + is_training=True) + convert_examples_to_features( + examples=train_examples, + tokenizer=tokenizer, + max_seq_length=FLAGS.max_seq_length, + doc_stride=FLAGS.doc_stride, + max_query_length=FLAGS.max_query_length, + is_training=True, + output_fn=train_writer.process_feature, + verbose_logging=FLAGS.verbose_logging) + train_writer.close() + + + eval_examples = read_squad_examples( + input_file=FLAGS.predict_file, is_training=False, + version_2_with_negative=FLAGS.version_2_with_negative) + + eval_writer = FeatureWriter( + filename=os.path.join(FLAGS.squad_dir, "final_tfrecords_sharded/eval.tf_record"), + is_training=False) + eval_features = [] + + def append_feature(feature): + eval_features.append(feature) + eval_writer.process_feature(feature) + + convert_examples_to_features( + examples=eval_examples, + tokenizer=tokenizer, + max_seq_length=FLAGS.max_seq_length, + doc_stride=FLAGS.doc_stride, + max_query_length=FLAGS.max_query_length, + is_training=False, + output_fn=append_feature, + verbose_logging=FLAGS.verbose_logging) + eval_writer.close() + +if __name__ == "__main__": + (npu_sess, npu_shutdown) = init_resource() + main() + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/dllogger_class.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/dllogger_class.py new file mode 100644 index 0000000000000000000000000000000000000000..57b693bbe68b9f3f2e50b5a81f52a617fb498c88 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/dllogger_class.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from dllogger import Logger, StdOutBackend, JSONStreamBackend, Verbosity +import numpy + +class dllogger_class(): + + def format_step(self, step): + if isinstance(step, str): + return step + elif isinstance(step, int): + return "Iteration: {} ".format(step) + elif len(step) > 0: + return "Iteration: {} ".format(step[0]) + else: + return "" + + def __init__(self, log_path="bert_dllog.json"): + self.logger = Logger([ + StdOutBackend(Verbosity.DEFAULT, step_format=self.format_step), + JSONStreamBackend(Verbosity.VERBOSE, log_path), + ]) + self.logger.metadata("mlm_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"}) + self.logger.metadata("nsp_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"}) + self.logger.metadata("avg_loss_step", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"}) + self.logger.metadata("total_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"}) + self.logger.metadata("loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"}) + self.logger.metadata("f1", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"}) + self.logger.metadata("precision", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"}) + self.logger.metadata("recall", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"}) + self.logger.metadata("mcc", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"}) + self.logger.metadata("exact_match", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"}) + self.logger.metadata( + "throughput_train", + {"unit": "seq/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "TRAIN"}, + ) + self.logger.metadata( + "throughput_inf", + {"unit": "seq/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "VAL"}, + ) + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/gpu_affinity.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/gpu_affinity.py new file mode 100644 index 0000000000000000000000000000000000000000..01bd3475b80dab20587048116af0287fbd532a90 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/gpu_affinity.py @@ -0,0 +1,80 @@ +# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import math +import os + +import pynvml + +pynvml.nvmlInit() + + +def systemGetDriverVersion(): + return pynvml.nvmlSystemGetDriverVersion() + + +def deviceGetCount(): + return pynvml.nvmlDeviceGetCount() + + +class device: + # assume nvml returns list of 64 bit ints + _nvml_affinity_elements = math.ceil(os.cpu_count() / 64) + + def __init__(self, device_idx): + super().__init__() + self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_idx) + + def getName(self): + return pynvml.nvmlDeviceGetName(self.handle) + + def getCpuAffinity(self): + affinity_string = '' + for j in pynvml.nvmlDeviceGetCpuAffinity( + self.handle, device._nvml_affinity_elements + ): + # assume nvml returns list of 64 bit ints + affinity_string = '{:064b}'.format(j) + affinity_string + affinity_list = [int(x) for x in affinity_string] + affinity_list.reverse() # so core 0 is in 0th element of list + + return [i for i, e in enumerate(affinity_list) if e != 0] + + +def set_affinity(gpu_id=None): + if gpu_id is None: + gpu_id = int(os.getenv('LOCAL_RANK', 0)) + + dev = device(gpu_id) + os.sched_setaffinity(0, dev.getCpuAffinity()) + + # list of ints representing the logical cores this process is now affinitied with + return os.sched_getaffinity(0) + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils.py b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/utils.py similarity index 40% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils.py rename to TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/utils.py index 56aa173a4054706da7e7064fe7f08336ba5892d7..ef32a31cfd754ed93e563291d149822e281aa2ab 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils.py +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/src/utils/utils.py @@ -1,5 +1,18 @@ -# coding=utf-8 -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,17 +25,31 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# + import tensorflow as tf import time +import os + +def setup_xla_flags(): + # causes memory fragmentation for bert leading to OOM + if os.environ.get("TF_XLA_FLAGS", None) is not None: + try: + os.environ["TF_XLA_FLAGS"] += " --tf_xla_enable_lazy_compilation=false" + except: #mpi 4.0.2 causes syntax error for = + os.environ["TF_XLA_FLAGS"] += " --tf_xla_enable_lazy_compilation false" + else: + try: + os.environ["TF_XLA_FLAGS"] = " --tf_xla_enable_lazy_compilation=false" + except: + os.environ["TF_XLA_FLAGS"] = " --tf_xla_enable_lazy_compilation false" # report latency and throughput during eval -class LogEvalRunHook(tf.train.SessionRunHook): +class LogEvalRunHook(tf.estimator.SessionRunHook): def __init__(self, global_batch_size, hvd_rank=-1): self.global_batch_size = global_batch_size self.hvd_rank = hvd_rank - self.total_time = 0.0 self.count = 0 - self.skipped = 0 self.time_list = [] def before_run(self, run_context): @@ -31,31 +58,27 @@ class LogEvalRunHook(tf.train.SessionRunHook): def after_run(self, run_context, run_values): elapsed_secs = time.time() - self.t0 self.count += 1 - - # Removing first 2 (arbitrary) number of startup iterations from perf evaluations - if self.count <= 2: - print("Skipping time record for ", self.count, " due to overhead") - self.skipped += 1 - else: - self.time_list.append(elapsed_secs) - self.total_time += elapsed_secs + self.time_list.append(elapsed_secs) # report throughput during training -class LogTrainRunHook(tf.train.SessionRunHook): - def __init__(self, global_batch_size, hvd_rank=-1, save_checkpoints_steps=1000): +class LogTrainRunHook(tf.estimator.SessionRunHook): + def __init__(self, global_batch_size, hvd_rank=-1, save_checkpoints_steps=1000, num_steps_ignore_xla=100): self.global_batch_size = global_batch_size self.hvd_rank = hvd_rank self.save_checkpoints_steps = save_checkpoints_steps self.total_time = 0.0 self.count = 0 # Holds number of iterations, including skipped iterations for fp16 loss scaling + self.skipped = 0 + self.num_steps_ignore_xla = num_steps_ignore_xla + #initial steps while xla is still compilingneed to be ignored from throughput computation def after_create_session(self, session, coord): self.init_global_step = session.run(tf.train.get_global_step()) def before_run(self, run_context): self.t0 = time.time() - return tf.train.SessionRunArgs( + return tf.estimator.SessionRunArgs( fetches=['step_update:0']) def after_run(self, run_context, run_values): @@ -63,14 +86,31 @@ class LogTrainRunHook(tf.train.SessionRunHook): self.global_step = run_values.results[0] self.count += 1 - # Removing first step + first two steps after every checkpoint save - if (self.global_step - self.init_global_step) % self.save_checkpoints_steps <= 1: + # Removing first 100 step + first five steps after every checkpoint save + if (self.global_step - self.init_global_step) <= self.num_steps_ignore_xla or (self.global_step - self.init_global_step) % self.save_checkpoints_steps < 5: print("Skipping time record for ", self.global_step, " due to checkpoint-saving/warmup overhead") + self.skipped += 1 else: self.total_time += elapsed_secs - def end(self, session): - num_global_steps = self.global_step - self.init_global_step +class ExamplesPerSecondHook(tf.estimator.SessionRunHook): + def __init__(self, batch_size, iterations_per_loop=1): + self._batch_size = batch_size + self._iter_per_loop = iterations_per_loop + self.start_time = 0 + self.end_time = 0 + + def before_run(self, run_context): # pylint: disable=unused-argument + self.start_time = time.time() + return tf.estimator.SessionRunArgs(fetches=[tf.compat.v1.train.get_global_step(), 'total_loss:0']) + + def after_run(self, run_context, run_values): + self.end_time = time.time() + elapsed_time = self.end_time - self.start_time + global_step, total_loss = run_values.results + global_step_per_sec = self._iter_per_loop / elapsed_time + examples_per_sec = self._batch_size * global_step_per_sec + tf.compat.v1.logging.info('loss = %.7f', total_loss) + tf.compat.v1.logging.info('global_step/sec: %g', global_step_per_sec) + tf.compat.v1.logging.info('examples/sec: %g', examples_per_sec) - self.skipped = (num_global_steps // self.save_checkpoints_steps) * 2 + \ - min(2, num_global_steps % self.save_checkpoints_steps) - 1 \ No newline at end of file diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_8p.sh index 22c6d2c0c807eabf73361a5da501a1faad2ae38f..969311af6d4e42b7683517785d9a6278d8a00926 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_8p.sh @@ -19,8 +19,10 @@ Network="Bert-base_ID0060_for_TensorFlow" train_epochs= #训练batch_size batch_size=128 -#训练step -train_steps=500000 +#训练step 1140000 / (128*8/256) +# warmup step 10000 / (128*8/256) +# lr = 1e-4 * (128*8/256) +train_steps=286000 #学习率 learning_rate= @@ -115,20 +117,21 @@ do --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --num_warmup_steps=2500 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=100 \ - --save_checkpoints_steps=1000 \ + --iterations_per_loop=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ @@ -150,7 +153,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +train_accuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh index 186d04e022b2c0caecae8c3c47a0966191fcf4b6..7aad180ab0d2b3b4d3f6f62bbf87e9739a6c2766 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_1p.sh @@ -103,13 +103,13 @@ do --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_8p.sh index bbf3fa24c21ce1358d45f27bc9275efee869645d..fe7f8a287cdfff7ce257f1ba569e64fca4533eca 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_performance_8p.sh @@ -115,13 +115,13 @@ do --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ @@ -163,7 +163,7 @@ CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "] loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_8p.sh index 6bd45a8f17431dc892bfb8a26dee2dd5497e23ef..a1fc1f5d2c503ca96bd5de14848cd4e42445df0e 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_8p.sh @@ -18,9 +18,10 @@ Network="BertLarge-128_ID3067_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 -#训练step -train_steps=32000 +batch_size=128 +#训练step 1140000 / (128*8/256) +# warmup step 10000 / (128*8/256) +train_steps=286000 #学习率 learning_rate= @@ -113,26 +114,26 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=1000 \ + --learning_rate=5e-5 \ + --num_warmup_steps=2500 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & @@ -152,7 +153,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_1p.sh index 94281301911bd64cb7f906bc27567a437663f8d7..422f2a8abd7af4bd77de6533971e1ee9821779ea 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_1p.sh @@ -17,9 +17,9 @@ Network="BertLarge-128_ID3067_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 +batch_size=128 #训练step -train_steps=100 +train_steps=1000 #学习率 learning_rate= @@ -102,25 +102,24 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ + --learning_rate=5e-5 \ --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=1000 \ --npu_bert_clip_by_global_norm=False \ --distributed=False \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_8p.sh index eaf3efa0848298b46b2e75f77632e802e47f8a56..e0113d2fc722d29cab9daf9322183d80edb6a06a 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_performance_8p.sh @@ -18,7 +18,7 @@ Network="BertLarge-128_ID3067_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 +batch_size=128 #训练step train_steps=1000 #学习率 @@ -113,14 +113,14 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --learning_rate=5e-5 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ @@ -132,7 +132,6 @@ do --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_8p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_8p_lamb_phase2.sh index bbd7ab172323decd0044080e173448a5e8efa658..b7672b0a752ba7a83bcf262d2e247ce860b11b65 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_8p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_8p_lamb_phase2.sh @@ -20,7 +20,7 @@ train_epochs=1 #训练batch_size batch_size=24 #训练step -train_steps=32000 +train_steps=50000 #学习率 learning_rate= @@ -111,28 +111,28 @@ do fi nohup python3.7 ${cur_path}/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ + --learning_rate=7e-4 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & @@ -152,7 +152,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh index b443d8f9e1d3ad02f5b33bc1b499ae56937e35a7..76e171c0c327f8f0929473a5dbf344374696b128 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_1p_lamb_phase2.sh @@ -19,7 +19,7 @@ train_epochs=1 #训练batch_size batch_size=24 #训练step -train_steps=100 +train_steps=200 #学习率 learning_rate= @@ -100,7 +100,7 @@ do #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ --num_warmup_steps=0 \ @@ -108,15 +108,15 @@ do --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=200 \ --npu_bert_clip_by_global_norm=False \ --distributed=False \ --npu_bert_loss_scale=0 \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_8p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_8p_lamb_phase2.sh index 052606c33900ae44598ac9f1342a12bfd6622b24..ff3afe2aa352229c48afd1d621e6e2d70e1a323d 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_8p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_performance_8p_lamb_phase2.sh @@ -20,7 +20,7 @@ train_epochs=1 #训练batch_size batch_size=24 #训练step -train_steps=100 +train_steps=200 #学习率 learning_rate= @@ -111,23 +111,23 @@ do fi nohup ${bind_core} python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ + --learning_rate=7e-4 \ --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=200 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_8p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_8p_lamb_phase2.sh index 71b9cc9758999bacaab1c57de8f9398730e49625..dd52a3b4b9f7e077368f03ba15074709748be2f5 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_8p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_8p_lamb_phase2.sh @@ -20,7 +20,7 @@ train_epochs= #训练batch_size batch_size=64 #训练step -train_steps=32000 +train_steps=50000 #学习率 learning_rate= @@ -111,23 +111,24 @@ do fi nohup python3.7 ${cur_path}/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ + --learning_rate=7e-4 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ @@ -151,7 +152,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_1p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_1p_lamb_phase2.sh index c4211aa680962e4a6397800fdc8947eeb950daca..7edd3382a576e0dd4e383800b7b91786b6229fa3 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_1p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_1p_lamb_phase2.sh @@ -100,16 +100,16 @@ do #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ - --num_warmup_steps=100 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_8p_lamb_phase2.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_8p_lamb_phase2.sh index c46b250a81e04c8721eec06edd11a27e0a1238b1..8786d384f2be2c5815091f6c677b27b818eb6b9e 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_8p_lamb_phase2.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_performance_8p_lamb_phase2.sh @@ -111,16 +111,16 @@ do fi nohup ${bind_core} python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=100 \ + --learning_rate=7e-4 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_8p.sh index 2da89be7d8602e38a7c3a1e70d3fa50ceda5a4d3..74fc8a03cd5d7124e40be1e676816d406a71d17f 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_8p.sh @@ -19,8 +19,9 @@ Network="BertBase-512_ID3206_for_TensorFlow" train_epochs= #训练batch_size batch_size=64 -#训练step -train_steps=32000 +#训练step 1144000 / (64*8/256) +# warmup step 10000 / (64*8/256) +train_steps=572000 #学习率 learning_rate= @@ -111,23 +112,24 @@ do fi nohup python3.7 ${cur_path}/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ + --num_warmup_steps=5000 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ @@ -151,7 +153,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_1p.sh index 5174684914cc707ec2f2550f4800b6ee19339f4d..5710366a9016ed7bfac0dc5276b3205830cace45 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_1p.sh @@ -100,16 +100,16 @@ do #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ - --num_warmup_steps=100 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_8p.sh index ae232a8bb20a6c4d0ea0981bf68a05cc3d01fc35..8b77a39c814e91f595a8dc0f1f5428e508cb19ef 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_performance_8p.sh @@ -111,16 +111,16 @@ do fi nohup ${bind_core} python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ - --num_warmup_steps=100 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_8p.sh index 34219b6ed76322d1158c08d180afb6f21ff67faf..21850e4c44c969dc58a5d629ed44665f458bfc2f 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_8p.sh @@ -19,8 +19,9 @@ Network="BertLarge-512_ID3207_for_TensorFlow" train_epochs=1 #训练batch_size batch_size=24 -#训练step -train_steps=32000 +#训练step 1144000 / (24*8/256) +# warmup step 10000 / (24*8/256) +train_steps=1144000 #学习率 learning_rate= @@ -111,28 +112,28 @@ do fi nohup python3.7 ${cur_path}/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ + --num_warmup_steps=10000 \ --num_train_steps=${train_steps} \ --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & @@ -152,7 +153,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_1p.sh index c6d99dbfc186c7368f55d9921b774afb9cac7150..5cb0aaaff4b4c33f8e1e016465b4337b5e128c52 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_1p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_1p.sh @@ -19,7 +19,7 @@ train_epochs=1 #训练batch_size batch_size=24 #训练step -train_steps=100 +train_steps=200 #学习率 learning_rate= @@ -100,7 +100,7 @@ do #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ --num_warmup_steps=0 \ @@ -108,19 +108,18 @@ do --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=200 \ --npu_bert_clip_by_global_norm=False \ --distributed=False \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_8p.sh index 413222136ed0b8bd761eabd3c31c6fbb612d9184..885c2bb10f31a23ae1a5c8c8bd1f47f9aea9e904 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_8p.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_performance_8p.sh @@ -20,7 +20,7 @@ train_epochs=1 #训练batch_size batch_size=24 #训练step -train_steps=100 +train_steps=200 #学习率 learning_rate= @@ -111,7 +111,7 @@ do fi nohup ${bind_core} python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ --max_seq_length=512 \ - --max_predictions_per_seq=76 \ + --max_predictions_per_seq=80 \ --train_batch_size=${batch_size} \ --learning_rate=5e-5 \ --num_warmup_steps=0 \ @@ -119,20 +119,19 @@ do --optimizer_type=adam \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ + --input_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_512_max_pred_80/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=200 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_8p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_8p_lamb_phase1.sh index 140a44570e3ac90a1338d7f656319818aec9b3ed..669bab2a1cb1a17901680505e5ecd0f4eaa1dc11 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_8p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_8p_lamb_phase1.sh @@ -19,8 +19,10 @@ Network="BertBase-128_ID3208_for_TensorFlow" train_epochs= #训练batch_size batch_size=128 -#训练step -train_steps=500000 +#训练step 1140000 / (128*8/256) +# warmup step 10000 / (128*8/256) +# lr = 1e-4 * (128*8/256) +train_steps=286000 #学习率 learning_rate= @@ -114,21 +116,22 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --learning_rate=5e-4 \ + --num_warmup_steps=2500 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=100 \ - --save_checkpoints_steps=1000 \ + --iterations_per_loop=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ @@ -150,7 +153,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +train_accuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_1p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_1p_lamb_phase1.sh index 852c9164a61fb63c4fcafe3a2bf64db31832bc4d..1d2bad58ea09856c3f4f93be4f53847ec2f5b42d 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_1p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_1p_lamb_phase1.sh @@ -102,14 +102,14 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --learning_rate=5e-4 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_8p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_8p_lamb_phase1.sh index a7270327ad511ea6ec94e9f1ca81bf88ba53e1ed..0dab73c7c101eb1517dc6752bf3d70c5d3497557 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_8p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_performance_8p_lamb_phase1.sh @@ -114,14 +114,14 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --learning_rate=5e-4 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ @@ -163,7 +163,7 @@ CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "] loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_1p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_1p_lamb_phase1.sh deleted file mode 100644 index 6e6b4f2b5067773a2c24d3447ff6f2ec49aeea35..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_1p_lamb_phase1.sh +++ /dev/null @@ -1,171 +0,0 @@ -#!/bin/bash - -#当前路径,不需要修改 -cur_path=`pwd` - -#集合通信参数,不需要修改 -export RANK_SIZE=1 -export JOB_ID=99990001 -RANK_ID_START=0 - -# 数据集路径,保持为空,不需要修改 -data_path="" - -#基础参数，需要模型审视修改 -#网络名称，同目录名称 -Network="BertLarge-128_ID3209_for_TensorFlow" -#训练epoch -train_epochs=1 -#训练batch_size -batch_size=24 -#训练step -train_steps=100000 -#学习率 -learning_rate= - -#维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" -#维持参数，以下不需要修改 -over_dump=False -data_dump_flag=False -data_dump_step="10" -profiling=False -autotune=False - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " - echo " " - echo "parameter explain: - --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message - " - exit 1 -fi - -#参数校验，不需要修改 -for para in $* -do - if [[ $para == --precision_mode* ]];then - precision_mode=`echo ${para#*=}` - elif [[ $para == --over_dump* ]];then - over_dump=`echo ${para#*=}` - over_dump_path=${cur_path}/output/overflow_dump - mkdir -p ${over_dump_path} - elif [[ $para == --data_dump_flag* ]];then - data_dump_flag=`echo ${para#*=}` - data_dump_path=${cur_path}/output/data_dump - mkdir -p ${data_dump_path} - elif [[ $para == --data_dump_step* ]];then - data_dump_step=`echo ${para#*=}` - elif [[ $para == --profiling* ]];then - profiling=`echo ${para#*=}` - profiling_dump_path=${cur_path}/output/profiling - mkdir -p ${profiling_dump_path} - elif [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - fi -done - -#校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be confing" - exit 1 -fi - -#训练开始时间，不需要修改 -start_time=$(date +%s) -#进入训练脚本目录，需要模型审视修改 -for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); -do - #设置环境变量，不需要修改 - echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$RANK_ID - - #创建DeviceID输出目录，不需要修改 - if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then - rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - fi - - #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ - --max_seq_length=128 \ - --max_predictions_per_seq=20 \ - --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=10000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=lamb \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & -done -wait - -#训练结束时间，不需要修改 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -#结果打印，不需要修改 -echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" - -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` -#打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" -echo "E2E Training Duration sec : $e2e_time" - -#稳定性精度看护结果汇总 -#训练用例信息，不需要修改 -BatchSize=${batch_size} -DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' - - -#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt - -#最后一个迭代loss值，不需要修改 -ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_8p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_8p_lamb_phase1.sh index e3a80ebb0f2207a3d1a3855af29d4b9d6407b1a1..06442a11f4536e46f56d3bfb85b1033ab1a42614 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_8p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_full_8p_lamb_phase1.sh @@ -18,9 +18,11 @@ Network="BertLarge-128_ID3209_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 -#训练step -train_steps=32000 +batch_size=128 +#训练step 1140000 / (128*8/256) +# warmup step 10000 / (128*8/256) +# lr = 1e-4 * (128*8/256) +train_steps=286000 #学习率 learning_rate= @@ -113,26 +115,26 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=1000 \ + --learning_rate=4e-4 \ + --num_warmup_steps=2500 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ + --do_eval=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ + --save_checkpoints_steps=10000 \ --npu_bert_clip_by_global_norm=False \ --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & @@ -152,7 +154,7 @@ TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ echo "Final Performance images/sec : $ActualFPS" #输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +TrainAccuracy=`grep "tensorflow: masked_lm_accuracy" $cur_path/output/0/train_0.log|awk 'END {print $4}'` #打印，不需要修改 echo "Final Train Accuracy : ${TrainAccuracy}" echo "E2E Training Duration sec : $e2e_time" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_1p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_1p_lamb_phase1.sh index 5127e43ddcc0b12879d8baf6ea7c7e1efc4a698c..7efe7affe36ca6967bc82a65c1982583904f66a2 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_1p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_1p_lamb_phase1.sh @@ -17,9 +17,9 @@ Network="BertLarge-128_ID3209_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 +batch_size=128 #训练step -train_steps=100 +train_steps=1000 #学习率 learning_rate= @@ -102,25 +102,24 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ + --learning_rate=5e-5 \ --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ --num_accumulation_steps=1 \ --npu_bert_job_start_file= \ - --iterations_per_loop=10 \ - --save_checkpoints_steps=100 \ + --iterations_per_loop=100 \ + --save_checkpoints_steps=1000 \ --npu_bert_clip_by_global_norm=False \ --distributed=False \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_8p_lamb_phase1.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_8p_lamb_phase1.sh index c0bfb6b7b04686e98cb18c3706406550d79726ee..8b6c7930f03310a420863a3f9307c7b5ab1d0d04 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_8p_lamb_phase1.sh +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3209_BertLarge-128_performance_8p_lamb_phase1.sh @@ -14,11 +14,11 @@ data_path="" #基础参数，需要模型审视修改 #网络名称，同目录名称 -Network="BertLarge-128_ID3067_for_TensorFlow" +Network="BertLarge-128_ID3209_for_TensorFlow" #训练epoch train_epochs=1 #训练batch_size -batch_size=24 +batch_size=128 #训练step train_steps=1000 #学习率 @@ -113,14 +113,14 @@ do --max_seq_length=128 \ --max_predictions_per_seq=20 \ --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ + --learning_rate=5e-5 \ + --num_warmup_steps=0 \ --num_train_steps=${train_steps} \ --optimizer_type=lamb \ --manual_fp16=True \ --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ + --input_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/training \ + --eval_files_dir=${data_path}/tfrecord/seq_len_128_max_pred_20/wikicorpus_en/test \ --npu_bert_debug=False \ --npu_bert_use_tdt=True \ --do_train=True \ @@ -132,7 +132,6 @@ do --distributed=True \ --npu_bert_tail_optimize=True \ --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ --over_dump=${over_dump} \ --over_dump_path=${over_dump_path} \ --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_full_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..98922c52a5f6173a4763268a47581b268ba4be2d --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_full_8p.sh @@ -0,0 +1,204 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad1.1_ID3217_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=2.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/model + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=True \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep "tensorflow:f1 =" $cur_path/output/0/train_0.log|awk 'END {print $3}'` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..98f551f0177d02dcd470d3fc1773ea9abae9b64e --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_1p.sh @@ -0,0 +1,200 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad1.1_ID3217_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/model + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --npu_bert_loss_scale=0 \ + --num_train_steps=1000 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..4501084fc3cd0f2b93aaa5d2d52ccb2865562914 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3217_BertBase-Squad1.1_performance_8p.sh @@ -0,0 +1,204 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad1.1_ID3217_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/model + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=${train_batch_size} \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_full_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..42d0cc1724ad00cf29859b4388425a016eb8a3f4 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_full_8p.sh @@ -0,0 +1,205 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad1.1_ID3218_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=2.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=True \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep "tensorflow:f1 =" $cur_path/output/0/train_0.log|awk 'END {print $3}'` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..ea4dd35628554658afffa06f06ec3149e5f346f5 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_1p.sh @@ -0,0 +1,201 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad1.1_ID3218_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=True \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --npu_bert_loss_scale=0 \ + --num_train_steps=1000 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..853d6af544efada28636bad191429f7e56b3f1d7 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3218_BertLarge-Squad1.1_performance_8p.sh @@ -0,0 +1,205 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad1.1_ID3218_for_TensorFlow" +#训练batch_size +train_batch_size=32 +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v1.1_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v1.1.json \ + --eval_script=${data_path}/dataset/evaluate-v1.1.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_full_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..1a0ad760ad3bb0b246b8d68420297def625bf41c --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_full_8p.sh @@ -0,0 +1,206 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad2.0_ID3219_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=2.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/uncased_L-12_H-768_A-12 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=True \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep "tensorflow:f1 =" $cur_path/output/0/train_0.log|awk 'END {print $3}'` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..0af37e1b1d0cfaec2aaafa066493ab21ec8649fa --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_1p.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad2.0_ID3219_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/uncased_L-12_H-768_A-12 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --npu_bert_loss_scale=0 \ + --num_train_steps=1000 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..4c3f6bd28281dfeb42f76c4bf0b66e0c23674288 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3219_BertBase-Squad2.0_performance_8p.sh @@ -0,0 +1,206 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertBase-Squad2.0_ID3219_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +model_path=${data_path}/uncased_L-12_H-768_A-12 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/bert_model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_full_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_full_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..d179d620316be3902847e4f8b4ead3795e7c54c9 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_full_8p.sh @@ -0,0 +1,207 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad2.0_ID3220_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=2.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=True \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep "tensorflow:f1 =" $cur_path/output/0/train_0.log|awk 'END {print $3}'` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_1p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..5a5a0d949a346bef275426d697027ca70de471e8 --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_1p.sh @@ -0,0 +1,204 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad2.0_ID3220_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --npu_bert_loss_scale=0 \ + --num_train_steps=1000 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_8p.sh b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_8p.sh new file mode 100644 index 0000000000000000000000000000000000000000..5f4d3604f069f4f0e0c3d307cd9aa019b613553b --- /dev/null +++ b/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3220_BertLarge-Squad2.0_performance_8p.sh @@ -0,0 +1,207 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +export RANK_SIZE=8 +export JOB_ID=99990001 +export RANK_TABLE_FILE=${cur_path}/../configs/8p.json +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge-Squad2.0_ID3220_for_TensorFlow" +#训练batch_size +train_batch_size=32 + +#训练ephch +num_train_epochs=1.0 +#学习率 +learning_rate=5e-6 +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +over_dump_path=${cur_path}/output/overflow_dump +data_dump_flag=False +data_dump_path=${cur_path}/output/data_dump +enable_exception_dump=False +data_dump_step="0" +profiling=False +autotune=False + +if [[ $1 == --help || $1 == -h ]];then + echo "usage: ./train_full_1p.sh " + + echo "" + echo "parameter explain: + --task_name finetune dataset + --data_path source data of training + --train_batch_size training batch + --learning_rate learning_rate + --enable_exception_dump enable_exception_dump + --num_train_epochs epochs + --output_dir output dir + -h/--help Show help message + " + exit 1 +fi + +for para in $* +do + if [[ $para == --task_name* ]];then + task_name=`echo ${para#*=}` + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --squad_version* ]];then + squad_version=`echo ${para#*=}` + elif [[ $para == --ckpt_path* ]];then + ckpt_path=`echo ${para#*=}` + elif [[ $para == --train_batch_size* ]];then + train_batch_size=`echo ${para#*=}` + elif [[ $para == --learning_rate* ]];then + learning_rate=`echo ${para#*=}` + elif [[ $para == --num_train_epochs* ]];then + num_train_epochs=`echo ${para#*=}` + elif [[ $para == --output_dir* ]];then + output_dir=`echo ${para#*=}` + elif [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + elif [[ $para == --data_dump_path* ]];then + data_dump_path=`echo ${para#*=}` + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --enable_exception_dump* ]];then + enable_exception_dump=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + fi +done + +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi +#model_path=${data_path}/uncased_L-24_H-1024_A-16 +model_path=${data_path}/bert_tf_ckpt_large_pretraining_amp_lamb_19.03.1 + +#训练开始时间，不需要修改 +start_time=$(date +%s) +#进入训练脚本目录，需要模型审视修改 +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + fi + + nohup python3.7 ${cur_path}/../src/run_squad.py \ + --precision_mode=$precision_mode \ + --vocab_file=${model_path}/vocab.txt \ + --bert_config_file=${model_path}/bert_config.json \ + --init_checkpoint=${model_path}/model.ckpt \ + --do_train=True \ + --train_file=${data_path}/dataset/squad_v2.0_train.tf_record \ + --do_predict=False \ + --predict_file=${data_path}/dataset/dev-v2.0.json \ + --eval_script=${data_path}/dataset/evaluate-v2.0.py \ + --train_batch_size=$train_batch_size \ + --learning_rate=$learning_rate \ + --num_train_epochs=$num_train_epochs \ + --save_checkpoints_steps=1000 \ + --distributed=True \ + --npu_bert_tail_optimize=True \ + --npu_bert_loss_scale=0 \ + --output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} \ + --version_2_with_negative=True \ + --enable_exception_dump=$enable_exception_dump\ + --data_dump_flag=$data_dump_flag \ + --data_dump_step=$data_dump_step\ + --data_dump_path=$data_dump_path\ + --over_dump=$over_dump \ + --over_dump_path=$over_dump_path > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +#输出性能FPS，需要模型审视修改 +FPS=`grep "tensorflow:examples/sec" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk 'END {print $2}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +#train_accuracy=`grep "f1 =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $3}'` +#打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" + +##获取性能数据 +#吞吐量 +ActualFPS=$FPS +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${train_batch_size}'*1000/'${FPS}'}'` + +##冒烟看护字段 +BatchSize=${train_batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 +grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改' +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`) + +#关键性息打印到CaseName.log中 +echo "Network = ${Network}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${Accuracy}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}">>$cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + + + + + + + + + + + + + + + + + + + + + diff --git a/TensorFlow/built-in/nlp/GRU4Rec_ID0128_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/nlp/GRU4Rec_ID0128_for_TensorFlow/test/train_full_1p.sh index c6b78abc7224cdf86e594512cfe915b9937a21bb..1d9183f5326395d4301ff2a8a165b4c47529692c 100644 --- a/TensorFlow/built-in/nlp/GRU4Rec_ID0128_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/built-in/nlp/GRU4Rec_ID0128_for_TensorFlow/test/train_full_1p.sh @@ -7,13 +7,14 @@ export JOB_ID=10087 RANK_ID_START=0 # 数据集路径,保持为空,不需要修改 data_path="" +data_file="/rsc15_train_full.txt" #设置默认日志级别,不需要修改 -export ASCEND_GLOBAL_LOG_LEVEL=3 +export ASCEND_GLOBAL_LOG_LEVEL_ETP=3 #基础参数，需要模型审视修改 #网络名称，同目录名称 Network="GRU4Rec_for_TensorFlow" #训练epoch -train_epochs=1 +train_epochs=10 #TF2.X独有，不需要修改 #export NPU_LOOP_SIZE=${train_steps} #维测参数，precision_mode需要模型审视修改 @@ -30,18 +31,18 @@ if [[ $1 == --help || $1 == -h ]];then echo " " echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message + --data_path source data of training + -h/--help show help message " exit 1 fi #参数校验，不需要修改 -for para in $* +for para in $* do if [[ $para == --precision_mode* ]];then precision_mode=`echo ${para#*=}` @@ -78,6 +79,7 @@ if [[ $data_path == "" ]];then exit 1 fi BatchSize=50 +`sed -i 's/batch_size = 4096/batch_size = 50/g' ${cur_path}/../gru4rec_BP/main.py` CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' #训练开始时间，不需要修改 start_time=$(date +%s) @@ -87,8 +89,8 @@ for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$RANK_ID - + export RANK_ID=$RANK_ID + #创建DeviceID输出目录，不需要修改 if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} @@ -101,7 +103,7 @@ do # let a=RANK_ID*12 # let b=RANK_ID+1 # let c=b*12-1 - + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune nohup python3 main.py \ @@ -109,13 +111,13 @@ do --path_to_test=${data_path} \ --train=1 \ --epoch=${train_epochs} \ - > ${cur_path}/output/${ASCEND_DEVICE_ID}/train.log \ - 2>&1 & + --train_dataset_file=${data_file} \ + > ${cur_path}/output/${ASCEND_DEVICE_ID}/train.log 2>&1 & #python3 main.py --train=1 --epoch=${train_epochs} \ # --over_dump=${over_dump} \ - # --over_dump_path=${over_dump_path} -done + # --over_dump_path=${over_dump_path} +done wait #训练结束时间，不需要修改 @@ -149,7 +151,7 @@ ActualFPS=${FPS} grep Each $cur_path/output/${ASCEND_DEVICE_ID}/train.log|awk '{print $6}'>>$cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_time.txt TrainingTime=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_time.txt` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep Epoch $cur_path/output/$ASCEND_DEVICE_ID/train.log|awk '{print $8}'>> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +grep Epoch $cur_path/output/$ASCEND_DEVICE_ID/train.log|awk '{print $8}'> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` @@ -166,4 +168,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/albert_config/vocab.txt b/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/albert_config/vocab.txt deleted file mode 100644 index ca4f9781030019ab9b253c6dcb8c7878b6dc87a5..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/nlp/albert_xlarge_zh_ID2348_for_TensorFlow/albert_config/vocab.txt +++ /dev/null @@ -1,21128 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] 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-##｡ -##｢ -##｣ -##､ -##･ -##ｯ -##ｰ -##ｲ -##ｸ -##ｼ -##ｽ -##ﾄ -##ﾉ -##ﾌ -##ﾗ -##ﾙ -##ﾝ -##ﾞ -##ﾟ -##￣ -##￥ -##👍 -##🔥 -##😂 -##😎 diff --git a/TensorFlow/built-in/recommendation/DIEN_ID0109_for_TensorFlow/script/model.py b/TensorFlow/built-in/recommendation/DIEN_ID0109_for_TensorFlow/script/model.py index f1ce1624351d27b8268344d25e812f9be01700f3..6a1d9fea62436f6707c2f2a5343b8d5054fc5d74 100644 --- a/TensorFlow/built-in/recommendation/DIEN_ID0109_for_TensorFlow/script/model.py +++ b/TensorFlow/built-in/recommendation/DIEN_ID0109_for_TensorFlow/script/model.py @@ -111,7 +111,10 @@ class Model(object): if self.use_negsampling: self.loss += self.aux_loss tf.summary.scalar('loss', self.loss) - self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.loss) + self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr) + loss_scale_manager = ExponentialUpdateLossScaleManager(init_loss_scale=2**32, incr_every_n_steps=1000, decr_every_n_nan_or_inf=2, decr_ratio=0.5) + self.optimizer = NPULossScaleOptimizer(self.optimizer, loss_scale_manager) + self.optimizer = self.optimizer.minimize(self.loss) # Accuracy metric self.accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.round(self.y_hat), self.target_ph), tf.float32)) diff --git a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/script/train.py b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/script/train.py index b2cc74f593dc4b65adc90774015b560aea69b212..1bdcadb6c0e4ca909200451ebe002e3d3e2951a2 100644 --- a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/script/train.py +++ b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/script/train.py @@ -200,7 +200,6 @@ def train( uids, mids, cats, mid_his, cat_his, mid_mask, target, sl, noclk_mids, noclk_cats = prepare_data(src, tgt, maxlen, return_neg=True) loss, acc, aux_loss = model.train(sess, [uids, mids, cats, mid_his, cat_his, mid_mask, target, sl, lr, noclk_mids, noclk_cats]) end_time = time.time() - print("step_time:", end_time - start_time) # tf.io.write_graph(sess.graph_def, '/data1/d00564369/dien-npu', 'train_graph.pbtxt') loss_sum += loss accuracy_sum += acc @@ -213,7 +212,6 @@ def train( avg_examples_per_second = batch_size/(end_time - start_time) print("avg_time_per_step: ", avg_time_per_step) print("avg_examples_per_second: ", avg_examples_per_second) - print("step_time:", end_time - start_time) print('[epoch: %d, iter: %d] ----> train_loss: %.4f ---- train_accuracy: %.4f ---- train_aux_loss: %.4f' % \ (itr, iter, loss_sum / test_iter, accuracy_sum / test_iter, aux_loss_sum / test_iter)) diff --git a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_full_1p.sh index ecbd254578c47a73f2f127a173e509ffad884af4..81b63f390314053587c00bb31270fd67647b31b6 100644 --- a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_full_1p.sh @@ -125,13 +125,12 @@ e2e_time=$(( $end_time - $start_time )) #结果打印，不需要修改 echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 -Time=`grep perf $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $14}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'}'` +FPS=`grep avg_examples_per_second $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F ":" 'END{print $2}'|sed s/[[:space:]]//g` #打印，不需要修改 echo "Final Performance item/sec : $FPS" #输出训练精度,需要模型审视修改 -train_accuracy=`grep "train_accuracy" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +train_accuracy=`grep "train_accuracy" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "train_accuracy:" 'END{print $2}' | awk -F ' ' '{print $1}' |sed s/[[:space:]]//g` #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" @@ -149,7 +148,7 @@ ActualFPS=${FPS} TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep train_loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $5}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +grep "train_loss" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "train_loss:" '{print $2}' | awk -F ' ' '{print $1}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` diff --git a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_performance_1p.sh index c9355df632285401a50ef3dcf58b08874b6f678f..bfc9a75e275ec7849269e3a67f98497511b6b9e4 100644 --- a/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/built-in/recommendation/DIEN_ID3065_for_TensorFlow/test/train_performance_1p.sh @@ -128,13 +128,12 @@ sed -i "s|break|pass|g" train.py #结果打印，不需要修改 echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 -Time=`grep perf $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $14}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'}'` +FPS=`grep avg_examples_per_second $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F ":" 'END{print $2}' |sed s/[[:space:]]//g` #打印，不需要修改 echo "Final Performance item/sec : $FPS" #输出训练精度,需要模型审视修改 -train_accuracy=`grep "train_accuracy" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +train_accuracy=`grep "train_accuracy" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "train_accuracy:" 'END{print $2}' | awk -F ' ' '{print $1}'|sed s/[[:space:]]//g` #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" @@ -152,46 +151,20 @@ ActualFPS=${FPS} TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep train_loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $5}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +grep "train_loss" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "train_loss:" '{print $2}' | awk -F ' ' '{print $1}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` #关键信息打印到${CaseName}.log中，不需要修改 -#echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log - -##获取错误信息 -#系统错误信息 -error_msg="cannot import name 'DynamicAUGRU' from 'npu_bridge.estimator.npu.npu_dynamic_rnn'" -#判断错误信息是否和历史状态一致，此处无需修改 -Status=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | wc -l` -#失败阶段，枚举值图准备FAIL/图拆分FAIL/图优化FAIL/图编译FAIL/图执行FAIL/流程OK -ModelStatus="图执行FAIL" -#DTS单号或者issue链接 -DTS_Number="AR0001TRFQ Florence V100R001C25_SF_02_020_SR_0800_AR_0001 -AR0001TRFR Florence V100R001C25_SF_02_020_SR_0800_AR_0002 -AR0001TRFS Florence V100R001C25_SF_02_020_SR_0800_AR_0003 -AR0001TRFT Florence V100R001C25_SF_02_020_SR_0801_AR_0001 -AR0001TRFU Florence V100R001C25_SF_02_020_SR_0801_AR_0002" - -#关键信息打印到CaseName.log中，此处无需修改 echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RankSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ModelStatus = ${ModelStatus}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DTS_Number = ${DTS_Number}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "Status = ${Status}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "error_msg = ${error_msg}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file + diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/rnn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/rnn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/rnn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/rnn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/rnn_v2.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/rnn_v2.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/rnn_v2.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/rnn_v2.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/utils.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/utils.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/contrib/utils.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/contrib/utils.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/feature_column.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/feature_column.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/feature_column.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/feature_column.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/inputs.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/inputs.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/inputs.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/inputs.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/afm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/afm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/afm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/afm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/autoint.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/autoint.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/autoint.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/autoint.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/ccpm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/ccpm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/ccpm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/ccpm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/dcn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/dcn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/dcn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/dcn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/deepfefm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/deepfefm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/deepfefm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/deepfefm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/deepfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/deepfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/deepfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/deepfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fibinet.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fibinet.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fibinet.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fibinet.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fnn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fnn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fnn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fnn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fwfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fwfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/fwfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/fwfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/nfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/nfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/nfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/nfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/pnn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/pnn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/pnn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/pnn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/wdl.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/wdl.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/wdl.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/wdl.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/xdeepfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/xdeepfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/models/xdeepfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/models/xdeepfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/utils.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/utils.py similarity index 98% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/utils.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/utils.py index 187354dd58ea20dc10819c825b047677dd920aab..639622f731b9f80c3b9e6922ae1e0c75bcab62a7 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/estimator/utils.py +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/estimator/utils.py @@ -138,12 +138,13 @@ class Head(_Head): training_loss = loss + reg_loss eval_metric_ops = self._eval_metric_ops(labels, logits, pred, unweighted_loss) - + training_Hook=tf.train.LoggingTensorHook({"loss":training_loss}, every_n_iter=1) return tf.estimator.EstimatorSpec( mode=mode, predictions=predictions, loss=training_loss, train_op=train_op_fn(training_loss), + training_hooks=[training_Hook], eval_metric_ops=eval_metric_ops, training_chief_hooks=training_chief_hooks) diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/feature_column.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/feature_column.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/feature_column.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/feature_column.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/inputs.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/inputs.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/inputs.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/inputs.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/activation.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/activation.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/activation.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/activation.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/core.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/core.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/core.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/core.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/interaction.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/interaction.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/interaction.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/interaction.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/normalization.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/normalization.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/normalization.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/normalization.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/sequence.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/sequence.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/sequence.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/sequence.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/utils.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/utils.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/layers/utils.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/layers/utils.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/afm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/afm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/afm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/afm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/autoint.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/autoint.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/autoint.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/autoint.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/ccpm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/ccpm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/ccpm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/ccpm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/dcn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/dcn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/dcn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/dcn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/dcnmix.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/dcnmix.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/dcnmix.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/dcnmix.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/deepfefm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/deepfefm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/deepfefm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/deepfefm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/deepfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/deepfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/deepfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/deepfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/difm.py 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TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/fibinet.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/fibinet.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/flen.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/flen.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/flen.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/flen.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/fnn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/fnn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/fnn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/fnn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/fwfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/fwfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/fwfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/fwfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/ifm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/ifm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/ifm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/ifm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/mlr.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/mlr.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/mlr.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/mlr.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/esmm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/esmm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/esmm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/esmm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/mmoe.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/mmoe.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/mmoe.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/mmoe.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/ple.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/ple.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/ple.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/ple.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/sharedbottom.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/sharedbottom.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/multitask/sharedbottom.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/multitask/sharedbottom.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/nfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/nfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/nfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/nfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/onn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/onn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/onn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/onn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/pnn.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/pnn.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/pnn.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/pnn.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/__init__.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/__init__.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/__init__.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/__init__.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/bst.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/bst.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/bst.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/bst.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/dien.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/dien.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/dien.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/dien.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/din.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/din.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/din.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/din.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/dsin.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/dsin.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/sequence/dsin.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/sequence/dsin.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/wdl.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/wdl.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/wdl.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/wdl.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/xdeepfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/xdeepfm.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/models/xdeepfm.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/models/xdeepfm.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/utils.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/utils.py similarity index 100% rename from TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/deepctr/utils.py rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/deepctr/utils.py diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/movielens_sample.txt b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/movielens_sample.txt deleted file mode 100644 index 9ffa14824b834e57a9aed7e616a2a3fc8785c734..0000000000000000000000000000000000000000 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/movielens_sample.txt +++ /dev/null @@ -1,201 +0,0 @@ -user_id,movie_id,rating,timestamp,title,genres,gender,age,occupation,zip -3299,235,4,968035345,Ed Wood (1994),Comedy|Drama,F,25,4,19119 -3630,3256,3,966536874,Patriot Games (1992),Action|Thriller,M,18,4,77005 -517,105,4,976203603,"Bridges of Madison County, The (1995)",Drama|Romance,F,25,14,55408 -785,2115,3,975430389,Indiana Jones and the Temple of Doom (1984),Action|Adventure,M,18,19,29307 -5848,909,5,957782527,"Apartment, The (1960)",Comedy|Drama,M,50,20,20009 -2996,2799,1,972769867,Problem Child 2 (1991),Comedy,M,18,0,63011 -3087,837,5,969738869,Matilda (1996),Children's|Comedy,F,1,1,90802 -872,3092,5,975273310,Chushingura (1962),Drama,M,50,1,20815 -4094,529,5,966223349,Searching for Bobby Fischer (1993),Drama,M,25,17,49017 -1868,3508,3,974694703,"Outlaw Josey Wales, The (1976)",Western,M,50,11,92346 -2913,1387,5,971769808,Jaws (1975),Action|Horror,F,35,20,98119 -380,3481,5,976316283,High Fidelity (2000),Comedy,M,25,2,92024 -2073,1784,5,974759084,As Good As It Gets (1997),Comedy|Drama,F,18,4,13148 -80,2059,3,977788576,"Parent Trap, The (1998)",Children's|Drama,M,56,1,49327 -3679,2557,1,976298130,I Stand Alone (Seul contre tous) (1998),Drama,M,25,4,68108 -2077,788,3,980013556,"Nutty Professor, The (1996)",Comedy|Fantasy|Romance|Sci-Fi,M,18,0,55112 -6036,2085,4,956716684,101 Dalmatians (1961),Animation|Children's,F,25,15,32603 -3675,532,3,966363610,Serial Mom (1994),Comedy|Crime|Horror,M,35,7,06680 -4566,3683,4,964489599,Blood Simple (1984),Drama|Film-Noir,M,35,17,19473 -2996,3763,3,972413564,F/X (1986),Action|Crime|Thriller,M,18,0,63011 -5831,2458,1,957898337,Armed and Dangerous (1986),Comedy|Crime,M,25,1,92120 -1869,1244,2,974695654,Manhattan (1979),Comedy|Drama|Romance,M,45,14,95148 -5389,2657,3,960328279,"Rocky Horror Picture Show, The (1975)",Comedy|Horror|Musical|Sci-Fi,M,45,7,01905 -1391,1535,3,974851275,Love! Valour! Compassion! (1997),Drama|Romance,M,35,15,20723 -3123,2407,3,969324381,Cocoon (1985),Comedy|Sci-Fi,M,25,2,90401 -4694,159,3,963602574,Clockers (1995),Drama,M,56,7,40505 -1680,1988,3,974709821,Hello Mary Lou: Prom Night II (1987),Horror,M,25,20,95380 -2002,1945,4,974677761,On the Waterfront (1954),Crime|Drama,F,56,13,02136-1522 -3430,2690,4,979949863,"Ideal Husband, An (1999)",Comedy,F,45,1,15208 -425,471,4,976284972,"Hudsucker Proxy, The (1994)",Comedy|Romance,M,25,12,55303 -1841,2289,2,974699637,"Player, The (1992)",Comedy|Drama,M,18,0,95037 -4964,2348,4,962619587,Sid and Nancy (1986),Drama,M,35,0,94110 -4520,2160,4,964883648,Rosemary's Baby (1968),Horror|Thriller,M,25,4,45810 -1265,2396,4,1011716691,Shakespeare in Love (1998),Comedy|Romance,F,18,20,49321 -2496,1278,5,974435324,Young Frankenstein (1974),Comedy|Horror,M,50,1,37932 -5511,2174,4,959787754,Beetlejuice (1988),Comedy|Fantasy,M,45,1,92407 -621,833,1,975799925,High School High (1996),Comedy,M,18,4,93560 -3045,2762,5,970189524,"Sixth Sense, The (1999)",Thriller,M,45,1,90631 -2050,2546,4,975522689,"Deep End of the Ocean, The (1999)",Drama,F,35,3,99504 -613,32,4,975812238,Twelve Monkeys (1995),Drama|Sci-Fi,M,35,20,10562 -366,1077,5,978471241,Sleeper (1973),Comedy|Sci-Fi,M,50,15,55126 -5108,367,4,962338215,"Mask, The (1994)",Comedy|Crime|Fantasy,F,25,9,93940 -4502,1960,4,965094644,"Last Emperor, The (1987)",Drama|War,M,50,0,01379 -5512,1801,5,959713840,"Man in the Iron Mask, The (1998)",Action|Drama|Romance,F,25,17,01701 -1861,2642,2,974699627,Superman III (1983),Action|Adventure|Sci-Fi,M,50,16,92129 -1667,1240,4,975016698,"Terminator, The (1984)",Action|Sci-Fi|Thriller,M,50,16,98516 -753,434,3,975460449,Cliffhanger (1993),Action|Adventure|Crime,M,1,10,42754 -1836,2736,5,974826228,Brighton Beach Memoirs (1986),Comedy,M,25,0,10016 -5626,474,5,959052158,In the Line of Fire (1993),Action|Thriller,M,56,16,32043 -1601,1396,4,978576948,Sneakers (1992),Crime|Drama|Sci-Fi,M,25,12,83001 -4725,1100,4,963369546,Days of Thunder (1990),Action|Romance,M,35,5,96707-1321 -2837,2396,5,972571456,Shakespeare in Love (1998),Comedy|Romance,M,18,0,49506 -1776,3882,4,1001558470,Bring It On (2000),Comedy,M,25,0,45801 -2820,457,2,972662398,"Fugitive, The (1993)",Action|Thriller,F,35,0,02138 -1834,2288,3,1038179198,"Thing, The (1982)",Action|Horror|Sci-Fi|Thriller,M,35,5,10990 -284,2716,4,976570902,Ghostbusters (1984),Comedy|Horror,M,25,12,91910 -2744,588,1,973215985,Aladdin (1992),Animation|Children's|Comedy|Musical,M,18,17,53818 -881,4,2,975264028,Waiting to Exhale (1995),Comedy|Drama,M,18,14,76401 -2211,916,3,974607067,Roman Holiday (1953),Comedy|Romance,M,45,6,01950 -2271,2671,4,1007158806,Notting Hill (1999),Comedy|Romance,M,50,14,13210 -1010,2953,1,975222613,Home Alone 2: Lost in New York (1992),Children's|Comedy,M,25,0,10310 -1589,2594,4,974735454,Open Your Eyes (Abre los ojos) (1997),Drama|Romance|Sci-Fi,M,25,0,95136 -1724,597,5,976441106,Pretty Woman (1990),Comedy|Romance,M,18,4,00961 -2590,2097,3,973840056,Something Wicked This Way Comes (1983),Children's|Horror,M,18,4,94044 -1717,1352,3,1009256707,Albino Alligator (1996),Crime|Thriller,F,50,6,30307 -1391,3160,2,974850796,Magnolia (1999),Drama,M,35,15,20723 -1941,1263,3,974954220,"Deer Hunter, The (1978)",Drama|War,M,35,17,94550 -3526,2867,4,966906064,Fright Night (1985),Comedy|Horror,M,35,2,62263-3004 -5767,198,3,958192148,Strange Days (1995),Action|Crime|Sci-Fi,M,25,2,75287 -5355,590,4,960596927,Dances with Wolves (1990),Adventure|Drama|Western,M,56,0,78232 -5788,156,4,958108785,Blue in the Face (1995),Comedy,M,25,0,92646 -1078,1307,4,974938851,When Harry Met Sally... (1989),Comedy|Romance,F,45,9,95661 -3808,61,2,965973222,Eye for an Eye (1996),Drama|Thriller,M,25,7,60010 -974,3897,4,975106398,Almost Famous (2000),Comedy|Drama,M,35,19,94930 -5153,1290,4,961972292,Some Kind of Wonderful (1987),Drama|Romance,M,25,7,60046 -5732,2115,3,958434069,Indiana Jones and the Temple of Doom (1984),Action|Adventure,F,25,11,02111 -4627,2478,3,964110136,Three Amigos! (1986),Comedy|Western,M,56,1,45224 -1884,1831,2,975648062,Lost in Space (1998),Action|Sci-Fi|Thriller,M,45,20,93108 -4284,517,4,965277546,Rising Sun (1993),Action|Drama|Mystery,M,50,7,40601 -1383,468,2,975979732,"Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)",Comedy|Romance,F,25,7,19806 -2230,2873,3,974599097,Lulu on the Bridge (1998),Drama|Mystery|Romance,F,45,1,60302 -2533,2266,4,974055724,"Butcher's Wife, The (1991)",Comedy|Romance,F,25,3,49423 -6040,3224,5,956716750,Woman in the Dunes (Suna no onna) (1964),Drama,M,25,6,11106 -4384,2918,5,965171739,Ferris Bueller's Day Off (1986),Comedy,M,25,0,43623 -5156,3688,3,961946487,Porky's (1981),Comedy,M,18,14,10024 -615,296,3,975805801,Pulp Fiction (1994),Crime|Drama,M,50,17,32951 -2753,3045,3,973198964,Peter's Friends (1992),Comedy|Drama,F,50,20,27516 -2438,1125,5,974259943,"Return of the Pink Panther, The (1974)",Comedy,M,35,1,22903 -5746,1242,4,958354460,Glory (1989),Action|Drama|War,M,18,15,94061 -5157,3462,5,961944604,Modern Times (1936),Comedy,M,35,1,74012 -3402,1252,5,967433929,Chinatown (1974),Film-Noir|Mystery|Thriller,M,35,20,30306 -76,593,5,977847255,"Silence of the Lambs, The (1991)",Drama|Thriller,M,35,7,55413 -2067,1019,3,974658834,"20,000 Leagues Under the Sea (1954)",Adventure|Children's|Fantasy|Sci-Fi,M,50,16,06430 -2181,2020,3,979353437,Dangerous Liaisons (1988),Drama|Romance,M,25,0,45245 -3947,593,5,965691680,"Silence of the Lambs, The (1991)",Drama|Thriller,M,25,0,90019 -546,218,4,976069421,Boys on the Side (1995),Comedy|Drama,F,25,0,37211 -1246,3030,5,1032056405,Yojimbo (1961),Comedy|Drama|Western,M,18,4,98225 -4214,3186,5,965319143,"Girl, Interrupted (1999)",Drama,F,25,0,20121 -2841,680,3,982805796,Alphaville (1965),Sci-Fi,M,50,12,98056 -4205,3175,4,965321085,Galaxy Quest (1999),Adventure|Comedy|Sci-Fi,F,25,15,87801 -1120,1097,4,974911354,E.T. the Extra-Terrestrial (1982),Children's|Drama|Fantasy|Sci-Fi,M,18,4,95616 -5371,3194,3,960481000,"Way We Were, The (1973)",Drama,M,25,11,55408 -2695,1278,5,973310827,Young Frankenstein (1974),Comedy|Horror,M,35,11,46033 -3312,520,2,976673070,Robin Hood: Men in Tights (1993),Comedy,F,18,4,90039 -5039,1792,1,962513044,U.S. Marshalls (1998),Action|Thriller,F,35,4,97068 -4655,2146,3,963903103,St. Elmo's Fire (1985),Drama|Romance,F,25,1,92037 -3558,1580,5,966802528,Men in Black (1997),Action|Adventure|Comedy|Sci-Fi,M,18,17,66044 -506,3354,1,976208080,Mission to Mars (2000),Sci-Fi,M,25,16,55103-1006 -3568,1230,3,966745594,Annie Hall (1977),Comedy|Romance,M,25,0,98503 -2943,1197,5,971319983,"Princess Bride, The (1987)",Action|Adventure|Comedy|Romance,M,35,12,95864 -716,737,3,982881364,Barb Wire (1996),Action|Sci-Fi,M,18,4,98188 -5964,454,3,956999469,"Firm, The (1993)",Drama|Thriller,M,18,5,97202 -4802,1208,4,996034747,Apocalypse Now (1979),Drama|War,M,56,1,40601 -1106,3624,4,974920622,Shanghai Noon (2000),Action,M,18,4,90241 -3410,2565,3,967419652,"King and I, The (1956)",Musical,M,35,1,20653 -1273,3095,5,974814536,"Grapes of Wrath, The (1940)",Drama,M,35,2,19123 -1706,1916,4,974709448,Buffalo 66 (1998),Action|Comedy|Drama,M,25,20,19134 -4889,590,5,962909224,Dances with Wolves (1990),Adventure|Drama|Western,M,18,4,63108 -4966,2100,3,962609782,Splash (1984),Comedy|Fantasy|Romance,M,50,14,55407 -4238,1884,4,965343416,Fear and Loathing in Las Vegas (1998),Comedy|Drama,M,35,16,44691 -5365,1042,3,960502974,That Thing You Do! (1996),Comedy,M,18,12,90250 -415,1302,3,977501743,Field of Dreams (1989),Drama,F,35,0,55406 -4658,1009,5,963966553,Escape to Witch Mountain (1975),Adventure|Children's|Fantasy,M,25,4,99163 -854,345,3,975357801,"Adventures of Priscilla, Queen of the Desert, The (1994)",Comedy|Drama,F,25,16,44092 -2857,436,4,972509362,Color of Night (1994),Drama|Thriller,M,25,0,10469 -1835,1330,4,974878241,April Fool's Day (1986),Comedy|Horror,M,25,19,11501 -1321,2240,3,974778494,My Bodyguard (1980),Drama,F,25,14,34639 -3274,3698,2,979767184,"Running Man, The (1987)",Action|Adventure|Sci-Fi,M,25,20,02062 -5893,2144,3,957470619,Sixteen Candles (1984),Comedy,M,25,7,02139 -3436,2724,3,967328026,Runaway Bride (1999),Comedy|Romance,M,35,0,98503 -3315,2918,5,967942960,Ferris Bueller's Day Off (1986),Comedy,M,25,12,78731 -5056,2700,5,962488280,"South Park: Bigger, Longer and Uncut (1999)",Animation|Comedy,M,45,1,16673 -5256,208,2,961271616,Waterworld (1995),Action|Adventure,M,25,16,30269 -4290,1193,4,965274348,One Flew Over the Cuckoo's Nest (1975),Drama,M,25,17,98661 -1010,1379,2,975220259,Young Guns II (1990),Action|Comedy|Western,M,25,0,10310 -829,904,4,975368038,Rear Window (1954),Mystery|Thriller,M,1,19,53711 -5953,480,4,957143581,Jurassic Park (1993),Action|Adventure|Sci-Fi,M,1,10,21030 -4732,3016,4,963332896,Creepshow (1982),Horror,M,25,14,24450 -4815,3181,5,972240802,Titus (1999),Drama,F,50,18,04849 -1164,1894,2,1004486985,Six Days Seven Nights (1998),Adventure|Comedy|Romance,F,25,19,90020 -4373,3167,5,965180829,Carnal Knowledge (1971),Drama,M,50,12,32920 -5293,1374,4,961055887,Star Trek: The Wrath of Khan (1982),Action|Adventure|Sci-Fi,M,25,12,95030 -1579,3101,4,981272057,Fatal Attraction (1987),Thriller,M,25,0,60201 -2600,3147,5,973804787,"Green Mile, The (1999)",Drama|Thriller,M,25,14,19312 -1283,480,4,974793389,Jurassic Park (1993),Action|Adventure|Sci-Fi,F,18,1,94607 -3242,3062,5,968341175,"Longest Day, The (1962)",Action|Drama|War,M,50,13,94089 -3618,3374,3,967116272,Daughters of the Dust (1992),Drama,M,56,17,22657 -3762,1337,4,966434517,"Body Snatcher, The (1945)",Horror,M,50,6,11746 -1015,1184,3,975018699,Mediterraneo (1991),Comedy|War,M,35,3,11220 -4645,2344,5,963976808,Runaway Train (1985),Action|Adventure|Drama|Thriller,F,50,6,48094 -3184,1397,4,968709039,Bastard Out of Carolina (1996),Drama,F,25,18,21214 -1285,1794,4,974833328,Love and Death on Long Island (1997),Comedy|Drama,M,35,4,98125 -5521,3354,2,959833154,Mission to Mars (2000),Sci-Fi,F,25,6,02118 -1472,2278,3,974767792,Ronin (1998),Action|Crime|Thriller,M,25,7,90248 -5630,21,4,980085414,Get Shorty (1995),Action|Comedy|Drama,M,35,17,06854 -3710,3033,5,966272980,Spaceballs (1987),Comedy|Sci-Fi,M,1,10,02818 -192,761,1,977028390,"Phantom, The (1996)",Adventure,M,18,1,10977 -1285,1198,5,974880310,Raiders of the Lost Ark (1981),Action|Adventure,M,35,4,98125 -2174,1046,4,974613044,Beautiful Thing (1996),Drama|Romance,M,50,12,87505 -635,1270,4,975768106,Back to the Future (1985),Comedy|Sci-Fi,M,56,17,33785 -910,412,5,975207742,"Age of Innocence, The (1993)",Drama,F,50,0,98226 -1752,2021,4,975729332,Dune (1984),Fantasy|Sci-Fi,M,25,3,96813 -1408,198,4,974762924,Strange Days (1995),Action|Crime|Sci-Fi,M,25,0,90046 -4738,1242,4,963279051,Glory (1989),Action|Drama|War,M,56,1,23608 -1503,1971,2,974748897,"Nightmare on Elm Street 4: The Dream Master, A (1988)",Horror,M,25,12,92688 -3053,1296,3,970601837,"Room with a View, A (1986)",Drama|Romance,F,25,3,55102 -3471,3614,2,973297828,Honeymoon in Vegas (1992),Comedy|Romance,M,18,4,80302 -678,1972,3,988638700,"Nightmare on Elm Street 5: The Dream Child, A (1989)",Horror,M,25,0,34952 -3483,2561,3,986327282,True Crime (1999),Crime|Thriller,F,45,7,30260 -3910,3108,5,965756244,"Fisher King, The (1991)",Comedy|Drama|Romance,M,25,20,91505 -182,1089,1,977085647,Reservoir Dogs (1992),Crime|Thriller,M,18,4,03052 -1755,1653,3,1036917836,Gattaca (1997),Drama|Sci-Fi|Thriller,F,18,4,77005 -3589,70,2,966658567,From Dusk Till Dawn (1996),Action|Comedy|Crime|Horror|Thriller,F,45,0,80010 -471,3481,4,976222483,High Fidelity (2000),Comedy,M,35,7,08904 -1141,813,2,974878678,Larger Than Life (1996),Comedy,F,25,3,84770 -5227,1196,2,961476022,Star Wars: Episode V - The Empire Strikes Back (1980),Action|Adventure|Drama|Sci-Fi|War,M,18,10,64050 -1303,2344,2,974837844,Runaway Train (1985),Action|Adventure|Drama|Thriller,M,25,19,94111 -5080,3102,5,962412804,Jagged Edge (1985),Thriller,F,50,12,95472 -2023,1012,4,1006290836,Old Yeller (1957),Children's|Drama,M,18,4,56001 -3759,2151,5,966094413,"Gods Must Be Crazy II, The (1989)",Comedy,M,35,6,54751 -1685,2664,2,974709721,Invasion of the Body Snatchers (1956),Horror|Sci-Fi,M,35,12,95833 -4715,1221,4,963508830,"Godfather: Part II, The (1974)",Action|Crime|Drama,M,25,2,97205 -1591,350,5,974742941,"Client, The (1994)",Drama|Mystery|Thriller,M,50,7,26501 -4227,3635,3,965411938,"Spy Who Loved Me, The (1977)",Action,M,25,19,11414-2520 -1908,36,5,974697744,Dead Man Walking (1995),Drama,M,56,13,95129 -5365,1892,4,960503255,"Perfect Murder, A (1998)",Mystery|Thriller,M,18,12,90250 -1579,2420,4,981272235,"Karate Kid, The (1984)",Drama,M,25,0,60201 -1866,3948,5,974753321,Meet the Parents (2000),Comedy,M,25,7,94043 -4238,3543,4,965415533,Diner (1982),Comedy|Drama,M,35,16,44691 -3590,2000,5,966657892,Lethal Weapon (1987),Action|Comedy|Crime|Drama,F,18,15,02115 -3401,3256,5,980115327,Patriot Games (1992),Action|Thriller,M,35,7,76109 -3705,540,2,966287116,Sliver (1993),Thriller,M,45,7,30076 -4973,1246,3,962607149,Dead Poets Society (1989),Drama,F,56,2,949702 -4947,380,4,962651180,True Lies (1994),Action|Adventure|Comedy|Romance,M,35,17,90035 -2346,1416,4,974413811,Evita (1996),Drama|Musical,F,1,10,48105 -1427,3596,3,974840560,Screwed (2000),Comedy,M,25,12,21401 -3868,1626,3,965855033,Fire Down Below (1997),Action|Drama|Thriller,M,18,12,73112 -249,2369,3,976730191,Desperately Seeking Susan (1985),Comedy|Romance,F,18,14,48126 -5720,349,4,958503395,Clear and Present Danger (1994),Action|Adventure|Thriller,M,25,0,60610 -877,1485,3,975270899,Liar Liar (1997),Comedy,M,25,0,90631 diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_classification_criteo.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_classification_criteo.py index 6462f295838e969ecd7a3f015298a68d1a50d9f0..877c0a1397535bf9d18c3f09497ec40a7ba81928 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_classification_criteo.py +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_classification_criteo.py @@ -36,10 +36,39 @@ from sklearn.preprocessing import LabelEncoder, MinMaxScaler from deepctr.models import DeepFM from deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names +import argparse if __name__ == "__main__": - npu_keras_sess = set_keras_session_npu_config() - data = pd.read_csv('./criteo_sample.txt') + + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', default="./", + help='data path for train') + parser.add_argument('--precision_mode', default='allow_fp32_to_fp16', + help='allow_fp32_to_fp16/force_fp16/ ' + 'must_keep_origin_dtype/allow_mix_precision.') + parser.add_argument('--data_dump_flag', action="store_true", + help='whether to enable dump data') + parser.add_argument('--data_dump_path', default="/home/data", + help='the path to save dump data') + parser.add_argument('--data_dump_step', default="0", + help='the step to dump') + args = parser.parse_args() + + sess_config = tf.ConfigProto() + custom_op = sess_config.graph_options.rewrite_options.custom_optimizers.add() + sess_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + sess_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(args.precision_mode) + if args.data_dump_flag: + print("start to config data dump...{}", args.data_dump_flag) + custom_op.parameter_map["enable_dump"].b = True + custom_op.parameter_map["dump_path"].s = tf.compat.as_bytes(args.data_dump_path) + custom_op.parameter_map["dump_step"].s = tf.compat.as_bytes(args.data_dump_step) + custom_op.parameter_map["dump_mode"].s = tf.compat.as_bytes("all") + + npu_keras_sess = set_keras_session_npu_config(config=sess_config) + data = pd.read_csv(os.path.join(args.data_dir, 'criteo_sample.txt')) sparse_features = ['C' + str(i) for i in range(1, 27)] dense_features = ['I' + str(i) for i in range(1, 14)] diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_estimator_tfrecord_classification.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_estimator_tfrecord_classification.py index 1ce5fac75631b9fdae6a883575115e20825ca08c..9ead9c2cde12c4f0cf2a046eea3a02192a9c1c47 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_estimator_tfrecord_classification.py +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_estimator_tfrecord_classification.py @@ -29,13 +29,45 @@ # from npu_bridge.npu_init import * import tensorflow as tf - +from tensorflow import keras from tensorflow.python.ops.parsing_ops import FixedLenFeature from deepctr.estimator import DeepFMEstimator from deepctr.estimator.inputs import input_fn_tfrecord +import argparse +import os +def main(): -if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', default="./", + help='data path for train') + parser.add_argument('--precision_mode', default='allow_fp32_to_fp16', + help='allow_fp32_to_fp16/force_fp16/ ' + 'must_keep_origin_dtype/allow_mix_precision.') + parser.add_argument('--profiling', default=False, + help='if or not profiling for performance debug, default is False') + parser.add_argument('--profiling_dump_path', default="/home/data", + help='the path to save profiling data') + args = parser.parse_args() + + sess_config = tf.ConfigProto() + custom_op = sess_config.graph_options.rewrite_options.custom_optimizers.add() + sess_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + sess_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(args.precision_mode) + + if args.profiling: + custom_op.parameter_map["profiling_mode"].b = True + custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes( + '{"output":"' + args.profiling_dump_path + '", \ + "training_trace":"on", \ + "task_trace":"on", \ + "aicpu":"on", \ + "aic_metrics":"PipeUtilization",\ + "fp_point":"concatenate_1/concat", \ + "bp_point":"training/Adam/gradients/gradients/AddN_38"}') + npu_keras_sess = set_keras_session_npu_config(config=sess_config) # 1.generate feature_column for linear part and dnn part sparse_features = ['C' + str(i) for i in range(1, 27)] @@ -59,17 +91,22 @@ if __name__ == "__main__": {k: FixedLenFeature(dtype=tf.float32, shape=1) for k in dense_features}) feature_description['label'] = FixedLenFeature(dtype=tf.float32, shape=1) - train_model_input = input_fn_tfrecord('./criteo_sample.tr.tfrecords', feature_description, 'label', batch_size=256, - num_epochs=1, shuffle_factor=10) + train_model_input = input_fn_tfrecord('./criteo_sample.tr.tfrecords', feature_description, 'label', batch_size=162, + num_epochs=5, shuffle_factor=10) test_model_input = input_fn_tfrecord('./criteo_sample.te.tfrecords', feature_description, 'label', batch_size=2 ** 14, num_epochs=1, shuffle_factor=0) # 3.Define Model,train,predict and evaluate model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary', config=tf.estimator.RunConfig(tf_random_seed=2021, save_summary_steps=0)) - + tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) model.train(train_model_input) eval_result = model.evaluate(test_model_input) print(eval_result) + close_session(npu_keras_sess) + +if __name__ == "__main__": + main() + diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_flen.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_flen.py index e083ffaf75cdc07cbaef58f34973ee9eb1b039f9..6373b34841a92f88aec2761cfe9a7c38890d528f 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_flen.py +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_flen.py @@ -28,6 +28,7 @@ # limitations under the License. # from npu_bridge.npu_init import * +from tensorflow import keras import pandas as pd from sklearn.metrics import log_loss, roc_auc_score from sklearn.model_selection import train_test_split @@ -35,9 +36,42 @@ from sklearn.preprocessing import LabelEncoder from deepctr.feature_column import SparseFeat,get_feature_names from deepctr.models import FLEN +import argparse +import os -if __name__ == "__main__": - data = pd.read_csv('./avazu_sample.txt') +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', default="./", + help='data path for train') + parser.add_argument('--precision_mode', default='allow_fp32_to_fp16', + help='allow_fp32_to_fp16/force_fp16/ ' + 'must_keep_origin_dtype/allow_mix_precision.') + parser.add_argument('--profiling', default=False, + help='if or not profiling for performance debug, default is False') + parser.add_argument('--profiling_dump_path', default="/home/data", + help='the path to save profiling data') + args = parser.parse_args() + + sess_config = tf.ConfigProto() + custom_op = sess_config.graph_options.rewrite_options.custom_optimizers.add() + sess_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + sess_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(args.precision_mode) + + if args.profiling: + custom_op.parameter_map["profiling_mode"].b = True + custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes( + '{"output":"' + args.profiling_dump_path + '", \ + "training_trace":"on", \ + "task_trace":"on", \ + "aicpu":"on", \ + "aic_metrics":"PipeUtilization",\ + "fp_point":"concatenate_1/concat", \ + "bp_point":"training/Adam/gradients/gradients/AddN_38"}') + + npu_keras_sess = set_keras_session_npu_config(config=sess_config) + data = pd.read_csv(os.path.join(args.data_dir,'./avazu_sample.txt')) data['day'] = data['hour'].apply(lambda x: str(x)[4:6]) data['hour'] = data['hour'].apply(lambda x: str(x)[6:]) @@ -88,8 +122,11 @@ if __name__ == "__main__": metrics=['binary_crossentropy'], ) history = model.fit(train_model_input, train[target].values, - batch_size=256, epochs=10, verbose=2, validation_split=0.2, ) - pred_ans = model.predict(test_model_input, batch_size=256) + batch_size=64, epochs=10, verbose=1, validation_split=0.2, ) + pred_ans = model.predict(test_model_input, batch_size=4) print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4)) print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4)) +if __name__ == "__main__": + main() + diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_multivalue_movielens.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_multivalue_movielens.py index a54fd6c851cd994d270d27961bf385b4d527e79c..9e61c348a04fd2d226455806a45ce231ff652cd8 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_multivalue_movielens.py +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_multivalue_movielens.py @@ -28,6 +28,7 @@ # limitations under the License. # from npu_bridge.npu_init import * +from tensorflow import keras import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder @@ -35,6 +36,8 @@ from tensorflow.python.keras.preprocessing.sequence import pad_sequences from deepctr.feature_column import SparseFeat, VarLenSparseFeat,get_feature_names from deepctr.models import DeepFM +import argparse +import os def split(x): @@ -45,10 +48,39 @@ def split(x): key2index[key] = len(key2index) + 1 return list(map(lambda x: key2index[x], key_ans)) - if __name__ == "__main__": - npu_keras_sess = set_keras_session_npu_config() - data = pd.read_csv("./movielens_sample.txt") + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', default="./", + help='data path for train') + parser.add_argument('--precision_mode', default='allow_fp32_to_fp16', + help='allow_fp32_to_fp16/force_fp16/ ' + 'must_keep_origin_dtype/allow_mix_precision.') + parser.add_argument('--profiling', default=False, + help='if or not profiling for performance debug, default is False') + parser.add_argument('--profiling_dump_path', default="/home/data", + help='the path to save profiling data') + args = parser.parse_args() + + sess_config = tf.ConfigProto() + custom_op = sess_config.graph_options.rewrite_options.custom_optimizers.add() + sess_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + sess_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(args.precision_mode) + + if args.profiling: + custom_op.parameter_map["profiling_mode"].b = True + custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes( + '{"output":"' + args.profiling_dump_path + '", \ + "training_trace":"on", \ + "task_trace":"on", \ + "aicpu":"on", \ + "aic_metrics":"PipeUtilization",\ + "fp_point":"concatenate_1/concat", \ + "bp_point":"training/Adam/gradients/gradients/AddN_38"}') + + npu_keras_sess = set_keras_session_npu_config(config=sess_config) + data = pd.read_csv(os.path.join(args.data_dir,"./movielens_sample.txt")) sparse_features = ["movie_id", "user_id", "gender", "age", "occupation", "zip", ] target = ['rating'] @@ -96,6 +128,6 @@ if __name__ == "__main__": model.compile("adam", "mse", metrics=['mse'], ) history = model.fit(model_input, data[target].values, - batch_size=256, epochs=10, verbose=2, validation_split=0.2, ) + batch_size=160, epochs=10, verbose=1, validation_split=0.2, ) close_session(npu_keras_sess) diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_full_1p.sh index 133ee0476685c328f1ae95a480d3064bc2138723..a6c8057d2bc0881b306c953a3361df3baba690d5 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_full_1p.sh @@ -120,7 +120,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh index 198e11fdf71d6d01f0e9a8ee9343b155fe8546bc..f192262101cc2881d480bfb3f5ac3a28ff13a438 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3057_FwFM_performance_1p.sh @@ -122,7 +122,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_full_1p.sh index f99ec75a9fe14f4df80a8c29056ca5aabcd9560f..57eb2a9c552ab9a9aa81d34d0137c52a05a34b57 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_full_1p.sh @@ -120,7 +120,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" '{print $1}'|awk '{sum+=$1} END {print"",sum/NR}'|awk '{print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh index a32f77fe128c32dde7c384cda8b2cd9cc921627d..77cce685ce5e0535792db212b895eaae27c87df5 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3058_MMoE_performance_1p.sh @@ -118,7 +118,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" '{print $1}'|awk '{sum+=$1} END {print"",sum/NR}'|awk '{print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_full_1p.sh index 8fb69eea2430c7aa0076fc011035289eef58cbe8..3340bf02547b0826fed5b695432b1448a95a9353 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_full_1p.sh @@ -29,8 +29,8 @@ learning_rate= precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False -data_dump_flag=False -data_dump_step="10" +data_dump_flag="" +data_dump_step="1" profiling=False # 帮助信息，不需要修改 @@ -40,8 +40,8 @@ if [[ $1 == --help || $1 == -h ]];then echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 + --data_dump_flag data dump flag, default is "" + --data_dump_step data dump step, default is 1 --profiling if or not profiling for performance debug, default is False --data_path source data of training -h/--help show help message @@ -101,7 +101,11 @@ do #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path - nohup python3 run_classification_criteo.py > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + nohup python3 run_classification_criteo.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + ${data_dump_flag} --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -114,7 +118,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END{print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh index 5af0754d360311aedd96924580ae477cc3450fd3..53fdf2d0970dd0e4d3362181cda056b79aca5c44 100644 --- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3062_DeepFM_performance_1p.sh @@ -29,8 +29,8 @@ learning_rate= precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False -data_dump_flag=False -data_dump_step="10" +data_dump_flag="" +data_dump_step="1" profiling=False # 帮助信息，不需要修改 @@ -40,8 +40,8 @@ if [[ $1 == --help || $1 == -h ]];then echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 + --data_dump_flag data dump flag, default is "" + --data_dump_step data dump step, default is 1 --profiling if or not profiling for performance debug, default is False --data_path source data of training -h/--help show help message @@ -59,7 +59,8 @@ do over_dump_path=${cur_path}/output/overflow_dump mkdir -p ${over_dump_path} elif [[ $para == --data_dump_flag* ]];then - data_dump_flag=`echo ${para#*=}` + # data_dump_flag=`echo ${para#*=}` + data_dump_flag="--data_dump_flag" data_dump_path=${cur_path}/output/data_dump mkdir -p ${data_dump_path} elif [[ $para == --data_dump_step* ]];then @@ -101,7 +102,11 @@ do #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path - nohup python3 run_classification_criteo.py > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + nohup python3 run_classification_criteo.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + ${data_dump_flag} --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait sed -i "s|epochs=5|epochs=10|g" run_classification_criteo.py @@ -114,7 +119,7 @@ echo "------------------ Final result ------------------" # #输出性能FPS，需要模型审视修改 Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END{print $1}'` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'*1000000}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 echo "Final Performance item/sec : $FPS" diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_1p_lamb_phase2.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_full_1p.sh similarity index 55% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_1p_lamb_phase2.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_full_1p.sh index 33aded8415373db9243b5768552f29ef05c7093f..dd228ad5143e65cda8db2de44feabc28f64242d5 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3068_BertLarge-512_full_1p_lamb_phase2.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_full_1p.sh @@ -1,171 +1,164 @@ -#!/bin/bash - -#当前路径,不需要修改 -cur_path=`pwd` - -#集合通信参数,不需要修改 -export RANK_SIZE=1 -export JOB_ID=99990001 -RANK_ID_START=0 - -# 数据集路径,保持为空,不需要修改 -data_path="" - -#基础参数，需要模型审视修改 -#网络名称，同目录名称 -Network="BertLarge-512_ID3068_for_TensorFlow" -#训练epoch -train_epochs=1 -#训练batch_size -batch_size=24 -#训练step -train_steps=100000 -#学习率 -learning_rate= - -#维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" -#维持参数，以下不需要修改 -over_dump=False -data_dump_flag=False -data_dump_step="10" -profiling=False -autotune=False - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " - echo " " - echo "parameter explain: - --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message - " - exit 1 -fi - -#参数校验，不需要修改 -for para in $* -do - if [[ $para == --precision_mode* ]];then - precision_mode=`echo ${para#*=}` - elif [[ $para == --over_dump* ]];then - over_dump=`echo ${para#*=}` - over_dump_path=${cur_path}/output/overflow_dump - mkdir -p ${over_dump_path} - elif [[ $para == --data_dump_flag* ]];then - data_dump_flag=`echo ${para#*=}` - data_dump_path=${cur_path}/output/data_dump - mkdir -p ${data_dump_path} - elif [[ $para == --data_dump_step* ]];then - data_dump_step=`echo ${para#*=}` - elif [[ $para == --profiling* ]];then - profiling=`echo ${para#*=}` - profiling_dump_path=${cur_path}/output/profiling - mkdir -p ${profiling_dump_path} - elif [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - fi -done - -#校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be confing" - exit 1 -fi - -#训练开始时间，不需要修改 -start_time=$(date +%s) -#进入训练脚本目录，需要模型审视修改 -for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); -do - #设置环境变量，不需要修改 - echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$RANK_ID - - #创建DeviceID输出目录，不需要修改 - if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then - rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - fi - - #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ - --max_seq_length=512 \ - --max_predictions_per_seq=76 \ - --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=lamb \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & -done -wait - -#训练结束时间，不需要修改 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -#结果打印，不需要修改 -echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" - -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` -#打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" -echo "E2E Training Duration sec : $e2e_time" - -#稳定性精度看护结果汇总 -#训练用例信息，不需要修改 -BatchSize=${batch_size} -DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' - - -#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt - -#最后一个迭代loss值，不需要修改 -ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 +RankSize=1 +# 数据集路径,保持为空,不需要修改 +data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="DeepFM_ID3202_for_TensorFlow" +#训练epoch +train_epochs=5 +#训练batch_size +batch_size=162 +#训练step +train_steps= +#学习率 +learning_rate= + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + +sed -i "s|epochs=10|epochs=5|g" run_multivalue_movielens.py + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + fi + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_estimator_tfrecord_classification.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +# #输出性能FPS，需要模型审视修改 +Time=`grep ":loss =" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk '{ print $4}'| awk -F "(" '{print $2}'|tail -n 2|awk '{sum+=$1} END {print"",sum/NR}'|awk '{print $1}' +` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'}'` +# #打印，不需要修改 +echo "Final Performance item/sec : $FPS" + +# #输出训练精度,需要模型审视修改 +train_accuracy=`grep "AUC = " $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk '{print $9}'|awk -F "," '{print $1}'` + + + +# #输出训练精度,需要模型审视修改 + +# #打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#性能看护结果汇总 +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'accu' + +##获取性能数据，不需要修改 +#吞吐量 +TrainingTime=`awk 'BEGIN{printf "%.6f\n",'${BatchSize}'/'${FPS}'}'` + +ActualFPS=${FPS} +grep ":loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk '{ print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_performance_1p.sh similarity index 55% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_1p.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_performance_1p.sh index 13bcf4fcd178e644c01f00d86c76080cd8b50258..4fc7f9bb29fc5a75df32334133d684646da3cc78 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3207_BertLarge-512_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3202_DeepFM_performance_1p.sh @@ -1,171 +1,164 @@ -#!/bin/bash - -#当前路径,不需要修改 -cur_path=`pwd` - -#集合通信参数,不需要修改 -export RANK_SIZE=1 -export JOB_ID=99990001 -RANK_ID_START=0 - -# 数据集路径,保持为空,不需要修改 -data_path="" - -#基础参数，需要模型审视修改 -#网络名称，同目录名称 -Network="BertLarge-512_ID3207_for_TensorFlow" -#训练epoch -train_epochs=1 -#训练batch_size -batch_size=24 -#训练step -train_steps=100000 -#学习率 -learning_rate= - -#维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" -#维持参数，以下不需要修改 -over_dump=False -data_dump_flag=False -data_dump_step="10" -profiling=False -autotune=False - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " - echo " " - echo "parameter explain: - --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message - " - exit 1 -fi - -#参数校验，不需要修改 -for para in $* -do - if [[ $para == --precision_mode* ]];then - precision_mode=`echo ${para#*=}` - elif [[ $para == --over_dump* ]];then - over_dump=`echo ${para#*=}` - over_dump_path=${cur_path}/output/overflow_dump - mkdir -p ${over_dump_path} - elif [[ $para == --data_dump_flag* ]];then - data_dump_flag=`echo ${para#*=}` - data_dump_path=${cur_path}/output/data_dump - mkdir -p ${data_dump_path} - elif [[ $para == --data_dump_step* ]];then - data_dump_step=`echo ${para#*=}` - elif [[ $para == --profiling* ]];then - profiling=`echo ${para#*=}` - profiling_dump_path=${cur_path}/output/profiling - mkdir -p ${profiling_dump_path} - elif [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - fi -done - -#校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be confing" - exit 1 -fi - -#训练开始时间，不需要修改 -start_time=$(date +%s) -#进入训练脚本目录，需要模型审视修改 -for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); -do - #设置环境变量，不需要修改 - echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$RANK_ID - - #创建DeviceID输出目录，不需要修改 - if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then - rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - fi - - #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ - --max_seq_length=512 \ - --max_predictions_per_seq=76 \ - --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=adam \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & -done -wait - -#训练结束时间，不需要修改 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -#结果打印，不需要修改 -echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" - -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` -#打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" -echo "E2E Training Duration sec : $e2e_time" - -#稳定性精度看护结果汇总 -#训练用例信息，不需要修改 -BatchSize=${batch_size} -DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' - - -#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt - -#最后一个迭代loss值，不需要修改 -ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 +RankSize=1 +# 数据集路径,保持为空,不需要修改 +data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="DeepFM_ID3202_for_TensorFlow" +#训练epoch +train_epochs=5 +#训练batch_size +batch_size=162 +#训练step +train_steps= +#学习率 +learning_rate= + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + +sed -i "s|epochs=10|epochs=5|g" run_multivalue_movielens.py + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt + fi + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_estimator_tfrecord_classification.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +# #输出性能FPS，需要模型审视修改 +Time=`grep ":loss =" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk '{ print $4}'| awk -F "(" '{print $2}'|tail -n 2|awk '{sum+=$1} END {print"",sum/NR}'|awk '{print $1}' +` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${Time}'}'` +# #打印，不需要修改 +echo "Final Performance item/sec : $FPS" + +# #输出训练精度,需要模型审视修改 +train_accuracy=`grep "AUC = " $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk '{print $9}'|awk -F "," '{print $1}'` + + + +# #输出训练精度,需要模型审视修改 + +# #打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#性能看护结果汇总 +#训练用例信息，不需要修改 +BatchSize=${batch_size} +DeviceType=`uname -m` +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据，不需要修改 +#吞吐量 +TrainingTime=`awk 'BEGIN{printf "%.6f\n",'${BatchSize}'/'${FPS}'}'` + +ActualFPS=${FPS} +grep ":loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk '{ print $3}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +#最后一个迭代loss值，不需要修改 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_1p_lamb_phase2.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_full_1p.sh similarity index 58% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_1p_lamb_phase2.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_full_1p.sh index 29a38da1f88ccf21ec184b03366decf0b26bd7e6..c180165beb55d300753d44d50a5aefe1278da1df 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3069_BertBase-512_full_1p_lamb_phase2.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_full_1p.sh @@ -4,47 +4,47 @@ cur_path=`pwd` #集合通信参数,不需要修改 + export RANK_SIZE=1 -export JOB_ID=99990001 +export JOB_ID=10087 RANK_ID_START=0 - +RankSize=1 # 数据集路径,保持为空,不需要修改 data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 #基础参数，需要模型审视修改 #网络名称，同目录名称 -Network="BertBase-512_ID3069_for_TensorFlow" +Network="DeepFM_ID3203_for_TensorFlow" #训练epoch -train_epochs=1 +train_epochs=10 #训练batch_size -batch_size=64 +batch_size=160 #训练step -train_steps=100000 +train_steps= #学习率 learning_rate= #维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" +precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False data_dump_flag=False data_dump_step="10" profiling=False -autotune=False # 帮助信息，不需要修改 if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " + echo"usage:./train_full_1P.sh " echo " " echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message " exit 1 fi @@ -81,48 +81,31 @@ fi #训练开始时间，不需要修改 start_time=$(date +%s) + #进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" export RANK_ID=$RANK_ID - + #创建DeviceID输出目录，不需要修改 if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt fi - + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ - --max_seq_length=512 \ - --max_predictions_per_seq=76 \ - --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=lamb \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_multivalue_movielens.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -132,27 +115,35 @@ e2e_time=$(( $end_time - $start_time )) #结果打印，不需要修改 echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" +# #输出性能FPS，需要模型审视修改 -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" +echo "Final Performance item/sec : $FPS" + + +# #输出训练精度,需要模型审视修改 +#train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'` +# #打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" -#稳定性精度看护结果汇总 +#性能看护结果汇总 #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' +##获取性能数据，不需要修改 +#吞吐量 + +ActualFPS=${FPS} +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +cat $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tr -d '\b\r'|grep -Eo " loss: [0-9]*\.[0-9]*"|awk -F " " '{print $2}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` @@ -164,7 +155,8 @@ echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = None" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_performance_1p.sh similarity index 57% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_1p.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_performance_1p.sh index 3238c377e44b8feffea4a142fb8dbeb3dbca4a05..f432f21ac4a6396ad97637534e4c6688c3f6ec69 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3206_BertBase-512_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3203_DeepFM_performance_1p.sh @@ -4,47 +4,47 @@ cur_path=`pwd` #集合通信参数,不需要修改 + export RANK_SIZE=1 -export JOB_ID=99990001 +export JOB_ID=10087 RANK_ID_START=0 - +RankSize=1 # 数据集路径,保持为空,不需要修改 data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 #基础参数，需要模型审视修改 #网络名称，同目录名称 -Network="BertBase-512_ID3206_for_TensorFlow" +Network="DeepFM_ID3203_for_TensorFlow" #训练epoch -train_epochs=1 +train_epochs=5 #训练batch_size -batch_size=64 +batch_size=160 #训练step -train_steps=100000 +train_steps= #学习率 learning_rate= #维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" +precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False data_dump_flag=False data_dump_step="10" profiling=False -autotune=False # 帮助信息，不需要修改 if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " + echo"usage:./train_performance_1P.sh " echo " " echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message " exit 1 fi @@ -81,48 +81,33 @@ fi #训练开始时间，不需要修改 start_time=$(date +%s) + #进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + +sed -i "s|epochs=10|epochs=5|g" run_multivalue_movielens.py + for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" export RANK_ID=$RANK_ID - + #创建DeviceID输出目录，不需要修改 if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt fi - + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ - --max_seq_length=512 \ - --max_predictions_per_seq=76 \ - --train_batch_size=${batch_size} \ - --learning_rate=5e-5 \ - --num_warmup_steps=1000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=adam \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train \ - --eval_files_dir=${data_path}/eval \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_multivalue_movielens.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -130,29 +115,39 @@ wait end_time=$(date +%s) e2e_time=$(( $end_time - $start_time )) +sed -i "s|epochs=5|epochs=10|g" run_multivalue_movielens.py + #结果打印，不需要修改 echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" +# #输出性能FPS，需要模型审视修改 -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" +echo "Final Performance item/sec : $FPS" + + +# #输出训练精度,需要模型审视修改 +#train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'` +# #打印，不需要修改 +#echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" -#稳定性精度看护结果汇总 +#性能看护结果汇总 #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据，不需要修改 +#吞吐量 +ActualFPS=${FPS} +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +cat $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tr -d '\b\r'|grep -Eo " loss: [0-9]*\.[0-9]*"|awk -F " " '{print $2}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` @@ -164,7 +159,8 @@ echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = None" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_1p.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_full_1p.sh similarity index 58% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_1p.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_full_1p.sh index 4e44bd480cfcef3b21fadb801c104eada561ca22..54d6021648f9e103a566525bf567678976bea124 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID0060_BertBase_full_1p.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_full_1p.sh @@ -4,47 +4,47 @@ cur_path=`pwd` #集合通信参数,不需要修改 + export RANK_SIZE=1 -export JOB_ID=99990001 +export JOB_ID=10087 RANK_ID_START=0 - +RankSize=1 # 数据集路径,保持为空,不需要修改 data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 #基础参数，需要模型审视修改 #网络名称，同目录名称 -Network="Bert-base_ID0060_for_TensorFlow" +Network="FLEN_ID3204_for_TensorFlow" #训练epoch -train_epochs=1 +train_epochs=10 #训练batch_size -batch_size=128 +batch_size=64 #训练step -train_steps=1000 +train_steps= #学习率 learning_rate= #维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" +precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False data_dump_flag=False data_dump_step="10" profiling=False -autotune=False # 帮助信息，不需要修改 if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " + echo"usage:./train_full_1P.sh " echo " " echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message " exit 1 fi @@ -81,48 +81,32 @@ fi #训练开始时间，不需要修改 start_time=$(date +%s) + #进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + + for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" export RANK_ID=$RANK_ID - + #创建DeviceID输出目录，不需要修改 if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt fi - + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ - --max_seq_length=128 \ - --max_predictions_per_seq=20 \ - --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ - --num_train_steps=${train_steps} \ - --optimizer_type=adam \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=100 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_flen.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -130,33 +114,42 @@ wait end_time=$(date +%s) e2e_time=$(( $end_time - $start_time )) + #结果打印，不需要修改 echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" +# #输出性能FPS，需要模型审视修改 -#输出训练精度,需要模型审视修改 -train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 +echo "Final Performance item/sec : $FPS" + + +# #输出训练精度,需要模型审视修改 +train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'` +# #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" -#稳定性精度看护结果汇总 +#性能看护结果汇总 #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' +##获取性能数据，不需要修改 +#吞吐量 + +ActualFPS=${FPS} +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +cat $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tr -d '\b\r'|grep -Eo " loss: [0-9]*\.[0-9]*"|awk -F " " '{print $2}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` -TrainAccuracy=${train_accuracy} + #关键信息打印到${CaseName}.log中，不需要修改 echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log @@ -164,7 +157,8 @@ echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_1p_lamb_phase1.sh b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_performance_1p.sh similarity index 57% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_1p_lamb_phase1.sh rename to TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_performance_1p.sh index f79248aa2dedc2ec5a6cec48ba428d0d0996b45d..a90a4f4399f9b000ea8d495517696c7a07e9f606 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3208_BertBase-128_full_1p_lamb_phase1.sh +++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/test/train_ID3204_FLEN_performance_1p.sh @@ -4,47 +4,47 @@ cur_path=`pwd` #集合通信参数,不需要修改 + export RANK_SIZE=1 -export JOB_ID=99990001 +export JOB_ID=10087 RANK_ID_START=0 - +RankSize=1 # 数据集路径,保持为空,不需要修改 data_path="" +#export ASCEND_SLOG_PRINT_TO_STDOUT=1 #基础参数，需要模型审视修改 #网络名称，同目录名称 -Network="BertBase-128_ID3208_for_TensorFlow" +Network="FLEN_ID3204_for_TensorFlow" #训练epoch -train_epochs=1 +train_epochs=5 #训练batch_size -batch_size=128 +batch_size=64 #训练step -train_steps=1000 +train_steps= #学习率 learning_rate= #维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" +precision_mode="allow_fp32_to_fp16" #维持参数，以下不需要修改 over_dump=False data_dump_flag=False data_dump_step="10" profiling=False -autotune=False # 帮助信息，不需要修改 if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " + echo"usage:./train_performance_1P.sh " echo " " echo "parameter explain: --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message " exit 1 fi @@ -81,48 +81,33 @@ fi #训练开始时间，不需要修改 start_time=$(date +%s) + #进入训练脚本目录，需要模型审视修改 +cd $cur_path/../examples + +sed -i "s|epochs=10|epochs=5|g" run_flen.py + for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" export RANK_ID=$RANK_ID - + #创建DeviceID输出目录，不需要修改 if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt fi - + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_base_config.json \ - --max_seq_length=128 \ - --max_predictions_per_seq=20 \ - --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=100 \ - --num_train_steps=${train_steps} \ - --optimizer_type=lamb \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=100 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path + nohup python3 run_flen.py \ + --data_dir=${data_path} \ + --precision_mode=${precision_mode} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & done wait @@ -130,33 +115,43 @@ wait end_time=$(date +%s) e2e_time=$(( $end_time - $start_time )) +sed -i "s|epochs=5|epochs=10|g" run_flen.py + #结果打印，不需要修改 echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" +# #输出性能FPS，需要模型审视修改 -#输出训练精度,需要模型审视修改 -train_accuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` +Time=`cat $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|tr -d '\b\r'|grep -Eo "[0-9]*us/sample"|awk -F "us/sample" 'END {print $1}'` +FPS=`awk 'BEGIN{printf "%.2f\n", 1 /'${Time}'*1000000}'` #打印，不需要修改 +echo "Final Performance item/sec : $FPS" + + +# #输出训练精度,需要模型审视修改 +train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'` +# #打印，不需要修改 echo "Final Train Accuracy : ${train_accuracy}" echo "E2E Training Duration sec : $e2e_time" -#稳定性精度看护结果汇总 +#性能看护结果汇总 #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据，不需要修改 +#吞吐量 +ActualFPS=${FPS} +#单迭代训练时长 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +cat $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tr -d '\b\r'|grep -Eo " loss: [0-9]*\.[0-9]*"|awk -F " " '{print $2}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt #最后一个迭代loss值，不需要修改 ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` -TrainAccuracy=${train_accuracy} + #关键信息打印到${CaseName}.log中，不需要修改 echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log @@ -164,7 +159,8 @@ echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/configs/config.py b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/configs/config.py index d2cd1a0f13cb7d3c8d2b4d03fdfb4bf93da3c920..aceafd8d3ecabd7f96ce940ac936349cae79302a 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/configs/config.py +++ b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/configs/config.py @@ -40,9 +40,9 @@ multi_hot_flags = [False] multi_hot_len = 1 ### #n_epoches =50 -#iterations_per_loop = 10 +iterations_per_loop = 10 n_epoches = 1 -iterations_per_loop = 1 +# iterations_per_loop = 1 #one_step = 50/iterations_per_loop # for one step debug one_step = 0 line_per_sample = 1000 diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/test/train_full_8p.sh index d83ef0dd5a3ce601505e87ebd03fe12c487e1045..81ff7c389e571bd87b0930259ec132d57af31165 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/test/train_full_8p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/test/train_full_8p.sh @@ -14,7 +14,7 @@ RANK_ID_START=0 data_path="" sed -i "s/n_epoches = 1/n_epoches = 50/g" `grep -rl "n_epoches = 1" ${cur_path}/../configs/config.py` -sed -i "s/iterations_per_loop = 1/iterations_per_loop = 10/g" `grep -rl "iterations_per_loop = 1" ${cur_path}/../configs/config.py` +#sed -i "s/iterations_per_loop = 1/iterations_per_loop = 10/g" `grep -rl "iterations_per_loop = 1" ${cur_path}/../configs/config.py` #基础参数 需要模型审视修改 #网络名称，同目录名称 Network="WideDeep_ID0028_for_TensorFlow" diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/train.py b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/train.py index 6852b54388f51c8d6e4b3347ebd381fdc23ee41e..c7c74ab324475a23d804c64111402acda4c281ce 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/train.py +++ b/TensorFlow/built-in/recommendation/WideDeep_ID0028_for_TensorFlow/train.py @@ -91,7 +91,7 @@ train_para = { 'test_per_epoch': config.test_size, 'batch_size': data_para['batch_size'], 'early_stop_epochs': 50, - # 'iterations_per_loop': config.iterations_per_loop + 'iterations_per_loop': config.iterations_per_loop } # set PIN model param @@ -383,6 +383,7 @@ if __name__ == '__main__': custom_op.parameter_map["min_group_size"].b = 1 custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") custom_op.parameter_map["hcom_parallel"].b = True + custom_op.parameter_map["iterations_per_loop"].i = config.iterations_per_loop if args.over_dump is True: print("NPU overflow dump is enabled") diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_4p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_4p.sh index 5b740ba40a1ad42b66cbbeac00cce9bbf50ab0f4..330625a23c667c976aa4e606aaa93a805611b478 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_4p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_4p.sh @@ -6,7 +6,7 @@ cur_path=`pwd` #集合通信参数,不需要修改 -export HCCL_CONNECT_TIMEOUT=300 +export HCCL_CONNECT_TIMEOUT=1200 #集合通信参数,不需要修改 export RANK_SIZE=4 diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh index 953c6a895c34a662e9bd5145413209f1c228195b..a577a9e1e8eaa998f7f8014e48ca70dfc2400ed7 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh @@ -6,7 +6,7 @@ cur_path=`pwd` #集合通信参数,不需要修改 -export HCCL_CONNECT_TIMEOUT=300 +export HCCL_CONNECT_TIMEOUT=1200 #集合通信参数,不需要修改 export RANK_SIZE=8 diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_4p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_4p.sh index be35617020cbb16f85e21961533089b5dd0d6c51..8296478223a22d687cde6e67747bab12d72d0946 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_4p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_4p.sh @@ -5,7 +5,7 @@ cur_path=`pwd` #export ASCEND_SLOG_PRINT_TO_STDOUT=1 #export GE_USE_STATIC_MEMORY=1 -export HCCL_CONNECT_TIMEOUT=300 +export HCCL_CONNECT_TIMEOUT=1200 #集合通信参数,不需要修改 export RANK_SIZE=4 @@ -24,8 +24,8 @@ RankSize=1 #参数配置 data_path="/npu/traindata/ID2940_CarPeting_TF_WideDeep_TF" -train_size=13107200 -display_step=1 +train_size=52428800 +display_step=10 n_epoches=4 #维持参数，以下不需要修改 diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_8p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_8p.sh index 55f195dd29fb41a89944b3bd9a63fc3663d1c2c5..bb0e0a8447cf22315bfaecd5e471ff866e14643e 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_8p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_performance_8p.sh @@ -5,7 +5,7 @@ cur_path=`pwd` #export ASCEND_SLOG_PRINT_TO_STDOUT=1 #export GE_USE_STATIC_MEMORY=1 -export HCCL_CONNECT_TIMEOUT=300 +export HCCL_CONNECT_TIMEOUT=1200 #集合通信参数,不需要修改 export RANK_SIZE=8 @@ -24,8 +24,8 @@ RankSize=1 #参数配置 data_path="/npu/traindata/ID2940_CarPeting_TF_WideDeep_TF" -train_size=13107200 -display_step=1 +train_size=52428800 +display_step=10 n_epoches=4 #维持参数，以下不需要修改 diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p.json index 96078ec8d6a851ca15a96cf8b68938913cf9c798..28426dea5096e8246f782ac2e253794aadbb79a6 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "0", - "device_ip": "192.1.2.8", - "rank_id": "0", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"0","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_0.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_0.json index 96078ec8d6a851ca15a96cf8b68938913cf9c798..28426dea5096e8246f782ac2e253794aadbb79a6 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_0.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_0.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "0", - "device_ip": "192.1.2.8", - "rank_id": "0", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"0","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_1.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_1.json index 8270cbe0e347f7e644c199804764a52690d8a456..d3e1c570bc4f79bcecbbfde405107a021be18098 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_1.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_1.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "1", - "device_ip": "192.1.2.8", - "rank_id": "1", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"1","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_2.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_2.json index 0438819ddf1e1c1425f5f3a706a5e5f7e9da0746..93c4a960c311ffba53d679224927aa01043e2328 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_2.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_2.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "2", - "device_ip": "192.1.2.8", - "rank_id": "2", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"2","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_3.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_3.json index ca9a5ff4ecdf11d5bed59358ccfeea9850d56697..4ed1fcf81f66bb111c3970bd91ee6358806eebe3 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_3.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_3.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "3", - "device_ip": "192.1.2.8", - "rank_id": "3", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"3","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_4.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_4.json index 90872c5e1a567ef0ba4145e9644005f87d4c1174..cdde74396d0e2ebb40ab88e835e0b60b4a713434 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_4.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_4.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "4", - "device_ip": "192.4.2.9", - "rank_id": "4", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"4","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_5.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_5.json index 1b1322f115c2a3dae81f85e46565068e87c8e50f..c0a7890298636b6b73b3f065dafff4c8602b5719 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_5.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_5.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "5", - "device_ip": "192.4.2.9", - "rank_id": "5", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"5","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_6.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_6.json index ea26227f6d4e233f61c3471a6b773f4ac432f4af..2c4b3211286d0efa409e74db5b2447ac23a79e36 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_6.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_6.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "6", - "device_ip": "192.4.2.9", - "rank_id": "6", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"6","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_7.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_7.json index 1ccc40abf4265a459877b44705d097bc9ab3db21..01399b72c57294348981db482d9e621f68669d77 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_7.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/1p_7.json @@ -1,18 +1,8 @@ { - "group_count": "1", - "group_list": - [ - { - "devices": [ - { - "device_id": "7", - "device_ip": "192.4.2.9", - "rank_id": "7", - } - ], - "server_id": "10.155.111.118" - } - ], - "status": "completed", - "version":"1.0" -} \ No newline at end of file +"server_count":"1", +"server_list":[{ + "device":[{"device_id":"7","device_ip":"192.168.1.195","rank_id":"0"}], + "server_id":"127.0.0.1"}], +"status":"completed", +"version":"1.0" +} diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/8p.json b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/8p.json index d13441e11288704f3e3ad5087dadd019d5481a15..3c329456ba1da36824150806dfbaae1b7beffa20 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/8p.json +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/config/8p.json @@ -11,10 +11,10 @@ {"device_id":"4","device_ip":"192.4.2.9","rank_id":"4"}, {"device_id":"5","device_ip":"192.4.2.9","rank_id":"5"}, {"device_id":"6","device_ip":"192.4.2.9","rank_id":"6"}, - {"device_id":"7","device_ip":"192.4.2.9","rank_id":"7"}, + {"device_id":"7","device_ip":"192.4.2.9","rank_id":"7"} ], - "server_id":"10.155.111.118" + "server_id":"127.0.0.1" } ], "status":"completed", diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/test/train_performance_1p.sh index 64ab2617e0318fcc0494239c6617026099635949..e5db039a1b812fe9099f2c8ccf020cbe280b16ea 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/test/train_performance_1p.sh @@ -27,6 +27,9 @@ RankSize=1 #训练epoch，可选 train_epochs=1 +#迭代下沉循环次数 +iteration_per_loop=0 + #参数配置 data_path="" @@ -93,36 +96,40 @@ else mkdir -p $cur_path/output/$ASCEND_DEVICE_ID/ckpt fi -if [ -d $cur_path/../config/1p_$ASCEND_DEVICE.json ];then - export RANK_TABLE_FILE=$cur_path/../config/1p_$ASCEND_DEVICE.json - export RANK_ID=$ASCEND_DEVICE_ID +if [ -f $cur_path/../config/1p_$ASCEND_DEVICE_ID.json ];then + export RANK_TABLE_FILE=$cur_path/../config/1p_$ASCEND_DEVICE_ID.json + export RANK_ID=0 else export RANK_TABLE_FILE=$cur_path/../config/1p_0.json export RANK_ID=0 fi + wait cd $cur_path/../ start=$(date +%s) -python3 -m trainer.task --gpu \ +python3 -m trainer.task \ --Adam \ + --iteration_per_loop=$iteration_per_loop \ --train_data_pattern=$data_path/outbrain/tfrecords/train/part* \ --eval_data_pattern=$data_path/outbrain/tfrecords/eval/part* \ --model_dir=$cur_path/output/$ASCEND_DEVICE_ID/ckpt \ --transformed_metadata_path=$data_path/outbrain/tfrecords \ - --num_epochs=$train_epochs > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & + --num_epochs=$train_epochs \ + --benchmark \ + --global_batch_size=$batch_size > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 & wait end=$(date +%s) -e2etime=$(( $end - $start )) +e2e_time=$(( $end - $start )) #结果打印，不需要修改 echo "------------------ Final result ------------------" #输出性能FPS，需要模型审视修改 -Time=`grep "INFO:tensorflow:global_step/sec: " $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk -F' ' '{print $2}' | tail -n 2 | head -n +1` -FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*'${Time}'}'` - +#Time=`grep "INFO:tensorflow:global_step/sec: " $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk -F' ' '{print $2}' | tail -n 2 | head -n +1` +#FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*'${Time}'}'` +FPS=`grep train_throughput $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk -F "train_throughput :" '{print $2}' | sed s/[[:space:]]//g` #打印，不需要修改 echo "Final Performance images/sec : $FPS" @@ -143,7 +150,7 @@ CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' #吞吐量，不需要修改 ActualFPS=${FPS} #单迭代训练时长，不需要修改 -TrainingTime=`awk -v x=320 -v y="$FPS" 'BECIN{printf "%3.f\n",y/x}'` +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'/'${FPS}'}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 loss=`grep 'INFO:tensorflow:loss' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | tr -d '\b\r' | grep -Eo "INFO:tensorflow:loss = [0-9]*\.[0-9]*" | awk -F' = ' '{print $2}'` diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/trainer/task.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/trainer/task.py index c22742f76797eca15694dd5b3995a332513e198b..dc8bc7bbc2038d522b28f696bc560f80135859ce 100644 --- a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/trainer/task.py +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/trainer/task.py @@ -27,11 +27,11 @@ import tensorflow as tf import tensorflow_transform as tft from tensorflow.core.protobuf import rewriter_config_pb2 from trainer import features -from utils.dataloader import separate_input_fn -from utils.hooks.benchmark_hooks import BenchmarkLoggingHook -from utils.metrics import map_custom_metric, map_custom_metric_with_leak -from utils.schedulers import learning_rate_scheduler - +from util.dataloader import separate_input_fn +from util.hooks.benchmark_hooks import BenchmarkLoggingHook +from util.metrics import map_custom_metric, map_custom_metric_with_leak +from util.schedulers import learning_rate_scheduler +from util.dnn_linear_combined import DNNLinearCombinedClassifier MODEL_TYPES = ['wide', 'deep', 'wide_n_deep'] WIDE, DEEP, WIDE_N_DEEP = MODEL_TYPES @@ -239,6 +239,11 @@ def create_parser(): help='Number of steps for train performance benchmark', type=int, default=100) + parser.add_argument( + '--iteration_per_loop', + help='Number of iters per loop', + type=int, + default=0) return parser @@ -262,7 +267,7 @@ def construct_estimator(model_type, run_config, optimizer=deep_optimizer) elif model_type == WIDE_N_DEEP: - estimator = tf.estimator.DNNLinearCombinedClassifier( + estimator = DNNLinearCombinedClassifier( config=npu_run_config_init(run_config=run_config), linear_feature_columns=wide_columns, linear_optimizer=wide_optimizer, @@ -329,15 +334,14 @@ def main(FLAGS): log_device_placement=FLAGS.log_device_placement ) else: - #session_config = tf.compat.v1.ConfigProto( - # device_count={'GPU': 0}, - # log_device_placement=FLAGS.log_device_placement - #) session_config = tf.ConfigProto() custom_op = session_config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") + if FLAGS.iteration_per_loop: + custom_op.parameter_map["enable_data_pre_proc"].b = True + custom_op.parameter_map["iterations_per_loop"].i = FLAGS.iteration_per_loop session_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF session_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF @@ -361,12 +365,7 @@ def main(FLAGS): int(FLAGS.eval_epoch_interval * steps_per_epoch) count_steps = FLAGS.benchmark_steps + 1 if FLAGS.benchmark else 100 - run_config = tf.estimator.RunConfig(model_dir=model_dir, save_summary_steps=0) \ - .replace(session_config=session_config, - save_checkpoints_steps=save_checkpoints_steps, - save_summary_steps=count_steps, - log_step_count_steps=count_steps, - keep_checkpoint_max=1) + run_config = tf.estimator.RunConfig(model_dir=model_dir, save_summary_steps=0, session_config=session_config, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=count_steps, keep_checkpoint_max=1) def wide_optimizer(): opt = tf.compat.v1.train.FtrlOptimizer( @@ -431,6 +430,8 @@ def main(FLAGS): estimator = tf.estimator.add_metrics(estimator, map_custom_metric_with_leak) hooks = [] + if FLAGS.iteration_per_loop: + hooks.append(npu_hook.SetIterationsVarHook(FLAGS.iteration_per_loop)) if FLAGS.hvd: hooks.append(NPUBroadcastGlobalVariablesHook(0, int(os.getenv('RANK_ID', '0')))) @@ -475,6 +476,7 @@ def main(FLAGS): else: # training if FLAGS.benchmark: + print("================is benchmark, not eval") benchmark_hook = BenchmarkLoggingHook(global_batch_size=FLAGS.global_batch_size, warmup_steps=FLAGS.benchmark_warmup_steps) hooks.append(benchmark_hook) @@ -482,6 +484,7 @@ def main(FLAGS): train_throughput = benchmark_hook.mean_throughput.value() dllogger.log(data={'train_throughput': train_throughput}, step=tuple()) else: + print('train and eval') train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=max_steps, hooks=hooks) @@ -498,18 +501,20 @@ def main(FLAGS): if __name__ == '__main__': + FLAGS = create_parser().parse_args() session_config = tf.ConfigProto() custom_op = session_config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") - custom_op.parameter_map["graph_memory_max_size"].s= tf.compat.as_bytes(str(16 * 1024 * 1024 * 1024)) - custom_op.parameter_map["variable_memory_max_size"].s = tf.compat.as_bytes(str(15 * 1024 * 1024 * 1024)) + if FLAGS.iteration_per_loop: + print('>>>>>>>>> iteration per loop var: %d'%(FLAGS.iteration_per_loop)) + custom_op.parameter_map["enable_data_pre_proc"].b = True + custom_op.parameter_map["iterations_per_loop"].i = FLAGS.iteration_per_loop session_config.graph_options.rewrite_options.remapping = RewriterConfig.OFF session_config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF (npu_sess, npu_shutdown) = init_resource(config=session_config) - FLAGS = create_parser().parse_args() main(FLAGS) shutdown_resource(npu_sess, npu_shutdown) close_session(npu_sess) diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/dataloader.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/dataloader.py similarity index 100% rename from TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/dataloader.py rename to TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/dataloader.py diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/dnn_linear_combined.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/dnn_linear_combined.py new file mode 100644 index 0000000000000000000000000000000000000000..08b0e2d88ef25cb41e67c05eaaa7eb5f531b8d50 --- /dev/null +++ b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/dnn_linear_combined.py @@ -0,0 +1,1152 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""TensorFlow estimators for Linear and DNN joined training models.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import six + +from tensorflow.python.framework import ops +from tensorflow.python.keras.utils import losses_utils +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import partitioned_variables +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops.losses import losses +from tensorflow.python.summary import summary +from tensorflow.python.training import sync_replicas_optimizer +from tensorflow.python.training import training_util +from tensorflow.python.util.tf_export import estimator_export +from tensorflow_estimator.python.estimator import estimator +from tensorflow_estimator.python.estimator.canned import dnn +from tensorflow_estimator.python.estimator.canned import head as head_lib +from tensorflow_estimator.python.estimator.canned import linear +from tensorflow_estimator.python.estimator.canned import optimizers +from tensorflow_estimator.python.estimator.head import head_utils +from tensorflow_estimator.python.estimator.head import regression_head +from tensorflow_estimator.python.estimator.mode_keys import ModeKeys + +# The default learning rates are a historical artifact of the initial +# implementation. +_DNN_LEARNING_RATE = 0.001 +_LINEAR_LEARNING_RATE = 0.005 + + +def _check_no_sync_replicas_optimizer(optimizer): + if isinstance(optimizer, sync_replicas_optimizer.SyncReplicasOptimizer): + raise ValueError( + 'SyncReplicasOptimizer does not support multi optimizers case. ' + 'Therefore, it is not supported in DNNLinearCombined model. ' + 'If you want to use this optimizer, please use either DNN or Linear ' + 'model.') + + +def _linear_learning_rate(num_linear_feature_columns): + """Returns the default learning rate of the linear model. + + The calculation is a historical artifact of this initial implementation, but + has proven a reasonable choice. + + Args: + num_linear_feature_columns: The number of feature columns of the linear + model. + + Returns: + A float. + """ + default_learning_rate = 1. / math.sqrt(num_linear_feature_columns) + return min(_LINEAR_LEARNING_RATE, default_learning_rate) + + +def _add_layer_summary(value, tag): + summary.scalar('%s/fraction_of_zero_values' % tag, nn.zero_fraction(value)) + summary.histogram('%s/activation' % tag, value) + + +def _validate_feature_columns(linear_feature_columns, dnn_feature_columns): + """Validates feature columns DNNLinearCombinedRegressor.""" + linear_feature_columns = linear_feature_columns or [] + dnn_feature_columns = dnn_feature_columns or [] + feature_columns = ( + list(linear_feature_columns) + list(dnn_feature_columns)) + if not feature_columns: + raise ValueError('Either linear_feature_columns or dnn_feature_columns ' + 'must be defined.') + return feature_columns + + +def _dnn_linear_combined_model_fn_v2( + features, + labels, + mode, + head, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + config=None, + batch_norm=False, + linear_sparse_combiner='sum', + loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE): + """Deep Neural Net and Linear combined model_fn. + + Args: + features: dict of `Tensor`. + labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of dtype + `int32` or `int64` in the range `[0, n_classes)`. + mode: Defines whether this is training, evaluation or prediction. See + `ModeKeys`. + head: A `Head` instance. + linear_feature_columns: An iterable containing all the feature columns used + by the Linear model. + linear_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the Linear model. Defaults to the Ftrl + optimizer. + dnn_feature_columns: An iterable containing all the feature columns used by + the DNN model. + dnn_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the DNN model. Defaults to the Adagrad + optimizer. + dnn_hidden_units: List of hidden units per DNN layer. + dnn_activation_fn: Activation function applied to each DNN layer. If `None`, + will use `tf.nn.relu`. + dnn_dropout: When not `None`, the probability we will drop out a given DNN + coordinate. + config: `RunConfig` object to configure the runtime settings. + batch_norm: Whether to use batch normalization after each hidden layer. + linear_sparse_combiner: A string specifying how to reduce the linear model + if a categorical column is multivalent. One of "mean", "sqrtn", and + "sum". + loss_reduction: One of `tf.keras.losses.Reduction` except `NONE`. Describes + how to reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`. + + Returns: + An `EstimatorSpec` instance. + + Raises: + ValueError: If both `linear_feature_columns` and `dnn_features_columns` + are empty at the same time, or `input_layer_partitioner` is missing, + or features has the wrong type. + """ + if not isinstance(features, dict): + raise ValueError('features should be a dictionary of `Tensor`s. ' + 'Given type: {}'.format(type(features))) + if not linear_feature_columns and not dnn_feature_columns: + raise ValueError( + 'Either linear_feature_columns or dnn_feature_columns must be defined.') + + del config + + # Build DNN Logits. + if not dnn_feature_columns: + dnn_logits = None + else: + if mode == ModeKeys.TRAIN: + dnn_optimizer = optimizers.get_optimizer_instance_v2( + dnn_optimizer, learning_rate=_DNN_LEARNING_RATE) + _check_no_sync_replicas_optimizer(dnn_optimizer) + + if not dnn_hidden_units: + raise ValueError( + 'dnn_hidden_units must be defined when dnn_feature_columns is ' + 'specified.') + dnn_logits, dnn_trainable_variables, dnn_update_ops = ( + dnn._dnn_model_fn_builder_v2( # pylint: disable=protected-access + units=head.logits_dimension, + hidden_units=dnn_hidden_units, + feature_columns=dnn_feature_columns, + activation_fn=dnn_activation_fn, + dropout=dnn_dropout, + batch_norm=batch_norm, + features=features, + mode=mode)) + + if not linear_feature_columns: + linear_logits = None + else: + if mode == ModeKeys.TRAIN: + linear_optimizer = optimizers.get_optimizer_instance_v2( + linear_optimizer, + learning_rate=_linear_learning_rate(len(linear_feature_columns))) + _check_no_sync_replicas_optimizer(linear_optimizer) + + linear_logits, linear_trainable_variables = ( + linear._linear_model_fn_builder_v2( # pylint: disable=protected-access + units=head.logits_dimension, + feature_columns=linear_feature_columns, + sparse_combiner=linear_sparse_combiner, + features=features)) + _add_layer_summary(linear_logits, 'linear') + + # Combine logits and build full model. + if dnn_logits is not None and linear_logits is not None: + logits = dnn_logits + linear_logits + elif dnn_logits is not None: + logits = dnn_logits + else: + logits = linear_logits + + def _train_op_fn(loss): + """Returns the op to optimize the loss.""" + train_ops = [] + # Scale loss by number of replicas. + if loss_reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE: + loss = losses_utils.scale_loss_for_distribution(loss) + + if dnn_logits is not None: + train_ops.extend( + dnn_optimizer.get_updates( + loss, + dnn_trainable_variables)) + if dnn_update_ops is not None: + train_ops.extend(dnn_update_ops) + if linear_logits is not None: + train_ops.extend( + linear_optimizer.get_updates( + loss, + linear_trainable_variables)) + train_op = control_flow_ops.group(*train_ops) + return train_op + + # In TRAIN mode, asssign global_step variable to optimizer.iterations to + # make global_step increased correctly, as Hooks relies on global step as + # step counter. Note that, Only one model's optimizer needs this assignment. + if mode == ModeKeys.TRAIN: + if dnn_logits is not None: + dnn_optimizer.iterations = training_util.get_or_create_global_step() + else: + linear_optimizer.iterations = training_util.get_or_create_global_step() + + return head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + +def _dnn_linear_combined_model_fn(features, + labels, + mode, + head, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + input_layer_partitioner=None, + config=None, + batch_norm=False, + linear_sparse_combiner='sum'): + """Deep Neural Net and Linear combined model_fn. + + Args: + features: dict of `Tensor`. + labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of dtype + `int32` or `int64` in the range `[0, n_classes)`. + mode: Defines whether this is training, evaluation or prediction. + See `ModeKeys`. + head: A `Head` instance. + linear_feature_columns: An iterable containing all the feature columns used + by the Linear model. + linear_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the Linear model. Defaults to the Ftrl + optimizer. + dnn_feature_columns: An iterable containing all the feature columns used by + the DNN model. + dnn_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the DNN model. Defaults to the Adagrad + optimizer. + dnn_hidden_units: List of hidden units per DNN layer. + dnn_activation_fn: Activation function applied to each DNN layer. If `None`, + will use `tf.nn.relu`. + dnn_dropout: When not `None`, the probability we will drop out a given DNN + coordinate. + input_layer_partitioner: Partitioner for input layer. + config: `RunConfig` object to configure the runtime settings. + batch_norm: Whether to use batch normalization after each hidden layer. + linear_sparse_combiner: A string specifying how to reduce the linear model + if a categorical column is multivalent. One of "mean", "sqrtn", and + "sum". + Returns: + An `EstimatorSpec` instance. + + Raises: + ValueError: If both `linear_feature_columns` and `dnn_features_columns` + are empty at the same time, or `input_layer_partitioner` is missing, + or features has the wrong type. + """ + if not isinstance(features, dict): + raise ValueError('features should be a dictionary of `Tensor`s. ' + 'Given type: {}'.format(type(features))) + if not linear_feature_columns and not dnn_feature_columns: + raise ValueError( + 'Either linear_feature_columns or dnn_feature_columns must be defined.') + + num_ps_replicas = config.num_ps_replicas if config else 0 + input_layer_partitioner = input_layer_partitioner or ( + partitioned_variables.min_max_variable_partitioner( + max_partitions=num_ps_replicas, + min_slice_size=64 << 20)) + + # Build DNN Logits. + dnn_parent_scope = 'dnn' + + if not dnn_feature_columns: + dnn_logits = None + else: + dnn_optimizer = optimizers.get_optimizer_instance( + dnn_optimizer, learning_rate=_DNN_LEARNING_RATE) + _check_no_sync_replicas_optimizer(dnn_optimizer) + if not dnn_hidden_units: + raise ValueError( + 'dnn_hidden_units must be defined when dnn_feature_columns is ' + 'specified.') + dnn_partitioner = ( + partitioned_variables.min_max_variable_partitioner( + max_partitions=num_ps_replicas)) + with variable_scope.variable_scope( + dnn_parent_scope, + values=tuple(six.itervalues(features)), + partitioner=dnn_partitioner) as scope: + dnn_absolute_scope = scope.name + dnn_logit_fn = dnn.dnn_logit_fn_builder( + units=head.logits_dimension, + hidden_units=dnn_hidden_units, + feature_columns=dnn_feature_columns, + activation_fn=dnn_activation_fn, + dropout=dnn_dropout, + batch_norm=batch_norm, + input_layer_partitioner=input_layer_partitioner) + dnn_logits = dnn_logit_fn(features=features, mode=mode) + + linear_parent_scope = 'linear' + + if not linear_feature_columns: + linear_logits = None + else: + linear_optimizer = optimizers.get_optimizer_instance( + linear_optimizer, + learning_rate=_linear_learning_rate(len(linear_feature_columns))) + _check_no_sync_replicas_optimizer(linear_optimizer) + with variable_scope.variable_scope( + linear_parent_scope, + values=tuple(six.itervalues(features)), + partitioner=input_layer_partitioner) as scope: + linear_absolute_scope = scope.name + logit_fn = linear.linear_logit_fn_builder( + units=head.logits_dimension, + feature_columns=linear_feature_columns, + sparse_combiner=linear_sparse_combiner) + linear_logits = logit_fn(features=features) + _add_layer_summary(linear_logits, scope.name) + + # Combine logits and build full model. + if dnn_logits is not None and linear_logits is not None: + logits = dnn_logits + linear_logits + elif dnn_logits is not None: + logits = dnn_logits + else: + logits = linear_logits + + def _train_op_fn(loss): + """Returns the op to optimize the loss.""" + train_ops = [] + global_step = training_util.get_global_step() + if dnn_logits is not None: + train_ops.append( + dnn_optimizer.minimize( + loss, + var_list=ops.get_collection( + ops.GraphKeys.TRAINABLE_VARIABLES, + scope=dnn_absolute_scope))) + if linear_logits is not None: + train_ops.append( + linear_optimizer.minimize( + loss, + var_list=ops.get_collection( + ops.GraphKeys.TRAINABLE_VARIABLES, + scope=linear_absolute_scope))) + + train_op = control_flow_ops.group(*train_ops, name='IterationOp') + with ops.control_dependencies([train_op]): + return state_ops.assign_add(global_step, 1).op + + return head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + +@estimator_export('estimator.DNNLinearCombinedClassifier', v1=[]) +class DNNLinearCombinedClassifierV2(estimator.EstimatorV2): + """An estimator for TensorFlow Linear and DNN joined classification models. + + Note: This estimator is also known as wide-n-deep. + + Example: + + ```python + numeric_feature = numeric_column(...) + categorical_column_a = categorical_column_with_hash_bucket(...) + categorical_column_b = categorical_column_with_hash_bucket(...) + + categorical_feature_a_x_categorical_feature_b = crossed_column(...) + categorical_feature_a_emb = embedding_column( + categorical_column=categorical_feature_a, ...) + categorical_feature_b_emb = embedding_column( + categorical_id_column=categorical_feature_b, ...) + + estimator = DNNLinearCombinedClassifier( + # wide settings + linear_feature_columns=[categorical_feature_a_x_categorical_feature_b], + linear_optimizer=tf.train.FtrlOptimizer(...), + # deep settings + dnn_feature_columns=[ + categorical_feature_a_emb, categorical_feature_b_emb, + numeric_feature], + dnn_hidden_units=[1000, 500, 100], + dnn_optimizer=tf.train.ProximalAdagradOptimizer(...), + # warm-start settings + warm_start_from="/path/to/checkpoint/dir") + + # To apply L1 and L2 regularization, you can set dnn_optimizer to: + tf.train.ProximalAdagradOptimizer( + learning_rate=0.1, + l1_regularization_strength=0.001, + l2_regularization_strength=0.001) + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. + + # Input builders + def input_fn_train: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_eval: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_predict: + # Returns tf.data.Dataset of (x, None) tuple. + pass + estimator.train(input_fn=input_fn_train, steps=100) + metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10) + predictions = estimator.predict(input_fn=input_fn_predict) + ``` + + Input of `train` and `evaluate` should have following features, + otherwise there will be a `KeyError`: + + * for each `column` in `dnn_feature_columns` + `linear_feature_columns`: + - if `column` is a `_CategoricalColumn`, a feature with `key=column.name` + whose `value` is a `SparseTensor`. + - if `column` is a `_WeightedCategoricalColumn`, two features: the first + with `key` the id column name, the second with `key` the weight column + name. Both features' `value` must be a `SparseTensor`. + - if `column` is a `_DenseColumn`, a feature with `key=column.name` + whose `value` is a `Tensor`. + + Loss is calculated by using softmax cross entropy. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ + + def __init__(self, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + n_classes=2, + weight_column=None, + label_vocabulary=None, + config=None, + warm_start_from=None, + loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE, + batch_norm=False, + linear_sparse_combiner='sum'): + """Initializes a DNNLinearCombinedClassifier instance. + + Args: + model_dir: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator + to continue training a previously saved model. + linear_feature_columns: An iterable containing all the feature columns + used by linear part of the model. All items in the set must be + instances of classes derived from `FeatureColumn`. + linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. + dnn_feature_columns: An iterable containing all the feature columns used + by deep part of the model. All items in the set must be instances of + classes derived from `FeatureColumn`. + dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. + dnn_hidden_units: List of hidden units per layer. All layers are fully + connected. + dnn_activation_fn: Activation function applied to each layer. If None, + will use `tf.nn.relu`. + dnn_dropout: When not None, the probability we will drop out + a given coordinate. + n_classes: Number of label classes. Defaults to 2, namely binary + classification. Must be > 1. + weight_column: A string or a `_NumericColumn` created by + `tf.feature_column.numeric_column` defining feature column representing + weights. It is used to down weight or boost examples during training. It + will be multiplied by the loss of the example. If it is a string, it is + used as a key to fetch weight tensor from the `features`. If it is a + `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, + then weight_column.normalizer_fn is applied on it to get weight tensor. + label_vocabulary: A list of strings represents possible label values. If + given, labels must be string type and have any value in + `label_vocabulary`. If it is not given, that means labels are + already encoded as integer or float within [0, 1] for `n_classes=2` and + encoded as integer values in {0, 1,..., n_classes-1} for `n_classes`>2 . + Also there will be errors if vocabulary is not provided and labels are + string. + config: RunConfig object to configure the runtime settings. + warm_start_from: A string filepath to a checkpoint to warm-start from, or + a `WarmStartSettings` object to fully configure warm-starting. If the + string filepath is provided instead of a `WarmStartSettings`, then all + weights are warm-started, and it is assumed that vocabularies and Tensor + names are unchanged. + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how + to reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`. + batch_norm: Whether to use batch normalization after each hidden layer. + linear_sparse_combiner: A string specifying how to reduce the linear model + if a categorical column is multivalent. One of "mean", "sqrtn", and + "sum" -- these are effectively different ways to do example-level + normalization, which can be useful for bag-of-words features. For more + details, see `tf.feature_column.linear_model`. + + Raises: + ValueError: If both linear_feature_columns and dnn_features_columns are + empty at the same time. + """ + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + head = head_utils.binary_or_multi_class_head( + n_classes, weight_column=weight_column, + label_vocabulary=label_vocabulary, + loss_reduction=loss_reduction) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn_v2( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + config=config, + batch_norm=batch_norm, + linear_sparse_combiner=linear_sparse_combiner, + loss_reduction=loss_reduction) + + super(DNNLinearCombinedClassifierV2, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config, + warm_start_from=warm_start_from) + + +#@estimator_export(v1=['estimator.DNNLinearCombinedClassifier']) # pylint: disable=missing-docstring +class DNNLinearCombinedClassifier(estimator.Estimator): + __doc__ = DNNLinearCombinedClassifierV2.__doc__.replace( + 'SUM_OVER_BATCH_SIZE', 'SUM') + + def __init__(self, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + n_classes=2, + weight_column=None, + label_vocabulary=None, + input_layer_partitioner=None, + config=None, + warm_start_from=None, + loss_reduction=losses.Reduction.SUM, + batch_norm=False, + linear_sparse_combiner='sum'): + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + head = head_lib._binary_logistic_or_multi_class_head( # pylint: disable=protected-access + n_classes, weight_column, label_vocabulary, loss_reduction) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + input_layer_partitioner=input_layer_partitioner, + config=config, + batch_norm=batch_norm, + linear_sparse_combiner=linear_sparse_combiner) + + super(DNNLinearCombinedClassifier, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config, + warm_start_from=warm_start_from) + + +def _init_dnn_linear_combined_estimator( + head, + linear_feature_columns, + linear_optimizer, + dnn_feature_columns, + dnn_optimizer, + dnn_hidden_units, + dnn_activation_fn, + dnn_dropout, + input_layer_partitioner, + linear_sparse_combiner): + """Helper function for the initialization of DNNLinearCombinedEstimator.""" + linear_feature_columns = linear_feature_columns or [] + dnn_feature_columns = dnn_feature_columns or [] + feature_columns = ( + list(linear_feature_columns) + list(dnn_feature_columns)) + if not feature_columns: + raise ValueError('Either linear_feature_columns or dnn_feature_columns ' + 'must be defined.') + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + input_layer_partitioner=input_layer_partitioner, + config=config, + linear_sparse_combiner=linear_sparse_combiner) + return feature_columns, _model_fn + + +# TODO(b/117517419): Update these contrib references once head moves to core. +# Also references to the "_Head" class need to be replaced with "Head". +@estimator_export('estimator.DNNLinearCombinedEstimator', v1=[]) +class DNNLinearCombinedEstimatorV2(estimator.EstimatorV2): + """An estimator for TensorFlow Linear and DNN joined models with custom head. + + Note: This estimator is also known as wide-n-deep. + + Example: + + ```python + numeric_feature = numeric_column(...) + categorical_column_a = categorical_column_with_hash_bucket(...) + categorical_column_b = categorical_column_with_hash_bucket(...) + + categorical_feature_a_x_categorical_feature_b = crossed_column(...) + categorical_feature_a_emb = embedding_column( + categorical_column=categorical_feature_a, ...) + categorical_feature_b_emb = embedding_column( + categorical_column=categorical_feature_b, ...) + + estimator = DNNLinearCombinedEstimator( + head=tf.contrib.estimator.multi_label_head(n_classes=3), + # wide settings + linear_feature_columns=[categorical_feature_a_x_categorical_feature_b], + linear_optimizer=tf.train.FtrlOptimizer(...), + # deep settings + dnn_feature_columns=[ + categorical_feature_a_emb, categorical_feature_b_emb, + numeric_feature], + dnn_hidden_units=[1000, 500, 100], + dnn_optimizer=tf.train.ProximalAdagradOptimizer(...)) + + # To apply L1 and L2 regularization, you can set dnn_optimizer to: + tf.train.ProximalAdagradOptimizer( + learning_rate=0.1, + l1_regularization_strength=0.001, + l2_regularization_strength=0.001) + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. + + # Input builders + def input_fn_train: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_eval: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_predict: + # Returns tf.data.Dataset of (x, None) tuple. + pass + estimator.train(input_fn=input_fn_train, steps=100) + metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10) + predictions = estimator.predict(input_fn=input_fn_predict) + ``` + + Input of `train` and `evaluate` should have following features, + otherwise there will be a `KeyError`: + + * for each `column` in `dnn_feature_columns` + `linear_feature_columns`: + - if `column` is a `_CategoricalColumn`, a feature with `key=column.name` + whose `value` is a `SparseTensor`. + - if `column` is a `_WeightedCategoricalColumn`, two features: the first + with `key` the id column name, the second with `key` the weight column + name. Both features' `value` must be a `SparseTensor`. + - if `column` is a `_DenseColumn`, a feature with `key=column.name` + whose `value` is a `Tensor`. + + Loss is calculated by using mean squared error. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ + + def __init__(self, + head, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + config=None, + linear_sparse_combiner='sum'): + """Initializes a DNNLinearCombinedEstimator instance. + + Args: + head: A `_Head` instance constructed with a method such as + `tf.contrib.estimator.multi_label_head`. + model_dir: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into an estimator + to continue training a previously saved model. + linear_feature_columns: An iterable containing all the feature columns + used by linear part of the model. All items in the set must be + instances of classes derived from `FeatureColumn`. + linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. + dnn_feature_columns: An iterable containing all the feature columns used + by deep part of the model. All items in the set must be instances of + classes derived from `FeatureColumn`. + dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. + dnn_hidden_units: List of hidden units per layer. All layers are fully + connected. + dnn_activation_fn: Activation function applied to each layer. If None, + will use `tf.nn.relu`. + dnn_dropout: When not None, the probability we will drop out + a given coordinate. + config: RunConfig object to configure the runtime settings. + linear_sparse_combiner: A string specifying how to reduce the linear model + if a categorical column is multivalent. One of "mean", "sqrtn", and + "sum" -- these are effectively different ways to do example-level + normalization, which can be useful for bag-of-words features. For more + details, see `tf.feature_column.linear_model`. + + Raises: + ValueError: If both linear_feature_columns and dnn_features_columns are + empty at the same time. + """ + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn_v2( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + config=config, + linear_sparse_combiner=linear_sparse_combiner) + + super(DNNLinearCombinedEstimatorV2, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config) + + +@estimator_export(v1=['estimator.DNNLinearCombinedEstimator']) # pylint: disable=missing-docstring +class DNNLinearCombinedEstimator(estimator.Estimator): + __doc__ = DNNLinearCombinedEstimatorV2.__doc__ + + def __init__(self, + head, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + input_layer_partitioner=None, + config=None, + linear_sparse_combiner='sum'): + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + input_layer_partitioner=input_layer_partitioner, + config=config, + linear_sparse_combiner=linear_sparse_combiner) + + super(DNNLinearCombinedEstimator, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config) + + +@estimator_export('estimator.DNNLinearCombinedRegressor', v1=[]) +class DNNLinearCombinedRegressorV2(estimator.EstimatorV2): + """An estimator for TensorFlow Linear and DNN joined models for regression. + + Note: This estimator is also known as wide-n-deep. + + Example: + + ```python + numeric_feature = numeric_column(...) + categorical_column_a = categorical_column_with_hash_bucket(...) + categorical_column_b = categorical_column_with_hash_bucket(...) + + categorical_feature_a_x_categorical_feature_b = crossed_column(...) + categorical_feature_a_emb = embedding_column( + categorical_column=categorical_feature_a, ...) + categorical_feature_b_emb = embedding_column( + categorical_column=categorical_feature_b, ...) + + estimator = DNNLinearCombinedRegressor( + # wide settings + linear_feature_columns=[categorical_feature_a_x_categorical_feature_b], + linear_optimizer=tf.train.FtrlOptimizer(...), + # deep settings + dnn_feature_columns=[ + categorical_feature_a_emb, categorical_feature_b_emb, + numeric_feature], + dnn_hidden_units=[1000, 500, 100], + dnn_optimizer=tf.train.ProximalAdagradOptimizer(...), + # warm-start settings + warm_start_from="/path/to/checkpoint/dir") + + # To apply L1 and L2 regularization, you can set dnn_optimizer to: + tf.train.ProximalAdagradOptimizer( + learning_rate=0.1, + l1_regularization_strength=0.001, + l2_regularization_strength=0.001) + # To apply learning rate decay, you can set dnn_optimizer to a callable: + lambda: tf.AdamOptimizer( + learning_rate=tf.exponential_decay( + learning_rate=0.1, + global_step=tf.get_global_step(), + decay_steps=10000, + decay_rate=0.96) + # It is the same for linear_optimizer. + + # Input builders + def input_fn_train: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_eval: + # Returns tf.data.Dataset of (x, y) tuple where y represents label's class + # index. + pass + def input_fn_predict: + # Returns tf.data.Dataset of (x, None) tuple. + pass + estimator.train(input_fn=input_fn_train, steps=100) + metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10) + predictions = estimator.predict(input_fn=input_fn_predict) + ``` + + Input of `train` and `evaluate` should have following features, + otherwise there will be a `KeyError`: + + * for each `column` in `dnn_feature_columns` + `linear_feature_columns`: + - if `column` is a `_CategoricalColumn`, a feature with `key=column.name` + whose `value` is a `SparseTensor`. + - if `column` is a `_WeightedCategoricalColumn`, two features: the first + with `key` the id column name, the second with `key` the weight column + name. Both features' `value` must be a `SparseTensor`. + - if `column` is a `_DenseColumn`, a feature with `key=column.name` + whose `value` is a `Tensor`. + + Loss is calculated by using mean squared error. + + @compatibility(eager) + Estimators can be used while eager execution is enabled. Note that `input_fn` + and all hooks are executed inside a graph context, so they have to be written + to be compatible with graph mode. Note that `input_fn` code using `tf.data` + generally works in both graph and eager modes. + @end_compatibility + """ + + def __init__(self, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + label_dimension=1, + weight_column=None, + config=None, + warm_start_from=None, + loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE, + batch_norm=False, + linear_sparse_combiner='sum'): + """Initializes a DNNLinearCombinedRegressor instance. + + Args: + model_dir: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator + to continue training a previously saved model. + linear_feature_columns: An iterable containing all the feature columns + used by linear part of the model. All items in the set must be + instances of classes derived from `FeatureColumn`. + linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the linear part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL + optimizer. + dnn_feature_columns: An iterable containing all the feature columns used + by deep part of the model. All items in the set must be instances of + classes derived from `FeatureColumn`. + dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the deep part of the model. Can also be a string (one of 'Adagrad', + 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad + optimizer. + dnn_hidden_units: List of hidden units per layer. All layers are fully + connected. + dnn_activation_fn: Activation function applied to each layer. If None, + will use `tf.nn.relu`. + dnn_dropout: When not None, the probability we will drop out + a given coordinate. + label_dimension: Number of regression targets per example. This is the + size of the last dimension of the labels and logits `Tensor` objects + (typically, these have shape `[batch_size, label_dimension]`). + weight_column: A string or a `_NumericColumn` created by + `tf.feature_column.numeric_column` defining feature column representing + weights. It is used to down weight or boost examples during training. It + will be multiplied by the loss of the example. If it is a string, it is + used as a key to fetch weight tensor from the `features`. If it is a + `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, + then weight_column.normalizer_fn is applied on it to get weight tensor. + config: RunConfig object to configure the runtime settings. + warm_start_from: A string filepath to a checkpoint to warm-start from, or + a `WarmStartSettings` object to fully configure warm-starting. If the + string filepath is provided instead of a `WarmStartSettings`, then all + weights are warm-started, and it is assumed that vocabularies and Tensor + names are unchanged. + loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how + to reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`. + batch_norm: Whether to use batch normalization after each hidden layer. + linear_sparse_combiner: A string specifying how to reduce the linear model + if a categorical column is multivalent. One of "mean", "sqrtn", and + "sum" -- these are effectively different ways to do example-level + normalization, which can be useful for bag-of-words features. For more + details, see `tf.feature_column.linear_model`. + + Raises: + ValueError: If both linear_feature_columns and dnn_features_columns are + empty at the same time. + """ + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + head = regression_head.RegressionHead( + label_dimension=label_dimension, + weight_column=weight_column, + loss_reduction=loss_reduction) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn_v2( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + config=config, + batch_norm=batch_norm, + linear_sparse_combiner=linear_sparse_combiner) + + super(DNNLinearCombinedRegressorV2, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config, + warm_start_from=warm_start_from) + + +@estimator_export(v1=['estimator.DNNLinearCombinedRegressor']) # pylint: disable=missing-docstring +class DNNLinearCombinedRegressor(estimator.Estimator): + __doc__ = DNNLinearCombinedRegressorV2.__doc__.replace( + 'SUM_OVER_BATCH_SIZE', 'SUM') + + def __init__(self, + model_dir=None, + linear_feature_columns=None, + linear_optimizer='Ftrl', + dnn_feature_columns=None, + dnn_optimizer='Adagrad', + dnn_hidden_units=None, + dnn_activation_fn=nn.relu, + dnn_dropout=None, + label_dimension=1, + weight_column=None, + input_layer_partitioner=None, + config=None, + warm_start_from=None, + loss_reduction=losses.Reduction.SUM, + batch_norm=False, + linear_sparse_combiner='sum'): + self._feature_columns = _validate_feature_columns( + linear_feature_columns=linear_feature_columns, + dnn_feature_columns=dnn_feature_columns) + + head = head_lib._regression_head( # pylint: disable=protected-access + label_dimension=label_dimension, + weight_column=weight_column, + loss_reduction=loss_reduction) + + def _model_fn(features, labels, mode, config): + """Call the _dnn_linear_combined_model_fn.""" + return _dnn_linear_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + linear_feature_columns=linear_feature_columns, + linear_optimizer=linear_optimizer, + dnn_feature_columns=dnn_feature_columns, + dnn_optimizer=dnn_optimizer, + dnn_hidden_units=dnn_hidden_units, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + input_layer_partitioner=input_layer_partitioner, + config=config, + batch_norm=batch_norm, + linear_sparse_combiner=linear_sparse_combiner) + + super(DNNLinearCombinedRegressor, self).__init__( + model_fn=_model_fn, + model_dir=model_dir, + config=config, + warm_start_from=warm_start_from) diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/hooks/benchmark_hooks.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/hooks/benchmark_hooks.py similarity index 100% rename from TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/hooks/benchmark_hooks.py rename to TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/hooks/benchmark_hooks.py diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/hooks/training_hooks.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/hooks/training_hooks.py similarity index 100% rename from TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/hooks/training_hooks.py rename to TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/hooks/training_hooks.py diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/metrics.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/metrics.py similarity index 100% rename from TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/metrics.py rename to TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/metrics.py diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/schedulers.py b/TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/schedulers.py similarity index 100% rename from TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/utils/schedulers.py rename to TensorFlow/built-in/recommendation/WideDeep_ID2940_for_TensorFlow/util/schedulers.py diff --git a/TensorFlow/built-in/recommendation/deepFM_unkownshape_ID0091_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/recommendation/deepFM_unkownshape_ID0091_for_TensorFlow/test/train_full_1p.sh index 81e47ad1016826a6bc2f92ba1d6a182fa4da476f..21a0df2a07b3d4693cebb762edd70db7aafaa101 100644 --- a/TensorFlow/built-in/recommendation/deepFM_unkownshape_ID0091_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/built-in/recommendation/deepFM_unkownshape_ID0091_for_TensorFlow/test/train_full_1p.sh @@ -102,7 +102,9 @@ fi - +#参数修改 +sed -i 's|"device\_id"\:"0"|"device_\id"\:"'$ASCEND_DEVICE_ID'"|g' $cur_path/../configs/hccl.json +wait #训练开始时间，不需要修改 @@ -114,7 +116,7 @@ for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); do #设置环境变量，不需要修改 echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$ASCEND_DEVICE_ID + export RANK_ID=$RANK_ID_START #自行设置变量 export RANK_TABLE_FILE=$cur_path/../configs/hccl.json diff --git a/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/README.md b/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/README.md index aff092c78f1ab1e0cb6bdf942db6799bc5a120c1..3ad066c0956ca2d105868d70ef5848c00aa8b501 100644 --- a/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/README.md @@ -141,24 +141,7 @@ Epoch 1/15: 0%| | 0/1000 [00:00性能指标 + +| gpu | npu | +|-----------|-------------| +|92.4 (it/s)|125.51 (it/s)| diff --git a/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/test/train_performance_1p.sh index 1bbb9a1daff5ad772f5af0dfb7a8db6980d01130..163217427d76372c3aa213224cb7f77631d6611d 100644 --- a/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/contrib/cv/ADAGAN_ID2115_for_TensorFlow/test/train_performance_1p.sh @@ -125,9 +125,11 @@ e2e_time=$(( $end_time - $start_time )) #结果打印，不需要修改 echo "------------------ Final result ------------------" +##获取性能数据，不需要修改 +#吞吐量 +ActualFPS=`cat ${cur_path}test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | grep -Eo "[0-9]*\.[0-9]*it/s" | tail -n 10 | awk -F "i" '{print $1}' | awk '{sum+=$1} END {print "", sum/NR}' | awk '{print $1}'` #输出性能FPS，需要模型审视修改 -TrainingTime1=`grep "Perf: " $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk 'END {print $2}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${TrainingTime1}'/'3'}'` +TrainingTime=`awk 'BEGIN{printf "%.2f\n", '1'/'${ActualFPS}'}'` #性能看护结果汇总 #训练用例信息，不需要修改 @@ -135,10 +137,6 @@ BatchSize=${batch_size} DeviceType=`uname -m` CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' -##获取性能数据，不需要修改 -#吞吐量 -ActualFPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${TrainingTime}'}'` - #获取模型精度 train_accuracy=`grep "C= " $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk 'END {print $8}'` diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/README.md b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/README.md index a6c4f49874e549c640a839502ad6b2004512370d..68fdd45c69f8fc6458cec5845ee4cb97e25296fe 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/README.md @@ -1,65 +1,239 @@ -### **BicycleGAN** +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：** ImageGeneration + +**版本（Version）：1.1** + +**修改时间（Modified） ：2022.04.21** + +**大小（Size）：** + +**框架（Framework）：TensorFlow 1.15.0** + +**模型格式（Model Format）：** + +**精度（Precision）：** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Research** + +**描述（Description）：基于TensorFlow框架的BicycleGAN图像生成网络训练代码** + +

概述

+ BicycleGAN模型是Toward Multimodal Image-to-Image Translation论文的Tensorflow的实现，该论文的核心思想体现在确保输入噪声向量与输出图像的双向映射一致性。BicycleGAN通过结合cVAE-GAN和cLR-GAN这两个方法来共同地促进隐层向量和输出图像在两个方向上的连接。通过BicycleGAN生成的图像多样性更好，且更具有视觉上的真实性。 +- 参考论文： + + [Zhu, Jun-Yan, et al. "Toward multimodal image-to-image translation." Advances in neural information processing systems 30 (2017).] + - arXiv:1711.11586(https://arxiv.org/pdf/arXiv:1711.11586.pdf) + +- 参考实现： + + https://github.com/prakashpandey9/BicycleGAN + +- 适配昇腾 AI 处理器的实现： + + + https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow + + + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +- 精度 + +| | GPU | NPU | +|-------|-------|-------| +| LPIPS | 0.412 | 0.392 | + +- 性能 + +| batchsize | image_size | GPU （v100） | NPU | +|-----------|------------|---|---| +| 1 | 256 x 256 | | | + + +## 默认配置 + +- 训练数据集预处理（以原论文的maps训练集为例，仅作为用户参考示例）： + + - 图像的输入尺寸为256*256 + - 图像输入格式：JEPG + - 输入图像进行归一化至[-1,1] + +- 测试数据集预处理（以原论文的maps验证集为例，仅作为用户参考示例） + + - 图像的输入尺寸为256*256 + - 图像输入格式：JEPG + - 输入图像进行归一化至[-1,1] + +- 训练超参 + + - Batch size: 1 + - Learning rate(LR): 0.0002 + - Optimizer: Adam + - Train epoch: 20 + +## 支持特性 + +| 特性列表 | 是否支持 | +|-------|------| +| 分布式训练 | 否 | +| 混合精度 | 否 | +| 并行数据 | 否 | + + -### **概述** +

训练环境准备

-迁移NIMA到ascend910平台 -将结果与原论文进行比较 +1. 硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。 +2. 宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。 - | | 论文 | ascend | -|----------------|------|--------| -| LIPIS Distance | 0.110±0.002 | 待测 | + 当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。 + **表 1** 镜像列表 -### Requirements + + + + + + + + + + + +

镜像名称

+

镜像版本

+

配套CANN版本

+
+

20.2.0

+

20.2

+
-1. Tensorflow 1.15 -### **代码及路径解释** +

快速上手

+- 数据集准备 +1. 模型训练使用原论文提供的maps数据集，数据集请用户自行获取。 +2. 获得数据集后，放入模型目录下，在训练脚本中指定数据集路径，可正常使用。 + + +## 模型训练 + +- 单击“立即下载”，并选择合适的下载方式下载源码包。 + +- 启动训练之前，首先要配置程序运行相关环境变量。 + + 环境变量配置信息参见： + + [Ascend 910训练平台环境变量设置](https://gitee.com/ascend/modelzoo/wikis/Ascend%20910%E8%AE%AD%E7%BB%83%E5%B9%B3%E5%8F%B0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F%E8%AE%BE%E7%BD%AE?sort_id=3148819) + +- 单卡训练 + + 1. 配置训练参数。 + + 首先在脚本test/train_full_1p.sh中，配置训练数据集路径，请用户根据实际路径配置，数据集参数如下所示： + + ``` + --data_path ./dataset + ``` + + 2. 启动训练。 + + 启动单卡训练 （脚本为modelarts_entry_acc.py） + + ``` + python3 modelarts_entry_acc.py + ``` + + + +

迁移学习指导

+ +- 数据集准备。 + + 数据集要求如下： + + 1. 获取数据。 + + 如果要使用自己的数据集，需要将数据集放到脚本参数data_path对应目录下。参考代码中的数据集存放路径如下： + + - 训练集： ./dataset/train + - 测试集： ./dataset/val + + 数据集也可以放在其它目录，则修改对应的脚本入参data_path即可。 + + +- 模型训练。 + + 参考“模型训练”中训练步骤。 + +- 模型评估。 + + 参考“模型训练”中验证步骤。 + +

高级参考

+ +## 脚本和示例代码 ``` -BicycleGAN +BicycyeGAN └─ ├─README.md - ├─folder_npu.py 用于检查文件夹结构 - ├─layers.py 用于创建基础的神经层 - ├─load_data_npu.py 用于创建数据流 - ├─log.py 用于创建训练日志 - ├─model_npu_tmp.py 用于定义模型结构 - ├─main_npu.py 用于启动训练和测试过程 - ├─maps 用于存放训练数据集 obs://bicyclegan/BicycleGAN2/maps/ + ├─dataset用于存放训练数据集 ├─train - └─... - ├─val - └─... - ├─checkpoints 用于存放训练好的模型文件 - ├─logs 用于存放训练日志 - ├─results 用于存放训练集和测试集的测试的结果 - ├─train_1p.sh 模型的启动脚本， - ├─test_1p.sh 模型的启动测试脚本 + └─val + ├─logs 用于存放日志文件 + ├─... + └─... + ├─weights 用于存放预训练模型 + ├─net-lin_alex_v0.1.pb + └─... + ├─results 用于模型生成的图片 + ├─net-lin_alex_v0.1.pb + └─... ``` -### **数据集和模型** -BicycleGAN模型所使用的数据集为Google maps-satellites,是一个pixel to pixel的风格迁移数据集，其中包括1096张实际街景图片和与之对应的地图标签。 +## 脚本参数 +``` + +--Z_dim Decoder后向量的维度,默认是8 +--reconst_coeff Reconstruction的系数,默认是10 +--latent_coeff Latent的系数,默认是0.5 +--kl_coeff KL的系数,默认是0.01 +--learning_rate 学习率,默认是0.0002 +--image_size 输入的图片大小,默认是256 +--batch_size 训练的batch大小，默认是1 +--epoch 训练的epoch数，默认是20 +--data_path 训练集文件路径 +--output_path 日志,模型文件等存放的路径 +``` -### 训练过程及结果 -epoch=200 \ -batch_size=1 \ -lr=0.0002 \ -耗费近1小时 +## 训练过程 -### 数据集百度云链接及提取码 -链接：https://pan.baidu.com/s/17rKdfkp_8_pvn89nII13fg -提取码：zdx1 +1. 通过“模型训练”中的训练指令启动单卡训练。 + +2. 参考脚本的模型存储路径为./checkpoint。 + + + +## 推理/验证过程 - **启动训练和测试过程** -执行shell脚本： -``` -bash train_1p.sh -``` \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/eval.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..73f2c567162854e2e12bdd8501861ddf529f7061 --- /dev/null +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/eval.py @@ -0,0 +1,77 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +from tqdm import tqdm +import tensorflow as tf +import lpips_tf +from PIL import Image +from npu_bridge.npu_init import * +from tensorflow_core.core.protobuf.rewriter_config_pb2 import RewriterConfig + + +def eval_tf(basedir): + # NPU config + config = tf.ConfigProto() + custom_op = config.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = "NpuOptimizer" + config.graph_options.rewrite_options.remapping = RewriterConfig.OFF # 必须显式关闭 + config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF # 必须显式关闭 + + with tf.Session(config=config) as sess: + image_dirs = [] + for root, dirs, files in os.walk(basedir): # find all dirs + if dirs != []: + for dirname in dirs: + full_dirname = os.path.join(root, dirname) + image_dirs.append(full_dirname) + + dist_consecutive = [] + image0_ph = tf.placeholder(tf.float32) + image1_ph = tf.placeholder(tf.float32) + dist_t = lpips_tf.lpips(image0_ph, image1_ph) + + for dir in tqdm(image_dirs): # find all pictures of the dir + lpips_pairs = [] + files = os.listdir(dir) + for file in files: + if file.startswith('random'): + path = os.path.join(dir, file) + image = Image.open(path) + image = np.asarray(image.resize((256, 256), Image.BICUBIC)) + # when evaluating,the image is normalized to [0,1], + # because the lpips will do the work that transforms [0,1] to [-1,1] + image = image.astype(np.float32) / 255.0 + lpips_pairs.append(image) + + for i in range(0, len(lpips_pairs) - 1): # consecutive test,computing (N-1) pairs + dist = sess.run(dist_t, feed_dict={image0_ph: lpips_pairs[i], image1_ph: lpips_pairs[i + 1]}) + dist_consecutive.append(dist) + + print('Final Average Distances : {}'.format(sum(dist_consecutive) / len(dist_consecutive))) diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder_npu.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder.py similarity index 96% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder_npu.py rename to TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder.py index 5c68ac5282cf60884ffee6af5d5d9f16cb94f618..8ebbc72083636f07eeecc0b144ff1678e57bcd7e 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder_npu.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/folder.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -28,11 +27,10 @@ # limitations under the License. import os -# import moxing as mox def check_folder(log_dir): if not os.path.exists(log_dir): - os.mkdir(log_dir) + os.makedirs(log_dir) # print (log_dir) return log_dir diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/layers.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/layers.py index 5fa36fb343798195e158ee050f9dea9b905e6153..40e780535ca73d35229f78cb1f5adb1cfa7859a8 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/layers.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/layers.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -104,8 +103,8 @@ def deconv2d_layer(x, out_channel, filter_height, filter_width, stride_height, s # Function for Residual Blocks def residual_block1(input, num_filters, filter_size, is_training, name="res_block"): with tf.variable_scope(name): - x_shortcut = input - x = lrelu_layer(bn_layer(conv2d_layer(input, num_filters, filter_size, filter_size, 2, 2, name='res_convd1'), + x_shortcut = x + x = lrelu_layer(bn_layer(conv2d_layer(x, num_filters, filter_size, filter_size, 2, 2, name='res_convd1'), is_training=is_training, scope='ebn_1')) x = bn_layer(conv2d_layer(x, num_filters, 1, 1, 1, 1, name='res_convd2'), is_training=is_training, scope='ebn_2') diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data_npu.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data.py similarity index 30% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data_npu.py rename to TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data.py index 08686cb8b9ee64b1106afaa5099cb134849070ff..12d148f10c189dd0fb26fef5246c1059656f4b7b 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data_npu.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/load_data.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -28,108 +27,36 @@ # limitations under the License. import numpy as np -import os -import h5py import glob -# import scipy.misc -# from scipy.misc import imread, imresize +import os from PIL import Image -# import moxing as mox -import matplotlib.image as mp - -imread = Image.open - - -def load_images(maps_dir): - train_all = glob.glob(maps_dir + "/train/*.jpg") - train_img_A = [] - train_img_B = [] - - for file in train_all: - full_image = np.array(imread(file).resize((512, 256), Image.ANTIALIAS)) - img_B = full_image[:, full_image.shape[1] // 2:, :] - img_A = full_image[:, :full_image.shape[1] // 2, :] - train_img_A.append(img_A) - train_img_B.append(img_B) - - train_A = np.asarray(train_img_A) - train_B = np.asarray(train_img_B) - print (train_A.shape) - print (train_B.shape) - - test_all = glob.glob(maps_dir + "/val/*.jpg") - test_img_A = [] - test_img_B = [] - - for file in test_all: - full_image = np.array(imread(file).resize((512, 256), Image.ANTIALIAS)) - img_B = full_image[:, full_image.shape[1] // 2:, :] - img_A = full_image[:, :full_image.shape[1] // 2, :] - test_img_A.append(img_A) - test_img_B.append(img_B) - - test_A = np.asarray(test_img_A) - test_B = np.asarray(test_img_B) - print (test_A.shape) - print (test_B.shape) - - return train_A, train_B, test_A, test_B - - -# train_all = glob.glob(maps_dir + "/train/*.jpg") -batch_size = 1 - - -def load_batch_image(idx, maps_dir): - train_all = glob.glob(maps_dir + "/train/*.jpg") - full_image = np.array(imread(train_all[idx]).resize((512,256), Image.ANTIALIAS)) - img_B = full_image[:, full_image.shape[1] // 2:, :] - img_A = full_image[:, :full_image.shape[1] // 2, :] - - return img_A, img_B - - -# test_all = glob.glob("maps/val/*.jpg") - - -def load_test_image(idx, maps_dir): - test_all = glob.glob(maps_dir + "/val/*.jpg") - full_image = np.array(imread(test_all[idx]).resize((512, 256), Image.ANTIALIAS)) - img_A = full_image[:, :full_image.shape[1] // 2, :] / 255. - - return img_A -def save_images(image, size, img_path): - return imsave(image, size, img_path) +def load_images(path, image_size): + train_all = sorted(glob.glob(os.path.join(path, "train/*.jpg"))) + test_all = sorted(glob.glob(os.path.join(path, "val/*.jpg"))) + train_input = [] + test_input = [] + train_output = [] + test_output = [] -def imsave(image, img_size, img_path): - # image = Image.fromarray(np.squeeze(image * 255.).astype(np.uint8)) - return Image.fromarray(np.squeeze(image * 255.).astype(np.uint8)).save(img_path) - # return mp.imsave(np.squeeze(img_path), image) + for img in train_all: + full_image = Image.open(img) + full_image = np.asarray(full_image.resize((2 * image_size, image_size), Image.BICUBIC)) + # in maps dataset,the input and output merge to one image + # and the output is the left part + train_output.append(full_image[:, :full_image.shape[1] // 2, :] / 255.) + train_input.append(full_image[:, full_image.shape[1] // 2:, :] / 255.) -def inverse_transform(image): - return (image + 1.) / 2. + for img in test_all: + full_image = Image.open(img) + full_image = np.asarray(full_image.resize((2 * image_size, image_size), Image.BICUBIC)) + test_output.append(full_image[:, :full_image.shape[1] // 2, :] / 255.) + test_input.append(full_image[:, full_image.shape[1] // 2:, :] / 255.) -def merge(images, size): - h, w = images.shape[1], images.shape[2] - if (images.shape[3] in (3, 4)): - c = images.shape[3] - img = np.zeros((h * size[0], w * size[1], c)) - for idx, image in enumerate(images): - i = idx % size[1] - j = idx // size[1] - img[j * h:j * h + h, i * w:i * w + w, :] = image - return img - elif images.shape[3] == 1: - img = np.zeros((h * size[0], w * size[1])) - for idx, image in enumerate(images): - i = idx % size[1] - j = idx // size[1] - img[j * h:j * h + h, i * w:i * w + w] = image[:, :, 0] - return img - else: - raise ValueError('In merge function, the first argument must have dimensions: HxW or HxWx3 or HxWx4') + # need to normalize to [-1,1] + return np.asarray(train_input) * 2 - 1, np.asarray(train_output) * 2 - 1, \ + np.asarray(test_input) * 2 - 1, np.asarray(test_output) * 2 - 1 diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/log.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/log.py index c3c36238be376cde2ea80305d7d7048bd6e62e30..c24ddf6c139127fe2d292dc0c40f0b90da045015 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/log.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/log.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -35,6 +34,7 @@ logging.info("Start BicycleGAN") logger = logging.getLogger('BicycleGAN') logger.setLevel(logging.INFO) + def makedirs(path): if not os.path.exists(path): - os.makedirs(path) + os.makedirs(path) diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/lpips_tf.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/lpips_tf.py new file mode 100644 index 0000000000000000000000000000000000000000..98d5d8991cd8d30e0840eeeb2acc1185dc030f3f --- /dev/null +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/lpips_tf.py @@ -0,0 +1,118 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys + +import tensorflow as tf +from six.moves import urllib + +_URL = 'http://rail.eecs.berkeley.edu/models/lpips' + + +def _download(url, output_dir): + """Downloads the `url` file into `output_dir`. + + Modified from https://github.com/tensorflow/models/blob/master/research/slim/datasets/dataset_utils.py + """ + filename = url.split('/')[-1] + filepath = os.path.join(output_dir, filename) + + def _progress(count, block_size, total_size): + sys.stdout.write('\r>> Downloading %s %.1f%%' % ( + filename, float(count * block_size) / float(total_size) * 100.0)) + sys.stdout.flush() + + filepath, _ = urllib.request.urlretrieve(url, filepath, _progress) + print() + statinfo = os.stat(filepath) + print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') + + +def lpips(input0, input1, model='net-lin', net='alex', version=0.1): + """ + Learned Perceptual Image Patch Similarity (LPIPS) metric. + + Args: + input0: An image tensor of shape `[..., height, width, channels]`, + with values in [0, 1]. + input1: An image tensor of shape `[..., height, width, channels]`, + with values in [0, 1]. + + Returns: + The Learned Perceptual Image Patch Similarity (LPIPS) distance. + + Reference: + Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang. + The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. + In CVPR, 2018. + """ + # flatten the leading dimensions + batch_shape = tf.shape(input0)[:-3] + input0 = tf.reshape(input0, tf.concat([[-1], tf.shape(input0)[-3:]], axis=0)) + input1 = tf.reshape(input1, tf.concat([[-1], tf.shape(input1)[-3:]], axis=0)) + # NHWC to NCHW + input0 = tf.transpose(input0, [0, 3, 1, 2]) + input1 = tf.transpose(input1, [0, 3, 1, 2]) + # normalize to [-1, 1] + input0 = input0 * 2.0 - 1.0 + input1 = input1 * 2.0 - 1.0 + + input0_name, input1_name = '0:0', '1:0' + + default_graph = tf.get_default_graph() + # producer_version = default_graph.graph_def_versions.producer + producer_version = 27 + cache_dir = './weights' + os.makedirs(cache_dir, exist_ok=True) + # files to try. try a specific producer version, but fallback to the version-less version (latest). + pb_fnames = [ + '%s_%s_v%s.pb' % (model, net, version), + '%s_%s_v%s_%d.pb' % (model, net, version, producer_version) + ] + for pb_fname in pb_fnames: + if not os.path.isfile(os.path.join(cache_dir, pb_fname)): + try: + _download(os.path.join(_URL, pb_fname), cache_dir) + except urllib.error.HTTPError: + pass + if os.path.isfile(os.path.join(cache_dir, pb_fname)): + break + + with open(os.path.join(cache_dir, pb_fname), 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + _ = tf.import_graph_def(graph_def, + input_map={input0_name: input0, input1_name: input1}) + distance, = default_graph.get_operations()[-1].outputs + + if distance.shape.ndims == 4: + distance = tf.squeeze(distance, axis=[-3, -2, -1]) + # reshape the leading dimensions + distance = tf.reshape(distance, batch_shape) + return distance diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main_npu.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main.py similarity index 43% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main_npu.py rename to TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main.py index 9e34fa655591d191a7080d9a8c11278069bd3cab..6216ef54a7d66ea2e863f33cb07ce51da6bd55c4 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main_npu.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/main.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -30,63 +29,41 @@ from __future__ import division from __future__ import print_function from __future__ import absolute_import -import sys import argparse -import numpy as np -import tensorflow as tf -from model_npu_tmp import BicycleGAN -from folder_npu import check_folder -# from load_data import load_images import os -# import moxing as mox -from npu_bridge.estimator import npu_ops -from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig -from tensorflow.python.framework import graph_util -from tensorflow.python import pywrap_tensorflow - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -os.environ['ASCEND_SLOG_PRINT_TO_STDOUT'] = "3" +import tensorflow as tf +from load_data import load_images +from model import BicycleGAN +from folder import check_folder +from eval import eval_tf +from npu_bridge.npu_init import * +from tensorflow_core.core.protobuf.rewriter_config_pb2 import RewriterConfig def parse_args(): - desc = "Tensorflow implementation of BicycleGAN" - parser = argparse.ArgumentParser(description=desc) + parser = argparse.ArgumentParser() parser.add_argument('--Z_dim', type=int, default=8, help='Size of latent vector') parser.add_argument('--reconst_coeff', type=float, default=10, help='Reconstruction Coefficient') parser.add_argument('--latent_coeff', type=float, default=0.5, help='Latent Coefficient') parser.add_argument('--kl_coeff', type=float, default=0.01, help='KL Coefficient') parser.add_argument('--learning_rate', type=float, default=0.0002, help='Learning Rate') parser.add_argument('--image_size', type=int, default=256, help='Image Size') - parser.add_argument('--batch_size', type=int, default=1, help='Size of the minibatch') - parser.add_argument('--gan_type', type=str, default='BicycleGAN', help='Type of GAN') - parser.add_argument('--dataset', type=str, default='./maps', help='The name of dataset') - parser.add_argument('--epoch', type=int, default=200, help='The number of epochs to run') - parser.add_argument('--checkpoint_dir', type=str, default='./checkpoints', - help='Directory name to save the checkpoints') - parser.add_argument('--train_url', type=str, default=None, help='train_url') - parser.add_argument('--data_url', type=str, default=None, help='data_url') - parser.add_argument('--result_dir', type=str, default='./results', help='Directory name to save the generated images') - parser.add_argument('--log_dir', type=str, default='./logs', help='Directory name to save training logs') + parser.add_argument('--batch_size', type=int, default=1, help='number of images in one minibatch') + parser.add_argument('--epoch', type=int, default=20, help='The number of epochs to run') + parser.add_argument('--data_path', type=str, default='', help='Datasets location') + parser.add_argument('--output_path', type=str, default='', help='Output location') return check_args(parser.parse_args()) -"""checking arguments""" - - def check_args(args): - # --checkpoint_dir - check_folder(args.checkpoint_dir) - - # --result_dir - check_folder(args.result_dir) - # --result_dir - check_folder(args.log_dir) + check_folder(args.output_path) # --epoch assert args.epoch > 0, 'Totral number of epochs must be greater than zero' # --batch_size + # Due to the limit of the network,the batch_size must be set to 1 currently assert args.batch_size > 0, 'Batch size must be greater than zero' # --z_dim @@ -95,76 +72,36 @@ def check_args(args): return args -"""main function""" - - def main(): # parse arguments args = parse_args() if args is None: exit() - # Open New Tensorflow Session - model = BicycleGAN - #add - # TMP_DATA_PATH = './' + args.dataset - # TMP_RESULTS_PATH = '.' + args.result_dir - # TMP_CHECKPOINT_PATH = './' + args.checkpoint_dir - # TMP_LOGS_PATH = './' + args.log_dir - - # OBS_DATA_PATH = 'obs://bicyclegan/BicycleGAN2/' + args.dataset - # OBS_RESULTS_PATH = 'obs://bicyclegan/BicycleGAN2/' + args.result_dir - # OBS_CHECKPOINT_DIR = 'obs://bicyclegan/BicycleGAN2/' + args.checkpoint_dir - # OBS_LOG_PATH = 'obs://bicyclegan/BicycleGAN2/' + args.log_dir - # mox.file.make_dirs(TMP_DATA_PATH) - # mox.file.make_dirs(TMP_RESULTS_PATH) - # mox.file.make_dirs(TMP_CHECKPOINT_PATH) - # mox.file.make_dirs(TMP_LOGS_PATH) - # mox.file.copy_parallel(OBS_RESULTS_PATH, TMP_RESULTS_PATH) - # mox.file.copy_parallel(OBS_DATA_PATH, TMP_DATA_PATH) - # mox.file.copy_parallel(OBS_LOG_PATH, TMP_LOGS_PATH) - # mox.file.copy_parallel(OBS_CHECKPOINT_DIR, TMP_CHECKPOINT_PATH) - config = tf.ConfigProto(allow_soft_placement=True) + # NPU config + config = tf.ConfigProto() custom_op = config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" - custom_op.parameter_map["use_off_line"].b = True - config.graph_options.rewrite_options.remapping = RewriterConfig.OFF + config.graph_options.rewrite_options.remapping = RewriterConfig.OFF # 必须显式关闭 + config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF # 必须显式关闭 - config.gpu_options.allow_growth = True + # Open New Tensorflow Session + model = BicycleGAN with tf.Session(config=config) as sess: - # Declare instance for GAN - - gan = None - if args.gan_type == model.model_name: - gan = model(sess, - epoch=args.epoch, - batch_size=args.batch_size, - Z_dim=args.Z_dim, - image_size=args.image_size, - dataset_name=args.dataset, - checkpoint_dir=args.checkpoint_dir, - result_dir=args.result_dir, - log_dir=args.log_dir) - if gan is None: - raise Exception("[!] There is no option for " + args.gan_type) - - # Build Tesnorflow Graph - gan.build_model() - - # show network architecture - # show_all_variables() - - # Launch the graph in a session - gan.train() + gan = model(sess=sess, args=args) + + train_A, train_B, test_A, test_B = load_images(args.data_path, args.image_size) + assert len(test_A) == len(test_B) + assert len(train_A) == len(train_B) + + gan.train(train_A=train_A, train_B=train_B) print(" [*] Training finished!") - # visualize learned generator - gan.test() + gan.test(test_A=test_A, test_B=test_B) print(" [*] Testing finished!") - # mox.file.copy_parallel(TMP_RESULTS_PATH, OBS_RESULTS_PATH) - # mox.file.copy_parallel(TMP_DATA_PATH, OBS_DATA_PATH) - # mox.file.copy_parallel(TMP_LOGS_PATH, OBS_LOG_PATH) - # mox.file.copy_parallel(TMP_CHECKPOINT_PATH, OBS_CHECKPOINT_DIR) + + path = os.path.join(args.output_path, "results", "test_results") + eval_tf(path) if __name__ == '__main__': diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model_npu_tmp.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model.py similarity index 69% rename from TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model_npu_tmp.py rename to TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model.py index dbbc737a1489382d43f32372fa77ea3ecbbd953d..e11930396c568016218246d68bf286f9c407ceb1 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model_npu_tmp.py +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/model.py @@ -1,4 +1,3 @@ - # Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -27,69 +26,106 @@ # See the License for the specific language governing permissions and # limitations under the License. -# -*- coding: utf-8 -*- from __future__ import division import os import time -import glob -import tensorflow as tf -import numpy as np -# import scipy.misc -from PIL import Image +from tqdm import trange from layers import * -# from tensorflow.contrib import layers -from folder_npu import check_folder -from load_data_npu import load_images, save_images, imsave, load_batch_image, load_test_image -import matplotlib - -matplotlib.use('Tkagg') -#import matplotlib.pyplot as plt -#import moxing as mox - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" +from folder import check_folder +from imageio import imwrite +import random class BicycleGAN(object): - model_name = "BicycleGAN" - def __init__(self, sess, epoch, batch_size, Z_dim, image_size, dataset_name, checkpoint_dir, result_dir, log_dir): - self.TMP_DATA_PATH = dataset_name - self.TMP_RESULTS_PATH = result_dir - self.TMP_CHECKPOINT_PATH = checkpoint_dir - self.TMP_LOGS_PATH = log_dir + def __init__(self, sess, args): self.sess = sess - self.dataset_name = dataset_name - self.checkpoint_dir = checkpoint_dir - self.result_dir = result_dir - self.log_dir = log_dir - self.epoch = epoch - self.batch_size = batch_size - self.image_size = image_size - - self.input_width = 256 - self.input_height = 256 - self.output_width = 256 - self.output_height = 256 - self.channels = 3 - - self.Z_dim = Z_dim + self.data_path = args.data_path + self.checkpoint_dir = os.path.join(args.output_path, 'checkpoints') + self.result_dir = os.path.join(args.output_path, 'results') + self.log_dir = os.path.join(args.output_path, 'logs') + self.epoch = args.epoch + self.batch_size = args.batch_size + self.image_size = args.image_size # train - self.learning_rate = 0.0002 - self.beta1 = 0.5 - self.beta2 = 0.999 - self.reconst_coeff = 10 - self.latent_coeff = 0.5 - self.kl_coeff = 0.01 + self.Z_dim = args.Z_dim + self.learning_rate = args.learning_rate + self.reconst_coeff = args.reconst_coeff + self.latent_coeff = args.latent_coeff + self.kl_coeff = args.kl_coeff # test - self.sample_num = 64 + self.sample_num = 20 # how many images will model generates for one input + + # Input Image A + self.image_A = tf.placeholder(tf.float32, [self.batch_size] + [self.image_size, self.image_size, 3], + name='input_images') + + # Output Image B + self.image_B = tf.placeholder(tf.float32, [self.batch_size] + [self.image_size, self.image_size, 3], + name='output_images') + + # Noise z + self.z = tf.placeholder(tf.float32, [self.batch_size, self.Z_dim], name='latent_vector') - # load data - # self.train_A, self.train_B, self.test_A, self.test_B = load_images() - #self.train_A = mox.file.glob(dataset_name + "/train/*.jpg") - self.train_A = glob.glob(dataset_name + "/train/*.jpg") - self.num_batches = len(self.train_A) // self.batch_size + ''' Implementation of cVAE-GAN: B -> z -> B' ''' + # Encoder is fed the correct output image B for encding it to the latent representation z to learn the distribution of z + # It outputs 3 things: Enocded value z as Q(z|B), mu of Q(z|B), log_sigma of Q(z|B) + self.encoded_true_img, self.encoded_mu, self.encoded_log_sigma = self.Encoder(self.image_B) + + # This encoded representation z along with the input image A is then fed to the Generator to output the image B' + self.desired_gen_img = self.Generator(self.image_A, self.encoded_true_img) # Image B_cap + + ''' Implementation of cLR-GAN: z -> B' -> z' ''' + # Now, z is sampled from a normal distribution N(z) which in addition to the input image A is fed to the Generator to output B' + self.LR_desired_img = self.Generator(self.image_A, self.z) # Generated Image B' + + # B' is then fed to the Encoder to output z' which we try to be close to N(z). + self.reconst_z, self.reconst_mu, self.reconst_log_sigma = self.Encoder(self.LR_desired_img) # Encoded z' + + self.P_real = self.Discriminator(self.image_B) # Probability of ground_truth/real image (B) as real/fake + self.P_fake = self.Discriminator( + self.LR_desired_img) # Probability of generated output images (G(A, N(z)) as real/fake + self.P_fake_encoded = self.Discriminator( + self.desired_gen_img) # Probability of generated output images (G(A, Q(z|B)) as real/fake + + self.loss_vae_gan_D = (tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) + tf.reduce_mean( + tf.square(self.P_fake_encoded))) + + self.loss_lr_gan_D = ( + tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) + tf.reduce_mean(tf.square(self.P_fake))) + + self.loss_vae_gan_GE = tf.reduce_mean(tf.squared_difference(self.P_fake_encoded, 0.9)) # G + + self.loss_gan_G = tf.reduce_mean(tf.squared_difference(self.P_fake, 0.9)) + + self.loss_vae_GE = tf.reduce_mean(tf.abs(self.image_B - self.desired_gen_img)) # G + + self.loss_latent_GE = tf.reduce_mean(tf.abs(self.z - self.reconst_z)) # G + + self.loss_kl_E = 0.5 * tf.reduce_mean( + -1 - self.encoded_log_sigma + self.encoded_mu ** 2 + tf.exp(self.encoded_log_sigma)) + + self.loss_D = self.loss_vae_gan_D + self.loss_lr_gan_D - tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) + self.loss_G = self.loss_vae_gan_GE + self.loss_gan_G + self.reconst_coeff * self.loss_vae_GE + self.latent_coeff * self.loss_latent_GE + self.loss_E = self.loss_vae_gan_GE + self.reconst_coeff * self.loss_vae_GE + self.latent_coeff * self.loss_latent_GE + self.kl_coeff * self.loss_kl_E + + # Optimizer + self.dis_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Discriminator") + self.gen_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Generator") + self.enc_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Encoder") + opt = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5) + + with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): + self.D_solver = opt.minimize(self.loss_D, var_list=self.dis_var) + self.G_solver = opt.minimize(self.loss_G, var_list=self.gen_var) + self.E_solver = opt.minimize(self.loss_E, var_list=self.enc_var) + + """ Summary """ + self.d_loss_sum = tf.summary.scalar("d_loss", self.loss_D) + self.g_loss_sum = tf.summary.scalar("g_loss", self.loss_G) + self.e_loss_sum = tf.summary.scalar("e_loss", self.loss_E) def Discriminator(self, x, is_training=True, reuse=True): with tf.variable_scope("Discriminator", reuse=tf.AUTO_REUSE): @@ -197,201 +233,108 @@ class BicycleGAN(object): return z, mu, log_sigma - def build_model(self): - image_dims = [self.input_width, self.input_height, self.channels] - - ''' Graph input ''' - # Input Image A - self.image_A = tf.placeholder(tf.float32, [self.batch_size] + image_dims, name='input_images') - - # Output Image B - self.image_B = tf.placeholder(tf.float32, [self.batch_size] + image_dims, name='output_images') - - # Noise z - self.z = tf.placeholder(tf.float32, [self.batch_size, self.Z_dim], name='latent_vector') - - ''' Implementation of cVAE-GAN: B -> z -> B' ''' - # Encoder is fed the correct output image B for encding it to the latent representation z to learn the distribution of z - # It outputs 3 things: Enocded value z as Q(z|B), mu of Q(z|B), log_sigma of Q(z|B) - self.encoded_true_img, self.encoded_mu, self.encoded_log_sigma = self.Encoder(self.image_B) - - # This encoded representation z along with the input image A is then fed to the Generator to output the image B' - self.desired_gen_img = self.Generator(self.image_A, self.encoded_true_img) # Image B_cap - - ''' Implementation of cLR-GAN: z -> B' -> z' ''' - # Now, z is sampled from a normal distribution N(z) which in addition to the input image A is fed to the Generator to output B' - self.LR_desired_img = self.Generator(self.image_A, self.z) # Generated Image B' - - # B' is then fed to the Encoder to output z' which we try to be close to N(z). - self.reconst_z, self.reconst_mu, self.reconst_log_sigma = self.Encoder(self.LR_desired_img) # Encoded z' - - self.P_real = self.Discriminator(self.image_B) # Probability of ground_truth/real image (B) as real/fake - self.P_fake = self.Discriminator( - self.LR_desired_img) # Probability of generated output images (G(A, N(z)) as real/fake - self.P_fake_encoded = self.Discriminator( - self.desired_gen_img) # Probability of generated output images (G(A, Q(z|B)) as real/fake - - self.loss_vae_gan_D = (tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) + tf.reduce_mean( - tf.square(self.P_fake_encoded))) - - self.loss_lr_gan_D = ( - tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) + tf.reduce_mean(tf.square(self.P_fake))) - - self.loss_vae_gan_GE = tf.reduce_mean(tf.squared_difference(self.P_fake_encoded, 0.9)) # G - - self.loss_gan_G = tf.reduce_mean(tf.squared_difference(self.P_fake, 0.9)) - - self.loss_vae_GE = tf.reduce_mean(tf.abs(self.image_B - self.desired_gen_img)) # G - - self.loss_latent_GE = tf.reduce_mean(tf.abs(self.z - self.reconst_z)) # G - - self.loss_kl_E = 0.5 * tf.reduce_mean( - -1 - self.encoded_log_sigma + self.encoded_mu ** 2 + tf.exp(self.encoded_log_sigma)) - - self.loss_D = self.loss_vae_gan_D + self.loss_lr_gan_D - tf.reduce_mean(tf.squared_difference(self.P_real, 0.9)) - self.loss_G = self.loss_vae_gan_GE + self.loss_gan_G + self.reconst_coeff * self.loss_vae_GE + self.latent_coeff * self.loss_latent_GE - self.loss_E = self.loss_vae_gan_GE + self.reconst_coeff * self.loss_vae_GE + self.latent_coeff * self.loss_latent_GE + self.kl_coeff * self.loss_kl_E - - # Optimizer - self.dis_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Discriminator") - self.gen_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Generator") - self.enc_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="Encoder") - opt = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5) - - with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): - self.D_solver = opt.minimize(self.loss_D, var_list=self.dis_var) - self.G_solver = opt.minimize(self.loss_G, var_list=self.gen_var) - self.E_solver = opt.minimize(self.loss_E, var_list=self.enc_var) - - """ Testing """ - # self.fake_images = self.Generator(self.image_A, self.z, is_training=False, reuse=True) - - """ Summary """ - - self.d_loss_sum = tf.summary.scalar("d_loss", self.loss_D) - self.g_loss_sum = tf.summary.scalar("g_loss", self.loss_G) - self.e_loss_sum = tf.summary.scalar("e_loss", self.loss_E) - - # final summary operations - # self.g_sum = tf.summary.merge([d_loss_fake_sum, g_loss_sum]) - # self.d_sum = tf.summary.merge([d_loss_real_sum, d_loss_sum]) - # self.q_sum = tf.summary.merge([q_loss_sum, q_disc_sum, q_cont_sum]) - - def train(self): - - # include code for logger.info() - + def train(self, train_A, train_B): # First initialize all variables tf.global_variables_initializer().run() - # Input to graph from training data - self.z_sample = np.random.normal(size=(self.batch_size, self.Z_dim)) - input_img1, batch_imagesB = load_batch_image(0, self.dataset_name) - self.input_img1 = np.expand_dims(input_img1, axis=0) - # self.input_img1 = self.train_A[0:self.batch_size] # training results for a single image # saving the model self.saver = tf.train.Saver() # summary writer - self.writer = tf.summary.FileWriter(self.log_dir + '/' + self.model_name, self.sess.graph) + self.writer = tf.summary.FileWriter(self.log_dir, self.sess.graph) + + self.num_batches = len(train_A) // self.batch_size + # restore check-point if it exits could_load, checkpoint_counter = self.load(self.checkpoint_dir) if could_load: start_epoch = (int)(checkpoint_counter / self.num_batches) - start_batch_id = checkpoint_counter - start_epoch * self.num_batches counter = checkpoint_counter print(" [*] Load SUCCESS") else: start_epoch = 0 - start_batch_id = 0 counter = 1 print(" [!] Load failed...") + # for generating temporary images during training + self.img_sample = np.expand_dims(train_A[0], axis=0) + # loop for epoch - for epoch in range(start_epoch, self.epoch): - - # get batch data - for idx in range(len(self.train_A)): + for idx in range(len(train_A)): start_time = time.time() - batch_imagesA, batch_imagesB = load_batch_image(idx, self.dataset_name) - batch_imagesA = np.expand_dims(batch_imagesA, axis=0) - batch_imagesB = np.expand_dims(batch_imagesB, axis=0) - batch_z = np.random.normal(size=(self.batch_size, self.Z_dim)) + + # get data + image_A = np.expand_dims(train_A[idx], axis=0) + image_B = np.expand_dims(train_B[idx], axis=0) + random_z = np.random.normal(size=(self.batch_size, self.Z_dim)) _, summary_str_d, D_loss_curr = self.sess.run([self.D_solver, self.d_loss_sum, self.loss_D], - feed_dict={self.image_A: batch_imagesA, - self.image_B: batch_imagesB, self.z: batch_z}) + feed_dict={self.image_A: image_A, self.image_B: image_B, + self.z: random_z}) self.writer.add_summary(summary_str_d, counter) _, summary_str_g, G_loss_curr = self.sess.run([self.G_solver, self.g_loss_sum, self.loss_G], - feed_dict={self.image_A: batch_imagesA, - self.image_B: batch_imagesB, self.z: batch_z}) + feed_dict={self.image_A: image_A, self.image_B: image_B, + self.z: random_z}) self.writer.add_summary(summary_str_g, counter) _, summary_str_e, E_loss_curr = self.sess.run([self.E_solver, self.e_loss_sum, self.loss_E], - feed_dict={self.image_A: batch_imagesA, - self.image_B: batch_imagesB, self.z: batch_z}) + feed_dict={self.image_A: image_A, self.image_B: image_B, + self.z: random_z}) self.writer.add_summary(summary_str_e, counter) - # display training status - counter += 1 - print("Epoch: [%2d] [%4d/%4d] time: %4.4f d_loss: %.8f g_loss: %.8f e_loss: %.8f" % ( - epoch, idx, self.num_batches, time.time() - start_time, D_loss_curr, G_loss_curr, E_loss_curr)) - # Saving training results for every 100 examples + temp_dir = check_folder(os.path.join(self.result_dir, 'temps')) if counter % 100 == 0: + z_sample = np.random.normal(size=(1, self.Z_dim)) samples = self.sess.run(self.LR_desired_img, - feed_dict={self.image_A: self.input_img1, self.z: self.z_sample}) - tot_num_samples = min(self.sample_num, self.batch_size) - manifold_h = int(np.floor(np.sqrt(tot_num_samples))) - manifold_w = int(np.floor(np.sqrt(tot_num_samples))) - save_images(samples[:manifold_h * manifold_w, :, :, :], [manifold_h, manifold_w], check_folder( - self.result_dir + '/' + self.model_dir) + '/' + self.model_name + '_train_{:02d}_{:04d}.png'.format( - epoch, idx)) - # mox.file.copy_parallel(self.TMP_RESULTS_PATH, self.OBS_RESULTS_PATH) - # mox.file.copy_parallel(self.TMP_DATA_PATH, self.OBS_DATA_PATH) - # mox.file.copy_parallel(self.TMP_LOGS_PATH, self.OBS_LOG_PATH) - # mox.file.copy_parallel(self.TMP_CHECKPOINT_PATH, self.OBS_CHECKPOINT_DIR) - # After an epoch, start_batch_id is set to zero - start_batch_id = 0 - # non-zero value is only for the first epoch after loading pre-trained model + feed_dict={self.image_A: self.img_sample, self.z: z_sample}) + # transform from [-1,1] to [0,255] + samples = (np.asarray(samples + 1) / 2 * 255).astype(np.uint8) + imwrite(os.path.join(temp_dir, f'train_{epoch}_{idx}.jpg'), + np.squeeze(samples)) + + # display training status + counter += 1 + cost_time = time.time() - start_time + print("epoch : {}----step : {}----|d_loss : {}----g_loss : {}----e_loss : {}|----sec/step : {}" + .format(epoch, counter, D_loss_curr, G_loss_curr, E_loss_curr, cost_time)) + # save model self.save(self.checkpoint_dir, counter) # save model for final step self.save(self.checkpoint_dir, counter) - def test(self): + def test(self, test_A, test_B): # generate images self.step = 0 - base_dir = os.path.join('test_results') - check_folder(os.path.join(self.result_dir, base_dir)) - #test_all = mox.file.glob(self.dataset_name + "/val/*.jpg") - test_all = glob.glob(self.dataset_name + "/val/*.jpg") - for idx in range(len(test_all)): - self.step += 1 - img_A = load_test_image(idx, self.dataset_name) - input_img = np.expand_dims(img_A, axis=0) - z = np.random.normal(size=(self.batch_size, self.Z_dim)) - LR_desired_img = self.sess.run(self.LR_desired_img, feed_dict={self.image_A: input_img, self.z: z}) - image = LR_desired_img[0] - image = Image.fromarray(np.uint8((np.concatenate((img_A * 255., image * 255.), axis=1)))) - image.save(os.path.join(self.result_dir, base_dir, 'random_{}.jpg'.format(self.step))) - - @property - def model_dir(self): - return "{}_{}_{}_{}".format(self.model_name, (self.dataset_name).split("/")[-1], self.batch_size, self.Z_dim) - - def save(self, checkpoint_dir, step): - checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir) + for idx in trange(len(test_A)): + self.step += 1 + save_dir = check_folder(os.path.join(self.result_dir, "test_results", str(self.step))) + + # get input and save groundtruth + image_A = np.expand_dims(test_A[idx], axis=0) + imwrite(os.path.join(save_dir, f'ground_truth.jpg'), + (np.asarray(test_B[idx] + 1) / 2 * 255).astype(np.uint8)) + + # generate images + for i in range(0, self.sample_num): + z = np.random.normal(size=(1, self.Z_dim)) + LR_desired_img = self.sess.run(self.LR_desired_img, + feed_dict={self.image_A: image_A, self.z: z}) + # transform from [-1,1] to [0,255] + LR_desired_img = (np.asarray(LR_desired_img + 1) / 2 * 255).astype(np.uint8) + imwrite(os.path.join(save_dir, f'random_{i + 1}.jpg'), + np.squeeze(LR_desired_img)) + + def save(self, checkpoint_dir, step): # save checkpoints if not os.path.exists(checkpoint_dir): - #mox.file.make_dirs(checkpoint_dir) - os.mkdir(checkpoint_dir) - self.saver.save(self.sess, os.path.join(checkpoint_dir, self.model_name + '.model'), global_step=step) + os.makedirs(checkpoint_dir) + self.saver.save(self.sess, os.path.join(checkpoint_dir, 'BicycleGAN.model'), global_step=step) - def load(self, checkpoint_dir): + def load(self, checkpoint_dir): # load checkpoint if it exits import re print(" [*] Reading checkpoints...") - checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir) ckpt = tf.train.get_checkpoint_state(checkpoint_dir) if ckpt and ckpt.model_checkpoint_path: diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_acc.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..13077b10e660de32d6f7861257a50e1a01ede9ba --- /dev/null +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_acc.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_perf.py new file mode 100644 index 0000000000000000000000000000000000000000..14384e227a0fa90a514254590aef5078c62ff700 --- /dev/null +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/modelarts_entry_perf.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_performance_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_full_1p.sh index a6b1b1f81516e54b703287701c163c04f59c44dd..60e2f4fd0414e5512f33377f1f5397b5de2e089a 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_full_1p.sh @@ -1,203 +1,184 @@ -Copyright 2019 The TensorFlow Authors. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. \ No newline at end of file +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./main.py --data_path=${data_path}/maps --output_path=${output_path} +else + python3.7 ./main.py --data_path=${data_path}/maps --output_path=${output_path} > ${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 7 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Average Distances :" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +#grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt +grep "d_loss :" ${print_log} | awk -F "|" '{print $2}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_performance_1p.sh index 613238bc25aa2b5c68efb2b7de41c8fd08b04445..e7b4b0ec33976f3904379eba15a771389d21eec7 100644 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/test/train_performance_1p.sh @@ -1,129 +1,184 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# !/bin/bash -cur_path=`pwd`/.. -echo $cur_path +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + export RANK_SIZE=1 +export RANK_ID=0 export JOB_ID=10087 -#export ASCEND_DEVICE_ID=0 -#模型训练参数 -Network="BicycleGAN_ID1287_for_TensorFlow" -data_path='' -result_path=${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt/ -batch_size=1 -epochs=2 +# 路径参数初始化 +data_path="" +output_path="" -# 帮助信息， +# 帮助信息，不需要修改 if [[ $1 == --help || $1 == -h ]];then echo"usage:./train_performance_1P.sh " echo " " echo "parameter explain: - --Network name of the network will be trained - --data_path source data of training , default is ${cur_path}/MNIST_data/ - --result_path output path, default is ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt/ - --batch_size batchsize of input per step, default is 256 - --epochs num of epochs, default is 1 - -h/--help show help message + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message " exit 1 fi -#参数校验，不需要修改 +# 参数校验，不需要修改 for para in $* do if [[ $para == --data_path* ]];then data_path=`echo ${para#*=}` - elif [[ $para == --result_path* ]];then - result_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` elif [[ $para == --batch_size* ]];then batch_size=`echo ${para#*=}` - elif [[ $para == --epochs* ]];then - epochs=`echo ${para#*=}` fi done -#检查data_path -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path \" must be config" - exit 1 +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running with modelarts..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" fi +echo "### get your log here : ${print_log}" -#训练过程 -cd ${cur_path} +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} -start=$(date +%s) +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 +epoch=1 -if [ -d ${cur_path}/test/output/${ASCEND_DEVICE_ID} ];then - rm -rf ${cur_path}/test/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt - else - mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./main.py --data_path=${data_path}/maps --output_path=${output_path} --epoch=${epoch} +else + python3.7 ./main.py --data_path=${data_path}/maps --output_path=${output_path} --epoch=${epoch} > ${print_log} 2>&1 fi -python3 main_npu.py \ - --dataset=${data_path}/maps/ \ - --checkpoint_dir=${result_path} \ - --epoch=${epochs} \ - --batch_size=${batch_size} > ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & -wait - -end=$(date +%s) -e2e_time=$(( $end - $start )) -echo "Final Training Duration sec : $e2e_time" - -#结果打印 + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 7 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Average Distances :" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +#grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt +grep "d_loss :" ${print_log} | awk -F "|" '{print $2}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + echo "------------------ Final result ------------------" -#输出性能 -TrainingTime=`grep "time" ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $6*1000}'` -FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'*1000/'${TrainingTime}'}'` -#accuracy=`grep "accuracy" ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $2}'| sed 's/,//g'` -##打印 -echo "Final Performance TrainingTime : $TrainingTime" +# 输出性能FPS/单step耗时/端到端耗时 echo "Final Performance images/sec : $FPS" -#echo "Final Accuracy : ${accuracy}" - -BatchSize=${batch_size} -DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' - -#train_loss -grep "d_loss:" ${cur_path}/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $8}' >> $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt -#最后一个迭代loss值，不需要修改 -ActualLoss=`awk 'END {print $1}' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` - -echo "Network = ${Network}" > $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = ${DeviceType}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${FPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -#echo "Accuracy = ${accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/train_1p.sh b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/train_1p.sh deleted file mode 100644 index 3f71b5f474747f973a4e45862c9e1fb816aad7d5..0000000000000000000000000000000000000000 --- a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/train_1p.sh +++ /dev/null @@ -1 +0,0 @@ -python main_npu.py \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1.pb b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1.pb new file mode 100644 index 0000000000000000000000000000000000000000..c8ec4cc41e4090f3957f157a2d7fb236f7e21f2d Binary files /dev/null and b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1.pb differ diff --git a/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1_27.pb b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1_27.pb new file mode 100644 index 0000000000000000000000000000000000000000..c8ec4cc41e4090f3957f157a2d7fb236f7e21f2d Binary files /dev/null and b/TensorFlow/contrib/cv/BicycleGAN_ID1287_for_TensorFlow/weights/net-lin_alex_v0.1_27.pb differ diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md index 4e3f0e500eece353e7187effff822b7b5a921534..81df7f3663fe0bfccb554e12a7f181a7a035193a 100644 --- a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md @@ -118,10 +118,11 @@ BlitzNet在一次前向传递中联合执行对象检测和语义分割，从而

快速上手

-- 数据集准备 - +- 训练数据集准备 + OBS下载地址：（下载的数据集为处理完的tf数据集） https://blitznets.obs.myhuaweicloud.com:443/Datasets/voc12-train-seg?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1661686224&Signature=QkWct66ZOwIUfNOYeoWFFZ/FTsk%3D + - ResNet预训练模型准备 OBS下载地址：（将下载的resnet50_full.ckpt文件置于Weights_imagenet中） @@ -140,26 +141,41 @@ BlitzNet在一次前向传递中联合执行对象检测和语义分割，从而 - 单卡训练 - 1. 配置训练参数。 + 1. 单卡性能训练。 - 首先在脚本run_1p.sh中，配置训练数据集路径，请用户根据实际路径配置，数据集参数如下所示： + 用户可以执行test/train_performance_1p.sh脚本执行少量step获取性能信息： ``` - - python3 ${code_dir}/train_1p.py --obs_dir=${obs_url} --run_name=BlitzNet300_x4_VOC12_detsegaug --dataset=voc12-train --trunk=resnet50 --x4 --batch_size=32 --optimizer=adam --detect --segment --max_iterations=40000 --lr_decay 25000 35000 + cd test + bash train_performance_1p.sh --data_path=数据集路径 + + train_performance_1p.sh中调用的训练命令示例如下： + python3 train_1p.py --obs_dir=${obs_url} --run_name=BlitzNet300_x4_VOC12_detsegaug --dataset=voc12-train --trunk=resnet50 --x4 --batch_size=16 --optimizer=adam --detect --segment --max_iterations=10 --lr_decay 25000 35000 ``` - 2. 启动训练。 + 2. 单卡精度训练。 - 启动单卡精度训练 （脚本为BlitzNet_ID0948_for_Tensorflow/train_testcase.sh） + 用户可以执行test/train_full_1p.sh脚本执行少量step获取性能信息： ``` - bash train_testcase.sh --code_url=/npu/traindata/cnews --data_url=/npu/traindata/cnews --result_url=/npu/traindata/cnews - ``` + cd test + bash train_full_1p.sh --data_path=数据集路径 -

高级参考

+ train_performance_1p.sh中调用的训练命令示例如下： + python3 train_1p.py --obs_dir=${obs_url} --run_name=BlitzNet300_x4_VOC12_detsegaug --dataset=voc12-train --trunk=resnet50 --x4 --batch_size=16 --optimizer=adam --detect --segment --max_iterations=40000 --lr_decay 25000 35000 + 3. 执行结果。 + |精度指标项|论文发布|GPU实测|NPU实测| + |---|---|---|---| + |ACC|xxx|0.88|0.88| + + |性能指标项|论文发布|GPU实测|NPU实测| + |---|---|---|---| + |FPS|XXX|0.35 sec/batch|0.23 sec/batch| + + +

高级参考

## 脚本和示例代码 @@ -174,39 +190,3 @@ BlitzNet在一次前向传递中联合执行对象检测和语义分割，从而 │ ├──train_testcase.sh //自测试用例脚本 ``` -## 脚本参数 - -``` -data_input_test.py ---obs_dir=${obs_url} ---run_name=BlitzNet300_x4_VOC12_detsegaug ---dataset=voc12-train ---trunk=resnet50 ---x4 ---batch_size=32 ---optimizer=adam ---detect ---segment ---max_iterations=40000 ---lr_decay 25000 35000 -``` - - -## 训练过程 - -1. 通过“模型训练”中的训练指令启动性能或者精度训练。性能和精度通过运行不同脚本，支持性能、精度网络训练。 - -2. 参考脚本的模型存储路径为test/output/*，训练脚本train_*.log中可查看性能、精度的相关运行状态。 - - -

精度测试

- -训练集：VOC12 train-seg-aug - -测试集：VOC12 val - -| | mIoU | mAP | -| ---------- | -------- | -------- | -| 论文精度 | 72.8 | 80.0 | -| GPU精度 | 72.8 | 80.0 | -| NPU精度 | 待测 | 待测 | \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..775d0e09f523b9fd28f7e965d696ff8d98d6fc62 --- /dev/null +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py @@ -0,0 +1,90 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from npu_bridge.npu_init import * +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +import os +from Train.config import args +from help_modelarts import modelarts_result2obs + +from Train.resnet import ResNet +from Train.config import config as net_config + +INIT_CKPT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'checkpoint65') +ckpt_path = os.path.join(INIT_CKPT_DIR, 'model.ckpt-65000') + +def main(): + print("start ckpt To pb") + print("ckpt_path") + tf.reset_default_graph() + img_ph = tf.placeholder(tf.float32, shape=[1, 300, 300, 3], name="input") + dataset_num_classes = 21 + + net = ResNet + depth = 50 + net = net(config=net_config, depth=depth, training=False) + + net.create_trunk(img_ph) + + if args.detect: + net.create_multibox_head(dataset_num_classes) + confidence = net.outputs['confidence'] + location = net.outputs['location'] + else: + location, confidence = None, None + + if args.segment: + net.create_segmentation_head(dataset_num_classes) + seg_logits = net.outputs['segmentation'] + else: + seg_logits = None + + print("confidence = ", confidence) + print("location = ", location) + print("seg_logits = ", seg_logits) + + with tf.Session() as sess: + tf.train.write_graph(sess.graph_def, args.result_dir, 'model.pb') + modelarts_result2obs(args) + freeze_graph.freeze_graph( + input_graph=os.path.join(args.result_dir, 'model.pb'), + input_saver='', + input_binary=False, + input_checkpoint=ckpt_path, + output_node_names="concat_1, concat_2, ssd_2/Conv_7/BiasAdd", # graph outputs node + restore_op_name='save/restore_all', + filename_tensor_name='save/Const:0', + output_graph=os.path.join(args.result_dir, 'bliznet_tf_310.pb'), # graph outputs name + clear_devices=False, + initializer_nodes="") + print("done") + + modelarts_result2obs(args) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_full_1p.sh index 5c7f983fdf736ff28c7884434e1d81ca88f5e22d..dae392c7169767e77e2b1b4f41cf561762367928 100644 --- a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_full_1p.sh @@ -24,9 +24,9 @@ Network="BlitzNet_ID0948_for_TensorFlow" #训练epoch train_epochs= #训练batch_size -batch_size=32 +batch_size=16 #训练step -train_steps=1000 +train_steps=40000 #学习率 learning_rate= @@ -129,7 +129,7 @@ do --dataset=voc12-train \ --trunk=resnet50 \ --x4 \ - --batch_size=32 \ + --batch_size=16 \ --optimizer=adam \ --detect \ --segment \ @@ -161,7 +161,7 @@ CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' ActualFPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${TrainingTime}'}'` #获取模型精度 -train_accuracy=`grep "acc =" $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk 'END {print $12}'|sed 's/,//g'` +train_accuracy=`grep "acc =" $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | tail -n 10 | awk '{print $12}' | awk -F"," '{print $1}' | awk '{sum+=$1} END {print sum/NR}'` #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 grep 'loss =' $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $9}'|sed 's/,//g' > $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_performance_1p.sh index fde86eb992fe0340df09bf596a2b2405d71bcd01..75cb8fede09eab7520ca6fc15daf13f5007f9246 100644 --- a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/test/train_performance_1p.sh @@ -24,7 +24,7 @@ Network="BlitzNet_ID0948_for_TensorFlow" #训练epoch train_epochs= #训练batch_size -batch_size=32 +batch_size=16 #训练step train_steps=10 #学习率 @@ -130,7 +130,7 @@ do --dataset=voc12-train \ --trunk=resnet50 \ --x4 \ - --batch_size=32 \ + --batch_size=16 \ --optimizer=adam \ --detect \ --segment \ diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9564af69a2a3d5cb8a83dc74fe53a79d99a885 --- /dev/null +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py @@ -0,0 +1,233 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from config import args +from getData.voc_loader import VOCLoader + +from tensorflow.python.ops.metrics_impl import mean_iou as streaming_mean_iou +from utils import decode_bboxes +from getData.boxer import PriorBoxGrid +from config import config as net_config +from detector import Detector +from tabulate import tabulate +import progressbar +import numpy as np +import logging +log = logging.getLogger() + +def eval_category(gt, dets, cid): + """Computes average precision for one category""" + cgt = gt[cid] + cdets = np.array(dets[cid]) + if (cdets.shape == (0, )): + return None, None + scores = cdets[:, 1] + sorted_inds = np.argsort(-scores) + image_ids = cdets[sorted_inds, 0].astype(int) + BB = cdets[sorted_inds] + + npos = 0 + for img_gt in cgt.values(): + img_gt['ignored'] = np.array(img_gt['difficult']) + img_gt['det'] = np.zeros(len(img_gt['difficult']), dtype=np.bool) + npos += np.sum(~img_gt['ignored']) + + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + ovmax = -np.inf + if image_ids[d] in cgt: + R = cgt[image_ids[d]] + bb = BB[d, 2:].astype(float) + + BBGT = R['bbox'].astype(float) + + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 0] + BBGT[:, 2], bb[0] + bb[2]) + iymax = np.minimum(BBGT[:, 1] + BBGT[:, 3], bb[1] + bb[3]) + iw = np.maximum(ixmax - ixmin, 0.) + ih = np.maximum(iymax - iymin, 0.) + inters = iw * ih + + # union + uni = (bb[2] * bb[3] + BBGT[:, 2] * BBGT[:, 3] - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > args.voc_iou_thresh: + if not R['ignored'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = True + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + N = float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = rec * N / np.maximum(rec * N + fp, np.finfo(np.float32).eps) + return rec, prec + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + p = 0 if np.sum(rec >= t) == 0 else np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + +def compute_ap(gt, dets, loader): + """computes average precision for all categories""" + aps = {} + for cid in range(1, loader.num_classes): + cat_name = loader.ids_to_cats[cid] + rec, prec = eval_category(gt, dets, cid) + ap = voc_ap(rec, prec, loader.year == '07') + aps[loader.ids_to_cats[cid]] = ap + return aps + +def make_detection_table(gt, dets, loader): + """creates a table with AP per category and mean AP""" + aps = compute_ap(gt, dets, loader) + print("ap = ", aps) + eval_cache = [aps] + + table = [] + for cid in range(1, loader.num_classes): + cat_name = loader.ids_to_cats[cid] + table.append((cat_name, ) + tuple(aps.get(cat_name, 'N/A') for aps in eval_cache)) + mean_ap = np.mean([a for a in list(aps.values()) if a >= 0]) + table.append(("AVERAGE", ) + tuple(np.mean(list(aps.values())) for aps in eval_cache)) + x = tabulate(table, headers=(["Category", "mAP (all)"]), + tablefmt='orgtbl', floatfmt=".3f") + log.info("Eval results:\n%s", x) + return table + +def compute_mean_iou(detector): + iou = detector.get_mean_iou() + print(iou) + log.info("\n Mean IoU is %f", iou) + return iou + +def main(argv=None): + if args.dataset == 'voc07' or args.dataset == 'voc07+12': + loader = VOCLoader('07', 'test') + if args.dataset == 'voc12-val': + loader = VOCLoader('12', 'val', segmentation=args.segment) + + with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, + log_device_placement=False)) as sess: + detector = Detector(sess, loader, net_config, no_gt=args.no_seg_gt) + + filenames = loader.get_filenames() + gt = {cid: {} for cid in range(1, loader.num_classes)} + dets = {cid: [] for cid in range(1, loader.num_classes)} + + bar = progressbar.ProgressBar()# 显示进度条 + # print("filenames = ", filenames) + + init_op = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer()) + sess.run(init_op) + for i in bar(range(len(filenames))): + name = filenames[i] + # print("name = ", name) + img_id = i + img = loader.load_image(name) # 获取图片 + # img = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32) + # img.shape = 1, 300, 300, 3 + gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息 + + confidence = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_0.txt".format(i)) + location = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_1.txt".format(i)) + seg_logits = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_2.txt".format(i)) + confidence.shape = 1, 45390, 21 + location.shape = 1, 45390, 4 + seg_logits.shape = 1, 75, 75, 21 + + for cid in np.unique(gt_cats): + mask = (gt_cats == cid) + bbox = gt_bboxes[mask] + diff = difficulty[mask] + det = np.zeros(len(diff), dtype=np.bool) + gt[cid][img_id] = {'bbox': bbox, 'difficult': diff, 'det': det} + + confidence1 = confidence + location1 = location + seg_logits1 = seg_logits + output = detector.feed_forward(img, seg_gt, confidence1, location1, seg_logits1, + w, h, name, gt_bboxes, gt_cats) # result + + if args.detect: + det_bboxes, det_probs, det_cats = output[:3] + for i in range(len(det_cats)): + dets[det_cats[i]].append((img_id, det_probs[i]) + tuple(det_bboxes[i])) + + # print("gt = ", gt) + # print("dets = ", dets) + print("table result:") + table = make_detection_table(gt, dets, loader) if args.detect else None + print("iou result:") + iou = compute_mean_iou(detector) if args.segment else None + + +if __name__ == '__main__': + tf.app.run() \ No newline at end of file diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/train_1p.py b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/train_1p.py index 3b46c37d3452f92268f832bd7727b14aa8a0f149..a44a8bba02b1ec2fea648409bfde09e7cd39b629 100644 --- a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/train_1p.py +++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/train_1p.py @@ -54,9 +54,9 @@ log = logging.getLogger() dataset_num_classes = len(VOC_CATS) -def npu_tf_optimizer(opt): - npu_opt = NPUDistributedOptimizer(opt) - return npu_opt +#def npu_tf_optimizer(opt): +# npu_opt = NPUDistributedOptimizer(opt) +# return npu_opt def objective(location, confidence, refine_ph, classes_ph, @@ -271,9 +271,11 @@ def train(net, config): learning_rate = cosine_decay(tf.to_int32(global_step), steps, learning_rates) if args.optimizer == 'adam': - opt = npu_tf_optimizer(tf.train.AdamOptimizer(learning_rate=learning_rate)) + # opt = npu_tf_optimizer(tf.train.AdamOptimizer(learning_rate=learning_rate)) + opt = tf.train.AdamOptimizer(learning_rate=learning_rate) elif args.optimizer == 'nesterov': - opt = npu_tf_optimizer(tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9, use_nesterov=True)) + # opt = npu_tf_optimizer(tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9, use_nesterov=True)) + opt = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9, use_nesterov=True) else: raise ValueError @@ -292,7 +294,7 @@ def train(net, config): custom_op = config_npu.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True - # custom_op.parameter_map["mix_compile_mode"].b = True + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") config_npu.graph_options.rewrite_options.remapping = RewriterConfig.OFF with tf.Session(config=config_npu) as sess: diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test/train_full_1p.sh index f85b93576acf9888c68abfe822c316ca49d2930a..898fc05da046dfb148bc057ad3952d00f907e6f2 100644 --- a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test/train_full_1p.sh @@ -130,7 +130,7 @@ StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` # 精度相关数据计算 -train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}' | awk -F ";" '{print $2}' | awk -F ":" '{print $2}'` +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}' | tr -d ":"` # 提取所有loss打印信息 #grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt diff --git a/TensorFlow/contrib/cv/DeltaEncoder_ID1273_for_TensorFlow/README.md b/TensorFlow/contrib/cv/DeltaEncoder_ID1273_for_TensorFlow/README.md index bd461c583ae922d4952505ba31c9cdcde775b6be..4d9d28737828f1fac692f364bfdea7e2e9d542f0 100644 --- a/TensorFlow/contrib/cv/DeltaEncoder_ID1273_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/DeltaEncoder_ID1273_for_TensorFlow/README.md @@ -99,6 +99,9 @@ num_ways: ways number. In the original paper, it was 5 |--|--| | GPU(V100)| 5.18s/epoch | | NPU(Ascend910)| 10s/epoch | +| NPU(Ascend910)-开启混合精度| 7s/epoch | +| NPU(Ascend910)-关闭日志| 3.5s/epoch | + #### 1-shot 5-way 精度结果 ##### GPU结果 ``` diff --git a/TensorFlow/contrib/cv/EfficientNet/EfficientNet_V2_ID1220_for_TensorFlow/efficientnet_model.py b/TensorFlow/contrib/cv/EfficientNet/EfficientNet_V2_ID1220_for_TensorFlow/efficientnet_model.py index 569e02c0190f07f2bd76e0b68a315104ac49bbd2..c653e87884793fb54f716fdda3abb5665333f2a0 100644 --- a/TensorFlow/contrib/cv/EfficientNet/EfficientNet_V2_ID1220_for_TensorFlow/efficientnet_model.py +++ b/TensorFlow/contrib/cv/EfficientNet/EfficientNet_V2_ID1220_for_TensorFlow/efficientnet_model.py @@ -367,6 +367,7 @@ class Model(tf.keras.Model): kernel_initializer=dense_kernel_initializer) if self._global_params.dropout_rate > 0: + from npu_bridge.estimator.npu import npu_convert_dropout self._dropout = tf.keras.layers.Dropout(self._global_params.dropout_rate) else: self._dropout = None diff --git a/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/freeze_graph.py b/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/freeze_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff8ed5112d25410cd6d3612d6bd9af22b2e8acb --- /dev/null +++ b/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/freeze_graph.py @@ -0,0 +1,81 @@ +# coding=utf-8 +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tensorflow.python.tools import freeze_graph +import argparse +import logging +import tensorflow as tf + +from maml_freeze import MAML + +logging.basicConfig(level=logging.INFO) +LOG = logging.getLogger('main') +parser = argparse.ArgumentParser() +parser.add_argument('--ckpt_path',type=str, default='/npu/ID1283/task00613907/MT-NET_ID1283_for_TensorFlow/mt-net-0419/ckpt/model59999',help='The path of checkpoint') + +#running function +def run(args): + ckpt_path = args.ckpt_path + + tf.reset_default_graph() + + inputa = tf.placeholder(tf.float32, shape=(4, 5, 1), name="inputa") + inputb = tf.placeholder(tf.float32, shape=(4, 5, 1), name="inputb") + labela = tf.placeholder(tf.float32, shape=(4, 5, 1), name="inputc") + labelb = tf.placeholder(tf.float32, shape=(4, 5, 1), name="inputd") + metaval_input_tensors = {'inputa': inputa, 'inputb': inputb, 'labela': labela, 'labelb': labelb} + + model = MAML(dim_input=1, dim_output=1, test_num_updates=1) + model.construct_model(input_tensors=metaval_input_tensors, prefix='metaval_') + + logits = model.metaval_total_loss1 + tf.identity(logits, name="output") + + with tf.Session() as sess: + tf.train.write_graph(sess.graph_def, './pb_model', 'output.pb') # save pb file with output node + freeze_graph.freeze_graph( + input_graph='./pb_model/output.pb', # the pb file with output node + input_saver='', + input_binary=False, + input_checkpoint=ckpt_path, # input checkpoint file path + output_node_names='output', # the name of output node in pb file + restore_op_name='save/restore_all', + filename_tensor_name='save/Const:0', + output_graph='./pb_model/mt-net.pb', # path of output graph + clear_devices=False, + initializer_nodes='') + logging.info('done') + + +if __name__ == "__main__": + args = parser.parse_args() + run(args) + + + diff --git a/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/maml_freeze.py b/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/maml_freeze.py new file mode 100644 index 0000000000000000000000000000000000000000..62a245d094f8c9e002c03ed52654c99bd5940e7d --- /dev/null +++ b/TensorFlow/contrib/cv/MT-NET_ID1283_for_TensorFlow/maml_freeze.py @@ -0,0 +1,531 @@ +# +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +""" Code for the MAML algorithm and network definitions. """ +from npu_bridge.npu_init import * +import numpy as np + +try: + import special_grads +except KeyError as e: + print ('WARNING: Cannot define MaxPoolGrad, likely already defined for this version of TensorFlow:', e) +import tensorflow as tf + +from tensorflow.python.platform import flags +from utils import mse, xent, conv_block, normalize + +FLAGS = flags.FLAGS + +## chip options +flags.DEFINE_string('chip', 'npu', "run on which chip, (npu or gpu or cpu)") +flags.DEFINE_string('platform', 'linux', 'runtime platform, linux or modelarts') +flags.DEFINE_string("obs_dir", '', 'obs result path, not need on gpu and apulis platform') +flags.DEFINE_boolean("profiling", False, "profiling for performance or not") + +## Dataset/method options + +flags.DEFINE_string('datasource', 'sinusoid', 'sinusoid or omniglot or miniimagenet') +flags.DEFINE_integer('num_classes', 5, 'number of classes used in classification (e.g. 5-way classification).') +flags.DEFINE_integer('num_train_classes', -1, 'number of classes to train on (-1 for all).') +# oracle means task id is input (only suitable for sinusoid) +flags.DEFINE_string('baseline', None, 'oracle, or None') + +## Training options +flags.DEFINE_integer('pretrain_iterations', 0, 'number of pre-training iterations.') +flags.DEFINE_integer('metatrain_iterations', 40000, 'number of metatraining iterations.') # 15k for omniglot, 50k for sinusoid +flags.DEFINE_integer('meta_batch_size', 1, 'number of tasks sampled per meta-update') +flags.DEFINE_float('meta_lr', 0.001, 'the base learning rate of the generator') +flags.DEFINE_integer('update_batch_size', 1, 'number of examples used for inner gradient update (K for K-shot learning).') +flags.DEFINE_float('update_lr', .01, 'step size alpha for inner gradient update.') # 0.1 for omniglot +flags.DEFINE_integer('num_updates', 1, 'number of inner gradient updates during training.') +flags.DEFINE_integer('poly_order', 1, 'order of polynomial to generate') + +## Model options +#flags.DEFINE_string('mod', '', 'modifications to original paper. None, split, both') +flags.DEFINE_bool('use_T', True, 'whether or not to use transformation matrix T') +flags.DEFINE_bool('use_M', True, 'whether or not to use mask M') +flags.DEFINE_bool('share_M', True, 'only effective if use_M is true, whether or not to ' + 'share masks between weights' + 'that contribute to the same activation') +flags.DEFINE_float('temp', 1, 'temperature for gumbel-softmax') +flags.DEFINE_float('logit_init', 0, 'initial logit') +flags.DEFINE_string('norm', 'None', 'batch_norm, layer_norm, or None') +flags.DEFINE_integer('dim_hidden', 40, 'dimension of fc layer') +flags.DEFINE_integer('num_filters', 64, 'number of filters for conv nets -- use 32 for ' + 'miniimagenet, 64 for omiglot.') +flags.DEFINE_bool('conv', True, 'whether or not to use a convolutional network, only applicable in some cases') +flags.DEFINE_bool('max_pool', True, 'Whether or not to use max pooling rather than strided convolutions') +flags.DEFINE_bool('stop_grad', False, 'if True, do not use second derivatives in meta-optimization (for speed)') + +## Logging, saving, and testing options +flags.DEFINE_bool('log', True, 'if false, do not log summaries, for debugging code.') +flags.DEFINE_string('logdir', 'logs/omniglot20way', 'directory for summaries and checkpoints.') +flags.DEFINE_bool('debug', False, 'debug mode. uses less data for fast evaluation.') +flags.DEFINE_bool('resume', True, 'resume training if there is a model available') +flags.DEFINE_bool('train', False, 'True to train, False to test.') +flags.DEFINE_integer('test_iter', -1, 'iteration to load model (-1 for latest model)') +flags.DEFINE_bool('test_set', False, 'Set to true to test on the the test set, False for the validation set.') +flags.DEFINE_integer('train_update_batch_size', -1, 'number of examples used for gradient update during training (use if you want to test with a different number).') +flags.DEFINE_float('train_update_lr', -1, 'value of inner gradient step step during training. (use if you want to test with a different value)') # 0.1 for omniglot + + +class MAML: + def __init__(self, dim_input=1, dim_output=1, test_num_updates=5): + """ must call construct_model() after initializing MAML! """ + self.dim_input = dim_input + self.dim_output = dim_output + self.update_lr = FLAGS.update_lr + self.meta_lr = tf.placeholder_with_default(FLAGS.meta_lr, ()) + self.classification = False + self.test_num_updates = test_num_updates + if FLAGS.datasource in ['sinusoid', 'polynomial']: + self.dim_hidden = [FLAGS.dim_hidden, FLAGS.dim_hidden] + if FLAGS.use_T: + self.forward = self.forward_fc_withT + else: + self.forward = self.forward_fc + self.construct_weights = self.construct_fc_weights + self.loss_func = mse + elif FLAGS.datasource == 'omniglot' or FLAGS.datasource == 'miniimagenet': + self.loss_func = xent + self.classification = True + if FLAGS.conv: + self.dim_hidden = FLAGS.num_filters + if FLAGS.use_T: + self.forward = self.forward_conv_withT + else: + self.forward = self.forward_conv + self.construct_weights = self.construct_conv_weights + else: + self.dim_hidden = [256, 128, 64, 64] + self.forward = self.forward_fc + self.construct_weights = self.construct_fc_weights + if FLAGS.datasource == 'miniimagenet': + self.channels = 3 + else: + self.channels = 1 + self.img_size = int(np.sqrt(self.dim_input / self.channels)) + else: + raise ValueError('Unrecognized data source.') + + def construct_model(self, input_tensors=None, prefix='metatrain_'): + # a: training data for inner gradient, b: test data for meta gradient + self.inputa = input_tensors['inputa'] + self.inputb = input_tensors['inputb'] + self.labela = input_tensors['labela'] + self.labelb = input_tensors['labelb'] + + with tf.variable_scope('model', reuse=None) as training_scope: + self.dropout_probs = {} + if 'weights' in dir(self): + training_scope.reuse_variables() + weights = self.weights + else: + # Define the weights + self.weights = weights = self.construct_weights() + + # outputbs[i] and lossesb[i] is the output and loss after i+1 gradient updates + lossesa, outputas, lossesb, outputbs = [], [], [], [] + accuraciesa, accuraciesb = [], [] + num_updates = max(self.test_num_updates, FLAGS.num_updates) + outputbs = [[]] * num_updates + lossesb = [[]] * num_updates + accuraciesb = [[]] * num_updates + + def task_metalearn(inp, reuse=True): + """ Perform gradient descent for one task in the meta-batch. """ + inputa, inputb, labela, labelb = inp + task_outputbs, task_lossesb = [], [] + mse_lossesb = [] + + if self.classification: + task_accuraciesb = [] + + train_keys = list(weights.keys()) + if FLAGS.use_M and FLAGS.share_M: + def make_shared_mask(key): + temperature = FLAGS.temp + logits = weights[key+'_prob'] + logits = tf.stack([logits, tf.zeros(logits.shape)], 1) + U = tf.random_uniform(logits.shape, minval=0, maxval=1) + gumbel = -tf.log(-tf.log(U + 1e-20) + 1e-20) + y = logits + gumbel + gumbel_softmax = tf.nn.softmax(y / temperature) + gumbel_hard = tf.cast(tf.equal(gumbel_softmax, tf.reduce_max(gumbel_softmax, 1, keep_dims=True)), tf.float32) + mask = tf.stop_gradient(gumbel_hard - gumbel_softmax) + gumbel_softmax + return mask[:, 0] + + def get_mask(masks, name): + mask = masks[[k for k in masks.keys() if name[-1] in k][0]] + if 'conv' in name: # Conv + mask = tf.reshape(mask, [1, 1, 1, -1]) + tile_size = weights[name].shape.as_list()[:3] + [1] + mask = tf.tile(mask, tile_size) + elif 'w' in name: # FC + mask = tf.reshape(mask, [1, -1]) + tile_size = weights[name].shape.as_list()[:1] + [1] + mask = tf.tile(mask, tile_size) + elif 'b' in name: # Bias + mask = tf.reshape(mask, [-1]) + return mask + if self.classification: + masks = {k: make_shared_mask(k) for k in ['conv1', 'conv2', 'conv3', 'conv4', 'w5']} + else: + masks = {k: make_shared_mask(k) for k in ['w1', 'w2', 'w3']} + + if FLAGS.use_M and not FLAGS.share_M: + def get_mask_noshare(key): + temperature = FLAGS.temp + logits = weights[key + '_prob'] + logits = tf.stack([logits, tf.zeros(logits.shape)], 1) + U = tf.random_uniform(logits.shape, minval=0, maxval=1) + gumbel = -tf.log(-tf.log(U + 1e-20) + 1e-20) + y = logits + gumbel + gumbel_softmax = tf.nn.softmax(y / temperature) + gumbel_hard = tf.cast(tf.equal(gumbel_softmax, tf.reduce_max(gumbel_softmax, 1, keep_dims=True)), tf.float32) + out = tf.stop_gradient(gumbel_hard - gumbel_softmax) + gumbel_softmax + return tf.reshape(out[:, 0], weights[key].shape) + + train_keys = [k for k in weights.keys() if 'prob' not in k and 'f' not in k] + train_weights = [weights[k] for k in train_keys] + task_outputa = self.forward(inputa, weights, reuse=reuse) # only reuse on the first iter + self.task_outputa = task_outputa + task_lossa = self.loss_func(task_outputa, labela) + grads = tf.gradients(task_lossa, train_weights) + if FLAGS.stop_grad: + grads = [tf.stop_gradient(grad) for grad in grads] + gradients = dict(zip(train_keys, grads)) + + fast_weights = dict(zip(weights.keys(), [weights[key] for key in weights.keys()])) + + def compute_weights(key): + prev_weights = fast_weights[key] + if key not in train_keys: + return prev_weights + if FLAGS.use_M and FLAGS.share_M: + mask = get_mask(masks, key) + new_weights = prev_weights - self.update_lr * mask * gradients[key] + elif FLAGS.use_M and not FLAGS.share_M: + mask = get_mask_noshare(key) + new_weights = prev_weights - self.update_lr * mask * gradients[key] + else: + new_weights = prev_weights - self.update_lr * gradients[key] + return new_weights + + fast_weights = dict(zip( + weights.keys(), [compute_weights(key) for key in weights.keys()])) + + output = self.forward(inputb, fast_weights, reuse=True) + task_outputbs.append(output) + loss = self.loss_func(output, labelb) + task_lossesb.append(loss) + + for j in range(num_updates - 1): + output = self.forward(inputa, fast_weights, reuse=True) + loss = self.loss_func(output, labela) + train_weights = [fast_weights[k] for k in train_keys] + grads = tf.gradients(loss, train_weights) + if FLAGS.stop_grad: + grads = [tf.stop_gradient(grad) for grad in grads] + gradients = dict(zip(train_keys, grads)) + + fast_weights = dict(zip( + weights.keys(), [compute_weights(key) for key in weights.keys()])) + + output = self.forward(inputb, fast_weights, reuse=True) + task_outputbs.append(output) + loss = self.loss_func(output, labelb) + task_lossesb.append(loss) + + task_output = [task_outputa, task_outputbs, task_lossa, task_lossesb] + + if self.classification: + task_accuracya = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputa), 1), + tf.argmax(labela, 1)) + for j in range(num_updates): + task_accuraciesb.append( + tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputbs[j]), 1), + tf.argmax(labelb, 1))) + task_output.extend([task_accuracya, task_accuraciesb]) + + return task_output + + out_dtype = [tf.float32, [tf.float32] * num_updates, tf.float32, [tf.float32] * num_updates] + if self.classification: + out_dtype.extend([tf.float32, [tf.float32] * num_updates]) + + if FLAGS.chip == 'npu': + if self.classification: + outputas, outputbs, lossesa, lossesb, accuraciesa, accuraciesb = [], [], [], [], [], [] + for i in range(FLAGS.meta_batch_size): + each_input = self.inputa[i], self.inputb[i], self.labela[i], self.labelb[i] + each_outputas, each_outputbs, each_lossesa, each_lossesb, each_accuraciesa, each_accuraciesb = task_metalearn( + each_input) + outputas.append(each_outputas) + outputbs.append(each_outputbs) + lossesa.append(each_lossesa) + lossesb.append(each_lossesb) + accuraciesa.append(each_accuraciesa) + accuraciesb.append(each_accuraciesb) + outputas = tf.stack(outputas) + outputbs = tf.unstack(tf.stack(outputbs), axis=1) + lossesa = tf.stack(lossesa) + lossesb = tf.unstack(tf.stack(lossesb), axis=1) + accuraciesa = tf.stack(accuraciesa) + accuraciesb = tf.unstack(tf.stack(accuraciesb), axis=1) + else: + outputas, outputbs, lossesa, lossesb = [], [], [], [] + for i in range(FLAGS.meta_batch_size): + each_input = self.inputa[i], self.inputb[i], self.labela[i], self.labelb[i] + each_outputas, each_outputbs, each_lossesa, each_lossesb = task_metalearn( + each_input) + outputas.append(each_outputas) + outputbs.append(each_outputbs) + lossesa.append(each_lossesa) + lossesb.append(each_lossesb) + outputas = tf.stack(outputas) + tmp = [] + for i in outputbs: + for j in i: + outputbs = tmp.append(i) + lossesa = tf.stack(lossesa) + lossesb = tf.unstack(tf.stack(lossesb), axis=1) + + logit_keys = sorted([k for k in weights.keys() if 'prob' in k]) + logit_weights = [-weights[k] for k in logit_keys] + probs = [tf.exp(w) / (1 + tf.exp(w)) for w in logit_weights] + self.total_probs = [tf.reduce_mean(p) for p in probs] + + ## Performance & Optimization + self.metaval_total_loss1 = total_loss1 = tf.reduce_sum(lossesa) / tf.to_float(FLAGS.meta_batch_size) + self.metaval_total_losses2 = total_losses2 = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) + for j in range(num_updates)] + if self.classification: + self.metaval_total_accuracy1 = total_accuracy1 = tf.reduce_sum(accuraciesa) / tf.to_float( + FLAGS.meta_batch_size) + self.metaval_total_accuracies2 = total_accuracies2 = [ + tf.reduce_sum(accuraciesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)] + + ## Summaries + tf.summary.scalar(prefix + 'change probs', tf.reduce_mean(self.total_probs)) + tf.summary.scalar(prefix + 'Pre-update loss', total_loss1) + if self.classification: + tf.summary.scalar(prefix + 'Pre-update accuracy', total_accuracy1) + + for j in range(num_updates): + tf.summary.scalar(prefix + 'Post-update loss, step ' + str(j + 1), total_losses2[j]) + if self.classification: + tf.summary.scalar(prefix + 'Post-update accuracy, step ' + str(j + 1), total_accuracies2[j]) + + for k, v in weights.items(): + tf.summary.histogram(k, v) + if 'prob' in k: + tf.summary.histogram('prob_'+k, tf.nn.softmax(tf.stack([v, tf.zeros(v.shape)], 1))[:, 0]) + + ### Network construction functions (fc networks and conv networks) + def construct_fc_weights(self): + weights = {} + weights['w1'] = tf.Variable(tf.truncated_normal([self.dim_input, self.dim_hidden[0]], stddev=0.01)) + weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden[0]])) + for i in range(1, len(self.dim_hidden)): + weights['w' + str(i + 1)] = tf.Variable( + tf.truncated_normal([self.dim_hidden[i - 1], self.dim_hidden[i]], stddev=0.01)) + weights['b' + str(i + 1)] = tf.Variable(tf.zeros([self.dim_hidden[i]])) + weights['w' + str(len(self.dim_hidden) + 1)] = tf.Variable( + tf.truncated_normal([self.dim_hidden[-1], self.dim_output], stddev=0.01)) + weights['b' + str(len(self.dim_hidden) + 1)] = tf.Variable(tf.zeros([self.dim_output])) + + if FLAGS.use_M and not FLAGS.share_M: + weights['w1_prob'] = tf.Variable(tf.truncated_normal([self.dim_input * self.dim_hidden[0]], stddev=.1)) + weights['b1_prob'] = tf.Variable(tf.truncated_normal([self.dim_hidden[0]], stddev=.1)) + for i in range(1, len(self.dim_hidden)): + weights['w' + str(i + 1) + '_prob'] = tf.Variable( + tf.truncated_normal([self.dim_hidden[i - 1] * self.dim_hidden[i]], stddev=.1)) + weights['b' + str(i + 1) + '_prob'] = tf.Variable( + tf.truncated_normal([self.dim_hidden[i]], stddev=.1)) + weights['w' + str(len(self.dim_hidden) + 1) + '_prob'] = tf.Variable( + tf.truncated_normal([self.dim_hidden[-1] * self.dim_output], stddev=0.1)) + weights['b' + str(len(self.dim_hidden) + 1) + '_prob'] = tf.Variable( + tf.truncated_normal([self.dim_output], stddev=.1)) + elif FLAGS.use_M and FLAGS.share_M: + weights['w1_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden[0]])) + for i in range(1, len(self.dim_hidden)): + weights['w' + str(i + 1) + '_prob'] = tf.Variable( + FLAGS.logit_init * tf.ones([self.dim_hidden[i]])) + weights['w' + str(len(self.dim_hidden) + 1) + '_prob'] = tf.Variable( + FLAGS.logit_init * tf.ones([self.dim_output])) + + if FLAGS.use_T: + weights['w1_f'] = tf.Variable(tf.eye(self.dim_hidden[0])) + weights['w2_f'] = tf.Variable(tf.eye(self.dim_hidden[1])) + weights['w3_f'] = tf.Variable(tf.eye(self.dim_output)) + return weights + + def forward_fc(self, inp, weights, reuse=False): + hidden = normalize(tf.matmul(inp, weights['w1']) + weights['b1'], + activation=tf.nn.relu, reuse=reuse, scope='0') + for i in range(1, len(self.dim_hidden)): + hidden = normalize(tf.matmul(hidden, weights['w' + str(i + 1)]) + weights['b' + str(i + 1)], + activation=tf.nn.relu, reuse=reuse, scope=str(i + 1)) + return tf.matmul(hidden, weights['w' + str(len(self.dim_hidden) + 1)]) + \ + weights['b' + str(len(self.dim_hidden) + 1)] + + def forward_fc_withT(self, inp, weights, reuse=False): + hidden = tf.matmul(tf.matmul(inp, weights['w1']) + weights['b1'], weights['w1_f']) + hidden = normalize(hidden, activation=tf.nn.relu, reuse=reuse, scope='1') + hidden = tf.matmul(tf.matmul(hidden, weights['w2']) + weights['b2'], weights['w2_f']) + hidden = normalize(hidden, activation=tf.nn.relu, reuse=reuse, scope='2') + hidden = tf.matmul(tf.matmul(hidden, weights['w3']) + weights['b3'], weights['w3_f']) + return hidden + + def construct_conv_weights(self): + weights = {} + dtype = tf.float32 + conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype) + fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype) + k = 3 + channels = self.channels + dim_hidden = self.dim_hidden + + def get_conv(name, shape): + return tf.get_variable(name, shape, initializer=conv_initializer, dtype=dtype) + + def get_identity(dim, conv=True): + return tf.Variable(tf.eye(dim, batch_shape=[1,1])) if conv \ + else tf.Variable(tf.eye(dim)) + + weights['conv1'] = get_conv('conv1', [k, k, channels, self.dim_hidden]) + weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden])) + weights['conv2'] = get_conv('conv2', [k, k, dim_hidden, self.dim_hidden]) + weights['b2'] = tf.Variable(tf.zeros([self.dim_hidden])) + weights['conv3'] = get_conv('conv3', [k, k, dim_hidden, self.dim_hidden]) + weights['b3'] = tf.Variable(tf.zeros([self.dim_hidden])) + weights['conv4'] = get_conv('conv4', [k, k, dim_hidden, self.dim_hidden]) + weights['b4'] = tf.Variable(tf.zeros([self.dim_hidden])) + if FLAGS.datasource == 'miniimagenet': + # assumes max pooling + assert FLAGS.max_pool + weights['w5'] = tf.get_variable('w5', [self.dim_hidden * 5 * 5, self.dim_output], + initializer=fc_initializer) + weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5') + + if FLAGS.use_M and not FLAGS.share_M: + weights['conv1_prob'] = tf.Variable(tf.truncated_normal([k * k * channels * self.dim_hidden], stddev=.01)) + weights['b1_prob'] = tf.Variable(tf.truncated_normal([self.dim_hidden], stddev=.01)) + weights['conv2_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['b2_prob'] = tf.Variable(tf.truncated_normal([self.dim_hidden], stddev=.01)) + weights['conv3_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['b3_prob'] = tf.Variable(tf.truncated_normal([self.dim_hidden], stddev=.01)) + weights['conv4_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['b4_prob'] = tf.Variable(tf.truncated_normal([self.dim_hidden], stddev=.01)) + weights['w5_prob'] = tf.Variable(tf.truncated_normal([dim_hidden *5*5* self.dim_output], stddev=.01)) + weights['b5_prob'] = tf.Variable(tf.truncated_normal([self.dim_output], stddev=.01)) + if FLAGS.use_M and FLAGS.share_M: + weights['conv1_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv2_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv3_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv4_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['w5_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_output])) + + if FLAGS.use_T: + weights['conv1_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv2_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv3_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv4_f'] = get_identity(self.dim_hidden, conv=True) + weights['w5_f'] = get_identity(self.dim_output, conv=False) + else: + weights['w5'] = tf.Variable(tf.random_normal([dim_hidden, self.dim_output]), name='w5') + weights['b5'] = tf.Variable(tf.zeros([self.dim_output]), name='b5') + if FLAGS.use_M and not FLAGS.share_M: + weights['conv1_prob'] = tf.Variable(tf.truncated_normal([k * k * channels * self.dim_hidden], stddev=.01)) + weights['conv2_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['conv3_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['conv4_prob'] = tf.Variable(tf.truncated_normal([k * k * dim_hidden * self.dim_hidden], stddev=.01)) + weights['w5_prob'] = tf.Variable(tf.truncated_normal([dim_hidden * self.dim_output], stddev=.01)) + if FLAGS.use_M and FLAGS.share_M: + weights['conv1_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv2_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv3_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['conv4_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_hidden])) + weights['w5_prob'] = tf.Variable(FLAGS.logit_init * tf.ones([self.dim_output])) + + if FLAGS.use_T: + weights['conv1_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv2_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv3_f'] = get_identity(self.dim_hidden, conv=True) + weights['conv4_f'] = get_identity(self.dim_hidden, conv=True) + weights['w5_f'] = get_identity(self.dim_output, conv=False) + return weights + + def forward_conv(self, inp, weights, reuse=False, scope=''): + # reuse is for the normalization parameters. + channels = self.channels + inp = tf.reshape(inp, [-1, self.img_size, self.img_size, channels]) + hidden1 = conv_block(inp, weights['conv1'], weights['b1'], reuse, scope + '0') + hidden2 = conv_block(hidden1, weights['conv2'], weights['b2'], reuse, scope + '1') + hidden3 = conv_block(hidden2, weights['conv3'], weights['b3'], reuse, scope + '2') + hidden4 = conv_block(hidden3, weights['conv4'], weights['b4'], reuse, scope + '3') + + if FLAGS.datasource == 'miniimagenet': + # last hidden layer is 6x6x64-ish, reshape to a vector + hidden4 = tf.reshape(hidden4, [-1, np.prod([int(dim) for dim in hidden4.get_shape()[1:]])]) + else: + hidden4 = tf.reduce_mean(hidden4, [1, 2]) + return tf.matmul(hidden4, weights['w5']) + weights['b5'] + + def forward_conv_withT(self, inp, weights, reuse=False, scope=''): + # reuse is for the normalization parameters. + def conv_tout(inp, cweight, bweight, rweight, reuse, scope, activation=tf.nn.relu, max_pool_pad='VALID', + residual=False): + stride, no_stride = [1, 2, 2, 1], [1, 1, 1, 1] + if FLAGS.max_pool: + conv_output = tf.nn.conv2d(inp, cweight, no_stride, 'SAME') + bweight + else: + conv_output = tf.nn.conv2d(inp, cweight, stride, 'SAME') + bweight + conv_output = tf.nn.conv2d(conv_output, rweight, no_stride, 'SAME') + normed = normalize(conv_output, activation, reuse, scope) + if FLAGS.max_pool: + normed = tf.nn.max_pool(normed, stride, stride, max_pool_pad) + return normed + + channels = self.channels + inp = tf.reshape(inp, [-1, self.img_size, self.img_size, channels]) + hidden1 = conv_tout(inp, weights['conv1'], weights['b1'], weights['conv1_f'], reuse, scope + '0') + hidden2 = conv_tout(hidden1, weights['conv2'], weights['b2'], weights['conv2_f'], reuse, scope + '1') + hidden3 = conv_tout(hidden2, weights['conv3'], weights['b3'], weights['conv3_f'], reuse, scope + '2') + hidden4 = conv_tout(hidden3, weights['conv4'], weights['b4'], weights['conv4_f'], reuse, scope + '3') + + if FLAGS.datasource == 'miniimagenet': + # last hidden layer is 6x6x64-ish, reshape to a vector + hidden4 = tf.reshape(hidden4, [-1, np.prod([int(dim) for dim in hidden4.get_shape()[1:]])]) + else: + hidden4 = tf.reduce_mean(hidden4, [1, 2]) + hidden5 = tf.matmul(hidden4, weights['w5']) + weights['b5'] + return tf.matmul(hidden5, weights['w5_f']) + diff --git a/TensorFlow/contrib/cv/RANDLA-NET_ID0850_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/RANDLA-NET_ID0850_for_TensorFlow/test/train_full_1p.sh index d89e020bbdebe9be2fbc66566b953c5344e85637..31c24b677841c564ecdcffa533984c0b9f112277 100644 --- a/TensorFlow/contrib/cv/RANDLA-NET_ID0850_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/contrib/cv/RANDLA-NET_ID0850_for_TensorFlow/test/train_full_1p.sh @@ -160,7 +160,7 @@ echo "E2E Training Duration sec : $e2e_time" #训练用例信息，不需要修改 BatchSize=${batch_size} DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' ##获取性能数据，不需要修改 #吞吐量 diff --git a/TensorFlow/contrib/cv/STNet_ID2360_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/STNet_ID2360_for_TensorFlow/test/train_performance_1p.sh index 408baf968c3a49ab59f2759daf07c25905428bae..68b7a0d5da9d16e59d3232ca82350cbe825dd9a7 100644 --- a/TensorFlow/contrib/cv/STNet_ID2360_for_TensorFlow/test/train_performance_1p.sh +++ b/TensorFlow/contrib/cv/STNet_ID2360_for_TensorFlow/test/train_performance_1p.sh @@ -120,7 +120,7 @@ else fi # 性能相关数据计算 -StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk -F':' '{print $5}' |cut -b -15 | awk '{sum+=$1} END {print sum/NR}'` FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` # 精度相关数据计算 diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/.keep b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/LICENSE b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..5ea8a5f7b6ae91ebb12b7f2fa71a5432bb89de63 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/LICENSE @@ 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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT +HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED +WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE +GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER +IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN +IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/README.md b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4306edd106e8cbbd614223f790a65d3a1ec68fd6 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/README.md @@ -0,0 +1,165 @@ +- [基本信息](#基本信息.md) +- [概述](#概述.md) +- [训练环境准备](#训练环境准备.md) +- [快速上手](#快速上手.md) +- [迁移学习指导](#迁移学习指导.md) +- [高级参考](#高级参考.md) +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Image Processing** + +**框架（Framework）：TensorFlow 1.15.0** + +**模型格式（Model Format）：ckpt** + +**精度（Precision）：Mixed** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Research** + +**描述（Description）： The paper propose +TVNet, a novel end-to-end trainable neural network, to learn +optical-flow-like features from data.** +

概述

+ + Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widelyused in video analysis tasks. To fill this gap, we proposeTVNet, a novel end-to-end trainable neural network, to learnoptical-flow-like features from data + +- 参考论文： + + https://openaccess.thecvf.com/content_cvpr_2018/papers/Fan_End-to-End_Learning_of_CVPR_2018_paper.pdf + + +- 适配昇腾 AI 处理器的实现： + + https://gitee.com/myd-git/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow + +- 通过Git获取对应commit\_id的代码方法如下： + + ``` + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + ``` + +## 默认配置 + +- 训练数据集预处理： + + - 图像的输入尺寸为1024*436 + - 图像输入格式：png + +- 测试数据集预处理 + + - 图像的输入尺寸为1024*436 + - 图像输入格式：png + +- 训练超参 + + - scale: Number of scales in TVNet (default: 1) + - warp: Number of warppings in TVNet (default: 1) + - iteration: Number of iterations in TVNet(default: 50) + - Train step: 10000 + - gpu: the gpu to run on (0-indexed, -1 for CPU) + +## 支持特性 + +| 特性列表 | 是否支持 | +|-------|------| +| 分布式训练 | 否 | +| 混合精度 | 是 | +| 并行数据 | 否 | + + +## 混合精度训练 + +昇腾910 AI处理器提供自动混合精度功能，可以针对全网中float32数据类型的算子，按照内置的优化策略，自动将部分float32的算子降低精度到float16，从而在精度损失很小的情况下提升系统性能并减少内存使用。 + +## 开启混合精度 + +脚本已默认开启混合精度，设置precision_mode参数的脚本参考如下。 + + ``` + custom_op = session_config.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = 'NpuOptimizer' + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(str(args.precision_mode)) + ``` + + +

快速上手

+ +- 数据集准备 +1. 数据集下载链接：obs://cann-id0951/dataset/ + + +## 模型训练 + +- 单卡训练 + +单卡训练 + +1. 配置训练参数 +2. 启动训练 +``` +bash train_full_1p.sh +``` + +

训练结果

+ + +- 精度结果比对 + +|精度指标项|GPU实测|NPU实测| +|---|---|---| +|LOSS|8.7241955|8.390178| + +- 性能结果比对 + +|性能指标项|GPU实测|NPU实测| +|---|---|---| +|FPS|0.9856|1.0684| + +

结果测试

+- 在test_sintel.py文件中修改测试的图片和指定模型的路径，然后运行该文件。 +``` +python test_sintel.py +``` +- 用visualize（可视化脚本集合）执行得到的.mat文件，得到如下的结果： +![输入图片说明](Result.png) + +

高级参考

+ +## 脚本和示例代码 + +``` +├── tvnet.py //模型搭建文件 +├── train_epe_sintel.py //读取数据进行训练 +├── test_sintel.py //测试模型结果 +├── spatial_transformer.py //Spatial Transformer Layer +├── template.py //一些模板参数的设置 +├── README.md //代码说明文档 +``` + +## 脚本参数 + + + + +``` +--data_path +--output_path +--Batch size: 1 +--Learning rate(LR): 1e-4 +--Optimizer: Adam +--Train steps:10000 +``` + +## 训练过程 + +1. 通过“模型训练”中的训练指令启动单卡卡训练。 + +2. 参考脚本的模型存储路径为train_url diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/Result.png b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/Result.png new file mode 100644 index 0000000000000000000000000000000000000000..7f8d8e7dc41b7b071ca1ce48ac17566640c20e21 Binary files /dev/null and b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/Result.png differ diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/loss perf_npu.txt b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/loss perf_npu.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_acc.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..13077b10e660de32d6f7861257a50e1a01ede9ba --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_acc.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_perf.py new file mode 100644 index 0000000000000000000000000000000000000000..14384e227a0fa90a514254590aef5078c62ff700 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelarts_entry_perf.py @@ -0,0 +1,63 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_performance_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelzoo_level.txt b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..012b8ef4f5a74d86a8555d86dacf4b10f807e129 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/modelzoo_level.txt @@ -0,0 +1,5 @@ +GPUStatus:OK +NPUMigrationStatus:OK +FuncStatus:OK +PerfStatus:OK +PrecisionStatus:OK \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/requirements.txt b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea314a5fa4a9e9a0a14b65d1dbb78d1412cae422 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/requirements.txt @@ -0,0 +1,8 @@ +python==3.6.0 +tensorflow==1.15.0 +scikit-learn==0.20 +matplotlib=3.3.4 +pandas==0.20.2 +numpy=1.19.2 +h5py +scipy=1.5.2 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/spatial_transformer.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/spatial_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f06b514cac283ec58225945d3c32c57e995f8fbb --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/spatial_transformer.py @@ -0,0 +1,220 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf + + +def transformer(U, theta, out_size, name='SpatialTransformer', **kwargs): + """Spatial Transformer Layer + + Implements a spatial transformer layer as described in [1]_. + Based on [2]_ and edited by David Dao for Tensorflow. + + Parameters + ---------- + U : float + The output of a convolutional net should have the + shape [num_batch, height, width, num_channels]. + theta: float + The output of the + localisation network should be [num_batch, 6]. + out_size: tuple of two ints + The size of the output of the network (height, width) + + References + ---------- + .. [1] Spatial Transformer Networks + Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu + Submitted on 5 Jun 2015 + .. [2] https://github.com/skaae/transformer_network/blob/master/transformerlayer.py + + Notes + ----- + To initialize the network to the identity transform init + ``theta`` to : + identity = np.array([[1., 0., 0.], + [0., 1., 0.]]) + identity = identity.flatten() + theta = tf.Variable(initial_value=identity) + + """ + + def _repeat(x, n_repeats): + with tf.variable_scope('_repeat'): + rep = tf.transpose( + tf.expand_dims(tf.ones(shape=tf.stack([n_repeats, ])), 1), [1, 0]) + rep = tf.cast(rep, 'int32') + x = tf.matmul(tf.reshape(x, (-1, 1)), rep) + return tf.reshape(x, [-1]) + + def _interpolate(im, x, y, out_size): + with tf.variable_scope('_interpolate'): + # constants + num_batch = tf.shape(im)[0] + height = tf.shape(im)[1] + width = tf.shape(im)[2] + channels = tf.shape(im)[3] + + x = tf.cast(x, 'float32') + y = tf.cast(y, 'float32') + height_f = tf.cast(height, 'float32') + width_f = tf.cast(width, 'float32') + out_height = out_size[0] + out_width = out_size[1] + zero = tf.zeros([], dtype='int32') + max_y = tf.cast(tf.shape(im)[1] - 1, 'int32') + max_x = tf.cast(tf.shape(im)[2] - 1, 'int32') + + # scale indices from [-1, 1] to [0, width/height-1] + x = (x + 1.0) * (width_f - 1) / 2.0 + y = (y + 1.0) * (height_f - 1) / 2.0 + + # do sampling + x0 = tf.cast(tf.floor(x), 'int32') + x1 = x0 + 1 + y0 = tf.cast(tf.floor(y), 'int32') + y1 = y0 + 1 + + x0 = tf.clip_by_value(x0, zero, max_x - 1) + x1 = tf.clip_by_value(x1, zero, max_x) + y0 = tf.clip_by_value(y0, zero, max_y - 1) + y1 = tf.clip_by_value(y1, zero, max_y) + dim2 = width + dim1 = width * height + base = _repeat(tf.range(num_batch) * dim1, out_height * out_width) + base_y0 = base + y0 * dim2 + base_y1 = base + y1 * dim2 + idx_a = base_y0 + x0 + idx_b = base_y1 + x0 + idx_c = base_y0 + x1 + idx_d = base_y1 + x1 + + # use indices to lookup pixels in the flat image and restore + # channels dim + im_flat = tf.reshape(im, tf.stack([-1, channels])) + im_flat = tf.cast(im_flat, 'float32') + Ia = tf.gather(im_flat, idx_a) + Ib = tf.gather(im_flat, idx_b) + Ic = tf.gather(im_flat, idx_c) + Id = tf.gather(im_flat, idx_d) + + # and finally calculate interpolated values + x0_f = tf.cast(x0, 'float32') + x1_f = tf.cast(x1, 'float32') + y0_f = tf.cast(y0, 'float32') + y1_f = tf.cast(y1, 'float32') + wa = tf.expand_dims(((x1_f - x) * (y1_f - y)), 1) + wb = tf.expand_dims(((x1_f - x) * (y - y0_f)), 1) + wc = tf.expand_dims(((x - x0_f) * (y1_f - y)), 1) + wd = tf.expand_dims(((x - x0_f) * (y - y0_f)), 1) + output = tf.add_n([wa * Ia, wb * Ib, wc * Ic, wd * Id]) + return output + + def _meshgrid(height, width): + with tf.variable_scope('_meshgrid'): + # This should be equivalent to: + # x_t, y_t = np.meshgrid(np.linspace(-1, 1, width), + # np.linspace(-1, 1, height)) + # ones = np.ones(np.prod(x_t.shape)) + # grid = np.vstack([x_t.flatten(), y_t.flatten(), ones]) + x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])), + tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0])) + y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1), + tf.ones(shape=tf.stack([1, width]))) + + x_t_flat = tf.reshape(x_t, (1, -1)) + y_t_flat = tf.reshape(y_t, (1, -1)) + + # ones = tf.ones_like(x_t_flat) + # grid = tf.concat(axis=0, values=[x_t_flat, y_t_flat, ones]) + grid = tf.concat(axis=0, values=[x_t_flat, y_t_flat]) + return grid + + def _transform(theta, input_dim, out_size): + with tf.variable_scope('_transform'): + num_batch = tf.shape(input_dim)[0] + height = tf.shape(input_dim)[1] + width = tf.shape(input_dim)[2] + num_channels = tf.shape(input_dim)[3] + # theta = tf.reshape(theta, (-1, 2, 3)) + theta = tf.cast(theta, 'float32') + + # grid of (x_t, y_t, 1), eq (1) in ref [1] + height_f = tf.cast(height, 'float32') + width_f = tf.cast(width, 'float32') + out_height = out_size[0] + out_width = out_size[1] + grid = _meshgrid(out_height, out_width) + grid = tf.expand_dims(grid, 0) + grid = tf.reshape(grid, [-1]) + grid = tf.tile(grid, tf.stack([num_batch])) + # grid = tf.reshape(grid, tf.stack([num_batch, 3, -1])) + grid = tf.reshape(grid, tf.stack([num_batch, 2, -1])) + + # Transform A x (x_t, y_t, 1)^T -> (x_s, y_s) + # T_g = tf.matmul(theta, grid) + T_g = theta + grid + x_s = tf.slice(T_g, [0, 0, 0], [-1, 1, -1]) + y_s = tf.slice(T_g, [0, 1, 0], [-1, 1, -1]) + x_s_flat = tf.reshape(x_s, [-1]) + y_s_flat = tf.reshape(y_s, [-1]) + + input_transformed = _interpolate( + input_dim, x_s_flat, y_s_flat, + out_size) + + output = tf.reshape( + input_transformed, tf.stack([num_batch, out_height, out_width, num_channels])) + + return output + + with tf.variable_scope(name): + output = _transform(theta, U, out_size) + return output + + +def batch_transformer(U, thetas, out_size, name='BatchSpatialTransformer'): + """Batch Spatial Transformer Layer + + Parameters + ---------- + + U : float + tensor of inputs [num_batch,height,width,num_channels] + thetas : float + a set of transformations for each input [num_batch,num_transforms,6] + out_size : int + the size of the output [out_height,out_width] + + Returns: float + Tensor of size [num_batch*num_transforms,out_height,out_width,num_channels] + """ + with tf.variable_scope(name): + num_batch, num_transforms = map(int, thetas.get_shape().as_list()[:2]) + indices = [[i] * num_transforms for i in range(num_batch)] + input_repeated = tf.gather(U, tf.reshape(indices, [-1])) + return transformer(input_repeated, thetas, out_size) diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_full_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..8917afd3cdf2778761304d2d822544867643f218 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_full_1p.sh @@ -0,0 +1,183 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 + +if [ x"${etp_flag}" != xtrue ]; +then + python3.7 ./train_epe_sintel.py --data_path=${data_path}/dataset --output_path=${output_path} +else + python3.7 ./train_epe_sintel.py --data_path=${data_path}/dataset --output_path=${output_path} > ${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | awk '{print $3}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}' ` + +# 精度相关数据计算 +train_accuracy='' +# 提取所有loss打印信息 +grep " loss =" ${print_log} | awk '{print $5}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=`grep "Average epeLoss:" ${print_log} | awk '{print $3}' | tr -d ';'` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..214a6728a0685e7bd249f6bb6cae33839fd63358 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test/train_performance_1p.sh @@ -0,0 +1,184 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +etp_flag=${etp_running_flag} +if [ x"${etp_flag}" != xtrue ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo ${print_log} + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=1 +train_epochs=2 +train_steps=100 + +if [ x"${etp_flag}" != xtrue ]; +then + python3.7 ./train_epe_sintel.py --data_path=${data_path}/dataset --output_path=${output_path} --steps=${train_steps} +else + python3.7 ./train_epe_sintel.py --data_path=${data_path}/dataset --output_path=${output_path} --steps=${train_steps} > ${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`cat ${print_log} | grep "sec/batch" | tail -n +2 | awk '{print $8}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy='' +# 提取所有loss打印信息 +grep " loss =" ${print_log} | awk '{print $5}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=`grep "Average epeLoss:" ${print_log} | awk '{print $3}' | tr -d ';'` + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test_sintel.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test_sintel.py new file mode 100644 index 0000000000000000000000000000000000000000..e307efb48e4dfef873bbb7251aa62bb9dc0cd21a --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/test_sintel.py @@ -0,0 +1,102 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import cv2 +import numpy as np +import scipy.io as sio +import tensorflow as tf +from npu_bridge.npu_init import * + +from tvnet import TVNet + +flags = tf.app.flags +scale = 1 +warp = 1 +iteration = 50 + +# 设置npu服务器上的路径 +data_path = '/home/ma-user/modelarts/inputs/data_url_0/' +output_path = '/home/ma-user/modelarts/outputs/train_url_0/' +print('data_url :' + data_path) +print('output_url :' + output_path) + + +eval_data = os.listdir(output_path) # 返回data_path下包含的文件或文件夹的名字的列表 +print('输出目录下的文件：') +for name in eval_data: + print(name) + +# load image ,指定测试的图片对 +img1 = cv2.imread(data_path + 'MPISintel_test/temple_2/frame_0001.png') +img2 = cv2.imread(data_path + 'MPISintel_test/temple_2/frame_0002.png') + +h, w, c = img1.shape + +# model construct +x1 = tf.placeholder(shape=[1, h, w, 3], dtype=tf.float32) +x2 = tf.placeholder(shape=[1, h, w, 3], dtype=tf.float32) +tvnet = TVNet() +u1, u2, rho = tvnet.tvnet_flow(x1, x2, max_scales=scale, + warps=warp, + max_iterations=iteration) +# init npu +# 变量初始化 +init = tf.global_variables_initializer() +# 创建session +config = tf.ConfigProto() +custom_op = config.graph_options.rewrite_options.custom_optimizers.add() +custom_op.name = "NpuOptimizer" +config.graph_options.rewrite_options.remapping = RewriterConfig.OFF # 必须显式关闭 +config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF # 必须显式关闭 +sess = tf.Session(config=config) + +saver = tf.train.Saver() +saver = tf.train.import_meta_graph(output_path + 'ckpt_gpu_epe1/nn_model_gpu_epe.ckpt.meta') # 加载模型结构 +saver.restore(sess, tf.train.latest_checkpoint(output_path + 'ckpt_gpu_epe1/')) # 只需要指定目录就可以恢复所有变量信息 +all_var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) # 从一个集合中取出变量 +# run model +u1_np, u2_np = sess.run([u1, u2], feed_dict={x1: img1[np.newaxis, ...], x2: img2[np.newaxis, ...]}) + +u1_np = np.squeeze(u1_np) +u2_np = np.squeeze(u2_np) +flow_mat = np.zeros([h, w, 2]) +flow_mat[:, :, 0] = u1_np +flow_mat[:, :, 1] = u2_np + + + +if not os.path.exists(output_path + 'result'): + os.mkdir(output_path + 'result') +res_path = os.path.join(output_path + 'result', '1.mat') +sio.savemat(res_path, {'flow': flow_mat}) +print("Extracting Flow finished!") + +# 关闭sess +sess.close() + diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/train_epe_sintel.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/train_epe_sintel.py new file mode 100644 index 0000000000000000000000000000000000000000..b025c3c63df26205a3b29f7e9eb0b2e73d584536 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/train_epe_sintel.py @@ -0,0 +1,211 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import datetime +import os +import time +import cv2 +import numpy as np +import tensorflow as tf +from npu_bridge.npu_init import * +import random +from tvnet import TVNet, batch_size +import argparse +import sys + +flags = tf.app.flags +scale = 1 +warp = 1 +iteration = 50 +print('TVNet Params:\n scale: %d\n warp: %d\n iteration: %d' \ + % (scale, warp, iteration)) + + +def get_config(args): + parser = argparse.ArgumentParser(description='Experiment parameters') + parser.add_argument('--data_path', default='./dataset', help='training input data path.') + parser.add_argument('--output_path', default='./output', help='prepocess result path.') + parser.add_argument('--steps', default='10000', help='train steps.') + parsed_args, unknown_args = parser.parse_known_args(args) + return parsed_args + + +def readflo(file_name): # 读取光流文件 + with open(file_name, 'rb') as f: + magic = np.fromfile(f, np.float32, count=1) + if 202021.25 != magic: + print('Magic number incorrect. Invalid .flo file') + else: + w = np.fromfile(f, np.int32, count=1) + h = np.fromfile(f, np.int32, count=1) + # print 'Reading %d x %d flo file' % (w, h) + data = np.array(np.fromfile(f, np.float32, count=2 * int(w) * int(h))) + # Reshape data into 3D array (columns, rows, bands) + # data2D = np.ndarray.reshape(data, (w, h, 2)) + data2D = data.reshape(int(h), int(w), 2) + return data2D + + +# other_data = os.listdir("./other_data/") + +def loadSintelData(data_url): # 加载训练数据 + data_path = os.path.join(data_url, "MPISintel_train/") + # print(data_url + '\n') + eval_data = os.listdir(data_path) # 返回data_path下包含的文件或文件夹的名字的列表 + # for name in eval_data: + # print(name + '/n') + img1 = np.zeros((batch_size, 436, 1024, 3)) + img2 = np.zeros((batch_size, 436, 1024, 3)) + label = np.zeros((batch_size, 436, 1024, 2)) + lod_folder = random.sample(eval_data, 1)[0] + train_dir = data_path + lod_folder + for j in range(batch_size): + i = random.randint(1, 49) + img1[j, :] = cv2.imread(train_dir + "/frame_" + str(i).zfill(4) + ".png") + img2[j, :] = cv2.imread(train_dir + "/frame_" + str(i + 1).zfill(4) + ".png") + label[j, :] = readflo(train_dir + "/frame_" + str(i).zfill(4) + ".flo") + # print("===>>>Flow File: "+ train_dir + "/frame_" + str(i).zfill(4) + ".flo") + return img1, img2, label + + +def calculate_epe(pr_u1, pr_u2, gt_u): + pr_u1 = tf.squeeze(pr_u1) + pr_u2 = tf.squeeze(pr_u2) + return tf.reduce_mean(tf.sqrt(tf.square(pr_u1 - gt_u[:, :, 0]) + tf.square(pr_u2 - gt_u[:, :, 1]))) + + +def calculate_loss(u1, u2, y): + loss = 0 + for j in range(batch_size): + y_1 = u1[j, :] + + y_2 = u2[j, :] + + gt = y[j, :] + + loss += calculate_epe(y_1, y_2, gt) + + return loss / batch_size + + +x1 = tf.placeholder(shape=[batch_size, 436, 1024, 3], dtype=tf.float32) # 函数作为一种占位符用于定义过程，可以理解为形参，在执行的时候再赋具体的值 +x2 = tf.placeholder(shape=[batch_size, 436, 1024, 3], dtype=tf.float32) +y = tf.placeholder(shape=[batch_size, 436, 1024, 2], dtype=tf.float32) +tf.summary.image('input', [x1, x2]) # 形成一张名为input的图像 + +loss_list = [] + +tvnet = TVNet() # 初始化TVnet类 + +u1_p, u2_p, rho = tvnet.tvnet_flow(x1, x2, max_scales=scale, + warps=warp, + max_iterations=iteration) + +loss = calculate_loss(u1_p, u2_p, y) # 计算loss +train_op = tf.train.AdamOptimizer(1e-4).minimize(loss) # 设置优化器 + +# 设置npu服务器上的路径 +args = get_config(sys.argv[1:]) +max_steps = int(args.steps) +print('data_url :' + args.data_path) +print('output_url :' + args.output_path) +print('steps:' + args.steps) +eval_data = os.listdir(args.data_path) # 返回data_url下包含的文件或文件夹的名字的列表 +for name in eval_data: + print(name) + +# init npu +# 变量初始化 +init = tf.global_variables_initializer() +# 创建session +config = tf.ConfigProto() +custom_op = config.graph_options.rewrite_options.custom_optimizers.add() +custom_op.name = "NpuOptimizer" +config.graph_options.rewrite_options.remapping = RewriterConfig.OFF # 必须显式关闭 +config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF # 必须显式关闭 +sess = tf.Session(config=config) +sess.run(init) + +saver = tf.train.Saver(tf.global_variables()) # 模型的保存和加载 + +start = datetime.datetime.now() +for step in range(max_steps): # 开始训练 + start_time = time.time() + img1, img2, label = loadSintelData(args.data_path) + # img1, img2 = loadData(batch_size) + _, loss_value = sess.run([train_op, loss], feed_dict={x1: img1, x2: img2, y: label}) # 带入具体的值 + duration = time.time() - start_time + loss_list.append(loss_value) + if step % 5 == 0: + examples_per_sec = batch_size / duration + sec_per_batch = float(duration) + format_str = 'step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)' + print(format_str % (step, loss_value, examples_per_sec, sec_per_batch)) +# Total time +# 按照格式输出单步训练的时间 +end = datetime.datetime.now() +timefortrain = (end - start).total_seconds() +cost_time = timefortrain / max_steps +print("sec/step : {}".format(cost_time)) +print("use second") +print(timefortrain) +strtime = '%dh%dm%ds' % (timefortrain / 3600, timefortrain % 3600 / 60, timefortrain % 3600 % 60) +print("===>>>Total train Time: " + strtime) # 输出训练时间 + +ckpt_path = os.path.join(args.output_path, 'ckpt_gpu_epe1') # 模型最终保存路径：./output/ckpt_gpu_epe1/ +if not os.path.exists(ckpt_path): # 判断模型保存的路径是否存在 + os.mkdir(ckpt_path) +checkpoint_path = os.path.join(ckpt_path, "nn_model_gpu_epe.ckpt") +print("===>>>checkpoint_path: " + checkpoint_path) +saver.save(sess, checkpoint_path) # 保存模型 +# 关闭sess +sess.close() + +loss_path = os.path.join(args.output_path, "loss_Sintel_gpu.log") # loss_log最终保存路径：./output +print("===>>>loss_path: " + loss_path) + +# Average and minal value of loss list +avg_loss = np.mean(loss_list) +min_loss = np.min(loss_list) +print("Average epeLoss: " + str(avg_loss) + "; Minimam Loss: " + str(min_loss)) + +# 开始写入loss +loss_list1 = [] +file = open(loss_path, 'w') +file.write("Total_train_Time: " + strtime) +file.write("Average_epeLoss: " + str(avg_loss) + "; Minimam_Loss: " + str(min_loss)) +for i in range(len(loss_list)): + loss_list1.append(np.mean(loss_list[0:i])) + file.write(str(loss_list[i])) + file.write("\n") +file.close() +loss_path1 = os.path.join(args.output_path, "epeloss1_Sintel_gpu.log") +print("===>>>loss1_path: " + loss_path1) + + + + diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/tvnet.py b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/tvnet.py new file mode 100644 index 0000000000000000000000000000000000000000..96d6fdce11581efd5de9c2deeae5ff7c197ecfae --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/tvnet.py @@ -0,0 +1,360 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import tensorflow as tf +import spatial_transformer + +# from train import batch_size +batch_size = 1 + + +def zoom_image(x, new_height, new_width): + assert len(x.shape) == 4 + delta = tf.zeros((tf.shape(x)[0], 2, new_height * new_width)) + zoomed_x = spatial_transformer.transformer(x, delta, (new_height, new_width)) + return tf.reshape(zoomed_x, [tf.shape(x)[0], new_height, new_width, x.shape[-1].value]) + + +class TVNet(object): + GRAD_IS_ZERO = 1e-12 + + def __init__(self): + pass + + def variable_with_weight_loss(self, shape, stddev): + var = tf.Variable(tf.truncated_normal(shape, stddev=stddev)) # tensorflow::ops::TruncatedNormal + return var + + def grey_scale_image(self, x): + assert len(x.shape) == 4 + assert x.shape[-1].value == 3, 'number of channels must be 3 (i.e. RGB)' + + ker_init = tf.constant_initializer([[0.114], [0.587], [0.299]]) + grey_x = tf.layers.conv2d(x, 1, [1, 1], padding='same', + kernel_initializer=ker_init, use_bias=False, trainable=False) + + return tf.floor(grey_x) + + def normalize_images(self, x1, x2): + reduction_axes = [i for i in range(1, len(x1.shape))] + min_x1 = tf.reduce_min(x1, axis=reduction_axes) + max_x1 = tf.reduce_max(x1, axis=reduction_axes) + + min_x2 = tf.reduce_min(x2, axis=reduction_axes) + max_x2 = tf.reduce_max(x2, axis=reduction_axes) + + min_val = tf.minimum(min_x1, min_x2) + max_val = tf.maximum(max_x1, max_x2) + + den = max_val - min_val + + expand_dims = [-1 if i == 0 else 1 for i in range(len(x1.shape))] + min_val_ex = tf.reshape(min_val, expand_dims) + den_ex = tf.reshape(den, expand_dims) + + x1_norm = tf.where(den > 0, 255. * (x1 - min_val_ex) / den_ex, x1) + x2_norm = tf.where(den > 0, 255. * (x2 - min_val_ex) / den_ex, x2) + + return x1_norm, x2_norm + + def gaussian_smooth(self, x): + assert len(x.shape) == 4 + ker_init = tf.constant_initializer([[0.000874, 0.006976, 0.01386, 0.006976, 0.000874], + [0.006976, 0.0557, 0.110656, 0.0557, 0.006976], + [0.01386, 0.110656, 0.219833, 0.110656, 0.01386], + [0.006976, 0.0557, 0.110656, 0.0557, 0.006976], + [0.000874, 0.006976, 0.01386, 0.006976, 0.000874]]) + smooth_x = tf.layers.conv2d(x, x.shape[-1].value, [5, 5], padding='same', + kernel_initializer=ker_init, use_bias=False, trainable=False) + + return smooth_x + + def warp_image(self, x, u, v): + assert len(x.shape) == 4 + assert len(u.shape) == 3 + assert len(v.shape) == 3 + u = u / x.shape[2].value * 2 + v = v / x.shape[1].value * 2 + + delta = tf.concat(axis=1, values=[u, v]) + return spatial_transformer.transformer(x, delta, (x.shape[-3].value, x.shape[-2].value)) + + def centered_gradient(self, x, name): + assert len(x.shape) == 4 + + with tf.variable_scope('centered_gradient'): + x_ker_init = tf.constant_initializer([-0.5, 0, 0.5]) # tf.constant_initializer([-0.5,0, 0.5]) + diff_x = tf.layers.conv2d(x, x.shape[-1].value, [1, 3], padding='same', + kernel_initializer=x_ker_init, use_bias=False, name=name + '_diff_x', + trainable=False) + + y_ker_init = tf.constant_initializer([[-0.5], [0], [0.5]]) + diff_y = tf.layers.conv2d(x, x.shape[-1].value, [3, 1], padding='same', + kernel_initializer=y_ker_init, use_bias=False, name=name + '_diff_y', + trainable=False) + # refine the boundary + + # x_ker_init = self.variable_with_weight_loss(shape=[1, 3, 1, 1], stddev=5e-2) + # diff_x = tf.nn.conv2d(x, x_ker_init, [1, 2, 2, 1], padding='SAME', name=name + '_diff_y') + + test1 = tf.slice(x, [0, 0, 1, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value]) + test2 = tf.slice(x, [0, 0, 0, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value]) + first_col = 0.5 * (tf.slice(x, [0, 0, 1, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value]) - + tf.slice(x, [0, 0, 0, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value])) + + last_col = 0.5 * ( + tf.slice(x, [0, 0, x.shape[2].value - 1, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value]) - + tf.slice(x, [0, 0, x.shape[2].value - 2, 0], [batch_size, x.shape[1].value, 1, x.shape[3].value])) + diff_x_valid = tf.slice(diff_x, begin=[0, 0, 1, 0], + size=[batch_size, x.shape[1].value, x.shape[2].value - 2, x.shape[3].value]) + diff_x = tf.concat(axis=2, values=[first_col, diff_x_valid, last_col]) + + first_row = 0.5 * (tf.slice(x, [0, 1, 0, 0], [batch_size, 1, x.shape[2].value, x.shape[3].value]) - + tf.slice(x, [0, 0, 0, 0], [batch_size, 1, x.shape[2].value, x.shape[3].value])) + last_row = 0.5 * ( + tf.slice(x, [0, x.shape[1].value - 1, 0, 0], [batch_size, 1, x.shape[2].value, x.shape[3].value]) - + tf.slice(x, [0, x.shape[1].value - 2, 0, 0], [batch_size, 1, x.shape[2].value, x.shape[3].value])) + diff_y_valid = tf.slice(diff_y, begin=[0, 1, 0, 0], + size=[batch_size, x.shape[1].value - 2, x.shape[2].value, x.shape[3].value]) + diff_y = tf.concat(axis=1, values=[first_row, diff_y_valid, last_row]) + + return diff_x, diff_y + + def forward_gradient(self, x, name): + assert len(x.shape) == 4 + + with tf.variable_scope('forward_gradient'): + x_ker_init = tf.constant_initializer([[-1, 1]]) + diff_x = tf.layers.conv2d(x, x.shape[-1].value, [1, 2], padding='same', + kernel_initializer=x_ker_init, use_bias=True, name=name + '_diff_x', + trainable=True) + + y_ker_init = tf.constant_initializer([[-1], [1]]) + diff_y = tf.layers.conv2d(x, x.shape[-1].value, [2, 1], padding='same', + kernel_initializer=y_ker_init, use_bias=True, name=name + '_diff_y', + trainable=True) + # refine the boundary + diff_x_valid = tf.slice(diff_x, begin=[0, 0, 0, 0], + size=[batch_size, x.shape[1].value, x.shape[2].value - 1, x.shape[3].value]) + last_col = tf.zeros([tf.shape(x)[0], x.shape[1].value, 1, x.shape[3].value], dtype=tf.float32) + diff_x = tf.concat(axis=2, values=[diff_x_valid, last_col]) + + diff_y_valid = tf.slice(diff_y, begin=[0, 0, 0, 0], + size=[batch_size, x.shape[1].value - 1, x.shape[2].value, x.shape[3].value]) + last_row = tf.zeros([tf.shape(x)[0], 1, x.shape[2].value, x.shape[3].value], dtype=tf.float32) + diff_y = tf.concat(axis=1, values=[diff_y_valid, last_row]) + + return diff_x, diff_y + + def divergence(self, x, y, name): + assert len(x.shape) == 4 + + with tf.variable_scope('divergence'): + x_valid = tf.slice(x, begin=[0, 0, 0, 0], + size=[batch_size, x.shape[1].value, x.shape[2].value - 1, x.shape[3].value]) + first_col = tf.zeros([tf.shape(x)[0], x.shape[1].value, 1, x.shape[3].value], dtype=tf.float32) + x_pad = tf.concat(axis=2, values=[first_col, x_valid]) + + y_valid = tf.slice(y, begin=[0, 0, 0, 0], + size=[batch_size, y.shape[1].value - 1, y.shape[2].value, y.shape[3].value]) + first_row = tf.zeros([tf.shape(y)[0], 1, y.shape[2].value, y.shape[3].value], dtype=tf.float32) + y_pad = tf.concat(axis=1, values=[first_row, y_valid]) + + x_ker_init = tf.constant_initializer([[-1, 1]]) + diff_x = tf.layers.conv2d(x_pad, x.shape[-1].value, [1, 2], padding='same', + kernel_initializer=x_ker_init, use_bias=True, name=name + '_diff_x', + trainable=True) + + y_ker_init = tf.constant_initializer([[-1], [1]]) + diff_y = tf.layers.conv2d(y_pad, y.shape[-1].value, [2, 1], padding='same', + kernel_initializer=y_ker_init, use_bias=True, name=name + '_diff_y', + trainable=True) + + div = diff_x + diff_y + return div + + def zoom_size(self, height, width, factor): + new_height = int(float(height) * factor + 0.5) + new_width = int(float(width) * factor + 0.5) + + return new_height, new_width + + def dual_tvl1_optic_flow(self, x1, x2, u1, u2, + tau=0.25, # time step + lbda=0.15, # weight parameter for the data term + theta=0.3, # weight parameter for (u - v)^2 + warps=5, # number of warpings per scale + max_iterations=50 # maximum number of iterations for optimization + ): + l_t = lbda * theta + taut = tau / theta + diff2_x, diff2_y = self.centered_gradient(x2, 'x2') # conv and slice + p11 = p12 = p21 = p22 = tf.zeros_like(x1) + for warpings in range(warps): # 1 + with tf.variable_scope('warping%d' % (warpings,)): + u1_flat = tf.reshape(u1, (tf.shape(x2)[0], 1, x2.shape[1].value * x2.shape[2].value)) + u2_flat = tf.reshape(u2, (tf.shape(x2)[0], 1, x2.shape[1].value * x2.shape[2].value)) + + x2_warp = self.warp_image(x2, u1_flat, u2_flat) + x2_warp = tf.reshape(x2_warp, tf.shape(x2)) + + diff2_x_warp = self.warp_image(diff2_x, u1_flat, u2_flat) + diff2_x_warp = tf.reshape(diff2_x_warp, tf.shape(diff2_x)) + + diff2_y_warp = self.warp_image(diff2_y, u1_flat, u2_flat) + diff2_y_warp = tf.reshape(diff2_y_warp, tf.shape(diff2_y)) + + diff2_x_sq = tf.square(diff2_x_warp) # square mat + diff2_y_sq = tf.square(diff2_y_warp) + + grad = diff2_x_sq + diff2_y_sq + self.GRAD_IS_ZERO + + rho_c = x2_warp - diff2_x_warp * u1 - diff2_y_warp * u2 - x1 + + for ii in range(max_iterations): # 50 + with tf.variable_scope('iter%d' % (ii,)): + rho = rho_c + diff2_x_warp * u1 + diff2_y_warp * u2 + self.GRAD_IS_ZERO + + masks1 = rho < -l_t * grad + d1_1 = tf.where(masks1, l_t * diff2_x_warp, tf.zeros_like(diff2_x_warp)) + d2_1 = tf.where(masks1, l_t * diff2_y_warp, tf.zeros_like(diff2_y_warp)) + + masks2 = rho > l_t * grad + d1_2 = tf.where(masks2, -l_t * diff2_x_warp, tf.zeros_like(diff2_x_warp)) + d2_2 = tf.where(masks2, -l_t * diff2_y_warp, tf.zeros_like(diff2_y_warp)) + masks3 = (~masks1) & (~masks2) & (grad > self.GRAD_IS_ZERO) + d1_3 = tf.where(masks3, -rho / grad * diff2_x_warp, tf.zeros_like(diff2_x_warp)) + d2_3 = tf.where(masks3, -rho / grad * diff2_y_warp, tf.zeros_like(diff2_y_warp)) + + v1 = d1_1 + d1_2 + d1_3 + u1 + v2 = d2_1 + d2_2 + d2_3 + u2 + + u1 = v1 + theta * self.divergence(p11, p12, 'div_p1') # slice->concat->conv->add + u2 = v2 + theta * self.divergence(p21, p22, 'div_p2') + + u1x, u1y = self.forward_gradient(u1, 'u1') + u2x, u2y = self.forward_gradient(u2, 'u2') + + p11 = (p11 + taut * u1x) / ( + 1.0 + taut * tf.sqrt(tf.square(u1x) + tf.square(u1y) + self.GRAD_IS_ZERO)) + p12 = (p12 + taut * u1y) / ( + 1.0 + taut * tf.sqrt(tf.square(u1x) + tf.square(u1y) + self.GRAD_IS_ZERO)) + p21 = (p21 + taut * u2x) / ( + 1.0 + taut * tf.sqrt(tf.square(u2x) + tf.square(u2y) + self.GRAD_IS_ZERO)) + p22 = (p22 + taut * u2y) / ( + 1.0 + taut * tf.sqrt(tf.square(u2x) + tf.square(u2y) + self.GRAD_IS_ZERO)) + return u1, u2, rho + + def tvnet_flow(self, x1, x2, + tau=0.25, # time step + lbda=0.15, # weight parameter for the data term + theta=0.3, # weight parameter for (u - v)^2 + warps=5, # number of warpings per scale + zfactor=0.5, # factor for building the image piramid + max_scales=5, # maximum number of scales for image piramid + max_iterations=50 # maximum number of iterations for optimization + ): + + for i in range(len(x1.shape)): + assert x1.shape[i].value == x2.shape[i].value + + zfactor = np.float32(zfactor) + + height = x1.shape[-3].value + width = x1.shape[-2].value + + n_scales = 1 + np.log(np.sqrt(height ** 2 + width ** 2) / 4.0) / np.log(1 / zfactor); # calculate n_scales + n_scales = min(n_scales, max_scales) + # n_scales = 1 + with tf.variable_scope('tvl1_flow'): + + grey_x1 = self.grey_scale_image(x1) # conv and floor + grey_x2 = self.grey_scale_image(x2) + + norm_imgs = self.normalize_images(grey_x1, grey_x2) # normalize to 0-255 + + smooth_x1 = self.gaussian_smooth(norm_imgs[0]) # conv + smooth_x2 = self.gaussian_smooth(norm_imgs[1]) + + for ss in range(n_scales - 1, -1, -1): + with tf.variable_scope('scale%d' % ss): + down_sample_factor = zfactor ** ss + down_height, down_width = self.zoom_size(height, width, down_sample_factor) + + if ss == n_scales - 1: + u1 = tf.get_variable('u1', shape=[1, down_height, down_width, 1], dtype=tf.float32, + initializer=tf.zeros_initializer) + u2 = tf.get_variable('u2', shape=[1, down_height, down_width, 1], dtype=tf.float32, + initializer=tf.zeros_initializer) + # print ([tf.shape(smooth_x1)[0], 1, 1, 1]) + + u1 = tf.tile(u1, [tf.shape(smooth_x1)[0], 1, 1, 1]) + u2 = tf.tile(u2, [tf.shape(smooth_x1)[0], 1, 1, 1]) + + down_x1 = zoom_image(smooth_x1, down_height, down_width) + down_x2 = zoom_image(smooth_x2, down_height, down_width) + + u1, u2, rho = self.dual_tvl1_optic_flow(down_x1, down_x2, u1, u2, + tau=tau, lbda=lbda, theta=theta, warps=warps, + max_iterations=max_iterations) + + if ss == 0: + return u1, u2, rho + + up_sample_factor = zfactor ** (ss - 1) + up_height, up_width = self.zoom_size(height, width, up_sample_factor) + u1 = zoom_image(u1, up_height, up_width) / zfactor + u2 = zoom_image(u2, up_height, up_width) / zfactor + + def get_loss(self, x1, x2, + tau=0.25, # time step + lbda=0.15, # weight parameter for the data term + theta=0.3, # weight parameter for (u - v)^2 + warps=5, # number of warpings per scale + zfactor=0.5, # factor for building the image piramid + max_scales=5, # maximum number of scales for image piramid + max_iterations=50 # maximum number of iterations for optimization + ): + + u1, u2, rho = self.tvnet_flow(x1, x2, + tau=tau, lbda=lbda, theta=theta, warps=warps, + zfactor=zfactor, max_scales=max_scales, + max_iterations=max_iterations) + + # computing loss + u1x, u1y = self.forward_gradient(u1, 'u1') + u2x, u2y = self.forward_gradient(u2, 'u2') + + u1_flat = tf.reshape(u1, (tf.shape(x2)[0], 1, x2.shape[1].value * x2.shape[2].value)) + u2_flat = tf.reshape(u2, (tf.shape(x2)[0], 1, x2.shape[1].value * x2.shape[2].value)) + + x2_warp = self.warp_image(x2, u1_flat, u2_flat) + x2_warp = tf.reshape(x2_warp, tf.shape(x2)) + loss = lbda * tf.reduce_mean(tf.abs(x2_warp - x1)) + tf.reduce_mean( + tf.abs(u1x) + tf.abs(u1y) + tf.abs(u2x) + tf.abs(u2y)) + return loss, u1, u2 diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/README.txt b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..79ff23ed3092c998fafef546655beb2968ea44e2 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/README.txt @@ -0,0 +1,9 @@ +Some utilities for reading, writing, and color-coding .flo images. + +Written according to the c++ source code of Daniel Scharstein + +Deqing Sun, 11/03/07 + +see colorTest for visualizing the encoding scheme, reading and writing .flo files. + +Run visualize.m to visualize the result generated by TVNet. \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/colorTest.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/colorTest.m new file mode 100644 index 0000000000000000000000000000000000000000..626099c7e4c8812b25a717a204fed57753251860 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/colorTest.m @@ -0,0 +1,84 @@ +function colorTest() + +% colorTest() creates a test image showing the color encoding scheme + +% According to the c++ source code of Daniel Scharstein +% Contact: schar@middlebury.edu + +% Author: Deqing Sun, Department of Computer Science, Brown University +% Contact: dqsun@cs.brown.edu +% $Date: 2007-10-31 20:22:10 (Wed, 31 Oct 2006) $ + +% Copyright 2007, Deqing Sun. +% +% All Rights Reserved +% +% Permission to use, copy, modify, and distribute this software and its +% documentation for any purpose other than its incorporation into a +% commercial product is hereby granted without fee, provided that the +% above copyright notice appear in all copies and that both that +% copyright notice and this permission notice appear in supporting +% documentation, and that the name of the author and Brown University not be used in +% advertising or publicity pertaining to distribution of the software +% without specific, written prior permission. +% +% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +% INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY +% PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR OR BROWN UNIVERSITY BE LIABLE FOR +% ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +% WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +% ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +% OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +%% test color pattern of Daniel's c++ code + +truerange = 1; +height = 151; +width = 151; +range = truerange * 1.04; + +s2 = round(height/2); + +[x y] = meshgrid(1:width, 1:height); + +u = x*range/s2 - range; +v = y*range/s2 - range; + +img = computeColor(u/truerange, v/truerange); + +img(s2,:,:) = 0; +img(:,s2,:) = 0; + +figure; +imshow(img); +title('test color pattern'); +pause; close; + +% test read and write flow +F(:,:,1) = u; +F(:,:,2) = v; +writeFlowFile(F, 'colorTest.flo'); +F2 = readFlowFile('colorTest.flo'); + +u2 = F2(:,:,1); +v2 = F2(:,:,2); + +img2 = computeColor(u2/truerange, v2/truerange); + +img2(s2,:,:) = 0; +img2(:,s2,:) = 0; + +figure; imshow(img2); +title('saved and reloaded test color pattern'); +pause; close; + +% color encoding scheme for optical flow +img = computeColor(u/range/sqrt(2), v/range/sqrt(2)); + +img(s2,:,:) = 0; +img(:,s2,:) = 0; + +figure; +imshow(img); +title('optical flow color encoding scheme'); +pause; close; diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/computeColor.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/computeColor.m new file mode 100644 index 0000000000000000000000000000000000000000..3566f4e08792e02a2328b9c64d2a0773b3f3d44d --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/computeColor.m @@ -0,0 +1,115 @@ +function img = computeColor(u,v) + +% computeColor color codes flow field U, V + +% According to the c++ source code of Daniel Scharstein +% Contact: schar@middlebury.edu + +% Author: Deqing Sun, Department of Computer Science, Brown University +% Contact: dqsun@cs.brown.edu +% $Date: 2007-10-31 21:20:30 (Wed, 31 Oct 2006) $ + +% Copyright 2007, Deqing Sun. +% +% All Rights Reserved +% +% Permission to use, copy, modify, and distribute this software and its +% documentation for any purpose other than its incorporation into a +% commercial product is hereby granted without fee, provided that the +% above copyright notice appear in all copies and that both that +% copyright notice and this permission notice appear in supporting +% documentation, and that the name of the author and Brown University not be used in +% advertising or publicity pertaining to distribution of the software +% without specific, written prior permission. +% +% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +% INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY +% PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR OR BROWN UNIVERSITY BE LIABLE FOR +% ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +% WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +% ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +% OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +nanIdx = isnan(u) | isnan(v); +u(nanIdx) = 0; +v(nanIdx) = 0; + +colorwheel = makeColorwheel(); +ncols = size(colorwheel, 1); + +rad = sqrt(u.^2+v.^2); + +a = atan2(-v, -u)/pi; + +fk = (a+1) /2 * (ncols-1) + 1; % -1~1 maped to 1~ncols + +k0 = floor(fk); % 1, 2, ..., ncols + +k1 = k0+1; +k1(k1==ncols+1) = 1; + +f = fk - k0; + +for i = 1:size(colorwheel,2) + tmp = colorwheel(:,i); + col0 = tmp(k0)/255; + col1 = tmp(k1)/255; + col = (1-f).*col0 + f.*col1; + + idx = rad <= 1; + col(idx) = 1-rad(idx).*(1-col(idx)); % increase saturation with radius + + col(~idx) = col(~idx)*0.75; % out of range + + img(:,:, i) = uint8(floor(255*col.*(1-nanIdx))); +end; + +%% +function colorwheel = makeColorwheel() + +% color encoding scheme + +% adapted from the color circle idea described at +% http://members.shaw.ca/quadibloc/other/colint.htm + + +RY = 15; +YG = 6; +GC = 4; +CB = 11; +BM = 13; +MR = 6; + +ncols = RY + YG + GC + CB + BM + MR; + +colorwheel = zeros(ncols, 3); % r g b + +col = 0; +%RY +colorwheel(1:RY, 1) = 255; +colorwheel(1:RY, 2) = floor(255*(0:RY-1)/RY)'; +col = col+RY; + +%YG +colorwheel(col+(1:YG), 1) = 255 - floor(255*(0:YG-1)/YG)'; +colorwheel(col+(1:YG), 2) = 255; +col = col+YG; + +%GC +colorwheel(col+(1:GC), 2) = 255; +colorwheel(col+(1:GC), 3) = floor(255*(0:GC-1)/GC)'; +col = col+GC; + +%CB +colorwheel(col+(1:CB), 2) = 255 - floor(255*(0:CB-1)/CB)'; +colorwheel(col+(1:CB), 3) = 255; +col = col+CB; + +%BM +colorwheel(col+(1:BM), 3) = 255; +colorwheel(col+(1:BM), 1) = floor(255*(0:BM-1)/BM)'; +col = col+BM; + +%MR +colorwheel(col+(1:MR), 3) = 255 - floor(255*(0:MR-1)/MR)'; +colorwheel(col+(1:MR), 1) = 255; \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/flowToColor.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/flowToColor.m new file mode 100644 index 0000000000000000000000000000000000000000..3e39e8e42068509afaf077af572d18f4bedac0ea --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/flowToColor.m @@ -0,0 +1,90 @@ +function img = flowToColor(flow, varargin) + +% flowToColor(flow, maxFlow) flowToColor color codes flow field, normalize +% based on specified value, +% +% flowToColor(flow) flowToColor color codes flow field, normalize +% based on maximum flow present otherwise + +% According to the c++ source code of Daniel Scharstein +% Contact: schar@middlebury.edu + +% Author: Deqing Sun, Department of Computer Science, Brown University +% Contact: dqsun@cs.brown.edu +% $Date: 2007-10-31 18:33:30 (Wed, 31 Oct 2006) $ + +% Copyright 2007, Deqing Sun. +% +% All Rights Reserved +% +% Permission to use, copy, modify, and distribute this software and its +% documentation for any purpose other than its incorporation into a +% commercial product is hereby granted without fee, provided that the +% above copyright notice appear in all copies and that both that +% copyright notice and this permission notice appear in supporting +% documentation, and that the name of the author and Brown University not be used in +% advertising or publicity pertaining to distribution of the software +% without specific, written prior permission. +% +% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +% INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY +% PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR OR BROWN UNIVERSITY BE LIABLE FOR +% ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +% WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +% ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +% OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +UNKNOWN_FLOW_THRESH = 1e9; +UNKNOWN_FLOW = 1e10; % + +[height widht nBands] = size(flow); + +if nBands ~= 2 + error('flowToColor: image must have two bands'); +end; + +u = flow(:,:,1); +v = flow(:,:,2); + +maxu = -999; +maxv = -999; + +minu = 999; +minv = 999; +maxrad = -1; + + +% fix unknown flow +idxUnknown = (abs(u)> UNKNOWN_FLOW_THRESH) | (abs(v)> UNKNOWN_FLOW_THRESH) ; + +u(idxUnknown) = 0; +v(idxUnknown) = 0; +max(u(:)) +maxu = max(maxu, max(u(:))); +minu = min(minu, min(u(:))); + +maxv = max(maxv, max(v(:))); +minv = min(minv, min(v(:))); + +rad = sqrt(u.^2+v.^2); +maxrad = max(maxrad, max(rad(:))); + +fprintf('max flow: %.4f flow range: u = %.3f .. %.3f; v = %.3f .. %.3f\n', maxrad, minu, maxu, minv, maxv); + +if isempty(varargin) ==0 + maxFlow = varargin{1}; + if maxFlow > 0 + maxrad = maxFlow; + end; +end; + +u = u/(maxrad+eps); +v = v/(maxrad+eps); + +% compute color + +img = computeColor(u, v); + +% unknown flow +IDX = repmat(idxUnknown, [1 1 3]); +img(IDX) = 0; \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/readFlowFile.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/readFlowFile.m new file mode 100644 index 0000000000000000000000000000000000000000..568d9b6296b958a3ef94b73482eaccaf5fdc6ebd --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/readFlowFile.m @@ -0,0 +1,84 @@ +function img = readFlowFile(filename) + +% readFlowFile read a flow file FILENAME into 2-band image IMG + +% According to the c++ source code of Daniel Scharstein +% Contact: schar@middlebury.edu + +% Author: Deqing Sun, Department of Computer Science, Brown University +% Contact: dqsun@cs.brown.edu +% $Date: 2007-10-31 16:45:40 (Wed, 31 Oct 2006) $ + +% Copyright 2007, Deqing Sun. +% +% All Rights Reserved +% +% Permission to use, copy, modify, and distribute this software and its +% documentation for any purpose other than its incorporation into a +% commercial product is hereby granted without fee, provided that the +% above copyright notice appear in all copies and that both that +% copyright notice and this permission notice appear in supporting +% documentation, and that the name of the author and Brown University not be used in +% advertising or publicity pertaining to distribution of the software +% without specific, written prior permission. +% +% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +% INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY +% PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR OR BROWN UNIVERSITY BE LIABLE FOR +% ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +% WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +% ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +% OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +TAG_FLOAT = 202021.25; % check for this when READING the file + +% sanity check +if isempty(filename) == 1 + error('readFlowFile: empty filename'); +end; + +idx = findstr(filename, '.'); +idx = idx(end); + +if length(filename(idx:end)) == 1 + error('readFlowFile: extension required in filename %s', filename); +end; + +if strcmp(filename(idx:end), '.flo') ~= 1 + error('readFlowFile: filename %s should have extension ''.flo''', filename); +end; + +fid = fopen(filename, 'r'); +if (fid < 0) + error('readFlowFile: could not open %s', filename); +end; + +tag = fread(fid, 1, 'float32'); +width = fread(fid, 1, 'int32'); +height = fread(fid, 1, 'int32'); + +% sanity check + +if (tag ~= TAG_FLOAT) + error('readFlowFile(%s): wrong tag (possibly due to big-endian machine?)', filename); +end; + +if (width < 1 || width > 99999) + error('readFlowFile(%s): illegal width %d', filename, width); +end; + +if (height < 1 || height > 99999) + error('readFlowFile(%s): illegal height %d', filename, height); +end; + +nBands = 2; + +% arrange into matrix form +tmp = fread(fid, inf, 'float32'); +tmp = reshape(tmp, [width*nBands, height]); +tmp = tmp'; +img(:,:,1) = tmp(:, (1:width)*nBands-1); +img(:,:,2) = tmp(:, (1:width)*nBands); + +fclose(fid); + diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/untitled.fig b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/untitled.fig new file mode 100644 index 0000000000000000000000000000000000000000..f6e57e0f77e6d673d414bae0130b80681f1caf9b Binary files /dev/null and b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/untitled.fig differ diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/visualize.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/visualize.m new file mode 100644 index 0000000000000000000000000000000000000000..03cb0187dee005fd2f0fa6c8ac6146b8e6546e27 --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/visualize.m @@ -0,0 +1,6 @@ +flow_file = './1.mat'; +load(flow_file); +img = flowToColor(flow); +figure; +imshow(img) +saveas(gcf,'test','png') \ No newline at end of file diff --git a/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/writeFlowFile.m b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/writeFlowFile.m new file mode 100644 index 0000000000000000000000000000000000000000..5fba45de70b6787f63a4687b569a3f90df46da4f --- /dev/null +++ b/TensorFlow/contrib/cv/TVNet_ID0951_for_TensorFlow/visualize/writeFlowFile.m @@ -0,0 +1,76 @@ +function writeFlowFile(img, filename) + +% writeFlowFile writes a 2-band image IMG into flow file FILENAME + +% According to the c++ source code of Daniel Scharstein +% Contact: schar@middlebury.edu + +% Author: Deqing Sun, Department of Computer Science, Brown University +% Contact: dqsun@cs.brown.edu +% $Date: 2007-10-31 15:36:40 (Wed, 31 Oct 2006) $ + +% Copyright 2007, Deqing Sun. +% +% All Rights Reserved +% +% Permission to use, copy, modify, and distribute this software and its +% documentation for any purpose other than its incorporation into a +% commercial product is hereby granted without fee, provided that the +% above copyright notice appear in all copies and that both that +% copyright notice and this permission notice appear in supporting +% documentation, and that the name of the author and Brown University not be used in +% advertising or publicity pertaining to distribution of the software +% without specific, written prior permission. +% +% THE AUTHOR AND BROWN UNIVERSITY DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, +% INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY +% PARTICULAR PURPOSE. IN NO EVENT SHALL THE AUTHOR OR BROWN UNIVERSITY BE LIABLE FOR +% ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +% WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +% ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +% OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +TAG_STRING = 'PIEH'; % use this when WRITING the file + +% sanity check +if isempty(filename) == 1 + error('writeFlowFile: empty filename'); +end; + +idx = findstr(filename, '.'); +idx = idx(end); % in case './xxx/xxx.flo' + +if length(filename(idx:end)) == 1 + error('writeFlowFile: extension required in filename %s', filename); +end; + +if strcmp(filename(idx:end), '.flo') ~= 1 + error('writeFlowFile: filename %s should have extension ''.flo''', filename); +end; + +[height width nBands] = size(img); + +if nBands ~= 2 + error('writeFlowFile: image must have two bands'); +end; + +fid = fopen(filename, 'w'); +if (fid < 0) + error('writeFlowFile: could not open %s', filename); +end; + +% write the header +fwrite(fid, TAG_STRING); +fwrite(fid, width, 'int32'); +fwrite(fid, height, 'int32'); + +% arrange into matrix form +tmp = zeros(height, width*nBands); + +tmp(:, (1:width)*nBands-1) = img(:,:,1); +tmp(:, (1:width)*nBands) = squeeze(img(:,:,2)); +tmp = tmp'; + +fwrite(fid, tmp, 'float32'); + +fclose(fid); diff --git a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/README.md b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/README.md index c9611df9b4e87f0dceef398098df3475a755cb5f..4d85bbb21718170990b4de1bf2ba25a885b5a4ac 100644 --- a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/README.md @@ -106,7 +106,7 @@ cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有 ## 模型训练 -在项目路径下执行如下 shell 命令进行训练： +在项目路径下执行如下 shell 命令进行训练与精度评估（顺序执行）： ``` python3.7 modelarts_entry_acc.py ``` @@ -155,15 +155,6 @@ session = tf.Session(config=config) -## 模型评估 - -运行如下 shell 命令来评估预训练模型精度： -``` -sh bash/evaluate_voiced_void.sh -``` - -可以替换 shell 脚本中的 restore_path 和 output_path 路径来评估自己的 checkpoints 。 - ## 评估结果 | | MAE | RMSE | iMAE | iRMSE | diff --git a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/bash/evaluate_voiced_void.sh b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/bash/evaluate_voiced_void.sh deleted file mode 100644 index 94361b2ebe951c4d9d955e2cb62cd1cbb536fedc..0000000000000000000000000000000000000000 --- a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/bash/evaluate_voiced_void.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -python src/evaluate_model.py \ ---image_path testing/void_test_image_1500.txt \ ---interp_depth_path testing/void_test_interp_depth_1500.txt \ ---validity_map_path testing/void_test_validity_map_1500.txt \ ---ground_truth_path testing/void_test_ground_truth_1500.txt \ ---start_idx 0 \ ---end_idx 800 \ ---n_batch 8 \ ---n_height 480 \ ---n_width 640 \ ---occ_threshold 1.5 \ ---occ_ksize 7 \ ---net_type vggnet11 \ ---im_filter_pct 0.75 \ ---sz_filter_pct 0.25 \ ---min_predict_z 0.1 \ ---max_predict_z 8.0 \ ---min_evaluate_z 0.2 \ ---max_evaluate_z 5.0 \ ---save_depth \ ---output_path trained_models/vggnet11_void_model/output \ ---restore_path trained_models/vggnet11_void_model/model.ckpt-100000 diff --git a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/src/evaluate_model.py b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/src/evaluate_model.py index 48cf8bbdbd966f1d8b652cabf1f3d807e5e8ca58..c834b6936f59ef04ea2a45d23398252634369bf7 100644 --- a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/src/evaluate_model.py +++ b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/src/evaluate_model.py @@ -177,7 +177,7 @@ with tf.Graph().as_default(): # Initialize Tensorflow session config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True - session = tf.Session(config=npu_config_proto(config_proto=config)) + session = tf.Session(config=config) # Load from checkpoint train_saver = tf.train.Saver() session.run(tf.global_variables_initializer()) diff --git a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/test/train_full_1p.sh index febcfdf2a4cbbe5efab3b411e74bb002d8f754c9..6c7127cc3f70ad653a15ba01d685c3dd74ba283d 100644 --- a/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/test/train_full_1p.sh +++ b/TensorFlow/contrib/cv/UDCVO_ID2359_for_TensorFlow/test/train_full_1p.sh @@ -1,5 +1,6 @@ #!/bin/bash - +export ASCEND_GLOBAL_LOG_LEVEL=0 +export ASCEND_SLOG_PRINT_TO_STDOUT=0 ########################################################## #########第3行 至 100行，请一定不要、不要、不要修改########## #########第3行 至 100行，请一定不要、不要、不要修改########## @@ -91,6 +92,33 @@ cd ${cur_path}/../ rm -rf ./test/output/${ASCEND_DEVICE_ID} mkdir -p ./test/output/${ASCEND_DEVICE_ID} +#move some data +cp -r ${data_path}/training ${cur_path}/../ +sed -i 's/^data//' training/void_train_image_1500.txt +sed -i "/void_voiced/s!^!${data_path}!" training/void_train_image_1500.txt + +sed -i 's/^data//' training/void_train_interp_depth_1500.txt +sed -i "/void_voiced/s!^!${data_path}!" training/void_train_interp_depth_1500.txt + +sed -i 's/^data//' training/void_train_validity_map_1500.txt +sed -i "/void_release/s!^!${data_path}!" training/void_train_validity_map_1500.txt + +sed -i 's/^data//' training/void_train_intrinsics_1500.txt +sed -i "/void_voiced/s!^!${data_path}!" training/void_train_intrinsics_1500.txt + +cp -r ${data_path}/testing ${cur_path}/../ +sed -i 's/^data//' testing/void_test_image_1500.txt +sed -i "/void_voiced/s!^!${data_path}!" testing/void_test_image_1500.txt + +sed -i 's/^data//' testing/void_test_interp_depth_1500.txt +sed -i "/void_voiced/s!^!${data_path}!" testing/void_test_interp_depth_1500.txt + +sed -i 's/^data//' testing/void_test_validity_map_1500.txt +sed -i "/void_release/s!^!${data_path}!" testing/void_test_validity_map_1500.txt + +sed -i 's/^data//' testing/void_test_ground_truth_1500.txt +sed -i "/void_release/s!^!${data_path}!" testing/void_test_ground_truth_1500.txt + # 训练开始时间记录，不需要修改 start_time=$(date +%s) ########################################################## @@ -112,11 +140,11 @@ batch_size=8 if [ x"${etp_flag}" != xtrue ]; then #python3.7 ./LeNet.py --data_path=${data_path} --output_path=${output_path} - python3.7 src/train_voiced.py \ - --train_image_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_image_1500.txt \ - --train_interp_depth_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_interp_depth_1500.txt \ - --train_validity_map_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_validity_map_1500.txt \ - --train_intrinsics_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_intrinsics_1500.txt \ + python3.7 ./src/train_voiced.py \ + --train_image_path training/void_train_image_1500.txt \ + --train_interp_depth_path training/void_train_interp_depth_1500.txt \ + --train_validity_map_path training/void_train_validity_map_1500.txt \ + --train_intrinsics_path training/void_train_intrinsics_1500.txt \ --n_batch 8 \ --n_height 480 \ --n_width 640 \ @@ -141,14 +169,38 @@ then --rot_param exponential \ --n_summary 1000 \ --n_checkpoint 5000 \ - --checkpoint_path /home/ma-user/modelarts/outputs/train_url_0/ + --checkpoint_path ${output_path} + + # 计算MAE,RMSE,iMAE,iRMSE + python3.7 ./src/evaluate_model.py \ + --image_path testing/void_test_image_1500.txt \ + --interp_depth_path testing/void_test_interp_depth_1500.txt \ + --validity_map_path testing/void_test_validity_map_1500.txt \ + --ground_truth_path testing/void_test_ground_truth_1500.txt \ + --start_idx 0 \ + --end_idx 800 \ + --n_batch 8 \ + --n_height 480 \ + --n_width 640 \ + --occ_threshold 1.5 \ + --occ_ksize 7 \ + --net_type vggnet11 \ + --im_filter_pct 0.75 \ + --sz_filter_pct 0.25 \ + --min_predict_z 0.1 \ + --max_predict_z 8.0 \ + --min_evaluate_z 0.2 \ + --max_evaluate_z 5.0 \ + --save_depth \ + --output_path ${output_path} \ + --restore_path ${output_path}/model.ckpt-103000 else #python3.7 ./LeNet.py --data_path=${data_path} --output_path=${output_path} > ${print_log} - python3.7 src/train_voiced.py \ - --train_image_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_image_1500.txt \ - --train_interp_depth_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_interp_depth_1500.txt \ - --train_validity_map_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_validity_map_1500.txt \ - --train_intrinsics_path /home/ma-user/modelarts/inputs/data_url_0/training/void_train_intrinsics_1500.txt \ + python3.7 ./src/train_voiced.py \ + --train_image_path training/void_train_image_1500.txt \ + --train_interp_depth_path training/void_train_interp_depth_1500.txt \ + --train_validity_map_path training/void_train_validity_map_1500.txt \ + --train_intrinsics_path training/void_train_intrinsics_1500.txt \ --n_batch 8 \ --n_height 480 \ --n_width 640 \ @@ -173,7 +225,31 @@ else --rot_param exponential \ --n_summary 1000 \ --n_checkpoint 5000 \ - --checkpoint_path /home/ma-user/modelarts/outputs/train_url_0/ + --checkpoint_path ${output_path} > ${print_log} 2>&1 + + # 计算MAE,RMSE,iMAE,iRMSE + python3.7 ./src/evaluate_model.py \ + --image_path testing/void_test_image_1500.txt \ + --interp_depth_path testing/void_test_interp_depth_1500.txt \ + --validity_map_path testing/void_test_validity_map_1500.txt \ + --ground_truth_path testing/void_test_ground_truth_1500.txt \ + --start_idx 0 \ + --end_idx 800 \ + --n_batch 8 \ + --n_height 480 \ + --n_width 640 \ + --occ_threshold 1.5 \ + --occ_ksize 7 \ + --net_type vggnet11 \ + --im_filter_pct 0.75 \ + --sz_filter_pct 0.25 \ + --min_predict_z 0.1 \ + --max_predict_z 8.0 \ + --min_evaluate_z 0.2 \ + --max_evaluate_z 5.0 \ + --save_depth \ + --output_path ${output_path} \ + --restore_path ${output_path}/model.ckpt-103000 >> ${print_log} 2>&1 fi # 性能相关数据计算 @@ -181,11 +257,10 @@ StepTime=`grep "StepTime: " ${print_log} | tail -n 10 | awk '{print $NF}' | awk FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` # 精度相关数据计算 -#train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +train_accuracy=`cat ${print_log} | grep -Eo " [0-9]*\.[0-9]*" | awk '{print $1}' | tail -n 1` # 提取所有loss打印信息 grep "loss: " ${print_log} | awk -F ":" '{print $2}' | awk -F " " '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt - ########################################################### #########后面的所有内容请不要修改########################### #########后面的所有内容请不要修改########################### @@ -239,4 +314,5 @@ echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}. echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/README.md b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/README.md index 125090097c7ff7b94097ef2618aaa277522de077..c7e754b4c2f4ffae4c5410f1632697e76eceee5b 100644 --- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/README.md @@ -162,7 +162,7 @@ 1. 配置训练参数。 - 首先在train.py中，配置参数。 + 在train.py中，配置参数。 ``` classes_path 指向model_data下的voc_classes.txt @@ -171,7 +171,18 @@ val_annotation_path 指向2007_val.txt' ``` - 2. 启动训练。 + 2. 配置测试参数。 + + 在yolo.py中，配置参数。 + ``` + model_path 指向训练好的模型 + ``` + 在get_map.py中，配置参数。 + ``` + VOCdevkit_path 指向VOC数据集位置 + ``` + + 3. 启动训练和测试。 ``` bash train_full_1p.sh ``` diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/fusion_result.json b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/fusion_result.json deleted file mode 100644 index bb2fbca3d4ee5dc86205264ea67a7d338e02a8af..0000000000000000000000000000000000000000 --- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/fusion_result.json +++ /dev/null @@ -1,1821 +0,0 @@ -{ - "graph_fusion": { - "RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - } - }, - "session_and_graph_id": "0_1" -}{ - "graph_fusion": { - "RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - }, - "TransdataCastFusionPass": { - "effect_times": "0", - "match_times": "1" - } - }, - "session_and_graph_id": "0_1001" -}{ - "graph_fusion": { - 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"RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - }, - "TransdataCastFusionPass": { - "effect_times": "0", - "match_times": "1" - } - }, - "session_and_graph_id": "0_961" -}{ - "graph_fusion": { - "RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - }, - "TransdataCastFusionPass": { - "effect_times": "0", - "match_times": "1" - } - }, - "session_and_graph_id": "0_971" -}{ - "graph_fusion": { - "RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - }, - "TransdataCastFusionPass": { - "effect_times": "0", - "match_times": "4" - } - }, - "session_and_graph_id": "0_981" -}{ - "graph_fusion": { - "RefreshInt64ToInt32FusionPass": { - "effect_times": "1", - "match_times": "1" - }, - "TransdataCastFusionPass": { - "effect_times": "0", - "match_times": "4" - } - }, - "session_and_graph_id": "0_991" -} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/get_map.py b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/get_map.py index 332fe1662f87c1dd054e78b41fadd02b2ac8a086..7fc37fe55a8a48df32ccddd7be4ec0280f6ab77e 100644 --- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/get_map.py +++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/get_map.py @@ -26,6 +26,8 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import argparse + import xml.etree.ElementTree as ET from PIL import Image @@ -36,6 +38,11 @@ from utils.utils import get_classes from utils.utils_map import get_coco_map, get_map if __name__ == "__main__": + # 解析输入参数data_url + parser = argparse.ArgumentParser() + parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") + config = parser.parse_args() + ''' Recall和Precision不像AP是一个面积的概念，在门限值不同时，网络的Recall和Precision值是不同的。 map计算结果中的Recall和Precision代表的是当预测时，门限置信度为0.5时，所对应的Recall和Precision值。 @@ -70,7 +77,7 @@ if __name__ == "__main__": # 指向VOC数据集所在的文件夹 # 默认指向根目录下的VOC数据集 #-------------------------------------------------------# - VOCdevkit_path = 'VOCdevkit' + VOCdevkit_path = config.data_url + '/VOCdevkit/' #-------------------------------------------------------# # 结果输出的文件夹，默认为map_out #-------------------------------------------------------# diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/model_data/simhei.ttf b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/model_data/simhei.ttf new file mode 100644 index 0000000000000000000000000000000000000000..5bd4687e7212775e23bea569f08fdd1cd7395dc3 Binary files /dev/null and b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/model_data/simhei.ttf differ diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_full_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..187030313808963456562bf6009350049b6a3504 --- /dev/null +++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_full_1p.sh @@ -0,0 +1,189 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=8 + +sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_train.txt +sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_val.txt + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./train.py --freeze_flag=0 + python3.7 ./get_map.py --data_url=${data_path} +else + python3.7 ./train.py --freeze_flag=0 1>${print_log} 2>&1 + python3.7 ./get_map.py --data_url=${data_path} 1>>${print_log} 2>&1 +fi + +# 性能相关数据计算 +#StepTime=`grep "each step time" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +#FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "mAP =" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss:" ${print_log} | awk '{print $NF}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +#echo "Final Performance images/sec : $FPS" +#echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..809516f649713a40f39f885878dd9056aeb87a29 --- /dev/null +++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh @@ -0,0 +1,181 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=8 + +sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_train.txt +sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_val.txt + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./train.py --epochs=8 --steps=48 --freeze_flag=0 +else + python3.7 ./train.py --epochs=8 --steps=48 --freeze_flag=0 1>${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "48/48" ${print_log} | grep -v "val_loss" | tail -n 5 | awk -F"48/48" '{print $2}' | awk '{print $4}' | awk -F"ms" '{print $1/1000}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 提取所有loss打印信息 +grep "loss:" ${print_log} | awk '{print $NF}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py index 52f68d694e3136267f1c13b981cba9c8c2851aab..4ffa36124e39f98dc6e1925db27808c95703a7ce 100644 --- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py +++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py @@ -1,35 +1,7 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from npu_bridge.npu_init import * import datetime import os -import tensorflow as tf -from tensorflow.python.keras import backend as K + +import tensorflow.keras.backend as K from tensorflow.keras.callbacks import (EarlyStopping, LearningRateScheduler, ModelCheckpoint, TensorBoard) from tensorflow.keras.layers import Conv2D, Dense, DepthwiseConv2D @@ -42,29 +14,19 @@ from utils.callbacks import LossHistory from utils.dataloader import YoloDatasets from utils.utils import get_anchors, get_classes from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig +from npu_bridge.npu_init import * -''' -训练自己的目标检测模型一定需要注意以下几点： -1、训练前仔细检查自己的格式是否满足要求，该库要求数据集格式为VOC格式，需要准备好的内容有输入图片和标签 - 输入图片为.jpg图片，无需固定大小，传入训练前会自动进行resize。 - 灰度图会自动转成RGB图片进行训练，无需自己修改。 - 输入图片如果后缀非jpg，需要自己批量转成jpg后再开始训练。 - - 标签为.xml格式，文件中会有需要检测的目标信息，标签文件和输入图片文件相对应。 +import argparse -2、训练好的权值文件保存在logs文件夹中，每个epoch都会保存一次，如果只是训练了几个step是不会保存的，epoch和step的概念要捋清楚一下。 - 在训练过程中，该代码并没有设定只保存最低损失的，因此按默认参数训练完会有100个权值，如果空间不够可以自行删除。 - 这个并不是保存越少越好也不是保存越多越好，有人想要都保存、有人想只保存一点，为了满足大多数的需求，还是都保存可选择性高。 +if __name__ == "__main__": -3、损失值的大小用于判断是否收敛，比较重要的是有收敛的趋势，即验证集损失不断下降，如果验证集损失基本上不改变的话，模型基本上就收敛了。 - 损失值的具体大小并没有什么意义，大和小只在于损失的计算方式，并不是接近于0才好。如果想要让损失好看点，可以直接到对应的损失函数里面除上10000。 - 训练过程中的损失值会保存在logs文件夹下的loss_%Y_%m_%d_%H_%M_%S文件夹中 + # 解析输入参数data_url + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=120) + parser.add_argument("--steps", type=int, default=-1) + parser.add_argument("--freeze_flag", type=int, default=1) + config = parser.parse_args() -4、调参是一门蛮重要的学问，没有什么参数是一定好的，现有的参数是我测试过可以正常训练的参数，因此我会建议用现有的参数。 - 但是参数本身并不是绝对的，比如随着batch的增大学习率也可以增大，效果也会好一些；过深的网络不要用太大的学习率等等。 - 这些都是经验上，只能靠各位同学多查询资料和自己试试了。 -''' -def main(): #---------------------------------------------------------------------# # classes_path 指向model_data下的txt，与自己训练的数据集相关 # 训练前一定要修改classes_path，使其对应自己的数据集 @@ -95,7 +57,6 @@ def main(): # 可以设置mosaic=True，直接随机初始化参数开始训练，但得到的效果仍然不如有预训练的情况。（像COCO这样的大数据集可以这样做） # 2、了解imagenet数据集，首先训练分类模型，获得网络的主干部分权值，分类模型的 主干部分 和该模型通用，基于此进行训练。 #----------------------------------------------------------------------------------------------------------------------------# - # model_path = 'model_data/yolov5_s.h5' model_path = '' #------------------------------------------------------# # input_shape 输入的shape大小，一定要是32的倍数 @@ -147,7 +108,7 @@ def main(): # (当Freeze_Train=False时失效) #------------------------------------------------------------------# Init_Epoch = 0 - Freeze_Epoch = 1 + Freeze_Epoch = 50 Freeze_batch_size = 16 #------------------------------------------------------------------# # 解冻阶段训练参数 @@ -156,14 +117,17 @@ def main(): # UnFreeze_Epoch 模型总共训练的epoch # Unfreeze_batch_size 模型在解冻后的batch_size #------------------------------------------------------------------# - UnFreeze_Epoch = 120 + UnFreeze_Epoch = config.epochs Unfreeze_batch_size = 8 #------------------------------------------------------------------# # Freeze_Train 是否进行冻结训练 # 默认先冻结主干训练后解冻训练。 # 如果设置Freeze_Train=False，建议使用优化器为sgd #------------------------------------------------------------------# - Freeze_Train = False + if config.freeze_flag != 0: + Freeze_Train = True + else: + Freeze_Train = False #------------------------------------------------------------------# # 其它训练参数：学习率、优化器、学习率下降有关 @@ -182,6 +146,7 @@ def main(): # 当使用SGD优化器时建议设置 Init_lr=1e-2 # momentum 优化器内部使用到的momentum参数 # weight_decay 权值衰减，可防止过拟合 + # adam会导致weight_decay错误，使用adam时建议设置为0。 #------------------------------------------------------------------# optimizer_type = "sgd" momentum = 0.937 @@ -193,7 +158,11 @@ def main(): #------------------------------------------------------------------# # save_period 多少个epoch保存一次权值，默认每个世代都保存 #------------------------------------------------------------------# - save_period = 1 + save_period = 20 + #------------------------------------------------------------------# + # save_dir 权值与日志文件保存的文件夹 + #------------------------------------------------------------------# + save_dir = 'logs' #------------------------------------------------------------------# # num_workers 用于设置是否使用多线程读取数据，1代表关闭多线程 # 开启后会加快数据读取速度，但是会占用更多内存 @@ -230,6 +199,8 @@ def main(): sess = tf.Session(config=sess_config) K.set_session(sess) + + #------------------------------------------------------# # 创建yolo模型 #------------------------------------------------------# @@ -239,8 +210,7 @@ def main(): # 载入预训练权重 #------------------------------------------------------# print('Load weights {}.'.format(model_path)) - model_body.load_weights(model_path, by_name=True) - # model_body.load_weights(model_path, by_name=True, skip_mismatch=True) + model_body.load_weights(model_path, by_name=False) model = get_train_model(model_body, input_shape, num_classes, anchors, anchors_mask, label_smoothing) @@ -281,12 +251,13 @@ def main(): start_epoch = Init_Epoch end_epoch = Freeze_Epoch if Freeze_Train else UnFreeze_Epoch + #-------------------------------------------------------------------# # 判断当前batch_size与64的差别，自适应调整学习率 #-------------------------------------------------------------------# nbs = 64 - Init_lr_fit = max(batch_size / nbs * Init_lr, 1e-4) - Min_lr_fit = max(batch_size / nbs * Min_lr, 1e-6) + Init_lr_fit = max(batch_size / nbs * Init_lr, 3e-4) + Min_lr_fit = max(batch_size / nbs * Min_lr, 3e-6) optimizer = { 'adam' : Adam(lr = Init_lr_fit, beta_1 = momentum), @@ -302,6 +273,10 @@ def main(): epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size + if config.steps != -1: + epoch_step = config.steps + epoch_step_val = config.steps + if epoch_step == 0 or epoch_step_val == 0: raise ValueError('数据集过小，无法进行训练，请扩充数据集。') @@ -316,14 +291,14 @@ def main(): # early_stopping 用于设定早停，val_loss多次不下降自动结束训练，表示模型基本收敛 #-------------------------------------------------------------------------------# time_str = datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S') - log_dir = os.path.join('logs', "loss_" + str(time_str)) + log_dir = os.path.join(save_dir, "loss_" + str(time_str)) logging = TensorBoard(log_dir) loss_history = LossHistory(log_dir) - checkpoint = ModelCheckpoint('ckpt/ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', + checkpoint = ModelCheckpoint(os.path.join(save_dir, "ep{epoch:03d}.h5"), monitor = 'val_loss', save_weights_only = True, save_best_only = False, period = save_period) early_stopping = EarlyStopping(monitor='val_loss', min_delta = 0, patience = 10, verbose = 1) lr_scheduler = LearningRateScheduler(lr_scheduler_func, verbose = 1) - callbacks = [logging, loss_history, checkpoint, lr_scheduler, early_stopping] + callbacks = [logging, loss_history, checkpoint, lr_scheduler] if start_epoch < end_epoch: print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) @@ -351,14 +326,14 @@ def main(): # 判断当前batch_size与64的差别，自适应调整学习率 #-------------------------------------------------------------------# nbs = 64 - Init_lr_fit = max(batch_size / nbs * Init_lr, 1e-4) - Min_lr_fit = max(batch_size / nbs * Min_lr, 1e-6) + Init_lr_fit = max(batch_size / nbs * Init_lr, 3e-4) + Min_lr_fit = max(batch_size / nbs * Min_lr, 3e-6) #---------------------------------------# # 获得学习率下降的公式 #---------------------------------------# lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) lr_scheduler = LearningRateScheduler(lr_scheduler_func, verbose = 1) - callbacks = [logging, loss_history, checkpoint, lr_scheduler, early_stopping] + callbacks = [logging, loss_history, checkpoint, lr_scheduler] for i in range(len(model.layers)): model.layers[i].trainable = True @@ -386,9 +361,3 @@ def main(): callbacks = callbacks ) sess.close() - -if __name__ == "__main__": - - print(1) - main() - \ No newline at end of file diff --git a/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/cfg.py b/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/cfg.py index a4a94ca5c3e2a17cdb907aeb4eb6d22a48e193e7..862f8a95766c33d41a709069255d4e55e401f06f 100644 --- a/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/cfg.py +++ b/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/cfg.py @@ -39,6 +39,7 @@ def make_config(FLAGS): custom_op = config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") config.graph_options.rewrite_options.remapping = RewriterConfig.OFF ## Auto Tune diff --git a/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/main.py b/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/main.py index 1af7f77f65cba4998449eb120231a2ac707b2ed1..f03b19841600fa7b736c1ea202bc620be5aead25 100644 --- a/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/main.py +++ b/TensorFlow/contrib/cv/fusiongan/FusionGAN_ID2124_for_TensorFlow/main.py @@ -75,7 +75,10 @@ def main(_): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) - + config_proto = tf.ConfigProto() + custom_op = config_proto.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = 'NpuOptimizer' + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") with tf.Session(config=config) as sess: srcnn = CGAN(sess, image_size=FLAGS.image_size, diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/ReadMe.md b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/ReadMe.md index 762b1b60f76d498b1eaeff8047263a6b4f56098b..4a333c10b6e37fafe9c3b193c6b0247215dc1292 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/ReadMe.md +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/ReadMe.md @@ -162,7 +162,9 @@ npu_device.global_options().precision_mode=FLAGS.precision_mode ├──eval_10k.tfrecord ``` +4、数据集pack (仅在使用pack策略进行训练时执行) +若训练时使用pack策略 （参看“模型训练” - 开始训练 - 4.pack策略）， 须将数据集进行处理，生成为pack后的数据集。再使用pack后的数据集进行训练。 数据集转换脚本在bert/data_pack/目录下，具体可参看该文件夹内README文件。 数据集pack过后，在指定的目录下生成“strategy_record”开头的一系列文件。 ## 模型训练 - 下载训练脚本。 @@ -245,7 +247,10 @@ npu_device.global_options().precision_mode=FLAGS.precision_mode bash test/train_performance_8p_192bs.sh --data_path=/home/tfrecord --precision_mode=allow_mix_precision - + 4. pack策略 + + 4.1 含pack策略的训练脚本（./test/目录下名字带有“_pack”的shell脚本即为包含pack策略的训练脚本） + 使用pack策略进行训练时，需使用pack过后的数据集（train, eval）及对应的预训练模型。若无对应的tensorflow-v2版本packed预训练模型。可由tensorflow-v1版本进行转换得来。模型转换相关脚本为bert/tf2_encoder_checkpoint_converter.py, 详见：“迁移学习指导” - 脚本和事例代码 - 模型转换脚本

高级参考

@@ -255,6 +260,7 @@ npu_device.global_options().precision_mode=FLAGS.precision_mode |--bert #网络代码目录 | |--tf2_common | |--modeling +| |--data_pack #pack脚本及说明所在目录 | |--...... |--configs #配置文件目录 | |--bert_config.json @@ -265,6 +271,13 @@ npu_device.global_options().precision_mode=FLAGS.precision_mode | |--...... ``` +添加： + +模型转换脚本（仅tensorflow_v1版本checkpoint转化为tensorflow_v2版本时使用） + +tensorflow-v1的checkpoint与tensorflow-v2的checkpoint从结构和使用上具有较大差异。迁移原有tensorflow-v1生成的checkpoint， 使其转换为tensorflow-v2环境中可使用的checkpoint， 需使用转换脚本， 脚本位置：“./bert/tf2_encoder_checkpoint_converter.py”。 +脚本使用示例： python3 tf2_encoder_checkpoint_converter.py --bert_config_file=/path/to/your/tensorflow_v1/bert_config.json --checkpoint_to_convert=/path/to/your/tensorflow_v1/model.ckpt-28252 --converted_checkpoint_path=/path/to/save/output_ckpt/output_ckpt + ## 脚本参数 ``` diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/bert_models.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/bert_models.py index 567c0c12c83f5a22504fd642a9d3596002341ae2..6709f33df29b3de93bb58a4306dd531a36510ff6 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/bert_models.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/bert_models.py @@ -29,7 +29,8 @@ from modeling.networks import bert_pretrainer from modeling.networks import bert_span_labeler from modeling.layers import bert_dropout from metrics_sparse_int32 import sparse_categorical_accuracy_int32 - +from absl import flags +FLAGS=flags.FLAGS class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer): """Returns layer that computes custom loss and metrics for pretraining.""" @@ -43,7 +44,7 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer): def _add_metrics(self, lm_output, lm_labels, lm_label_weights, lm_example_loss, sentence_output, sentence_labels, - next_sentence_loss): + next_sentence_loss, next_sentence_weights=None): """Adds metrics.""" #masked_lm_accuracy = tf.keras.metrics.sparse_categorical_accuracy( # lm_labels, lm_output) @@ -59,6 +60,11 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer): #next_sentence_accuracy = tf.keras.metrics.sparse_categorical_accuracy( # sentence_labels, sentence_output) next_sentence_accuracy = sparse_categorical_accuracy_int32(sentence_labels, sentence_output) + if FLAGS.use_packed_model: + print("next_sentence_accuracy, next_sentence_weights=====", next_sentence_accuracy, next_sentence_weights) + next_sentence_numerator = tf.reduce_sum(next_sentence_accuracy * next_sentence_weights) + next_sentence_denominator = tf.reduce_sum(next_sentence_weights) + next_sentence_accuracy = next_sentence_numerator / next_sentence_denominator next_sentence_num = tf.reduce_sum(next_sentence_accuracy) next_sentence_denom = tf.size(next_sentence_accuracy) # self.add_metric( @@ -81,15 +87,24 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer): def call(self, lm_output, sentence_output, lm_label_ids, lm_label_weights, - sentence_labels): + sentence_labels, next_sentence_weights=None): """Implements call() for the layer.""" lm_label_weights = tf.cast(lm_label_weights, tf.float32) + if FLAGS.use_packed_model: + lm_label_weights = tf.minimum(lm_label_weights, 1.0) lm_output = tf.cast(lm_output, tf.float32) sentence_output = tf.cast(sentence_output, tf.float32) mask_label_loss = losses.weighted_sparse_categorical_crossentropy_loss( labels=lm_label_ids, predictions=lm_output, weights=lm_label_weights) - sentence_loss = losses.weighted_sparse_categorical_crossentropy_loss( + if FLAGS.use_packed_model: + # change shape [B, 3] to [B*3, ], keep batch normal + sentence_labels = tf.reshape(sentence_labels, [-1,]) + next_sentence_weights = tf.reshape(next_sentence_weights, [-1,]) + sentence_loss = losses.weighted_sparse_categorical_crossentropy_loss( + labels=sentence_labels, predictions=sentence_output, weights=next_sentence_weights) + else: + sentence_loss = losses.weighted_sparse_categorical_crossentropy_loss( labels=sentence_labels, predictions=sentence_output) loss = mask_label_loss + sentence_loss batch_shape = tf.slice(tf.shape(sentence_labels), [0], [1]) @@ -100,9 +115,15 @@ class BertPretrainLossAndMetricLayer(tf.keras.layers.Layer): # TODO(b/122840926): metrics use distribution strategy merge_call() and do # not work with tf.function(compile=True). Either fix this issue or move # metric aggregation outside the model. - metric_outputs = self._add_metrics(lm_output, lm_label_ids, lm_label_weights, - mask_label_loss, sentence_output, sentence_labels, - sentence_loss) + if FLAGS.use_packed_model: + next_sentence_weights = tf.cast(next_sentence_weights,dtype=tf.float32) + metric_outputs = self._add_metrics(lm_output, lm_label_ids, lm_label_weights, + mask_label_loss, sentence_output, sentence_labels, + sentence_loss,next_sentence_weights) + else: + metric_outputs = self._add_metrics(lm_output, lm_label_ids, lm_label_weights, + mask_label_loss, sentence_output, sentence_labels, + sentence_loss) return final_loss, bs, metric_outputs @@ -194,8 +215,17 @@ def pretrain_model(bert_config, shape=(max_predictions_per_seq,), name='masked_lm_weights', dtype=tf.int32) - next_sentence_labels = tf.keras.layers.Input( - shape=(1,), name='next_sentence_labels', dtype=tf.int32) + + if FLAGS.use_packed_model: + next_sentence_weights = tf.keras.layers.Input( + shape=(FLAGS.max_sequences_per_pack,), name='next_sentence_weights', dtype=tf.int32) + next_sentence_positions = tf.keras.layers.Input( + shape=(FLAGS.max_sequences_per_pack,), dtype=tf.int32, name='next_sentence_positions') + next_sentence_labels = tf.keras.layers.Input( + shape=(FLAGS.max_sequences_per_pack,), name='next_sentence_labels', dtype=tf.int32) + else: + next_sentence_labels = tf.keras.layers.Input( + shape=(1,), name='next_sentence_labels', dtype=tf.int32) transformer_encoder = get_transformer_encoder(bert_config, seq_length) if initializer is None: @@ -208,15 +238,24 @@ def pretrain_model(bert_config, activation=tf_utils.get_activation(bert_config.hidden_act), initializer=initializer, output='predictions') - - lm_output, sentence_output = pretrainer_model( - [input_word_ids, input_mask, input_type_ids, masked_lm_positions]) + + if FLAGS.use_packed_model: + lm_output, sentence_output = pretrainer_model( + [input_word_ids, input_mask, input_type_ids, next_sentence_positions, masked_lm_positions]) + else: + lm_output, sentence_output = pretrainer_model( + [input_word_ids, input_mask, input_type_ids, masked_lm_positions]) pretrain_loss_layer = BertPretrainLossAndMetricLayer( vocab_size=bert_config.vocab_size) - output_loss = pretrain_loss_layer(lm_output, sentence_output, masked_lm_ids, - masked_lm_weights, next_sentence_labels) - keras_model = tf.keras.Model( + if FLAGS.use_packed_model: + output_loss = pretrain_loss_layer(lm_output, sentence_output, masked_lm_ids, + masked_lm_weights, next_sentence_labels, next_sentence_weights) + else: + output_loss = pretrain_loss_layer(lm_output, sentence_output, masked_lm_ids, + masked_lm_weights, next_sentence_labels) + if FLAGS.use_packed_model: + keras_model = tf.keras.Model( inputs={ 'input_word_ids': input_word_ids, 'input_mask': input_mask, @@ -225,6 +264,20 @@ def pretrain_model(bert_config, 'masked_lm_ids': masked_lm_ids, 'masked_lm_weights': masked_lm_weights, 'next_sentence_labels': next_sentence_labels, + 'next_sentence_weights': next_sentence_weights, + 'next_sentence_positions': next_sentence_positions, + }, + outputs=output_loss) + else: + keras_model = tf.keras.Model( + inputs={ + 'input_word_ids': input_word_ids, + 'input_mask': input_mask, + 'input_type_ids': input_type_ids, + 'masked_lm_positions': masked_lm_positions, + 'masked_lm_ids': masked_lm_ids, + 'masked_lm_weights': masked_lm_weights, + 'next_sentence_labels': next_sentence_labels, }, outputs=output_loss) return keras_model, transformer_encoder, pretrainer_model diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/input_pipeline.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/input_pipeline.py index 724d2887f2fe528b719eb37957a445d94c45eccb..37f34748d411ec1c5af8410d8ef5f986f417aace 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/input_pipeline.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/input_pipeline.py @@ -40,7 +40,8 @@ import npu_device as npu from npu_device.npu_device import global_npu_ctx import numpy as np from tf2_common.utils.dataset_unpad import _batch_examples - +from absl import flags +FLAGS=flags.FLAGS def decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" @@ -121,22 +122,44 @@ def create_pretrain_dataset(input_patterns, input_pipeline_context=None, num_eval_samples=None): """Creates input dataset from (tf)records files for pretraining.""" - name_to_features = { + if FLAGS.use_packed_model: + name_to_features = { 'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64), 'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64), 'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64), + 'positions_ids': + tf.io.FixedLenFeature([seq_length], tf.int64), 'masked_lm_positions': tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), 'masked_lm_ids': tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), 'masked_lm_weights': - tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32), - 'next_sentence_labels': - tf.io.FixedLenFeature([1], tf.int64), - } + tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), + + 'next_sentence_positions': tf.io.FixedLenFeature([FLAGS.max_sequences_per_pack], tf.int64), + 'next_sentence_labels': tf.io.FixedLenFeature([FLAGS.max_sequences_per_pack], tf.int64), + 'next_sentence_weights': tf.io.FixedLenFeature([FLAGS.max_sequences_per_pack], tf.int64), + } + else: + name_to_features = { + 'input_ids': + tf.io.FixedLenFeature([seq_length], tf.int64), + 'input_mask': + tf.io.FixedLenFeature([seq_length], tf.int64), + 'segment_ids': + tf.io.FixedLenFeature([seq_length], tf.int64), + 'masked_lm_positions': + tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), + 'masked_lm_ids': + tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), + 'masked_lm_weights': + tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32), + 'next_sentence_labels': + tf.io.FixedLenFeature([1], tf.int64), + } if use_synthetic: dataset = create_synthetic_pretrain_dataset( @@ -188,15 +211,28 @@ def create_pretrain_dataset(input_patterns, def _select_data_from_record(record): """Filter out features to use for pretraining.""" - x = { - 'input_word_ids': record['input_ids'], - 'input_mask': record['input_mask'], - 'input_type_ids': record['segment_ids'], - 'masked_lm_positions': record['masked_lm_positions'], - 'masked_lm_ids': record['masked_lm_ids'], - 'masked_lm_weights': record['masked_lm_weights'], - 'next_sentence_labels': record['next_sentence_labels'], - } + if FLAGS.use_packed_model: + x = { + 'input_word_ids': record['input_ids'], + 'input_mask': record['input_mask'], + 'input_type_ids': record['segment_ids'], + 'masked_lm_positions': record['masked_lm_positions'], + 'masked_lm_ids': record['masked_lm_ids'], + 'masked_lm_weights': record['masked_lm_weights'], + 'next_sentence_labels': record['next_sentence_labels'], + 'next_sentence_positions': record['next_sentence_positions'], + 'next_sentence_weights': record['next_sentence_weights'], + } + else: + x = { + 'input_word_ids': record['input_ids'], + 'input_mask': record['input_mask'], + 'input_type_ids': record['segment_ids'], + 'masked_lm_positions': record['masked_lm_positions'], + 'masked_lm_ids': record['masked_lm_ids'], + 'masked_lm_weights': record['masked_lm_weights'], + 'next_sentence_labels': record['next_sentence_labels'], + } y = record['masked_lm_weights'] diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/layers/self_attention_mask.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/layers/self_attention_mask.py index 7569e3c25b2407a44e97a3f87e96139cb7d1e722..edda39d164328bd403c096627a1c00097bdf85d9 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/layers/self_attention_mask.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/layers/self_attention_mask.py @@ -21,6 +21,8 @@ from __future__ import print_function import tensorflow as tf from tf2_common.modeling import tf_utils +from absl import flags +FLAGS=flags.FLAGS @tf.keras.utils.register_keras_serializable(package='Text') class SelfAttentionMask(tf.keras.layers.Layer): @@ -44,19 +46,30 @@ class SelfAttentionMask(tf.keras.layers.Layer): to_shape = tf_utils.get_shape_list(to_mask, expected_rank=2) to_seq_length = to_shape[1] - to_mask = tf.cast( - tf.reshape(to_mask, [batch_size, 1, to_seq_length]), - dtype=from_tensor.dtype) + if FLAGS.use_packed_model: + mask_tile = tf.tile(to_mask, [1, to_seq_length]) + mask_tile = tf.reshape(mask_tile, (batch_size * to_seq_length, to_seq_length)) + reshape_mask = tf.reshape(to_mask, (1, -1)) + broadcast_mask = tf.broadcast_to(reshape_mask, (to_seq_length, batch_size * to_seq_length)) + transpose_mask = tf.transpose(broadcast_mask, (1, 0)) + equal_mask = tf.equal(mask_tile, transpose_mask) + equal_mask = tf.cast(equal_mask, dtype=tf.float32) + mask = tf.reshape(equal_mask, [batch_size, to_seq_length, to_seq_length]) - # We don't assume that `from_tensor` is a mask (although it could be). We - # don't actually care if we attend *from* padding tokens (only *to* padding) - # tokens so we create a tensor of all ones. - # - # `broadcast_ones` = [batch_size, from_seq_length, 1] - broadcast_ones = tf.ones( - shape=[batch_size, from_seq_length, 1], dtype=from_tensor.dtype) + else: + to_mask = tf.cast( + tf.reshape(to_mask, [batch_size, 1, to_seq_length]), + dtype=from_tensor.dtype) - # Here we broadcast along two dimensions to create the mask. - mask = broadcast_ones * to_mask + # We don't assume that `from_tensor` is a mask (although it could be). We + # don't actually care if we attend *from* padding tokens (only *to* padding) + # tokens so we create a tensor of all ones. + # + # `broadcast_ones` = [batch_size, from_seq_length, 1] + broadcast_ones = tf.ones( + shape=[batch_size, from_seq_length, 1], dtype=from_tensor.dtype) + + # Here we broadcast along two dimensions to create the mask. + mask = broadcast_ones * to_mask return mask diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/networks/transformer_encoder.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/networks/transformer_encoder.py index ec6a120ce7c2d5047024c1c7407f7d56a187a4b6..b1528c473b31807f927c9cc2b4c5bd200a1e6f02 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/networks/transformer_encoder.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/modeling/networks/transformer_encoder.py @@ -26,6 +26,10 @@ from modeling.layers import util from modeling.layers import bert_dropout from modeling.layers import bert_layernorm +from tf2_common.modeling import tf_utils +from absl import flags +FLAGS = flags.FLAGS + @tf.keras.utils.register_keras_serializable(package='Text') class TransformerEncoder(tf.keras.Model): """Bi-directional Transformer-based encoder network. @@ -106,6 +110,10 @@ class TransformerEncoder(tf.keras.Model): shape=(sequence_length,), dtype=tf.int32, name='input_mask') type_ids = tf.keras.layers.Input( shape=(sequence_length,), dtype=tf.int32, name='input_type_ids') + + if FLAGS.use_packed_model: + next_sentence_starts = tf.keras.layers.Input( + shape=([FLAGS.max_sequences_per_pack,]), dtype=tf.int32, name='next_sentence_positions') self._embedding_layer = layers.OnDeviceEmbedding( vocab_size=vocab_size, @@ -156,10 +164,20 @@ class TransformerEncoder(tf.keras.Model): self._transformer_layers.append(layer) data = layer([data, attention_mask]) encoder_outputs.append(data) - - first_token_tensor = ( - tf.keras.layers.Lambda(lambda x: tf.squeeze(x[:, 0:1, :], axis=1))( - encoder_outputs[-1])) + if FLAGS.use_packed_model: + def first_token(args): + encoder_outputs, next_sentence_starts = args[0],args[1] + sequence_output = encoder_outputs[-1] + first_token_tensor = tf.gather(sequence_output, next_sentence_starts, axis=1, batch_dims=1, name=None) + first_token_tensor = tf.reshape(first_token_tensor, [-1, hidden_size]) + return first_token_tensor + + first_token_tensor = ( + tf.keras.layers.Lambda(first_token)([encoder_outputs, next_sentence_starts])) + else: + first_token_tensor = ( + tf.keras.layers.Lambda(lambda x: tf.squeeze(x[:, 0:1, :], axis=1))( + encoder_outputs[-1])) cls_output = tf.keras.layers.Dense( units=hidden_size, activation='tanh', @@ -171,9 +189,12 @@ class TransformerEncoder(tf.keras.Model): outputs = [encoder_outputs, cls_output] else: outputs = [encoder_outputs[-1], cls_output] - - super(TransformerEncoder, self).__init__( - inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs) + if FLAGS.use_packed_model: + super(TransformerEncoder, self).__init__( + inputs=[word_ids, mask, type_ids, next_sentence_starts], outputs=outputs, **kwargs) + else: + super(TransformerEncoder, self).__init__( + inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs) def get_embedding_table(self): return self._embedding_layer.embeddings diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining.py index 1d5959077af10cfef9e12a6760941ed67791a89b..2f7a2efb79f2ed27391b41397eda44a6ad305b7e 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining.py @@ -120,6 +120,21 @@ flags.DEFINE_integer('num_accumulation_steps', 1, 'number of steps to accumulate with large batch size.') flags.DEFINE_float('stop_threshold', 0.912, 'Stop threshold for MLPerf.') flags.DEFINE_float('poly_power', 1.0, 'The power of poly decay.') + +flags.DEFINE_boolean(name='use_packed_model', default=False, help='whether to enable packed model, default is True.') +flags.DEFINE_integer( + "max_sequences_per_pack", 3, + "Maximum number of sequences per sequence. " + "Must match data generation.") +flags.DEFINE_float( + "average_sequences_per_sample", 1.999, + "average number of sequences per sample. " + "Must match data generation.") +flags.DEFINE_float( + "average_sequences_per_eval_sample", 1.73, + "average number of sequences per sample. " + "Must match data generation.") + common_flags.define_common_bert_flags() FLAGS = flags.FLAGS diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining_bucket.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining_bucket.py index f9b770b7c6dace47809f548895fd5ba96de1267b..a1f4dc045a1577559c3bd21bcfebc084eebe0e3b 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining_bucket.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/run_pretraining_bucket.py @@ -121,7 +121,7 @@ flags.DEFINE_integer('num_accumulation_steps', 1, flags.DEFINE_float('stop_threshold', 0.912, 'Stop threshold for MLPerf.') flags.DEFINE_float('poly_power', 1.0, 'The power of poly decay.') -flags.DEFINE_multi_integer("seq_len_buckets", [64,128,192,256,384,512], +flags.DEFINE_multi_integer("seq_len_buckets", [64,128,192,256,320,384,448,512], "sequence length bucketizations boundaries") flags.DEFINE_integer('max_tockens_num', 12288, 'max_tockens_num = bs * seq_len') @@ -132,9 +132,22 @@ FLAGS = flags.FLAGS def npu_config(): FLAGS = flags.FLAGS - npu_device.global_options().input_shape = "data_0:-1,-1;data_1:-1,-1;data_2:-1,-1;data_3:-1,-1;data_4:-1,-1;data_5:-1,-1;data_6:-1,-1" - npu_device.global_options().dynamic_node_type = "0" - npu_device.global_options().dynamic_dims = "192,64,192,64,192,64,192,76,192,76,192,76,192,1;96,128,96,128,96,128,96,76,96,76,96,76,96,1;64,192,64,192,64,192,64,76,64,76,64,76,64,1;48,256,48,256,48,256,48,76,48,76,48,76,48,1;32,384,32,384,32,384,32,76,32,76,32,76,32,1;24,512,24,512,24,512,24,76,24,76,24,76,24,1" + npu_device.global_options().experimental.multi_branches_config.input_shape = "data_0:-1,-1;" \ + "data_1:-1,-1;" \ + "data_2:-1,-1;" \ + "data_3:-1,-1;" \ + "data_4:-1,-1;" \ + "data_5:-1,-1;" \ + "data_6:-1,-1" + npu_device.global_options().experimental.multi_branches_config.dynamic_node_type = "0" + npu_device.global_options().experimental.multi_branches_config.dynamic_dims = "192,64,192,64,192,64,192,76,192,76,192,76,192,1;" \ + "96,128,96,128,96,128,96,76,96,76,96,76,96,1;" \ + "64,192,64,192,64,192,64,76,64,76,64,76,64,1;" \ + "48,256,48,256,48,256,48,76,48,76,48,76,48,1;" \ + "38,320,38,320,38,320,38,76,38,76,38,76,38,1;" \ + "32,384,32,384,32,384,32,76,32,76,32,76,32,1;" \ + "28,448,28,448,28,448,28,76,28,76,28,76,28,1;" \ + "24,512,24,512,24,512,24,76,24,76,24,76,24,1" if FLAGS.data_dump_flag: npu_device.global_options().dump_config.enable_dump = True diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/tf2_common/modeling/model_training_utils.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/tf2_common/modeling/model_training_utils.py index 1458b80d0a64c17e5db219c09a9d4dfc794baf1e..5dc20c6019a3ab9d03530489922a4d5db602cee8 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/tf2_common/modeling/model_training_utils.py +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/bert/tf2_common/modeling/model_training_utils.py @@ -36,6 +36,7 @@ import numpy as np import json import os import time +import math from absl import logging import tensorflow as tf @@ -44,6 +45,9 @@ from tf2_common.utils.misc import distribution_utils from tf2_common.utils.mlp_log import mlp_log import npu_device as npu +from absl import flags +FLAGS = flags.FLAGS + _SUMMARY_TXT = 'training_summary.txt' _MIN_SUMMARY_STEPS = 10 @@ -211,6 +215,8 @@ def run_customized_training_loop( by `model_fn` is None. """ mlperf_block_number = 1 + if FLAGS.use_packed_model: + eval_steps = int(math.floor(eval_steps / FLAGS.average_sequences_per_eval_sample)) if _sentinel is not None: raise ValueError('only call `run_customized_training_loop()` ' diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/set_ranktable.py b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/set_ranktable.py new file mode 100644 index 0000000000000000000000000000000000000000..c25b51462c5df2325462786688d4a206ee29fb9a --- /dev/null +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/set_ranktable.py @@ -0,0 +1,1740 @@ +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('-n', '--npu_nums', type=int, default='2', help='nums of npu') +parser.add_argument('-c', '--conf_path', type=str, default='./', help='the path of server_info') +FLAGS = parser.parse_args() + +import json +import os +server = [] +server_conf = [] +server_list = ["0", "1", "2", "3", "4", "5", "6", "7"] +if os.path.isdir(FLAGS.conf_path): + for f in os.listdir(FLAGS.conf_path): + if (f.split("_")[-1]).split(".")[0] in server_list and (f.split("_")[-1]).split(".")[1] == 'info' and f.split("_")[0] == 'server': + server_conf.append(f) + + + + + + +rank_address = [] +for i in range(FLAGS.npu_nums): + for x in server_conf: + if (x.split("_")[-1]).split(".")[0] == str(i): + server.append(x.split("_")[1]) + l = FLAGS.conf_path + "/" + x + with open(l, "r") as a: + s = a.readlines() + for s_ in s: + if 'address_0' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_1' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_2' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_3' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_4' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_5' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_6' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + for s_ in s: + if 'address_7' in s_: + rank_address.append(s_.split("=")[-1][:-1]) + +if FLAGS.npu_nums == 1: + rank = { + "server_count":"1", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 2: + rank = { + "server_count":"2", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]} + ], + + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 3: + rank = { + "server_count":"3", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 4: + rank = { + "server_count":"4", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]} + ], + "status":"completed", + "version":"1.0" + } +elif FLAGS.npu_nums == 5: + rank = { + "server_count":"5", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + +elif FLAGS.npu_nums == 6: + rank = { + "server_count":"6", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + +elif FLAGS.npu_nums == 7: + rank = { + "server_count":"7", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +elif FLAGS.npu_nums == 8: + rank = { + "server_count":"8", + "server_list":[ + { + "server_id":server[0], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[0], + "rank_id":"0" + }, + { + "device_id":"1", + "device_ip":rank_address[1], + "rank_id":"1" + }, + { + "device_id":"2", + "device_ip":rank_address[2], + "rank_id":"2" + }, + { + "device_id":"3", + "device_ip":rank_address[3], + "rank_id":"3" + }, + { + "device_id":"4", + "device_ip":rank_address[4], + "rank_id":"4" + }, + { + "device_id":"5", + "device_ip":rank_address[5], + "rank_id":"5" + }, + { + "device_id":"6", + "device_ip":rank_address[6], + "rank_id":"6" + }, + { + "device_id":"7", + "device_ip":rank_address[7], + "rank_id":"7" + } + ]}, + + + { + "server_id":server[1], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[8], + "rank_id":"8" + }, + { + "device_id":"1", + "device_ip":rank_address[9], + "rank_id":"9" + }, + { + "device_id":"2", + "device_ip":rank_address[10], + "rank_id":"10" + }, + { + "device_id":"3", + "device_ip":rank_address[11], + "rank_id":"11" + }, + { + "device_id":"4", + "device_ip":rank_address[12], + "rank_id":"12" + }, + { + "device_id":"5", + "device_ip":rank_address[13], + "rank_id":"13" + }, + { + "device_id":"6", + "device_ip":rank_address[14], + "rank_id":"14" + }, + { + "device_id":"7", + "device_ip":rank_address[15], + "rank_id":"15" + } + ]}, + { + "server_id":server[2], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[16], + "rank_id":"16" + }, + { + "device_id":"1", + "device_ip":rank_address[17], + "rank_id":"17" + }, + { + "device_id":"2", + "device_ip":rank_address[18], + "rank_id":"18" + }, + { + "device_id":"3", + "device_ip":rank_address[19], + "rank_id":"19" + }, + { + "device_id":"4", + "device_ip":rank_address[20], + "rank_id":"20" + }, + { + "device_id":"5", + "device_ip":rank_address[21], + "rank_id":"21" + }, + { + "device_id":"6", + "device_ip":rank_address[22], + "rank_id":"22" + }, + { + "device_id":"7", + "device_ip":rank_address[23], + "rank_id":"23" + } + ]}, + { + "server_id":server[3], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[24], + "rank_id":"24" + }, + { + "device_id":"1", + "device_ip":rank_address[25], + "rank_id":"25" + }, + { + "device_id":"2", + "device_ip":rank_address[26], + "rank_id":"26" + }, + { + "device_id":"3", + "device_ip":rank_address[27], + "rank_id":"27" + }, + { + "device_id":"4", + "device_ip":rank_address[28], + "rank_id":"28" + }, + { + "device_id":"5", + "device_ip":rank_address[29], + "rank_id":"29" + }, + { + "device_id":"6", + "device_ip":rank_address[30], + "rank_id":"30" + }, + { + "device_id":"7", + "device_ip":rank_address[31], + "rank_id":"31" + } + ]}, + { + "server_id":server[4], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[32], + "rank_id":"32" + }, + { + "device_id":"1", + "device_ip":rank_address[33], + "rank_id":"33" + }, + { + "device_id":"2", + "device_ip":rank_address[34], + "rank_id":"34" + }, + { + "device_id":"3", + "device_ip":rank_address[35], + "rank_id":"35" + }, + { + "device_id":"4", + "device_ip":rank_address[36], + "rank_id":"36" + }, + { + "device_id":"5", + "device_ip":rank_address[37], + "rank_id":"37" + }, + { + "device_id":"6", + "device_ip":rank_address[38], + "rank_id":"38" + }, + { + "device_id":"7", + "device_ip":rank_address[39], + "rank_id":"39" + } + ]}, + { + "server_id":server[5], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[40], + "rank_id":"40" + }, + { + "device_id":"1", + "device_ip":rank_address[41], + "rank_id":"41" + }, + { + "device_id":"2", + "device_ip":rank_address[42], + "rank_id":"42" + }, + { + "device_id":"3", + "device_ip":rank_address[43], + "rank_id":"43" + }, + { + "device_id":"4", + "device_ip":rank_address[44], + "rank_id":"44" + }, + { + "device_id":"5", + "device_ip":rank_address[45], + "rank_id":"45" + }, + { + "device_id":"6", + "device_ip":rank_address[46], + "rank_id":"46" + }, + { + "device_id":"7", + "device_ip":rank_address[47], + "rank_id":"47" + } + ]}, + { + "server_id":server[6], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[48], + "rank_id":"48" + }, + { + "device_id":"1", + "device_ip":rank_address[49], + "rank_id":"49" + }, + { + "device_id":"2", + "device_ip":rank_address[50], + "rank_id":"50" + }, + { + "device_id":"3", + "device_ip":rank_address[51], + "rank_id":"51" + }, + { + "device_id":"4", + "device_ip":rank_address[52], + "rank_id":"52" + }, + { + "device_id":"5", + "device_ip":rank_address[53], + "rank_id":"53" + }, + { + "device_id":"6", + "device_ip":rank_address[54], + "rank_id":"54" + }, + { + "device_id":"7", + "device_ip":rank_address[55], + "rank_id":"55" + } + ]}, + { + "server_id":server[7], + "device":[ + { + "device_id":"0", + "device_ip":rank_address[56], + "rank_id":"56" + }, + { + "device_id":"1", + "device_ip":rank_address[57], + "rank_id":"57" + }, + { + "device_id":"2", + "device_ip":rank_address[58], + "rank_id":"58" + }, + { + "device_id":"3", + "device_ip":rank_address[59], + "rank_id":"59" + }, + { + "device_id":"4", + "device_ip":rank_address[60], + "rank_id":"60" + }, + { + "device_id":"5", + "device_ip":rank_address[61], + "rank_id":"61" + }, + { + "device_id":"6", + "device_ip":rank_address[62], + "rank_id":"62" + }, + { + "device_id":"7", + "device_ip":rank_address[63], + "rank_id":"63" + } + ]} + ], + "status":"completed", + "version":"1.0" + } + + + + +with open("rank_table.json", "w") as f: + json.dump(rank, f) + + + + + + diff --git a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_1p.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_1p_24bs_packed.sh similarity index 51% rename from TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_1p.sh rename to TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_1p_24bs_packed.sh index f6d490715962f2d7da47144fb144f11ff9609f6c..60d5c60352100e5a344766175e44272efbd988a1 100644 --- a/TensorFlow/built-in/nlp/BertNV_Series_for_TensorFlow/test/train_ID3067_BertLarge-128_full_1p.sh +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_1p_24bs_packed.sh @@ -1,171 +1,218 @@ -#!/bin/bash - -#当前路径,不需要修改 -cur_path=`pwd` - -#集合通信参数,不需要修改 -export RANK_SIZE=1 -export JOB_ID=99990001 -RANK_ID_START=0 - -# 数据集路径,保持为空,不需要修改 -data_path="" - -#基础参数，需要模型审视修改 -#网络名称，同目录名称 -Network="BertLarge-128_ID3067_for_TensorFlow" -#训练epoch -train_epochs=1 -#训练batch_size -batch_size=24 -#训练step -train_steps=100000 -#学习率 -learning_rate= - -#维测参数，precision_mode需要模型审视修改 -#precision_mode="allow_mix_precision" -#维持参数，以下不需要修改 -over_dump=False -data_dump_flag=False -data_dump_step="10" -profiling=False -autotune=False - -# 帮助信息，不需要修改 -if [[ $1 == --help || $1 == -h ]];then - echo"usage:./train_full_1p.sh " - echo " " - echo "parameter explain: - --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) - --over_dump if or not over detection, default is False - --data_dump_flag data dump flag, default is False - --data_dump_step data dump step, default is 10 - --profiling if or not profiling for performance debug, default is False - --autotune whether to enable autotune, default is False - --data_path source data of training - -h/--help show help message - " - exit 1 -fi - -#参数校验，不需要修改 -for para in $* -do - if [[ $para == --precision_mode* ]];then - precision_mode=`echo ${para#*=}` - elif [[ $para == --over_dump* ]];then - over_dump=`echo ${para#*=}` - over_dump_path=${cur_path}/output/overflow_dump - mkdir -p ${over_dump_path} - elif [[ $para == --data_dump_flag* ]];then - data_dump_flag=`echo ${para#*=}` - data_dump_path=${cur_path}/output/data_dump - mkdir -p ${data_dump_path} - elif [[ $para == --data_dump_step* ]];then - data_dump_step=`echo ${para#*=}` - elif [[ $para == --profiling* ]];then - profiling=`echo ${para#*=}` - profiling_dump_path=${cur_path}/output/profiling - mkdir -p ${profiling_dump_path} - elif [[ $para == --data_path* ]];then - data_path=`echo ${para#*=}` - fi -done - -#校验是否传入data_path,不需要修改 -if [[ $data_path == "" ]];then - echo "[Error] para \"data_path\" must be confing" - exit 1 -fi - -#训练开始时间，不需要修改 -start_time=$(date +%s) -#进入训练脚本目录，需要模型审视修改 -for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); -do - #设置环境变量，不需要修改 - echo "Device ID: $ASCEND_DEVICE_ID" - export RANK_ID=$RANK_ID - - #创建DeviceID输出目录，不需要修改 - if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then - rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - else - mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt${ASCEND_DEVICE_ID} - fi - - #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 - #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune - nohup python3.7 $cur_path/../src/run_pretraining.py --bert_config_file=${cur_path}/../configs/bert_large_config.json \ - --max_seq_length=128 \ - --max_predictions_per_seq=20 \ - --train_batch_size=${batch_size} \ - --learning_rate=1e-4 \ - --num_warmup_steps=10000 \ - --num_train_steps=${train_steps} \ - --optimizer_type=adam \ - --manual_fp16=True \ - --use_fp16_cls=True \ - --input_files_dir=${data_path}/train_phase1 \ - --eval_files_dir=${data_path}/eval_phase1 \ - --npu_bert_debug=False \ - --npu_bert_use_tdt=True \ - --do_train=True \ - --num_accumulation_steps=1 \ - --npu_bert_job_start_file= \ - --iterations_per_loop=1000 \ - --save_checkpoints_steps=1000 \ - --npu_bert_clip_by_global_norm=False \ - --distributed=False \ - --npu_bert_loss_scale=0 \ - --init_loss_scale_value=1 \ - --over_dump=${over_dump} \ - --over_dump_path=${over_dump_path} \ - --output_dir=${cur_path}/output/${ASCEND_DEVICE_ID}/ckpt${ASCEND_DEVICE_ID} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & -done -wait - -#训练结束时间，不需要修改 -end_time=$(date +%s) -e2e_time=$(( $end_time - $start_time )) - -#结果打印，不需要修改 -echo "------------------ Final result ------------------" -#输出性能FPS，需要模型审视修改 -ActualFPS=`grep Throughput ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $6}'` -TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}' * '${RANK_SIZE}' / '${ActualFPS}'}'` -#打印，不需要修改 -echo "Final Performance images/sec : $ActualFPS" - -#输出训练精度,需要模型审视修改 -TrainAccuracy=`grep -A 1 top1 $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $3}'` -#打印，不需要修改 -echo "Final Train Accuracy : ${TrainAccuracy}" -echo "E2E Training Duration sec : $e2e_time" - -#稳定性精度看护结果汇总 -#训练用例信息，不需要修改 -BatchSize=${batch_size} -DeviceType=`uname -m` -CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc' - - -#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 -grep "tensorflow:loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss = " '{print $2}' | awk -F "," '{print $1}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt - -#最后一个迭代loss值，不需要修改 -ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` - -#关键信息打印到${CaseName}.log中，不需要修改 -echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log -echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge_ID0634_for_TensorFlow2.X" + +#训练batch_size +eval_batch_size=4 +batch_size=24 +average_sequences_per_sample=2 +#训练step +train_steps=1000 +#训练epoch +train_epochs=`expr 768 / ${batch_size}` +#学习率 +learning_rate=0.000058711 + +#TF2.X独有，不需要修改 +#export NPU_LOOP_SIZE=${train_steps} +export NPU_LOOP_SIZE=1000 +export GE_USE_STATIC_MEMORY=1 + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_1p.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +init_ckpt_path=${data_path}/'output_ckpt/model.ckpt-28252' #need modify to actual path +train_files_path=${data_path}/'train_packed/*' #need modify to actual path +eval_files_path=${data_path}/'eval_packed/*' #need modify to actual path + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup taskset -c $a-$c python3 ../bert/run_pretraining.py \ + --use_packed_model=True \ + --all_reduce_alg=nccl \ + --bert_config_file=../configs/bert_config.json \ + --beta_1=0.91063 \ + --beta_2=0.96497 \ + --device_warmup=False \ + --do_eval=True \ + --dtype=fp16 \ + --eval_batch_size=${eval_batch_size} \ + --init_checkpoint=${init_ckpt_path} \ + --train_files=${train_files_path} \ + --eval_files=${eval_files_path} \ + --learning_rate=${learning_rate} \ + --loss_scale=dynamic \ + --max_predictions_per_seq=79 \ + --max_seq_length=512 \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} \ + --num_accumulation_steps=1 \ + --distribution_strategy=one_device \ + --num_gpus=1 \ + --num_steps_per_epoch=8000 \ + --num_train_epochs=${train_epochs} \ + --optimizer_type=lamb \ + --scale_loss=False \ + --stop_threshold=0.95 \ + --steps_between_eval=1000 \ + --steps_per_loop=${NPU_LOOP_SIZE} \ + --stop_steps=100000 \ + --enable_checkpoint_and_summary=True \ + --train_batch_size=${batch_size} \ + --verbosity=0 \ + --warmup_steps=0 \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${batch_size}'*'${average_sequences_per_sample}'}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p_packed'_'acc' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_bucket.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_bucket.sh index c6e9d5be8bddb513a44ef59641a610de85af041c..9f6dac2b527452cd9eebf9cb5aa1135a68e7648a 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_bucket.sh +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_bucket.sh @@ -177,9 +177,14 @@ e2e_time=$(( $end_time - $start_time )) #############结果处理######################### echo "------------------ Final result ------------------" #输出性能FPS，需要模型审视修改 -single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` -avg_bs=`grep avg_bs $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` -FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${avg_bs}'*8}'` +FPS=0.0 +for((ID=0; ID<8; ID++)) +do + single_batch_step_sec=`grep TimeHistory $cur_path/output/${ID}/train_${ID}.log|awk 'END {print $8}'` + avg_bs=`grep avg_bs $cur_path/output/${ID}/train_${ID}.log|awk 'END {print $8}'` + PER_FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${avg_bs}'}'` + FPS=`awk 'BEGIN{printf "%.2f\n",'${PER_FPS}'+'${FPS}'}'` +done #打印，不需要修改 echo "Final Performance images/sec : $FPS" diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_packed.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_packed.sh new file mode 100644 index 0000000000000000000000000000000000000000..e7157da722666ba45a73ef09471b16a8fd4b8ff3 --- /dev/null +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_full_8p_192bs_packed.sh @@ -0,0 +1,229 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下 +export RANK_SIZE=8 +export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数 需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge_ID0634_for_TensorFlow2.X" + + +#训练batch_size +batch_size=192 +eval_batch_size=16 +average_sequences_per_sample=2 +#训练step +train_steps=1000 +#训练epoch +train_epochs=`expr 768 / ${batch_size}` +#学习率 +learning_rate=0.0002 + +#TF2.X独有，需要模型审视修改 +export NPU_LOOP_SIZE=1000 +export GE_USE_STATIC_MEMORY=1 + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_8p_32bs.sh " + + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is 0 + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，需要模型审视修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --bind_core* ]]; then + bind_core=`echo ${para#*=}` + name_bind="_bindcore" + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +init_ckpt_path=${data_path}/'output_ckpt/model.ckpt-28252' #need modify to actual path +train_files_path=${data_path}/'train_packed/*' #need modify to actual path +eval_files_path=${data_path}/'eval_packed/*' #need modify to actual path + + + +start_time=$(date +%s) +#############执行训练######################### +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + ASCEND_DEVICE_ID=$RANK_ID + + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + if [ "x${bind_core}" != x ];then + bind_core="taskset -c $a-$c" + fi + nohup ${bind_core} python3 ../bert/run_pretraining.py \ + --use_packed_model=True \ + --all_reduce_alg=nccl \ + --bert_config_file=../configs/bert_config.json \ + --beta_1=0.91063 \ + --beta_2=0.96497 \ + --device_warmup=False \ + --do_eval=True \ + --dtype=fp16 \ + --eval_batch_size=${eval_batch_size} \ + --init_checkpoint=${init_ckpt_path} \ + --train_files=${train_files_path} \ + --eval_files=${eval_files_path} \ + --learning_rate=${learning_rate} \ + --loss_scale=dynamic \ + --max_predictions_per_seq=79 \ + --max_seq_length=512 \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} \ + --num_accumulation_steps=1 \ + --distribution_strategy=one_device \ + --num_gpus=1 \ + --use_npu_lamb=True \ + --use_mixlist=True \ + --num_steps_per_epoch=8000 \ + --stop_threshold=0.95 \ + --num_train_epochs=${train_epochs} \ + --optimizer_type=lamb \ + --enable_checkpoint_and_summary=True \ + --scale_loss=False \ + --steps_between_eval=1000 \ + --steps_per_loop=${NPU_LOOP_SIZE} \ + --stop_steps=32000 \ + --train_batch_size=${batch_size} \ + --verbosity=0 \ + --warmup_steps=0 \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${batch_size}'*'${average_sequences_per_sample}'}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p_packed'_'acc' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + sed -i "/AttributeError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log + sed -i "/ModuleNotFoundError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log +done \ No newline at end of file diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_16p_384bs.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_16p_384bs.sh new file mode 100644 index 0000000000000000000000000000000000000000..f2b82938dc2a36324d04e30a1ec70b9c73c6f02c --- /dev/null +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_16p_384bs.sh @@ -0,0 +1,231 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下 +export RANK_SIZE=16 +#export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json +export JOB_ID=10087 +RANK_ID_START=0 + +# 数据集路径,保持为空,不需要修改 +data_path="" +server_index="" +conf_path="" +#基础参数 需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge_ID0634_for_TensorFlow2.X" +#训练batch_size +batch_size=384 +eval_batch_size=32 +#训练step +train_steps=1000 +#训练epoch +train_epochs=`expr 768 / ${batch_size}` +#学习率 +learning_rate=0.0007 + +#TF2.X独有，需要模型审视修改 +export NPU_LOOP_SIZE=100 +export GE_USE_STATIC_MEMORY=1 + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_8p_32bs.sh " + + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is 0 + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，需要模型审视修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --bind_core* ]]; then + bind_core=`echo ${para#*=}` + name_bind="_bindcore" + elif [[ $para == --server_index* ]];then + server_index=`echo ${para#*=}` + elif [[ $para == --conf_path* ]];then + conf_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +init_ckpt_path=${data_path}/tf2_ckpt/model.ckpt-28252 #need modify to actual path +train_files_path=${data_path}/'train/*' #need modify to actual path +eval_files_path=${data_path}/'eval/eval.tfrecord' #need modify to actual path + +rank_size=8 +nohup python3 set_ranktable.py --npu_nums=$((RANK_SIZE/rank_size)) --conf_path=$conf_path +export RANK_TABLE_FILE=${cur_path}/rank_table.json +export HCCL_CONNECT_TIMEOUT=600 +RANK_ID_START=0 + +start_time=$(date +%s) +#############执行训练######################### +for((RANK_ID=$((rank_size*server_index));RANK_ID<$((((server_index+1))*rank_size));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + ASCEND_DEVICE_ID=`expr ${RANK_ID} - $((rank_size*server_index))` + + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + if [ "x${bind_core}" != x ];then + bind_core="taskset -c $a-$c" + fi + nohup ${bind_core} python3 ../bert/run_pretraining.py \ + --all_reduce_alg=nccl \ + --bert_config_file=../configs/bert_config.json \ + --beta_1=0.91063 \ + --beta_2=0.96497 \ + --device_warmup=False \ + --do_eval=True \ + --dtype=fp16 \ + --init_checkpoint=${init_ckpt_path} \ + --eval_batch_size=${eval_batch_size} \ + --train_files=${train_files_path} \ + --eval_files=${eval_files_path} \ + --learning_rate=${learning_rate} \ + --loss_scale=dynamic \ + --max_predictions_per_seq=76 \ + --max_seq_length=512 \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} \ + --num_accumulation_steps=1 \ + --distribution_strategy=one_device \ + --num_gpus=1 \ + --enable_checkpoint_and_summary=True \ + --num_steps_per_epoch=1000 \ + --num_train_epochs=${train_epochs} \ + --optimizer_type=lamb \ + --scale_loss=False \ + --steps_between_eval=100 \ + --steps_per_loop=${NPU_LOOP_SIZE} \ + --stop_steps=200 \ + --train_batch_size=${batch_size} \ + --verbosity=0 \ + --warmup_steps=0 \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${batch_size}'}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + sed -i "/AttributeError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log + sed -i "/ModuleNotFoundError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log +done diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_1p_24bs_packed.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_1p_24bs_packed.sh new file mode 100644 index 0000000000000000000000000000000000000000..3ddc8aef045b7a786518437cead3bf1fc43b5c6f --- /dev/null +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_1p_24bs_packed.sh @@ -0,0 +1,219 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +RANK_ID_START=0 + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge_ID0634_for_TensorFlow2.X" +#训练batch_size +eval_batch_size=4 +batch_size=24 +average_sequences_per_sample=2 +#训练step +train_steps=1000 +#训练epoch +train_epochs=`expr 768 / ${batch_size}` +#学习率 +learning_rate=0.000058711 + +#TF2.X独有，不需要修改 +#export NPU_LOOP_SIZE=${train_steps} +export NPU_LOOP_SIZE=100 +export GE_USE_STATIC_MEMORY=1 +export NPU_ENABLE_PERF=true + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_1p.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +init_ckpt_path=${data_path}/'output_ckpt/model.ckpt-28252' #need modify to actual path +train_files_path=${data_path}/'train_packed/*' #need modify to actual path +eval_files_path=${data_path}/'eval_packed/*' #need modify to actual path + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup taskset -c $a-$c python3 ../bert/run_pretraining.py \ + --use_packed_model=True \ + --all_reduce_alg=nccl \ + --bert_config_file=../configs/bert_config.json \ + --beta_1=0.91063 \ + --beta_2=0.96497 \ + --device_warmup=False \ + --do_eval=True \ + --dtype=fp16 \ + --eval_batch_size=${eval_batch_size} \ + --init_checkpoint=${init_ckpt_path} \ + --train_files=${train_files_path} \ + --eval_files=${eval_files_path} \ + --learning_rate=${learning_rate} \ + --loss_scale=dynamic \ + --max_predictions_per_seq=79 \ + --max_seq_length=512 \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} \ + --num_accumulation_steps=1 \ + --distribution_strategy=one_device \ + --num_gpus=1 \ + --num_steps_per_epoch=1000 \ + --num_train_epochs=${train_epochs} \ + --optimizer_type=lamb \ + --scale_loss=False \ + --steps_between_eval=100 \ + --steps_per_loop=${NPU_LOOP_SIZE} \ + --stop_steps=200 \ + --enable_checkpoint_and_summary=True \ + --train_batch_size=${batch_size} \ + --verbosity=0 \ + --warmup_steps=0 \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#结果打印，不需要修改 +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${batch_size}'*'${average_sequences_per_sample}'}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p_packed'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +sed -i "/AttributeError/d" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log \ No newline at end of file diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs.sh index f40ad087a7c25549c789b02d7bf403961a0a15e5..b9832cc804195b14e5e6fae62ae2d204483da387 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs.sh +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#'!/bin/bash #当前路径,不需要修改 cur_path=`pwd` @@ -88,7 +88,7 @@ if [[ $data_path == "" ]];then exit 1 fi -init_ckpt_path=${data_path}/tf2_ckpt/model.ckpt-28252 #need modify to actual path +init_ckpt_path=${data_path}/'tf2_ckpt/model.ckpt-28252' #need modify to actual path train_files_path=${data_path}/'train/*' #need modify to actual path eval_files_path=${data_path}/'eval/eval.tfrecord' #need modify to actual path diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_bucket.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_bucket.sh index b82b734fe13b7551a3e5f5c42b5392d102c45a4d..225afcb73aa0ae9ac7ee01ef069df6d96b1a0225 100644 --- a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_bucket.sh +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_bucket.sh @@ -173,9 +173,14 @@ e2e_time=$(( $end_time - $start_time )) #############结果处理######################### echo "------------------ Final result ------------------" #输出性能FPS，需要模型审视修改 -single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` -avg_bs=`grep avg_bs $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` -FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${avg_bs}'*8}'` +FPS=0.0 +for((ID=0; ID<8; ID++)) +do + single_batch_step_sec=`grep TimeHistory $cur_path/output/${ID}/train_${ID}.log|awk 'END {print $8}'` + avg_bs=`grep avg_bs $cur_path/output/${ID}/train_${ID}.log|awk 'END {print $8}'` + PER_FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${avg_bs}'}'` + FPS=`awk 'BEGIN{printf "%.2f\n",'${PER_FPS}'+'${FPS}'}'` +done #打印，不需要修改 echo "Final Performance images/sec : $FPS" diff --git a/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_packed.sh b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_packed.sh new file mode 100644 index 0000000000000000000000000000000000000000..25e660e708e4e9df418cd224847613ffe1800547 --- /dev/null +++ b/TensorFlow2/built-in/nlp/BertLarge_ID0634_for_TensorFlow2.X/test/train_performance_8p_192bs_packed.sh @@ -0,0 +1,225 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 +#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下 +export RANK_SIZE=8 +export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json +export JOB_ID=10087 +RANK_ID_START=0 + +export NPU_ENABLE_PERF=true +# 数据集路径,保持为空,不需要修改 +data_path="" + +#基础参数 需要模型审视修改 +#网络名称，同目录名称 +Network="BertLarge_ID0634_for_TensorFlow2.X" +#训练batch_size +batch_size=192 +eval_batch_size=16 +average_sequences_per_sample=2 +#训练step +train_steps=1000 +#训练epoch +train_epochs=`expr 768 / ${batch_size}` +#学习率 +learning_rate=0.000144 + +#TF2.X独有，需要模型审视修改 +export NPU_LOOP_SIZE=100 +export GE_USE_STATIC_MEMORY=1 + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_fp32_to_fp16" +#维持参数，不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_8p_32bs.sh " + + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is 0 + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，需要模型审视修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --bind_core* ]]; then + bind_core=`echo ${para#*=}` + name_bind="_bindcore" + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +init_ckpt_path=${data_path}/'output_ckpt/model.ckpt-28252' #need modify to actual path +train_files_path=${data_path}/'train_packed/*' #need modify to actual path +eval_files_path=${data_path}/'eval_packed/*' #need modify to actual path + + + +start_time=$(date +%s) +#############执行训练######################### +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $RANK_ID" + export RANK_ID=$RANK_ID + export ASCEND_DEVICE_ID=$RANK_ID + ASCEND_DEVICE_ID=$RANK_ID + + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + if [ "x${bind_core}" != x ];then + bind_core="taskset -c $a-$c" + fi + nohup ${bind_core} python3 ../bert/run_pretraining.py \ + --use_packed_model=True \ + --all_reduce_alg=nccl \ + --bert_config_file=../configs/bert_config.json \ + --beta_1=0.91063 \ + --beta_2=0.96497 \ + --device_warmup=False \ + --do_eval=True \ + --dtype=fp16 \ + --eval_batch_size=${eval_batch_size} \ + --init_checkpoint=${init_ckpt_path} \ + --train_files=${train_files_path} \ + --eval_files=${eval_files_path} \ + --learning_rate=${learning_rate} \ + --loss_scale=dynamic \ + --max_predictions_per_seq=79 \ + --max_seq_length=512 \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} \ + --num_accumulation_steps=1 \ + --distribution_strategy=one_device \ + --num_gpus=1 \ + --enable_checkpoint_and_summary=True \ + --num_steps_per_epoch=1000 \ + --num_train_epochs=${train_epochs} \ + --optimizer_type=lamb \ + --scale_loss=False \ + --steps_between_eval=100 \ + --steps_per_loop=${NPU_LOOP_SIZE} \ + --stop_steps=200 \ + --train_batch_size=${batch_size} \ + --verbosity=0 \ + --warmup_steps=0 \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############结果处理######################### +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'*'${batch_size}'*'${average_sequences_per_sample}'}'` +#打印，不需要修改 +echo "Final Performance images/sec : $FPS" + +#输出训练精度,需要模型审视修改 +train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p_packed'_'perf' + +##获取性能数据 +#吞吐量，不需要修改 +ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + sed -i "/AttributeError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log + sed -i "/ModuleNotFoundError/d" $cur_path/output/${RANK_ID}/train_${RANK_ID}.log +done \ No newline at end of file diff --git a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_4096bs_dynamic_noeval.sh b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_4096bs_dynamic_noeval.sh new file mode 100644 index 0000000000000000000000000000000000000000..632d4525fb67313007945d08934b57988b6df877 --- /dev/null +++ b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_4096bs_dynamic_noeval.sh @@ -0,0 +1,220 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +export RANK_ID_START=0 +export PYTHONPATH=../transformer:$PYTHONPATH + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="Transformer_ID0633_for_TensorFlow2.X" +#训练batch_size +batch_size=4096 +#训练step +train_steps=400000 + +#TF2.X独有，不需要修改 +#export NPU_ENABLE_PERF=true + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_1p.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup taskset -c $a-$c python3 ../transformer/official/nlp/transformer/transformer_main.py \ + --data_dir=${data_path} \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ + --vocab_file=${data_path}/vocab.ende.32768 \ + --param_set=big \ + --train_steps=${train_steps} \ + --batch_size=${batch_size} \ + --steps_between_evals=10000 \ + --max_length=64 \ + --mode=train \ + --decode_batch_size=32 \ + --decode_max_length=97 \ + --padded_decode=False \ + --num_gpus=1 \ + --dtype=fp16 \ + --distribution_strategy='one_device' \ + --enable_time_history=true \ + --log_steps=1000 \ + --loss_scale='dynamic' \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +# single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +# FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'}'` +#打印，不需要修改 +# echo "Final Performance images/sec : $FPS" + +# grep "Train history" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$8}'|sed 's/,//g'|sed 's/\[//g'|sed 's/\]//g' |sed 's/\}//g'>> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +# ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` +#输出训练精度,需要模型审视修改 +# grep "Train history" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$8}'|sed 's/,//g'|sed 's/\[//g'|sed 's/\]//g' |sed 's/\}//g'>> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt +# train_accuracy=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt` +#train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +# echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + + +#fail info +ModelStatus="图执行FAIL" +DTS_Number="DTS2022042410927" +error_msg="op\[SoftmaxCrossEntropyWithLogitsTiling\], not supported shape\[FUNC:DoNdTiling\]" +Status=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|wc -l` +error_msg=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tail -1` +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 + +#最后一个迭代loss值，不需要修改 + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ModelStatus = ${ModelStatus}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DTS_Number = ${DTS_Number}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "Status = ${Status}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "error_msg = ${error_msg}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +##获取性能数据 +#吞吐量，不需要修改 +# ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +# TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +#grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +#ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +#echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_6144bs_dynamic_noeval.sh b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_6144bs_dynamic_noeval.sh new file mode 100644 index 0000000000000000000000000000000000000000..adba4519454eeb9e1aca00cdfa9e12394192cc00 --- /dev/null +++ b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/test/train_full_1p_6144bs_dynamic_noeval.sh @@ -0,0 +1,221 @@ +#!/bin/bash + +#当前路径,不需要修改 +cur_path=`pwd` + +#集合通信参数,不需要修改 + +export RANK_SIZE=1 +export JOB_ID=10087 +export RANK_ID_START=0 +export PYTHONPATH=../transformer:$PYTHONPATH + + +# 数据集路径,保持为空,不需要修改 +data_path="" + +#设置默认日志级别,不需要修改 + +#基础参数，需要模型审视修改 +#网络名称，同目录名称 +Network="Transformer_ID0633_for_TensorFlow2.X" +#训练batch_size +batch_size=6144 +#训练step +train_steps=250000 + +#TF2.X独有，不需要修改 +#export NPU_ENABLE_PERF=true + +#维测参数，precision_mode需要模型审视修改 +precision_mode="allow_mix_precision" +#维持参数，以下不需要修改 +over_dump=False +data_dump_flag=False +data_dump_step="10" +profiling=False + + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_full_1p.sh " + echo " " + echo "parameter explain: + --precision_mode precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision) + --over_dump if or not over detection, default is False + --data_dump_flag data dump flag, default is False + --data_dump_step data dump step, default is 10 + --profiling if or not profiling for performance debug, default is False + --data_path source data of training + -h/--help show help message + " + exit 1 +fi + +#参数校验，不需要修改 +for para in $* +do + if [[ $para == --precision_mode* ]];then + precision_mode=`echo ${para#*=}` + elif [[ $para == --over_dump* ]];then + over_dump=`echo ${para#*=}` + over_dump_path=${cur_path}/output/overflow_dump + mkdir -p ${over_dump_path} + elif [[ $para == --data_dump_flag* ]];then + data_dump_flag=`echo ${para#*=}` + data_dump_path=${cur_path}/output/data_dump + mkdir -p ${data_dump_path} + elif [[ $para == --data_dump_step* ]];then + data_dump_step=`echo ${para#*=}` + elif [[ $para == --profiling* ]];then + profiling=`echo ${para#*=}` + profiling_dump_path=${cur_path}/output/profiling + mkdir -p ${profiling_dump_path} + elif [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + fi +done + +#校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be confing" + exit 1 +fi + +#训练开始时间，不需要修改 +start_time=$(date +%s) + +#进入训练脚本目录，需要模型审视修改 + +for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++)); +do + #设置环境变量，不需要修改 + echo "Device ID: $ASCEND_DEVICE_ID" + export RANK_ID=$RANK_ID + + + + #创建DeviceID输出目录，不需要修改 + if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then + rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID} + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + else + mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt_${learning_rate} + fi + + #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改 + cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'` + cpustep=`expr $cpucount / 8` + echo "taskset c steps:" $cpustep + let a=RANK_ID*$cpustep + let b=RANK_ID+1 + let c=b*$cpustep-1 + + #执行训练脚本，以下传参不需要修改，其他需要模型审视修改 + #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune + nohup taskset -c $a-$c python3 ../transformer/official/nlp/transformer/transformer_main.py \ + --data_dir=${data_path} \ + --model_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \ + --vocab_file=${data_path}/vocab.ende.32768 \ + --param_set=big \ + --train_steps=${train_steps} \ + --batch_size=${batch_size} \ + --steps_between_evals=10000 \ + --max_length=64 \ + --mode=train \ + --decode_batch_size=32 \ + --decode_max_length=97 \ + --padded_decode=False \ + --num_gpus=1 \ + --dtype=fp32 \ + --enable_metrics_in_training=true \ + --distribution_strategy='one_device' \ + --enable_time_history=true \ + --log_steps=1000 \ + --loss_scale='dynamic' \ + --precision_mode=${precision_mode} \ + --over_dump=${over_dump} \ + --over_dump_path=${over_dump_path} \ + --data_dump_flag=${data_dump_flag} \ + --data_dump_step=${data_dump_step} \ + --data_dump_path=${data_dump_path} \ + --profiling=${profiling} \ + --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 & +done +wait + +#训练结束时间，不需要修改 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +#############冒烟看护######################### +BatchSize=${batch_size} +#设备类型 +DeviceType=`uname -m` +#用例名称 +CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf' + +echo "------------------ Final result ------------------" +#输出性能FPS，需要模型审视修改 +# single_batch_step_sec=`grep TimeHistory $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print $8}'` +# FPS=`awk 'BEGIN{printf "%.2f\n",'${single_batch_step_sec}'}'` +#打印，不需要修改 +# echo "Final Performance images/sec : $FPS" + +# grep "Train history" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$8}'|sed 's/,//g'|sed 's/\[//g'|sed 's/\]//g' |sed 's/\}//g'>> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt +# ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` +#输出训练精度,需要模型审视修改 +# grep "Train history" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$8}'|sed 's/,//g'|sed 's/\[//g'|sed 's/\]//g' |sed 's/\}//g'>> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt +# train_accuracy=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_acc.txt` +#train_accuracy=`grep eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'|sed 's/,//g'|cut -c 1-5` +#打印，不需要修改 +# echo "Final Train Accuracy : ${train_accuracy}" +echo "E2E Training Duration sec : $e2e_time" + + +#fail info +ModelStatus="图执行FAIL" +DTS_Number="DTS2022042214040" +error_msg="Param:owner_graph is nullptr, check invalid" +Status=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|wc -l` +error_msg=`grep "${error_msg}" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|tail -1` +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视 + +#最后一个迭代loss值，不需要修改 + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ModelStatus = ${ModelStatus}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DTS_Number = ${DTS_Number}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "Status = ${Status}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "error_msg = ${error_msg}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log + +##获取性能数据 +#吞吐量，不需要修改 +# ActualFPS=${FPS} +#单迭代训练时长，不需要修改 +# TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'` + +##获取Loss +#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中 +#grep loss $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print$11}'|grep -v instead|grep -v masked_lm_loss|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt + +#最后一个迭代loss值 +#ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt` + +#关键信息打印到${CaseName}.log中 +#echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +#echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log diff --git a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/misc.py b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/misc.py index fd20e215d96aedacf4afd8d1f5ac9d02adf39e64..0678b396efb3f45c3a751d0ba1c9aa9e0f5807d7 100644 --- a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/misc.py +++ b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/misc.py @@ -217,6 +217,11 @@ def define_transformer_flags(): 'If True, then only the model\'s weights will be saved ' '(`model.save_weights(filepath)`), else the full model is saved ' '(`model.save(filepath)`)')) + flags.DEFINE_bool( + name='dynamic_eval', + default=False, + help=flags_core.help_wrap( + 'use dynamic eval')) flags_core.set_defaults( data_dir='/tmp/translate_ende', diff --git a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/transformer_main.py b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/transformer_main.py index 5b650f074a2a5e7c2022438a2893e99af6102c19..8f52dec383785e603a5935962df6790d44cf3717 100644 --- a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/transformer_main.py +++ b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/transformer_main.py @@ -233,6 +233,7 @@ class TransformerTask(object): params["steps_between_evals"] = flags_obj.steps_between_evals params["enable_checkpointing"] = flags_obj.enable_checkpointing params["save_weights_only"] = flags_obj.save_weights_only + params["dynamic_eval"] = flags_obj.dynamic_eval self.distribution_strategy = distribute_utils.get_distribution_strategy( distribution_strategy=flags_obj.distribution_strategy, diff --git a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/translate.py b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/translate.py index b0fb14827275b3434ab47d4afbd03102752c2aa7..485db5d75cace7a31b3d0fe18b795a12aa19b73c 100644 --- a/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/translate.py +++ b/TensorFlow2/built-in/nlp/Transformer_ID0633_for_TensorFlow2.X/transformer/official/nlp/transformer/translate.py @@ -65,6 +65,31 @@ def _get_sorted_inputs(filename,batch_size): return sorted_inputs, sorted_keys +def _get_sorted_inputs_dynamic(filename): + """Read and sort lines from the file sorted by decreasing length. + Args: + filename: String name of file to read inputs from. + Returns: + Sorted list of inputs, and dictionary mapping original index->sorted index + of each element. + """ + with tf.io.gfile.GFile(filename) as f: + records = f.read().split("\n") + inputs = [record.strip() for record in records] + if not inputs[-1]: + inputs.pop() + + input_lens = [(i, len(line.split())) for i, line in enumerate(inputs)] + sorted_input_lens = sorted(input_lens, key=lambda x: x[1], reverse=True) + + sorted_inputs = [None] * len(sorted_input_lens) + sorted_keys = [0] * len(sorted_input_lens) + for i, (index, _) in enumerate(sorted_input_lens): + sorted_inputs[i] = inputs[index] + sorted_keys[index] = i + return sorted_inputs, sorted_keys + + def _encode_and_add_eos(line, subtokenizer): """Encode line with subtokenizer, and add EOS id to the end.""" return subtokenizer.encode(line) + [tokenizer.EOS_ID] @@ -107,7 +132,11 @@ def translate_file(model, # Read and sort inputs by length. Keep dictionary (original index-->new index # in sorted list) to write translations in the original order. - sorted_inputs, sorted_keys = _get_sorted_inputs(input_file,batch_size) + + if params['dynamic_eval']: + sorted_inputs, sorted_keys = _get_sorted_inputs_dynamic(input_file) + else: + sorted_inputs, sorted_keys = _get_sorted_inputs(input_file,batch_size) total_samples = len(sorted_inputs) num_decode_batches = (total_samples - 1) // batch_size + 1 #static input modify diff --git a/image.png b/image.png new file mode 100644 index 0000000000000000000000000000000000000000..84fcb3b5438eb548cf1b6e490d934b02948e2530 Binary files /dev/null and b/image.png differ