diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/HybridSN-Spectral-Net.py b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/HybridSN-Spectral-Net.py new file mode 100644 index 0000000000000000000000000000000000000000..bc98fc58f06e406ee3b8519e37a258cf11c8463f --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/HybridSN-Spectral-Net.py @@ -0,0 +1,393 @@ +""" +# 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.keras +import tensorflow as tf +# import npu_bridge +from npu_bridge.npu_init import * +# from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization +from tensorflow.keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape +from tensorflow.keras.layers import Dropout, Input +from tensorflow.keras.models import Model +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint +# from tensorflow.keras.utils import np_utils +from tensorflow.python.keras.utils.np_utils import to_categorical + +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score + +from operator import truediv +import os + +os.system('pip install --upgrade pip') +os.system('pip install plotly') +os.system('pip install spectral') +# os.system('pip install nets') +from plotly.offline import init_notebook_mode +from PIL import Image + +import numpy as np +import matplotlib.pyplot as plt +import scipy.io as sio + +import spectral +import argparse + + + +# init_notebook_mode(connected=True) +# %matplotlib inline +from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig + +parser = argparse.ArgumentParser() +parser.add_argument("--data_path", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--output_path", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +parser.add_argument("--batch_size", default=256) +parser.add_argument("--train_epochs", default=2) +args = parser.parse_args() +config = tf.compat.v1.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 +custom_op.parameter_map["dynamic_input"].b = True +custom_op.parameter_map["dynamic_graph_execute_mode"].s = tf.compat.as_bytes("lazy_recompile") + +flags = tf.compat.v1.flags +FLAGS = flags.FLAGS +# +# # Required parameters +# flags.DEFINE_string( +# "train_url", "/home/ma-user/modelarts/outputs/train_url_0/", +# "The output directory where the model checkpoints will be written.") +# +# flags.DEFINE_string("data_url", "/home/ma-user/modelarts/inputs/data_url_0/", +# "dataset path") +# # flags.DEFINE_string("data_path", "data_url/data", +# # "dataset") + +# flags.DEFINE_integer( +# "batch_size", 32, +# "batch size for one NPU") +# +# flags.DEFINE_integer( +# "train_step", 100, +# "total epochs for training") + + +## GLOBAL VARIABLES +dataset = 'IP' +test_ratio = 0.7 +windowSize = 25 + + +def loadData(name): #根据变量dataset选择的数据集加载对应数据集，该加载数据集函数返回对应数据集及标签 + global data, labels + # data_path = os.path.join(os.getcwd(), 'data') + # data_url = os.path.join(FLAGS.data_url, data) + if name == 'IP': + data = sio.loadmat(os.path.join(args.data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected'] + labels = sio.loadmat(os.path.join(args.data_path, 'Indian_pines_gt.mat'))['indian_pines_gt'] + elif name == 'SA': + data = sio.loadmat(os.path.join(args.data_path, 'Salinas_corrected.mat'))['salinas_corrected'] + labels = sio.loadmat(os.path.join(args.data_path, 'Salinas_gt.mat'))['salinas_gt'] + elif name == 'PU': + data = sio.loadmat(os.path.join(args.data_path, 'PaviaU.mat'))['paviaU'] + labels = sio.loadmat(os.path.join(args.data_path, 'PaviaU_gt.mat'))['paviaU_gt'] + + return data, labels + + +def splitTrainTestSet(X, y, testRatio, randomState=345): + #划分训练集及测试集，返回训练集和测试集的数据集标签 + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, + stratify=y) + return X_train, X_test, y_train, y_test + + +def applyPCA(X, numComponents=75): + newX = np.reshape(X, (-1, X.shape[2])) + pca = PCA(n_components=numComponents, whiten=True) + newX = pca.fit_transform(newX) + newX = np.reshape(newX, (X.shape[0], X.shape[1], numComponents)) + return newX, pca + + +def padWithZeros(X, margin=2): #实现数据集图片变换，并返回新数据集图片 + newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2 * margin, X.shape[2])) + x_offset = margin + y_offset = margin + newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X + return newX + + +def createImageCubes(X, y, windowSize=5, removeZeroLabels=True): + #设置不同窗口大小，划分对应patch块，及patch块对应的标签大小，返回patch块的数据集和标签 + margin = int((windowSize - 1) / 2) + zeroPaddedX = padWithZeros(X, margin=margin) + # split patches + patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2])) + patchesLabels = np.zeros((X.shape[0] * X.shape[1])) + patchIndex = 0 + for r in range(margin, zeroPaddedX.shape[0] - margin): + for c in range(margin, zeroPaddedX.shape[1] - margin): + patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1] + patchesData[patchIndex, :, :, :] = patch + patchesLabels[patchIndex] = y[r - margin, c - margin] + patchIndex = patchIndex + 1 + if removeZeroLabels: + patchesData = patchesData[patchesLabels > 0, :, :, :] + patchesLabels = patchesLabels[patchesLabels > 0] + patchesLabels -= 1 + return patchesData, patchesLabels + +with tf.compat.v1.Session(config=config) as sess: + init_op = tf.group(tf.compat.v1.local_variables_initializer(), tf.compat.v1.global_variables_initializer()) + sess.run(init_op) + # dataset = "IP" + X, y = loadData(dataset) + + print(X.shape, y.shape) + + K = X.shape[2] + + K = 30 if dataset == 'IP' else 15 + X, pca = applyPCA(X, numComponents=K) + + print(X.shape) + + X, y = createImageCubes(X, y, windowSize=windowSize) + + print(X.shape, y.shape) + + Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, test_ratio) + + print(Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape) + + Xtrain = Xtrain.reshape(-1, windowSize, windowSize, K, 1) + print(Xtrain.shape) + + # ytrain = np_utils.to_categorical(ytrain) + ytrain = to_categorical(ytrain) + print(ytrain.shape) + + S = windowSize + L = K + output_units = 9 if (dataset == 'PU' or dataset == 'PC') else 16 + + ## input layer + input_layer = Input((S, S, L, 1)) + + ## convolutional layers + conv_layer1 = Conv3D(filters=8, kernel_size=(3, 3, 7), activation='relu')(input_layer) + conv_layer2 = Conv3D(filters=16, kernel_size=(3, 3, 5), activation='relu')(conv_layer1) + conv_layer3 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(conv_layer2) + # print(conv_layer3._keras_shape) + print(conv_layer3.shape) + # conv3d_shape = conv_layer3._keras_shape + conv3d_shape = conv_layer3.shape + conv_layer3 = Reshape((conv3d_shape[1], conv3d_shape[2], conv3d_shape[3] * conv3d_shape[4]))(conv_layer3) + conv_layer4 = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(conv_layer3) + + flatten_layer = Flatten()(conv_layer4) + + ## fully connected layers + dense_layer1 = Dense(units=256, activation='relu')(flatten_layer) + dense_layer1 = Dropout(0.4)(dense_layer1) + dense_layer2 = Dense(units=128, activation='relu')(dense_layer1) + dense_layer2 = Dropout(0.4)(dense_layer2) + output_layer = Dense(units=output_units, activation='softmax')(dense_layer2) + + # define the model with input layer and output layer + model = Model(inputs=input_layer, outputs=output_layer) + + model.summary() + + # compiling the model + adam = Adam(lr=0.001, decay=1e-06) + # model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy']) + model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['acc']) + + # checkpoint + filepath = args.output_path + "best-model.h5" + + checkpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, save_weights_only=False, + mode='max') + callbacks_list = [checkpoint] + history = model.fit(x=Xtrain, y=ytrain, batch_size=args.batch_size, epochs=args.train_epochs, callbacks=callbacks_list) + model.save(filepath)# Pass callback to training + + # load best weights + # model.load_weights("best-model.hdf5") + model.load_weights(filepath) + model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy']) + + Xtest = Xtest.reshape(-1, windowSize, windowSize, K, 1) + print(Xtest.shape) + for i in range(Xtest.shape[0]): + Xtest[i].tofile(args.output_path + "IPtest/IPtest{%s}.bin" %i) + + # ytest = np_utils.to_categorical(ytest) + ytest = to_categorical(ytest) + print(ytest.shape) + for i in range(ytest.shape[0]): + ytest[i].tofile(args.output_path+"IPytest/IPytest{%s}.bin" % i) + print(ytest[i].shape) + + Y_pred_test = model.predict(Xtest) + y_pred_test = np.argmax(Y_pred_test, axis=1) + + classification = classification_report(np.argmax(ytest, axis=1), y_pred_test) + print(classification) + + + def AA_andEachClassAccuracy(confusion_matrix): + #根据混淆矩阵计算准确率及其平均准确率，返回准确率和平均准确率 + counter = confusion_matrix.shape[0] + list_diag = np.diag(confusion_matrix) + list_raw_sum = np.sum(confusion_matrix, axis=1) + each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum)) + average_acc = np.mean(each_acc) + return each_acc, average_acc + + + def reports(X_test, y_test, name): + #根据dataset所选取的数据集加载对应数据集的标签，并预测测试集准确率，oa，混淆矩阵，平均准确率，Kappa，训练损失，训练准确率 + # start = time.time() + Y_pred = model.predict(X_test) + y_pred = np.argmax(Y_pred, axis=1) + # end = time.time() + # print(end - start) + if name == 'IP': + target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn' + , 'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed', + 'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill', + 'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives', + 'Stone-Steel-Towers'] + elif name == 'SA': + target_names = ['Brocoli_green_weeds_1', 'Brocoli_green_weeds_2', 'Fallow', 'Fallow_rough_plow', + 'Fallow_smooth', + 'Stubble', 'Celery', 'Grapes_untrained', 'Soil_vinyard_develop', + 'Corn_senesced_green_weeds', + 'Lettuce_romaine_4wk', 'Lettuce_romaine_5wk', 'Lettuce_romaine_6wk', 'Lettuce_romaine_7wk', + 'Vinyard_untrained', 'Vinyard_vertical_trellis'] + elif name == 'PU': + target_names = ['Asphalt', 'Meadows', 'Gravel', 'Trees', 'Painted metal sheets', 'Bare Soil', 'Bitumen', + 'Self-Blocking Bricks', 'Shadows'] + + classification = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=target_names) + oa = accuracy_score(np.argmax(y_test, axis=1), y_pred) + confusion = confusion_matrix(np.argmax(y_test, axis=1), y_pred) + each_acc, aa = AA_andEachClassAccuracy(confusion) + kappa = cohen_kappa_score(np.argmax(y_test, axis=1), y_pred) + score = model.evaluate(X_test, y_test, batch_size=32) + Test_Loss = score[0] * 100 + Test_accuracy = score[1] * 100 + # namedtuple = classification, confusion, Test_Loss, Test_accuracy, oa * 100, each_acc *100, aa *100, kappa *100 + # return namedtuple + + return classification, confusion, Test_Loss, Test_accuracy, oa * 100, each_acc * 100, aa * 100, kappa * 100 + + + classification, confusion, Test_loss, Test_accuracy, oa, each_acc, aa, kappa = reports(Xtest, ytest, dataset) + classification = str(classification) + confusion = str(confusion) + file_name = args.output_path + "classification_report.txt" + + with open(file_name, 'w') as x_file: + x_file.write('{} Test loss (%)'.format(Test_loss)) + x_file.write('\n') + x_file.write('{} Test accuracy (%)'.format(Test_accuracy)) + x_file.write('\n') + x_file.write('\n') + x_file.write('{} Kappa accuracy (%)'.format(kappa)) + x_file.write('\n') + x_file.write('{} Overall accuracy (%)'.format(oa)) + x_file.write('\n') + x_file.write('{} Average accuracy (%)'.format(aa)) + x_file.write('\n') + x_file.write('\n') + x_file.write('{}'.format(classification)) + x_file.write('\n') + x_file.write('{}'.format(confusion)) + + + def Patch(data, height_index, width_index): #设置生成预测图窗口的大小 + height_slice = slice(height_index, height_index + PATCH_SIZE) + width_slice = slice(width_index, width_index + PATCH_SIZE) + patch = data[height_slice, width_slice, :] + + return patch + + + # load the original image + X, y = loadData(dataset) + + height = y.shape[0] + width = y.shape[1] + PATCH_SIZE = windowSize + numComponents = K + + X, pca = applyPCA(X, numComponents=numComponents) + + X = padWithZeros(X, PATCH_SIZE // 2) + + # calculate the predicted image + outputs = np.zeros((height, width)) + for i in range(height): + for j in range(width): + target = int(y[i, j]) + if target == 0: + continue + else: + image_patch = Patch(X, i, j) + X_test_image = image_patch.reshape(1, image_patch.shape[0], image_patch.shape[1], image_patch.shape[2], + 1).astype('float32') + prediction = (model.predict(X_test_image)) + prediction = np.argmax(prediction, axis=1) + outputs[i][j] = prediction + 1 + + ground_truth = spectral.imshow(classes=y.astype(int), figsize=(7, 7)) + + predict_image = spectral.imshow(classes=outputs.astype(int), figsize=(7, 7)) + + spectral.save_rgb(args.output_path + "predictions.jpg", outputs.astype(int), colors=spectral.spy_colors) + spectral.save_rgb(args.output_path + str(dataset) + "_ground_truth.jpg", y, colors=spectral.spy_colors) + + + + + + + + diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..871ce8e638ad6d763308e44411d2c4a2e658cf55 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. Therefore, you have +certain responsibilities if you distribute copies of the software, or if +you modify it: responsibilities to respect the freedom of others. + + For example, if you distribute copies of such a program, whether +gratis or for a fee, you must pass on to the recipients the same +freedoms that you received. You must make sure that they, too, receive +or can get the source code. And you must show them these terms so they +know their rights. + + Developers that use the GNU GPL protect your rights with two steps: +(1) assert copyright on the software, and (2) offer you this License +giving you legal permission to copy, distribute and/or modify it. + + For the developers' and authors' protection, the GPL clearly explains +that there is no warranty for this free software. For both users' and +authors' sake, the GPL requires that modified versions be marked as +changed, so that their problems will not be attributed erroneously to +authors of previous versions. + + Some devices are designed to deny users access to install or run +modified versions of the software inside them, although the manufacturer +can do so. This is fundamentally incompatible with the aim of +protecting users' freedom to change the software. The systematic +pattern of such abuse occurs in the area of products for individuals to +use, which is precisely where it is most unacceptable. Therefore, we +have designed this version of the GPL to prohibit the practice for those +products. If such problems arise substantially in other domains, we +stand ready to extend this provision to those domains in future versions +of the GPL, as needed to protect the freedom of users. + + Finally, every program is threatened constantly by software patents. +States should not allow patents to restrict development and use of +software on general-purpose computers, but in those that do, we wish to +avoid the special danger that patents applied to a free program could +make it effectively proprietary. To prevent this, the GPL assures that +patents cannot be used to render the program non-free. + + The precise terms and conditions for copying, distribution and +modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of +works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this +License. Each licensee is addressed as "you". "Licensees" and +"recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work +in a fashion requiring copyright permission, other than the making of an +exact copy. The resulting work is called a "modified version" of the +earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based +on the Program. + + To "propagate" a work means to do anything with it that, without +permission, would make you directly or secondarily liable for +infringement under applicable copyright law, except executing it on a +computer or modifying a private copy. Propagation includes copying, +distribution (with or without modification), making available to the +public, and in some countries other activities as well. + + To "convey" a work means any kind of propagation that enables other +parties to make or receive copies. Mere interaction with a user through +a computer network, with no transfer of a copy, is not conveying. + + An interactive user interface displays "Appropriate Legal Notices" +to the extent that it includes a convenient and prominently visible +feature that (1) displays an appropriate copyright notice, and (2) +tells the user that there is no warranty for the work (except to the +extent that warranties are provided), that licensees may convey the +work under this License, and how to view a copy of this License. If +the interface presents a list of user commands or options, such as a +menu, a prominent item in the list meets this criterion. + + 1. Source Code. + + The "source code" for a work means the preferred form of the work +for making modifications to it. "Object code" means any non-source +form of a work. + + A "Standard Interface" means an interface that either is an official +standard defined by a recognized standards body, or, in the case of +interfaces specified for a particular programming language, one that +is widely used among developers working in that language. + + The "System Libraries" of an executable work include anything, other +than the work as a whole, that (a) is included in the normal form of +packaging a Major Component, but which is not part of that Major +Component, and (b) serves only to enable use of the work with that +Major Component, or to implement a Standard Interface for which an +implementation is available to the public in source code form. A +"Major Component", in this context, means a major essential component +(kernel, window system, and so on) of the specific operating system +(if any) on which the executable work runs, or a compiler used to +produce the work, or an object code interpreter used to run it. + + The "Corresponding Source" for a work in object code form means all +the source code needed to generate, install, and (for an executable +work) run the object code and to modify the work, including scripts to +control those activities. However, it does not include the work's +System Libraries, or general-purpose tools or generally available free +programs which are used unmodified in performing those activities but +which are not part of the work. For example, Corresponding Source +includes interface definition files associated with source files for +the work, and the source code for shared libraries and dynamically +linked subprograms that the work is specifically designed to require, +such as by intimate data communication or control flow between those +subprograms and other parts of the work. + + The Corresponding Source need not include anything that users +can regenerate automatically from other parts of the Corresponding +Source. + + The Corresponding Source for a work in source code form is that +same work. + + 2. Basic Permissions. + + All rights granted under this License are granted for the term of +copyright on the Program, and are irrevocable provided the stated +conditions are met. This License explicitly affirms your unlimited +permission to run the unmodified Program. The output from running a +covered work is covered by this License only if the output, given its +content, constitutes a covered work. This License acknowledges your +rights of fair use or other equivalent, as provided by copyright law. + + You may make, run and propagate covered works that you do not +convey, without conditions so long as your license otherwise remains +in force. You may convey covered works to others for the sole purpose +of having them make modifications exclusively for you, or provide you +with facilities for running those works, provided that you comply with +the terms of this License in conveying all material for which you do +not control copyright. Those thus making or running the covered works +for you must do so exclusively on your behalf, under your direction +and control, on terms that prohibit them from making any copies of +your copyrighted material outside their relationship with you. + + Conveying under any other circumstances is permitted solely under +the conditions stated below. Sublicensing is not allowed; section 10 +makes it unnecessary. + + 3. Protecting Users' Legal Rights From Anti-Circumvention Law. + + No covered work shall be deemed part of an effective technological +measure under any applicable law fulfilling obligations under article +11 of the WIPO copyright treaty adopted on 20 December 1996, or +similar laws prohibiting or restricting circumvention of such +measures. + + When you convey a covered work, you waive any legal power to forbid +circumvention of technological measures to the extent such circumvention +is effected by exercising rights under this License with respect to +the covered work, and you disclaim any intention to limit operation or +modification of the work as a means of enforcing, against the work's +users, your or third parties' legal rights to forbid circumvention of +technological measures. + + 4. Conveying Verbatim Copies. + + You may convey verbatim copies of the Program's source code as you +receive it, in any medium, provided that you conspicuously and +appropriately publish on each copy an appropriate copyright notice; +keep intact all notices stating that this License and any +non-permissive terms added in accord with section 7 apply to the code; +keep intact all notices of the absence of any warranty; and give all +recipients a copy of this License along with the Program. + + You may charge any price or no price for each copy that you convey, +and you may offer support or warranty protection for a fee. + + 5. Conveying Modified Source Versions. + + You may convey a work based on the Program, or the modifications to +produce it from the Program, in the form of source code under the +terms of section 4, provided that you also meet all of these conditions: + + a) The work must carry prominent notices stating that you modified + it, and giving a relevant date. + + b) The work must carry prominent notices stating that it is + released under this License and any conditions added under section + 7. This requirement modifies the requirement in section 4 to + "keep intact all notices". + + c) You must license the entire work, as a whole, under this + License to anyone who comes into possession of a copy. This + License will therefore apply, along with any applicable section 7 + additional terms, to the whole of the work, and all its parts, + regardless of how they are packaged. This License gives no + permission to license the work in any other way, but it does not + invalidate such permission if you have separately received it. + + d) If the work has interactive user interfaces, each must display + Appropriate Legal Notices; however, if the Program has interactive + interfaces that do not display Appropriate Legal Notices, your + work need not make them do so. + + A compilation of a covered work with other separate and independent +works, which are not by their nature extensions of the covered work, +and which are not combined with it such as to form a larger program, +in or on a volume of a storage or distribution medium, is called an +"aggregate" if the compilation and its resulting copyright are not +used to limit the access or legal rights of the compilation's users +beyond what the individual works permit. Inclusion of a covered work +in an aggregate does not cause this License to apply to the other +parts of the aggregate. + + 6. Conveying Non-Source Forms. + + You may convey a covered work in object code form under the terms +of sections 4 and 5, provided that you also convey the +machine-readable Corresponding Source under the terms of this License, +in one of these ways: + + a) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by the + Corresponding Source fixed on a durable physical medium + customarily used for software interchange. + + b) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by a + written offer, valid for at least three years and valid for as + long as you offer spare parts or customer support for that product + model, to give anyone who possesses the object code either (1) a + copy of the Corresponding Source for all the software in the + product that is covered by this License, on a durable physical + medium customarily used for software interchange, for a price no + more than your reasonable cost of physically performing this + conveying of source, or (2) access to copy the + Corresponding Source from a network server at no charge. + + c) Convey individual copies of the object code with a copy of the + written offer to provide the Corresponding Source. This + alternative is allowed only occasionally and noncommercially, and + only if you received the object code with such an offer, in accord + with subsection 6b. + + d) Convey the object code by offering access from a designated + place (gratis or for a charge), and offer equivalent access to the + Corresponding Source in the same way through the same place at no + further charge. You need not require recipients to copy the + Corresponding Source along with the object code. If the place to + copy the object code is a network server, the Corresponding Source + may be on a different server (operated by you or a third party) + that supports equivalent copying facilities, provided you maintain + clear directions next to the object code saying where to find the + Corresponding Source. Regardless of what server hosts the + Corresponding Source, you remain obligated to ensure that it is + available for as long as needed to satisfy these requirements. + + e) Convey the object code using peer-to-peer transmission, provided + you inform other peers where the object code and Corresponding + Source of the work are being offered to the general public at no + charge under subsection 6d. + + A separable portion of the object code, whose source code is excluded +from the Corresponding Source as a System Library, need not be +included in conveying the object code work. + + A "User Product" is either (1) a "consumer product", which means any +tangible personal property which is normally used for personal, family, +or household purposes, or (2) anything designed or sold for incorporation +into a dwelling. In determining whether a product is a consumer product, +doubtful cases shall be resolved in favor of coverage. For a particular +product received by a particular user, "normally used" refers to a +typical or common use of that class of product, regardless of the status +of the particular user or of the way in which the particular user +actually uses, or expects or is expected to use, the product. A product +is a consumer product regardless of whether the product has substantial +commercial, industrial or non-consumer uses, unless such uses represent +the only significant mode of use of the product. + + "Installation Information" for a User Product means any methods, +procedures, authorization keys, or other information required to install +and execute modified versions of a covered work in that User Product from +a modified version of its Corresponding Source. The information must +suffice to ensure that the continued functioning of the modified object +code is in no case prevented or interfered with solely because +modification has been made. + + If you convey an object code work under this section in, or with, or +specifically for use in, a User Product, and the conveying occurs as +part of a transaction in which the right of possession and use of the +User Product is transferred to the recipient in perpetuity or for a +fixed term (regardless of how the transaction is characterized), the +Corresponding Source conveyed under this section must be accompanied +by the Installation Information. But this requirement does not apply +if neither you nor any third party retains the ability to install +modified object code on the User Product (for example, the work has +been installed in ROM). + + The requirement to provide Installation Information does not include a +requirement to continue to provide support service, warranty, or updates +for a work that has been modified or installed by the recipient, or for +the User Product in which it has been modified or installed. Access to a +network may be denied when the modification itself materially and +adversely affects the operation of the network or violates the rules and +protocols for communication across the network. + + Corresponding Source conveyed, and Installation Information provided, +in accord with this section must be in a format that is publicly +documented (and with an implementation available to the public in +source code form), and must require no special password or key for +unpacking, reading or copying. + + 7. Additional Terms. + + "Additional permissions" are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. +Additional permissions that are applicable to the entire Program shall +be treated as though they were included in this License, to the extent +that they are valid under applicable law. If additional permissions +apply only to part of the Program, that part may be used separately +under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + + When you convey a copy of a covered work, you may at your option +remove any additional permissions from that copy, or from any part of +it. (Additional permissions may be written to require their own +removal in certain cases when you modify the work.) You may place +additional permissions on material, added by you to a covered work, +for which you have or can give appropriate copyright permission. + + Notwithstanding any other provision of this License, for material you +add to a covered work, you may (if authorized by the copyright holders of +that material) supplement the terms of this License with terms: + + a) Disclaiming warranty or limiting liability differently from the + terms of sections 15 and 16 of this License; or + + b) Requiring preservation of specified reasonable legal notices or + author attributions in that material or in the Appropriate Legal + Notices displayed by works containing it; or + + c) Prohibiting misrepresentation of the origin of that material, or + requiring that modified versions of such material be marked in + reasonable ways as different from the original version; or + + d) Limiting the use for publicity purposes of names of licensors or + authors of the material; or + + e) Declining to grant rights under trademark law for use of some + trade names, trademarks, or service marks; or + + f) Requiring indemnification of licensors and authors of that + material by anyone who conveys the material (or modified versions of + it) with contractual assumptions of liability to the recipient, for + any liability that these contractual assumptions directly impose on + those licensors and authors. + + All other non-permissive additional terms are considered "further +restrictions" within the meaning of section 10. If the Program as you +received it, or any part of it, contains a notice stating that it is +governed by this License along with a term that is a further +restriction, you may remove that term. If a license document contains +a further restriction but permits relicensing or conveying under this +License, you may add to a covered work material governed by the terms +of that license document, provided that the further restriction does +not survive such relicensing or conveying. + + If you add terms to a covered work in accord with this section, you +must place, in the relevant source files, a statement of the +additional terms that apply to those files, or a notice indicating +where to find the applicable terms. + + Additional terms, permissive or non-permissive, may be stated in the +form of a separately written license, or stated as exceptions; +the above requirements apply either way. + + 8. Termination. + + You may not propagate or modify a covered work except as expressly +provided under this License. Any attempt otherwise to propagate or +modify it is void, and will automatically terminate your rights under +this License (including any patent licenses granted under the third +paragraph of section 11). + + However, if you cease all violation of this License, then your +license from a particular copyright holder is reinstated (a) +provisionally, unless and until the copyright holder explicitly and +finally terminates your license, and (b) permanently, if the copyright +holder fails to notify you of the violation by some reasonable means +prior to 60 days after the cessation. + + Moreover, your license from a particular copyright holder is +reinstated permanently if the copyright holder notifies you of the +violation by some reasonable means, this is the first time you have +received notice of violation of this License (for any work) from that +copyright holder, and you cure the violation prior to 30 days after +your receipt of the notice. + + Termination of your rights under this section does not terminate the +licenses of parties who have received copies or rights from you under +this License. If your rights have been terminated and not permanently +reinstated, you do not qualify to receive new licenses for the same +material under section 10. + + 9. Acceptance Not Required for Having Copies. + + You are not required to accept this License in order to receive or +run a copy of the Program. Ancillary propagation of a covered work +occurring solely as a consequence of using peer-to-peer transmission +to receive a copy likewise does not require acceptance. However, +nothing other than this License grants you permission to propagate or +modify any covered work. These actions infringe copyright if you do +not accept this License. Therefore, by modifying or propagating a +covered work, you indicate your acceptance of this License to do so. + + 10. Automatic Licensing of Downstream Recipients. + + Each time you convey a covered work, the recipient automatically +receives a license from the original licensors, to run, modify and +propagate that work, subject to this License. You are not responsible +for enforcing compliance by third parties with this License. + + An "entity transaction" is a transaction transferring control of an +organization, or substantially all assets of one, or subdividing an +organization, or merging organizations. If propagation of a covered +work results from an entity transaction, each party to that +transaction who receives a copy of the work also receives whatever +licenses to the work the party's predecessor in interest had or could +give under the previous paragraph, plus a right to possession of the +Corresponding Source of the work from the predecessor in interest, if +the predecessor has it or can get it with reasonable efforts. + + You may not impose any further restrictions on the exercise of the +rights granted or affirmed under this License. For example, you may +not impose a license fee, royalty, or other charge for exercise of +rights granted under this License, and you may not initiate litigation +(including a cross-claim or counterclaim in a lawsuit) alleging that +any patent claim is infringed by making, using, selling, offering for +sale, or importing the Program or any portion of it. + + 11. Patents. + + A "contributor" is a copyright holder who authorizes use under this +License of the Program or a work on which the Program is based. The +work thus licensed is called the contributor's "contributor version". + + A contributor's "essential patent claims" are all patent claims +owned or controlled by the contributor, whether already acquired or +hereafter acquired, that would be infringed by some manner, permitted +by this License, of making, using, or selling its contributor version, +but do not include claims that would be infringed only as a +consequence of further modification of the contributor version. For +purposes of this definition, "control" includes the right to grant +patent sublicenses in a manner consistent with the requirements of +this License. + + Each contributor grants you a non-exclusive, worldwide, royalty-free +patent license under the contributor's essential patent claims, to +make, use, sell, offer for sale, import and otherwise run, modify and +propagate the contents of its contributor version. + + In the following three paragraphs, a "patent license" is any express +agreement or commitment, however denominated, not to enforce a patent +(such as an express permission to practice a patent or covenant not to +sue for patent infringement). To "grant" such a patent license to a +party means to make such an agreement or commitment not to enforce a +patent against the party. + + If you convey a covered work, knowingly relying on a patent license, +and the Corresponding Source of the work is not available for anyone +to copy, free of charge and under the terms of this License, through a +publicly available network server or other readily accessible means, +then you must either (1) cause the Corresponding Source to be so +available, or (2) arrange to deprive yourself of the benefit of the +patent license for this particular work, or (3) arrange, in a manner +consistent with the requirements of this License, to extend the patent +license to downstream recipients. "Knowingly relying" means you have +actual knowledge that, but for the patent license, your conveying the +covered work in a country, or your recipient's use of the covered work +in a country, would infringe one or more identifiable patents in that +country that you have reason to believe are valid. + + If, pursuant to or in connection with a single transaction or +arrangement, you convey, or propagate by procuring conveyance of, a +covered work, and grant a patent license to some of the parties +receiving the covered work authorizing them to use, propagate, modify +or convey a specific copy of the covered work, then the patent license +you grant is automatically extended to all recipients of the covered +work and works based on it. + + A patent license is "discriminatory" if it does not include within +the scope of its coverage, prohibits the exercise of, or is +conditioned on the non-exercise of one or more of the rights that are +specifically granted under this License. You may not convey a covered +work if you are a party to an arrangement with a third party that is +in the business of distributing software, under which you make payment +to the third party based on the extent of your activity of conveying +the work, and under which the third party grants, to any of the +parties who would receive the covered work from you, a discriminatory +patent license (a) in connection with copies of the covered work +conveyed by you (or copies made from those copies), or (b) primarily +for and in connection with specific products or compilations that +contain the covered work, unless you entered into that arrangement, +or that patent license was granted, prior to 28 March 2007. + + Nothing in this License shall be construed as excluding or limiting +any implied license or other defenses to infringement that may +otherwise be available to you under applicable patent law. + + 12. No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. Use with the GNU Affero General Public License. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU Affero General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the special requirements of the GNU Affero General Public License, +section 13, concerning interaction through a network will apply to the +combination as such. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. If the +Program specifies that a certain numbered version of the GNU General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/README.md new file mode 100644 index 0000000000000000000000000000000000000000..370db2181830ba1f719ee716302bbabeda2671fb --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/README.md @@ -0,0 +1,48 @@ +## HybridSN + +HybridSN推理部分实现如下 + +## 训练环境 + +1）TensorFlow 1.15.0 + +2）Python3.7.0 + +## 代码及路径解释 + +``` +HybridSN_ID1160_for_ACL +|---h52pb.py h5模型固化为pb +|---atc.sh atc工具 pb转om转换命令 +|---msame.sh msame工具：om离线推理命令 +|---offline.py 离线推理 +``` + +## 数据处理过程 + +推理数据预处理在训练中进行保存，具体见HybridSN_ID1160_for_TensorFlow增加数据处理代码 + +## 模型文件 + +初始h5， 固化pb，以及推理om文件链接： 链接：https://pan.baidu.com/s/1QK0r_GtbKIQDEiVRM4SKLw +提取码：xi7o + +## ATC工具进行模型转换时可参考atc.sh + +``` +atc --model=/usr/hy.pb --framework=3 --output=/usr/l3 --soc_version=Ascend310 --out_nodes="Identity:0" --input_shape="input_1:1, 25, 25, 30, 1" +``` + +## msame工具进行推理时可参考msame.sh + +``` +./msame --model "/usr/l3.om" --input "/home/HwHiAiUser/data2" --output "/usr" --outfmt TXT +``` + +input参数根据自己的文件位置进行修改 + +## 推理结果 + +![输入图片说明](../../../../image.png) +一个batch数据的推理性能约为0.99ms，这里只展示了一部分 + diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/atc.sh b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/atc.sh new file mode 100644 index 0000000000000000000000000000000000000000..34d0ee346e69d9eed6349ef94efa58a5bdf9cc7f --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/atc.sh @@ -0,0 +1 @@ +atc --model=/usr/hy.pb --framework=3 --output=/usr/l3 --soc_version=Ascend310 --out_nodes="Identity:0" --input_shape="input_1:1, 25, 25, 30, 1" \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/h52pb.py b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/h52pb.py new file mode 100644 index 0000000000000000000000000000000000000000..95159e11746a404d84ff92d2253cbfe220e7dddb --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/h52pb.py @@ -0,0 +1,45 @@ +import tensorflow as tf +from tensorflow_core.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + +def frozen_graph(h5_file_path, pb_model_path): + """ + 冻结模型，可以将训练好的.h5模型文件转成.pb文件 + :param h5_file_path: h5模型文件路径 + :param pb_model_path: pb模型文件保存路径 + :return: + """ + # 加载模型，如有自定义层请参考方法二末尾处如何加载 + model = tf.keras.models.load_model(h5_file_path, compile=False) + model.summary() + + full_model = tf.function(lambda input_1: model(input_1)) + full_model = full_model.get_concrete_function(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype)) + + # Get frozen ConcreteFunction + frozen_func = convert_variables_to_constants_v2(full_model) + frozen_func.graph.as_graph_def() + + layers = [op.name for op in frozen_func.graph.get_operations()] + print("-" * 50) + print("Frozen model layers: ") + for layer in layers: + print(layer) + + print("-" * 50) + print("Frozen model inputs: ") + print(frozen_func.inputs) + print("Frozen model outputs: ") + print(frozen_func.outputs) + + # Save frozen graph from frozen ConcreteFunction to hard drive + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, + logdir=pb_model_path, + name="hy.pb", + as_text=False) + print('model has been saved') + + +h5_file_path = 'D:\PytorchProgram\HY\\best-model.h5' +pb_model_path = 'D:\PytorchProgram\HY/' +frozen_graph(h5_file_path, pb_model_path) diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/masme.sh b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/masme.sh new file mode 100644 index 0000000000000000000000000000000000000000..68193a24c120a5f0010c7a944a3494102f295c43 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/masme.sh @@ -0,0 +1 @@ +./msame --model "/usr/l3.om" --input "/home/HwHiAiUser/data2" --output "/usr" --outfmt TXT \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelarts_entry_acc.py b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelarts_entry_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..5a3a86c258148afbb4ebf9ef8824a9d2a8bbac54 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelarts_entry_acc.py @@ -0,0 +1,65 @@ +# 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)) +#os.system("bash test/train_full_1p.sh") + + +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/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelarts_entry_perf.py b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelarts_entry_perf.py new file mode 100644 index 0000000000000000000000000000000000000000..14384e227a0fa90a514254590aef5078c62ff700 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/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/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..2e5e535e31b3c98d5fa96cee6da012045cac26f4 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/modelzoo_level.txt @@ -0,0 +1,3 @@ +FuncStatus: OK +PerfStatus: OK +PrecisionStatus: NOK \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/offline.py b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/offline.py new file mode 100644 index 0000000000000000000000000000000000000000..2776e6941c6c59481be253e9faf3f29051243dc8 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/offline.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 +from operator import truediv + +import numpy as np + +from sklearn.metrics import confusion_matrix, accuracy_score, cohen_kappa_score + + + +def AA_andEachClassAccuracy(confusion_matrix): + # 根据混淆矩阵计算准确率及其平均准确率，返回准确率和平均准确率 + counter = confusion_matrix.shape[0] + list_diag = np.diag(confusion_matrix) + list_raw_sum = np.sum(confusion_matrix, axis=1) + each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum)) + average_acc = np.mean(each_acc) + return each_acc, average_acc + +predict_file = "D:\PytorchProgram\HY\\2022223_10_28_5_968623" +label_file = "D:\PytorchProgram\HY\IPytest" + +def read_file(predict_file, label_file): + files_predict = os.listdir(predict_file) + # print(files_predict) + files_label = os.listdir(label_file) + + pred = list() + label = list() + for files in files_predict: + if files.endswith(".txt"): + # print(files) + tmp = np.loadtxt(predict_file + '/' + files, dtype='float32') + pred.append(tmp) + + for file in files_label: + if file.endswith('.bin'): + tmp = np.fromfile(label_file + '/' + file, dtype='float32') + label.append(tmp) + return pred, label + +pred, label = read_file(predict_file, label_file) +confusion = confusion_matrix(np.argmax(label, axis=1), np.argmax(pred, axis=1)) +each_acc, aa = AA_andEachClassAccuracy(confusion) +oa = accuracy_score(np.argmax(label, axis=1), np.argmax(pred, axis=1)) +kappa = cohen_kappa_score(np.argmax(label, axis=1), np.argmax(pred, axis=1)) + + +print(aa*100) +print(oa*100) +print(kappa*100) diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/.keep b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/.keep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/train_full_1p.sh b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/train_full_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..1981fdb63649a6fa12e52a9d2f5f83cfcaf1abab --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/train_full_1p.sh @@ -0,0 +1,188 @@ +#!/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 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 HybridSN-Spectral-Net.py --data_path=${data_path} --output_path=${output_path} +else + python3.7 HybridSN-Spectral-Net.py --data_path=${data_path} --output_path=${output_path} 1>${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +#FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '3074'/'${batch_size}'}'` + + +# 精度相关数据计算 +#train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +train_accuracy=`grep "acc" ${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 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/HybridSN_ID1160_for_ACL/test/train_performance_1p.sh b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/train_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..b36af7d3ae80713e46cc2d0ab3cca8b15f951d9b --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/HybridSN_ID1160_for_ACL/test/train_performance_1p.sh @@ -0,0 +1,187 @@ +#!/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"${modelarts_flag}" != x ]; +then + echo "running with modelarts..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${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 +train_epochs=2 +train_steps= +batch_size=256 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 HybridSN-Spectral-Net.py --data_path=${data_path} --output_path=${output_path} --train_epochs=${train_epochs} +else + python3.7 HybridSN-Spectral-Net.py --data_path=${data_path} --output_path=${output_path} --train_epochs=${train_epochs} 1>${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +#FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '3074'/'${batch_size}'}'` + +# 精度相关数据计算 +#train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +train_accuracy=`grep "acc" ${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 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/image.png b/image.png new file mode 100644 index 0000000000000000000000000000000000000000..6b8e9794033a2d2847fe4e959af3f7cdaf4c00d6 Binary files /dev/null and b/image.png differ