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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 +. diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/CFL_StdConvs.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/CFL_StdConvs.py new file mode 100644 index 0000000000000000000000000000000000000000..f2e5c53a0c3a9d76ff91e860a54d8e7fd7a3fc16 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/CFL_StdConvs.py @@ -0,0 +1,257 @@ +# 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 * +from .network import Network +import tensorflow as tf + + +class LayoutEstimator_StdConvs(Network): + def setup(self): + feed_dict_test = {} + feed_dict_train = {} + self.nname = "edge-estimator" + with tf.variable_scope(self.nname): + (self.feed('rgb_input') + .conv(7, 7, 64, 2, 2, relu=False, name='conv1') + .batch_normalization(relu=True, name='bn_conv1') + .max_pool(3, 3, 2, 2, name='pool1') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1') + .batch_normalization(name='bn2a_branch1')) + + (self.feed('pool1') + .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a') + .batch_normalization(relu=True, name='bn2a_branch2a') + .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b') + .batch_normalization(relu=True, name='bn2a_branch2b') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c') + .batch_normalization(name='bn2a_branch2c')) + + (self.feed('bn2a_branch1', + 'bn2a_branch2c') + .add(name='res2a') + .relu(name='res2a_relu') + .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a') + .batch_normalization(relu=True, name='bn2b_branch2a') + .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b') + .batch_normalization(relu=True, name='bn2b_branch2b') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c') + .batch_normalization(name='bn2b_branch2c')) + + (self.feed('res2a_relu', + 'bn2b_branch2c') + .add(name='res2b') + .relu(name='res2b_relu') + .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a') + .batch_normalization(relu=True, name='bn2c_branch2a') + .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b') + .batch_normalization(relu=True, name='bn2c_branch2b') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c') + .batch_normalization(name='bn2c_branch2c')) + + (self.feed('res2b_relu', + 'bn2c_branch2c') + .add(name='res2c') + .relu(name='res2c_relu') + .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res3a_branch1') + .batch_normalization(name='bn3a_branch1')) + + (self.feed('res2c_relu') + .conv(1, 1, 128, 2, 2, biased=False, relu=False, name='res3a_branch2a') + .batch_normalization(relu=True, name='bn3a_branch2a') + .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3a_branch2b') + .batch_normalization(relu=True, name='bn3a_branch2b') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3a_branch2c') + .batch_normalization(name='bn3a_branch2c')) + + (self.feed('bn3a_branch1', + 'bn3a_branch2c') + .add(name='res3a') + .relu(name='res3a_relu') + .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b_branch2a') + .batch_normalization(relu=True, name='bn3b_branch2a') + .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b_branch2b') + .batch_normalization(relu=True, name='bn3b_branch2b') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b_branch2c') + .batch_normalization(name='bn3b_branch2c')) + + (self.feed('res3a_relu', + 'bn3b_branch2c') + .add(name='res3b') + .relu(name='res3b_relu') + .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3c_branch2a') + .batch_normalization(relu=True, name='bn3c_branch2a') + .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3c_branch2b') + .batch_normalization(relu=True, name='bn3c_branch2b') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3c_branch2c') + .batch_normalization(name='bn3c_branch2c')) + + (self.feed('res3b_relu', + 'bn3c_branch2c') + .add(name='res3c') + .relu(name='res3c_relu') + .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3d_branch2a') + .batch_normalization(relu=True, name='bn3d_branch2a') + .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3d_branch2b') + .batch_normalization(relu=True, name='bn3d_branch2b') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3d_branch2c') + .batch_normalization(name='bn3d_branch2c')) + + (self.feed('res3c_relu', + 'bn3d_branch2c') + .add(name='res3d') + .relu(name='res3d_relu') + .conv(1, 1, 1024, 2, 2, biased=False, relu=False, name='res4a_branch1') + .batch_normalization(name='bn4a_branch1')) + + (self.feed('res3d_relu') + .conv(1, 1, 256, 2, 2, biased=False, relu=False, name='res4a_branch2a') + .batch_normalization(relu=True, name='bn4a_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4a_branch2b') + .batch_normalization(relu=True, name='bn4a_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4a_branch2c') + .batch_normalization(name='bn4a_branch2c')) + + (self.feed('bn4a_branch1', + 'bn4a_branch2c') + .add(name='res4a') + .relu(name='res4a_relu') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b_branch2a') + .batch_normalization(relu=True, name='bn4b_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b_branch2b') + .batch_normalization(relu=True, name='bn4b_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b_branch2c') + .batch_normalization(name='bn4b_branch2c')) + + (self.feed('res4a_relu', + 'bn4b_branch2c') + .add(name='res4b') + .relu(name='res4b_relu') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4c_branch2a') + .batch_normalization(relu=True, name='bn4c_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4c_branch2b') + .batch_normalization(relu=True, name='bn4c_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4c_branch2c') + .batch_normalization(name='bn4c_branch2c')) + + (self.feed('res4b_relu', + 'bn4c_branch2c') + .add(name='res4c') + .relu(name='res4c_relu') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4d_branch2a') + .batch_normalization(relu=True, name='bn4d_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4d_branch2b') + .batch_normalization(relu=True, name='bn4d_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4d_branch2c') + .batch_normalization(name='bn4d_branch2c')) + + (self.feed('res4c_relu', + 'bn4d_branch2c') + .add(name='res4d') + .relu(name='res4d_relu') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4e_branch2a') + .batch_normalization(relu=True, name='bn4e_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4e_branch2b') + .batch_normalization(relu=True, name='bn4e_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4e_branch2c') + .batch_normalization(name='bn4e_branch2c')) + + (self.feed('res4d_relu', + 'bn4e_branch2c') + .add(name='res4e') + .relu(name='res4e_relu') + .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4f_branch2a') + .batch_normalization(relu=True, name='bn4f_branch2a') + .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4f_branch2b') + .batch_normalization(relu=True, name='bn4f_branch2b') + .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4f_branch2c') + .batch_normalization(name='bn4f_branch2c')) + + (self.feed('res4e_relu', + 'bn4f_branch2c') + .add(name='res4f') + .relu(name='res4f_relu') + .conv(1, 1, 2048, 2, 2, biased=False, relu=False, name='res5a_branch1') + .batch_normalization(name='bn5a_branch1')) + + (self.feed('res4f_relu') + .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res5a_branch2a') + .batch_normalization(relu=True, name='bn5a_branch2a') + .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5a_branch2b') + .batch_normalization(relu=True, name='bn5a_branch2b') + .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch2c') + .batch_normalization(name='bn5a_branch2c')) + + (self.feed('bn5a_branch1', + 'bn5a_branch2c') + .add(name='res5a') + .relu(name='res5a_relu') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5b_branch2a') + .batch_normalization(relu=True, name='bn5b_branch2a') + .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5b_branch2b') + .batch_normalization(relu=True, name='bn5b_branch2b') + .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5b_branch2c') + .batch_normalization(name='bn5b_branch2c')) + + drop_out_d = tf.placeholder(tf.float32, name="drop_out_d") + feed_dict_train[drop_out_d] = 0.5 # 0.5 + feed_dict_test[drop_out_d] = 1.0 + + (self.feed('res5a_relu', + 'bn5b_branch2c') + .add(name='res5b') + .relu(name='res5b_relu') + .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5c_branch2a') + .batch_normalization(relu=True, name='bn5c_branch2a', dropout=drop_out_d) # def + .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5c_branch2b') + .batch_normalization(relu=True, name='bn5c_branch2b', dropout=drop_out_d) + .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5c_branch2c') + .batch_normalization(name='bn5c_branch2c')) + + # ------------------------------------------------------------------------------------ + # decoder EDGE MAPS & CORNERS MAPS + (self.feed('bn5c_branch2c') + .upconv(None, 512, ksize=5, stride=2, name='d_2x', biased=True, relu=True)) + (self.feed('d_2x', 'res4f_relu') + .concat(axis=3, name="d_concat_2x") + .upconv(None, 256, ksize=5, stride=2, name='d_4x', biased=True, relu=True) + .upconv(None, 2, ksize=3, stride=1, biased=True, relu=False, name='output4X_likelihood')) + (self.feed('d_4x', 'res3d_relu', 'output4X_likelihood') + .concat(axis=3, name="d_concat_4x") + .upconv(None, 128, ksize=5, stride=2, biased=True, relu=True, name='d_8x') + .upconv(None, 2, ksize=3, stride=1, relu=False, biased=True, name='output8X_likelihood')) + (self.feed('d_8x', 'res2c_relu', 'output8X_likelihood') + .concat(axis=3, name="d_concat_8x") + .upconv(None, 64, ksize=5, stride=2, biased=True, relu=True, name='d_16x') + .upconv(None, 2, ksize=3, stride=1, relu=False, biased=True, name='output16X_likelihood')) + (self.feed('d_16x', 'bn_conv1', 'output16X_likelihood') + .concat(axis=3, name="d_concat_16x") + .upconv(None, 64, ksize=3, stride=1, biased=True, relu=True, name='d_16x_conv1') + .upconv(None, 2, ksize=3, stride=1, biased=True, relu=False, name='output_likelihood')) + + self.fd_test = feed_dict_test + self.fd_train = feed_dict_train \ No newline at end of file diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/__init__.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d90e902da62c77a09c1b6e58d8e71155b65bc74d --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/__init__.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. +from npu_bridge.npu_init import * +from. CFL_StdConvs import LayoutEstimator_StdConvs diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/network.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/network.py new file mode 100644 index 0000000000000000000000000000000000000000..945d5a4de6a2bd4c40292406521b8e234c18ea65 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/Models/network.py @@ -0,0 +1,708 @@ +# 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 numpy as np +import tensorflow as tf +import re +import math +from config import * +from npu_bridge.estimator import npu_ops + + +DEFAULT_PADDING = 'SAME' +DEFAULT_TYPE = tf.float32 + + +def include_original(dec): + """ Meta decorator, which make the original function callable (via f._original() )""" + + def meta_decorator(f): + decorated = dec(f) + decorated._original = f + return decorated + + return meta_decorator + + +summary = True + + +def ActivationSummary(layer): # tensorBoard (jmfacil) + if summary: + TOWER_NAME = 'tower' + tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', layer.op.name) + tf.summary.histogram(tensor_name + '/activations', layer) + + +@include_original +def layer(op): + def layer_decorated(self, *args, **kwargs): + # Automatically set a name if not provided. + name = kwargs.setdefault('name', self.get_unique_name(op.__name__)) + # Figure out the layer inputs. + if len(self.inputs) == 0: + raise RuntimeError('No input variables found for layer %s.' % name) + elif len(self.inputs) == 1: + layer_input = self.inputs[0] + else: + layer_input = list(self.inputs) + # Perform the operation and get the output. + layer_output = op(self, layer_input, *args, **kwargs) + # Add to layer LUT. + self.layers[name] = layer_output + # This output is now the input for the next layer. + self.feed(layer_output) + # Return self for chained calls. + return self + + return layer_decorated + + +class Network(object): + + def __init__(self, inputs, trainable=True, is_training=True, bs=16): # ,reuse=None): #cfernandez + self.inputs = [] + self.batch_size = bs + self.layers = dict(inputs) + self.trainable = trainable + self.is_training = is_training + self.setup() + + def setup(self): + raise NotImplementedError('Must be subclassed.') + + def load(self, data_path, session, ignore_missing=False): + def transform_names(k): + if k == 'mean': + return 'moving_mean' + if k == 'variance': + return 'moving_variance' + if k == 'scale': + return 'gamma' + if k == 'offset': + return 'beta' + return k + + print(data_path) + data_dict = np.load(data_path, encoding='latin1').item() + for key in data_dict: + superkey = self.nname + "/" + key + with tf.variable_scope(superkey, reuse=True): + for subkey in data_dict[key]: + try: + nsubkey = transform_names(subkey) + var = tf.get_variable(nsubkey) + session.run(var.assign(data_dict[key][subkey])) + except ValueError: + print("ignore " + key, subkey) + print(superkey, tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=superkey)) + if not ignore_missing: + raise + print("Loaded weitghts") + + def feed(self, *args): + assert len(args) != 0 + self.inputs = [] + for layer in args: + if isinstance(layer, str): + try: + layer = self.layers[layer] + print(layer) + except KeyError: + print(list(self.layers.keys())) + raise KeyError('Unknown layer name fed: %s' % layer) + self.inputs.append(layer) + return self + + def get_output(self, layer): + try: + layer = self.layers[layer] + except KeyError: + print(list(self.layers.keys())) + raise KeyError('Unknown layer name fed: %s' % layer) + return layer + + def get_layer_output(self, name): + return self.layers[name] + + def get_unique_name(self, prefix): + id = sum(t.startswith(prefix) for t, _ in list(self.layers.items())) + 1 + return '%s_%d' % (prefix, id) + + def make_var(self, name, shape, initializer=None, trainable=True, regularizer=None): + return tf.get_variable(name, shape, initializer=initializer, trainable=trainable, regularizer=regularizer) + + def validate_padding(self, padding): + assert padding in ('SAME', 'VALID') + + def filler(self, params): # chema + # print "Filler: "+str(params) + value = params.get("value", 0.0) + mean = params.get("mean", 0.0) + std = params.get("std", 0.1) + dtype = params.get("dtype", DEFAULT_TYPE) + name = params.get("name", None) + uniform = params.get("uniform", False) + return { + "xavier_conv2d": tf.contrib.layers.xavier_initializer_conv2d(uniform=uniform), + "t_normal": tf.truncated_normal_initializer(mean=mean, stddev=std, dtype=dtype), + "constant": tf.constant_initializer(value=value, dtype=dtype) + }[params.get("type", "t_normal")] + + @layer + def conv(self, input, k_h, k_w, c_o, s_h, s_w, name, rate=1, biased=True, relu=True, padding=DEFAULT_PADDING, + trainable=True, initializer=None): + """ contribution by miraclebiu, and biased option""" + self.validate_padding(padding) + c_i = input.get_shape()[-1] + convolve = lambda i, k: tf.nn.convolution( + i, k, padding=padding, strides=[s_h, s_w], dilation_rate=[rate, rate]) + with tf.variable_scope(name, reuse=False) as scope: # cfernandez reuse + + # init_weights = tf.truncated_normal_initializer(0.0, stddev=0.001) + init_weights = tf.zeros_initializer() if initializer is 'zeros' else tf.contrib.layers.variance_scaling_initializer( + factor=0.01, mode='FAN_AVG', uniform=False) + init_biases = tf.constant_initializer(0.0) + # kernel = self.make_var('weights', [k_h, k_w, c_i, c_o], init_weights, trainable, + # regularizer=self.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)) + kernel = self.make_var('weights', shape=[k_h, k_w, c_i // 1, c_o], + initializer=self.filler({"type": "t_normal", # cfernandez + "mean": 0.0, + "std": 0.1 + }), + regularizer=self.l2_regularizer(args.weight_decay)) # 0.0005 cfg.TRAIN.WEIGHT_DECAY + + if biased: + biases = self.make_var('biases', [c_o], init_biases, trainable) + conv = convolve(input, kernel) + if relu: + bias = tf.nn.bias_add(conv, biases) + output = tf.nn.relu(bias) + output = tf.nn.bias_add(conv, biases) + + else: + conv = convolve(input, kernel) + if relu: + output = tf.nn.relu(conv) + output = conv + + return output + + @staticmethod + def rotation_matrix(axis, theta): + """ + Return the rotation matrix associated with counterclockwise rotation about + the given axis by theta radians. + """ + axis = np.asarray(axis) + axis = axis / math.sqrt(np.dot(axis, axis)) + a = math.cos(theta / 2.0) + b, c, d = -axis * math.sin(theta / 2.0) + aa, bb, cc, dd = a * a, b * b, c * c, d * d + bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d + return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)], + [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)], + [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]]) + + @staticmethod + def equi_coord(pano_W, pano_H, k_W, k_H, u, v): + """ contribution by cfernandez and jmfacil """ + fov_w = k_W * np.deg2rad(360. / float(pano_W)) + focal = (float(k_W) / 2) / np.tan(fov_w / 2) + c_x = 0 + c_y = 0 + + u_r, v_r = u, v + u_r, v_r = u_r - float(pano_W) / 2., v_r - float(pano_H) / 2. + phi, theta = u_r / (pano_W) * (np.pi) * 2, -v_r / (pano_H) * (np.pi) + + ROT = Network.rotation_matrix((0, 1, 0), phi) + ROT = np.matmul(ROT, Network.rotation_matrix((1, 0, 0), theta)) # np.eye(3) + + h_range = np.array(range(k_H)) + w_range = np.array(range(k_W)) + w_ones = (np.ones(k_W)) + h_ones = (np.ones(k_H)) + h_grid = np.matmul(np.expand_dims(h_range, -1), np.expand_dims(w_ones, 0)) + 0.5 - float(k_H) / 2 + w_grid = np.matmul(np.expand_dims(h_ones, -1), np.expand_dims(w_range, 0)) + 0.5 - float(k_W) / 2 + + K = np.array([[focal, 0, c_x], [0, focal, c_y], [0., 0., 1.]]) + inv_K = np.linalg.inv(K) + rays = np.stack([w_grid, h_grid, np.ones(h_grid.shape)], 0) + rays = np.matmul(inv_K, rays.reshape(3, k_H * k_W)) + rays /= np.linalg.norm(rays, axis=0, keepdims=True) + rays = np.matmul(ROT, rays) + rays = rays.reshape(3, k_H, k_W) + + phi = np.arctan2(rays[0, ...], rays[2, ...]) + theta = np.arcsin(np.clip(rays[1, ...], -1, 1)) + x = (pano_W) / (2. * np.pi) * phi + float(pano_W) / 2. + y = (pano_H) / (np.pi) * theta + float(pano_H) / 2. + + roi_y = h_grid + v_r + float(pano_H) / 2. + roi_x = w_grid + u_r + float(pano_W) / 2. + + new_roi_y = (y) + new_roi_x = (x) + + offsets_x = (new_roi_x - roi_x) + offsets_y = (new_roi_y - roi_y) + + return offsets_x, offsets_y + + @staticmethod + def equi_coord_fixed_resoltuion(pano_W, pano_H, k_W, k_H, u, v, pano_Hf=-1, pano_Wf=-1): + """ contribution by cfernandez and jmfacil """ + pano_Hf = pano_H if pano_Hf <= 0 else pano_H / pano_Hf + pano_Wf = pano_W if pano_Wf <= 0 else pano_W / pano_Wf + fov_w = k_W * np.deg2rad(360. / float(pano_Wf)) + focal = (float(k_W) / 2) / np.tan(fov_w / 2) + c_x = 0 + c_y = 0 + + u_r, v_r = u, v + u_r, v_r = u_r - float(pano_W) / 2., v_r - float(pano_H) / 2. + phi, theta = u_r / (pano_W) * (np.pi) * 2, -v_r / (pano_H) * (np.pi) + + ROT = Network.rotation_matrix((0, 1, 0), phi) + ROT = np.matmul(ROT, Network.rotation_matrix((1, 0, 0), theta)) # np.eye(3) + + h_range = np.array(range(k_H)) + w_range = np.array(range(k_W)) + w_ones = (np.ones(k_W)) + h_ones = (np.ones(k_H)) + h_grid = np.matmul(np.expand_dims(h_range, -1), np.expand_dims(w_ones, 0)) + 0.5 - float(k_H) / 2 + w_grid = np.matmul(np.expand_dims(h_ones, -1), np.expand_dims(w_range, 0)) + 0.5 - float(k_W) / 2 + + K = np.array([[focal, 0, c_x], [0, focal, c_y], [0., 0., 1.]]) + inv_K = np.linalg.inv(K) + rays = np.stack([w_grid, h_grid, np.ones(h_grid.shape)], 0) + rays = np.matmul(inv_K, rays.reshape(3, k_H * k_W)) + rays /= np.linalg.norm(rays, axis=0, keepdims=True) + rays = np.matmul(ROT, rays) + rays = rays.reshape(3, k_H, k_W) + + phi = np.arctan2(rays[0, ...], rays[2, ...]) + theta = np.arcsin(np.clip(rays[1, ...], -1, 1)) + x = (pano_W) / (2. * np.pi) * phi + float(pano_W) / 2. + y = (pano_H) / (np.pi) * theta + float(pano_H) / 2. + + roi_y = h_grid + v_r + float(pano_H) / 2. + roi_x = w_grid + u_r + float(pano_W) / 2. + + new_roi_y = (y) + new_roi_x = (x) + + offsets_x = (new_roi_x - roi_x) + offsets_y = (new_roi_y - roi_y) + + return offsets_x, offsets_y + + @staticmethod + def distortion_aware_map(pano_W, pano_H, k_W, k_H, s_width=1, s_height=1, bs=16): + """ contribution by cfernandez and jmfacil """ + n = 1 + offset = np.zeros(shape=[pano_H, pano_W, k_H * k_W * 2]) + print(offset.shape) + + for v in range(0, pano_H, s_height): + for u in range(0, pano_W, s_width): + offsets_x, offsets_y = Network.equi_coord_fixed_resoltuion(pano_W, pano_H, k_W, k_H, u, v, 1, 1) + offsets = np.concatenate((np.expand_dims(offsets_y, -1), np.expand_dims(offsets_x, -1)), axis=-1) + total_offsets = offsets.flatten().astype("float32") + offset[v, u, :] = total_offsets + + offset = tf.constant(offset) + offset = tf.expand_dims(offset, 0) + offset = tf.concat([offset for _ in range(bs)], axis=0) + offset = tf.cast(offset, tf.float32) + + return offset + + @layer + def equi_conv(self, input, k_h, k_w, c_o, s_h, s_w, num_deform_group, name, num_groups=1, rate=1, biased=True, + relu=True, + padding=DEFAULT_PADDING, trainable=True, initializer=None): + """ contribution by cfernandez and jmfacil """ + self.validate_padding(padding) + data = input + n, h, w, _ = tuple(data.get_shape().as_list()) + data_shape = data.shape + offset = tf.stop_gradient( + Network.distortion_aware_map(w, h, k_w, k_h, s_width=s_w, s_height=s_h, bs=self.batch_size)) + + c_i = data.get_shape()[-1] + trans2NCHW = lambda x: tf.transpose(x, [0, 3, 1, 2]) + trans2NHWC = lambda x: tf.transpose(x, [0, 2, 3, 1]) + # deform conv only supports NCHW + data = trans2NCHW(data) + offset = trans2NCHW(offset) + dconvolve = lambda i, k, o: deform_conv_op.deform_conv_op( + i, k, o, strides=[1, 1, s_h, s_w], rates=[1, 1, rate, rate], padding=padding, num_groups=num_groups, + deformable_group=num_deform_group) + with tf.variable_scope(name, reuse=False) as scope: + + init_weights = tf.zeros_initializer() if initializer is 'zeros' else tf.contrib.layers.variance_scaling_initializer( + factor=0.01, mode='FAN_AVG', uniform=False) + init_biases = tf.constant_initializer(0.0) + kernel = self.make_var('weights', [k_h, k_w, c_i, c_o], init_weights, trainable, + regularizer=self.l2_regularizer(args.weight_decay)) + kernel = tf.transpose(kernel, [3, 2, 0, 1]) + ActivationSummary(offset) + + print(data, kernel, offset) + dconv = trans2NHWC(dconvolve(data, kernel, offset)) + if biased: + biases = self.make_var('biases', [c_o], init_biases, trainable) + if relu: + bias = tf.nn.bias_add(dconv, biases) + return tf.nn.relu(bias) + return tf.nn.bias_add(dconv, biases) + else: + if relu: + return tf.nn.relu(dconv) + return dconv + + @layer + def upconv(self, input, shape, c_o, ksize=4, stride=2, name='upconv', biased=False, relu=True, + padding=DEFAULT_PADDING, + trainable=True, initializer=None): + """ up-conv""" + self.validate_padding(padding) + + c_in = input.get_shape()[3].value + in_shape_d = tf.shape(input) + in_shape = input.shape.as_list() + if shape is None: + h = ((in_shape[1]) * stride) + w = ((in_shape[2]) * stride) + new_shape = [in_shape_d[0], h, w, c_o] + else: + new_shape = [in_shape_d[0], shape[1], shape[2], c_o] + output_shape = tf.stack(new_shape) + + filter_shape = [ksize, ksize, c_o, c_in] + + with tf.variable_scope(name, reuse=False) as scope: + init_weights = tf.zeros_initializer() if initializer is 'zeros' else tf.contrib.layers.variance_scaling_initializer( + factor=0.01, mode='FAN_AVG', uniform=False) # cfernandez + filters = self.make_var('weights', filter_shape, init_weights, trainable, + regularizer=self.l2_regularizer(args.weight_decay)) # cfg.TRAIN.WEIGHT_DECAY + deconv = tf.nn.conv2d_transpose(input, filters, output_shape, + strides=[1, stride, stride, 1], padding=DEFAULT_PADDING, name=scope.name) + # coz de-conv losses shape info, use reshape to re-gain shape + deconv = tf.reshape(deconv, new_shape) + + if biased: + init_biases = tf.constant_initializer(0.0) + biases = self.make_var('biases', [c_o], init_biases, trainable) + if relu: + bias = tf.nn.bias_add(deconv, biases) + output = tf.nn.relu(bias) + output = tf.nn.bias_add(deconv, biases) + + else: + if relu: + output = tf.nn.relu(deconv) + output = devonv + return output + + @layer + def reduce_max(self, input_data, name): + return tf.reduce_max(input_data, axis=1, keep_dims=True) + + @layer + def reduce_mean(self, input_data, name): + return tf.reduce_mean(input_data, axis=1, keep_dims=True) + + @layer + def argmax(self, input_data, name): + return tf.argmax(input_data, axis=1) + + @layer + def bilinear_unpool(self, input_data, mul_factor, name): + _, h, w, _ = tuple(input_data.get_shape().as_list()) + return tf.image.resize_bilinear(input_data, (h * mul_factor, w * mul_factor), align_corners=True, name=name) + + @layer + def mul_grad(self, input_data, mul, name): + return (1.0 - mul) * tf.stop_gradient(input_data) + (mul) * input_data + + @layer + def relu(self, input, name): + return tf.nn.relu(input, name=name) + + @layer + def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING): + self.validate_padding(padding) + return tf.nn.max_pool(input, + ksize=[1, k_h, k_w, 1], + strides=[1, s_h, s_w, 1], + padding=padding, + name=name) + + @layer + def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING): + self.validate_padding(padding) + return tf.nn.avg_pool(input, + ksize=[1, k_h, k_w, 1], + strides=[1, s_h, s_w, 1], + padding=padding, + name=name) + + @layer + def roi_pool(self, input, pooled_height, pooled_width, spatial_scale, name): + # only use the first input + if isinstance(input[0], tuple): + input[0] = input[0][0] + + if isinstance(input[1], tuple): + input[1] = input[1][0] + + print(input) + return roi_pool_op.roi_pool(input[0], input[1], + pooled_height, + pooled_width, + spatial_scale, + name=name)[0] + + @layer + def psroi_pool(self, input, output_dim, group_size, spatial_scale, name): + """contribution by miraclebiu""" + # only use the first input + if isinstance(input[0], tuple): + input[0] = input[0][0] + + if isinstance(input[1], tuple): + input[1] = input[1][0] + + return psroi_pooling_op.psroi_pool(input[0], input[1], + output_dim=output_dim, + group_size=group_size, + spatial_scale=spatial_scale, + name=name)[0] + + @layer + def reshape_layer(self, input, d, name): + input_shape = tf.shape(input) + if name == 'rpn_cls_prob_reshape': + # + # transpose: (1, AxH, W, 2) -> (1, 2, AxH, W) + # reshape: (1, 2xA, H, W) + # transpose: -> (1, H, W, 2xA) + return tf.transpose(tf.reshape(tf.transpose(input, [0, 3, 1, 2]), + [input_shape[0], + int(d), + tf.cast( + tf.cast(input_shape[1], tf.float32) / tf.cast(d, tf.float32) * tf.cast( + input_shape[3], tf.float32), tf.int32), + input_shape[2] + ]), + [0, 2, 3, 1], name=name) + else: + return tf.transpose(tf.reshape(tf.transpose(input, [0, 3, 1, 2]), + [input_shape[0], + int(d), + tf.cast(tf.cast(input_shape[1], tf.float32) * ( + tf.cast(input_shape[3], tf.float32) / tf.cast(d, tf.float32)), + tf.int32), + input_shape[2] + ]), + [0, 2, 3, 1], name=name) + + @layer + def reshape(self, input, shape, name): + return tf.reshape(input, shape=shape, name=name) + + @layer + def spatial_reshape_layer(self, input, d, name): + input_shape = tf.shape(input) + # transpose: (1, H, W, A x d) -> (1, H, WxA, d) + return tf.reshape(input, \ + [input_shape[0], \ + input_shape[1], \ + -1, \ + int(d)]) + + @layer + def lrn(self, input, radius, alpha, beta, name, bias=1.0): + return tf.nn.local_response_normalization(input, + depth_radius=radius, + alpha=alpha, + beta=beta, + bias=bias, + name=name) + + @layer + def concat(self, inputs, axis, name): + return tf.concat(axis=axis, values=inputs, name=name) + + @layer + def flatten_data(self, input, name): + return tf.reshape(input, shape=[input.shape[0], -1], name=name) + + @layer + def softmax(self, input, name): + input_shape = tf.shape(input) + if name == 'rpn_cls_prob': + return tf.reshape(tf.nn.softmax(tf.reshape(input, [-1, input_shape[3]])), + [-1, input_shape[1], input_shape[2], input_shape[3]], name=name) + else: + return tf.nn.softmax(input, name=name) + + @layer + def spatial_softmax(self, input, name): + input_shape = tf.shape(input) + # d = input.get_shape()[-1] + return tf.reshape(tf.nn.softmax(tf.reshape(input, [-1, input_shape[3]])), + [-1, input_shape[1], input_shape[2], input_shape[3]], name=name) + + @layer + def add(self, input, name): + """contribution by miraclebiu""" + return tf.add(input[0], input[1], name=name) + + # The original + @layer + def batch_normalization(self, input, name, relu=True, dropout=None): # , is_training= True): #, is_training= True + # jmfacil/cfernandez: dropout added based on pix2pix + is_training = self.is_training + # is_training=False + if dropout is not None and is_training: + temp_layer = tf.contrib.layers.batch_norm(input, scale=True, center=True, is_training=is_training, + scope=name) + if relu: + temp_layer = tf.nn.relu(temp_layer) + # output = tf.nn.dropout(temp_layer,dropout) + #return tf.nn.dropout(temp_layer,dropout) + return npu_ops.dropout(temp_layer, dropout) + + """contribution by miraclebiu""" + if relu: + temp_layer = tf.contrib.layers.batch_norm(input, scale=True, center=True, is_training=is_training, + scope=name) + # output = tf.nn.relu(temp_layer) + return tf.nn.relu(temp_layer) + else: + # output = tf.contrib.layers.batch_norm(input,scale=True,center=True,is_training=is_training,scope=name) + return tf.contrib.layers.batch_norm(input, scale=True, center=True, is_training=is_training, scope=name) + + # ActivationSummary(output) + # return output + + @layer + def batch_normalization0(self, input, name, relu=True, is_training=True, dropout=None, scale_offset=True, + decay=0.999): + is_training=self.is_training + shape = [input.get_shape()[-1]] + with tf.variable_scope(name, reuse=False) as scope: + if scale_offset: + scale = self.make_var('gamma', shape=shape, + initializer=self.filler( + {"type": "constant", + "value": 1.0} + ) + ) + offset = self.make_var('beta', shape=shape) + else: + scale, offset = (None, None) + + pop_mean = self.make_var('moving_mean', shape=shape) + pop_var = self.make_var('moving_variance', shape=shape, + initializer=self.filler( + {"type": "constant", + "value": 1.0} + ), + regularizer=False) + + if is_training: + batch_mean, batch_var = tf.nn.moments(input, [0, 1, 2], name='moments') + train_mean = tf.assign(pop_mean, + pop_mean * decay + batch_mean * (1 - decay)) + train_var = tf.assign(pop_var, + pop_var * decay + batch_var * (1 - decay)) + with tf.control_dependencies([train_mean, train_var]): + epsilon = 1e-4 + output = tf.nn.batch_normalization(input, + batch_mean, batch_var, offset, scale, epsilon) + else: + epsilon = 1e-4 + output = tf.nn.batch_normalization(input, pop_mean, pop_var, offset, scale, epsilon) + # jmfacil/cfernandez: dropout added based on pix2pix + if dropout is not None and is_training: + # temp_layer=tf.contrib.layers.batch_norm(input,scale=True,center=True,is_training=is_training,scope=name) + # if relu: + # temp_layer = tf.nn.relu(temp_layer) + #output = tf.nn.dropout(output,dropout) + output = npu_ops.dropout(output,dropout) + + """contribution by miraclebiu""" + if relu: + # temp_layer=tf.contrib.layers.batch_norm(input,scale=True,center=True,is_training=is_training,scope=name) + output = tf.nn.relu(output) + # else: + # return tf.contrib.layers.batch_norm(input,scale=True,center=True,is_training=is_training,scope=name) + return output + + @layer + def scale(self, input, c_in, name): + with tf.variable_scope(name, reuse=False) as scope: + alpha = tf.get_variable('alpha', shape=[c_in, ], dtype=tf.float32, + initializer=tf.constant_initializer(1.0), trainable=True, + regularizer=self.l2_regularizer(0.00001)) + beta = tf.get_variable('beta', shape=[c_in, ], dtype=tf.float32, + initializer=tf.constant_initializer(0.0), trainable=True, + regularizer=self.l2_regularizer(0.00001)) + return tf.add(tf.multiply(input, alpha), beta) + + @layer + def dropout(self, input, keep_prob, name): + # return tf.nn.dropout(input, keep_prob, name=name) + is_training=self.is_training + if is_training: + return npu_ops.dropout(input, keep_prob, name=name) + else: + return None + def l2_regularizer(self, weight_decay=0.0005, scope=None): + def regularizer(tensor): + with tf.name_scope(scope, default_name='l2_regularizer', values=[tensor]): + l2_weight = tf.convert_to_tensor(weight_decay, + dtype=tensor.dtype.base_dtype, + name='weight_decay') + return tf.multiply(l2_weight, tf.nn.l2_loss(tensor), name='value') + + return regularizer + + def smooth_l1_dist(self, deltas, sigma2=9.0, name='smooth_l1_dist'): + with tf.name_scope(name=name) as scope: + deltas_abs = tf.abs(deltas) + smoothL1_sign = tf.cast(tf.less(deltas_abs, 1.0 / sigma2), tf.float32) + return tf.square(deltas) * 0.5 * sigma2 * smoothL1_sign + \ + (deltas_abs - 0.5 / sigma2) * tf.abs(smoothL1_sign - 1) + + diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/README.md b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/README.md new file mode 100644 index 0000000000000000000000000000000000000000..307afcd65969c53fc690c2d241e417db1a910a8c --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/README.md @@ -0,0 +1,171 @@ +# 基本信息 + +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Computer Vision** + +**版本（Version）：1.1** + +**修改时间（Modified）：2022.03.09** + +**大小（Size）：117KB** + +**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）：基于Tensorflow框架对360°全景图片实现3D布局恢复的测试代码** + +# 模型概述 + +CFL模型是CFL: End-to-End Layout Recovery from 360 Images论文的Tensorflow实现，该论文的核心思想是使用StdConvs模型和EquiConvs模型分别在360°全景图片上实现3D布局恢复，并生成边图和角图。需要注意的是，此脚本是使用了StdConvs模型。 + +- 参考论文 + + [Corners for Layout: End-to-End Layout Recovery from 360 Images (cfernandezlab.github.io)](https://cfernandezlab.github.io/CFL/) + +- 参考实现 + + [GitHub - cfernandezlab/CFL: Tensorflow implementation of our end-to-end model to recover 3D layouts. Also with equirectangular convolutions!](https://github.com/cfernandezlab/CFL) + +# 默认配置 + +- 测试数据预处理（以SUN360测试集为例，仅作为用户参考示例） + - 图像的输入尺寸：128×256 + - 图像的输入格式：jpg +- 测试超参 + - Batch size：16 + - Test epoch：1 + - Test step：72 + +# 支持特性 + +| 特性列表 | 是否支持 | +| :------: | :------: | +| 分布式 | 否 | +| 混合精度 | 是 | + +# 混合精度 + +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。 + +脚本已默认开启混合精度，设置precision_mode参数的脚本参考如下。 + +```python +custom_op = config.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") +``` + +# 环境准备 + +- 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://gitee.com/link?target=https%3A%2F%2Fsupport.huawei.com%2Fenterprise%2Fzh%2Fcategory%2Fai-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。 +- 宿主机上需要安装Docker并登录[Ascend Hub中心](https://gitee.com/link?target=https%3A%2F%2Fascendhub.huawei.com%2F%23%2Fdetail%3Fname%3Dascend-tensorflow-arm)获取镜像。 +- 安装必要的python依赖 +`pip install -r requirements.txt` + + +# 快速上手 + +模型测试之前的准备工作：模型使用SUN360数据集和CFL模型训练得到的ckpt文件（见参考实现），数据集和ckpt文件请用户自行获取。 + +# 模型测试 + +- 单击“立即下载”，并选择合适的下载方式下载源码包。 + +- 启动测试之前，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/%E5%85%B6%E4%BB%96%E6%A1%88%E4%BE%8B/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) + +- 单卡测试 + + - 配置参数 + + 首先在脚本test/train_full_1p.sh中，配置data_path、output_path等参数，请用户根据实际路径配置data_path和output_path，或者在启动测试的命令行中以参数形式下发。 + + ```python + batch_size=16 + data_path=./data_weights + output_path=./output + ``` + + - 启动测试 + + 启动单卡测试（脚本为test/train_full_1p.sh） + + `bash test/train_full_1p.sh --data_path=./data_weights --output_path=./output` + +# 测试结果 + +- 精度结果对比 + + - EDGES + + | 精度指标项 | 论文发布 | GPU实测 | NPU实测 | + | :--------: | :------: | :-----: | :-----: | + | IoU | 0.575 | 0.588 | 0.583 | + | Accuracy | 0.931 | 0.933 | 0.931 | + | Precision | 0.789 | 0.782 | 0.818 | + | Recall | 0.667 | 0.691 | 0.661 | + | f1 score | 0.722 | 0.733 | 0.730 | + + - CORNERS + + | 精度指标项 | 论文发布 | GPU实测 | NPU实测 | + | :--------: | :------: | :-----: | :-----: | + | IoU | 0.460 | 0.465 | 0.457 | + | Accuracy | 0.974 | 0.974 | 0.974 | + | Precision | 0.887 | 0.872 | 0.885 | + | Recall | 0.488 | 0.498 | 0.484 | + | f1 score | 0.627 | 0.632 | 0.624 | + + + +# 高级参考 + +##### 文件说明 + +```python +|--Models + |--__init__.py //网络初始化 + |--CFL_StdConvs.py //网络构建 + |--network.py //网络结构 +|--test + |--train_full_1p.sh //单卡全量启动脚本 +|--License //声明 +|--README.md //代码说明文档 +|--config.py //参数设置文件 +|--modelarts_entry_acc.py //拉起测试文件 +|--modelzoo_level.txt //网络进度 +|--requirements.txt //python依赖列表 +|--test_CFL.py //网络测试代码 +|--output //测试结果存放路径 +|--data_weights //数据集和ckpt文件存放路径 + |--Datasets + |--SUN360 + |--test + |--CM_gt + |--pano_0b9db1eaf8b73158dd047b8f810cf0cc_CM.jpg + ... + |--pano_azzfywvfwnlpcl_CM.jpg + |--EM_gt + |--pano_0b9db1eaf8b73158dd047b8f810cf0cc_EM.jpg + ... + |--pano_azzfywvfwnlpcl_EM.jpg + |--RGB + |--pano_0b9db1eaf8b73158dd047b8f810cf0cc.jpg + ... + |--pano_azzfywvfwnlpcl.jpg +``` + +##### 脚本参数 + +```python +--batch_size 每个NPU的batch size,默认:16 +--data_path 数据集路径,默认:./data_weights +--output_path 结果输出路径,默认:./output +``` diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/config.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/config.py new file mode 100644 index 0000000000000000000000000000000000000000..e4a138d0c30c381c080b52d62699c6527142a488 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/config.py @@ -0,0 +1,89 @@ +# 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 argparse +import os +from npu_bridge.npu_init import * + +## PATHS: Please, change this before execution if needed. + +# Creates a directory in case it doesn't exist +def check(dirname): + if not os.path.exists(dirname): + os.makedirs(dirname) + return dirname + +# The project directory +CFL_DIR = os.path.dirname(os.path.realpath(__file__)) + +# --------------------------------------------------------------- + +## Configuration of CFL + +# Mean color to subtract before propagating an image through a DNN +MEAN_COLOR = [103.939, 116.779, 123.68] + +parser = argparse.ArgumentParser() + +# The dataset you want to train/test the model on +parser.add_argument('--dataset', required=True, type=str, help='Path to dataset folders. It must contain RGB/, CM_gt/ and EM_gt/.') + +# CFL architecture +parser.add_argument('--network', default='StdConvs', choices=['StdConvs','EquiConvs'], help='CFL architecture') + +# Path to weights +parser.add_argument('--weights', required=True, help= 'Path to weights (eg. weights/StdConvs.ckpt') + +# Path to results folder +parser.add_argument('--results', default=os.path.join(CFL_DIR, 'results/'), help= 'Path to results folder. It will generate the folder if it does not exist.') + +# GPU to be used +parser.add_argument('--gpu', default="0", help= 'GPU to be used') + +# Ignore missing params +parser.add_argument('--ignore', action="store_true", default=False, help= 'Ignore missing params') + +# TEST config +parser.add_argument("--im_height", default=128, type=int) +parser.add_argument("--im_width", default=256, type=int) +parser.add_argument("--im_ch", default=3, type=int) + +# TRAIN config +parser.add_argument("--weight_decay", default=0.0005, type=int) + +# Modelarts +parser.add_argument('--platform', default='modelarts', help='runtime platform, linux or modelarts') +parser.add_argument('--chip', default='gpu', help='device identifier -- gpu, tpu or npu') + +parser.add_argument('--logdir', default='/tmp/data', help='directory for summaries and checkpoints.') +parser.add_argument('--obs_dir', default='obs://eric-mt-net/log/g', help='device identifier -- gpu, tpu or npu') + + + +args = parser.parse_args() + diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/modelarts_entry_acc.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/modelarts_entry_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..13077b10e660de32d6f7861257a50e1a01ede9ba --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_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/CFL_ID1230_for_TensorFlow/modelzoo_level.txt b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..e34a411924aa7b6a688e60dd22cea2ea08a843f3 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/modelzoo_level.txt @@ -0,0 +1,4 @@ +GPUStatus:OK +NPUMigrationStatus:OK +FuncStatus:OK +PrecisionStatus:OK \ No newline at end of file diff --git a/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/requirements.txt b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4b8d744da315fd22d382abf35a53edd1917ae4a0 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/requirements.txt @@ -0,0 +1,34 @@ +absl-py==0.7.1 +astor==0.7.1 +certifi==2019.3.9 +chardet==3.0.4 +cycler==0.10.0 +Cython==0.29.6 +easydict==1.9 +gast==0.2.2 +graphviz==0.8.4 +grpcio==1.19.0 +h5py==2.9.0 +idna==2.8 +Keras-Applications==1.0.8 +Keras-Preprocessing==1.0.9 +kiwisolver==1.0.1 +Markdown==3.0.1 +matplotlib==3.0.3 +mock==2.0.0 +numpy==1.16.2 +opencv-python==4.0.0.21 +pbr==5.1.3 +Pillow==5.4.1 +protobuf==3.7.0 +pyparsing==2.3.1 +python-dateutil==2.8.0 +requests==2.21.0 +scipy==1.2.1 +six==1.12.0 +tensorboard==1.15.0 +tensorflow-estimator==1.15.1 +tensorflow-gpu==1.15.0 +termcolor==1.1.0 +urllib3==1.24.1 +Werkzeug==0.15.0 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 new file mode 100644 index 0000000000000000000000000000000000000000..030e50c4deebcf1dca8b667f2f831cb320a943ff --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test/train_full_1p.sh @@ -0,0 +1,191 @@ +#!/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=16 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./test_CFL.py \ + --dataset=${data_path}"/Datasets/SUN360/test/" \ + --weights=${data_path}"/Weights/StdConvs/model.ckpt" \ + --results=${output_path}"/results" \ + --network="StdConvs" +else + python3.7 ./test_CFL.py \ + --dataset=${data_path}"/Datasets/SUN360/test/" \ + --weights=${data_path}"/Weights/StdConvs/model.ckpt" \ + --results=${output_path}"/results" \ + --network="StdConvs" >${print_log} +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 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/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test_CFL.py b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test_CFL.py new file mode 100644 index 0000000000000000000000000000000000000000..959d338fd9e3231d4c38c34fa827c96100cadd20 --- /dev/null +++ b/TensorFlow/contrib/cv/CFL_ID1230_for_TensorFlow/test_CFL.py @@ -0,0 +1,175 @@ +# 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 argparse +import os +import numpy as np +import tensorflow as tf +import scipy.misc +from scipy import misc +from matplotlib import pyplot as plt +import imageio +from PIL import Image +import glob +import time +import math +import os.path +import Models +from npu_bridge.npu_init import * +from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig + +from config import * + +def preprocess(img): + mean_color = [103.939, 116.779, 123.68] + r, g, b = tf.split(axis=3, num_or_size_splits=3, value=img) + bgr = tf.concat(values=[b - mean_color[0], g - mean_color[1], r - mean_color[2]], axis=3) + return bgr + + +def evaluate(map): + if map == 'edges': + prediction_path_list = glob.glob(os.path.join(args.results, 'EM_test') + '/*.jpg') + gt_path_list = glob.glob(os.path.join(args.dataset, 'EM_gt') + '/*.jpg') + if map == 'corners': + prediction_path_list = glob.glob(os.path.join(args.results, 'CM_test') + '/*.jpg') + gt_path_list = glob.glob(os.path.join(args.dataset, 'CM_gt') + '/*.jpg') + prediction_path_list.sort() + gt_path_list.sort() + + P, R, Acc, f1, IoU = [], [], [], [], [] + prediction = Image.open(prediction_path_list[0]) + for im in range(len(prediction_path_list)): + # predicted image + prediction = Image.open(prediction_path_list[im]) + pred_W, pred_H = prediction.size + prediction = np.array(prediction) / 255. + # gt image + gt = Image.open(gt_path_list[im]) + gt = gt.resize([pred_W, pred_H]) + gt = np.array(gt) / 255. + gt = (gt >= 0.01).astype(int) + + th = 0.1 + tp = np.sum(np.logical_and(gt == 1, prediction > th)) + tn = np.sum(np.logical_and(gt == 0, prediction <= th)) + fp = np.sum(np.logical_and(gt == 0, prediction > th)) + fn = np.sum(np.logical_and(gt == 1, prediction <= th)) + + # How accurate the positive predictions are + P.append(tp / (tp + fp)) + # Coverage of actual positive sample + R.append(tp / (tp + fn)) + # Overall performance of model + Acc.append((tp + tn) / (tp + tn + fp + fn)) + # Hybrid metric useful for unbalanced classes + f1.append(2 * (tp / (tp + fp)) * (tp / (tp + fn)) / ((tp / (tp + fp)) + (tp / (tp + fn)))) + # Intersection over Union + IoU.append(tp / (tp + fp + fn)) + + return np.mean(P), np.mean(R), np.mean(Acc), np.mean(f1), np.mean(IoU) + + +def predict(image_path_list): + rgb_ph1 = tf.compat.v1.placeholder(tf.float32, shape=(None, args.im_height, args.im_width, args.im_ch)) + rgb_ph = preprocess(rgb_ph1) + + net = Models.LayoutEstimator_StdConvs({'rgb_input': rgb_ph}, is_training=False) + + saver = tf.train.Saver() + config = tf.ConfigProto(log_device_placement=False,allow_soft_placement=True) + custom_op = config.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") + config.graph_options.rewrite_options.remapping = RewriterConfig.OFF # 必须显式关闭remap + config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF # 必须显式关闭 + with tf.Session(config=config) as sess: + + print('Loading the model') + + saver.restore(sess, args.weights) + + print('model loaded') + + # Obtain network predictions + for image_path in image_path_list: + + name = str(image_path) + filename = os.path.basename(name) + + img = Image.open(image_path) + img = img.resize([args.im_width, args.im_height], Image.ANTIALIAS) + img = np.array(img).astype('float32') + img = np.expand_dims(np.asarray(img), axis=0) + + fd = net.fd_test + fd[rgb_ph1] = img + + prediction = net.get_layer_output("output_likelihood") + pred_edges, pred_corners = tf.split(prediction, [1, 1], 3) + + tt = time.time(); + emap, cmap = sess.run([tf.nn.sigmoid(pred_edges), tf.nn.sigmoid(pred_corners)], feed_dict=fd) + print("sec/step :", time.time() - tt) + + + # Save results + imageio.imwrite(os.path.join(args.results, 'EM_test', filename + "_emap.jpg"), emap[0, :, :, 0]) + imageio.imwrite(os.path.join(args.results, 'CM_test', filename + "_emap.jpg"), cmap[0, :, :, 0]) + + + + +def main(): + + t = time.time() + + if not os.path.exists(os.path.join(args.results, 'EM_test')): os.makedirs(os.path.join(args.results, 'EM_test')) + if not os.path.exists(os.path.join(args.results, 'CM_test')): os.makedirs(os.path.join(args.results, 'CM_test')) + pred = predict(glob.glob(os.path.join(args.dataset, 'RGB') + '/*.jpg')) + elapsed = time.time() - t + print('Total time in seconds:', elapsed / 1) + + ## Give metrics + P_e, R_e, Acc_e, f1_e, IoU_e = evaluate('edges') + print('EDGES: IoU: ' + str('%.3f' % IoU_e) + '; Accuracy: ' + str('%.3f' % Acc_e) + '; Precision: ' + str( + '%.3f' % P_e) + '; Recall: ' + str('%.3f' % R_e) + '; f1 score: ' + str('%.3f' % f1_e)) + P_c, R_c, Acc_c, f1_c, IoU_c = evaluate('corners') + print('CORNERS: IoU: ' + str('%.3f' % IoU_c) + '; Accuracy: ' + str('%.3f' % Acc_c) + '; Precision: ' + str( + '%.3f' % P_c) + '; Recall: ' + str('%.3f' % R_c) + '; f1 score: ' + str('%.3f' % f1_c)) + + print("Final Accuracy accuracy :"+str('%.3f'%Acc_c)) + # latex format + latex = [str('$%.3f$' % IoU_c) + " & " + str('$%.3f$' % Acc_c) + " & " + str('$%.3f$' % P_c) + " & " + str( + '$%.3f$' % R_c) + " & " + str('$%.3f$' % f1_c)] + print(latex) + + + +if __name__ == '__main__': + main() \ No newline at end of file