X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v4.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v4.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=b4f07ea70edf69ecac94fad26fb949295a41eac0;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py deleted file mode 100755 index b4f07ea..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py +++ /dev/null @@ -1,323 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Contains the definition of the Inception V4 architecture. - -As described in http://arxiv.org/abs/1602.07261. - - Inception-v4, Inception-ResNet and the Impact of Residual Connections - on Learning - Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim - - -def block_inception_a(inputs, scope=None, reuse=None): - """Builds Inception-A block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def block_reduction_a(inputs, scope=None, reuse=None): - """Builds Reduction-A block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - - -def block_inception_b(inputs, scope=None, reuse=None): - """Builds Inception-B block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1') - branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7') - branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1') - branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7') - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def block_reduction_b(inputs, scope=None, reuse=None): - """Builds Reduction-B block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1') - branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - - -def block_inception_c(inputs, scope=None, reuse=None): - """Builds Inception-C block for Inception v4 network.""" - # By default use stride=1 and SAME padding - with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], - stride=1, padding='SAME'): - with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = tf.concat(axis=3, values=[ - slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'), - slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')]) - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1') - branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3') - branch_2 = tf.concat(axis=3, values=[ - slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'), - slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')]) - with tf.variable_scope('Branch_3'): - branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') - branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1') - return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - - -def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None): - """Creates the Inception V4 network up to the given final endpoint. - - Args: - inputs: a 4-D tensor of size [batch_size, height, width, 3]. - final_endpoint: specifies the endpoint to construct the network up to. - It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', - 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', - 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', - 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', - 'Mixed_7d'] - scope: Optional variable_scope. - - Returns: - logits: the logits outputs of the model. - end_points: the set of end_points from the inception model. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - """ - end_points = {} - - def add_and_check_final(name, net): - end_points[name] = net - return name == final_endpoint - - with tf.variable_scope(scope, 'InceptionV4', [inputs]): - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # 299 x 299 x 3 - net = slim.conv2d(inputs, 32, [3, 3], stride=2, - padding='VALID', scope='Conv2d_1a_3x3') - if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points - # 149 x 149 x 32 - net = slim.conv2d(net, 32, [3, 3], padding='VALID', - scope='Conv2d_2a_3x3') - if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points - # 147 x 147 x 32 - net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3') - if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points - # 147 x 147 x 64 - with tf.variable_scope('Mixed_3a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_0a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', - scope='Conv2d_0a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_3a', net): return net, end_points - - # 73 x 73 x 160 - with tf.variable_scope('Mixed_4a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7') - branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1') - branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID', - scope='Conv2d_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_4a', net): return net, end_points - - # 71 x 71 x 192 - with tf.variable_scope('Mixed_5a'): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID', - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1]) - if add_and_check_final('Mixed_5a', net): return net, end_points - - # 35 x 35 x 384 - # 4 x Inception-A blocks - for idx in range(4): - block_scope = 'Mixed_5' + chr(ord('b') + idx) - net = block_inception_a(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - - # 35 x 35 x 384 - # Reduction-A block - net = block_reduction_a(net, 'Mixed_6a') - if add_and_check_final('Mixed_6a', net): return net, end_points - - # 17 x 17 x 1024 - # 7 x Inception-B blocks - for idx in range(7): - block_scope = 'Mixed_6' + chr(ord('b') + idx) - net = block_inception_b(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - - # 17 x 17 x 1024 - # Reduction-B block - net = block_reduction_b(net, 'Mixed_7a') - if add_and_check_final('Mixed_7a', net): return net, end_points - - # 8 x 8 x 1536 - # 3 x Inception-C blocks - for idx in range(3): - block_scope = 'Mixed_7' + chr(ord('b') + idx) - net = block_inception_c(net, block_scope) - if add_and_check_final(block_scope, net): return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v4(inputs, num_classes=1001, is_training=True, - dropout_keep_prob=0.8, - reuse=None, - scope='InceptionV4', - create_aux_logits=True): - """Creates the Inception V4 model. - - Args: - inputs: a 4-D tensor of size [batch_size, height, width, 3]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: float, the fraction to keep before final layer. - reuse: whether or not the network and its variables should be reused. To be - able to reuse 'scope' must be given. - scope: Optional variable_scope. - create_aux_logits: Whether to include the auxiliary logits. - - Returns: - logits: the logits outputs of the model. - end_points: the set of end_points from the inception model. - """ - end_points = {} - with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v4_base(inputs, scope=scope) - - with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], - stride=1, padding='SAME'): - # Auxiliary Head logits - if create_aux_logits: - with tf.variable_scope('AuxLogits'): - # 17 x 17 x 1024 - aux_logits = end_points['Mixed_6h'] - aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, - padding='VALID', - scope='AvgPool_1a_5x5') - aux_logits = slim.conv2d(aux_logits, 128, [1, 1], - scope='Conv2d_1b_1x1') - aux_logits = slim.conv2d(aux_logits, 768, - aux_logits.get_shape()[1:3], - padding='VALID', scope='Conv2d_2a') - aux_logits = slim.flatten(aux_logits) - aux_logits = slim.fully_connected(aux_logits, num_classes, - activation_fn=None, - scope='Aux_logits') - end_points['AuxLogits'] = aux_logits - - # Final pooling and prediction - with tf.variable_scope('Logits'): - # 8 x 8 x 1536 - net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', - scope='AvgPool_1a') - # 1 x 1 x 1536 - net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b') - net = slim.flatten(net, scope='PreLogitsFlatten') - end_points['PreLogitsFlatten'] = net - # 1536 - logits = slim.fully_connected(net, num_classes, activation_fn=None, - scope='Logits') - end_points['Logits'] = logits - end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') - return logits, end_points -inception_v4.default_image_size = 299 - - -inception_v4_arg_scope = inception_utils.inception_arg_scope