X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Fvgg.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Fvgg.py;h=4c74c5a340c866c16ebb82ea2cd97b9fddf3bfba;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py new file mode 100755 index 0000000..4c74c5a --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/vgg.py @@ -0,0 +1,265 @@ +# 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 model definitions for versions of the Oxford VGG network. + +These model definitions were introduced in the following technical report: + + Very Deep Convolutional Networks For Large-Scale Image Recognition + Karen Simonyan and Andrew Zisserman + arXiv technical report, 2015 + PDF: http://arxiv.org/pdf/1409.1556.pdf + ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf + CC-BY-4.0 + +More information can be obtained from the VGG website: +www.robots.ox.ac.uk/~vgg/research/very_deep/ + +Usage: + with slim.arg_scope(vgg.vgg_arg_scope()): + outputs, end_points = vgg.vgg_a(inputs) + + with slim.arg_scope(vgg.vgg_arg_scope()): + outputs, end_points = vgg.vgg_16(inputs) + +@@vgg_a +@@vgg_16 +@@vgg_19 +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim + + +def vgg_arg_scope(weight_decay=0.0005): + """Defines the VGG arg scope. + + Args: + weight_decay: The l2 regularization coefficient. + + Returns: + An arg_scope. + """ + with slim.arg_scope([slim.conv2d, slim.fully_connected], + activation_fn=tf.nn.relu, + weights_regularizer=slim.l2_regularizer(weight_decay), + biases_initializer=tf.zeros_initializer()): + with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc: + return arg_sc + + +def vgg_a(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_a', + fc_conv_padding='VALID'): + """Oxford Net VGG 11-Layers version A Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +vgg_a.default_image_size = 224 + + +def vgg_16(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_16', + fc_conv_padding='VALID'): + """Oxford Net VGG 16-Layers version D Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + # yjr_feature = tf.squeeze(net) + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + # end_points['yjr_feature'] = yjr_feature + end_points['predictions'] = slim.softmax(net, scope='predictions') + return net, end_points +vgg_16.default_image_size = 224 + + +def vgg_19(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_19', + fc_conv_padding='VALID'): + """Oxford Net VGG 19-Layers version E Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +vgg_19.default_image_size = 224 + +# Alias +vgg_d = vgg_16 +vgg_e = vgg_19