X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Foverfeat.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Foverfeat.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=64a542523a1df7079ab3cca1a0137a60d592ec70;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py deleted file mode 100755 index 64a5425..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat.py +++ /dev/null @@ -1,118 +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 model definition for the OverFeat network. - -The definition for the network was obtained from: - OverFeat: Integrated Recognition, Localization and Detection using - Convolutional Networks - Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and - Yann LeCun, 2014 - http://arxiv.org/abs/1312.6229 - -Usage: - with slim.arg_scope(overfeat.overfeat_arg_scope()): - outputs, end_points = overfeat.overfeat(inputs) - -@@overfeat -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def overfeat_arg_scope(weight_decay=0.0005): - 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'): - with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: - return arg_sc - - -def overfeat(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.5, - spatial_squeeze=True, - scope='overfeat'): - """Contains the model definition for the OverFeat network. - - The definition for the network was obtained from: - OverFeat: Integrated Recognition, Localization and Detection using - Convolutional Networks - Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and - Yann LeCun, 2014 - http://arxiv.org/abs/1312.6229 - - Note: All the fully_connected layers have been transformed to conv2d layers. - To use in classification mode, resize input to 231x231. To use in fully - convolutional mode, set spatial_squeeze to false. - - 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. - - Returns: - the last op containing the log predictions and end_points dict. - - """ - with tf.variable_scope(scope, 'overfeat', [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.conv2d(inputs, 64, [11, 11], 4, padding='VALID', - scope='conv1') - net = slim.max_pool2d(net, [2, 2], scope='pool1') - net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2') - net = slim.max_pool2d(net, [2, 2], scope='pool2') - net = slim.conv2d(net, 512, [3, 3], scope='conv3') - net = slim.conv2d(net, 1024, [3, 3], scope='conv4') - net = slim.conv2d(net, 1024, [3, 3], scope='conv5') - net = slim.max_pool2d(net, [2, 2], scope='pool5') - with slim.arg_scope([slim.conv2d], - weights_initializer=trunc_normal(0.005), - biases_initializer=tf.constant_initializer(0.1)): - # Use conv2d instead of fully_connected layers. - net = slim.conv2d(net, 3072, [6, 6], padding='VALID', 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, - biases_initializer=tf.zeros_initializer(), - 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 -overfeat.default_image_size = 231