removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / networks / slim_nets / overfeat.py

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 (executable)
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