removed exmple apps code

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

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py
deleted file mode 100755 (executable)
index 4e7e563..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/alexnet.py
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@@ -1,125 +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 a model definition for AlexNet.
-
-This work was first described in:
-  ImageNet Classification with Deep Convolutional Neural Networks
-  Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton
-
-and later refined in:
-  One weird trick for parallelizing convolutional neural networks
-  Alex Krizhevsky, 2014
-
-Here we provide the implementation proposed in "One weird trick" and not
-"ImageNet Classification", as per the paper, the LRN layers have been removed.
-
-Usage:
-  with slim.arg_scope(alexnet.alexnet_v2_arg_scope()):
-    outputs, end_points = alexnet.alexnet_v2(inputs)
-
-@@alexnet_v2
-"""
-
-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 alexnet_v2_arg_scope(weight_decay=0.0005):
-  with slim.arg_scope([slim.conv2d, slim.fully_connected],
-                      activation_fn=tf.nn.relu,
-                      biases_initializer=tf.constant_initializer(0.1),
-                      weights_regularizer=slim.l2_regularizer(weight_decay)):
-    with slim.arg_scope([slim.conv2d], padding='SAME'):
-      with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
-        return arg_sc
-
-
-def alexnet_v2(inputs,
-               num_classes=1000,
-               is_training=True,
-               dropout_keep_prob=0.5,
-               spatial_squeeze=True,
-               scope='alexnet_v2'):
-  """AlexNet version 2.
-
-  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
-  Parameters from:
-  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
-  layers-imagenet-1gpu.cfg
-
-  Note: All the fully_connected layers have been transformed to conv2d layers.
-        To use in classification mode, resize input to 224x224. To use in fully
-        convolutional mode, set spatial_squeeze to false.
-        The LRN layers have been removed and change the initializers from
-        random_normal_initializer to xavier_initializer.
-
-  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, 'alexnet_v2', [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, [3, 3], 2, scope='pool1')
-      net = slim.conv2d(net, 192, [5, 5], scope='conv2')
-      net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
-      net = slim.conv2d(net, 384, [3, 3], scope='conv3')
-      net = slim.conv2d(net, 384, [3, 3], scope='conv4')
-      net = slim.conv2d(net, 256, [3, 3], scope='conv5')
-      net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')
-
-      # Use conv2d instead of fully_connected layers.
-      with slim.arg_scope([slim.conv2d],
-                          weights_initializer=trunc_normal(0.005),
-                          biases_initializer=tf.constant_initializer(0.1)):
-        net = slim.conv2d(net, 4096, [5, 5], 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
-alexnet_v2.default_image_size = 224