example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_utils.py

   1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
   2 #
   3 # Licensed under the Apache License, Version 2.0 (the "License");
   4 # you may not use this file except in compliance with the License.
   5 # You may obtain a copy of the License at
   6 #
   7 # http://www.apache.org/licenses/LICENSE-2.0
   8 #
   9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 # ==============================================================================
  15 """Contains common code shared by all inception models.
  16
  17 Usage of arg scope:
  18   with slim.arg_scope(inception_arg_scope()):
  19     logits, end_points = inception.inception_v3(images, num_classes,
  20                                                 is_training=is_training)
  21
  22 """
  23 from __future__ import absolute_import
  24 from __future__ import division
  25 from __future__ import print_function
  26
  27 import tensorflow as tf
  28
  29 slim = tf.contrib.slim
  30
  31
  32 def inception_arg_scope(weight_decay=0.00004,
  33                         use_batch_norm=True,
  34                         batch_norm_decay=0.9997,
  35                         batch_norm_epsilon=0.001):
  36   """Defines the default arg scope for inception models.
  37
  38   Args:
  39     weight_decay: The weight decay to use for regularizing the model.
  40     use_batch_norm: "If `True`, batch_norm is applied after each convolution.
  41     batch_norm_decay: Decay for batch norm moving average.
  42     batch_norm_epsilon: Small float added to variance to avoid dividing by zero
  43       in batch norm.
  44
  45   Returns:
  46     An `arg_scope` to use for the inception models.
  47   """
  48   batch_norm_params = {
  49       # Decay for the moving averages.
  50       'decay': batch_norm_decay,
  51       # epsilon to prevent 0s in variance.
  52       'epsilon': batch_norm_epsilon,
  53       # collection containing update_ops.
  54       'updates_collections': tf.GraphKeys.UPDATE_OPS,
  55   }
  56   if use_batch_norm:
  57     normalizer_fn = slim.batch_norm
  58     normalizer_params = batch_norm_params
  59   else:
  60     normalizer_fn = None
  61     normalizer_params = {}
  62   # Set weight_decay for weights in Conv and FC layers.
  63   with slim.arg_scope([slim.conv2d, slim.fully_connected],
  64                       weights_regularizer=slim.l2_regularizer(weight_decay)):
  65     with slim.arg_scope(
  66         [slim.conv2d],
  67         weights_initializer=slim.variance_scaling_initializer(),
  68         activation_fn=tf.nn.relu,
  69         normalizer_fn=normalizer_fn,
  70         normalizer_params=normalizer_params) as sc:
  71       return sc