# 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

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# ==============================================================================

"""Contains common code shared by all inception models.

Usage of arg scope:

  with slim.arg_scope(inception_arg_scope()):

    logits, end_points = inception.inception_v3(images, num_classes,

                                                is_training=is_training)

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim

def inception_arg_scope(weight_decay=0.00004,

                        use_batch_norm=True,

                        batch_norm_decay=0.9997,

                        batch_norm_epsilon=0.001,

                        activation_fn=tf.nn.relu):

  """Defines the default arg scope for inception models.

  Args:

    weight_decay: The weight decay to use for regularizing the model.

    use_batch_norm: "If `True`, batch_norm is applied after each convolution.

    batch_norm_decay: Decay for batch norm moving average.

    batch_norm_epsilon: Small float added to variance to avoid dividing by zero

      in batch norm.

    activation_fn: Activation function for conv2d.

  Returns:

    An `arg_scope` to use for the inception models.

  """

  batch_norm_params = {

      # Decay for the moving averages.

      'decay': batch_norm_decay,

      # epsilon to prevent 0s in variance.

      'epsilon': batch_norm_epsilon,

      # collection containing update_ops.

      'updates_collections': tf.GraphKeys.UPDATE_OPS,

      # use fused batch norm if possible.

      'fused': None,

  }

  if use_batch_norm:

    normalizer_fn = slim.batch_norm

    normalizer_params = batch_norm_params

  else:

    normalizer_fn = None

    normalizer_params = {}

  # Set weight_decay for weights in Conv and FC layers.

  with slim.arg_scope([slim.conv2d, slim.fully_connected],

                      weights_regularizer=slim.l2_regularizer(weight_decay)):

    with slim.arg_scope(

        [slim.conv2d],

        weights_initializer=slim.variance_scaling_initializer(),

        activation_fn=activation_fn,

        normalizer_fn=normalizer_fn,

        normalizer_params=normalizer_params) as sc:

      return sc