text: # 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 definition for inception v1 classification network."""
  
  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 inception_v1_base(inputs,
                        final_endpoint='Mixed_5c',
                        scope='InceptionV1'):
    """Defines the Inception V1 base architecture.
  
    This architecture is defined in:
      Going deeper with convolutions
      Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
      Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
      http://arxiv.org/pdf/1409.4842v1.pdf.
  
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      final_endpoint: specifies the endpoint to construct the network up to. It
        can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
        'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
        'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
        'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
      scope: Optional variable_scope.
  
    Returns:
      A dictionary from components of the network to the corresponding activation.
  
    Raises:
      ValueError: if final_endpoint is not set to one of the predefined values.
    """
    end_points = {}
    with tf.variable_scope(scope, 'InceptionV1', [inputs]):
      with slim.arg_scope(
          [slim.conv2d, slim.fully_connected],
          weights_initializer=trunc_normal(0.01)):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                            stride=1, padding='SAME'):
          end_point = 'Conv2d_1a_7x7'
          net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
          end_point = 'MaxPool_2a_3x3'
          net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
          end_point = 'Conv2d_2b_1x1'
          net = slim.conv2d(net, 64, [1, 1], scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
          end_point = 'Conv2d_2c_3x3'
          net = slim.conv2d(net, 192, [3, 3], scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
          end_point = 'MaxPool_3a_3x3'
          net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_3b'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_3c'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'MaxPool_4a_3x3'
          net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_4b'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_4c'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_4d'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_4e'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_4f'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'MaxPool_5a_2x2'
          net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_5b'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
  
          end_point = 'Mixed_5c'
          with tf.variable_scope(end_point):
            with tf.variable_scope('Branch_0'):
              branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
            with tf.variable_scope('Branch_1'):
              branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
              branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_2'):
              branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
              branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3')
            with tf.variable_scope('Branch_3'):
              branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
              branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1')
            net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
          end_points[end_point] = net
          if final_endpoint == end_point: return net, end_points
      raise ValueError('Unknown final endpoint %s' % final_endpoint)
  
  
  def inception_v1(inputs,
                   num_classes=1000,
                   is_training=True,
                   dropout_keep_prob=0.8,
                   prediction_fn=slim.softmax,
                   spatial_squeeze=True,
                   reuse=None,
                   scope='InceptionV1'):
    """Defines the Inception V1 architecture.
  
    This architecture is defined in:
  
      Going deeper with convolutions
      Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
      Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
      http://arxiv.org/pdf/1409.4842v1.pdf.
  
    The default image size used to train this network is 224x224.
  
    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      num_classes: number of predicted classes.
      is_training: whether is training or not.
      dropout_keep_prob: the percentage of activation values that are retained.
      prediction_fn: a function to get predictions out of logits.
      spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
          of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
      reuse: whether or not the network and its variables should be reused. To be
        able to reuse 'scope' must be given.
      scope: Optional variable_scope.
  
    Returns:
      logits: the pre-softmax activations, a tensor of size
        [batch_size, num_classes]
      end_points: a dictionary from components of the network to the corresponding
        activation.
    """
    # Final pooling and prediction
    with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes],
                           reuse=reuse) as scope:
      with slim.arg_scope([slim.batch_norm, slim.dropout],
                          is_training=is_training):
        net, end_points = inception_v1_base(inputs, scope=scope)
        with tf.variable_scope('Logits'):
          net = slim.avg_pool2d(net, [7, 7], stride=1, scope='MaxPool_0a_7x7')
          net = slim.dropout(net,
                             dropout_keep_prob, scope='Dropout_0b')
          logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                               normalizer_fn=None, scope='Conv2d_0c_1x1')
          if spatial_squeeze:
            logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
  
          end_points['Logits'] = logits
          end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
    return logits, end_points
  inception_v1.default_image_size = 224
  
  
  def inception_v1_arg_scope(weight_decay=0.00004,
                             use_batch_norm=True):
    """Defines the default InceptionV1 arg scope.
  
    Note: Althougth the original paper didn't use batch_norm we found it useful.
  
    Args:
      weight_decay: The weight decay to use for regularizing the model.
      use_batch_norm: "If `True`, batch_norm is applied after each convolution.
  
    Returns:
      An `arg_scope` to use for the inception v3 model.
    """
    batch_norm_params = {
        # Decay for the moving averages.
        'decay': 0.9997,
        # epsilon to prevent 0s in variance.
        'epsilon': 0.001,
        # collection containing update_ops.
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
    }
    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=tf.nn.relu,
          normalizer_fn=normalizer_fn,
          normalizer_params=normalizer_params) as sc:
        return sc
  