text: # Copyright 2017 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 definitions for the preactivation form of Residual Networks.
  
  Residual networks (ResNets) were originally proposed in:
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Deep Residual Learning for Image Recognition. arXiv:1512.03385
  
  The full preactivation 'v2' ResNet variant implemented in this module was
  introduced by:
  [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
      Identity Mappings in Deep Residual Networks. arXiv: 1603.05027
  
  The key difference of the full preactivation 'v2' variant compared to the
  'v1' variant in [1] is the use of batch normalization before every weight layer
  rather than after.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  _BATCH_NORM_DECAY = 0.997
  _BATCH_NORM_EPSILON = 1e-5
  
  
  def batch_norm_relu(inputs, is_training, data_format):
    """Performs a batch normalization followed by a ReLU."""
    # We set fused=True for a significant performance boost. See
    # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
    inputs = tf.layers.batch_normalization(
        inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
        momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
        scale=True, training=is_training, fused=True)
    inputs = tf.nn.relu(inputs)
    return inputs
  
  
  def fixed_padding(inputs, kernel_size, data_format):
    """Pads the input along the spatial dimensions independently of input size.
  
    Args:
      inputs: A tensor of size [batch, channels, height_in, width_in] or
        [batch, height_in, width_in, channels] depending on data_format.
      kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
                   Should be a positive integer.
      data_format: The input format ('channels_last' or 'channels_first').
  
    Returns:
      A tensor with the same format as the input with the data either intact
      (if kernel_size == 1) or padded (if kernel_size > 1).
    """
    pad_total = kernel_size - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg
  
    if data_format == 'channels_first':
      padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
                                      [pad_beg, pad_end], [pad_beg, pad_end]])
    else:
      padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
                                      [pad_beg, pad_end], [0, 0]])
    return padded_inputs
  
  
  def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
    """Strided 2-D convolution with explicit padding."""
    # The padding is consistent and is based only on `kernel_size`, not on the
    # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
    if strides > 1:
      inputs = fixed_padding(inputs, kernel_size, data_format)
  
    return tf.layers.conv2d(
        inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
        padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
        kernel_initializer=tf.variance_scaling_initializer(),
        data_format=data_format)
  
  
  def building_block(inputs, filters, is_training, projection_shortcut, strides,
                     data_format):
    """Standard building block for residual networks with BN before convolutions.
  
    Args:
      inputs: A tensor of size [batch, channels, height_in, width_in] or
        [batch, height_in, width_in, channels] depending on data_format.
      filters: The number of filters for the convolutions.
      is_training: A Boolean for whether the model is in training or inference
        mode. Needed for batch normalization.
      projection_shortcut: The function to use for projection shortcuts (typically
        a 1x1 convolution when downsampling the input).
      strides: The block's stride. If greater than 1, this block will ultimately
        downsample the input.
      data_format: The input format ('channels_last' or 'channels_first').
  
    Returns:
      The output tensor of the block.
    """
    shortcut = inputs
    inputs = batch_norm_relu(inputs, is_training, data_format)
  
    # The projection shortcut should come after the first batch norm and ReLU
    # since it performs a 1x1 convolution.
    if projection_shortcut is not None:
      shortcut = projection_shortcut(inputs)
  
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=3, strides=strides,
        data_format=data_format)
  
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=3, strides=1,
        data_format=data_format)
  
    return inputs + shortcut
  
  
  def bottleneck_block(inputs, filters, is_training, projection_shortcut,
                       strides, data_format):
    """Bottleneck block variant for residual networks with BN before convolutions.
  
    Args:
      inputs: A tensor of size [batch, channels, height_in, width_in] or
        [batch, height_in, width_in, channels] depending on data_format.
      filters: The number of filters for the first two convolutions. Note that the
        third and final convolution will use 4 times as many filters.
      is_training: A Boolean for whether the model is in training or inference
        mode. Needed for batch normalization.
      projection_shortcut: The function to use for projection shortcuts (typically
        a 1x1 convolution when downsampling the input).
      strides: The block's stride. If greater than 1, this block will ultimately
        downsample the input.
      data_format: The input format ('channels_last' or 'channels_first').
  
    Returns:
      The output tensor of the block.
    """
    shortcut = inputs
    inputs = batch_norm_relu(inputs, is_training, data_format)
  
    # The projection shortcut should come after the first batch norm and ReLU
    # since it performs a 1x1 convolution.
    if projection_shortcut is not None:
      shortcut = projection_shortcut(inputs)
  
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=1, strides=1,
        data_format=data_format)
  
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=3, strides=strides,
        data_format=data_format)
  
    inputs = batch_norm_relu(inputs, is_training, data_format)
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=4 * filters, kernel_size=1, strides=1,
        data_format=data_format)
  
    return inputs + shortcut
  
  
  def block_layer(inputs, filters, block_fn, blocks, strides, is_training, name,
                  data_format):
    """Creates one layer of blocks for the ResNet model.
  
    Args:
      inputs: A tensor of size [batch, channels, height_in, width_in] or
        [batch, height_in, width_in, channels] depending on data_format.
      filters: The number of filters for the first convolution of the layer.
      block_fn: The block to use within the model, either `building_block` or
        `bottleneck_block`.
      blocks: The number of blocks contained in the layer.
      strides: The stride to use for the first convolution of the layer. If
        greater than 1, this layer will ultimately downsample the input.
      is_training: Either True or False, whether we are currently training the
        model. Needed for batch norm.
      name: A string name for the tensor output of the block layer.
      data_format: The input format ('channels_last' or 'channels_first').
  
    Returns:
      The output tensor of the block layer.
    """
    # Bottleneck blocks end with 4x the number of filters as they start with
    filters_out = 4 * filters if block_fn is bottleneck_block else filters
  
    def projection_shortcut(inputs):
      return conv2d_fixed_padding(
          inputs=inputs, filters=filters_out, kernel_size=1, strides=strides,
          data_format=data_format)
  
    # Only the first block per block_layer uses projection_shortcut and strides
    inputs = block_fn(inputs, filters, is_training, projection_shortcut, strides,
                      data_format)
  
    for _ in range(1, blocks):
      inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
  
    return tf.identity(inputs, name)
  
  
  def cifar10_resnet_v2_generator(resnet_size, num_classes, data_format=None):
    """Generator for CIFAR-10 ResNet v2 models.
  
    Args:
      resnet_size: A single integer for the size of the ResNet model.
      num_classes: The number of possible classes for image classification.
      data_format: The input format ('channels_last', 'channels_first', or None).
        If set to None, the format is dependent on whether a GPU is available.
  
    Returns:
      The model function that takes in `inputs` and `is_training` and
      returns the output tensor of the ResNet model.
  
    Raises:
      ValueError: If `resnet_size` is invalid.
    """
    if resnet_size % 6 != 2:
      raise ValueError('resnet_size must be 6n + 2:', resnet_size)
  
    num_blocks = (resnet_size - 2) // 6
  
    if data_format is None:
      data_format = (
          'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
  
    def model(inputs, is_training):
      """Constructs the ResNet model given the inputs."""
      if data_format == 'channels_first':
        # Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
        # This provides a large performance boost on GPU. See
        # https://www.tensorflow.org/performance/performance_guide#data_formats
        inputs = tf.transpose(inputs, [0, 3, 1, 2])
  
      inputs = conv2d_fixed_padding(
          inputs=inputs, filters=16, kernel_size=3, strides=1,
          data_format=data_format)
      inputs = tf.identity(inputs, 'initial_conv')
  
      inputs = block_layer(
          inputs=inputs, filters=16, block_fn=building_block, blocks=num_blocks,
          strides=1, is_training=is_training, name='block_layer1',
          data_format=data_format)
      inputs = block_layer(
          inputs=inputs, filters=32, block_fn=building_block, blocks=num_blocks,
          strides=2, is_training=is_training, name='block_layer2',
          data_format=data_format)
      inputs = block_layer(
          inputs=inputs, filters=64, block_fn=building_block, blocks=num_blocks,
          strides=2, is_training=is_training, name='block_layer3',
          data_format=data_format)
  
      inputs = batch_norm_relu(inputs, is_training, data_format)
      inputs = tf.layers.average_pooling2d(
          inputs=inputs, pool_size=8, strides=1, padding='VALID',
          data_format=data_format)
      inputs = tf.identity(inputs, 'final_avg_pool')
      inputs = tf.reshape(inputs, [-1, 64])
      inputs = tf.layers.dense(inputs=inputs, units=num_classes)
      inputs = tf.identity(inputs, 'final_dense')
      return inputs
  
    return model
  
  
  def imagenet_resnet_v2_generator(block_fn, layers, num_classes,
                                   data_format=None):
    """Generator for ImageNet ResNet v2 models.
  
    Args:
      block_fn: The block to use within the model, either `building_block` or
        `bottleneck_block`.
      layers: A length-4 array denoting the number of blocks to include in each
        layer. Each layer consists of blocks that take inputs of the same size.
      num_classes: The number of possible classes for image classification.
      data_format: The input format ('channels_last', 'channels_first', or None).
        If set to None, the format is dependent on whether a GPU is available.
  
    Returns:
      The model function that takes in `inputs` and `is_training` and
      returns the output tensor of the ResNet model.
    """
    if data_format is None:
      data_format = (
          'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
  
    def model(inputs, is_training):
      """Constructs the ResNet model given the inputs."""
      if data_format == 'channels_first':
        # Convert the inputs from channels_last (NHWC) to channels_first (NCHW).
        # This provides a large performance boost on GPU. See
        # https://www.tensorflow.org/performance/performance_guide#data_formats
        inputs = tf.transpose(inputs, [0, 3, 1, 2])
  
      inputs = conv2d_fixed_padding(
          inputs=inputs, filters=64, kernel_size=7, strides=2,
          data_format=data_format)
      inputs = tf.identity(inputs, 'initial_conv')
      inputs = tf.layers.max_pooling2d(
          inputs=inputs, pool_size=3, strides=2, padding='SAME',
          data_format=data_format)
      inputs = tf.identity(inputs, 'initial_max_pool')
  
      inputs = block_layer(
          inputs=inputs, filters=64, block_fn=block_fn, blocks=layers[0],
          strides=1, is_training=is_training, name='block_layer1',
          data_format=data_format)
      inputs = block_layer(
          inputs=inputs, filters=128, block_fn=block_fn, blocks=layers[1],
          strides=2, is_training=is_training, name='block_layer2',
          data_format=data_format)
      inputs = block_layer(
          inputs=inputs, filters=256, block_fn=block_fn, blocks=layers[2],
          strides=2, is_training=is_training, name='block_layer3',
          data_format=data_format)
      inputs = block_layer(
          inputs=inputs, filters=512, block_fn=block_fn, blocks=layers[3],
          strides=2, is_training=is_training, name='block_layer4',
          data_format=data_format)
  
      inputs = batch_norm_relu(inputs, is_training, data_format)
      inputs = tf.layers.average_pooling2d(
          inputs=inputs, pool_size=7, strides=1, padding='VALID',
          data_format=data_format)
      inputs = tf.identity(inputs, 'final_avg_pool')
      inputs = tf.reshape(inputs,
                          [-1, 512 if block_fn is building_block else 2048])
      inputs = tf.layers.dense(inputs=inputs, units=num_classes)
      inputs = tf.identity(inputs, 'final_dense')
      return inputs
  
    return model
  
  
  def imagenet_resnet_v2(resnet_size, num_classes, data_format=None):
    """Returns the ResNet model for a given size and number of output classes."""
    model_params = {
        18: {'block': building_block, 'layers': [2, 2, 2, 2]},
        34: {'block': building_block, 'layers': [3, 4, 6, 3]},
        50: {'block': bottleneck_block, 'layers': [3, 4, 6, 3]},
        101: {'block': bottleneck_block, 'layers': [3, 4, 23, 3]},
        152: {'block': bottleneck_block, 'layers': [3, 8, 36, 3]},
        200: {'block': bottleneck_block, 'layers': [3, 24, 36, 3]}
    }
  
    if resnet_size not in model_params:
      raise ValueError('Not a valid resnet_size:', resnet_size)
  
    params = model_params[resnet_size]
    return imagenet_resnet_v2_generator(
        params['block'], params['layers'], num_classes, data_format)
  