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 Residual Networks.
  
  Residual networks ('v1' 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 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 = 0.9
  _BATCH_NORM_EPSILON = 1e-5
  DEFAULT_VERSION = 2
  DEFAULT_DTYPE = tf.float32
  CASTABLE_TYPES = (tf.float16,tf.bfloat16)
  ALLOWED_TYPES = (DEFAULT_DTYPE,) + CASTABLE_TYPES
  
  
  ################################################################################
  # Convenience functions for building the ResNet model.
  ################################################################################
  def batch_norm(inputs, training, data_format):
    """Performs a batch normalization using a standard set of parameters."""
    # We set fused=True for a significant performance boost. See
    # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
    outputs = tf.compat.v1.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=training, fused=True)
  
  
    return outputs
  
  
  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(tensor=inputs, paddings=[[0, 0], [0, 0],
                                      [pad_beg, pad_end], [pad_beg, pad_end]])
    else:
      padded_inputs = tf.pad(tensor=inputs, paddings=[[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).
  
    inputs_for_logging = inputs
    if strides > 1:
      inputs = fixed_padding(inputs, kernel_size, data_format)
  
    outputs = tf.compat.v1.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.compat.v1.variance_scaling_initializer(
            distribution="truncated_normal"),
        data_format=data_format)
  
  
    return outputs
  
  
  ################################################################################
  # ResNet block definitions.
  ################################################################################
  def _building_block_v1(inputs, filters, training, projection_shortcut, strides,
                         data_format):
    raise NotImplementedError
  
  
  def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
                         data_format):
    raise NotImplementedError
  
  
  def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
                           strides, data_format):
    """A single block for ResNet v1, with a bottleneck.
  
    Similar to _building_block_v1(), except using the "bottleneck" blocks
    described in:
      Convolution then batch normalization then ReLU as described by:
        Deep Residual Learning for Image Recognition
        https://arxiv.org/pdf/1512.03385.pdf
        by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
  
    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.
      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; shape should match inputs.
    """
  
    shortcut = inputs
  
    if projection_shortcut is not None:
      shortcut = projection_shortcut(inputs)
      shortcut = batch_norm(inputs=shortcut, training=training,
                            data_format=data_format)
  
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=1, strides=1,
        data_format=data_format)
    inputs = batch_norm(inputs, training, data_format)
  
    inputs = tf.nn.relu(inputs)
  
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=filters, kernel_size=3, strides=strides,
        data_format=data_format)
    inputs = batch_norm(inputs, training, data_format)
  
    inputs = tf.nn.relu(inputs)
  
    inputs = conv2d_fixed_padding(
        inputs=inputs, filters=4 * filters, kernel_size=1, strides=1,
        data_format=data_format)
    inputs = batch_norm(inputs, training, data_format)
  
    # Remove it later once we optimize this in graph.
    inputs = tf.math.add_n([inputs, shortcut])
  
    inputs = tf.nn.relu(inputs)
  
    return inputs
  
  
  def _bottleneck_block_v2(inputs, filters, training, projection_shortcut,
                           strides, data_format):
    raise NotImplementedError
  
  
  def block_layer(inputs, filters, bottleneck, block_fn, blocks, strides,
                  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.
      bottleneck: Is the block created a bottleneck block.
      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.
      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 = filters * 4 if bottleneck 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, training, projection_shortcut, strides,
                      data_format)
  
    for _ in range(1, blocks):
      inputs = block_fn(inputs, filters, training, None, 1, data_format)
  
    return tf.identity(inputs, name)
  
  
  class Model(object):
    """Base class for building the Resnet Model."""
  
    def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
                 kernel_size,
                 conv_stride, first_pool_size, first_pool_stride,
                 second_pool_size, second_pool_stride, block_sizes, block_strides,
                 final_size, version=DEFAULT_VERSION, data_format=None,
                 dtype=DEFAULT_DTYPE):
      """Creates a model for classifying an image.
  
      Args:
        resnet_size: A single integer for the size of the ResNet model.
        bottleneck: Use regular blocks or bottleneck blocks.
        num_classes: The number of classes used as labels.
        num_filters: The number of filters to use for the first block layer
          of the model. This number is then doubled for each subsequent block
          layer.
        kernel_size: The kernel size to use for convolution.
        conv_stride: stride size for the initial convolutional layer
        first_pool_size: Pool size to be used for the first pooling layer.
          If none, the first pooling layer is skipped.
        first_pool_stride: stride size for the first pooling layer. Not used
          if first_pool_size is None.
        second_pool_size: Pool size to be used for the second pooling layer.
        second_pool_stride: stride size for the final pooling layer
        block_sizes: A list containing n values, where n is the number of sets of
          block layers desired. Each value should be the number of blocks in the
          i-th set.
        block_strides: List of integers representing the desired stride size for
          each of the sets of block layers. Should be same length as block_sizes.
        final_size: The expected size of the model after the second pooling.
        version: Integer representing which version of the ResNet network to use.
          See README for details. Valid values: [1, 2]
        data_format: Input format ('channels_last', 'channels_first', or None).
          If set to None, the format is dependent on whether a GPU is available.
        dtype: The TensorFlow dtype to use for calculations. If not specified
          tf.float32 is used.
  
      Raises:
        ValueError: if invalid version is selected.
      """
      self.resnet_size = resnet_size
  
      if not data_format:
        data_format = (
            'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
  
      self.resnet_version = version
      if version not in (1, 2):
        raise ValueError(
            'Resnet version should be 1 or 2. See README for citations.')
  
      self.bottleneck = bottleneck
      if bottleneck:
        if version == 1:
          self.block_fn = _bottleneck_block_v1
        else:
          self.block_fn = _bottleneck_block_v2
      else:
        if version == 1:
          self.block_fn = _building_block_v1
        else:
          self.block_fn = _building_block_v2
  
      if dtype not in ALLOWED_TYPES:
        raise ValueError('dtype must be one of: {}'.format(ALLOWED_TYPES))
  
      self.data_format = data_format
      self.num_classes = num_classes
      self.num_filters = num_filters
      self.kernel_size = kernel_size
      self.conv_stride = conv_stride
      self.first_pool_size = first_pool_size
      self.first_pool_stride = first_pool_stride
      self.second_pool_size = second_pool_size
      self.second_pool_stride = second_pool_stride
      self.block_sizes = block_sizes
      self.block_strides = block_strides
      self.final_size = final_size
      self.dtype = dtype
      self.pre_activation = version == 2
  
    def _custom_dtype_getter(self, getter, name, shape=None, dtype=DEFAULT_DTYPE,
                             *args, **kwargs):
      """Creates variables in fp32, then casts to fp16 if necessary.
  
      This function is a custom getter. A custom getter is a function with the
      same signature as tf.get_variable, except it has an additional getter
      parameter. Custom getters can be passed as the `custom_getter` parameter of
      tf.variable_scope. Then, tf.get_variable will call the custom getter,
      instead of directly getting a variable itself. This can be used to change
      the types of variables that are retrieved with tf.get_variable.
      The `getter` parameter is the underlying variable getter, that would have
      been called if no custom getter was used. Custom getters typically get a
      variable with `getter`, then modify it in some way.
  
      This custom getter will create an fp32 variable. If a low precision
      (e.g. float16) variable was requested it will then cast the variable to the
      requested dtype. The reason we do not directly create variables in low
      precision dtypes is that applying small gradients to such variables may
      cause the variable not to change.
  
      Args:
        getter: The underlying variable getter, that has the same signature as
          tf.get_variable and returns a variable.
        name: The name of the variable to get.
        shape: The shape of the variable to get.
        dtype: The dtype of the variable to get. Note that if this is a low
          precision dtype, the variable will be created as a tf.float32 variable,
          then cast to the appropriate dtype
        *args: Additional arguments to pass unmodified to getter.
        **kwargs: Additional keyword arguments to pass unmodified to getter.
  
      Returns:
        A variable which is cast to fp16 if necessary.
      """
  
      if dtype in CASTABLE_TYPES:
        var = getter(name, shape, tf.float32, *args, **kwargs)
        return tf.cast(var, dtype=dtype, name=name + '_cast')
      else:
        return getter(name, shape, dtype, *args, **kwargs)
  
    def _model_variable_scope(self):
      """Returns a variable scope that the model should be created under.
  
      If self.dtype is a castable type, model variable will be created in fp32
      then cast to self.dtype before being used.
  
      Returns:
        A variable scope for the model.
      """
  
      return tf.compat.v1.variable_scope('resnet_model',
                               custom_getter=self._custom_dtype_getter)
  
    def __call__(self, inputs, training):
      """Add operations to classify a batch of input images.
  
      Args:
        inputs: A Tensor representing a batch of input images.
        training: A boolean. Set to True to add operations required only when
          training the classifier.
  
      Returns:
        A logits Tensor with shape [<batch_size>, self.num_classes].
      """
  
  
      with self._model_variable_scope():
        if self.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(a=inputs, perm=[0, 3, 1, 2])
  
        inputs = conv2d_fixed_padding(
            inputs=inputs, filters=self.num_filters, kernel_size=self.kernel_size,
            strides=self.conv_stride, data_format=self.data_format)
        inputs = tf.identity(inputs, 'initial_conv')
  
        # We do not include batch normalization or activation functions in V2
        # for the initial conv1 because the first ResNet unit will perform these
        # for both the shortcut and non-shortcut paths as part of the first
        # block's projection. Cf. Appendix of [2].
        if self.resnet_version == 1:
          inputs = batch_norm(inputs, training, self.data_format)
  
          inputs = tf.nn.relu(inputs)
  
        if self.first_pool_size:
          pooled_inputs = tf.compat.v1.layers.max_pooling2d(
              inputs=inputs, pool_size=self.first_pool_size,
              strides=self.first_pool_stride, padding='SAME',
              data_format=self.data_format)
          inputs = tf.identity(pooled_inputs, 'initial_max_pool')
  
        for i, num_blocks in enumerate(self.block_sizes):
          num_filters = self.num_filters * (2**i)
          inputs = block_layer(
              inputs=inputs, filters=num_filters, bottleneck=self.bottleneck,
              block_fn=self.block_fn, blocks=num_blocks,
              strides=self.block_strides[i], training=training,
              name='block_layer{}'.format(i + 1), data_format=self.data_format)
  
        # Only apply the BN and ReLU for model that does pre_activation in each
        # building/bottleneck block, eg resnet V2.
        if self.pre_activation:
          inputs = batch_norm(inputs, training, self.data_format)
  
          inputs = tf.nn.relu(inputs)
  
        # The current top layer has shape
        # `batch_size x pool_size x pool_size x final_size`.
        # ResNet does an Average Pooling layer over pool_size,
        # but that is the same as doing a reduce_mean. We do a reduce_mean
        # here because it performs better than AveragePooling2D.
        axes = [2, 3] if self.data_format == 'channels_first' else [1, 2]
        inputs = tf.reduce_mean(input_tensor=inputs, axis=axes, keepdims=True)
        inputs = tf.identity(inputs, 'final_reduce_mean')
  
        inputs = tf.reshape(inputs, [-1, self.final_size])
        inputs = tf.compat.v1.layers.dense(
          inputs=inputs,
          units=self.num_classes,
          kernel_initializer=tf.compat.v1.random_normal_initializer(stddev=.01))
        inputs = tf.identity(inputs, 'final_dense')
  
        # Drop batch size from shape logging.
        return inputs
  