Python tensorflow.contrib.slim.nets.resnet_v2.resnet_arg_scope() Examples

def forward(self, inputs, num_classes, data_format, is_training):
        sc = resnet_arg_scope(
            weight_decay=0.0001,
            data_format=data_format,
            batch_norm_decay=0.997,
            batch_norm_epsilon=1e-5,
            batch_norm_scale=True,
            activation_fn=tf.nn.relu,
            use_batch_norm=True,
            is_training=is_training)
        with slim.arg_scope(sc):
            logits, end_points = resnet_v2_50(
                inputs,
                num_classes=num_classes,
                is_training=is_training,
                global_pool=True,
                output_stride=None,
                reuse=None,
                scope=self.scope)
            return logits, end_points 

def forward(self, inputs, num_classes, data_format, is_training):
        sc = resnet_arg_scope(
            weight_decay=0.0001,
            data_format=data_format,
            batch_norm_decay=0.997,
            batch_norm_epsilon=1e-5,
            batch_norm_scale=True,
            activation_fn=tf.nn.relu,
            use_batch_norm=True,
            is_training=is_training)
        with slim.arg_scope(sc):
            logits, end_points = resnet_v2_101(
                inputs,
                num_classes=num_classes,
                is_training=is_training,
                global_pool=True,
                output_stride=None,
                reuse=None,
                scope=self.scope)
            return logits, end_points 

def forward(self, inputs, num_classes, data_format, is_training):
        sc = resnet_arg_scope(
            weight_decay=0.0001,
            data_format=data_format,
            batch_norm_decay=0.997,
            batch_norm_epsilon=1e-5,
            batch_norm_scale=True,
            activation_fn=tf.nn.relu,
            use_batch_norm=True,
            is_training=is_training)
        with slim.arg_scope(sc):
            logits, end_points = resnet_v2_152(
                inputs,
                num_classes=num_classes,
                is_training=is_training,
                global_pool=True,
                output_stride=None,
                reuse=None,
                scope=self.scope)
            return logits, end_points

# =========================================================================== #
# Functional definition.
# =========================================================================== # 

def get_model(model_name, num_classes):
  """Returns function which creates model.

  Args:
    model_name: Name of the model.
    num_classes: Number of classes.

  Raises:
    ValueError: If model_name is invalid.

  Returns:
    Function, which creates model when called.
  """
  if model_name.startswith('resnet'):
    def resnet_model(images, is_training, reuse=tf.AUTO_REUSE):
      with tf.contrib.framework.arg_scope(resnet_v2.resnet_arg_scope()):
        resnet_fn = RESNET_MODELS[model_name]
        logits, _ = resnet_fn(images, num_classes, is_training=is_training,
                              reuse=reuse)
        logits = tf.reshape(logits, [-1, num_classes])
      return logits
    return resnet_model
  else:
    raise ValueError('Invalid model: %s' % model_name) 

def get_model(model_name, num_classes):
  """Returns function which creates model.

  Args:
    model_name: Name of the model.
    num_classes: Number of classes.

  Raises:
    ValueError: If model_name is invalid.

  Returns:
    Function, which creates model when called.
  """
  if model_name.startswith('resnet'):
    def resnet_model(images, is_training, reuse=tf.AUTO_REUSE):
      with tf.contrib.framework.arg_scope(resnet_v2.resnet_arg_scope()):
        resnet_fn = RESNET_MODELS[model_name]
        logits, _ = resnet_fn(images, num_classes, is_training=is_training,
                              reuse=reuse)
        logits = tf.reshape(logits, [-1, num_classes])
      return logits
    return resnet_model
  else:
    raise ValueError('Invalid model: %s' % model_name) 

def get_model(model_name, num_classes):
  """Returns function which creates model.

  Args:
    model_name: Name of the model.
    num_classes: Number of classes.

  Raises:
    ValueError: If model_name is invalid.

  Returns:
    Function, which creates model when called.
  """
  if model_name.startswith('resnet'):
    def resnet_model(images, is_training, reuse=tf.AUTO_REUSE):
      with tf.contrib.framework.arg_scope(resnet_v2.resnet_arg_scope()):
        resnet_fn = RESNET_MODELS[model_name]
        logits, _ = resnet_fn(images, num_classes, is_training=is_training,
                              reuse=reuse)
        logits = tf.reshape(logits, [-1, num_classes])
      return logits
    return resnet_model
  else:
    raise ValueError('Invalid model: %s' % model_name) 

def get_model(model_name, num_classes):
  """Returns function which creates model.

  Args:
    model_name: Name of the model.
    num_classes: Number of classes.

  Raises:
    ValueError: If model_name is invalid.

  Returns:
    Function, which creates model when called.
  """
  if model_name.startswith('resnet'):
    def resnet_model(images, is_training, reuse=tf.AUTO_REUSE):
      with tf.contrib.framework.arg_scope(resnet_v2.resnet_arg_scope()):
        resnet_fn = RESNET_MODELS[model_name]
        logits, _ = resnet_fn(images, num_classes, is_training=is_training,
                              reuse=reuse)
        logits = tf.reshape(logits, [-1, num_classes])
      return logits
    return resnet_model
  else:
    raise ValueError('Invalid model: %s' % model_name) 

def resnet_arg_scope(weight_decay=0.0001,
                     activation_fn=tf.nn.relu,
                     use_layer_norm=True):
  """Defines the default ResNet arg scope.

  TODO(gpapan): The batch-normalization related default values above are
    appropriate for use in conjunction with the reference ResNet models
    released at https://github.com/KaimingHe/deep-residual-networks. When
    training ResNets from scratch, they might need to be tuned.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    activation_fn: The activation function which is used in ResNet.
    use_layer_norm: Whether or not to use layer normalization.

  Returns:
    An `arg_scope` to use for the resnet models.
  """

  with slim.arg_scope(
      [slim.conv2d],
      weights_regularizer=slim.l2_regularizer(weight_decay),
      weights_initializer=slim.variance_scaling_initializer(),
      activation_fn=activation_fn,
      normalizer_fn=slim.layer_norm if use_layer_norm else None,
      normalizer_params=None):
    # The following implies padding='SAME' for pool1, which makes feature
    # alignment easier for dense prediction tasks. This is also used in
    # https://github.com/facebook/fb.resnet.torch. However the accompanying
    # code of 'Deep Residual Learning for Image Recognition' uses
    # padding='VALID' for pool1. You can switch to that choice by setting
    # slim.arg_scope([slim.max_pool2d], padding='VALID').
    with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
      return arg_sc 

def resnet_arg_scope(is_training=True, # 训练标记
                     weight_decay=0.0001, # 权重衰减速率
                     batch_norm_decay=0.997, # BN的衰减速率
                     batch_norm_epsilon=1e-5, #  BN的epsilon默认1e-5
                     batch_norm_scale=True): # BN的scale默认值

  batch_norm_params = { # 定义batch normalization（标准化）的参数字典
      'is_training': is_training,
      'decay': batch_norm_decay,
      'epsilon': batch_norm_epsilon,
      'scale': batch_norm_scale,
      'updates_collections': tf.GraphKeys.UPDATE_OPS,
  }

  with slim.arg_scope( # 通过slim.arg_scope将[slim.conv2d]的几个默认参数设置好
      [slim.conv2d],
      weights_regularizer=slim.l2_regularizer(weight_decay), # 权重正则器设置为L2正则 
      weights_initializer=slim.variance_scaling_initializer(), # 权重初始化器
      activation_fn=tf.nn.relu, # 激活函数
      normalizer_fn=slim.batch_norm, # 标准化器设置为BN
      normalizer_params=batch_norm_params):
    with slim.arg_scope([slim.batch_norm], **batch_norm_params):
      with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc: # ResNet原论文是VALID模式，SAME模式可让特征对齐更简单
        return arg_sc # 最后将基层嵌套的arg_scope作为结果返回



# 定义核心的bottleneck残差学习单元 

def atrous_spatial_pyramid_pooling(inputs, output_stride, batch_norm_decay, is_training, depth=256):
    """Atrous Spatial Pyramid Pooling.

    Args:
      inputs: A tensor of size [batch, height, width, channels].
      output_stride: The ResNet unit's stride. Determines the rates for atrous convolution.
        the rates are (6, 12, 18) when the stride is 16, and doubled when 8.
      batch_norm_decay: The moving average decay when estimating layer activation
        statistics in batch normalization.
      is_training: A boolean denoting whether the input is for training.
      depth: The depth of the ResNet unit output.

    Returns:
      The atrous spatial pyramid pooling output.
    """
    with tf.variable_scope("aspp"):
        if output_stride not in [8, 16]:
            raise ValueError('output_stride must be either 8 or 16.')

        atrous_rates = [6, 12, 18]
        if output_stride == 8:
            atrous_rates = [2 * rate for rate in atrous_rates]

        with tf.contrib.slim.arg_scope(resnet_v2.resnet_arg_scope(batch_norm_decay=batch_norm_decay)):
            with arg_scope([layers.batch_norm], is_training=is_training):
                inputs_size = tf.shape(inputs)[1:3]
                # (a) one 1x1 convolution and three 3x3 convolutions with rates = (6, 12, 18) when output stride = 16.
                # the rates are doubled when output stride = 8.
                conv_1x1 = layers_lib.conv2d(inputs, depth, [1, 1], stride=1, scope="conv_1x1")
                conv_3x3_1 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[0],
                                               scope='conv_3x3_1')
                conv_3x3_2 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[1],
                                               scope='conv_3x3_2')
                conv_3x3_3 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[2],
                                               scope='conv_3x3_3')

                # (b) the image-level features
                with tf.variable_scope("image_level_features"):
                    # global average pooling
                    image_level_features = tf.reduce_mean(inputs, [1, 2], name='global_average_pooling', keepdims=True)
                    # 1x1 convolution with 256 filters( and batch normalization)
                    image_level_features = layers_lib.conv2d(image_level_features, depth, [1, 1], stride=1,
                                                             scope='conv_1x1')
                    # bilinearly upsample features
                    image_level_features = tf.image.resize_bilinear(image_level_features, inputs_size, name='upsample')

                net = tf.concat([conv_1x1, conv_3x3_1, conv_3x3_2, conv_3x3_3, image_level_features], axis=3,
                                name='concat')
                net = layers_lib.conv2d(net, depth, [1, 1], stride=1, scope='conv_1x1_concat')

                return net 

def atrous_spatial_pyramid_pooling(inputs, output_stride, batch_norm_decay, is_training, depth=256):
  """Atrous Spatial Pyramid Pooling.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    output_stride: The ResNet unit's stride. Determines the rates for atrous convolution.
      the rates are (6, 12, 18) when the stride is 16, and doubled when 8.
    batch_norm_decay: The moving average decay when estimating layer activation
      statistics in batch normalization.
    is_training: A boolean denoting whether the input is for training.
    depth: The depth of the ResNet unit output.

  Returns:
    The atrous spatial pyramid pooling output.
  """
  with tf.variable_scope("aspp"):
    if output_stride not in [8, 16]:
      raise ValueError('output_stride must be either 8 or 16.')

    atrous_rates = [6, 12, 18]
    if output_stride == 8:
      atrous_rates = [2*rate for rate in atrous_rates]

    with tf.contrib.slim.arg_scope(resnet_v2.resnet_arg_scope(batch_norm_decay=batch_norm_decay)):
      with arg_scope([layers.batch_norm], is_training=is_training):
        inputs_size = tf.shape(inputs)[1:3]
        # (a) one 1x1 convolution and three 3x3 convolutions with rates = (6, 12, 18) when output stride = 16.
        # the rates are doubled when output stride = 8.
        conv_1x1 = layers_lib.conv2d(inputs, depth, [1, 1], stride=1, scope="conv_1x1")
        conv_3x3_1 = resnet_utils.conv2d_same(inputs, depth, 3, stride=1, rate=atrous_rates[0], scope='conv_3x3_1')
        conv_3x3_2 = resnet_utils.conv2d_same(inputs, depth, 3, stride=1, rate=atrous_rates[1], scope='conv_3x3_2')
        conv_3x3_3 = resnet_utils.conv2d_same(inputs, depth, 3, stride=1, rate=atrous_rates[2], scope='conv_3x3_3')

        # (b) the image-level features
        with tf.variable_scope("image_level_features"):
          # global average pooling
          image_level_features = tf.reduce_mean(inputs, [1, 2], name='global_average_pooling', keepdims=True)
          # 1x1 convolution with 256 filters( and batch normalization)
          image_level_features = layers_lib.conv2d(image_level_features, depth, [1, 1], stride=1, scope='conv_1x1')
          # bilinearly upsample features
          image_level_features = tf.image.resize_bilinear(image_level_features, inputs_size, name='upsample')

        net = tf.concat([conv_1x1, conv_3x3_1, conv_3x3_2, conv_3x3_3, image_level_features], axis=3, name='concat')
        net = layers_lib.conv2d(net, depth, [1, 1], stride=1, scope='conv_1x1_concat')

        return net 

def atrous_spatial_pyramid_pooling(inputs, output_stride, batch_norm_decay, is_training, depth=256):
  """Atrous Spatial Pyramid Pooling.

  Args:
    inputs: A tensor of size [batch, height, width, channels].
    output_stride: The ResNet unit's stride. Determines the rates for atrous convolution.
      the rates are (6, 12, 18) when the stride is 16, and doubled when 8.
    batch_norm_decay: The moving average decay when estimating layer activation
      statistics in batch normalization.
    is_training: A boolean denoting whether the input is for training.
    depth: The depth of the ResNet unit output.

  Returns:
    The atrous spatial pyramid pooling output.
  """
  with tf.variable_scope("aspp"):
    if output_stride not in [8, 16]:
      raise ValueError('output_stride must be either 8 or 16.')

    atrous_rates = [6, 12, 18]
    if output_stride == 8:
      atrous_rates = [2*rate for rate in atrous_rates]

    with tf.contrib.slim.arg_scope(resnet_v2.resnet_arg_scope(batch_norm_decay=batch_norm_decay)):
      with arg_scope([layers.batch_norm], is_training=is_training):
        inputs_size = tf.shape(inputs)[1:3]
        # (a) one 1x1 convolution and three 3x3 convolutions with rates = (6, 12, 18) when output stride = 16.
        # the rates are doubled when output stride = 8.
        conv_1x1 = layers_lib.conv2d(inputs, depth, [1, 1], stride=1, scope="conv_1x1")
        conv_3x3_1 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[0], scope='conv_3x3_1')
        conv_3x3_2 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[1], scope='conv_3x3_2')
        conv_3x3_3 = layers_lib.conv2d(inputs, depth, [3, 3], stride=1, rate=atrous_rates[2], scope='conv_3x3_3')

        # (b) the image-level features
        with tf.variable_scope("image_level_features"):
          # global average pooling
          image_level_features = tf.reduce_mean(inputs, [1, 2], name='global_average_pooling', keepdims=True)
          # 1x1 convolution with 256 filters( and batch normalization)
          image_level_features = layers_lib.conv2d(image_level_features, depth, [1, 1], stride=1, scope='conv_1x1')
          # bilinearly upsample features
          image_level_features = tf.image.resize_bilinear(image_level_features, inputs_size, name='upsample')

        net = tf.concat([conv_1x1, conv_3x3_1, conv_3x3_2, conv_3x3_3, image_level_features], axis=3, name='concat')
        net = layers_lib.conv2d(net, depth, [1, 1], stride=1, scope='conv_1x1_concat')

        return net 

def resnet_arg_scope(is_training=True,
                     weight_decay=0.0001,
                     batch_norm_decay=0.997,
                     batch_norm_epsilon=1e-5,
                     batch_norm_scale=True):
  """Defines the default ResNet arg scope.

  TODO(gpapan): The batch-normalization related default values above are
    appropriate for use in conjunction with the reference ResNet models
    released at https://github.com/KaimingHe/deep-residual-networks. When
    training ResNets from scratch, they might need to be tuned.

  Args:
    is_training: Whether or not we are training the parameters in the batch
      normalization layers of the model.
    weight_decay: The weight decay to use for regularizing the model.
    batch_norm_decay: The moving average decay when estimating layer activation
      statistics in batch normalization.
    batch_norm_epsilon: Small constant to prevent division by zero when
      normalizing activations by their variance in batch normalization.
    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
      activations in the batch normalization layer.

  Returns:
    An `arg_scope` to use for the resnet models.
  """
  batch_norm_params = {
      'is_training': is_training,
      'decay': batch_norm_decay,
      'epsilon': batch_norm_epsilon,
      'scale': batch_norm_scale,
      'updates_collections': tf.GraphKeys.UPDATE_OPS,
  }

  with slim.arg_scope(
      [slim.conv2d],
      weights_regularizer=slim.l2_regularizer(weight_decay),
      weights_initializer=slim.variance_scaling_initializer(),
      activation_fn=tf.nn.relu,
      normalizer_fn=slim.batch_norm,
      normalizer_params=batch_norm_params):
    with slim.arg_scope([slim.batch_norm], **batch_norm_params):
      # The following implies padding='SAME' for pool1, which makes feature
      # alignment easier for dense prediction tasks. This is also used in
      # https://github.com/facebook/fb.resnet.torch. However the accompanying
      # code of 'Deep Residual Learning for Image Recognition' uses
      # padding='VALID' for pool1. You can switch to that choice by setting
      # slim.arg_scope([slim.max_pool2d], padding='VALID').
      with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
        return arg_sc 

def build(self, images):
    """Builds a ResNet50 embedder for the input images.

    It assumes that the range of the pixel values in the images tensor is
      [0,255] and should be castable to tf.uint8.

    Args:
      images: a tensor that contains the input images which has the shape of
          NxTxHxWx3 where N is the batch size, T is the maximum length of the
          sequence, H and W are the height and width of the images and C is the
          number of channels.
    Returns:
      The embedding of the input image with the shape of NxTxL where L is the
        embedding size of the output.

    Raises:
      ValueError: if the shape of the input does not agree with the expected
      shape explained in the Args section.
    """
    shape = images.get_shape().as_list()
    if len(shape) != 5:
      raise ValueError(
          'The tensor shape should have 5 elements, {} is provided'.format(
              len(shape)))
    if shape[4] != 3:
      raise ValueError('Three channels are expected for the input image')

    images = tf.cast(images, tf.uint8)
    images = tf.reshape(images,
                        [shape[0] * shape[1], shape[2], shape[3], shape[4]])
    with slim.arg_scope(resnet_v2.resnet_arg_scope()):

      def preprocess_fn(x):
        x = tf.expand_dims(x, 0)
        x = tf.image.resize_bilinear(x, [299, 299],
                                       align_corners=False)
        return(tf.squeeze(x, [0]))

      images = tf.map_fn(preprocess_fn, images, dtype=tf.float32)

      net, _ = resnet_v2.resnet_v2_50(
          images, is_training=False, global_pool=True)
      output = tf.reshape(net, [shape[0], shape[1], -1])
      return output 

def build(self, images):
    """Builds a ResNet50 embedder for the input images.

    It assumes that the range of the pixel values in the images tensor is
      [0,255] and should be castable to tf.uint8.

    Args:
      images: a tensor that contains the input images which has the shape of
          NxTxHxWx3 where N is the batch size, T is the maximum length of the
          sequence, H and W are the height and width of the images and C is the
          number of channels.
    Returns:
      The embedding of the input image with the shape of NxTxL where L is the
        embedding size of the output.

    Raises:
      ValueError: if the shape of the input does not agree with the expected
      shape explained in the Args section.
    """
    shape = images.get_shape().as_list()
    if len(shape) != 5:
      raise ValueError(
          'The tensor shape should have 5 elements, {} is provided'.format(
              len(shape)))
    if shape[4] != 3:
      raise ValueError('Three channels are expected for the input image')

    images = tf.cast(images, tf.uint8)
    images = tf.reshape(images,
                        [shape[0] * shape[1], shape[2], shape[3], shape[4]])
    with slim.arg_scope(resnet_v2.resnet_arg_scope()):

      def preprocess_fn(x):
        x = tf.expand_dims(x, 0)
        x = tf.image.resize_bilinear(x, [299, 299],
                                       align_corners=False)
        return(tf.squeeze(x, [0]))

      images = tf.map_fn(preprocess_fn, images, dtype=tf.float32)

      net, _ = resnet_v2.resnet_v2_50(
          images, is_training=False, global_pool=True)
      output = tf.reshape(net, [shape[0], shape[1], -1])
      return output 

def build(self, images):
    """Builds a ResNet50 embedder for the input images.

    It assumes that the range of the pixel values in the images tensor is
      [0,255] and should be castable to tf.uint8.

    Args:
      images: a tensor that contains the input images which has the shape of
          NxTxHxWx3 where N is the batch size, T is the maximum length of the
          sequence, H and W are the height and width of the images and C is the
          number of channels.
    Returns:
      The embedding of the input image with the shape of NxTxL where L is the
        embedding size of the output.

    Raises:
      ValueError: if the shape of the input does not agree with the expected
      shape explained in the Args section.
    """
    shape = images.get_shape().as_list()
    if len(shape) != 5:
      raise ValueError(
          'The tensor shape should have 5 elements, {} is provided'.format(
              len(shape)))
    if shape[4] != 3:
      raise ValueError('Three channels are expected for the input image')

    images = tf.cast(images, tf.uint8)
    images = tf.reshape(images,
                        [shape[0] * shape[1], shape[2], shape[3], shape[4]])
    with slim.arg_scope(resnet_v2.resnet_arg_scope()):

      def preprocess_fn(x):
        x = tf.expand_dims(x, 0)
        x = tf.image.resize_bilinear(x, [299, 299],
                                       align_corners=False)
        return(tf.squeeze(x, [0]))

      images = tf.map_fn(preprocess_fn, images, dtype=tf.float32)

      net, _ = resnet_v2.resnet_v2_50(
          images, is_training=False, global_pool=True)
      output = tf.reshape(net, [shape[0], shape[1], -1])
      return output