Python utils.convert_data_format方法代码示例

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    # Apply the actual ops.
    if self.data_format == 'channels_last':
      strides = (1,) + self.strides + (1,)
    else:
      strides = (1, 1) + self.strides
    outputs = nn.separable_conv2d(
        inputs,
        self.depthwise_kernel,
        self.pointwise_kernel,
        strides=strides,
        padding=self.padding.upper(),
        rate=self.dilation_rate,
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.use_bias:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    if self.data_format == 'channels_last':
      pool_shape = (1,) + self.pool_size + (1,)
      strides = (1,) + self.strides + (1,)
    else:
      pool_shape = (1, 1) + self.pool_size
      strides = (1, 1) + self.strides
    outputs = self.pool_function(
        inputs,
        ksize=pool_shape,
        strides=strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, 4))
    return outputs 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    outputs = nn.convolution(
        input=inputs,
        filter=self.kernel,
        dilation_rate=self.dilation_rate,
        strides=self.strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, self.rank + 2))

    if self.bias is not None:
      if self.data_format == 'channels_first':
        # bias_add only supports NHWC.
        # TODO(fchollet): remove this when `bias_add` is feature-complete.
        if self.rank == 1:
          bias = array_ops.reshape(self.bias, (1, self.filters, 1))
          outputs += bias
        if self.rank == 2:
          bias = array_ops.reshape(self.bias, (1, self.filters, 1, 1))
          outputs += bias
        if self.rank == 3:
          # As of Mar 2017, direct addition is significantly slower than
          # bias_add when computing gradients. To use bias_add, we collapse Z
          # and Y into a single dimension to obtain a 4D input tensor.
          outputs_shape = outputs.shape.as_list()
          outputs_4d = array_ops.reshape(outputs,
                                         [outputs_shape[0], outputs_shape[1],
                                          outputs_shape[2] * outputs_shape[3],
                                          outputs_shape[4]])
          outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
          outputs = array_ops.reshape(outputs_4d, outputs_shape)
      else:
        outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')

    if self.activation is not None:
      return self.activation(outputs)
    return outputs 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    if self.data_format == 'channels_last':
      pool_shape = (1,) + self.pool_size + (1,)
      strides = (1,) + self.strides + (1,)
    else:
      pool_shape = (1, 1) + self.pool_size
      strides = (1, 1) + self.strides
    return self.pool_function(
        inputs,
        ksize=pool_shape,
        strides=strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, 4)) 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    outputs = nn.convolution(
        input=inputs,
        filter=self.kernel,
        dilation_rate=self.dilation_rate,
        strides=self.strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, self.rank + 2))
    if self.bias is not None:
      if self.rank != 2 and self.data_format == 'channels_first':
        # bias_add does not support channels_first for non-4D inputs.
        if self.rank == 1:
          bias = array_ops.reshape(self.bias, (1, self.filters, 1))
        if self.rank == 3:
          bias = array_ops.reshape(self.bias, (1, self.filters, 1, 1))
        outputs += bias
      else:
        outputs = nn.bias_add(
            outputs,
            self.bias,
            data_format=utils.convert_data_format(self.data_format, 4))
        # Note that we passed rank=4 because bias_add will only accept
        # NHWC and NCWH even if the rank of the inputs is 3 or 5.

    if self.activation is not None:
      return self.activation(outputs)
    return outputs 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def call(self, inputs):
    if self.data_format == 'channels_first':
      # Reshape to channels last
      inputs = array_ops.transpose(inputs, (0, 2, 3, 1))

    # Apply the actual ops.
    outputs = separable_conv2d_tf_nn(
        inputs,
        self.depthwise_kernel,
        self.pointwise_kernel,
        strides=(1,) + self.strides + (1,),
        padding=self.padding.upper(),
        rate=self.dilation_rate)

    if self.data_format == 'channels_first':
      # Reshape to channels first
      outputs = array_ops.transpose(outputs, (0, 3, 1, 2))

    if self.bias is not None:
      outputs = nn.bias_add(
          outputs,
          self.bias,
          data_format=utils.convert_data_format(self.data_format, ndim=4))

    if self.activation is not None:
      return self.activation(outputs)
    return outputs 

# 需要导入模块: from tensorflow.python.layers import utils [as 别名]
# 或者: from tensorflow.python.layers.utils import convert_data_format [as 别名]
def build(self, input_shape):
    input_shape = tensor_shape.TensorShape(input_shape)
    if self.data_format == 'channels_first':
      channel_axis = 1
    else:
      channel_axis = -1
    if input_shape[channel_axis].value is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    input_dim = input_shape[channel_axis].value
    kernel_shape = self.kernel_size + (input_dim, self.filters)

    self.kernel = self.add_variable(name='kernel',
                                    shape=kernel_shape,
                                    initializer=self.kernel_initializer,
                                    regularizer=self.kernel_regularizer,
                                    constraint=self.kernel_constraint,
                                    trainable=True,
                                    dtype=self.dtype)
    if self.use_bias:
      self.bias = self.add_variable(name='bias',
                                    shape=(self.filters,),
                                    initializer=self.bias_initializer,
                                    regularizer=self.bias_regularizer,
                                    constraint=self.bias_constraint,
                                    trainable=True,
                                    dtype=self.dtype)
    else:
      self.bias = None
    self.input_spec = base.InputSpec(ndim=self.rank + 2,
                                     axes={channel_axis: input_dim})
    self._convolution_op = nn_ops.Convolution(
        input_shape,
        filter_shape=self.kernel.get_shape(),
        dilation_rate=self.dilation_rate,
        strides=self.strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format,
                                              self.rank + 2))
    self.built = True