Python nn.conv2d_transpose方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import conv2d_transpose [as 別名]
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    # Infer the dynamic output shape:
    out_height = utils.deconv_output_length(height,
                                            kernel_h,
                                            self.padding,
                                            stride_h)
    out_width = utils.deconv_output_length(width,
                                           kernel_w,
                                           self.padding,
                                           stride_w)
    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    # Infer the static output shape:
    out_shape = inputs.get_shape().as_list()
    out_shape[c_axis] = self.filters
    out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis],
                                                   kernel_h,
                                                   self.padding,
                                                   stride_h)
    out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis],
                                                   kernel_w,
                                                   self.padding,
                                                   stride_w)
    outputs.set_shape(out_shape)

    if self.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 

# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import conv2d_transpose [as 別名]
def conv2d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None):
  """2D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      output_shape: 1D int tensor for the output shape.
      strides: strides tuple.
      padding: string, `"same"` or `"valid"`.
      data_format: `"channels_last"` or `"channels_first"`.
          Whether to use Theano or TensorFlow data format
          for inputs/kernels/ouputs.

  Returns:
      A tensor, result of transposed 2D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format ' + str(data_format))
  if isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(output_shape)

  x = _preprocess_conv2d_input(x, data_format)
  output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format)
  padding = _preprocess_padding(padding)
  strides = (1,) + strides + (1,)

  x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding)
  x = _postprocess_conv2d_output(x, data_format)
  return x 

# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import conv2d_transpose [as 別名]
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    def get_deconv_dim(dim_size, stride_size, kernel_size, padding):
      if isinstance(dim_size, ops.Tensor):
        dim_size = math_ops.multiply(dim_size, stride_size)
      elif dim_size is not None:
        dim_size *= stride_size

      if padding == 'valid' and dim_size is not None:
        dim_size += max(kernel_size - stride_size, 0)
      return dim_size

    # Infer the dynamic output shape:
    out_height = get_deconv_dim(height, stride_h, kernel_h, self.padding)
    out_width = get_deconv_dim(width, stride_w, kernel_w, self.padding)

    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    # Infer the static output shape:
    out_shape = inputs.get_shape().as_list()
    out_shape[c_axis] = self.filters
    out_shape[h_axis] = get_deconv_dim(
        out_shape[h_axis], stride_h, kernel_h, self.padding)
    out_shape[w_axis] = get_deconv_dim(
        out_shape[w_axis], stride_w, kernel_w, self.padding)
    outputs.set_shape(out_shape)

    if self.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 

# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import conv2d_transpose [as 別名]
def conv2d_transpose(x,
                     kernel,
                     output_shape,
                     strides=(1, 1),
                     padding='valid',
                     data_format=None):
  """2D deconvolution (i.e.

  transposed convolution).

  Arguments:
      x: Tensor or variable.
      kernel: kernel tensor.
      output_shape: 1D int tensor for the output shape.
      strides: strides tuple.
      padding: string, `"same"` or `"valid"`.
      data_format: `"channels_last"` or `"channels_first"`.
          Whether to use Theano or TensorFlow data format
          for inputs/kernels/outputs.

  Returns:
      A tensor, result of transposed 2D convolution.

  Raises:
      ValueError: if `data_format` is neither `channels_last` or
      `channels_first`.
  """
  if data_format is None:
    data_format = image_data_format()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format ' + str(data_format))
  if isinstance(output_shape, (tuple, list)):
    output_shape = array_ops.stack(output_shape)

  x = _preprocess_conv2d_input(x, data_format)
  output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format)
  padding = _preprocess_padding(padding)
  strides = (1,) + strides + (1,)

  x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding)
  x = _postprocess_conv2d_output(x, data_format)
  return x 

# 需要導入模塊: from tensorflow.python.ops import nn [as 別名]
# 或者: from tensorflow.python.ops.nn import conv2d_transpose [as 別名]
def call(self, inputs):
    inputs_shape = array_ops.shape(inputs)
    batch_size = inputs_shape[0]
    if self.data_format == 'channels_first':
      c_axis, h_axis, w_axis = 1, 2, 3
    else:
      c_axis, h_axis, w_axis = 3, 1, 2

    height, width = inputs_shape[h_axis], inputs_shape[w_axis]
    kernel_h, kernel_w = self.kernel_size
    stride_h, stride_w = self.strides

    # Infer the dynamic output shape:
    out_height = utils.deconv_output_length(height,
                                            kernel_h,
                                            self.padding,
                                            stride_h)
    out_width = utils.deconv_output_length(width,
                                           kernel_w,
                                           self.padding,
                                           stride_w)
    if self.data_format == 'channels_first':
      output_shape = (batch_size, self.filters, out_height, out_width)
      strides = (1, 1, stride_h, stride_w)
    else:
      output_shape = (batch_size, out_height, out_width, self.filters)
      strides = (1, stride_h, stride_w, 1)

    output_shape_tensor = array_ops.stack(output_shape)
    outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

    if context.in_graph_mode():
      # Infer the static output shape:
      out_shape = inputs.get_shape().as_list()
      out_shape[c_axis] = self.filters
      out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis],
                                                     kernel_h,
                                                     self.padding,
                                                     stride_h)
      out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis],
                                                     kernel_w,
                                                     self.padding,
                                                     stride_w)
      outputs.set_shape(out_shape)

    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