Python tensor_shape.as_dimension方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import as_dimension [as 別名]
def get_conv_output_size(input_size, filter_size, strides, padding_type):
  """Returns the spatial size of a n-d convolution/pooling output."""
  input_size = tuple([tensor_shape.as_dimension(x).value for x in input_size])
  filter_size = tuple([tensor_shape.as_dimension(x).value for x in filter_size])
  strides = [int(x) for x in strides]

  if all(x == 1 for x in input_size) and all(x == 1 for x in filter_size):
    return input_size

  if any(x is not None and y is not None and x > y for x, y in
         zip(filter_size, input_size)):
    raise ValueError("Filter must not be larger than the input: "
                     "Filter: %r Input: %r" % (filter_size, input_size))

  if padding_type == b"VALID":

    def _valid(in_dim, k_dim, s_dim):
      if in_dim is not None and k_dim is not None:
        return (in_dim - k_dim + s_dim) // s_dim
      else:
        return None

    output_size = [
        _valid(in_dim, k_dim, s_dim)
        for in_dim, k_dim, s_dim in zip(input_size, filter_size, strides)
    ]
  elif padding_type == b"SAME":

    def _same(in_dim, s_dim):
      if in_dim is not None:
        return (in_dim + s_dim - 1) // s_dim
      else:
        return None

    output_size = [_same(in_dim, s_dim)
                   for in_dim, s_dim in zip(input_size, strides)]
  else:
    raise ValueError("Invalid padding: %r" % padding_type)

  return tuple(output_size) 

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import as_dimension [as 別名]
def testAsDimension(self):
    self.assertEqual(tensor_shape.Dimension(12),
                     tensor_shape.as_dimension(tensor_shape.Dimension(12)))
    self.assertEqual(tensor_shape.Dimension(12), tensor_shape.as_dimension(12))
    self.assertEqual(
        tensor_shape.Dimension(None).value,
        tensor_shape.as_dimension(tensor_shape.Dimension(None)).value)
    self.assertEqual(tensor_shape.Dimension(None).value,
                     tensor_shape.as_dimension(None).value) 

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import as_dimension [as 別名]
def get2d_deconv_output_size(input_height, input_width, filter_height,
                           filter_width, row_stride, col_stride, padding_type):
    """Returns the number of rows and columns in a convolution/pooling output."""
    input_height = tensor_shape.as_dimension(input_height)
    input_width = tensor_shape.as_dimension(input_width)
    filter_height = tensor_shape.as_dimension(filter_height)
    filter_width = tensor_shape.as_dimension(filter_width)
    row_stride = int(row_stride)
    col_stride = int(col_stride)

    # Compute number of rows in the output, based on the padding.
    if input_height.value is None or filter_height.value is None:
      out_rows = None
    elif padding_type == "VALID":
      out_rows = (input_height.value - 1) * row_stride + filter_height.value 
    elif padding_type == "SAME":
      out_rows = input_height.value * row_stride
    else:
      raise ValueError("Invalid value for padding: %r" % padding_type)

    # Compute number of columns in the output, based on the padding.
    if input_width.value is None or filter_width.value is None:
      out_cols = None
    elif padding_type == "VALID":
      out_cols = (input_width.value - 1) * col_stride + filter_width.value
    elif padding_type == "SAME":
      out_cols = input_width.value * col_stride

    return out_rows, out_cols 

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import as_dimension [as 別名]
def get2d_deconv_output_size(input_height, input_width, filter_height,
                           filter_width, row_stride, col_stride, padding_type):
    """Returns the number of rows and columns in a convolution/pooling output."""
    input_height = tensor_shape.as_dimension(input_height)
    input_width = tensor_shape.as_dimension(input_width)
    filter_height = tensor_shape.as_dimension(filter_height)
    filter_width = tensor_shape.as_dimension(filter_width)
    row_stride = int(row_stride)
    col_stride = int(col_stride)

    # Compute number of rows in the output, based on the padding.
    if input_height.value is None or filter_height.value is None:
      out_rows = None
    elif padding_type == "VALID":
      out_rows = (input_height.value - 1) * row_stride + filter_height.value
    elif padding_type == "SAME":
      out_rows = input_height.value * row_stride
    else:
      raise ValueError("Invalid value for padding: %r" % padding_type)

    # Compute number of columns in the output, based on the padding.
    if input_width.value is None or filter_width.value is None:
      out_cols = None
    elif padding_type == "VALID":
      out_cols = (input_width.value - 1) * col_stride + filter_width.value
    elif padding_type == "SAME":
      out_cols = input_width.value * col_stride

    return out_rows, out_cols