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tf.compat.v1.space_to_batch

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SpaceToBatch for 4-D tensors of type T.

Aliases:

  • tf.compat.v1.nn.space_to_batch
tf.compat.v1.space_to_batch(
    input,
    paddings,
    block_size=None,
    name=None,
    block_shape=None
)

This is a legacy version of the more general SpaceToBatchND.

Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the batch dimension. After the zero-padding, both height and width of the input must be divisible by the block size.

Args:

  • input: A Tensor. 4-D with shape [batch, height, width, depth].
  • paddings: A Tensor. Must be one of the following types: int32, int64. 2-D tensor of non-negative integers with shape [2, 2]. It specifies the padding of the input with zeros across the spatial dimensions as follows:

    paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
    

    The effective spatial dimensions of the zero-padded input tensor will be:

    height_pad = pad_top + height + pad_bottom
    width_pad = pad_left + width + pad_right
    

    The attr block_size must be greater than one. It indicates the block size.

    • Non-overlapping blocks of size block_size x block size in the height and width dimensions are rearranged into the batch dimension at each location.
    • The batch of the output tensor is batch * block_size * block_size.
    • Both height_pad and width_pad must be divisible by block_size.

    The shape of the output will be:

    [batchblock_sizeblock_size, height_pad/block_size, width_pad/block_size, depth]

    Some examples:

    (1) For the following input of shape [1, 2, 2, 1] and block_size of 2:

x = [[[[1], [2]], [[3], [4]]]]

The output tensor has shape [4, 1, 1, 1] and value:

[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]

(2) For the following input of shape [1, 2, 2, 3] and block_size of 2:

x = [[[[1, 2, 3], [4, 5, 6]],
      [[7, 8, 9], [10, 11, 12]]]]

The output tensor has shape [4, 1, 1, 3] and value:

[[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]

(3) For the following input of shape [1, 4, 4, 1] and block_size of 2:

x = [[[[1],   [2],  [3],  [4]],
      [[5],   [6],  [7],  [8]],
      [[9],  [10], [11],  [12]],
      [[13], [14], [15],  [16]]]]

The output tensor has shape [4, 2, 2, 1] and value:

x = [[[[1], [3]], [[9], [11]]],
     [[[2], [4]], [[10], [12]]],
     [[[5], [7]], [[13], [15]]],
     [[[6], [8]], [[14], [16]]]]

(4) For the following input of shape [2, 2, 4, 1] and block_size of 2:

x = [[[[1],   [2],  [3],  [4]],
      [[5],   [6],  [7],  [8]]],
     [[[9],  [10], [11],  [12]],
      [[13], [14], [15],  [16]]]]

The output tensor has shape [8, 1, 2, 1] and value:

x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
     [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]

Among others, this operation is useful for reducing atrous convolution into regular convolution. * block_size: An int that is >= 2. * name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.