tf.space_to_batch(input, paddings, block_size, name=None)

tf.space_to_batch(input, paddings, block_size, name=None)

See the guide: Tensor Transformations > Slicing and Joining

SpaceToBatch for 4-D tensors of type T.

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:

    [batch*block_size*block_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]], [[5], [7]]],
         [[[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.

Defined in tensorflow/python/ops/array_ops.py.