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:

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

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

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

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

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

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

  prettyprint [[[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:

  prettyprint 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:

  prettyprint 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:

  prettyprint 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:

  prettyprint 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.