tf.batch_to_space(input, crops, block_size, name=None)

tf.batch_to_space(input, crops, block_size, name=None)

See the guide: Tensor Transformations > Slicing and Joining

BatchToSpace for 4-D tensors of type T.

This is a legacy version of the more general BatchToSpaceND.

Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the batch dimension are moved in spatial blocks to the height and width dimensions, followed by cropping along the height and width dimensions.

Args:

  • input: A Tensor. 4-D tensor with shape [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth]. Note that the batch size of the input tensor must be divisible by block_size * block_size.
  • crops: A Tensor. Must be one of the following types: int32, int64. 2-D tensor of non-negative integers with shape [2, 2]. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows:

    crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
    
  • block_size: An int that is >= 2.

  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input. 4-D with shape [batch, height, width, depth], where:

    height = height_pad - crop_top - crop_bottom
    width = width_pad - crop_left - crop_right

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

Some examples:

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

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

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

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

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

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

The output tensor has shape [1, 2, 2, 3] and value:

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

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

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

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

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

(4) For the following input of shape [8, 1, 2, 1] and block_size of 2:

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

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

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

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