BatchToSpace

public final class BatchToSpace

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.

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of a tensor.
static <T, U extends Number> BatchToSpace<T>
create(Scope scope, Operand<T> input, Operand<U> crops, Long blockSize)
Factory method to create a class wrapping a new BatchToSpace operation.
Output<T>
output()
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.

Inherited Methods

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static BatchToSpace<T> create (Scope scope, Operand<T> input, Operand<U> crops, Long blockSize)

Factory method to create a class wrapping a new BatchToSpace operation.

Parameters
scope current scope
input 4-D tensor with shape `[batchblock_sizeblock_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 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]]
Returns
  • a new instance of BatchToSpace

public Output<T> output ()

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]], [[9], [11]]],
      [[[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]]]]