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SpaceToBatch

public final class SpaceToBatch

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.

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output <T>
asOutput ()
Returns the symbolic handle of the tensor.
static <T extends TType > SpaceToBatch <T>
create ( Scope scope, Operand <T> input, Operand <? extends TNumber > paddings, Long blockSize)
Factory method to create a class wrapping a new SpaceToBatch operation.
Output <T>

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "SpaceToBatch"

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of the 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 SpaceToBatch <T> create ( Scope scope, Operand <T> input, Operand <? extends TNumber > paddings, Long blockSize)

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

Parameters
scope current scope
input 4-D with shape `[batch, height, width, depth]`.
paddings 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]], [[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.

Returns
  • a new instance of SpaceToBatch

public Output <T> output ()