tensorflow::ops::SpaceToBatchND

#include <array_ops.h>

SpaceToBatch for N-D tensors of type T.

Summary

This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according to paddings. See below for a precise description.

Arguments:

• scope: A Scope object
• input: N-D with shape input_shape = [batch] + spatial_shape + remaining_shape, where spatial_shape has M dimensions.
• block_shape: 1-D with shape [M], all values must be >= 1.
• paddings: 2-D with shape [M, 2], all values must be >= 0. paddings[i] = [pad_start, pad_end] specifies the padding for input dimension i + 1, which corresponds to spatial dimension i. It is required that block_shape[i] divides input_shape[i + 1] + pad_start + pad_end.

This operation is equivalent to the following steps:

1. Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings to produce padded of shape padded_shape.
2. Reshapepadded to reshaped_padded of shape:[batch] + [padded_shape[1] / block_shape[0], block_shape[0], ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape
3. Permute dimensions of reshaped_padded to produce permuted_reshaped_padded of shape:block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape
4. Reshapepermuted_reshaped_padded to flatten block_shape into the batch dimension, producing an output tensor of shape:[batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

Some examples:

(1) For the following input of shape [1, 2, 2, 1], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]:

```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], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]:

```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], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]:

```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], block_shape = [2, 2], and paddings = [[0, 0], [2, 0]]:

```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, 3, 1] and value:

```prettyprint x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], [[[0], [6], [8]]], [[[0], [14], [16]]]] ```

Among others, this operation is useful for reducing atrous convolution into regular convolution.

Returns:

• Output: The output tensor.

Public attributes

::tensorflow::Output output

Public functions

 SpaceToBatchND(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input block_shape,
  ::tensorflow::Input paddings
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const