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mindspore.ops.SpaceToBatchND

class mindspore.ops.SpaceToBatchND(block_shape, paddings)

Divides spatial dimensions into blocks and combines the block size with the original batch.

This operation will divide spatial dimensions (H, W) into blocks with block_shape, the output tensor’s H and W dimension is the corresponding number of blocks after division. The output tensor’s batch dimension is the product of the original batch and the product of block_shape. Before division, the spatial dimensions of the input are zero padded according to paddings if necessary.

Parameters

• block_shape (Union[list(int), tuple(int), int]) – The block shape of dividing block with all value greater than 1. If block_shape is a tuple or list, the length of block_shape is M corresponding to the number of spatial dimensions. If block_shape is a int, the block size of M dimendions are the same, equal to block_shape. M must be 2.

• paddings (Union[tuple, list]) – The padding values for H and W dimension, containing 2 subtraction list. Each contains 2 integer value. All values must be greater than 0. paddings[i] specifies the paddings for the spatial dimension i, which corresponds to the input dimension i+2. It is required that input_shape[i+2]+paddings[i][0]+paddings[i][1] is divisible by block_shape[i].

Inputs:

• input_x (Tensor) - The input tensor. It must be a 4-D tensor.

Outputs:

Tensor, the output tensor with the same data type as input. Assume input shape is $(n,c,h,w)$ with $block\mathrm{\_}shape$ and $padddings$. The shape of the output tensor will be $(n^{\prime },c^{\prime },h^{\prime },w^{\prime })$, where

$n^{\prime }=n\ast (block\mathrm{\_}shape[0]\ast block\mathrm{\_}shape[1])$

$c^{\prime }=c$

$h^{\prime }=(h+paddings[0][0]+paddings[0][1])//block\mathrm{\_}shape[0]$

$w^{\prime }=(w+paddings[1][0]+paddings[1][1])//block\mathrm{\_}shape[1]$

Raises

• TypeError – If block_shape is not one of list, tuple, int.

• TypeError – If paddings is neither list nor tuple.

• ValueError – If length of shape of block_shape is not equal to 1.

• ValueError – If length of block_shape or paddings is not equal to 2.

Supported Platforms:

Ascend

Examples

>>> block_shape = [2, 2]
>>> paddings = [[0, 0], [0, 0]]
>>> space_to_batch_nd = ops.SpaceToBatchND(block_shape, paddings)
>>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
>>> output = space_to_batch_nd(input_x)
>>> print(output)
[[[[1.]]]
 [[[2.]]]
 [[[3.]]]
 [[[4.]]]]