# mindspore.ops.SpaceToBatchND¶

class mindspore.ops.SpaceToBatchND(*args, **kwargs)[source]

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]+paddings[i] 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 $$block\_shape$$ and $$padddings$$. The shape of the output tensor will be $$(n', c', h', w')$$, where

$$n' = n*(block\_shape*block\_shape)$$

$$c' = c$$

$$h' = (h+paddings+paddings)//block\_shape$$

$$w' = (w+paddings+paddings)//block\_shape$$

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]]