class mindspore.ops.SpaceToBatchND(block_shape, paddings)[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.

  • 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 an int, the block size of M dimensions 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].

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


Tensor, the output tensor with the same data type as input. Assume input shape is \((n, c, h, w)\) with \(block\_shape\) and \(paddings\). The shape of the output tensor will be \((n', c', h', w')\), where

\(n' = n*(block\_shape[0]*block\_shape[1])\)

\(c' = c\)

\(h' = (h+paddings[0][0]+paddings[0][1])//block\_shape[0]\)

\(w' = (w+paddings[1][0]+paddings[1][1])//block\_shape[1]\)

  • 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:



>>> 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)