class mindspore.ops.SpaceToBatch(block_size, paddings)[source]

SpaceToBatch is deprecated. Please use mindspore.ops.SpaceToBatchND instead. 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_size, 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 square of block_size. Before division, the spatial dimensions of the input are zero padded according to paddings if necessary.

  • block_size (int) – The block size of dividing blocks with value greater than or equal to 2.

  • paddings (Union[tuple, list]) – The padding values for H and W dimension, containing 2 subtraction lists. Each subtraction list 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_size.

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


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

\(n' = n*(block\_size*block\_size)\)

\(c' = c\)

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

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

Supported Platforms:



>>> block_size = 2
>>> paddings = [[0, 0], [0, 0]]
>>> space_to_batch = ops.SpaceToBatch(block_size, paddings)
>>> input_x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mindspore.float32)
>>> output = space_to_batch(input_x)
>>> print(output)