# mindspore.ops.BatchToSpaceND

class mindspore.ops.BatchToSpaceND(block_shape, crops)[source]

Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions.

This operation will divide batch dimension N into blocks with block_shape, the output tensor’s N dimension is the corresponding number of blocks after division. The output tensor’s H, W dimension is the product of original H, W dimension and block_shape with given amount to crop from dimension, respectively.

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 an int, the block size of M dimensions are the same, equal to block_shape. M must be 2.

• crops (Union[list(int), tuple(int)]) –

The crop value for H and W dimension, containing 2 subtraction list, each containing 2 int value. All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to input dimension i+2. It is required that

$$input\_shape[i+2]*block\_shape[i] > crops[i][0]+crops[i][1]$$

Inputs:
• input_x (Tensor) - The input tensor. It must be a 4-D tensor, dimension 0 must be divisible by product of block_shape. The data type is float16 or float32.

Outputs:

Tensor, the output tensor with the same type as input. Assume input shape is (n, c, h, w) with block_shape and crops. The output shape will be (n’, c’, h’, w’), where

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

$$c' = c$$

$$h' = h*block\_shape[0]-crops[0][0]-crops[0][1]$$

$$w' = w*block\_shape[1]-crops[1][0]-crops[1][1]$$

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

• TypeError – If crops is neither list nor tuple.

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

Supported Platforms:

Ascend

Examples

>>> block_shape = [2, 2]
>>> crops = [[0, 0], [0, 0]]
>>> batch_to_space_nd = ops.BatchToSpaceND(block_shape, crops)
>>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32)
>>> output = batch_to_space_nd(input_x)
>>> print(output)
[[[[1.  2.]
[3.  4.]]]]