mindspore.ops.DepthToSpace

class mindspore.ops.DepthToSpace(block_size)

Rearrange blocks of depth data into spatial dimensions.

This is the reverse operation of SpaceToDepth.

The depth of output tensor is $input\mathrm{\_}depth/(block\mathrm{\_}size\ast block\mathrm{\_}size)$.

The output tensor's height dimension is $height\ast block\mathrm{\_}size$.

The output tensor's weight dimension is $weight\ast block\mathrm{\_}size$.

The input tensor's depth must be divisible by block_size * block_size. The data format is "NCHW".

Parameters

block_size (int) – The block size used to divide depth data. It must be >= 2.

Inputs:

• x (Tensor) - The target tensor. It must be a 4-D tensor with shape $(N,C_{in},H_{in},W_{in})$. The data type is Number.

Outputs:

Tensor of shape $(N,C_{in}/{\text{block\_size}}^2,H_{in}\ast \text{block\_size},W_{in}\ast \text{block\_size})$.

Raises

• TypeError – If block_size is not an int.

• ValueError – If block_size is less than 2.

• ValueError – If length of shape of x is not equal to 4.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.rand(1, 12, 1, 1), mindspore.float32)
>>> block_size = 2
>>> depth_to_space = ops.DepthToSpace(block_size)
>>> output = depth_to_space(x)
>>> print(output.shape)
(1, 3, 2, 2)