mindspore.ops.SpaceToDepth
- class mindspore.ops.SpaceToDepth(block_size)[source]
Rearrange blocks of spatial data into depth.
The output tensor's height dimension is \(height / block\_size\).
The output tensor's weight dimension is \(weight / block\_size\).
The depth of output tensor is \(block\_size * block\_size * input\_depth\).
The input tensor's height and width must be divisible by block_size. The data format is "NCHW".
- Parameters
block_size (int) – The block size used to divide spatial data. It must be >= 2.
- Inputs:
x (Tensor) - The target tensor. The data type is Number. It must be a 4-D tensor.
- Outputs:
Tensor, the same data type as x. It must be a 4-D tensor. Tensor of shape \((N, (C_{in} * \text{block_size} * 2), H_{in} / \text{block_size}, W_{in} / \text{block_size})\).
- 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(1,3,2,2), mindspore.float32) >>> block_size = 2 >>> space_to_depth = ops.SpaceToDepth(block_size) >>> output = space_to_depth(x) >>> print(output.shape) (1, 12, 1, 1)