mindspore.ops.BatchToSpace
- class mindspore.ops.BatchToSpace(block_size, crops)[源代码]
Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions.
This operation will divide batch dimension N into blocks with block_size, the output tensor’s N dimension is the corresponding number of blocks after division. The output tensor’s H, W dimension is product of original H, W dimension and block_size with given amount to crop from dimension, respectively.
- Parameters
block_size (int) – The block size of division, has the value not less than 2.
crops (Union[list(int), tuple(int)]) –
The crop value for H and W dimension, containing 2 subtraction lists. Each list contains 2 integers. All values must be not less than 0. crops[i] specifies the crop values for the spatial dimension i, which corresponds to the input dimension i+2. It is required that
\(input\_shape[i+2]*block\_size >= crops[i][0]+crops[i][1]\)
- 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_size and crops. The output shape will be (n’, c’, h’, w’), where
\(n' = n//(block\_size*block\_size)\)
\(c' = c\)
\(h' = h*block\_size-crops[0][0]-crops[0][1]\)
\(w' = w*block\_size-crops[1][0]-crops[1][1]\)
- Raises
TypeError – If block_size or element of crops is not an int.
TypeError – If crops is neither list nor tuple.
ValueError – If block_size is less than 2.
- Supported Platforms:
Ascend
GPU
Examples
>>> block_size = 2 >>> crops = [[0, 0], [0, 0]] >>> batch_to_space = ops.BatchToSpace(block_size, crops) >>> input_x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mindspore.float32) >>> output = batch_to_space(input_x) >>> print(output) [[[[1. 2.] [3. 4.]]]]