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mindspore.mint.nn.functional.fold

mindspore.mint.nn.functional.fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1)

Combines an array of sliding local blocks into a large containing tensor.

Consider a batched input tensor of shape $(N,C\times \prod (\text{kernel\_size}),L)$ , where $N$ is the batch dimension, $C\times \prod (\text{kernel\_size})$ is the total number of values within each block (a block has $\prod (\text{kernel\_size})$ spatial locations each containing a C-channeled vector), and $L$ is the total number of such blocks:

$$L=\prod _d\lfloor \frac{\text{output\_size}[d]+2\times \text{padding}[d]-\text{dilation}[d]\times (\text{kernel\_size}[d]-1)-1}{\text{stride}[d]}+1\rfloor ,$$

where $d$ is over all spatial dimensions.

Therefore, output_size is the spatial shape of the large containing tensor of the sliding local blocks.

The dilation, padding and stride arguments specify how the sliding blocks are retrieved.

Warning

Currently, only unbatched(3D) or batched(4D) image-like output tensors are supported.

Parameters

• input (Tensor) – 2-D or 3-D Tensor.

• output_size (Union[int, tuple[int], list[int]]) – The shape of the spatial dimensions of the output(i.e., output.shape[2:]).

• kernel_size (Union[int, tuple[int], list[int]]) – The size of the kernel, should be two int for height and width. If type is int, it means that height equal with width. Must be specified.

• dilation (Union[int, tuple[int], list[int]], optional) – The size of the dilation, should be two int for height and width. If type is int, it means that height equal with width. Default: 1 .

• padding (Union[int, tuple[int], list[int]], optional) – The size of the padding, should be two int for height and width. If type is int, it means that height equal with width. Default: 0 .

• stride (Union[int, tuple[int], list[int]], optional) – The size of the stride, should be two int for height and width. If type is int, it means that height equal with width. Default: 1 .

Returns

A Tensor, with same type as input .

Shape:

• Input: $(N,C\times \prod (\text{kernel\_size}),L)$ or $(C\times \prod (\text{kernel\_size}),L)$

• Output: $(N,C,output\mathrm{\_}size[0],output\mathrm{\_}size[1],...)$ or $(C,output\mathrm{\_}size[0],output\mathrm{\_}size[1],...)$

Raises

• TypeError – If output_size, kernel_size, stride, dilation, padding data type is not int, tuple or list.

• ValueError – If output_size, kernel_size, dilation, stride value is not greater than zero or elements number invalid.

• ValueError – If padding value is less than zero or elements number invalid.

• ValueError – If input.shape[-2] can't be divisible by the product of kernel_size.

• ValueError – If input.shape[-1] is not equal to the calculated number of sliding blocks L.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.random.rand(16, 64, 25).astype(np.float32))
>>> output = mint.nn.functional.fold(x, (8, 8), [2, 2], [2, 2], [2, 2], [2, 2])
>>> print(output.shape)
(16, 16, 8, 8)