mindspore.mint.nn.functional.unfold

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

Extracts sliding local blocks from a batched input tensor.

Consider a batched input tensor of shape $(N,C,\ast )$, where $N$ is the batch dimension, $C$ is the channel dimension, and $\ast$ represent arbitrary spatial dimensions. This operation flattens each sliding Kernel_size- sized block within the spatial dimensions of input into a column (i.e., last dimension) of a 3-D output tensor of shape $(N,C\times \prod (\text{kernel\_size}),L)$, where $C\times \prod (\text{kernel\_size})$ 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:

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

where $\text{spatial\_size}$ is formed by the spatial dimensions of input ($\ast$ above), and $d$ is over all spatial dimensions.

Therefore, indexing output at the last dimension (column dimension) gives all values within a certain block.

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

Warning

• Currently, batched(4D) image-like tensors are supported.

• For Ascend, it is only supported on platforms above Atlas A2.

Parameters

• input (Tensor) – 4-D Tensor.

• 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 dilation of the window, 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 pad of the window, 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 stride of the window, 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: $(N,C,\ast )$

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

Raises

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

• ValueError – If kernel_size, dilation, stride value is not greater than zero or elements number more than 2.

• ValueError – If padding value is less than zero.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.random.rand(4, 4, 32, 32), mindspore.float32)
>>> output = mint.nn.functional.unfold(x, kernel_size=3, dilation=1, stride=1)
>>> print(output.shape)
(4, 36, 900)