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mindspore.nn.Unfold

class mindspore.nn.Unfold(ksizes, strides, rates, padding='valid')

Extracts patches from images. The input tensor must be a 4-D tensor and the data format is NCHW.

Parameters

• ksizes (Union[tuple[int], list[int]]) – The size of sliding window, must be a tuple or a list of integers, and the format is [1, ksize_row, ksize_col, 1].

• strides (Union[tuple[int], list[int]]) – Distance between the centers of the two consecutive patches, must be a tuple or list of int, and the format is [1, stride_row, stride_col, 1].

• rates (Union[tuple[int], list[int]]) – In each extracted patch, the gap between the corresponding dimension pixel positions, must be a tuple or a list of integers, and the format is [1, rate_row, rate_col, 1].

• padding (str) –

  The type of padding algorithm, is a string whose value is "same" or "valid" , not case sensitive. Default: "valid" .

  • "same": Means that the patch can take the part beyond the original image, and this part is filled with 0.

  • "valid": Means that the taken patch area must be completely covered in the original image.

Inputs:

• x (Tensor) - A 4-D tensor whose shape is $[in\mathrm{\_}batch,in\mathrm{\_}depth,in\mathrm{\_}row,in\mathrm{\_}col]$ and data type is number.

Outputs:

Tensor, a 4-D tensor whose data type is same as x, and the shape is $(out\mathrm{\_}batch,out\mathrm{\_}depth,out\mathrm{\_}row,out\mathrm{\_}col)$ where out_batch is the same as the in_batch.

• $out\mathrm{\_}depth=ksize\mathrm{\_}row\ast ksize\mathrm{\_}col\ast in\mathrm{\_}depth$

• $out\mathrm{\_}row=(in\mathrm{\_}row-(ksize\mathrm{\_}row+(ksize\mathrm{\_}row-1)\ast (rate\mathrm{\_}row-1)))//stride\mathrm{\_}row+1$

• $out\mathrm{\_}col=(in\mathrm{\_}col-(ksize\mathrm{\_}col+(ksize\mathrm{\_}col-1)\ast (rate\mathrm{\_}col-1)))//stride\mathrm{\_}col+1$

Raises

• TypeError – If ksizes, strides or rates is neither a tuple nor list.

• ValueError – If shape of ksizes, strides or rates is not $(1,x\mathrm{\_}row,x\mathrm{\_}col,1)$.

• ValueError – If the second and third element of ksizes, strides or rates is less than 1.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> net = nn.Unfold(ksizes=[1, 2, 2, 1], strides=[1, 2, 2, 1], rates=[1, 2, 2, 1])
>>> # As stated in the above code:
>>> # ksize_row = 2, ksize_col = 2, rate_row = 2, rate_col = 2, stride_row = 2, stride_col = 2.
>>> image = Tensor(np.ones([2, 3, 6, 6]), dtype=mindspore.float16)
>>> # in_batch = 2, in_depth = 3, in_row = 6, in_col = 6.
>>> # Substituting the formula to get:
>>> # out_batch = in_batch = 2
>>> # out_depth = 2 * 2 * 3 = 12
>>> # out_row = (6 - (2 + (2 - 1) * (2 - 1))) // 2 + 1 = 2
>>> # out_col = (6 - (2 + (2 - 1) * (2 - 1))) // 2 + 1 = 2
>>> output = net(image)
>>> print(output.shape)
(2, 12, 2, 2)