mindspore.ops.ExtractVolumePatches

class mindspore.ops.ExtractVolumePatches(kernel_size, strides, padding)[source]

Extract patches from input and put them in the “depth” output dimension. “depth” dimension is the second dim of output.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • kernel_size (Union[int, tuple[int], list[int]]) – A list of ints which’s length is 3 or 5. The size of the sliding window for each dimension of input. Must be: \([1, 1, k_d, k_h, k_w]\) or \([k_d, k_h, k_w]\). If \(k_d = k_h = k_w\), you can enter an integer.

  • strides (Union[int, tuple[int], list[int]]) – A list of ints which’s length is 3 or 5. How far the centers of two consecutive patches are in input. Must be: \([1, 1, s_d, s_h, s_w]\) or \([s_d, s_h, s_w]\). If \(s_d = s_h = s_w\), you can enter an integer.

  • padding (str) – A string from: "SAME" , "VALID" . The type of padding algorithm to use.

Inputs:

  • input_x (Tensor) - A Tensor. 5-D Tensor with shape \((x_n, x_c, x_d, x_h, x_w)\).

Outputs:

Tensor, has the same type as input. If padding is “VALID”, the shape is \((x_n, k_d * k_h * k_w * x_c, 1 + (x_d - k_d) / s_d, 1 + (x_h - k_h) / s_h, 1 + (x_w - k_w) / s_w)\); if padding is “SAME”, the shape is \(( x_n, k_d * k_h * k_w * x_c, (x_d + s_d - 1) / s_d, (x_h + s_h - 1) / s_h, (x_w + s_w - 1) / s_w)\).

Raises

  • TypeError – If kernel_size or strides is not a list, a tuple or an int.

  • TypeError – If input_x is not a tensor.

  • TypeError – If padding is not str.

  • ValueError – If the length of kernel_size is neither 3 nor 5 and kernel_size is not an integer.

  • ValueError – If the length of strides is neither 3 nor 5 and strides is not an integer.

  • ValueError – If padding is neither "VALID" nor "SAME" .

  • ValueError – If elements of kernel_size or strides are not positive integer.

  • ValueError – If input_x is not a tensor in dimension 5.

  • ValueError – If input_x’s shape has zero.

  • ValueError – If one of kernel_size or strides’ first two numbers is not 1.

  • ValueError – If padding = “VALID” and \(input\_x - kernel\_size\) is less than 0 in d, h or w dimension.

  • ValueError – If padding = “SAME” and \(padding\_needed = ((input\_x + strides - 1) / strides - 1) * strides + kernel\_size - input\_x\) is less than 0 in d, h or w dimension.

  • ValueError – If x_h is not 1 or x_w is not 1 and \(x_w + padding\_needed - k_w - s_w\) is less than 0.

  • ValueError – If \(x_d * x_h * x_w\) is greater than 2048.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> kernel_size = (1, 1, 2, 2, 2)
>>> strides = (1, 1, 1, 1, 1)
>>> padding = "VALID"
>>> input_x = ops.Reshape()(Tensor(np.arange(1, 28), mstype.float16), (1, 1, 3, 3, 3))
>>> output_y = ops.ExtractVolumePatches(kernel_size, strides, padding)(input_x)
>>> print(output_y.shape)
(1, 8, 2, 2, 2)