mindspore.mint.nn.MaxUnpool2d

class mindspore.mint.nn.MaxUnpool2d(kernel_size, stride=None, padding=0)

Computes the inverse of Maxpool2d.

MaxUnpool2d keeps the maximal value and set all position of non-maximal values to zero. Typically the input is of shape $(N,C,H_{in},W_{in})$ or $(C,H_{in},W_{in})$, and the output is of shape $(N,C,H_{out},W_{out})$ or $(C,H_{out},W_{out})$. The operation is as follows.

$$\begin{array}{r}\begin{array}{l}\\ H_{out}=(H_{in}-1)\times stride[0]-2\times padding[0]+kernel\mathrm{\_}size[0]\\ W_{out}=(W_{in}-1)\times stride[1]-2\times padding[1]+kernel\mathrm{\_}size[1]\end{array}\end{array}$$

Warning

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

Parameters

• kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum value, an int number that represents height and width of the kernel, or a tuple of two int numbers that represent height and width respectively.

• stride (Union[int, tuple[int]], optional) – The distance of kernel moving, an int number that represents the height and width of movement are both stride, or a tuple of two int numbers that represent height and width of movement respectively. Default: None , which indicates the moving step is kernel_size .

• padding (Union[int, tuple[int]], optional) – The pad value to be filled. Default: 0 . If padding is an integer, the paddings of height and width are the same, equal to padding. If padding is a tuple of two integers, the padding of height and width equal to padding[0] and padding[1] correspondingly.

Inputs:

• input (Tensor) - The input Tensor to invert. Tensor of shape $(N,C,H_{in},W_{in})$ or $(C,H_{in},W_{in})$.

• indices (Tensor) - Max values' index represented by the indices. Tensor of shape must be same with input 'input'. Values of indices must belong to $[0,H_{in}\times W_{in}-1]$. Data type must be in int32 or int64.

• output_size (tuple[int], optional) - The target output size. Default: None . If output_size == (), then the shape of output computed by kernel_size, stride and padding. If output_size != (), then output_size must be $(N,C,H,W)$ , $(C,H,W)$ or $(H,W)$ and output_size must belong to $[(N,C,H_{out}-stride[0],W_{out}-stride[1]),(N,C,H_{out}+stride[0],W_{out}+stride[1])]$.

Outputs:

Tensor, with shape $(N,C,H_{out},W_{out})$ or $(C,H_{out},W_{out})$, with the same data type with input.

Raises

• TypeError – If data type of input or indices is not supported.

• TypeError – If kernel_size, stride or padding is neither an int nor a tuple.

• ValueError – If numbers in stride, padding or kernel_size is not positive.

• ValueError – If the shapes of input and indices are not equal.

• ValueError – If input whose length is not 3 or 4.

• ValueError – If output_size whose type is not tuple.

• ValueError – If output_size is not close to output size computed by attr kernel_size, stride, padding.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([[[[0, 1], [8, 9]]]]).astype(np.float32))
>>> indices = Tensor(np.array([[[[0, 1], [2, 3]]]]).astype(np.int64))
>>> net =  mint.nn.MaxUnpool2d(1, stride=1, padding=0)
>>> output = net(input, indices)
>>> print(output.asnumpy())
[[[[0. 1.]
   [8. 9.]]]]