mindspore.ops.max_unpool1d

mindspore.ops.max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=None)

Computes the inverse of max_pool1d.

max_unpool1d keeps the maximal value and set all position of non-maximal values to zero. Typically the input is of shape $(N,C,H_{in})$ or $(C,H_{in})$, and the output is of shape $(N,C,H_{out})$ or $(C,H_{out})$. The operation is as follows.

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

Parameters

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

• indices (Tensor) – Index of maximum value. Tensor of shape must be same with input 'x'. Values of indices must belong to $[0,H_{in}-1]$. Data type must be in int32 or int64.

• kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum value.

• stride (Union[int, tuple[int]]) – The distance of kernel moving, If stride is 0, (0) or None , then stride equal to kernel_size. Default: None , which indicates the moving step is kernel_size .

• padding (Union[int, tuple[int]]) – The pad value to be filled. Default: 0 .

• output_size (tuple[int], optional) – The output shape. 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 $(N,C,H)$ , $(C,H)$ or $(H)$ and output_size must belong to $[(N,C,H_{out}-stride[0]),(N,C,H_{out}+stride[0])]$.

Returns

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

Raises

• TypeError – If data type of x 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 (also support 0 and (0)) or kernel_size is not positive.

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

• ValueError – If x whose length is not 2 or 3.

• ValueError – If type of output_size is not tuple.

• ValueError – If output_size whose length is not 0, 2 or 3.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[2, 4, 6, 8]]).astype(np.float32))
>>> indices = Tensor(np.array([[1, 3, 5, 7]]).astype(np.int64))
>>> output = ops.max_unpool1d(x, indices, kernel_size =2, stride=2, padding=0)
>>> print(output.asnumpy())
[[0. 2. 0. 4. 0. 6. 0. 8.]]