mindspore.nn.MaxUnpool1d
- class mindspore.nn.MaxUnpool1d(kernel_size, stride=None, padding=0)[source]
Computes the inverse of
mindspore.nn.MaxPool1d
.MaxUnpool1d 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.
\[\begin{split}\begin{array}{ll} \\ H_{out} = (H_{in} - 1) \times stride[0] - 2 \times padding[0] + kernel\_size[0] \\ \end{array}\end{split}\]- Parameters
- Inputs:
x (Tensor) - The input Tensor to invert. Tensor of shape \((N, C, H_{in})\) or \((C, H_{in})\).
indices (Tensor) - Max values' index represented by the indices. 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.
output_size (tuple[int], optional) - The output size. Default:
None
. If output_size isNone
, then the shape of output computed by kernel_size, stride and padding. If output_size is notNone
, then output_size must be \((N, C, H)\) , \((C, H)\) or \((H)\) and output_size must belong to \([(N, C, H_{out} - stride[0]), (N, C, H_{out} + stride[0])]\).
- Outputs:
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:
GPU
CPU
Examples
>>> import mindspore as ms >>> import numpy as np >>> x = ms.Tensor(np.array([[2, 4, 6, 8]]).astype(np.float32)) >>> indices = ms.Tensor(np.array([[1, 3, 5, 7]]).astype(np.int64)) >>> maxunpool1d = ms.nn.MaxUnpool1d(kernel_size =2, stride=2, padding=0) >>> output = maxunpool1d(x, indices) >>> print(output.asnumpy()) [[0. 2. 0. 4. 0. 6. 0. 8.]]