MaxUnpool2d¶
- class torch.nn.MaxUnpool2d(kernel_size, stride=None, padding=0)[source][source]¶
Computes a partial inverse of
MaxPool2d
.MaxPool2d
is not fully invertible, since the non-maximal values are lost.MaxUnpool2d
takes in as input the output ofMaxPool2d
including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero.Note
This operation may behave nondeterministically when the input indices has repeat values. See https://github.com/pytorch/pytorch/issues/80827 and Reproducibility for more information.
Note
MaxPool2d
can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argumentoutput_size
in the forward call. See the Inputs and Example below.- Parameters
- Inputs:
input: the input Tensor to invert
indices: the indices given out by
MaxPool2d
output_size (optional): the targeted output size
- Shape:
Input: or .
Output: or , where
or as given by
output_size
in the call operator
Example:
>>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) >>> unpool = nn.MaxUnpool2d(2, stride=2) >>> input = torch.tensor([[[[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., 10., 11., 12.], [13., 14., 15., 16.]]]]) >>> output, indices = pool(input) >>> unpool(output, indices) tensor([[[[ 0., 0., 0., 0.], [ 0., 6., 0., 8.], [ 0., 0., 0., 0.], [ 0., 14., 0., 16.]]]]) >>> # Now using output_size to resolve an ambiguous size for the inverse >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], [ 6., 7., 8., 9., 10.], [11., 12., 13., 14., 15.], [16., 17., 18., 19., 20.]]]]) >>> output, indices = pool(input) >>> # This call will not work without specifying output_size >>> unpool(output, indices, output_size=input.size()) tensor([[[[ 0., 0., 0., 0., 0.], [ 0., 7., 0., 9., 0.], [ 0., 0., 0., 0., 0.], [ 0., 17., 0., 19., 0.]]]])