Splits input, a tensor with three or more dimensions, into multiple tensors depthwise according to indices_or_sections. Each split is a view of input.
This is equivalent to calling torch.tensor_split(input, indices_or_sections, dim=2) (the split dimension is 2), except that if indices_or_sections is an integer it must evenly divide the split dimension or a runtime error will be thrown.
>>> t = torch.arange(16.0).reshape(2, 2, 4)
>>> t
tensor([[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.]],
[[ 8., 9., 10., 11.],
[12., 13., 14., 15.]]])
>>> torch.dsplit(t, 2)
(tensor([[[ 0., 1.],
[ 4., 5.]],
[[ 8., 9.],
[12., 13.]]]),
tensor([[[ 2., 3.],
[ 6., 7.]],
[[10., 11.],
[14., 15.]]]))
>>> torch.dsplit(t, [3, 6])
(tensor([[[ 0., 1., 2.],
[ 4., 5., 6.]],
[[ 8., 9., 10.],
[12., 13., 14.]]]),
tensor([[[ 3.],
[ 7.]],
[[11.],
[15.]]]),
tensor([], size=(2, 2, 0)))
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4