Unflatten¶
- class torch.nn.Unflatten(dim, unflattened_size)[source]¶
将张量维度展开到所需的形状。用于
Sequential
。dim
指定要展开的输入张量的维度,当使用 Tensor 或 NamedTensor 时,它可以是 int 或 str。unflattened_size
是张量展开维度的新的形状,对于 Tensor 输入,它可以是 tuple 整数或 list 整数或 torch.Size;对于 NamedTensor 输入,它可以是 NamedShape((name, size) 元组的元组)。
- 形状
输入:, 其中 是维度
dim
的大小,而 表示任意数量的维度,包括无维度。输出:,其中 =
unflattened_size
且 .
- 参数
unflattened_size (Union[torch.Size, Tuple, List, NamedShape]) – 展开后维度的新的形状
示例
>>> input = torch.randn(2, 50) >>> # With tuple of ints >>> m = nn.Sequential( >>> nn.Linear(50, 50), >>> nn.Unflatten(1, (2, 5, 5)) >>> ) >>> output = m(input) >>> output.size() torch.Size([2, 2, 5, 5]) >>> # With torch.Size >>> m = nn.Sequential( >>> nn.Linear(50, 50), >>> nn.Unflatten(1, torch.Size([2, 5, 5])) >>> ) >>> output = m(input) >>> output.size() torch.Size([2, 2, 5, 5]) >>> # With namedshape (tuple of tuples) >>> input = torch.randn(2, 50, names=('N', 'features')) >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) >>> output = unflatten(input) >>> output.size() torch.Size([2, 2, 5, 5])