<>拼接张量：torch.cat() 、torch.stack()

* torch.cat(inputs, dimension=0) → Tensor

>>> import torch >>> x = torch.randn(2, 3) >>> x tensor([[-0.1997, -0.6900,
0.7039], [ 0.0268, -1.0140, -2.9764]]) >>> torch.cat((x, x, x), 0) # 在 0

0.1997, -0.6900, 0.7039], [ 0.0268, -1.0140, -2.9764], [-0.1997, -0.6900, 0.7039
], [ 0.0268, -1.0140, -2.9764]]) >>> torch.cat((x, x, x), 1) # 在 1 维(横向)进行拼接
tensor([[-0.1997, -0.6900, 0.7039, -0.1997, -0.6900, 0.7039, -0.1997, -0.6900,
0.7039], [ 0.0268, -1.0140, -2.9764, 0.0268, -1.0140, -2.9764, 0.0268, -1.0140,
-2.9764]]) >>> y1 = torch.randn(5, 3, 6) >>> y2 = torch.randn(5, 3, 6) >>> torch
.cat([y1, y2], 2).size() torch.Size([5, 3, 12]) >>> torch.cat([y1, y2], 1).size(
) torch.Size([5, 6, 6])

* torch.stack(sequence, dim=0)

>>> x1 = torch.randn(2, 3) >>> x2 = torch.randn(2, 3) >>> torch.stack((x1, x2),
0).size() # 在 0 维插入一个维度，进行区分拼接 torch.Size([2, 2, 3]) >>> torch.stack((x1, x2), 1
).size() # 在 1 维插入一个维度，进行组合拼接 torch.Size([2, 2, 3]) >>> torch.stack((x1, x2), 2)
.size() torch.Size([2, 3, 2]) >>> torch.stack((x1, x2), 0) tensor([[[-0.3499, -
0.6124, 1.4332], [ 0.1516, -1.5439, -0.1758]], [[-0.4678, -1.1430, -0.5279], [-
0.4917, -0.6504, 2.2512]]]) >>> torch.stack((x1, x2), 1) tensor([[[-0.3499, -
0.6124, 1.4332], [-0.4678, -1.1430, -0.5279]], [[ 0.1516, -1.5439, -0.1758], [-
0.4917, -0.6504, 2.2512]]]) >>> torch.stack((x1, x2), 2) tensor([[[-0.3499, -
0.4678], [-0.6124, -1.1430], [ 1.4332, -0.5279]], [[ 0.1516, -0.4917], [-1.5439,
-0.6504], [-0.1758, 2.2512]]])

<>拆分张量：torch.split()、torch.chunk()

* torch.split(tensor, split_size, dim=0)

>>> x = torch.randn(3, 10, 6) >>> a, b, c = x.split(1, 0) # 在 0 维进行间隔维 1 的拆分 >>
> a.size(), b.size(), c.size() (torch.Size([1, 10, 6]), torch.Size([1, 10, 6]),
torch.Size([1, 10, 6])) >>> d, e = x.split(2, 0) # 在 0 维进行间隔维 2 的拆分 >>> d.size()
, e.size() (torch.Size([2, 10, 6]), torch.Size([1, 10, 6]))

* torch.chunk(tensor, chunks, dim=0)

>>> l, m, n = x.chunk(3, 0) # 在 0 维上拆分成 3 份 >>> l.size(), m.size(), n.size() (
torch.Size([1, 10, 6]), torch.Size([1, 10, 6]), torch.Size([1, 10, 6])) >>> u, v
= x.chunk(2, 0) # 在 0 维上拆分成 2 份 >>> u.size(), v.size() (torch.Size([2, 10, 6]),
torch.Size([1, 10, 6]))