1 torch.cat
torch.cat((A, B), dim)
将两个tensor在指定维度进行拼接
A = torch.zeros(2,3)
B = torch.zeros(2,3)
C = torch.cat((A,B), 0) ## shape [4,3]
D = torch.cat((A,B), 1) ## shape [2,6]
2 torch.stack
torch.stack((A, B), dim)
增加新的维度进行堆叠
A = torch.zeros(1,3)
B = torch.zeros(1,3)
C = torch.stack((A,B), 0) ## [2, 1, 3]
D = torch.stack((A,B), 1) ## [1, 2, 3]
E = torch.stack((A,B), 2) ## [1, 3, 2]
3 torch.permute
A = A.permute(0, 2, 3, 1)
调整tensor的维度顺序，相当于更灵活的transpose
A = torch.zeros(32, 3, 18, 18) ## [32, 3, 18, 18]
B = A.permute(0, 2, 3, 1) ##[32, 18, 18, 3]
4 tensor.contiguous
view只能用在contiguous的tensor上。如果在view之前用了transpose, permute等，需要用contiguous()来返回一个contiguous copy。
eg:
v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
5 tensor.squeeze
A = A.squeeze(dim)
去掉tensor的维度为1的维度，该维度可以通过参数dim指定，也可以不加参数，默认找到维度为1的维度然后去掉
A = torch.zeros(1, 18, 18) ## [1, 18, 18]
B = A.squeeze(0) ## [18, 18]
6 tensor.unsqueeze
A = A.unsqueee(dim)
在tensor中增加一个新的指定维度，新维度放在指定位置 原来维度序列向两边移动
A = torch.zeros(2, 3, 4) ## [2, 3, 4]
B = A.unsqueeze(0) ## [1, 2, 3, 4]
C = A.unsqueeze(1) ## [2, 1, 3, 4]
D = A.unsqueeze(2) ## [2, 3, 1, 4]
E = A.unsqueeze(3) ## [2, 3, 4, 1]
7 tensor.expand
A = A.expand()
在指定维度上扩展数据， 该指定维度长度为1，否则报错。（此时扩展仅是创建新的视图，并不进行数据复制）
A = torch.zeros(2, 3, 1) ## [2, 3, 1]
B = A.expand(2, 3, 3) ## [2, 3, 3]
8 tensor.clone()
clone() 得到的tensor不仅拷贝了原始的value，而且会计算梯度传播信息
b = a.clone()
9 tensor.copy_(src_tensor)
只拷贝src_tensor的数据到dst_tensor上，并返回self
a = torch.ones([3,4])
b = torch.zeros([3,4])
b.copy_(a)
10 生成特定尺度、特定数值的tensor
a = torch.Tensor(3,5).fill_(0)
a = torch.full((3,5), 0, dtype=torch.IntTensor)