创建tensor
torch.zeros
torch.arange(start, end, step)
>>> torch.arange(5)
tensor([ 0, 1, 2, 3, 4])
>>> torch.arange(1, 4)
tensor([ 1, 2, 3])
>>> torch.arange(1, 2.5, 0.5)
tensor([ 1.0000, 1.5000, 2.0000])
减小维度
torch.squeeze()
将维度为1的部分删掉。比如:
>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])
>>> y = torch.squeeze(x, (1, 2, 3))
torch.Size([2, 2, 2])
增加维度
torch.unsqueeze(input, dim)
和torch.squeeze为反操作，在dim维度加上一个维度：
# x.shape: (4)
>>> x = torch.tensor([1, 2, 3, 4])
# after unsqueeze on dimension 0: (1, 4)
>>> torch.unsqueeze(x, 0)
tensor([[ 1, 2, 3, 4]])
# after unsquezze on dimension 1: (4, 1)
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
[ 2],
[ 3],
[ 4]])
torch.stack(tensors, dim=0, *, out=None)
Concatenates a sequence of tensors along a new dimension. All tensors need to be of the same size.对于size不同的tensor可以使用cat。
改变维度
torch.cat(tensors, dim)
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
# after cat: (6,3)
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
# after cat: (2, 9)
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580,
-1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034,
-0.5790, 0.1497]])
torch.transpose(input, dim0, dim1)
互换dim0和dim1的维度。
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 1.0028, -0.9893, 0.5809],
[-0.1669, 0.7299, 0.4942]])
# after transpose: (3, 2)
>>> torch.transpose(x, 0, 1)
tensor([[ 1.0028, -0.1669],
[-0.9893, 0.7299],
[ 0.5809, 0.4942]])
Tensor.expand(*sizes)
把tensor的维度进行扩展。如果设为-1，表示这个维度不变。这个括号里的size是变化后的形状size。扩展就是按照维度来复制。举例如下：
>>> x = torch.tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> x.expand(3, 4) # expand后形状从[3,1]变为了[3,4]
tensor([[ 1, 1, 1, 1],
[ 2, 2, 2, 2],
[ 3, 3, 3, 3]])
>>> x.expand(-1, 4) # -1 means not changing the size of that dimension
tensor([[ 1, 1, 1, 1],
[ 2, 2, 2, 2],
[ 3, 3, 3, 3]]) # expand后形状从[3,1]变为了[3,4]
也可以用expand来增加维度，增加的维度会放在最前面。举例如下：
>>> x = torch.tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> y = x.expand(2, 3, 4)
>>> y
tensor([[[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]],
[[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]]])
>>> y.size()
torch.Size([2, 3, 4])
expand不会增加内存，只是在原先的tensor加了view。所以对expand后的tensor做操作需要小心，因为它本质上没有clone。
其他
torch.split(tensor, split_size, dim)
将tensor拆成多个tensors，返回拆分后得到的tensor列表。split_size可以是一个列表，它会根据列表给出的size拆分。
>>> a = torch.arange(10).reshape(5, 2)
# a.shape: (5, 2)
>>> a
tensor([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
# split后得到列表，默认在dim=0分裂，得到的tensor有3个
>>> torch.split(a, 2)
(tensor([[0, 1],
[2, 3]]),
tensor([[4, 5],
[6, 7]]),
tensor([[8, 9]]))
# 得到的tensor列表有2个，分别是(1,2)和(4,2)
>>> torch.split(a, [1, 4])
(tensor([[0, 1]]),
tensor([[2, 3],
[4, 5],
[6, 7],
[8, 9]]))
torch.tile(input, dims)
Constructs a tensor by repeating the elements of input. 可以用于重复tensor中的元素。dims来指定重复的次数和对应维度。
>>> x = torch.tensor([1, 2, 3])
# dims必须是tuple，如果只是在一个维度上重复，参考这种写法，不要写(2)，会报错X
>>> x.tile((2,))
tensor([1, 2, 3, 1, 2, 3])
# y.shape: (2,2)
>>> y = torch.tensor([[1, 2], [3, 4]])
>>> torch.tile(y, (2, 2))
# y.shape: (4,4)
tensor([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
当dim的维度比input的维度要大的时候：
# prompt.shape: (2,3,4)
>>> prompt = torch.zeros((2,3,4))
# prompt扩充为(1,2,3,4)，然后tile
>>> y = torch.tile(prompt, (1,10,1,1))
>>> y.shape
>>> (1,20,3,4)
当input的维度比dim的维度要大的时候：
# prompt.shape: (2,3,4)
>>> prompt = torch.zeros((2,3,4))
# dims扩充为(1,10,1)，然后tile
>>> y = torch.tile(prompt, (10,1))
>>> y.shape
>>> (2,30,4)