https://zhuanlan.zhihu.com/p/474153365
torch.repeat
使张量沿着某个维度进行复制, 并且不仅可以复制张量，也可以拓展张量的维度:
import torch
x = torch.randn(2, 4)
# 1. 沿着某个维度复制
x.repeat(1, 1).size() # torch.Size([2, 4])
x.repeat(2, 1).size() # torch.Size([4, 4])
x.repeat(1, 2).size() # torch.Size([2, 8])
# 2. 不仅可以复制维度, 还可以拓展维度
x.repeat(1, 1, 1).size() # torch.Size([1, 2, 4])
x.repeat(2, 1, 1).size() # torch.Size([2, 2, 4])
x.repeat(1, 1, 1, 1).size() # torch.Size([1, 1, 2, 4])
# 3. repeat中传入的参数不可以少于x的维度
x.repeat(1) # 报错
torch.repeat_interleave
torch.repeat_interleave的行为与numpy.repeat类似，但是和torch.repeat不同，这边还是以代码为例:
import torch
x = torch.randn(2, 2)
print(x)
>>> tensor([[ 0.4332, 0.1172],
[ 0.8808, -1.7127]])
print(x.repeat(2, 1))
>>> tensor([[ 0.4332, 0.1172],
[ 0.8808, -1.7127],
[ 0.4332, 0.1172],
[ 0.8808, -1.7127]])
print(x.repeat_interleave(2, dim=0))
>>> tensor([[ 0.4332, 0.1172],
[ 0.4332, 0.1172],
[ 0.8808, -1.7127],
[ 0.8808, -1.7127]])
print(x.repeat_interleave(2, dim=1))
>>> tensor([[ 0.4332, 0.4332, 0.1172, 0.1172],
[ 0.8808, 0.8808, -1.7127, -1.7127]])
# 如果不传dim参数, 则默认复制后拉平
print(x.repeat_interleave(2))
>>> tensor([ 0.4332, 0.4332, 0.1172, 0.1172, 0.8808, 0.8808, -1.7127, -1.7127])
从这个代码可以看出来torch.repeat更像是把tensor作为一个整体进行复制, 而torch.repeat_interleave更是针对tensor里的每个元素进行复制，并且torch.repeat_interleave可以通过传入一个一维的torch.Tensor来指定每个元素复制的次数
import torch
x = torch.tensor([[1, 2], [3, 4]])
result = torch.repeat_interleave(x, torch.tensor([1, 3]), dim=0)
print(result)
>>> tensor([[1, 2],
[3, 4],
[3, 4],
[3, 4]])
torch.tile
torch.tile函数也是元素复制的一个函数, 但是在传参上和torch.repeat不同，但是也是以input为一个整体进行复制, torch.tile如果只传入一个参数的话, 默认是沿着行进行复制
import torch
x = torch.tensor([[1, 2], [3, 4]])
# 只传入一个参数
print(x.tile((2, )))
>>> tensor([[1, 2, 1, 2],
[3, 4, 3, 4]])
print(x.repeat(1, 2))
>>> tensor([[1, 2, 1, 2],
[3, 4, 3, 4]])
torch.tile传入一个元组的话, 表示(行复制次数, 列复制次数)
import torch
x = torch.tensor([[1, 2], [3, 4]])
print(x.tile((2, 2)))
>>> tensor([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
print(x.repeat(2, 2))
>>> tensor([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
当传入的参数少于需要复制的元素的维度时, 如果一个tensor的形状为(2, 2, 2)，传入tile中的参数为(2, 2)时, 会默认表示为(1, 2, 2)
import torch
x = torch.randn(2, 2, 2)
print(x)
>>> tensor([[[ 0.8517, 0.8721],
[-1.1591, -0.2000]],
[[ 0.3888, -0.8365],
[-1.6383, -0.1539]]])
print(x.tile((2, 2)))
>>> tensor([[[ 0.8517, 0.8721, 0.8517, 0.8721],
[-1.1591, -0.2000, -1.1591, -0.2000],
[ 0.8517, 0.8721, 0.8517, 0.8721],
[-1.1591, -0.2000, -1.1591, -0.2000]],
[[ 0.3888, -0.8365, 0.3888, -0.8365],
[-1.6383, -0.1539, -1.6383, -0.1539],
[ 0.3888, -0.8365, 0.3888, -0.8365],
[-1.6383, -0.1539, -1.6383, -0.1539]]])
当传入的参数多于需要复制的元素维度时，会拓展维度
import torch
x = torch.randn(2, 2)
print(x)
>>> tensor([[ 1.1165, -0.5559],
[-0.6341, 0.5215]])
print(x.tile((2, 2, 2)))
>>> tensor([[[ 1.1165, -0.5559, 1.1165, -0.5559],
[-0.6341, 0.5215, -0.6341, 0.5215],
[ 1.1165, -0.5559, 1.1165, -0.5559],
[-0.6341, 0.5215, -0.6341, 0.5215]],
[[ 1.1165, -0.5559, 1.1165, -0.5559],
[-0.6341, 0.5215, -0.6341, 0.5215],
[ 1.1165, -0.5559, 1.1165, -0.5559],
[-0.6341, 0.5215, -0.6341, 0.5215]]])
使用tile和reshape代替repeat_interleave
import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6]]) # shape: (2, 3)
y = torch.repeat_interleave(x, repeats=3, dim=0)
print(y)
>>> tensor([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6],
[4, 5, 6]])
# 直接使用tile, 无法得到类似的结果
z = torch.tile(x, (3, ))
print(z)
>>> tensor([[1, 2, 3, 1, 2, 3, 1, 2, 3],
[4, 5, 6, 4, 5, 6, 4, 5, 6]])
z = torch.tile(x, (3, 1))
print(z)
>>> tensor([[1, 2, 3],
[4, 5, 6],
[1, 2, 3],
[4, 5, 6],
[1, 2, 3],
[4, 5, 6]])
# 需要使用 tile + reshape 才可以得到类似的结果
z = torch.tile(x, (3, ))
print(z.shape) # (2, 9)
print(z.reshape(6, 3)) # 得到了和y一样的输出
>>> tensor([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6],
[4, 5, 6]])
