Merge or split
Cat
Statistics about scores
[class1-4,students,scores]
[class5-9,students,scores]
import torch
a = torch.rand(4,32,8)
b = torch.rand(5,32,8)
print(torch.cat([a,b],dim=0).shape)
out: torch.Size([9, 3, 32, 32])
a1 = torch.rand(4,3,32,32)
a2 = torch.rand(5,3,32,32)
print(torch.cat([a1,a2],dim=0).shape)
out: torch.Size([9, 3, 32, 32])
a1 = torch.rand(4,3,32,32)
a2 = torch.rand(4,1,32,32)
# print(torch.cat([a1,a2],dim=0).shape) 报错
print(torch.cat([a1,a2],dim=1).shape)
out: 报错:Sizes of tensors must match except in dimension 0, 是因为维度dim=1的shape不一样造成的，而cat维度的shape可以不一样。
out: torch.Size([4, 4, 32, 32])
a1 = torch.rand(4,3,16,32)
a2 = torch.rand(4,3,16,32)
print(torch.cat([a1,a2],dim=2).shape)
out: torch.Size([4, 3, 32, 32])
Along distinct dim/axis
Stack
a1 = torch.rand(4,3,16,32)
a2 = torch.rand(4,3,16,32)
print(torch.stack([a1,a2],dim=2).shape) # torch.Size([4,3,2,16,32])
a = torch.rand(32,8)
b = torch.rand(32,8)
print(torch.stack([a,b],dim=0).shape) # torch.Size([2,32,8])
print(torch.cat([a,b],dim=0).shape) # torch.Size([64,8])
Stack与Cat最根本区别在于是联合还是合并。举一个简单例子，把a看作班级32个同学8门课程的成绩，把b看作另一个班级32个同学8门课程的成绩，用Stack看作是两个班级的联合[2,32,8]，而用cat看作两个班级是一个整体[64,8]。对于Stack而言两个维度都必须一致，而对于Cat而言拼接的那个维度可以不一样
b = torch.rand([30,8])
# print(torch.stack([a,b],dim=0)) 报错
print(torch.cat([a,b],dim=0).shape) # torch.Size([62, 8])
Split
a = torch.rand(32,8)
b = torch.rand(32,8)
c = torch.stack([a,b],dim=0)
print(c.shape) #torch.Size([64,8])
按长度拆分
长度不一样，可以直接给定一个list,[1,1]切片，其实就是拆成2块，每块长度是1。
如果是给定[1,2,3]就代表拆成3块，每块长度分别是1，2，3
aa,bb = c.split([1,1],dim=0)
print(aa.shape,bb.shape) # torch.Size([1, 32, 8]) torch.Size([1, 32, 8])
长度一样就设一个固定值，每块长度是1，拆分成n块，
aa,bb = c.split(1,dim=0)
print(aa.shape,bb.shape) # torch.Size([1, 32, 8]) torch.Size([1, 32, 8])
ValueError: not enough values to unpack (expected 2, got 1)
拆分成n块，每块长度是2，但是c只能拆成1个，所以返回1个tensor，不能用2个tensor接受
# aa,bb = c.split(2,dim=0) 报错
Chunk
按数量拆分，拆分成2块，每块长度是2/2
aa,bb = c.chunk(2,dim=0)
print(aa.shape,bb.shape) # torch.Size([1, 32, 8]) torch.Size([1, 32, 8])