torch.stack方法详解
Parameters
tensors：张量序列，也就是要进行stack操作的对象们，可以有很多个张量。
dim：按照dim的方式对这些张量进行stack操作，也就是你要按照哪种堆叠方式对张量进行堆叠。dim的取值范围为闭区间0,输入Tensor的维数
return
堆叠后的张量
二、例子
2.1 一维tensor进行stack操作
python
import torch as t
x = t.tensor([1, 2, 3, 4])
y = t.tensor([5, 6, 7, 8])
print(x.shape)
print(y.shape)
z1 = t.stack((x, y), dim=0)
print(z1)
print(z1.shape)
z2 = t.stack((x, y), dim=1)
print(z2)
print(z2.shape)
python
torch.Size([4])
torch.Size([4])
tensor([[1, 2, 3, 4],
[5, 6, 7, 8]])
torch.Size([2, 4])
tensor([[1, 5],
[2, 6],
[3, 7],
[4, 8]])
torch.Size([4, 2])
2.2 2个二维tensor进行stack操作
python
import torch as t
x = t.tensor([[1,2,3],[4,5,6]])
y = t.tensor([[7,8,9],[10,11,12]])
print(x.shape)
print(y.shape)
z1 = t.stack((x,y), dim=0)
print(z1)
print(z1.shape)
z2 = t.stack((x,y), dim=1)
print(z2)
print(z2.shape)
z3 = t.stack((x,y), dim=2)
print(z3)
print(z3.shape)
python
torch.Size([2, 3])
torch.Size([2, 3])
tensor([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
torch.Size([2, 2, 3])
tensor([[[ 1, 2, 3],
[ 7, 8, 9]],
[[ 4, 5, 6],
[10, 11, 12]]])
torch.Size([2, 2, 3])
tensor([[[ 1, 7],
[ 2, 8],
[ 3, 9]],
[[ 4, 10],
[ 5, 11],
[ 6, 12]]])
torch.Size([2, 3, 2])
2.3 多个二维tensor进行stack操作
python
import torch
x = torch.tensor([[1,2,3],[4,5,6]])
y = torch.tensor([[7,8,9],[10,11,12]])
z = torch.tensor([[13,14,15],[16,17,18]])
print(x.shape)
print(y.shape)
print(z.shape)
r1 = torch.stack((x,y,z),dim=0)
print(r1)
print(r1.shape)
r2 = torch.stack((x,y,z),dim=1)
print(r2)
print(r2.shape)
r3 = torch.stack((x,y,z),dim=2)
print(r3)
print(r3.shape)
python
torch.Size([2, 3])
torch.Size([2, 3])
torch.Size([2, 3])
tensor([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]],
[[13, 14, 15],
[16, 17, 18]]])
torch.Size([3, 2, 3])
tensor([[[ 1, 2, 3],
[ 7, 8, 9],
[13, 14, 15]],
[[ 4, 5, 6],
[10, 11, 12],
[16, 17, 18]]])
torch.Size([2, 3, 3])
tensor([[[ 1, 7, 13],
[ 2, 8, 14],
[ 3, 9, 15]],
[[ 4, 10, 16],
[ 5, 11, 17],
[ 6, 12, 18]]])
torch.Size([2, 3, 3])2.4 2个三维tensor进行stack操作
python
import torch
x= torch.tensor([[[1,2,3],[4,5,6]],
[[2,3,4],[5,6,7]]])
y = torch.tensor([[[7,8,9],[10,11,12]],
[[8,9,10],[11,12,13]]])
print(x.shape)
print(y.shape)
z1 = torch.stack((x,y),dim=0)
print(z1)
print(z1.shape)
z2 = torch.stack((x,y),dim=1)
print(z2)
print(z2.shape)
z3 = torch.stack((x,y),dim=2)
print(z3)
print(z3.shape)
z4 = torch.stack((x,y),dim=3)
print(z4)
print(z4.shape)
python
torch.Size([2, 2, 3])
torch.Size([2, 2, 3])
tensor([[[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 2, 3, 4],
[ 5, 6, 7]]],
[[[ 7, 8, 9],
[10, 11, 12]],
[[ 8, 9, 10],
[11, 12, 13]]]])
torch.Size([2, 2, 2, 3])
tensor([[[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]],
[[[ 2, 3, 4],
[ 5, 6, 7]],
[[ 8, 9, 10],
[11, 12, 13]]]])
torch.Size([2, 2, 2, 3])
tensor([[[[ 1, 2, 3],
[ 7, 8, 9]],
[[ 4, 5, 6],
[10, 11, 12]]],
[[[ 2, 3, 4],
[ 8, 9, 10]],
[[ 5, 6, 7],
[11, 12, 13]]]])
torch.Size([2, 2, 2, 3])
tensor([[[[ 1, 7],
[ 2, 8],
[ 3, 9]],
[[ 4, 10],
[ 5, 11],
[ 6, 12]]],
[[[ 2, 8],
[ 3, 9],
[ 4, 10]],
[[ 5, 11],
[ 6, 12],
[ 7, 13]]]])
torch.Size([2, 2, 3, 2])