【Pytorch之一】--torch.stack()方法详解

torch.stack方法详解

pytorch官网注释

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

import torch as tx = 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)

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

 import torch as tx = 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)

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

import torchx = 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)

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

import torchx= 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)

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])

参考文献

[1] PyTorch基础（18）-- torch.stack()方法

[2]pytorch官网注释