一、torch.cat与torch.stack的区别

torch.cat用于在给定的维度上连接多个张量，它将这些张量沿着指定维度堆叠在一起。

torch.stack用于在新的维度上堆叠多个张量，它会创建一个新的维度，并将这些张量沿着这个新维度堆叠在一起。

二、torch.cat

Example1：

py 复制代码

import torch

tensor1 = torch.tensor([[1, 2], [3, 4]])
tensor2 = torch.tensor([[5, 6], [7, 8]])

result1 = torch.cat((tensor1, tensor2), dim=0)
result2 = torch.cat((tensor1, tensor2), dim=1)

print(result1.shape)
print(result1)
print(result2.shape)
print(result2)

lua 复制代码

torch.Size([4, 2])
tensor([[1, 2],
        [3, 4],
        [5, 6],
        [7, 8]])
torch.Size([2, 4])
tensor([[1, 2, 5, 6],
        [3, 4, 7, 8]])

三、torch.stack

Example1：

py 复制代码

import torch

tensor1 = torch.tensor([1, 2, 3])
tensor2 = torch.tensor([4, 5, 6])

result1 = torch.stack((tensor1, tensor2), dim=0)
result2 = torch.stack((tensor1, tensor2), dim=1)

print(result1.shape)
print(result1)
print(result2.shape)
print(result2)

lua 复制代码

torch.Size([2, 3])
tensor([[1, 2, 3],
        [4, 5, 6]])
torch.Size([3, 2])
tensor([[1, 4],
        [2, 5],
        [3, 6]])

Example2：

py 复制代码

import torch

tensor1 = torch.tensor([[1, 2], [3, 4], [5, 6]])
tensor2 = torch.tensor([[7, 8], [9, 10], [11, 12]])
tensor3 = torch.tensor([[13, 14], [15, 16], [17, 18]])

result1 = torch.stack((tensor1, tensor2, tensor3), dim=0)
result2 = torch.stack((tensor1, tensor2, tensor3), dim=1)

print(result1.shape)
print(result1)
print(result2.shape)
print(result2)

lua 复制代码

torch.Size([3, 3, 2])
tensor([[[ 1,  2],
         [ 3,  4],
         [ 5,  6]],

        [[ 7,  8],
         [ 9, 10],
         [11, 12]],

        [[13, 14],
         [15, 16],
         [17, 18]]])
torch.Size([3, 3, 2])
tensor([[[ 1,  2],
         [ 7,  8],
         [13, 14]],

        [[ 3,  4],
         [ 9, 10],
         [15, 16]],

        [[ 5,  6],
         [11, 12],
         [17, 18]]])