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：
import torchtensor1 = 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)
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：
import torchtensor1 = 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)
torch.Size([2, 3])
tensor([[1, 2, 3],[4, 5, 6]])
torch.Size([3, 2])
tensor([[1, 4],[2, 5],[3, 6]])
Example2：
import torchtensor1 = 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)
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]]])
