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张量拼接操作

news 2025/7/13 6:00:29

一.前言

本章节来介绍一下张量拼接的操作，掌握torch.cat torch.stack使⽤，张量的拼接操作在神经⽹络搭建过程中是⾮常常⽤的⽅法，例如: 在后⾯将要学习到的残差⽹络、注意⼒机制中都使⽤到了张量拼接。

二.torch.cat 函数的使用

torch.cat 函数可以将两个张量根据指定的维度拼接起来.

import torchdef test():data1 = torch.randint(0, 10, [3, 5, 4])data2 = torch.randint(0, 10, [3, 5, 4])print(data1)print(data2)print('-' * 50)# 1. 按0维度拼接new_data = torch.cat([data1, data2], dim=0)print(new_data.shape)print('-' * 50)# 2. 按1维度拼接new_data = torch.cat([data1, data2], dim=1)print(new_data.shape)print('-' * 50)# 3. 按2维度拼接new_data = torch.cat([data1, data2], dim=2)print(new_data.shape)if __name__ == '__main__':test()

结果展示：

tensor([[[6, 7, 2, 6],
[4, 6, 4, 3],
[5, 3, 4, 9],
[8, 8, 6, 7],
[0, 3, 3, 0]],
[[6, 1, 2, 0],
[5, 6, 7, 0],
[6, 4, 8, 0],
[2, 2, 8, 3],
[0, 1, 6, 8]],
[[3, 5, 0, 8],
[6, 2, 1, 7],
[8, 9, 9, 8],
[3, 8, 8, 0],
[5, 8, 4, 4]]])
tensor([[[7, 2, 2, 1],
[8, 0, 6, 6],
[9, 0, 6, 5],
[1, 3, 7, 7],
[7, 0, 5, 1]],
[[0, 7, 3, 1],
[9, 2, 9, 0],
[9, 6, 2, 1],
[9, 3, 5, 0],
[8, 8, 6, 2]],
[[1, 8, 9, 9],
[4, 3, 0, 9],
[7, 3, 3, 8],
[2, 4, 6, 9],
[2, 1, 0, 5]]])
--------------------------------------------------
torch.Size([6, 5, 4])
--------------------------------------------------
torch.Size([3, 10, 4])
--------------------------------------------------
torch.Size([3, 5, 8])

三.torch.stack 函数的使用

torch.stack 函数可以将两个张量根据指定的维度叠加起来.

import torchdef test():data1 = torch.randint(0, 10, [2, 3])data2 = torch.randint(0, 10, [2, 3])print(data1)print(data2)print("="*50)new_data = torch.stack([data1, data2], dim=0)print(new_data.shape)print(new_data)print("=" * 50)new_data = torch.stack([data1, data2], dim=1)print(new_data.shape)print(new_data)print("=" * 50)new_data = torch.stack([data1, data2], dim=2)print(new_data.shape)print(new_data)if __name__ == '__main__':test()

结果展示：

tensor([[6, 9, 6],
[3, 2, 7]])
tensor([[3, 3, 4],
[9, 1, 4]])
==================================================
torch.Size([2, 2, 3])
tensor([[[6, 9, 6],
[3, 2, 7]],
[[3, 3, 4],
[9, 1, 4]]])
==================================================
torch.Size([2, 2, 3])
tensor([[[6, 9, 6],
[3, 3, 4]],
[[3, 2, 7],
[9, 1, 4]]])
==================================================
torch.Size([2, 3, 2])
tensor([[[6, 3],
[9, 3],
[6, 4]],
[[3, 9],
[2, 1],
[7, 4]]])