一、cat函数
torch.catimport torch
a=torch.randn(3,4) #随机生成一个shape（3，4）的tensort
b=torch.randn(2,4) #随机生成一个shape（2，4）的tensor
print("a:")
print(a)
print("b:")
print(b)
print("拼接结果：")
#print(torch.cat([a,b],dim=1))#由于3！=2（a,b的行数不同故无法拼接）
print(torch.cat([a,b],dim=0))#也就是原来的第0维维数增多
#print(torch.cat([a,b],dim=0))返回一个shape（5，4）的tensor
#把a和b拼接成一个shape（5，4）的tensor，
#可理解为沿着行增加的方向（即纵向）拼接
a:
tensor([[ 2.1754, 1.4698, 1.4103, -0.2498],
[ 0.3248, -1.9372, -0.9310, -0.3833],
[-0.3603, -0.0271, -0.1942, -0.0345]])
b:
tensor([[-0.3917, 0.1332, -1.0066, 0.3633],
[-0.2378, 0.5224, 1.1371, 0.6401]])
拼接结果：
tensor([[ 2.1754, 1.4698, 1.4103, -0.2498],
[ 0.3248, -1.9372, -0.9310, -0.3833],
[-0.3603, -0.0271, -0.1942, -0.0345],
[-0.3917, 0.1332, -1.0066, 0.3633],
[-0.2378, 0.5224, 1.1371, 0.6401]])
二、stack函数
创建一个新的维度。
要求：两个tensor拼接前“拼接维度”的形状要完全一致
torch.stack要求：两个tensor拼接前的形状完全一致import torch
a=torch.randn(3,4)
b=torch.randn(3,4)
print("a: ")
print(a)
print("b: ")
print(b)
c=torch.stack([a,b],dim=0)
#返回一个shape(2,3,4)的tensor,新增的维度2分别指向a和b
# print("c: ")
# print(c)
# a:
# tensor([[ 0.9722, 0.7518, 0.8787, 1.1068],
# [-0.3760, -0.3623, -0.9563, 0.3909],
# [ 0.7292, -0.0121, -0.4910, 2.1195]])
# b:
# tensor([[-0.4713, -2.5941, 1.8245, 0.2314],
# [ 1.3405, 0.3472, 1.1083, 0.7682],
# [-1.1995, 0.6853, -0.7180, 0.7114]])
# c:
# tensor([[[ 0.9722, 0.7518, 0.8787, 1.1068],
# [-0.3760, -0.3623, -0.9563, 0.3909],
# [ 0.7292, -0.0121, -0.4910, 2.1195]],
#
# [[-0.4713, -2.5941, 1.8245, 0.2314],
# [ 1.3405, 0.3472, 1.1083, 0.7682],
# [-1.1995, 0.6853, -0.7180, 0.7114]]])
d=torch.stack([a,b],dim=1)
#返回一个shape（3,2,4）的tensor，新增的维度2分别指向相应的a的第i行和b的第i行
print("d: ")
print(d)
# a:
# tensor([[ 0.9923, 0.2121, -0.8024, 0.4230],
# [-0.6697, -2.7528, -1.2073, 0.9505],
# [ 0.5162, -0.9078, -0.6087, -0.4061]])
# b:
# tensor([[ 1.7505, 1.0785, 0.8404, 0.2812],
# [ 0.9416, -0.7041, -1.6120, 0.3687],
# [ 0.4658, 0.1827, 0.2341, -0.1813]])
# d:
# tensor([[[ 0.9923, 0.2121, -0.8024, 0.4230],
# [ 1.7505, 1.0785, 0.8404, 0.2812]],
#
# [[-0.6697, -2.7528, -1.2073, 0.9505],
# [ 0.9416, -0.7041, -1.6120, 0.3687]],
#
# [[ 0.5162, -0.9078, -0.6087, -0.4061],
# [ 0.4658, 0.1827, 0.2341, -0.1813]]])
这里的关键词参数dim的理解和cat方法中有些区别。
cat方法中可以理解为原tensor的维度，dim=0，就是沿着原来的0轴进行拼接，dim=1，就是沿着原来的1轴进行拼接。
stack方法中的dim则是指向新增维度的位置，dim=0，就是在新形成的tensor的维度的第0个位置新插入维度
三、split函数
split是根据长度去拆分tensor
import torch
a=torch.randn(3,4)
print('a :')
print(a)
print("按维度0拆分 ： ")print(a.split([1,2],dim=0))
#把维度0按照长度[1,2]拆分，形成2个tensor，
#shape（1，4）和shape（2，4）
# a :
# tensor([[ 0.2626, 0.9178, -1.3622, -0.9441],
# [-0.1259, -0.3336, 0.2441, -0.2219],
# [ 1.5535, 0.7683, -1.7978, -1.1680]])
# 按维度0拆分 ：
# (tensor([[ 0.2626, 0.9178, -1.3622, -0.9441]]), tensor([[-0.1259, -0.3336, 0.2441, -0.2219],
# [ 1.5535, 0.7683, -1.7978, -1.1680]]))
print("按维度1拆分 ： ")
print(a.split([2,2],dim=1))
#把维度1按照长度[2,2]拆分，形成2个tensor，
#shape（3，2）和shape（3，2）
# a :
# tensor([[-0.5111, 1.3557, -0.1616, -0.2014],
# [-1.1011, 1.0982, -0.1794, -0.3510],
# [-1.4451, -2.1550, -1.9542, -1.6998]])
# 按维度1拆分 ：
# (tensor([[-0.5111, 1.3557],
# [-1.1011, 1.0982],
# [-1.4451, -2.1550]]), tensor([[-0.1616, -0.2014],
# [-0.1794, -0.3510],
# [-1.9542, -1.6998]]))
四、chunk函数
chunk可以理解为均等分的split，但是当维度长度不能被等分份数整除时，虽然不会报错，但可能结果与预期的不一样，建议只在可以被整除的情况下运用
import torch
a=torch.randn(4,6)
print("a :")
print(a)
#print(a.chunk(2,dim=0))
#返回2个shape（2，6）的tensor
# a :
# tensor([[ 0.8008, 0.6612, -0.0450, 0.3255, -0.4714, -0.2343],
# [ 1.3068, -0.2587, 1.4938, 0.1859, 0.4674, 0.0086],
# [ 0.4522, 0.0220, -0.2653, -0.0588, 1.1987, 0.7340],
# [ 0.1547, -0.2052, -0.8919, -0.8763, -0.6897, 0.2474]])
# (tensor([[ 0.8008, 0.6612, -0.0450, 0.3255, -0.4714, -0.2343],
# [ 1.3068, -0.2587, 1.4938, 0.1859, 0.4674, 0.0086]]), tensor([[ 0.4522, 0.0220, -0.2653, -0.0588, 1.1987, 0.7340],
# [ 0.1547, -0.2052, -0.8919, -0.8763, -0.6897, 0.2474]]))
print(a.chunk(2,dim=1))
#返回2个shape（4，3）的tensor]]))
a :
tensor([[-0.4875, 1.4914, 0.2244, -0.5883, -0.5951, -0.4857],
[-0.1344, -0.6973, -0.2042, 2.5817, -0.7972, -0.6522],
[ 1.4379, -0.1185, 0.4457, -1.1168, 1.0184, -0.5088],
[-0.7692, 1.4040, -0.2799, 1.1515, 0.2329, 0.4926]])
(tensor([[-0.4875, 1.4914, 0.2244],
[-0.1344, -0.6973, -0.2042],
[ 1.4379, -0.1185, 0.4457],
[-0.7692, 1.4040, -0.2799]]), tensor([[-0.5883, -0.5951, -0.4857],
[ 2.5817, -0.7972, -0.6522],
[-1.1168, 1.0184, -0.5088],
[ 1.1515, 0.2329, 0.4926]]))