【python】pytorch: tensor类型的构建与相互转换

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我是靠谱客的博主迷路画板，最近开发中收集的这篇文章主要介绍【python】pytorch: tensor类型的构建与相互转换，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

Summary
主要包括以下三种途径：

使用独立的函数；
使用torch.type()函数；
使用type_as(tesnor)将张量转换为给定类型的张量。
使用独立函数
import torch

tensor = torch.randn(3, 5)
print(tensor)

# torch.long() 将tensor投射为long类型
long_tensor = tensor.long()
print(long_tensor)

# torch.half()将tensor投射为半精度浮点类型
half_tensor = tensor.half()
print(half_tensor)

# torch.int()将该tensor投射为int类型
int_tensor = tensor.int()
print(int_tensor)

# torch.double()将该tensor投射为double类型
double_tensor = tensor.double()
print(double_tensor)

# torch.float()将该tensor投射为float类型
float_tensor = tensor.float()
print(float_tensor)

# torch.char()将该tensor投射为char类型
char_tensor = tensor.char()
print(char_tensor)

# torch.byte()将该tensor投射为byte类型
byte_tensor = tensor.byte()
print(byte_tensor)

# torch.short()将该tensor投射为short类型
short_tensor = tensor.short()
print(short_tensor)

-0.5841 -1.6370 0.1353 0.6334 -3.0761
-0.2628 0.1245 0.8626 0.4095 -0.3633
1.3605 0.5055 -2.0090 0.8933 -0.6267
[torch.FloatTensor of size 3x5]

0 -1 0 0 -3
0 0 0 0 0
1 0 -2 0 0
[torch.LongTensor of size 3x5]

-0.5840 -1.6367 0.1353 0.6333 -3.0762
-0.2627 0.1245 0.8628 0.4094 -0.3633
1.3604 0.5054 -2.0098 0.8936 -0.6265
[torch.HalfTensor of size 3x5]

0 -1 0 0 -3
0 0 0 0 0
1 0 -2 0 0
[torch.IntTensor of size 3x5]

-0.5841 -1.6370 0.1353 0.6334 -3.0761
-0.2628 0.1245 0.8626 0.4095 -0.3633
1.3605 0.5055 -2.0090 0.8933 -0.6267
[torch.DoubleTensor of size 3x5]

-0.5841 -1.6370 0.1353 0.6334 -3.0761
-0.2628 0.1245 0.8626 0.4095 -0.3633
1.3605 0.5055 -2.0090 0.8933 -0.6267
[torch.FloatTensor of size 3x5]

0 -1 0 0 -3
0 0 0 0 0
1 0 -2 0 0
[torch.CharTensor of size 3x5]

0 255 0 0 253
0 0 0 0 0
1 0 254 0 0
[torch.ByteTensor of size 3x5]

0 -1 0 0 -3
0 0 0 0 0
1 0 -2 0 0
[torch.ShortTensor of size 3x5]

其中，torch.Tensor、torch.rand、torch.randn 均默认生成 torch.FloatTensor型：

import torch

tensor = torch.Tensor(3, 5)
assert isinstance(tensor, torch.FloatTensor)

tensor = torch.rand(3, 5)
assert isinstance(tensor, torch.FloatTensor)

tensor = torch.randn(3, 5)
assert isinstance(tensor, torch.FloatTensor)

使用torch.type()函数
type(new_type=None, async=False)

import torch

tensor = torch.randn(3, 5)
print(tensor)

int_tensor = tensor.type(torch.IntTensor)
print(int_tensor)

-0.4449 0.0332 0.5187 0.1271 2.2303
1.3961 -0.1542 0.8498 -0.3438 -0.2834
-0.5554 0.1684 1.5216 2.4527 0.0379
[torch.FloatTensor of size 3x5]

0 0 0 0 2
1 0 0 0 0
0 0 1 2 0
[torch.IntTensor of size 3x5]

使用type_as(tesnor)将张量转换为给定类型的张量
import torch

tensor_1 = torch.FloatTensor(5)

tensor_2 = torch.IntTensor([10, 20])
tensor_1 = tensor_1.type_as(tensor_2)
assert isinstance(tensor_1, torch.IntTensor)

作者：JNingWei
来源：CSDN
原文：https://blog.csdn.net/jningwei/article/details/79849600