Summary

主要包括以下三种途径：

1. 使用独立的函数；
2. 使用torch.type()函数；
3. 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)

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-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]

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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)

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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)

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-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]

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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)

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[1] pytorch张量torch.Tensor类型的构建与相互转换以及torch.type()和torch.type_as()的用法