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 size3x5] 0 -1 0 0 -3 0 0 0 0 0 1 0 -2 0 0
[torch.LongTensor of size3x5] -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 size3x5] 0 -1 0 0 -3 0 0 0 0 0 1 0 -2 0 0 [torch.IntTensor
of size3x5] -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 size3x5]
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 size3x5] 0 0 0 0 2 1 0 0 0 0 0 0 1 2 0 [torch.IntTensor
of size3x5]
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)
[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
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