pytorch: tensor类型的构建与相互转换实例


            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)





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