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 torchtensor = 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 torchtensor = 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 torchtensor = 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 torchtensor_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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