pytorch中tensor张量数据类型的转化方式

tensor ----->numpy

import torch
a=torch.ones([2,5])

tensor([[1., 1., 1., 1., 1.],
    [1., 1., 1., 1., 1.]])
# **********************************    
b=a.numpy()

array([[1., 1., 1., 1., 1.],
    [1., 1., 1., 1., 1.]], dtype=float32)

numpy ----->tensor

import numpy as np
a=np.ones([2,5])

array([[1., 1., 1., 1., 1.],
    [1., 1., 1., 1., 1.]])
# **********************************    
b=torch.from_numpy(a)

tensor([[1., 1., 1., 1., 1.],
    [1., 1., 1., 1., 1.]], dtype=torch.float64)

tensor—>list

a=torch.ones([1,5])

tensor([[1., 1., 1., 1., 1.]])
# ***********************************
b=a.tolist()

[[1.0, 1.0, 1.0, 1.0, 1.0]]



list—>tensor

a=list(range(1,6))

[1, 2, 3, 4, 5]
# **********************************
b=torch.tensor(a)

tensor([1, 2, 3, 4, 5])

tensor = torch.Tensor(3, 5)

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

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

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

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

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

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

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

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

>>> a=torch.Tensor(2,5)
>>> a
tensor([[1.9431e-19, 4.8613e+30, 1.4603e-19, 2.0704e-19, 4.7429e+30],
    [1.6530e+19, 1.8254e+31, 1.4607e-19, 6.8801e+16, 1.8370e+25]])
>>> b=torch.IntTensor(1,2)
>>> b
tensor([[16843009,    1]], dtype=torch.int32)
>>> a.type_as(b)
tensor([[     0, -2147483648,      0,      0, -2147483648],
    [-2147483648, -2147483648,      0, -2147483648, -2147483648]],
    dtype=torch.int32)
>>> a
tensor([[1.9431e-19, 4.8613e+30, 1.4603e-19, 2.0704e-19, 4.7429e+30],
    [1.6530e+19, 1.8254e+31, 1.4607e-19, 6.8801e+16, 1.8370e+25]])