PyTorch中torch.tensor()和torch.to_tensor()的区别

# 如下写法报错
 feature = torch.tensor(image, dtype=torch.float32)
 
# 改为
feature = torch.as_tensor(image, dtype=torch.float32)

new_data = torch.as_tensor(data, dtype=None,device=None)->Tensor

import numpy
import torch

a = numpy.array([1, 2, 3])
t = torch.as_tensor(a)
t[0] = -1

print(a)   # [-1  2  3]
print(a.dtype)   # int64
print(t)   # tensor([-1,  2,  3])
print(t.dtype)   # torch.int64

import numpy
import torch

a = torch.tensor([1, 2, 3], device=torch.device('cuda'))
t = torch.as_tensor(a)
t[0] = -1

print(a)   # tensor([-1,  2,  3], device='cuda:0')
print(t)   # tensor([-1,  2,  3], device='cuda:0')

import numpy
import torch

import numpy
a = numpy.array([1, 2, 3])
t = torch.as_tensor(a, device=torch.device('cuda'))
t[0] = -1

print(a)   # [1 2 3]
print(a.dtype)   # int64
print(t)   # tensor([-1,  2,  3], device='cuda:0')
print(t.dtype)   # torch.int64

import numpy
import torch

a = numpy.array([1, 2, 3])
t = torch.as_tensor(a, dtype=torch.float32)
t[0] = -1

print(a)   # [1 2 3]
print(a.dtype)   # int64
print(t)   # tensor([-1.,  2.,  3.])
print(t.dtype)   # torch.float32

# 原数据类型是：tensor 会发出警告
import numpy
import torch

a = torch.tensor([1, 2, 3], device=torch.device('cuda'))
t = torch.tensor(a)
t[0] = -1

print(a)
print(t)

# 输出：
# tensor([1, 2, 3], device='cuda:0')
# tensor([-1,  2,  3], device='cuda:0')
# /opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).

# 原数据类型是：list, tuple, NumPy ndarray, scalar, and other types， 没警告
import torch
import numpy

a =  numpy.array([1, 2, 3])
t = torch.tensor(a) 

b = [1,2,3]
t= torch.tensor(b)

c = (1,2,3)
t= torch.tensor(c)