PyTorch中torch.tensor()和torch.to_tensor()的区别
Enzo 想砸电脑 人气:0前言
在跑模型的时候,遇到如下报错
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).
网上查了一下,发现将 torch.tensor()
改写成 torch.as_tensor()
就可以避免报错了。
# 如下写法报错 feature = torch.tensor(image, dtype=torch.float32) # 改为 feature = torch.as_tensor(image, dtype=torch.float32)
然后就又仔细研究了下 torch.as_tensor()
和 torch.tensor()
的区别,在此记录。
1、torch.as_tensor()
new_data = torch.as_tensor(data, dtype=None,device=None)->Tensor
作用:生成一个新的 tensor, 这个新生成的tensor 会根据原数据的实际情况,来决定是进行浅拷贝,还是深拷贝。当然,会优先浅拷贝,浅拷贝会共享内存,并共享 autograd 历史记录。
情况一:数据类型相同 且 device相同,会进行浅拷贝,共享内存
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')
情况二: 数据类型相同,但是device不同,深拷贝,不再共享内存
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
情况三:device相同,但数据类型不同,深拷贝,不再共享内存
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
2、torch.tensor()
torch.tensor()
是深拷贝方式。
torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False)
深拷贝:会拷贝 数据类型 和 device,不会记录 autograd 历史 (also known as a “leaf tensor” 叶子tensor)
重点是:
- 如果原数据的数据类型是:list, tuple, NumPy ndarray, scalar, and other types,不会 waring
- 如果原数据的数据类型是:tensor,使用 torch.tensor(data) 就会报waring
# 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)
结论就是:以后尽量用 torch.as_tensor()
吧
总结
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