CUDA报错：RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available()

问题

Traceback (most recent call last):
  File "test.py", line 22, in <module>
    model = loadmodel()
  File "/home/joshuayun/Desktop/IBD/loader/model_loader.py", line 48, in loadmodel
    checkpoint = torch.load(settings.MODEL_FILE)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 387, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 574, in _load
    result = unpickler.load()
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 537, in persistent_load
    deserialized_objects[root_key] = restore_location(obj, location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 119, in default_restore_location
    result = fn(storage, location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 95, in _cuda_deserialize
    device = validate_cuda_device(location)
  File "/home/joshuayun/.local/lib/python3.6/site-packages/torch/serialization.py", line 79, in validate_cuda_device
    raise RuntimeError('Attempting to deserialize object on a CUDA '
    
RuntimeError: Attempting to deserialize object on a CUDA device but 
  torch.cuda.is_available() is False. If you are running on a CPU-only machine, 
  please use torch.load with map_location='cpu' to map your storages to the CPU.

原因

pytorch中model在加载预训练模型时，保存的模型未读取到对应合适位置；

本文仅给出机器只有CPU情形，而保存的模型是使用CUDA格式，无法正常读取，还有其他情形：

1. 模型cpu，读取到gpu
2. 模型gpu，读取到cpu
3. 模型gpu1，读取到gpu2

解决方案

在读取模型时，指定准确硬件即可：

model = torch.load('model/pytorch_resnet50.pth',map_location ='cpu')

model = torch.load('model/pytorch_resnet50.pth',map_location ='cuda:0')

model = torch.load('model/pytorch_resnet50.pth',map_location ={'cuda:0':'cuda:1'})

注意读取模型时，是设置torch.load中读取位置，而不是model.load_state_dict。