validate_cuda_device raise RuntimeError('Attempting to deserialize object on CUDA device ' RuntimeError: Attempting to deserialize object on CUDA device 3 but torch.cuda.device_count() is 1. Please use torch.load with map_location to map your storages to an existing device.
时间: 2023-10-14 12:53:11 浏览: 41
这个错误提示意味着你正在尝试将一个在 CUDA 设备上序列化的对象反序列化到另一个 CUDA 设备上,但是目标设备的数量与源设备数量不匹配。
为了解决这个问题,你可以使用 `torch.load` 函数时设置 `map_location` 参数,将序列化的对象映射到一个现有的 CUDA 设备上。你可以按照以下步骤进行操作:
1. 使用 `torch.cuda.device_count()` 函数检查当前系统中可用的 CUDA 设备数量。确保你的系统中有足够的 CUDA 设备来匹配序列化对象所需的设备数量。
2. 在 `torch.load` 函数中使用 `map_location` 参数,将序列化的对象映射到一个可用的 CUDA 设备上。例如,如果你希望将对象映射到第一个 CUDA 设备上,可以使用以下代码:
```python
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torch.load("model.pt", map_location=device)
```
这将把序列化的对象加载到指定的 CUDA 设备上,或者如果 CUDA 不可用,则加载到 CPU 上。
通过这种方式,你应该能够成功地将序列化的对象加载到指定的 CUDA 设备上,解决这个错误。
相关问题
Traceback (most recent call last): File "D:/LPRNet_Pytorch-master/LPRNet_Pytorch-master/train_LPRNet.py", line 268, in <module> train() File "D:/LPRNet_Pytorch-master/LPRNet_Pytorch-master/train_LPRNet.py", line 107, in train lprnet.load_state_dict(torch.load(args.pretrained_model)) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 608, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 787, in _legacy_load result = unpickler.load() File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 743, in persistent_load deserialized_objects[root_key] = restore_location(obj, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 175, in default_restore_location result = fn(storage, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 151, in _cuda_deserialize device = validate_cuda_device(location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 135, 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=torch.device('cpu') to map your storages to the CPU.
这个错误是因为你在尝试从一个CUDA设备上加载模型,但是当前的机器上没有GPU。你需要使用`torch.load`函数时传入`map_location=torch.device('cpu')`的参数来将模型加载到CPU上。 例如:
```python
lprnet.load_state_dict(torch.load(args.pretrained_model, map_location=torch.device('cpu')))
```
请解释一下这段报错 Traceback (most recent call last): File "D:/yolov7-lpr/yolov7_plate-master/plate_recognition/lprnet_plate_recognition.py", line 41, in <module> result = lprnet_plate_recognition("D:\yolov7-lpr\yolov7_plate-master\imgs\police.jpg", "D:\yolov7-lpr\yolov7_plate-master\weights\Final_LPRNet_model.pth") File "D:/yolov7-lpr/yolov7_plate-master/plate_recognition/lprnet_plate_recognition.py", line 9, in lprnet_plate_recognition model = torch.load(model_path) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 608, in load return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 787, in _legacy_load result = unpickler.load() File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 743, in persistent_load deserialized_objects[root_key] = restore_location(obj, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 175, in default_restore_location result = fn(storage, location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 151, in _cuda_deserialize device = validate_cuda_device(location) File "D:\Anaconda\lib\site-packages\torch\serialization.py", line 135, 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=torch.device('cpu') to map your storages to the CPU.
这个报错是因为您正在尝试在 CUDA 设备上反序列化对象,但是您的机器上没有安装 CUDA 或者没有正确配置 CUDA。您可以尝试使用 `torch.load` 并传递 `map_location=torch.device('cpu')` 参数来将模型加载到 CPU 上进行运算。例如,您可以尝试使用以下代码进行模型加载:
```python
model = torch.load(model_path, map_location=torch.device('cpu'))
```
这将把模型加载到 CPU 上,即使您的机器本来是支持 CUDA 的。这样,您就可以在不需要 CUDA 的机器上运行代码。