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.
时间: 2023-06-24 08:05:01 浏览: 200
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。
相关问题
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 10, in lprnet_plate_recognition model.eval() AttributeError: 'collections.OrderedDict' object has no attribute 'eval'这段报错是什么意思
这报错意味着你的代码中使用了一个名为"model"的对象,但它是一个有序字典(OrderedDict)类型的对象,该类型没有名为"eval"的属性。这通常表示你的模型没有被正确加载或初始化。你需要检查你的代码,确保正确加载了你的模型,并且确保模型是PyTorch的nn.Module类型的对象,才能调用eval()方法。
Traceback (most recent call last): File "D:/yolov7-lpr/yolov7_plate-master/detect_rec_plate.py", line 9, in <module> from models.experimental import attempt_load File "D:\yolov7-lpr\yolov7_plate-master\models\experimental.py", line 7, in <module>
这个错误提示不够完整,无法判断具体原因。但是根据错误提示的第9行和第7行可以判断,这段代码引用了自定义的模块,并且该自定义模块依赖于其他模块或库。可能是你没有正确安装或配置这些依赖项导致的错误。建议你检查一下依赖项是否都已经正确安装,并且确认路径是否正确。如果问题仍然存在,你可以提供更详细的错误信息,以便更好地诊断问题。