RuntimeError: logs/ep500-loss1.779-val_loss2.101.pth is a zip archive (did you mean to use torch.jit.load()?)
时间: 2024-04-15 07:30:10 浏览: 14
这个错误是由于使用了错误的加载函数导致的。根据错误提示,你可能使用了torch.load()函数来加载模型参数,但是文件实际上是一个压缩文件(zip归档文件)。正确的加载方式是使用torch.jit.load()函数来加载压缩文件。你可以尝试使用以下代码来加载模型:
```python
import torch
model = torch.jit.load('logs/ep500-loss1.779-val_loss2.101.pth')
```
请确保你已经安装了torch和torchvision库,并且将文件路径替换为你实际的文件路径。
相关问题
RuntimeError: log/sem_seg/pointnet2_sem_seg//checkpoints/best_model.pth is a zip archive (did you mean to use torch.jit.load()?)
This error message suggests that the PyTorch model checkpoint file 'best_model.pth' is a zip archive, which is not the expected format for loading a PyTorch model checkpoint. The error message also suggests using 'torch.jit.load()' instead of the current method of loading the model checkpoint.
To resolve this issue, you can try using 'torch.jit.load()' to load the model checkpoint. This function is specifically designed for loading PyTorch models that have been serialized using the JIT compiler. Here's an example of how you can use 'torch.jit.load()' to load a model checkpoint:
```
import torch
# Specify the path to the model checkpoint file
model_path = 'log/sem_seg/pointnet2_sem_seg/checkpoints/best_model.pth'
# Load the model checkpoint using torch.jit.load()
model = torch.jit.load(model_path)
# Use the loaded model for inference or further training
```
Note that you may need to adjust the code above depending on the specifics of your application and the structure of your model checkpoint file.
怎么解决RuntimeError: ./data/Prior.ckpt is a zip archive (did you mean to use torch.jit.load()?)
这个错误通常是因为使用了错误的方法加载模型。正确的方法是使用`torch.jit.load()`方法加载模型,而不是使用`torch.load()`方法。`torch.jit.load()`方法是用于加载序列化的TorchScript模型的,而`torch.load()`方法是用于加载Python模型的。如果您使用的是Python模型,则需要在保存模型时使用`torch.save()`将其保存,并在加载模型时使用`torch.load()`加载它。如果您使用的是TorchScript模型,则需要使用`torch.jit.save()`将其保存,并在加载模型时使用`torch.jit.load()`加载它。
如果您已经使用了`torch.load()`方法加载模型,您可以尝试使用`torch.jit.load()`方法来加载模型,例如:
```
model = torch.jit.load('model.pt')
```
如果您仍然遇到问题,请检查您的模型文件是否已经被压缩成.zip文件,如果是,请解压缩它并使用正确的方法加载模型。