Introduction

As you dive deeper into the world of PyTorch, you'll quickly realize the importance of saving and loading models. Whether you're training a model for days or want to share your work with others, understanding how to persist your models is crucial. In this blog post, we'll explore various techniques for saving and loading PyTorch models, from simple methods to more advanced approaches.

Basic Model Saving and Loading

Saving a Model

The most straightforward way to save a PyTorch model is by using torch.save(). This function serializes the entire model or just its state dictionary.

import torch
import torch.nn as nn

# Define a simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(10, 5)

    def forward(self, x):
        return self.fc(x)

# Create an instance of the model
model = SimpleModel()

# Save the entire model
torch.save(model, 'simple_model.pth')

# Save only the model's state dictionary
torch.save(model.state_dict(), 'simple_model_state_dict.pth')

Loading a Model

To load a model, you can use torch.load(). If you saved the entire model, you can load it directly. If you saved only the state dictionary, you'll need to create an instance of the model first and then load the state dictionary.

# Load the entire model
loaded_model = torch.load('simple_model.pth')

# Load the state dictionary
model = SimpleModel()
model.load_state_dict(torch.load('simple_model_state_dict.pth'))

Best Practices for Model Persistence

Use State Dictionaries

Saving and loading state dictionaries is generally preferred over saving entire models. This approach is more flexible and allows for easier sharing and versioning of models.

Include Metadata

When saving models, it's often useful to include additional metadata such as the epoch number, loss, and other relevant information.

torch.save({
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}, 'checkpoint.pth')

To load this checkpoint:

checkpoint = torch.load('checkpoint.pth')
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']

Advanced Techniques

Saving and Loading Models with Custom Classes

If your model uses custom classes or functions, you'll need to make sure these are available when loading the model. One way to do this is by including the necessary imports in your loading script.

Handling Device Compatibility

When loading a model saved on a different device (e.g., GPU to CPU), you may need to specify the map_location parameter:

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = torch.load('model.pth', map_location=device)

Saving and Loading Models for Inference

When preparing a model for inference, you might want to convert it to a traced or scripted model using TorchScript:

# Trace the model
traced_model = torch.jit.trace(model, torch.randn(1, 10))
torch.jit.save(traced_model, 'traced_model.pt')

# Load the traced model
loaded_traced_model = torch.jit.load('traced_model.pt')

Common Pitfalls and How to Avoid Them

1. Forgetting to set the model to eval mode: Always remember to call model.eval() before performing inference.

2. Not handling custom layers properly: If your model contains custom layers, make sure to implement __getstate__ and __setstate__ methods for proper serialization.

3. Ignoring version compatibility: Be aware of PyTorch version differences when sharing models between environments.

Conclusion

Mastering the art of saving and loading PyTorch models is essential for any serious deep learning practitioner. By following the techniques and best practices outlined in this guide, you'll be well-equipped to manage your models effectively, whether you're working on a personal project or collaborating with a team.