torch.load加载本地pth模型

Daniel Parker

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25-02-2025

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Loading Local PTH Models with torch.load: A Comprehensive Guide

PyTorch's .pth files are the standard way to save and load trained models. This article provides a thorough guide on how to effectively load local .pth model files using torch.load. We'll cover various scenarios and best practices to ensure smooth model loading and prevent common pitfalls. Understanding this process is crucial for any PyTorch developer.

Understanding torch.load

The core function for loading PyTorch models is torch.load. This function reads serialized objects from a file (typically a .pth file), reconstructing the model's architecture and weights. The simplicity of this function belies its importance in the PyTorch workflow. Loading your trained model correctly is essential for inference or further training.

Basic Model Loading

The simplest way to load a model is using the torch.load function directly:

import torch

# Path to your saved model
model_path = 'path/to/your/model.pth'

# Load the model
model = torch.load(model_path)

# Now you can use the loaded model
# ... your code to utilize the model ...

Important Considerations: This method assumes your .pth file contains the entire model state dictionary. This is the most common approach, but more complex situations require additional steps.

Handling Model Architecture Separately

Sometimes, the model's architecture is defined separately from the weight parameters. In this case, you need to first define your model architecture and then load the weights into it:

import torch

# Define your model architecture
class MyModel(torch.nn.Module):
    # ... your model definition ...
    pass

model = MyModel()

# Load the state dictionary
model_path = 'path/to/your/model.pth'
model_state_dict = torch.load(model_path)['state_dict'] #assuming the weights are stored under the key 'state_dict'

# Load the weights
model.load_state_dict(model_state_dict)

# Ensure the model is in evaluation mode for inference
model.eval()

# Now you can use the loaded model
# ... your code to utilize the model ...

This approach provides better separation of concerns and allows for easier model versioning and reproducibility. Always check the contents of your .pth file to understand how the model and its weights are stored.

Loading on Different Devices (CPU vs. GPU)

The location of your model (CPU or GPU) during saving affects how you load it. If you saved the model on a GPU, attempting to load it directly onto a CPU might cause an error. The map_location argument in torch.load addresses this:

import torch

model_path = 'path/to/your/model.pth'

# Load the model onto the CPU, even if it was saved on a GPU
model = torch.load(model_path, map_location=torch.device('cpu'))

# Alternatively, load onto a specific GPU (if available)
# model = torch.load(model_path, map_location=torch.device('cuda:0'))

map_location ensures compatibility across different hardware setups. Always specify the target device to avoid runtime errors.

Troubleshooting Common Issues

• RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False.: This indicates you're trying to load a GPU-saved model onto a system without a compatible CUDA setup. Use map_location=torch.device('cpu').

• KeyError: 'state_dict': This typically means the saved .pth file doesn't contain a key named 'state_dict'. Check the structure of your saved file using a text editor or Python's print(torch.load(model_path)). Adjust your loading code accordingly.

• RuntimeError: Error(s) in loading state_dict for MyModel:: This points to a mismatch between your defined model architecture and the weights in the .pth file. Double-check that the architecture is identical to the one used during training. Inconsistent layer names or structures are common causes.

Best Practices

• Always save the model architecture: This ensures reproducibility and avoids potential issues due to architecture mismatches.

• Use descriptive filenames: Clearly label your .pth files with relevant information (e.g., model type, dataset, training parameters).

• Version control: Use Git or a similar version control system to track changes to your models and training scripts.

By following these guidelines and troubleshooting tips, you can confidently load your local .pth PyTorch models and seamlessly integrate them into your projects. Remember to always adapt the code snippets to match the specific structure of your saved model files.