Metadata-Version: 2.1
Name: torch-featurelayer
Version: 0.1.1
Summary: Useful utility functions and wrappers for hooking onto layers within PyTorch models for feature extraction.
Author-email: spencerwooo <spencer.woo@outlook.com>
License: MIT License
        
        Copyright (c) 2024 Spencer (Shangbo Wu)
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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Project-URL: Repository, https://github.com/spencerwooo/torch-featurelayer
Project-URL: Documentation, https://github.com/spencerwooo/torch-featurelayer/blob/main/README.md
Keywords: torch,pytorch,torchvision,feature-extraction
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: <3.13,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch <2.3,>=2.0
Provides-Extra: dev
Requires-Dist: mypy ; extra == 'dev'
Requires-Dist: ruff ; extra == 'dev'
Provides-Extra: test
Requires-Dist: torchvision <0.18,>=0.15 ; extra == 'test'

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# torch-featurelayer

🧠 Simple utility functions and wrappers for hooking onto layers within PyTorch models for feature extraction.

> [!TIP]
> This library is intended to be a simplified and well-documented implementation for extracting a PyTorch model's intermediate layer output(s). For a more sophisticated and complete implementation, either consider using [`torchvision.models.feature_extraction`](https://pytorch.org/vision/stable/feature_extraction.html), or check the official [`torch.fx`](https://pytorch.org/docs/stable/fx.html). 

## Usage

```python
import torch
from torchvision.models import vgg11

from torch_featurelayer import FeatureLayer

# Load a pretrained VGG-11 model
model = vgg11(weights='DEFAULT').eval()

# Hook onto layer `features.15` of the model
layer_path = 'features.15'
hooked_model = FeatureLayer(model, layer_path)

# Forward pass an input tensor through the model
x = torch.randn(1, 3, 224, 224)
feature_output, output = hooked_model(x)

# Print the output shape
print(f'Feature layer output shape: {feature_output.shape}')  # [1, 512, 14, 14]
print(f'Model output shape: {output.shape}')  # [1, 1000]
```

Check the [examples](./examples/) directory for more.

## API

> `torch_featurelayer.FeatureLayer(model: torch.nn.Module, feature_layer_path: str)`

> `torch_featurelayer.FeatureLayers(model: torch.nn.Module, feature_layer_paths: list[str])`

> `torch_featurelayer.get_layer_candidates(module: nn.Module, max_depth: int = 1)`

## License

[MIT](./LICENSE)
