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Conflicting bundles

This is the original implementation of the conflicting bundles paper.

@inproceedings{peer2021conflicting,
  title={Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks},
  author={Peer, David and Stabinger, Sebastian and Rodriguez-Sanchez, Antonio},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={256--265},
  year={2021}
}

Dependencies

For this installation we assume that python3, pip3 and all nvidia drivers (GPU support) are already installed. Then execute the following to create a virtual environment and install all necessary dependencies:

  1. Create virtual environment: python3 -m venv env
  2. Activate venv: source env/bin/activate
  3. Update your pip installation: pip3 install --upgrade pip
  4. Install all requirements. Use requirements-gpu if a gpu is available, requirements-cpu otherwise: pip3 install -r requirements.txt

Note: If the dataset is not available in your home dir, it will be downloaded automatically and can take a few minutes.

Execute

We provide a run.sh file to show how all experiments can be trained and evaluated. Note that it is important to train the model to check reproducibility and therefore, no checkpoints are provided.

Training

Models are either trained on a single or on multiple gpu's. A checkpoint is created after each epoch to be able to evaluate the test-accuracy and to evaluate the bundle entropy. Therefore, about 2TB of storage are needed. Details of all parameters that are available can be found in config.py.

Evaluation

To evaluate a model trained with fixed architecture setups or for a model pruned with auto-tune, the evaluate.py script can be used. Results are written into a csv files to e.g. create graphs automatically or also tensorboard can be used to show all results (gradients, bundle entropy etc.). Per default only the last layer L is evaluated. To evaluate the bundle entropy for each layer set --all_conflict_layers="True" . Details of all parameters that are available can be found in config.py.