nn-training

Code for training neural networks

Usage

1. Git fork the repo
2. Create a python/conda envrionment with python >= 3.9 (other versions might work, but I personally use python 3.9) and activate it
3. Inside the project root directory, pip install the package locally with the command

pip install -r requirements.txt -e .

4. The file src/nn_training/config.ini inside the project directory shows what paths are used by default. The code saves model checkpoints at the path indicated by the checkpoints variable. Tensorboard log files are saved at the tensorboard variable. Imagenet is assumed to be located at the imagenet variable. Change any of these variables as necessary.
5. Now you can run the code for training models. For example, to train a supervised resnet50 with factorization loss with weight 1.0 using distributed data parallel and also log training information to tensorboard, do

python main.py disentangle --co --fw_1.0 -m -t

You can see all the general training options available in src/nn_training/main.py and the options specific to factorization in src/nn_training/nn_modules/disentangle.py.