Project Template

This template project is based on pytorch lightning and lightning-template. Please read the docs of them before using this template.

Installation

We recommend you use conda to install this project and all required packages with the specific version to recurrent our experiments and results of them. The following commands can be used to install this project and all required packages.

conda env create -f requirements/conda.yml -n <env_name>
conda activate <env_name>
pip install -e .

See installation docs for details.

Usage

Training

You can train the model with the following command.

CUDA_VISIBLE_DEVICES=<gpu_ids> cli fit --config-file configs/path/to/config.yaml

where the configs/path/to/config.yaml is the path to the config file you want to use.

Evaluation

You can evaluate your model trained in the previous step on validation or test dataset with the following command.

CUDA_VISIBLE_DEVICES=<gpu_ids> cli {validate, test} --config-file configs/path/to/config.yaml --ckpt_path work_dirs/<run_name>/<run_id>/checkpoints/<ckpt_name>.ckpt

where the {validate, test} determines the dataset you want to evaluate, the configs/path/to/config.yaml is the path to the config file you want to use, and the work_dirs/<run_name>/<run_id>/checkpoints/<ckpt_name>.ckpt is the path to the checkpoint you want to evaluate.

Prediction

You can predict the outcome of the clinical trials and plot the weights of the Sparse Mixture-of-Experts and Mixture-of-Experts modules with the following command.

CUDA_VISIBLE_DEVICES=<gpu_ids> cli predict --config-file configs/path/to/config.yaml --ckpt_path work_dirs/<run_name>/<run_id>/checkpoints/<ckpt_name>.ckpt

Similarly, the configs/path/to/config.yaml is the path to the config file you want to use, and the work_dirs/<run_name>/<run_id>/checkpoints/<ckpt_name>.ckpt is the path to the checkpoint you want to predict.

Contribution

See contribution docs for details.