We recommend using anaconda or miniconda for python. Our code has been tested with python=3.8 on linux.
Create a conda environment from the yml file and activate it.
conda env create -f environment.yml
conda activate dbviz_env
Make sure the following requirements are met
- torch>=1.8.1
- torchvision>=0.9.1
We used wandb to log most of the outputs.
conda install -c conda-forge wandb
We provide wide variety of models, please see models folder to see the exhasutive list. Train the model with following command.
python main.py --net <model_name> --set_seed <init_seed> --save_net <model_save_path> --active_log --epochs <number_epochs> --lr <suitable_learningrate>
Once you have a saved model, we save prediction arrays for this model by running the following command:
python save_preds.py --load_net /path/to/your/saved/models --epochs 500 --resolution 50
Here, epochs is a stand-in for number of runs, and resolution determines the grid resolution for sampling.
Then, we calculate the reproducibility matrix by running the following command:
python calculate_iou.py --load_net /path/to/your/saved/models
These two scripts also contain more information about the structure of saved models that is needed.