1) Install Dependencies

pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

Install other dependencies:

pip install -r requirements.txt

2) Running experiments

Start by running

source init_env.sh

Now, you can run experiments for the different modalities as follows:

scripts/run_ecg.py config/ecg/pretrain_viewmaker_ptb_xl_simclr.json --gpu-device 0

Scripts contributed for COS429 Final Project:

The scripts directory holds:

• run_ecg.py: for pretraining and running linear evaluation on PTB-XL with spectrogram inputs
• run_ecg_1d.py: for pretraining and running linear evaluation on PTB-XL with 1D ECG time series inputs

The config/ecg directory holds all experiment configuration files. The first field in every config file is exp_base which specifies the base directory to save experiment outputs, which you should change for your own setup.

The src/datasets directory holds:

• ptb_xl.py: for loading PTB-XL batch inputs in spectrogram format
• ptb_xl_1d.py: for loading PTB-XL batch inputs in 1D time series signal format

The src/models directory holds:

• resnet_1d.py: for running a ResNet18 on 1D inputs. Taken from 3KG codebase.
• viewmaker_1d.py: for running a Viewmaker network on 1D inputs. Inspired by resnet_1d.py.
• aug_3kg.py: for applying 3KG's benchmark augmentations. Taken directly from the original publication codebase for comparison purposes.
• vcg.py: for transforming between 1D ECG space and 3D VCG space in 3KG's implementation. Taken directly from the original publication codebase for comparison purposes.

The src/systems directory holds:

• ecg_systems.py: for initializing pretraining and transfer learning models with spectrogram inputs
• ecg_1d_systems.py: initializing pretraining and transfer learning models with 1D time series signal inputs

All WandB logged experiments can be found here: https://wandb.ai/viewmaker-ecg/ecg