The overall structure: 1. Data is in the home directory. 2. Scripts: testes' scripts pretrain.sh: setting up cuda environment, sending what GPU cards are assigned; call run.py 3. run.py: Is called by shell scripts (e.g., pretrain.sh) to run experiments. Main job: call appropriate experiment main.py file and pass on env args. (1) Get config file & validate settings (2) Parse GPU device info (3) Set experiment seed (4) Run experiment via the corresponding emain.py 4. experiments/pretrain/emain.py: is called by run.py. Is just the main train loop. The main thing is setup to get all the model components. 5. lib/utils: the training utils. images.py: for visualization. statistics.py: track the experiments using weights and biases API. WandBTracker object is initialized at the beginning of the training and and is updated during the training and connected to the weights and biases database.
charzharr/3D-medseg-pretraining
Project on discovering new effective pretraining methods for 3D medical image segmentation.
Jupyter Notebook