Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Solution writeup: https://www.kaggle.com/c/g2net-gravitational-wave-detection/discussion/275341

Instructions

1. Download data

You have to download the competition dataset from competition website, and place the files in input/ directory.

┣ input/
┃   ┣ training_labels.csv
┃   ┣ sample_submission.csv
┃   ┣ train/
┃   ┣ test/
┃
┣ configs.py
┣ ...

(Optional:) Add your hardware configurations

# configs.py
HW_CFG = {
    'RTX3090': (16, 128, 1, 24), # CPU count, RAM amount(GB), GPU count, GPU RAM(GB)
    'A100': (9, 60, 1, 40), 
    'Your config', (128, 512, 8, 40) # add your hardware config!
}

2. Setup python environment

conda

conda env create -n kumaconda -f=environment.yaml
conda activate kumaconda

docker

WIP

3. Prepare data

Two new files - input/train.csv and input/test/.csv will be created.

python prep_data.py

(Optional:) Prepare waveform cache

Optionally you can speed up training by making waveform cache.
This is not recommend if your machine has RAM size smaller than 32GB.
input/train_cache.pickle and input/test_cache.pickle will be created.

python prep_data.py --cache

Then, add cache path to Baseline class in configs.py.

# configs.py
class Baseline:
    name = 'baseline'
    seed = 2021
    train_path = INPUT_DIR/'train.csv'
    test_path = INPUT_DIR/'test.csv'
    train_cache = INPUT_DIR/'train_cache.pickle' # here
    test_cache = INPUT_DIR/'test_cache.pickle' # here
    cv = 5

4. Train nueral network

Each experiment class has a name (e.g. name for Nspec16 is nspec_16).
Outputs of an experiment are

• outoffolds.npy : (train size, 1) np.float32
• predictions.npy : (cv fold, test size, 1) np.float32
• {name}_{timestamp}.log : training log
• foldx.pt : pytorch checkpoint

All outputs will be created in results/{name}/.

python train.py --config {experiment class}
# [Options]
# --progress_bar    : Everyone loves progress bar
# --inference       : Run inference only
# --tta             : Run test time augmentations (FlipWave)
# --limit_fold x    : Train a single fold x. You must run inference again by yourself.

5. Train neural network again (pseudo-label)

For experiments with name starting with Pseudo, you must use train_pseudo.py.
Outputs and options are the same as train.py.
Make sure the dependent experiment (see the table below) was successfully run.

python train_pseudo.py --config {experiment class}

Experiments