train.py

import argparse
from trainer import Trainer
import torch
import numpy as np
import pandas as pd

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='SAILER')
    parser.add_argument('--name', default='main', type=str, help='name of the experiment')
    parser.add_argument('--log', default='train_log.csv', type=str, help='name of log file')
    parser.add_argument('-l', '--load_ckpt', default=False, type=str, help='path to ckpt loaded')
    parser.add_argument('-cuda', '--cuda_dev', default=[0], type=int, help='GPU want to use')
    parser.add_argument('--sample_batch', default=False, type=bool, help='Add batch effect correction')
    parser.add_argument('--max_epoch', default=400, type=int, help='maximum training epoch')
    parser.add_argument('--start_epoch', default=0, type=int, help='starting epoch')
    parser.add_argument('-b', '--batch_size', default=100, type=int, help='batch size')
    parser.add_argument('--start_save', default=350, type=int, help='epoch starting to save models')
    parser.add_argument('--conv', default=False, type=bool, help='use conv vae')
    parser.add_argument('--model_type', default='inv', type=str, help='model type')
    parser.add_argument('-d', '--data_type', type=str, help='dataset')
    parser.add_argument('--lr', default=1e-3, type=float, help='learning rate')
    parser.add_argument('--pos_w', default=30, type=float, help='BCE positive weight')
    parser.add_argument('--weight_decay', default=5e-4, type=str, help='weight decay for adam')
    parser.add_argument('--z_dim', default=10, type=int, help='latent dim')
    parser.add_argument('--out_every', default=1, type=int, help='save ckpt every x epoch')
    parser.add_argument('--ckpt_dir', default='./models/', type=str, help='out directory')
    parser.add_argument('--setting', default=2, type=int, help='setting for sim data')
    parser.add_argument('--signal', default=0.35, type=float, help='signal to noise ratio')
    parser.add_argument('--frags', default=3000, type=int, help='num of fragments')
    parser.add_argument('--bin_size', default=10000, type=int, help='size of each bin')
    parser.add_argument('--LAMBDA', default=1, type=float, help='lambda value')
    args = parser.parse_args()

    solver = Trainer(args)
    solver.warm_up()
    solver.inv_train()