QD-RetNet/main.py at main · ashutoshkr45/QD-RetNet · GitHub

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import os
import random
import torch
import numpy as np
import argparse
from data.DataLoaders import MultiDataLoader_I, MultiDataLoader_II
from dml_trainer import MutualTrainer
from utils import load_config, splitprint, setup_logging


def parse_args():
    parser = argparse.ArgumentParser(description="training")
    parser.add_argument("--train_collection", type=str,
                        default='image_data/topcon-mm/train',
                        help="train collection path")
    parser.add_argument("--val_collection", type=str,
                        default='image_data/topcon-mm/val',
                        help="val collection path")
    parser.add_argument("--test_collection", type=str,
                        default='image_data/topcon-mm/test',
                        help="test collection path")
    parser.add_argument("--print_freq", default=30, type=int, help="print frequent (default: 30)")
    parser.add_argument("--model_configs", type=str, default='config.py',
                        help="filename of the model configuration file.")
    parser.add_argument("--run_id", default=0, type=int, help="run_id (default: 0)")
    parser.add_argument('--device_id', default='0,1', type=str, help='select device id')
    parser.add_argument('--device', default='cuda', type=str, help='device: cuda or cpu')
    parser.add_argument("--num_workers", default=4, type=int, help="number of threads for sampling. (default: 0)")
    parser.add_argument("--overwrite", default=True, type=bool, help="overwrite existing files")
    parser.add_argument("--checkpoint_f", default="image_data/topcon-mm/train/models/val/config_fundus.py_s1/run_0/best_model.pth",
                         type=str, help="fundus model checkpoint path")
    parser.add_argument("--checkpoint_o", default="image_data/topcon-mm/train/models/val/config_oct.py_s2/run_0/best_model.pth",
                         type=str, help="oct model checkpoint path")
    parser.add_argument("--batch_size", default=32, type=int, help="size of a batch")
    parser.add_argument("--distill_epoch", default=0, type=float, help="epoch to start distillation")
    parser.add_argument("--seed", default=100, type=int)
    parser.add_argument("--temperature", default=4, type=float)
    parser.add_argument("--alpha", default=2, type=float)
    parser.add_argument("--beta", default=1, type=float)
    args = parser.parse_args()
    return args


def main(opts):
    # Set up logging
    log_filename = setup_logging()
    print(f"Logging training information to: {log_filename}\n")

    # load model configs
    configs = load_config(opts.model_configs)

    splitprint()

    # Set GPU devices to use
    os.environ['CUDA_VISIBLE_DEVICES'] = opts.device_id

    # Print GPU info
    print(f"Using CUDA_VISIBLE_DEVICES={os.environ.get('CUDA_VISIBLE_DEVICES')}")
    if torch.cuda.is_available():
        print(f"Number of CUDA devices available: {torch.cuda.device_count()}")
        for i in range(torch.cuda.device_count()):
            print(f"Device {i}: {torch.cuda.get_device_name(i)}")
    else:
        print("No CUDA devices available")

    device = torch.device(opts.device if torch.cuda.is_available() else "cpu")
    torch.backends.cudnn.benchmark = True

    # get trainset and testset dataloaders
    data_initializer1 = MultiDataLoader_I(opts, configs)
    train_loader1, val_loader1, test_loader1 = data_initializer1.get_training_dataloader()

    data_initializer2 = MultiDataLoader_II(opts, configs)
    train_loader2, val_loader2, test_loader2 = data_initializer2.get_training_dataloader()

    splitprint()

    trainer = MutualTrainer(configs, opts, device)

    trainer.train(train_loader_I=train_loader1,
                  test_loader_I=test_loader1,
                  train_loader_II=train_loader2,
                  test_loader_II=test_loader2)

    print(f"\nTraining completed.")
    print(f"All training logs have been saved to: {log_filename}")


if __name__ == "__main__":
    opts = parse_args()

    random.seed(opts.seed)
    np.random.seed(opts.seed)
    torch.manual_seed(opts.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(opts.seed)
    main(opts)