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import sys
from pathlib import Path
import hydra
import torch
from loguru import logger
project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(project_root))
from yolo.config.config import Config
from yolo.tools.data_loader import create_dataloader
from yolo.tools.dataset_preparation import prepare_dataset
from yolo.tools.trainer import ModelTrainer
from yolo.utils.logging_utils import custom_logger, validate_log_directory
@hydra.main(config_path="../yolo/config", config_name="config", version_base=None)
def main(cfg: Config):
custom_logger()
save_path = validate_log_directory(cfg.hyper.general, cfg.name)
if cfg.download.auto:
prepare_dataset(cfg.download)
dataloader = create_dataloader(cfg)
# TODO: get_device or rank, for DDP mode
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trainer = ModelTrainer(cfg, save_path, device)
trainer.train(dataloader, cfg.hyper.train.epoch)
if __name__ == "__main__":
main()
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