DEIM/train.py at main · logivations/DEIM · GitHub

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"""
DEIM: DETR with Improved Matching for Fast Convergence
Copyright (c) 2024 The DEIM Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
Copyright (c) 2023 lyuwenyu. All Rights Reserved.
"""
import json
import os
import sys
import pickle
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))

import argparse

from engine.misc import dist_utils
from engine.core import YAMLConfig, yaml_utils
from engine.solver import TASKS
from tools.utils import apply_ls_params
debug=False

if debug:
    import torch
    def custom_repr(self):
        return f'{{Tensor:{tuple(self.shape)}}} {original_repr(self)}'
    original_repr = torch.Tensor.__repr__
    torch.Tensor.__repr__ = custom_repr

def main(cmd_args: argparse.Namespace, ) -> None:
    """main
    """
    dist_utils.setup_distributed(cmd_args.print_rank, cmd_args.print_method, seed=cmd_args.seed)

    assert not all([cmd_args.tuning, cmd_args.resume]), \
        'Only support from_scrach or resume or tuning at one time'


    update_dict = yaml_utils.parse_cli(cmd_args.update)
    update_dict.update({k: v for k, v in cmd_args.__dict__.items() \
        if k not in ['update', ] and v is not None})

    cfg = YAMLConfig(cmd_args.config, **update_dict)

    if cmd_args.resume or cmd_args.tuning:
        if 'HGNetv2' in cfg.yaml_cfg:
            cfg.yaml_cfg['HGNetv2']['pretrained'] = False

    cfg = apply_ls_params(cmd_args, cfg)
    print('cfg: ', cfg.__dict__)

    solver = TASKS[cfg.yaml_cfg['task']](cfg)

    if cmd_args.test_only:
        solver.val()
    else:
        solver.fit()

    dist_utils.cleanup()


if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    # priority 0
    parser.add_argument('-c', '--config', type=str, required=True)
    parser.add_argument('-r', '--resume', type=str, help='resume from checkpoint')
    parser.add_argument('-t', '--tuning', type=str, help='tuning from checkpoint')
    parser.add_argument('-d', '--device', type=str, help='device',)
    parser.add_argument('--seed', type=int, help='exp reproducibility')
    parser.add_argument('--use-amp', action='store_true', help='auto mixed precision training')
    parser.add_argument('--output-dir', type=str, help='output directoy')
    parser.add_argument('--summary-dir', type=str, help='tensorboard summry')
    parser.add_argument('--test-only', action='store_true', default=False,)
    # LabelStudio
    parser.add_argument('--train-epochs', type=int, default=72,)
    parser.add_argument(
        '--training-res',
        type=int,
        default=[512, 512],
        nargs='+',
        help='Training resolution')

    # priority 1
    parser.add_argument('-u', '--update', nargs='+', help='update yaml config')

    # env
    parser.add_argument('--print-method', type=str, default='builtin', help='print method')
    parser.add_argument('--print-rank', type=int, default=0, help='print rank id')

    parser.add_argument('--local-rank', type=int, help='local rank id')
    args = parser.parse_args()

    main(args)