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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 os
import sys
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
try:
import wandb
_WANDB_AVAILABLE = True
except ImportError:
_WANDB_AVAILABLE = False
wandb = None
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(args, ) -> None:
"""main
"""
dist_utils.setup_distributed(args.print_rank, args.print_method, seed=args.seed)
assert not all([args.tuning, args.resume]), \
'Only support from_scrach or resume or tuning at one time'
update_dict = yaml_utils.parse_cli(args.update)
update_dict.update({k: v for k, v in args.__dict__.items() \
if k not in ['update', ] and v is not None})
cfg = YAMLConfig(args.config, **update_dict)
if args.resume or args.tuning:
if 'HGNetv2' in cfg.yaml_cfg:
cfg.yaml_cfg['HGNetv2']['pretrained'] = False
# Initialize wandb if enabled and on main process
wandb_run = None
if _WANDB_AVAILABLE and args.use_wandb and dist_utils.is_main_process():
# Override wandb enabled setting from CLI
if not cfg.wandb:
cfg.wandb = {}
cfg.wandb['enabled'] = True
if args.wandb_id:
cfg.wandb['id'] = args.wandb_id
wandb_run = wandb.init(
project=cfg.wandb.get('project', 'DEIM-detection'),
entity=cfg.wandb.get('entity', None),
name=cfg.wandb.get('name', None),
id=cfg.wandb.get('id', None),
config=cfg.yaml_cfg,
resume='auto' if cfg.wandb.get('id') else False,
reinit=True,
tags=cfg.wandb.get('tags', []),
notes=cfg.wandb.get('notes', None),
group=cfg.wandb.get('group', None),
job_type=cfg.wandb.get('job_type', 'training'),
anonymous=cfg.wandb.get('anonymous', None),
mode=cfg.wandb.get('mode', 'online'),
save_code=cfg.wandb.get('save_code', True),
)
cfg.wandb_run = wandb_run
print("WandB initialized successfully!")
elif args.use_wandb and not _WANDB_AVAILABLE:
print("Warning: --use-wandb specified but wandb is not available. Please install wandb.")
elif args.use_wandb and not dist_utils.is_main_process():
print("WandB will only log from main process in distributed training.")
print('cfg: ', cfg.__dict__)
solver = TASKS[cfg.yaml_cfg['task']](cfg)
if args.test_only:
solver.val()
else:
solver.fit()
# Finish wandb run
if wandb_run is not None:
wandb_run.finish()
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,)
# wandb arguments
parser.add_argument('--use-wandb', action='store_true', help='enable wandb logging')
parser.add_argument('--wandb-id', type=str, help='resume existing run id')
parser.add_argument('--enable-vis', action='store_true', help='enable visualization of augmented images during training')
# 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)