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RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models
Copyright (c) 2025 The RT-DETRv4 Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from DEIM: DETR with Improved Matching for Fast Convergence
Copyright (c) 2024 The DEIM Authors. All Rights Reserved.
"""
import time
import json
import datetime
import math
import torch
from ..misc import dist_utils, stats
from ._solver import BaseSolver
from .det_engine import train_one_epoch, evaluate
from ..optim.lr_scheduler import FlatCosineLRScheduler
class DetSolver(BaseSolver):
def fit(self, ):
self.train()
args = self.cfg
n_parameters, model_stats = stats(self.cfg)
print(model_stats)
print("-"*42 + "Start training" + "-"*43)
self.self_lr_scheduler = False
if args.lrsheduler is not None:
iter_per_epoch = len(self.train_dataloader)
print(" ## Using Self-defined Scheduler-{} ## ".format(args.lrsheduler))
self.lr_scheduler = FlatCosineLRScheduler(self.optimizer, args.lr_gamma, iter_per_epoch, total_epochs=args.epoches,
warmup_iter=args.warmup_iter, flat_epochs=args.flat_epoch, no_aug_epochs=args.no_aug_epoch)
self.self_lr_scheduler = True
n_parameters = sum([p.numel() for p in self.model.parameters() if p.requires_grad])
print(f'number of trainable parameters: {n_parameters}')
top1 = 0
best_stat = {'epoch': -1, }
# evaluate again before resume training
if self.last_epoch > 0:
module = self.ema.module if self.ema else self.model
test_stats, coco_evaluator = evaluate(
module,
self.criterion,
self.postprocessor,
self.val_dataloader,
self.evaluator,
self.device
)
for k in test_stats:
best_stat['epoch'] = self.last_epoch
best_stat[k] = test_stats[k][0]
top1 = test_stats[k][0]
print(f'best_stat: {best_stat}')
best_stat_print = best_stat.copy()
start_time = time.time()
start_epoch = self.last_epoch + 1
for epoch in range(start_epoch, args.epoches):
self.train_dataloader.set_epoch(epoch)
# self.train_dataloader.dataset.set_epoch(epoch)
if dist_utils.is_dist_available_and_initialized():
self.train_dataloader.sampler.set_epoch(epoch)
if epoch == self.train_dataloader.collate_fn.stop_epoch:
self.load_resume_state(str(self.output_dir / 'best_stg1.pth'))
self.ema.decay = self.train_dataloader.collate_fn.ema_restart_decay
print(f'Refresh EMA at epoch {epoch} with decay {self.ema.decay}')
train_stats, grad_percentages = train_one_epoch(
self.self_lr_scheduler,
self.lr_scheduler,
self.model,
self.criterion,
self.train_dataloader,
self.optimizer,
self.device,
epoch,
max_norm=args.clip_max_norm,
print_freq=args.print_freq,
ema=self.ema,
scaler=self.scaler,
lr_warmup_scheduler=self.lr_warmup_scheduler,
writer=self.writer,
teacher_model=self.teacher_model, # NEW: Pass teacher model to train_one_epoch
)
if not self.self_lr_scheduler: # update by epoch
if self.lr_warmup_scheduler is None or self.lr_warmup_scheduler.finished():
self.lr_scheduler.step()
self.last_epoch += 1
if dist_utils.is_main_process() and hasattr(self.criterion, 'distill_adaptive_params') and \
self.criterion.distill_adaptive_params and self.criterion.distill_adaptive_params.get('enabled', False):
params = self.criterion.distill_adaptive_params
default_weight = params.get('default_weight')
avg_percentage = sum(grad_percentages) / len(grad_percentages) if grad_percentages else 0.0
current_weight = self.criterion.weight_dict.get('loss_distill', 0.0)
new_weight = current_weight
reason = 'unchanged'
if avg_percentage < 1e-6:
if default_weight is not None:
new_weight = default_weight
reason = 'reset_to_default_zero_grad'
elif epoch >= self.train_dataloader.collate_fn.stop_epoch:
if default_weight is not None:
new_weight = default_weight
reason = 'ema_phase_default'
else:
rho = params['rho']
delta = params['delta']
lower_bound = rho - delta
upper_bound = rho + delta
if not (lower_bound <= avg_percentage <= upper_bound):
target_percentage = upper_bound if avg_percentage < lower_bound else lower_bound
if current_weight > 1e-6:
p_current = avg_percentage / 100.0
p_target = target_percentage / 100.0
numerator = p_target * (1.0 - p_current)
denominator = p_current * (1.0 - p_target)
if abs(denominator) >= 1e-9:
ratio = numerator / denominator
ratio = max(ratio, 0.1) # clamp non-positive to 0.1
new_weight = current_weight * ratio
new_weight = min(max(new_weight, current_weight / 10.0), current_weight * 10.0)
reason = f'adjusted_to_{target_percentage:.2f}%'
if abs(new_weight - current_weight) > 0:
self.criterion.weight_dict['loss_distill'] = new_weight
print(f"Epoch {epoch}: avg encoder grad {avg_percentage:.2f}% | distill {current_weight:.6f} -> {new_weight:.6f} ({reason})")
if self.output_dir and epoch < self.train_dataloader.collate_fn.stop_epoch:
checkpoint_paths = [self.output_dir / 'last.pth']
# extra checkpoint before LR drop and every 100 epochs
if (epoch + 1) % args.checkpoint_freq == 0:
checkpoint_paths.append(self.output_dir / f'checkpoint{epoch:04}.pth')
for checkpoint_path in checkpoint_paths:
dist_utils.save_on_master(self.state_dict(), checkpoint_path)
module = self.ema.module if self.ema else self.model
test_stats, coco_evaluator = evaluate(
module,
self.criterion,
self.postprocessor,
self.val_dataloader,
self.evaluator,
self.device
)
# TODO
for k in test_stats:
if self.writer and dist_utils.is_main_process():
for i, v in enumerate(test_stats[k]):
self.writer.add_scalar(f'Test/{k}_{i}'.format(k), v, epoch)
if k in best_stat:
best_stat['epoch'] = epoch if test_stats[k][0] > best_stat[k] else best_stat['epoch']
best_stat[k] = max(best_stat[k], test_stats[k][0])
else:
best_stat['epoch'] = epoch
best_stat[k] = test_stats[k][0]
if best_stat[k] > top1:
best_stat_print['epoch'] = epoch
top1 = best_stat[k]
if self.output_dir:
if epoch >= self.train_dataloader.collate_fn.stop_epoch:
dist_utils.save_on_master(self.state_dict(), self.output_dir / 'best_stg2.pth')
else:
dist_utils.save_on_master(self.state_dict(), self.output_dir / 'best_stg1.pth')
best_stat_print[k] = max(best_stat[k], top1)
print(f'best_stat: {best_stat_print}') # global best
if best_stat['epoch'] == epoch and self.output_dir:
if epoch >= self.train_dataloader.collate_fn.stop_epoch:
if test_stats[k][0] > top1:
top1 = test_stats[k][0]
dist_utils.save_on_master(self.state_dict(), self.output_dir / 'best_stg2.pth')
else:
top1 = max(test_stats[k][0], top1)
dist_utils.save_on_master(self.state_dict(), self.output_dir / 'best_stg1.pth')
elif epoch >= self.train_dataloader.collate_fn.stop_epoch:
best_stat = {'epoch': -1, }
self.ema.decay -= 0.0001
self.load_resume_state(str(self.output_dir / 'best_stg1.pth'))
print(f'Refresh EMA at epoch {epoch} with decay {self.ema.decay}')
log_stats = {
**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters
}
if self.output_dir and dist_utils.is_main_process():
with (self.output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
# for evaluation logs
if coco_evaluator is not None:
(self.output_dir / 'eval').mkdir(exist_ok=True)
if "bbox" in coco_evaluator.coco_eval:
filenames = ['latest.pth']
if epoch % 50 == 0:
filenames.append(f'{epoch:03}.pth')
for name in filenames:
torch.save(coco_evaluator.coco_eval["bbox"].eval,
self.output_dir / "eval" / name)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def val(self, ):
self.eval()
module = self.ema.module if self.ema else self.model
test_stats, coco_evaluator = evaluate(module, self.criterion, self.postprocessor,
self.val_dataloader, self.evaluator, self.device)
if self.output_dir:
dist_utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, self.output_dir / "eval.pth")
return
def state_dict(self):
"""State dict, train/eval"""
state = {}
state['date'] = datetime.datetime.now().isoformat()
# For resume
state['last_epoch'] = self.last_epoch
for k, v in self.__dict__.items():
if k == 'teacher_model':
continue
if hasattr(v, 'state_dict'):
v = dist_utils.de_parallel(v)
state[k] = v.state_dict()
return state |