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main.py
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main.py
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# Copyright (c) 2022 IDEA. All Rights Reserved.
# ------------------------------------------------------------------------
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import os, sys
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
from util.get_param_dicts import get_param_dict
from util.logger import setup_logger
from util.slconfig import DictAction, SLConfig
from util.utils import ModelEma, BestMetricHolder
import util.misc as utils
import datasets
from datasets import build_dataset, get_coco_api_from_dataset
from engine import evaluate, train_one_epoch, test, train_one_epoch_with_self_training
from models.dino import EMA
def get_args_parser():
parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
parser.add_argument('--config_file', '-c', type=str, required=True)
parser.add_argument('--options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file.')
# dataset parameters
parser.add_argument('--dataset_file', default='city2bdd100k')
parser.add_argument('--coco_path', type=str, default='')
parser.add_argument('--coco_panoptic_path', type=str)
parser.add_argument('--remove_difficult', action='store_true')
parser.add_argument('--fix_size', action='store_true')
# training parameters
parser.add_argument('--output_dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--note', default='',
help='add some notes to the experiment')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--pretrain_model_path', help='load from other checkpoint')
parser.add_argument('--finetune_ignore', type=str, nargs='+')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--test', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--find_unused_params', action='store_true')
parser.add_argument('--save_results', action='store_true')
parser.add_argument('--save_log', action='store_true')
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--rank', default=0, type=int,
help='number of distributed processes')
parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel')
parser.add_argument('--amp', action='store_true',
help="Train with mixed precision")
return parser
def build_model_main(args):
# we use register to maintain models from catdet6 on.
from models.registry import MODULE_BUILD_FUNCS
assert args.modelname in MODULE_BUILD_FUNCS._module_dict
build_func = MODULE_BUILD_FUNCS.get(args.modelname)
model, criterion, postprocessors = build_func(args)
return model, criterion, postprocessors
def main(args):
utils.init_distributed_mode(args)
# load cfg file and update the args
print("Loading config file from {}".format(args.config_file))
time.sleep(args.rank * 0.02)
cfg = SLConfig.fromfile(args.config_file)
if args.options is not None:
cfg.merge_from_dict(args.options)
if args.rank == 0:
save_cfg_path = os.path.join(args.output_dir, "config_cfg.py")
cfg.dump(save_cfg_path)
save_json_path = os.path.join(args.output_dir, "config_args_raw.json")
with open(save_json_path, 'w') as f:
json.dump(vars(args), f, indent=2)
cfg_dict = cfg._cfg_dict.to_dict()
args_vars = vars(args)
for k,v in cfg_dict.items():
if k not in args_vars:
setattr(args, k, v)
else:
raise ValueError("Key {} can used by args only".format(k))
# update some new args temporally
if not getattr(args, 'use_ema', None):
args.use_ema = False
if not getattr(args, 'debug', None):
args.debug = False
# setup logger
os.makedirs(args.output_dir, exist_ok=True)
logger = setup_logger(output=os.path.join(args.output_dir, 'info.txt'), distributed_rank=args.rank, color=False, name="detr")
logger.info("git:\n {}\n".format(utils.get_sha()))
logger.info("Command: "+' '.join(sys.argv))
if args.rank == 0:
save_json_path = os.path.join(args.output_dir, "config_args_all.json")
with open(save_json_path, 'w') as f:
json.dump(vars(args), f, indent=2)
logger.info("Full config saved to {}".format(save_json_path))
logger.info('world size: {}'.format(args.world_size))
logger.info('rank: {}'.format(args.rank))
logger.info('local_rank: {}'.format(args.local_rank))
logger.info("args: " + str(args) + '\n')
if args.frozen_weights is not None:
assert args.masks, "Frozen training is meant for segmentation only"
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# build model
model, criterion, postprocessors = build_model_main(args)
wo_class_error = False
model.to(device)
# ema
if args.use_ema:
ema_m = ModelEma(model, args.ema_decay)
else:
ema_m = None
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info('number of params:'+str(n_parameters))
logger.info("params:\n"+json.dumps({n: p.numel() for n, p in model.named_parameters() if p.requires_grad}, indent=2))
param_dicts = get_param_dict(args, model_without_ddp)
optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
weight_decay=args.weight_decay)
dataset_train = build_dataset(image_set='train', args=args)
dataset_val = build_dataset(image_set='val', args=args)
if args.strong_aug: #是否创建半监督强增广dataset
dataset_train_strong_aug = build_dataset(image_set='train', args=args,strong_aug = True)
else:
dataset_train_strong_aug = None
if args.distributed:
sampler_train = DistributedSampler(dataset_train)
sampler_val = DistributedSampler(dataset_val, shuffle=False)
if dataset_train_strong_aug is not None: # 半监督强增广使用
sampler_train_strong_aug = DistributedSampler(dataset_train_strong_aug)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if dataset_train_strong_aug is not None: # 半监督强增广使用
sampler_train_strong_aug = torch.utils.data.RandomSampler(dataset_train_strong_aug)
batch_sampler_train = torch.utils.data.BatchSampler(
sampler_train, args.batch_size, drop_last=True)
data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,
collate_fn=utils.collate_fn_da, num_workers=args.num_workers)
data_loader_val = DataLoader(dataset_val, 1, sampler=sampler_val,
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)
if dataset_train_strong_aug is not None: # 半监督强增广使用
batch_sampler_train_strong_aug = torch.utils.data.BatchSampler(
sampler_train_strong_aug, args.batch_size, drop_last=True)
data_loader_train_strong_aug = DataLoader(dataset_train_strong_aug, batch_sampler=batch_sampler_train_strong_aug,
collate_fn=utils.collate_fn_da, num_workers=args.num_workers)
else:
data_loader_train_strong_aug = None
if args.onecyclelr:
lr_scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=args.lr, steps_per_epoch=len(data_loader_train), epochs=args.epochs, pct_start=0.2)
elif args.multi_step_lr:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_drop_list)
else:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)
if args.dataset_file == "coco_panoptic":
# We also evaluate AP during panoptic training, on original coco DS
coco_val = datasets.coco.build("val", args)
base_ds = get_coco_api_from_dataset(coco_val)
else:
base_ds = get_coco_api_from_dataset(dataset_val)
if args.frozen_weights is not None:
checkpoint = torch.load(args.frozen_weights, map_location='cpu')
model_without_ddp.detr.load_state_dict(checkpoint['model'])
output_dir = Path(args.output_dir)
if os.path.exists(os.path.join(args.output_dir, 'checkpoint.pth')):
args.resume = os.path.join(args.output_dir, 'checkpoint.pth')
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
args.resume, map_location='cpu', check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if args.use_ema:
if 'ema_model' in checkpoint:
ema_m.module.load_state_dict(utils.clean_state_dict(checkpoint['ema_model']))
else:
del ema_m
ema_m = ModelEma(model, args.ema_decay)
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if (not args.resume) and args.pretrain_model_path:
checkpoint = torch.load(args.pretrain_model_path, map_location='cpu')['model']
from collections import OrderedDict
_ignorekeywordlist = args.finetune_ignore if args.finetune_ignore else []
ignorelist = []
def check_keep(keyname, ignorekeywordlist):
for keyword in ignorekeywordlist:
if keyword in keyname:
ignorelist.append(keyname)
return False
return True
logger.info("Ignore keys: {}".format(json.dumps(ignorelist, indent=2)))
_tmp_st = OrderedDict({k:v for k, v in utils.clean_state_dict(checkpoint).items() if check_keep(k, _ignorekeywordlist)})
_load_output = model_without_ddp.load_state_dict(_tmp_st, strict=False)
logger.info(str(_load_output))
if args.use_ema:
if 'ema_model' in checkpoint:
ema_m.module.load_state_dict(utils.clean_state_dict(checkpoint['ema_model']))
else:
del ema_m
ema_m = ModelEma(model, args.ema_decay)
if args.eval:
os.environ['EVAL_FLAG'] = 'TRUE'
test_stats, coco_evaluator = evaluate(model, criterion, postprocessors,
data_loader_val, base_ds, device, args.output_dir, wo_class_error=wo_class_error, args=args)
if args.output_dir:
utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
log_stats = {**{f'test_{k}': v for k, v in test_stats.items()} }
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
return
#==========self-training准备工作,创建EMA Teacher模型=====================
#----EMA
#ema_teacher= ModelEma(model, args.ema_decay_teacher) #teacher model
ema_teacher= EMA.ModelEMA(model, decay = args.ema_decay_teacher) #teacher model
best_ema_model = None #由于self-training训练存在较大波动,用于保存最优模型
#========best_ema_model 指标记录=======
#student model(原版模型) 指标记录
best_checkpoint_fitness = 0
#最终的最优模型
best_ema_model_fitness = 0
cache_best_ema_model_epoch = 0
# teacher model 指标记录
best_ema_teacher_fitness = 0
cache_best_ema_teacher_epoch = 0
# ---记录评估指标---
ema_teacher_eval = []
best_ema_model_eval = []
#-----------
print("Start training")
args.start_epoch = 0 #----修改初始位置用于DEBUG
start_time = time.time()
best_map_holder = BestMetricHolder(use_ema=args.use_ema)
for epoch in range(args.start_epoch, args.epochs):
epoch_start_time = time.time()
if args.distributed:
sampler_train.set_epoch(epoch)
# ----1.不采用self-training训练
if epoch < cfg.burn_epochs:
#when lr drop ,加载最优模型
if epoch == cfg.lr_drop:
checkpoint = torch.load(os.path.join(output_dir, 'best_ema_teacher.pth'), map_location='cpu')
if not args.distributed:
model_without_ddp.load_state_dict(checkpoint['ema_model'], strict=True)
else: # DDP
state_dict = {k.replace("module.", ""): v for k, v in checkpoint['ema_model'].items()}
model_without_ddp.load_state_dict(state_dict, strict=True)
# eval
test_stats, coco_evaluator = evaluate(
model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
#---标准训练 with Domain Adaptation
train_stats = train_one_epoch(
model, criterion, data_loader_train, optimizer, device, epoch,
args.clip_max_norm, wo_class_error=wo_class_error, lr_scheduler=lr_scheduler, args=args, logger=(logger if args.save_log else None), ema_m=ema_m)
# ----2.加入伪标签,self-training训练
else:
if args.distributed:
sampler_train.set_epoch(epoch)
sampler_train_strong_aug.set_epoch(epoch)
#----准备工作
if epoch == cfg.burn_epochs:
# 考虑重新设置学习率,学习率*10(待实现)
print('学习率:')
for p in optimizer.param_groups:
print(p['lr'])
#(0)----------加载最优检测模型----------------
checkpoint = torch.load(os.path.join(output_dir, 'best_ema_teacher.pth'), map_location='cpu')
if not args.distributed:
model_without_ddp.load_state_dict(checkpoint['ema_model'], strict=True)
ema_teacher.ema.load_state_dict(checkpoint['ema_model'], strict=True)
else: # DDP
state_dict = {k.replace("module.", ""): v for k, v in checkpoint['ema_model'].items()}
model_without_ddp.load_state_dict(state_dict, strict=True)
ema_teacher.ema.load_state_dict(state_dict, strict=True)
# 评估,用于debug
test_stats, coco_evaluator = evaluate(
ema_teacher.ema, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
#(1)----------由于学生模型学习可能存在不稳定情况,创建额外的EMA用于保存最优结果---------------)
best_ema_model = EMA.CosineEMA(ema_teacher.ema,decay_start = args.ema_decay_best_model,
total_epoch = args.epochs - args.burn_epochs) #使用较大的ema_decay加快模型学习效率
#(2)----------self-training训练 with Domain Adaptation---------------)
train_stats = train_one_epoch_with_self_training(
model, ema_teacher ,criterion, data_loader_train, data_loader_train_strong_aug,optimizer, device, epoch,
args.clip_max_norm, wo_class_error=wo_class_error, lr_scheduler=lr_scheduler, args=args, logger=(logger if args.save_log else None), ema_m=ema_m)
# ----3.训练后,使用EMA更新teacher model和 best_ema_model
#(1)---------- 更新teacher model -----------------
ema_teacher.update(model)
#(2)---------- 更新best_ema_model model -----------------
if best_ema_model:
best_ema_model.update_decay(epoch - args.burn_epochs) # 更新semi_ema的decay
best_ema_model.update(ema_teacher.ema)
# ----4.评估并保存模型
#(1)---------- 官方保存每一个epoch student model-----------------
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
if not args.onecyclelr:
lr_scheduler.step()
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
# extra checkpoint before LR drop and every 100 epochs
if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % args.save_checkpoint_interval == 0:
checkpoint_paths.append(output_dir / f'checkpoint{epoch:04}.pth')
for checkpoint_path in checkpoint_paths:
weights = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}
if args.use_ema:
weights.update({
'ema_model': ema_m.module.state_dict(),
})
utils.save_on_master(weights, checkpoint_path)
#(2)---------- 评估结果-----------------
#----官方原版student eval
test_stats, coco_evaluator = evaluate(
model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
if test_stats['coco_eval_bbox'][1] >= best_checkpoint_fitness:
best_checkpoint_fitness = test_stats['coco_eval_bbox'][1]
cache_best_checkpoint_epoch = epoch # 记录epoch数
map_regular = test_stats['coco_eval_bbox'][0]
_isbest = best_map_holder.update(map_regular, epoch, is_ema=False)
if _isbest:
checkpoint_path = output_dir / 'checkpoint_best_regular.pth'
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}, checkpoint_path)
log_stats = {
**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
}
# eval ema
if args.use_ema:
ema_test_stats, ema_coco_evaluator = evaluate(
ema_m.module, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
log_stats.update({f'ema_test_{k}': v for k,v in ema_test_stats.items()})
map_ema = ema_test_stats['coco_eval_bbox'][0]
_isbest = best_map_holder.update(map_ema, epoch, is_ema=True)
if _isbest:
checkpoint_path = output_dir / 'checkpoint_best_ema.pth'
utils.save_on_master({
'model': ema_m.module.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}, checkpoint_path)
log_stats.update(best_map_holder.summary())
#----增添EMA模型评估
#存在best_ema则评估best_ema
if epoch >= args.burn_epochs:
test_stats_best_ema, coco_evaluator_best_ema = evaluate(
best_ema_model.ema, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
else:#不存在则评估ema_teacher
test_stats_ema_teacher, coco_evaluator_ema_teacher = evaluate(
ema_teacher.ema, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
wo_class_error=wo_class_error, args=args, logger=(logger if args.save_log else None)
)
#----增添保存最优模型
if args.output_dir and utils.is_main_process():
# 1)存储最佳best_ema
if epoch >= args.burn_epochs:
best_ema_model_eval.append(test_stats_best_ema['coco_eval_bbox'][1])
# 记录结果
with open(output_dir / "best_ema_model_eval.txt", 'w') as f:
for i in best_ema_model_eval:
f.write('%s\n' % i)
if test_stats_best_ema['coco_eval_bbox'][1] >= best_ema_model_fitness:
best_ema_model_fitness = test_stats_best_ema['coco_eval_bbox'][1]
checkpoint_path = output_dir / 'best_ema_model.pth'
cache_best_ema_model_epoch = epoch # 记录epoch数
utils.save_on_master({
'ema_model': best_ema_model.ema.state_dict(),
'epoch': epoch,
}, checkpoint_path)
#2)存储最佳ema teacher
if epoch < args.burn_epochs:
ema_teacher_eval.append(test_stats_ema_teacher['coco_eval_bbox'][1])
#记录结果
with open(output_dir / "ema_teacher_eval.txt", 'w') as f:
for i in ema_teacher_eval:
f.write('%s\n'%i)
if test_stats_ema_teacher['coco_eval_bbox'][1] >= best_ema_teacher_fitness:
best_ema_teacher_fitness = test_stats_ema_teacher['coco_eval_bbox'][1]
checkpoint_path = output_dir / 'best_ema_teacher.pth'
cache_best_ema_teacher_epoch = epoch # 记录epoch数
utils.save_on_master({
'ema_model': ema_teacher.ema.state_dict(),
'epoch': epoch,
}, checkpoint_path)
# (3)记录日志
with open(output_dir / "log_best.txt", 'w') as f:
f.write('best_checkpoint --> map50:%s , epoch:%s\n' % (
best_checkpoint_fitness, cache_best_checkpoint_epoch))
f.write(
'best_semi_ema --> map50:%s , epoch:%s\n' % (best_ema_model_fitness, cache_best_ema_model_epoch))
f.write('best_teacher --> map50:%s , epoch:%s\n' % (best_ema_teacher_fitness, cache_best_ema_teacher_epoch))
ep_paras = {
'epoch': epoch,
'n_parameters': n_parameters
}
log_stats.update(ep_paras)
try:
log_stats.update({'now_time': str(datetime.datetime.now())})
except:
pass
epoch_time = time.time() - epoch_start_time
epoch_time_str = str(datetime.timedelta(seconds=int(epoch_time)))
log_stats['epoch_time'] = epoch_time_str
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
# for evaluation logs
if coco_evaluator is not None:
(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,
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))
# remove the copied files.
copyfilelist = vars(args).get('copyfilelist')
if copyfilelist and args.local_rank == 0:
from datasets.data_util import remove
for filename in copyfilelist:
print("Removing: {}".format(filename))
remove(filename)
if __name__ == '__main__':
parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)