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tools/train.py
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233
tools/train.py
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import _init_path
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import argparse
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import datetime
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import glob
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import os
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from pathlib import Path
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from test import repeat_eval_ckpt
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import torch
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import torch.nn as nn
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from tensorboardX import SummaryWriter
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from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
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from pcdet.datasets import build_dataloader
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from pcdet.models import build_network, model_fn_decorator
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from pcdet.utils import common_utils
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from train_utils.optimization import build_optimizer, build_scheduler
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from train_utils.train_utils import train_model
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def parse_config():
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parser = argparse.ArgumentParser(description='arg parser')
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parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
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parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
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parser.add_argument('--epochs', type=int, default=None, required=False, help='number of epochs to train for')
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parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader')
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parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
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parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
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parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model')
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parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
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parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
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parser.add_argument('--sync_bn', action='store_true', default=False, help='whether to use sync bn')
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parser.add_argument('--fix_random_seed', action='store_true', default=False, help='')
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parser.add_argument('--ckpt_save_interval', type=int, default=1, help='number of training epochs')
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parser.add_argument('--local_rank', type=int, default=None, help='local rank for distributed training')
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parser.add_argument('--max_ckpt_save_num', type=int, default=30, help='max number of saved checkpoint')
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parser.add_argument('--merge_all_iters_to_one_epoch', action='store_true', default=False, help='')
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parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
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help='set extra config keys if needed')
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parser.add_argument('--max_waiting_mins', type=int, default=0, help='max waiting minutes')
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parser.add_argument('--start_epoch', type=int, default=0, help='')
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parser.add_argument('--num_epochs_to_eval', type=int, default=0, help='number of checkpoints to be evaluated')
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parser.add_argument('--save_to_file', action='store_true', default=False, help='')
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parser.add_argument('--use_tqdm_to_record', action='store_true', default=False, help='if True, the intermediate losses will not be logged to file, only tqdm will be used')
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parser.add_argument('--logger_iter_interval', type=int, default=50, help='')
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parser.add_argument('--ckpt_save_time_interval', type=int, default=300, help='in terms of seconds')
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parser.add_argument('--wo_gpu_stat', action='store_true', help='')
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parser.add_argument('--use_amp', action='store_true', help='use mix precision training')
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args = parser.parse_args()
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cfg_from_yaml_file(args.cfg_file, cfg)
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cfg.TAG = Path(args.cfg_file).stem
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cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
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args.use_amp = args.use_amp or cfg.OPTIMIZATION.get('USE_AMP', False)
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if args.set_cfgs is not None:
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cfg_from_list(args.set_cfgs, cfg)
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return args, cfg
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def main():
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args, cfg = parse_config()
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if args.launcher == 'none':
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dist_train = False
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total_gpus = 1
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else:
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if args.local_rank is None:
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args.local_rank = int(os.environ.get('LOCAL_RANK', '0'))
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total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
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args.tcp_port, args.local_rank, backend='nccl'
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)
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dist_train = True
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if args.batch_size is None:
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args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
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else:
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assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
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args.batch_size = args.batch_size // total_gpus
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args.epochs = cfg.OPTIMIZATION.NUM_EPOCHS if args.epochs is None else args.epochs
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if args.fix_random_seed:
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common_utils.set_random_seed(666 + cfg.LOCAL_RANK)
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output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
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ckpt_dir = output_dir / 'ckpt'
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output_dir.mkdir(parents=True, exist_ok=True)
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ckpt_dir.mkdir(parents=True, exist_ok=True)
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log_file = output_dir / ('train_%s.log' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
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logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
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# log to file
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logger.info('**********************Start logging**********************')
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gpu_list = os.environ['CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys() else 'ALL'
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logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
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if dist_train:
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logger.info('Training in distributed mode : total_batch_size: %d' % (total_gpus * args.batch_size))
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else:
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logger.info('Training with a single process')
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for key, val in vars(args).items():
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logger.info('{:16} {}'.format(key, val))
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log_config_to_file(cfg, logger=logger)
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if cfg.LOCAL_RANK == 0:
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os.system('cp %s %s' % (args.cfg_file, output_dir))
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tb_log = SummaryWriter(log_dir=str(output_dir / 'tensorboard')) if cfg.LOCAL_RANK == 0 else None
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logger.info("----------- Create dataloader & network & optimizer -----------")
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train_set, train_loader, train_sampler = build_dataloader(
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dataset_cfg=cfg.DATA_CONFIG,
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class_names=cfg.CLASS_NAMES,
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batch_size=args.batch_size,
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dist=dist_train, workers=args.workers,
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logger=logger,
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training=True,
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merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch,
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total_epochs=args.epochs,
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seed=666 if args.fix_random_seed else None
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)
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model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=train_set)
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if args.sync_bn:
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
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model.cuda()
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optimizer = build_optimizer(model, cfg.OPTIMIZATION)
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# load checkpoint if it is possible
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start_epoch = it = 0
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last_epoch = -1
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if args.pretrained_model is not None:
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model.load_params_from_file(filename=args.pretrained_model, to_cpu=dist_train, logger=logger)
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if args.ckpt is not None:
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it, start_epoch = model.load_params_with_optimizer(args.ckpt, to_cpu=dist_train, optimizer=optimizer, logger=logger)
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last_epoch = start_epoch + 1
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else:
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ckpt_list = glob.glob(str(ckpt_dir / '*.pth'))
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if len(ckpt_list) > 0:
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ckpt_list.sort(key=os.path.getmtime)
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while len(ckpt_list) > 0:
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try:
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it, start_epoch = model.load_params_with_optimizer(
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ckpt_list[-1], to_cpu=dist_train, optimizer=optimizer, logger=logger
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)
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last_epoch = start_epoch + 1
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break
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except:
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ckpt_list = ckpt_list[:-1]
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model.train() # before wrap to DistributedDataParallel to support fixed some parameters
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if dist_train:
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model = nn.parallel.DistributedDataParallel(model, device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()])
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logger.info(f'----------- Model {cfg.MODEL.NAME} created, param count: {sum([m.numel() for m in model.parameters()])} -----------')
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logger.info(model)
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lr_scheduler, lr_warmup_scheduler = build_scheduler(
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optimizer, total_iters_each_epoch=len(train_loader), total_epochs=args.epochs,
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last_epoch=last_epoch, optim_cfg=cfg.OPTIMIZATION
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)
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# -----------------------start training---------------------------
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logger.info('**********************Start training %s/%s(%s)**********************'
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% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
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train_model(
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model,
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optimizer,
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train_loader,
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model_func=model_fn_decorator(),
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lr_scheduler=lr_scheduler,
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optim_cfg=cfg.OPTIMIZATION,
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start_epoch=start_epoch,
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total_epochs=args.epochs,
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start_iter=it,
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rank=cfg.LOCAL_RANK,
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tb_log=tb_log,
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ckpt_save_dir=ckpt_dir,
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train_sampler=train_sampler,
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lr_warmup_scheduler=lr_warmup_scheduler,
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ckpt_save_interval=args.ckpt_save_interval,
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max_ckpt_save_num=args.max_ckpt_save_num,
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merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch,
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logger=logger,
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logger_iter_interval=args.logger_iter_interval,
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ckpt_save_time_interval=args.ckpt_save_time_interval,
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use_logger_to_record=not args.use_tqdm_to_record,
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show_gpu_stat=not args.wo_gpu_stat,
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use_amp=args.use_amp,
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cfg=cfg
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)
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if hasattr(train_set, 'use_shared_memory') and train_set.use_shared_memory:
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train_set.clean_shared_memory()
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logger.info('**********************End training %s/%s(%s)**********************\n\n\n'
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% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
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logger.info('**********************Start evaluation %s/%s(%s)**********************' %
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(cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
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test_set, test_loader, sampler = build_dataloader(
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dataset_cfg=cfg.DATA_CONFIG,
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class_names=cfg.CLASS_NAMES,
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batch_size=args.batch_size,
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dist=dist_train, workers=args.workers, logger=logger, training=False
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)
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eval_output_dir = output_dir / 'eval' / 'eval_with_train'
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eval_output_dir.mkdir(parents=True, exist_ok=True)
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args.start_epoch = max(args.epochs - args.num_epochs_to_eval, 0) # Only evaluate the last args.num_epochs_to_eval epochs
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repeat_eval_ckpt(
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model.module if dist_train else model,
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test_loader, args, eval_output_dir, logger, ckpt_dir,
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dist_test=dist_train
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)
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logger.info('**********************End evaluation %s/%s(%s)**********************' %
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(cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
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if __name__ == '__main__':
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main()
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