import _init_path import argparse import datetime import glob import os import re import time from pathlib import Path import numpy as np import torch from tensorboardX import SummaryWriter from eval_utils import eval_utils from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file from pcdet.datasets import build_dataloader from pcdet.models import build_network from pcdet.utils import common_utils def parse_config(): parser = argparse.ArgumentParser(description='arg parser') parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training') parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training') parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader') parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment') parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from') parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model') parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none') parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training') parser.add_argument('--local_rank', type=int, default=None, help='local rank for distributed training') parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER, help='set extra config keys if needed') parser.add_argument('--max_waiting_mins', type=int, default=30, help='max waiting minutes') parser.add_argument('--start_epoch', type=int, default=0, help='') parser.add_argument('--eval_tag', type=str, default='default', help='eval tag for this experiment') parser.add_argument('--eval_all', action='store_true', default=False, help='whether to evaluate all checkpoints') parser.add_argument('--ckpt_dir', type=str, default=None, help='specify a ckpt directory to be evaluated if needed') parser.add_argument('--save_to_file', action='store_true', default=False, help='') parser.add_argument('--infer_time', action='store_true', default=False, help='calculate inference latency') args = parser.parse_args() cfg_from_yaml_file(args.cfg_file, cfg) cfg.TAG = Path(args.cfg_file).stem cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml' np.random.seed(1024) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs, cfg) return args, cfg def eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id, dist_test=False): # load checkpoint model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=dist_test, pre_trained_path=args.pretrained_model) model.cuda() # start evaluation eval_utils.eval_one_epoch( cfg, args, model, test_loader, epoch_id, logger, dist_test=dist_test, result_dir=eval_output_dir ) def get_no_evaluated_ckpt(ckpt_dir, ckpt_record_file, args): ckpt_list = glob.glob(os.path.join(ckpt_dir, '*checkpoint_epoch_*.pth')) ckpt_list.sort(key=os.path.getmtime) evaluated_ckpt_list = [float(x.strip()) for x in open(ckpt_record_file, 'r').readlines()] for cur_ckpt in ckpt_list: num_list = re.findall('checkpoint_epoch_(.*).pth', cur_ckpt) if num_list.__len__() == 0: continue epoch_id = num_list[-1] if 'optim' in epoch_id: continue if float(epoch_id) not in evaluated_ckpt_list and int(float(epoch_id)) >= args.start_epoch: return epoch_id, cur_ckpt return -1, None def repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir, dist_test=False): # evaluated ckpt record ckpt_record_file = eval_output_dir / ('eval_list_%s.txt' % cfg.DATA_CONFIG.DATA_SPLIT['test']) with open(ckpt_record_file, 'a'): pass # tensorboard log if cfg.LOCAL_RANK == 0: tb_log = SummaryWriter(log_dir=str(eval_output_dir / ('tensorboard_%s' % cfg.DATA_CONFIG.DATA_SPLIT['test']))) total_time = 0 first_eval = True while True: # check whether there is checkpoint which is not evaluated cur_epoch_id, cur_ckpt = get_no_evaluated_ckpt(ckpt_dir, ckpt_record_file, args) if cur_epoch_id == -1 or int(float(cur_epoch_id)) < args.start_epoch: wait_second = 30 if cfg.LOCAL_RANK == 0: print('Wait %s seconds for next check (progress: %.1f / %d minutes): %s \r' % (wait_second, total_time * 1.0 / 60, args.max_waiting_mins, ckpt_dir), end='', flush=True) time.sleep(wait_second) total_time += 30 if total_time > args.max_waiting_mins * 60 and (first_eval is False): break continue total_time = 0 first_eval = False model.load_params_from_file(filename=cur_ckpt, logger=logger, to_cpu=dist_test) model.cuda() # start evaluation cur_result_dir = eval_output_dir / ('epoch_%s' % cur_epoch_id) / cfg.DATA_CONFIG.DATA_SPLIT['test'] tb_dict = eval_utils.eval_one_epoch( cfg, args, model, test_loader, cur_epoch_id, logger, dist_test=dist_test, result_dir=cur_result_dir ) if cfg.LOCAL_RANK == 0: for key, val in tb_dict.items(): tb_log.add_scalar(key, val, cur_epoch_id) # record this epoch which has been evaluated with open(ckpt_record_file, 'a') as f: print('%s' % cur_epoch_id, file=f) logger.info('Epoch %s has been evaluated' % cur_epoch_id) def main(): args, cfg = parse_config() if args.infer_time: os.environ['CUDA_LAUNCH_BLOCKING'] = '1' if args.launcher == 'none': dist_test = False total_gpus = 1 else: if args.local_rank is None: args.local_rank = int(os.environ.get('LOCAL_RANK', '0')) total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)( args.tcp_port, args.local_rank, backend='nccl' ) dist_test = True if args.batch_size is None: args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU else: assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus' args.batch_size = args.batch_size // total_gpus output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag output_dir.mkdir(parents=True, exist_ok=True) eval_output_dir = output_dir / 'eval' if not args.eval_all: num_list = re.findall(r'\d+', args.ckpt) if args.ckpt is not None else [] epoch_id = num_list[-1] if num_list.__len__() > 0 else 'no_number' eval_output_dir = eval_output_dir / ('epoch_%s' % epoch_id) / cfg.DATA_CONFIG.DATA_SPLIT['test'] else: eval_output_dir = eval_output_dir / 'eval_all_default' if args.eval_tag is not None: eval_output_dir = eval_output_dir / args.eval_tag eval_output_dir.mkdir(parents=True, exist_ok=True) log_file = eval_output_dir / ('log_eval_%s.txt' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S')) logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK) # log to file logger.info('**********************Start logging**********************') gpu_list = os.environ['CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys() else 'ALL' logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list) if dist_test: logger.info('total_batch_size: %d' % (total_gpus * args.batch_size)) for key, val in vars(args).items(): logger.info('{:16} {}'.format(key, val)) log_config_to_file(cfg, logger=logger) ckpt_dir = args.ckpt_dir if args.ckpt_dir is not None else output_dir / 'ckpt' test_set, test_loader, sampler = build_dataloader( dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, batch_size=args.batch_size, dist=dist_test, workers=args.workers, logger=logger, training=False ) model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=test_set) with torch.no_grad(): if args.eval_all: repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir, dist_test=dist_test) else: eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id, dist_test=dist_test) if __name__ == '__main__': main()