from loguru import logger import torch import torch.backends.cudnn as cudnn from torch.nn.parallel import DistributedDataParallel as DDP from yolox.core import launch from yolox.exp import get_exp from yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger from yolox.evaluators import MOTEvaluator import argparse import os import random import warnings import glob import motmetrics as mm from collections import OrderedDict from pathlib import Path def make_parser(): parser = argparse.ArgumentParser("YOLOX Eval") parser.add_argument("-expn", "--experiment-name", type=str, default=None) parser.add_argument("-n", "--name", type=str, default=None, help="model name") # distributed parser.add_argument( "--dist-backend", default="nccl", type=str, help="distributed backend" ) parser.add_argument( "--dist-url", default=None, type=str, help="url used to set up distributed training", ) parser.add_argument("-b", "--batch-size", type=int, default=64, help="batch size") parser.add_argument( "-d", "--devices", default=None, type=int, help="device for training" ) parser.add_argument( "--local_rank", default=0, type=int, help="local rank for dist training" ) parser.add_argument( "--num_machines", default=1, type=int, help="num of node for training" ) parser.add_argument( "--machine_rank", default=0, type=int, help="node rank for multi-node training" ) parser.add_argument( "-f", "--exp_file", default=None, type=str, help="pls input your expriment description file", ) parser.add_argument( "--fp16", dest="fp16", default=False, action="store_true", help="Adopting mix precision evaluating.", ) parser.add_argument( "--fuse", dest="fuse", default=False, action="store_true", help="Fuse conv and bn for testing.", ) parser.add_argument( "--trt", dest="trt", default=False, action="store_true", help="Using TensorRT model for testing.", ) parser.add_argument( "--test", dest="test", default=False, action="store_true", help="Evaluating on test-dev set.", ) parser.add_argument( "--speed", dest="speed", default=False, action="store_true", help="speed test only.", ) parser.add_argument( "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER, ) # det args parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt for eval") parser.add_argument("--conf", default=0.1, type=float, help="test conf") parser.add_argument("--nms", default=0.7, type=float, help="test nms threshold") parser.add_argument("--tsize", default=None, type=int, help="test img size") parser.add_argument("--seed", default=None, type=int, help="eval seed") # tracking args parser.add_argument("--track_thresh", type=float, default=0.6, help="tracking confidence threshold") parser.add_argument("--track_buffer", type=int, default=30, help="the frames for keep lost tracks") parser.add_argument("--match_thresh", type=int, default=0.9, help="matching threshold for tracking") parser.add_argument('--min-box-area', type=float, default=100, help='filter out tiny boxes') # deepsort args parser.add_argument("--model_folder", type=str, default='pretrained/ckpt.t7', help="reid model folder") return parser def compare_dataframes(gts, ts): accs = [] names = [] for k, tsacc in ts.items(): if k in gts: logger.info('Comparing {}...'.format(k)) accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5)) names.append(k) else: logger.warning('No ground truth for {}, skipping.'.format(k)) return accs, names @logger.catch def main(exp, args, num_gpu): if args.seed is not None: random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True warnings.warn( "You have chosen to seed testing. This will turn on the CUDNN deterministic setting, " ) is_distributed = num_gpu > 1 # set environment variables for distributed training cudnn.benchmark = True rank = args.local_rank # rank = get_local_rank() file_name = os.path.join(exp.output_dir, args.experiment_name) if rank == 0: os.makedirs(file_name, exist_ok=True) results_folder = os.path.join(file_name, "track_results_deepsort") os.makedirs(results_folder, exist_ok=True) model_folder = args.model_folder setup_logger(file_name, distributed_rank=rank, filename="val_log.txt", mode="a") logger.info("Args: {}".format(args)) if args.conf is not None: exp.test_conf = args.conf if args.nms is not None: exp.nmsthre = args.nms if args.tsize is not None: exp.test_size = (args.tsize, args.tsize) model = exp.get_model() logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size))) #logger.info("Model Structure:\n{}".format(str(model))) #evaluator = exp.get_evaluator(args.batch_size, is_distributed, args.test) val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test) evaluator = MOTEvaluator( args=args, dataloader=val_loader, img_size=exp.test_size, confthre=exp.test_conf, nmsthre=exp.nmsthre, num_classes=exp.num_classes, ) torch.cuda.set_device(rank) model.cuda(rank) model.eval() if not args.speed and not args.trt: if args.ckpt is None: ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar") else: ckpt_file = args.ckpt logger.info("loading checkpoint") loc = "cuda:{}".format(rank) ckpt = torch.load(ckpt_file, map_location=loc) # load the model state dict model.load_state_dict(ckpt["model"]) logger.info("loaded checkpoint done.") if is_distributed: model = DDP(model, device_ids=[rank]) if args.fuse: logger.info("\tFusing model...") model = fuse_model(model) if args.trt: assert ( not args.fuse and not is_distributed and args.batch_size == 1 ), "TensorRT model is not support model fusing and distributed inferencing!" trt_file = os.path.join(file_name, "model_trt.pth") assert os.path.exists( trt_file ), "TensorRT model is not found!\n Run tools/trt.py first!" model.head.decode_in_inference = False decoder = model.head.decode_outputs else: trt_file = None decoder = None # start evaluate *_, summary = evaluator.evaluate_deepsort( model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder, model_folder ) logger.info("\n" + summary) # evaluate MOTA mm.lap.default_solver = 'lap' gt_type = '_val_half' #gt_type = '' print('gt_type', gt_type) gtfiles = glob.glob( os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type))) print('gt_files', gtfiles) tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')] logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles))) logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers)) logger.info('Default LAP solver \'{}\''.format(mm.lap.default_solver)) logger.info('Loading files.') gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles]) ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles]) mh = mm.metrics.create() accs, names = compare_dataframes(gt, ts) logger.info('Running metrics') metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked', 'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects'] summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True) # summary = mh.compute_many(accs, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True) # print(mm.io.render_summary( # summary, formatters=mh.formatters, # namemap=mm.io.motchallenge_metric_names)) div_dict = { 'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'], 'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']} for divisor in div_dict: for divided in div_dict[divisor]: summary[divided] = (summary[divided] / summary[divisor]) fmt = mh.formatters change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked', 'partially_tracked', 'mostly_lost'] for k in change_fmt_list: fmt[k] = fmt['mota'] print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names)) metrics = mm.metrics.motchallenge_metrics + ['num_objects'] summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True) print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names)) logger.info('Completed') if __name__ == "__main__": args = make_parser().parse_args() exp = get_exp(args.exp_file, args.name) exp.merge(args.opts) if not args.experiment_name: args.experiment_name = exp.exp_name num_gpu = torch.cuda.device_count() if args.devices is None else args.devices assert num_gpu <= torch.cuda.device_count() launch( main, num_gpu, args.num_machines, args.machine_rank, backend=args.dist_backend, dist_url=args.dist_url, args=(exp, args, num_gpu), )