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| 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 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 | |
| # evaluate MOTA | |
| results_folder = 'YOLOX_outputs/yolox_x_ablation/track_results' | |
| 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=0.6)) 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') |