85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
from loguru import logger
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import torch
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import torch.backends.cudnn as cudnn
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from torch.nn.parallel import DistributedDataParallel as DDP
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from yolox.core import launch
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from yolox.exp import get_exp
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from yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger
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from yolox.evaluators import MOTEvaluator
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import argparse
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import os
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import random
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import warnings
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import glob
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import motmetrics as mm
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from collections import OrderedDict
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from pathlib import Path
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def compare_dataframes(gts, ts):
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accs = []
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names = []
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for k, tsacc in ts.items():
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if k in gts:
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logger.info('Comparing {}...'.format(k))
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accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))
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names.append(k)
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else:
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logger.warning('No ground truth for {}, skipping.'.format(k))
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return accs, names
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# evaluate MOTA
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results_folder = 'YOLOX_outputs/yolox_x_ablation/track_results'
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mm.lap.default_solver = 'lap'
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gt_type = '_val_half'
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#gt_type = ''
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print('gt_type', gt_type)
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gtfiles = glob.glob(
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os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))
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print('gt_files', gtfiles)
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tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]
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logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))
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logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))
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logger.info('Default LAP solver \'{}\''.format(mm.lap.default_solver))
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logger.info('Loading files.')
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gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])
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ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1.0)) for f in tsfiles])
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mh = mm.metrics.create()
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accs, names = compare_dataframes(gt, ts)
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logger.info('Running metrics')
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metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',
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'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',
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'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']
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summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)
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# summary = mh.compute_many(accs, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)
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# print(mm.io.render_summary(
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# summary, formatters=mh.formatters,
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# namemap=mm.io.motchallenge_metric_names))
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div_dict = {
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'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],
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'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}
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for divisor in div_dict:
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for divided in div_dict[divisor]:
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summary[divided] = (summary[divided] / summary[divisor])
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fmt = mh.formatters
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change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',
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'partially_tracked', 'mostly_lost']
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for k in change_fmt_list:
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fmt[k] = fmt['mota']
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print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))
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metrics = mm.metrics.motchallenge_metrics + ['num_objects']
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summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)
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print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))
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logger.info('Completed')
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