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| """ run_tao.py | |
| Run example: | |
| run_tao_ow.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL Tracktor++ | |
| Command Line Arguments: Defaults, # Comments | |
| Eval arguments: | |
| 'USE_PARALLEL': False, | |
| 'NUM_PARALLEL_CORES': 8, | |
| 'BREAK_ON_ERROR': True, | |
| 'PRINT_RESULTS': True, | |
| 'PRINT_ONLY_COMBINED': False, | |
| 'PRINT_CONFIG': True, | |
| 'TIME_PROGRESS': True, | |
| 'OUTPUT_SUMMARY': True, | |
| 'OUTPUT_DETAILED': True, | |
| 'PLOT_CURVES': True, | |
| Dataset arguments: | |
| 'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'), # Location of GT data | |
| 'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'), # Trackers location | |
| 'OUTPUT_FOLDER': None, # Where to save eval results (if None, same as TRACKERS_FOLDER) | |
| 'TRACKERS_TO_EVAL': None, # Filenames of trackers to eval (if None, all in folder) | |
| 'CLASSES_TO_EVAL': None, # Classes to eval (if None, all classes) | |
| 'SPLIT_TO_EVAL': 'training', # Valid: 'training', 'val' | |
| 'PRINT_CONFIG': True, # Whether to print current config | |
| 'TRACKER_SUB_FOLDER': 'data', # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER | |
| 'OUTPUT_SUB_FOLDER': '', # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER | |
| 'TRACKER_DISPLAY_NAMES': None, # Names of trackers to display, if None: TRACKERS_TO_EVAL | |
| 'MAX_DETECTIONS': 300, # Number of maximal allowed detections per image (0 for unlimited) | |
| 'SUBSET': 'unknown', # Evaluate on the following subsets ['all', 'known', 'unknown', 'distractor'] | |
| Metric arguments: | |
| 'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP'] | |
| """ | |
| import sys | |
| import os | |
| import argparse | |
| from multiprocessing import freeze_support | |
| sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) | |
| import trackeval # noqa: E402 | |
| if __name__ == '__main__': | |
| freeze_support() | |
| # Command line interface: | |
| default_eval_config = trackeval.Evaluator.get_default_eval_config() | |
| # print only combined since TrackMAP is undefined for per sequence breakdowns | |
| default_eval_config['PRINT_ONLY_COMBINED'] = True | |
| default_eval_config['DISPLAY_LESS_PROGRESS'] = True | |
| default_dataset_config = trackeval.datasets.TAO_OW.get_default_dataset_config() | |
| default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']} | |
| config = {**default_eval_config, **default_dataset_config, **default_metrics_config} # Merge default configs | |
| parser = argparse.ArgumentParser() | |
| for setting in config.keys(): | |
| if type(config[setting]) == list or type(config[setting]) == type(None): | |
| parser.add_argument("--" + setting, nargs='+') | |
| else: | |
| parser.add_argument("--" + setting) | |
| args = parser.parse_args().__dict__ | |
| for setting in args.keys(): | |
| if args[setting] is not None: | |
| if type(config[setting]) == type(True): | |
| if args[setting] == 'True': | |
| x = True | |
| elif args[setting] == 'False': | |
| x = False | |
| else: | |
| raise Exception('Command line parameter ' + setting + 'must be True or False') | |
| elif type(config[setting]) == type(1): | |
| x = int(args[setting]) | |
| elif type(args[setting]) == type(None): | |
| x = None | |
| else: | |
| x = args[setting] | |
| config[setting] = x | |
| eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()} | |
| dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()} | |
| metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()} | |
| # Run code | |
| evaluator = trackeval.Evaluator(eval_config) | |
| dataset_list = [trackeval.datasets.TAO_OW(dataset_config)] | |
| metrics_list = [] | |
| # for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.CLEAR, trackeval.metrics.Identity, | |
| # trackeval.metrics.HOTA]: | |
| for metric in [trackeval.metrics.HOTA]: | |
| if metric.get_name() in metrics_config['METRICS']: | |
| metrics_list.append(metric()) | |
| if len(metrics_list) == 0: | |
| raise Exception('No metrics selected for evaluation') | |
| evaluator.evaluate(dataset_list, metrics_list) |