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""" run_youtube_vis.py
Run example:
run_youtube_vis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL STEm_Seg
Command Line Arguments: Defaults, # Comments
Eval arguments:
'USE_PARALLEL': False,
'NUM_PARALLEL_CORES': 8,
'BREAK_ON_ERROR': True, # Raises exception and exits with error
'RETURN_ON_ERROR': False, # if not BREAK_ON_ERROR, then returns from function on error
'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'), # if not None, save any errors into a log file.
'PRINT_RESULTS': True,
'PRINT_ONLY_COMBINED': False,
'PRINT_CONFIG': True,
'TIME_PROGRESS': True,
'DISPLAY_LESS_PROGRESS': True,
'OUTPUT_SUMMARY': True,
'OUTPUT_EMPTY_CLASSES': True, # If False, summary files are not output for classes with no detections
'OUTPUT_DETAILED': True,
'PLOT_CURVES': True,
Dataset arguments:
'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/youtube_vis_training'), # Location of GT data
'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/youtube_vis_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
'OUTPUT_SUB_FOLDER': '', # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
'TRACKER_SUB_FOLDER': 'data', # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
'TRACKER_DISPLAY_NAMES': None, # Names of trackers to display, if None: TRACKERS_TO_EVAL
Metric arguments:
'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']
"""
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_dataset_config = trackeval.datasets.YouTubeVIS.get_default_dataset_config()
default_metrics_config = {'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']}
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.YouTubeVIS(dataset_config)]
metrics_list = []
for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,
trackeval.metrics.Identity]:
if metric.get_name() in metrics_config['METRICS']:
# specify TrackMAP config for YouTubeVIS
if metric == trackeval.metrics.TrackMAP:
default_track_map_config = metric.get_default_metric_config()
default_track_map_config['USE_TIME_RANGES'] = False
default_track_map_config['AREA_RANGES'] = [[0 ** 2, 128 ** 2],
[ 128 ** 2, 256 ** 2],
[256 ** 2, 1e5 ** 2]]
metrics_list.append(metric(default_track_map_config))
else:
metrics_list.append(metric())
if len(metrics_list) == 0:
raise Exception('No metrics selected for evaluation')
evaluator.evaluate(dataset_list, metrics_list) |