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SAM3 Video Segmentation - Clean deployment
14114e8
# flake8: noqa
"""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 argparse
import os
import sys
from multiprocessing import freeze_support
from . import trackeval
def run_ytvis_eval(args=None, gt_json=None, dt_json=None):
# 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": ["HOTA"]}
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(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)
# allow directly specifying the GT JSON data and Tracker (result)
# JSON data as Python objects, without reading from files.
dataset_config["GT_JSON_OBJECT"] = gt_json
dataset_config["TRACKER_JSON_OBJECT"] = dt_json
dataset_list = [trackeval.datasets.YouTubeVIS(dataset_config)]
metrics_list = []
# for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,
# trackeval.metrics.Identity]:
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")
output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)
return output_res, output_msg
if __name__ == "__main__":
import sys
freeze_support()
run_ytvis_eval(sys.argv[1:])