| import sys |
| import os |
| import numpy as np |
| from multiprocessing import freeze_support |
|
|
| sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) |
| import trackeval |
|
|
| |
| if __name__ == '__main__': |
| freeze_support() |
|
|
|
|
| eval_config = {'USE_PARALLEL': False, |
| 'NUM_PARALLEL_CORES': 8, |
| 'PRINT_RESULTS': False, |
| 'PRINT_CONFIG': True, |
| 'TIME_PROGRESS': True, |
| 'DISPLAY_LESS_PROGRESS': True, |
| 'OUTPUT_SUMMARY': False, |
| 'OUTPUT_EMPTY_CLASSES': False, |
| 'OUTPUT_DETAILED': False, |
| 'PLOT_CURVES': False, |
| } |
| evaluator = trackeval.Evaluator(eval_config) |
| metrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity(), |
| trackeval.metrics.JAndF()] |
|
|
| tests = [ |
| {'SPLIT_TO_EVAL': 'val', 'TRACKERS_TO_EVAL': ['ags']}, |
| ] |
|
|
| for dataset_config in tests: |
|
|
| dataset_list = [trackeval.datasets.DAVIS(dataset_config)] |
| file_loc = os.path.join('davis', 'davis_unsupervised_' + dataset_config['SPLIT_TO_EVAL']) |
|
|
| raw_results, messages = evaluator.evaluate(dataset_list, metrics_list) |
|
|
| classes = dataset_list[0].config['CLASSES_TO_EVAL'] |
| tracker = dataset_config['TRACKERS_TO_EVAL'][0] |
| test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', file_loc) |
|
|
| for cls in classes: |
| results = {seq: raw_results['DAVIS'][tracker][seq][cls] for seq in raw_results['DAVIS'][tracker].keys()} |
| current_metrics_list = metrics_list + [trackeval.metrics.Count()] |
| metric_names = trackeval.utils.validate_metrics_list(current_metrics_list) |
|
|
| |
| test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, cls + '_detailed.csv')) |
|
|
| |
| for seq in test_data.keys(): |
| assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys()) |
|
|
| details = [] |
| for metric, metric_name in zip(current_metrics_list, metric_names): |
| table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()} |
| details.append(metric.detailed_results(table_res)) |
| res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], []) |
| res_values = sum([list(s[seq].values()) for s in details], []) |
| res_dict = dict(zip(res_fields, res_values)) |
|
|
| for field in test_data[seq].keys(): |
| assert np.isclose(res_dict[field], test_data[seq][field]), seq + ': ' + cls + ': ' + field |
|
|
| print('Tracker %s tests passed' % tracker) |
| print('All tests passed') |
