def test(self):
self.cfg.model_flow = 'test'
print('======test======')
score_strs = []
score_summary = []
for test_dataset_name, test_dict in self.test_loader.items():
self.cfg.eval.test_feat_cache_file = osp.join(self.cfg.log.exp_dir, '{}_to_{}_feat_cache.pkl'.format(
self.cfg.dataset.train.source.name, test_dataset_name))
self.cfg.eval.score_prefix = '{} -> {}'.format(self.cfg.dataset.train.source.name, test_dataset_name).ljust(30)
score_dict = evaluation_creation(self.model_for_eval,
test_dict['query'],
test_dict['gallery'],
deepcopy(self.cfg))
score_strs.append(score_dict['scores_str'])
score_summary.append("{}->{}: {} ({})".format(self.cfg.dataset.train.source.name,
test_dataset_name,
score_str(score_dict['cmc_scores'][0]).replace('%', ''),
score_str(score_dict['mAP']).replace('%', '')))
score_str_ = join_str(score_strs, '\n')
score_summary = ('Epoch {}'.format(self.current_ep)).ljust(12) + ', '.join(score_summary) + '\n'
write_to_file(self.cfg.log.score_file, score_summary, append=True)
self.cfg.model_flow = 'train'
return score_str_
def _evaluation(self, score_strs, score_summary, test_dataset_name,
test_dict, use_gcn, use_gm):
score_dict = evaluation_creation(self.model_for_eval,
test_dict['query'],
test_dict['gallery'],
deepcopy(self.cfg),
use_gcn=use_gcn,
use_gm=use_gm)
score_strs.append(score_dict['scores_str'])
score_summary.append("{}->{}: {} ({})".format(
self.cfg.dataset.train.source.name, test_dataset_name,
score_str(score_dict['cmc_scores'][0]).replace('%', ''),
score_str(score_dict['mAP']).replace('%', '')))
def get_scores_str(mAP, CMC_scores, score_prefix):
return score_prefix + '[mAP: {}], [cmc1: {}], [cmc5: {}], [cmc10: {}]'.format(
score_str(mAP), score_str(CMC_scores[0]), score_str(CMC_scores[4]), score_str(CMC_scores[9]))