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 print_log(cfg, current_ep, current_step, optimizer, loss_functions,
analyze_functions, epoch_start_time):
time_log = 'Ep {}, Step {}, {:.2f}s'.format(current_ep + 1,
current_step + 1,
time.time() - epoch_start_time)
lr_log = 'lr {}'.format(get_optimizer_lr_str(cfg, optimizer))
loss_meter_log = join_str([
m.avg_str for lf in loss_functions.values()
for m in lf.meter_dict.values()
], ', ')
if analyze_functions is not None:
analyze_meter_log = join_str([
m.avg_str for lf in analyze_functions.values()
for m in lf.meter_dict.values()
], ', ')
else:
analyze_meter_log = None
if analyze_meter_log is not None:
log = join_str([time_log, lr_log, loss_meter_log, analyze_meter_log],
', ')
else:
log = join_str([time_log, lr_log, loss_meter_log], ', ')
return print(log)
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)
if self.cfg.only_test is True:
self._evaluation(score_strs,
score_summary,
test_dataset_name,
test_dict,
use_gcn=False,
use_gm=False)
self._evaluation(score_strs,
score_summary,
test_dataset_name,
test_dict,
use_gcn=True,
use_gm=False)
self._evaluation(score_strs,
score_summary,
test_dataset_name,
test_dict,
use_gcn=True,
use_gm=True)
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_