def display_save(self):
args = vars(self.opt)
# Display settings
print('------------ Options -------------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
print("[#GPUs] Using %d / Available %d " % (len(self.opt.device_ids), torch.cuda.device_count()))
# Save settings
if not isinstance(self, TestOptions):
option_file_path = os.path.join(self.opt.results_dir, 'opt.json') # not yaml file indeed
save_json_pretty(args, option_file_path)
def display_save(self, options, results_dir):
"""save config info for future reference, and print"""
args = vars(options) # type == dict
# Display settings
print('------------ Options -------------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
# Save settings
if not isinstance(self, TestOptions):
option_file_path = os.path.join(results_dir, 'opt.json') # not yaml file indeed
save_json_pretty(args, option_file_path)
def main_eval():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--gt_path", type=str, default="data/tvqa_plus_val.json",
help="ground-truth json file path")
parser.add_argument("--pred_path", type=str,
help="input prediction json file path, the same format as the results "
"returned by load_tvqa_plus_annotation func")
parser.add_argument("--word2idx_path", type=str, default="data/word2idx.json",
help="word2idx json file path, provided with the evaluation code")
parser.add_argument("--output_path", type=str,
help="path to store the calculated metrics")
parser.add_argument("--no_preproc_pred", action="store_true",)
args = parser.parse_args()
# Display settings
print('------------ Options -------------')
for k, v in sorted(vars(args).items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
groundtruth = load_tvqa_plus_annotation(args.gt_path)
if args.no_preproc_pred:
prediction = load_json(args.pred_path)
else:
prediction = load_predictions(args.pred_path, args.gt_path, args.word2idx_path)
word2idx = load_json(args.word2idx_path)
bbox_metrics = compute_att_metrics_using_maskrcnn_voc(prediction["bbox"], groundtruth["bbox"], word2idx)
temporal_metrics = compute_temporal_metrics(prediction["ts_answer"], groundtruth["ts_answer"])
all_metrics = merge_dicts([bbox_metrics, temporal_metrics])
print("QA Acc. {}\nGrd. mAP {}\nTemp. mIoU{}\nASA {}"
.format(all_metrics["qa_acc"], all_metrics["overall_map"],
all_metrics["miou"], all_metrics["[email protected]"]))
if args.output_path:
save_json_pretty(all_metrics, args.output_path)