Is the metric or something wrong?

Thank you for sharing your code.

I pulled your code repository and set up the Human3.6M dataset according to your instruction. After that, I’d like to test “Algebraic triangulation” so ran the following command: python3 train.py --eval --eval_dataset train --config eval/human36m_alg.yaml --logdir ./logs And then, I got the following message:

args: Namespace(config=‘eval/human36m_alg.yaml’, eval=True, eval_dataset=‘train’, local_rank=None, logdir=‘./logs’, seed=42) Number of available GPUs: 1 Loading pretrained weights from: ./data/pretrained/human36m/pose_resnet_4.5_pixels_human36m.pth Reiniting final layer filters: module.final_layer.weight Reiniting final layer biases: module.final_layer.bias Successfully loaded pretrained weights for backbone Successfully loaded pretrained weights for whole model Loading data… Experiment name: eval_human36m_alg_AlgebraicTriangulationNet@11.11.2019-21:14:55 /home/akihiko/MV3Dpose/learnable-triangulation-pytorch/mvn/datasets/human36m.py:220: RuntimeWarning: invalid value encountered in true_divide action_scores[k] = v[‘total_loss’] / v[‘frame_count’] /home/akihiko/MV3Dpose/learnable-triangulation-pytorch/mvn/datasets/human36m.py:220: RuntimeWarning: invalid value encountered in double_scalars action_scores[k] = v[‘total_loss’] / v[‘frame_count’] Done.

It seems to work but I got a huge error. For example, the average “per pose error” is about 1200 and it means 1200 mm, doesn’t it?

Thank you.

[qiuzhongwei-USTB]

@aki34 v[‘frame_count’] could be zero. I obatin right results by adding the following codes. for k, v in action_scores.items(): if v[‘frame_count’] > 0: action_scores[k] = v[‘total_loss’] / v[‘frame_count’] else: action_scores[k] = 0

[shrubb]

@aki34 I just pushed a large reproducibility update, please check if it brings you a lower error.