RuntimeErrror: There were no tensor arguments to this function - torch.cat(joints.shuffle) about rignet HOT 9 CLOSED

This seems like the joint prediction step doesn't get any joints. Could you check why there is no joint extracted by visualizing that step only? It might because of the threshold or because of some misalignment of vox and mesh.

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Does the learning threshold impact as well?( I tried to change ROOTNET's joints_shuffle to torch.tensor, but the problem remained, also tweaked quick_start's threshold a bit). For visualization I assume using from utils.vis_utils import draw_shifted_pts, show_obj_skel, show_mesh_vox. Thanks.

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The network only learns the bandwidth. The threshold need to be set by the user. Yes you can use draw_shifted_pts to visualize the shifted points, draw_joints to visualize intermedia joints after meanshift.

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What threshold would you advise for training human models only?(my collected dataset consists only of human models)

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I think this issue comes from run_root_cls.py. Joints_shuffle are not obsolete and in run_root_cls.py the last checkpoint rushes out an error, and I think that it corrupts the file.

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Epoch50. train_loss: 0.101977. Epoch50. val_loss: 0.817965. val_acc: 0.880000 Epoch50. test_loss: 1.348911. test_acc: 0.909091 => loading checkpoint 'checkpoints/rootnet\model_best.pth.tar' => loaded checkpoint 'checkpoints/rootnet\model_best.pth.tar' (epoch 41) Traceback (most recent call last): File "run_root_cls.py", line 184, in <module> main(parser.parse_args()) File "run_root_cls.py", line 111, in main test_loss, test_acc = test(test_loader, model, args) TypeError: test() takes 2 positional arguments but 3 were given

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I visualized and no joints were found. You were right, but what may be the problem? I viewvox-ed the binvox-es, and the axis are aligned with obj.s's axes. Do you have any other idea why this error might persist?

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I am closing this issue, because the problem is clear, but I need your advising. Sincerely,Artur.

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Running all the files in given order and deleting the former checkpoints that I used to override upon, solved the issue. But the prediction isn't as good, maybe because only 50 models were given. Anyway, thanks.

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