Error Loading pretrained weights

[is-cs]

Error Loading pretrained weights

[lshiwjx]

For the first issue, try to change the name of self.down to self.conv_res in

2s-AGCN/model/agcn.py

Line 72 in d4880e1

self.down = nn.Sequential(

For the second issue, try to change the name of self.l1 to self.l1a in

2s-AGCN/model/agcn.py

Line 145 in d4880e1

self.l1 = TCN_GCN_unit(3, 64, A, residual=False)

Or just re-train the model.
Sorry for this mistake, but it has been a long day and I forgot some details.

[is-cs]

Hi. Thank you for replying. There were other issues similar to these, so now i am retraining. But it will be great if you could release a working set of weights if you get time. Thanks again!

[xuanshibin]

thank your source code, but when I run this code, The following error occurs:
ValueError: num_samples should be a positive integer value, but got num_samples=0

I've run the program 'python data_gen/ntu_gendata.py 'before, and some documents were generated :
train_data_joint.npy
train_label.pkl
val_data_joint.npy
val_label.pkl

but their size are all 1K

How should I deal with, trouble you give directions.

thanks

[erinchen824]

For the first issue, try to change the name of self.down to self.conv_res in

2s-AGCN/model/agcn.py

Line 72 in d4880e1

self.down = nn.Sequential(

For the second issue, try to change the name of self.l1 to self.l1a in

2s-AGCN/model/agcn.py

Line 145 in d4880e1

self.l1 = TCN_GCN_unit(3, 64, A, residual=False)

Or just re-train the model.
Sorry for this mistake, but it has been a long day and I forgot some details.

I have the same problem, it seems only test joints on ntu-crossview, it will work. otherwise there will be the same problem of the model mismatch even if I try the above modifications.
Could you please help me with this? If you get any time, Thanks

[erinchen824]

[ Sun Oct 25 02:03:25 2020 ] Load weights from ./runs/ntu_cv_agcn_joint-49-29400.pt. [ Sun Oct 25 02:03:25 2020 ] using warm up, epoch: 0 [ Sun Oct 25 02:03:25 2020 ] Model: model.agcn.Model. [ Sun Oct 25 02:03:25 2020 ] Weights: ./runs/ntu_cv_agcn_joint-49-29400.pt. [ Sun Oct 25 02:03:25 2020 ] Eval epoch: 1 main.py:460: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad():instead. volatile=True) main.py:464: UserWarning: volatile was removed and now has no effect. Usewith torch.no_grad():instead. volatile=True) 100%|███████████████████████████████████████████| 74/74 [03:43<00:00, 3.03s/it] Accuracy: 0.012465666596239171 model: ./runs/ntu_cv_agcn_test_joint [ Sun Oct 25 02:07:09 2020 ] Mean test loss of 74 batches: 16.15059603871526. [ Sun Oct 25 02:07:09 2020 ] Top1: 1.25% [ Sun Oct 25 02:07:09 2020 ] Top5: 8.16% [ Sun Oct 25 02:07:10 2020 ] Done.

there might be some problem with the provided model.
I try it with ntu-cross-view on joints data with provided model, the result is too poor.
Is there anything wrong with the model or Did I do something wrong? Thanks

[erinchen824]

[ Sun Oct 25 02:03:25 2020 ] Load weights from ./runs/ntu_cv_agcn_joint-49-29400.pt. [ Sun Oct 25 02:03:25 2020 ] using warm up, epoch: 0 [ Sun Oct 25 02:03:25 2020 ] Model: model.agcn.Model. [ Sun Oct 25 02:03:25 2020 ] Weights: ./runs/ntu_cv_agcn_joint-49-29400.pt. [ Sun Oct 25 02:03:25 2020 ] Eval epoch: 1 main.py:460: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad():instead. volatile=True) main.py:464: UserWarning: volatile was removed and now has no effect. Usewith torch.no_grad():instead. volatile=True) 100%|███████████████████████████████████████████| 74/74 [03:43<00:00, 3.03s/it] Accuracy: 0.012465666596239171 model: ./runs/ntu_cv_agcn_test_joint [ Sun Oct 25 02:07:09 2020 ] Mean test loss of 74 batches: 16.15059603871526. [ Sun Oct 25 02:07:09 2020 ] Top1: 1.25% [ Sun Oct 25 02:07:09 2020 ] Top5: 8.16% [ Sun Oct 25 02:07:10 2020 ] Done.

there might be some problem with the provided model.
I try it with ntu-cross-view on joints data with provided model, the result is too poor.
Is there anything wrong with the model or Did I do something wrong? Thanks

I changed the layers name of the mismatched ones and reload the model, however, it seems all of the provided model isn't working well with a poor performance.
Start training from scratch.