byungjae89 / mahalanobisad-pytorch Goto Github PK

@byungjae89 I have test the code,but I cant get the results.
bottle ROCAUC: 1.000
cable ROCAUC: 0.940
capsule ROCAUC: 0.923
carpet ROCAUC: 0.955
grid ROCAUC: 0.929
hazelnut ROCAUC: 0.987
leather ROCAUC: 1.000
metal_nut ROCAUC: 0.931
pill ROCAUC: 0.834
screw ROCAUC: 0.812
tile ROCAUC: 0.974
toothbrush ROCAUC: 0.958
transistor ROCAUC: 0.959
wood ROCAUC: 0.976
zipper ROCAUC: 0.979
Average ROCAUC: 0.944

Loaded pretrained weights for efficientnet-b0
| feature extraction | train | bottle |: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:32<00:00, 4.58s/it]
Traceback (most recent call last):
File "main.py", line 150, in
main()
File "main.py", line 66, in main
mean = torch.mean(torch.cat(train_output, 0).squeeze(), dim=0).cpu().detach().numpy()
RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, Autograd, Profiler, Tracer, Autocast]

Thank you very much for your excellent work！
In the evaluation part of your code, only the auroc part is calculated. If it is used in inference, how to calculate the threshold of anomaly detection?