Pre-trained Model

[Xadra-T]

Hi there,
When I tried to test the pre-trained model, it said:

loading model from ./results/hourglass_1.pth
{'epoch': 14, 'MPE': 7.700112, 'AUC': 0.8504827899520097}

and the result was:

[epoch -1][MPE 25.175][AUC 0.530]

I tried two times with Google Colab. One time I installed the requirements and the other time I did not. Both gave the same result. Any help is appreciated.

(Btw, the hourglass_1 results (hourglass_1.txt) gives the expected error value (7.7)).

[Elody-07]

Hi，

Sorry for the delay. We didn't specify in the paper, but the hourglass model is trained with kernel_size=0.4 while resnet is trained with kernel_size=0.8. You may try out different settings. We have updated and debugged our codes. You may get the expected result by running train.py now.

[Xadra-T]

Hi,
Thanks for the updates.
I'm getting memory error with the new test function. Could you please look into it? (running without the train part)

I trained the model with the new train.py. The only differences that I know of are

1. 4 workers instead of 8
2. torch version is 1.10.0+cu113

Using the old test function, still the error is too high (1-stage HG 10.2 vs 7.7). Could it be because of these two?

In the line 42 of the config, it says:
kernel_size = 0.4 # 0.4 for hourglass and 1 for resnet
perhaps the 1 should change to 0.8

Also in the line 160 (and 149) of hourglass.py there is this line:
combined_feature.append(feature)
but combined_feature is not used anywhere. Was it for experiments?

[Elody-07]

Hi,
Sorry for the delay, I have missed the notifications of github. If you have trouble with memory during inference, you can try decrease batch_size or num_workers. This should not affect the network's performance.

combined_feature is for experiments.

If you find errors in our code, feel free to pull a request.