NAN in training process about iim

[taohan10200]

You can lower the learning rate of the threshold encoder in config.py, such as 1e-7.

if __C.OPT == 'Adam':
    __C.LR_BASE_NET = 1e-5  # learning rate
    __C.LR_BM_NET =  1e-7    #1e-6  # learning rate'

Thanks for your attention!

[Nikumata]

adjust the learning rate does works! Thanks for your reply.

[Nikumata]

Hi taohan@taohan10200 , after lowing the learning rate, NAN still appeared after 87 iterations. I saved the model and weights every 20 iterations, and felt amazed that based on 80th model, the model can be trained normally without NAN. Do you have any good suggestions?

By the way, there is no read_pred_and_gt module in misc.utils.py, causes vis4val.py cannot work properly, would you please commit this part codes？Thanks。

[taohan10200]

In our training, NaN would appear even if we lowered the threshold some times. At this time, we usually lower the threshold again to avoid this problem. We recommend using the experimental configuration we provide under folder saved_exp_results. In general, it may be the inverse gradients that make the module's training is instability. We have tried to solve this problem by optimizing the threshold learner, but it is still in testing, and we will update the new solution in the future.

We have updated the read_pred_and_gt module in misc.utils.py.

Thanks~

[henbucuoshanghai]

where is the path of the save model?