training loss curve and weights for AEloss

[coordxyz]

hi @anewell , Thank you for releasing the training code. I tried to train the network on coco2014 with original settings except {'batchsize': 8, 'input_res': 256, 'output_res': 64}. The training loss curve looks so weird but the output map look right. I'm quite confused. Does this normal? Could you please share your training log?

In the line 102 of my_lib.c for push_loss calculation, why do you need to multiply the push_loss with 0.5?

100.     if(current_people>1)
     

101.         output_tmp[0]/= current_people*(current_people-1)/2;
     

102.     output_tmp[0] *= 0.5;
     

Why are the weights for pull_loss and push_loss so small(1e-3)? How did you choose the weights for pull_loss, push_loss and detection_loss？

[jiachen0212]

hello @bhyzhao , need you help.... when i cd /extensions/AE and run python build.py install i meet the bug: cffi.VerificationError: CompileError: command 'gcc' failed with exit status 1

i'm using py3.6 and pytorch 0.4 gcc is 4.85 (also i tried 4.91) . so i wonder which type of gcc is suitable?

[abcxs]

I want to know why use so small weights(1e-3), it is best ?

[coordxyz]

hello @bhyzhao , need you help.... when i cd /extensions/AE and run python build.py install i meet the bug: cffi.VerificationError: CompileError: command 'gcc' failed with exit status 1

i'm using py3.6 and pytorch 0.4 gcc is 4.85 (also i tried 4.91) . so i wonder which type of gcc is suitable?

hi, there, I'm using python 3.6.5 pytorch 0.4.0 gcc version 5.4.0

[coordxyz]

I want to know why use so small weights(1e-3), it is best ?

I tried to adjust it to higher value but failed, e.g. 1e-2. It seems 1e-3 just work well for this problem but I don't know why. And the training is really frustrated slow.