Open MokkeMeguru opened 4 years ago
Tensorflow Probability の実装はオリジナルの Glow のそれと違う気がする…
ちゃんと数値実験して見る必要がありそう
The sampled images are a bit dark as iterations increase... Do you think this is a problem caused by conv1x1?
It's preprocess problem... I 'm working more better repo now.
@gitlabspy sorry, I takes two weeks to generate the best example...
Can you check this simple packaged example?
It is the trained result with 2048 epoch. but you can get the same result with 1024 epochs. You can try
python task.py --epochs=1024
https://drive.google.com/file/d/1Zyo7DEA7fX5VNf9MhRkDZ8q9n9AxRFjR/view?usp=sharing
and then, you can check the result using tensorboard. (If you can checked it, please comment me)
tensorbaord --logdir outputs/
@MokkeMeguru Sorry my friend, I didn't notice that you relied. Thing is I am having trouble downloading things from google drive so that I can not test it for you... I am so sorry...
I found a line of code which kinda looks like a little bug to me: https://github.com/MokkeMeguru/TFGENZOO/blob/3dc42af0fea028e74b78dda989d6da77c8854379/TFGENZOO/flows/factor_out.py#L77 I think it should be mean 0 and std 1 , right? std 0 means all data points are identical. So I think it might be caused by copy-paste right? 😄
not std, its "logged std" https://github.com/MokkeMeguru/TFGENZOO/blob/3dc42af0fea028e74b78dda989d6da77c8854379/TFGENZOO/flows/utils/gaussianize.py#L6
so... if logstd = 0, then std = exp(logstd) = exp(0) = 1
and sorry, I did not know how to share my result... can you give me any advice? I need the method to share the experiment result with this problem and also, with other problems.
You mean results from your trained model?
Maybe randomly sample some images from the model and display them into one single figure? Or calculate the bit/dim
?
so that, i should write more better docs in this repos. wait a moment... i'll working heavily in my new company now...
😂 I heard of some stories about how serious overtime is in Japan... Take care my friend!
@MokkeMeguru Hi, recently I found that using bigger size glow (level=4 or more K=18 something) will cause large memory allocation in physical memory of CPU like 20G and more and memory leaking slowly. When bigger dataset like 600K+ images it will allocate 180G RAM... GPU's memory usage is fine though. Do you know what causes this? Thx!
I think,
or ... I don't know what happened...
Hmm... I think it might be something related to the autograph compilation, what you think about this?
tensorflow probability's inv1x1conv implementation is stable than original one.
but I don't know whether formula is correct or not.
and generate images are not good...
TODO: check the formula more carefully.