using --mode=visualize after trained the model, but I got the wrong pred_x.png

[ChinaYi]

the output file pred_x.png has no color except black.

[ChinaYi]

ok. I've seen the same problem in the previous issues. I'll try to add the batchsize up to 16,and try again. And I wish it will work.

[yeshenlin]

@ChinaYi Maybe you can use "logs/images/Image_Cmaped.ipynb" to visualize your output.

[ChinaYi]

@yeshenlin thanks, I've tried this, but did not work, actually, it turns an all-black png to an all-blue one.

[shekkizh]

@ChinaYi Is it possible that the model hasn't been trained for enough epochs?

[ChinaYi]

@shekkizh thanks for your help. My iteration times are set to 1e+5, and the best valid_loss is 0.566078. Results are similiar to groud_truth, but not enough comparing to your report. Is there any possible reasons?

[placentian]

Hi @ChinaYi : I am experiencing the same problem as you previously got. After training for 100K (default) iterations, the prediction image is just black. Did you do anything special to fix this issue?

Step: 99800, Train_loss:3.27614
Step: 99810, Train_loss:4.61153
Step: 99820, Train_loss:3.18354
Step: 99830, Train_loss:2.48784
Step: 99840, Train_loss:2.74431
Step: 99850, Train_loss:3.29662
Step: 99860, Train_loss:3.19142
Step: 99870, Train_loss:4.58435
Step: 99880, Train_loss:2.86515
Step: 99890, Train_loss:3.80537
Step: 99900, Train_loss:2.84969
Step: 99910, Train_loss:3.47618
Step: 99920, Train_loss:3.40244
Step: 99930, Train_loss:3.72526
Step: 99940, Train_loss:3.00145
Step: 99950, Train_loss:3.51405
Step: 99960, Train_loss:2.82784
Step: 99970, Train_loss:2.75893
Step: 99980, Train_loss:2.42421
Step: 99990, Train_loss:3.21162
Step: 100000, Train_loss:2.66081

[ChinaYi]

@placentian it seems that your loss has been fluctuated with the iteration times and haven't reached the bottom. This is not a good training process. You'd better enlarge the batch size and try to reduce to learning rate. I have been suffered from the same issues like you, you got black picture since you have a large loss. cheer up!

[placentian]

Thanks a lot for your encouragement, @ChinaYi . Do I also need to change the iteration to some other numbers (like 200K or 500K)?

What kind of reasonable learning rate and batch size would you recommend by any chance?

[placentian]

I got better results after reducing the learingrate but they are not as good as @shekkizh 's results. Dont know what else we should try.

[ChinaYi]

@placentian can you plot you loss curve? Or paste the Train_loss for each iteration

[CLegendBuptSun]

Maybe I can explain why we got black outputs.
When I run the code on 1 GPU Titan Xp (memory 12GB), the original code's performance is good.(Batchsize=2 ,lr=1e-4,itr=30000)
When I run the code on 2 GPU Titan Xp (memory 24GB), the original code's performance is terrible.(Batchsize=2 ,lr=1e-4,itr=30000).But I changed the Batchsize=8 and lr=1e-4 when itr<20000,lr=1e-5 when lr>20000,the performance is better than run on 1GPU.
So your configuration of computer may affect your results.
So try to change these basic arguments, you will get the satisfied output.