Hlings
Training Error!
Hello,
When I run the code for the training, I get an error. The error:
Can you share the versions of the python, torch etc. that you used?
Thank you in advance!
Hi I ran the experiments on Python 3.7 or 3.8 and pytorch 1.7. If the env can run the YOLO v5, then it can also run this repo. And, btw, I haven't met such bug :( This part is the same as YOLO v5 official repo.
Hi, You can fix the above bug by changing the line number 288 in loss.py indices.append((b, a, gj.clamp_(0, gain[3].long() - 1), gi.clamp_(0, gain[2].long() - 1))) just add .long() to gain[3] anf gain[2] to match the data types.
I hope it helps!
Hello again,
Thank you both. It works now. I have two more questions. Why are multiple ap values calculated even though I only have one class for two domains? As you can see in the fig. Which one should I take as the correct value?
And also can you explain the variables in the pic.? Why are you using these variables?
Hi, Good that it worked!
As for the multiple AP values values in the list, I think they are per sample (image in validation set) ap values. But for overall you would be getting map@50 and map@50 - 95, consider the one as per your requirement.
The above variables basically indicate the total batch size, for example 12 , then how much proportion must be taken from real source dataloader (bs_source) i.e 11, the rest must be taken from target dataloader (bs_target), i.e 1 as per the proportions taken by author.
Among the source samples what proportion should be taken for the target-similar samples (bs_topk) i.e 8. Only bs_topk are taken closest distance wise, rest are not considered, to compensate for the discarded amount of samples, bs_add amount, i.e 3, are added back to the batch, concat bs_add the with closest distance again...
At the end a batch is contains [(8 target-similar source samples), (1 real target samples), (3 top most target-similar source samples )].
This is what I understood from the code, If you want any other details or if I was not able to make it clear enough, please refer to code lines 276-355 in train_MMD.py.
Hope it Hepls!