non_max_suppression causes CUDA out of memory.

[wongkinyiu]

non_max_suppression causes CUDA out of memory.

[WongKinYiu]

you could increase epoch number for start test
https://github.com/WongKinYiu/yolor/blob/main/train.py#L335

or pass conf_thres=0.1 into test function
https://github.com/WongKinYiu/yolor/blob/main/train.py#L336

[trungpham2606]

@ixez
I have the same problem with you, but then I can train the model by setting the value of rect in here to store_false.

But the problem is after nearly 100 epochs; during inference, the model outputs nothing at all.

[trungpham2606]

Dear @WongKinYiu ,
I have a question, the rect option is set to False as default, so what is the difference between setting it to False or True ?
And when set it to False, I cant train the yolor because of OOM.
I had set the conf_thres=0.0001, the results are so random. The loss during training was around 0.02

[WongKinYiu]

--rect is not suggested to use due to it do not shuffle data in default.
with --rect: no mosaic augmentation, so the gt will be ~1/4, and the memory used for compute loss will reduce to 1/4.

[trungpham2606]

@WongKinYiu
So with 11Gb memory of card, how can I do to be able to train the yolor ?

[ixez]

@WongKinYiu
Thanks for the reply.
Starting testing early doesn't solve the problem, but delays it.

Any other insights about the cause?

[ixez]

@WongKinYiu Regarding the second advice, is the problem caused by too many bboxes when testing?

[janchk]

@WongKinYiu Regarding the second advice, is the problem caused by too many bboxes when testing?

Yes. I figured out that it is.
To fix this you could uncomment this lline

yolor/utils/general.py

Line 336 in b168a4d

# x = x[x[:, 4].argsort(descending=True)]

And add some clipping for reasonable amount of predictions for example
x = x[0:10000, :]