jinhuan-hit
jinhuan-hit
@zhufengli Hi,I meet the same problem.Coould you please tell me how to solve it? MY ENV ubuntu 16.04 tensorflow 1.10.0 python 2.7 cuda 9.0 cudnn 7.5
I will update it for multi-gpus after I reproduce it. Maybe next week, now the time is not enough.
> @lorenmt Good point! Thanks a lot! > @jinhuan-hit If you're still working on this, `Accelerate` seems a good place to start. And it's perfectly ok if you don't want...
> @jinhuan-hit Thanks a lot! I still don't have the hardware to debug multi-GPU for now. But hopefully I'll be able to debug this month/the next. > The problem seems...
> > > @lorenmt Good point! Thanks a lot! > > > @jinhuan-hit If you're still working on this, `Accelerate` seems a good place to start. And it's perfectly ok...
> @jinhuan-hit If the results are similar compared to single card under mixed precision, maybe you'd like to send a pull request for this? Yeah, I'm checking the results now....
> Thanks a lot! If a PyTorch version update that concerns code change is necessary for using Accelerate, please make the version update & multi-GPU in 2 separate PRs, if...
I have checked the result and it looks normal!
@soldatjiang Thank you for sharing the way to predict bbox and the result is ok.However,why is the bbox like parallelograms？
@dingshenglan @lx-onism You can use them by giving them as arguments,check issue #5,for example: ./imagenet_classifier.py --batch_size=128 --model_path=./models/imagenet/m1/m1.ckpt --train_or_validation=train