Closed salehhp closed 5 months ago
Hi @salehhp,
I do not think there should be significant changes that are needed to run the toolbox on windows (apart from needing to re-format data paths to match the expected windows style). If you are using conda, you should hopefully be able to set up your environment properly using setup.sh
.
Outside of this there may be some light debugging needed.
In terms of CPU functionality - currently the toolbox only support GPU based training and inference. We will likely add CPU based inference as some point though not in the immediate future. If you need to adapt the code to run on cpu I would do the following things:
rPPG-Toolbox/neural_methods/trainer/DeepPhysTrainer.py
) change self.device = torch.device(config.DEVICE)
to self.device = torch.device('cpu')
. This will force tell the pipeline to use the CPU (instead of possible available gpus).torch.nn.DataParallel
in the trainer as well. These lines are used to split workloads across multiple compute devices. I think these two changes should get you 90% of the way there. I would start with DeepPhys, and ensure that you are able to run data from a known dataset (try UBFC-rppg) through the pipeline (to get acquainted with the toolbox) - This will likely save you debugging time later if you are running inference on custom videos.
Hope this helps!
Closing due to inactivity Feel free to re-open if need be.
Hi First of all thank you for sharing this toolbox. I need to use your toolbox in my project and test the captured videos with different algorithms of your toolbox. But I don't have access to Linux and in the toolbox description file, there is no explanation on how to use the toolbox in Windows 10. Please guide me how I can use your toolbox in Windows 10. Another question is whether it is possible to use toolbox by CPU? Thanks