Muhammad khubaibraza imkhubaibraza
| sudo add-apt-repository ppa:graphics-drivers/ppa | |
| sudo apt update | |
| sudo apt upgrade | |
| wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin | |
| sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600 | |
| wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb | |
| sudo dpkg -i cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb | |
| sudo apt-key add /var/cuda-repo-ubuntu2004-11-1-local/7fa2af80.pub | |
| sudo apt-get update |
Here's a simple implementation of bilinear interpolation on tensors using PyTorch.
I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).
For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).
In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle
