Kornia VS CenterNet

👉 https://github.com/kornia/kornia/releases/tag/v0.6.6

Kornia: Differential Computer Vision\ (3 comments)

Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Between "not being sure yet" about GPU operations in pre-processing and choosing high-end CPUs, I think you are overthinking the wrong alternative. Besides DALI, check whether you are using codecs besides nvidia/torchvision-supported jpeg and png, and if other GPU CV libraries meet your needs: torchvision kornia

Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia

CenterNet comes to mind

Found relevant code at https://github.com/xingyizhou/CenterNet + all code implementations here

I am trying the implement this paper https://github.com/xingyizhou/CenterNet

Take a look at this centernet architecture.

Yeah, I'd run into this 2 years ago and ended up also reporting an issue on the Centernet repo [1]

The solution I have in that repo adapts from the very helpful discussions in the original Pytorch issue [2]

I will admit that this is *very* easy to mess up as evidenced by the fact that examples in the official tutorials for Pytorch and other well known code-bases suffer from it. In the Pytorch training framework I've developed at work, we've implemented a custom `worker_init_fn` as outlined in [1] that is the default for all "trainer" instances who are responsible for instantiating DataLoaders in 99% of our training runs.

[1] https://github.com/xingyizhou/CenterNet/issues/233

[2] https://github.com/pytorch/pytorch/issues/5059