training speed about 4k4d

[dendenxu]

Hi, thanks for using our code first! Sorry for the late reply.

For the dynamic dataset, the released default config trains for 800k iterations (defined in r4dv.yaml with the epochs parameter). It typically only requires 400k iterations (epochs=800) to converge. Another thing to note is that we test the training speed without evaluation (runner_cfg.eval_ep=800) and report training metrics only every 100 iterations (runner_cfg.log_interval=100) to reflect the real training time.

The same story goes for the static scene. It only takes 2-3k iterations to converge.

The iteration speed looks fine (60-70ms) though. I'm not sure about the cause for the two experiments showing up, the VRAM usage seems OK.

Another thing to do to speed up the training is to use our latest CUDA-backend implementation, you can enable it via this option: https://github.com/zju3dv/4K4D/blob/712eccb0e0eeef744c19eb221cfb424a2915b474/easyvolcap/models/samplers/r4dv_sampler.py#L43C18-L43C27

As for the training PSNR, the 0013_01 scene is the harder of all four for the DNA-Rendering dataset thus its training PSNR is slightly lower.