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FirasGit / medicaldiffusion

Medical Diffusion: This repository contains the code to our paper Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Synthesis

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Insufficient GPU memory

sunck1 opened this issue · comments

commented

Dear authors, I encountered an insufficient GPU memory problem, when I trained the VQGAN model based on Kits19 dataset. I found you trained this medical diffusion model with a 24GB memory GPU. According to your default parameter setting, I failed to train the VQGAN model( n_codes = 16384) on a 50GB memory GPU (although the batch size = 1). It seems like this model can be adopted to process the whole CT scan without cropping. What should I do to cope with this problem, not affecting the performance of your model? I would appreciate if you can give me a hand!!

commented

I encountered the same problem as well. When training on the A800 with 80GB of VRAM, I also experience out-of-memory issues.

commented

Dear authors, I encountered an insufficient GPU memory problem, when I trained the VQGAN model based on Kits19 dataset. I found you trained this medical diffusion model with a 24GB memory GPU. According to your default parameter setting, I failed to train the VQGAN model( n_codes = 16384) on a 50GB memory GPU (although the batch size = 1). It seems like this model can be adopted to process the whole CT scan without cropping. What should I do to cope with this problem, not affecting the performance of your model? I would appreciate if you can give me a hand!!

I have fixed this issue, and the modifications I made are as follows:

  1. I downgraded the PyTorch version from 2.0 to 1.20 to match the version used by the author.
  2. I modified the "downsample" parameter to be consistent with the supplementary materials of the paper.
commented

Dear authors, I encountered an insufficient GPU memory problem, when I trained the VQGAN model based on Kits19 dataset. I found you trained this medical diffusion model with a 24GB memory GPU. According to your default parameter setting, I failed to train the VQGAN model( n_codes = 16384) on a 50GB memory GPU (although the batch size = 1). It seems like this model can be adopted to process the whole CT scan without cropping. What should I do to cope with this problem, not affecting the performance of your model? I would appreciate if you can give me a hand!!

I have fixed this issue, and the modifications I made are as follows:

  1. I downgraded the PyTorch version from 2.0 to 1.20 to match the version used by the author.
  2. I modified the "downsample" parameter to be consistent with the supplementary materials of the paper.

I also encounter this Problem, could you explain it more in detail. Thank you so much for your help!!