Comments (11)
suagr_model will load the dataset to the GPU by default, which will consume a lot of GPU memory.
So, you can try the fllowing steps:
- modified this code,
image_height=image_height, image_width=image_width, data_device='cpu')
- move the image to cuda during training process, modified this code
gt_image = nerfmodel.get_gt_image(camera_indices=camera_indices).cuda()
- same action in
coarse_density.py
andrefined.py
By use this method,
2000+images on my 24GB device 4090 is okay.
Hope this help.
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My GPU is OK: 12G Beggar's,700*1200 size, 240 images.
- reduce image size
- reduce gauss-points (important, BUT maybe your images are very complex.)
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if the error is directly related to pytorch reserving GPU memory, you can try adding:
os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"]= "1"
and clearing the cache with
torch.cuda.empty_cache()
Hope that helps
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Julius, Are you reducing image size bwfore processing? Got OOM again. My dataset is only 26 images at 1900x1423.Best regards,Reynel RodriguezOn Dec 22, 2023 01:40, Reynel Rodriguez @.> wrote: Please disregard previous email, I had mistyped the suggestion and formatted the parentheses in …camera_indices).cuda() as …camera_indices.cuda()) My mistake. I’m rerunning my datasets again now. Will report back with results. Anttwo, please let me know if you would like me to share results of my dataset with you to showcase quality. Sent from Mail for Windows From: julius Sent: Thursday, December 21, 2023 9:09 PM To: Anttwo/SuGaR Cc: Cuban Tony Stark; Comment Subject: Re: [Anttwo/SuGaR] cuda out of memory (Issue #40) suagr_model will load the dataset to the GPU by default, which will consume a lot of GPU memory. So, you can try the fllowing steps: modified this code, image_height=image_height, image_width=image_width, data_device='cpu') move the image to cuda during training process, modified this code gt_image = nerfmodel.get_gt_image(camera_indices=camera_indices).cuda() same action in coarse_density.py and refined.py By use this method, 2000+images on my 24GB device 4090 is okay. Hope this help. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.>
No,I did not reduce the images size.
What GPU are you using and how much memory?
And which step does OOM happen?
In my experiments, I found that the final texture mesh extraction stage requires a large amount of GPU memory.
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suagr_model will load the dataset to the GPU by default, which will consume a lot of GPU memory.
So, you can try the fllowing steps:
- modified this code,
image_height=image_height, image_width=image_width, data_device='cpu')
- move the image to cuda during training process, modified this code
gt_image = nerfmodel.get_gt_image(camera_indices=camera_indices).cuda()
- same action in
coarse_density.py
andrefined.py
By use this method, 2000+images on my 24GB device 4090 is okay.
Hope this help.
It's very useful, thanks!!!
from sugar.
suagr_model will load the dataset to the GPU by default, which will consume a lot of GPU memory.
So, you can try the fllowing steps:
modified this code, image_height=image_height, image_width=image_width, data_device='cpu')
move the image to cuda during training process, modified this code gt_image = nerfmodel.get_gt_image(camera_indices=camera_indices).cuda()
same action in coarse_density.py and refined.py
By use this method, 2000+images on my 24GB device 4090 is okay.Hope this help.
Verified this works well!
Besides the suggestion above, i also change device type here from cuda to cpu
from sugar.
Related Issues (20)
- About SDF of space position p
- About extract_refined_mesh_with_texture.py HOT 1
- Confusion about cum_probs in sample_points_in_gaussians() HOT 2
- OSError: [WinError 123] The filename, directory name, or volume label syntax is incorrect: 'C:\\Users\\ruibm\\Desktop\\SugerTests\\sugarfine_C:'
- Artifacts on a reomved/black_background data HOT 10
- Cuda device shows not available on EC2 instance HOT 2
- Windows Implement, Success! HOT 11
- CUDA mismatch error while installing in docker. HOT 2
- Question about the Equation(1) implementation HOT 2
- Blank Viewer - no issues in inspect
- Foreground and Background meshes are empty HOT 3
- The process of generating mesh takes too long
- AssertionError: Could not recognize scene type! HOT 2
- Bin size was too small in the coarse rasterization phase. This caused an overflow, meaning output may be incomplete.
- index error
- Joint Refine
- Simultaneous editing of Gaussian and mesh
- Problem Extracting Mesh Without 'use_centers_to_extract_mesh' Option
- Difficulty in extracting an accurate mesh in a case study using SuGaR HOT 1
- simple knn install error
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