Comments (6)
alihassanijr
commented on June 27, 2024
Thank you for your interest.
Unfortunately we don't have a cpu version yet. Were you unable to install the extension, or do you not have CUDA?
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AntonyDrovalov
commented on June 27, 2024
Hello!
Can I run inference without using a cuda extension? (for example, if I have macos operating system)
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alihassanijr
commented on June 27, 2024
Unfortunately that is not possible at the moment because we don't have a cpu implementation. We plan to release one in the future.
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AntonyDrovalov
commented on June 27, 2024
Unfortunately that is not possible at the moment because we don't have a cpu implementation. We plan to release one in the future.
Thanks for reply. Looking forward to this update.
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Geek-lixiang
commented on June 27, 2024
Thank you very much for your reply.
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alihassanijr
commented on June 27, 2024
We released the pure PyTorch version (PR #23 ) along with a new version of the kernel.
I'd like to thank @qwopqwop200 for their implementation of our RPB, which was used instead of our own in the PyTorch version.
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Related Issues (20)
- Can you release your training log of NAT? I mean, the summary.csv in output folder. HOT 3
- ONNX HOT 2
- How to visualize the attention map? HOT 3
- Welcome update to OpenMMLab 2.0 HOT 1
- Is it possible to do upsampling using NAT ? HOT 2
- Where is natten.py
- May I ask whether the code of coco instance segmentation mask2former is dinat or NAT? HOT 1
- some problem during train HOT 9
- Is DiNAT code is runnable? HOT 2
- Is dectect model available? HOT 2
- freeze_at be set to 2 to freeze the pretrained weight downloaded from the official website? HOT 2
- About the receptive field of image pixel HOT 4
- NAT Tiny performance on ImageNet 1k HOT 7
- training from scratch with different size for height and width HOT 3
- Cannot repeat the results of Mask2Former+DiNAT-Large on ADE20K HOT 12
- mmdetection on COCO2017 not converge HOT 1
- How to calculate the number of params? HOT 1
- For 3D segmentation HOT 2
- instance segmentation mask2former + dinat HOT 1
- Some comparisons against Deformable Attention HOT 4
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