Comments (2)
Hi, I did not find a fast and straightforward CUDA implementation of standard convolutions so I was not able to integrate the method. Standard convolutions are hard to implement efficiently and cuDNN is closed source.
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Thanks for your reply, hoping that the method will be applied to standard convolutions and more other applications.
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Related Issues (14)
- A question about soft-mask calculation HOT 2
- question about the sparsity_target HOT 2
- About pose environment HOT 1
- Questions about mask generation HOT 2
- Questions about mask usage in convolution
- 发生异常: RuntimeError CUDA error: the launch timed out and was terminated File "/home/lym/Compare experiment new/classification/main_cifar.py", line 72, in main model = net_module(sparse=args.budget >= 0, pretrained=args.pretrained).to(device=device) File "/home/lym/Compare experiment new/classification/main_cifar.py", line 232, in <module> main()
- About multi-gpu training HOT 5
- Mask calculation HOT 6
- About the "Classification with efficient sparse MobileNetV2" HOT 2
- Ponder_Cost_Plotting
- Training on Google Colab HOT 1
- /annot/valid.json is missing HOT 3
- license HOT 3
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