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Scaling up the kernel size with D3D modules
Hi, thank you for the great work and currently I have used the D3D packages to perform deformable convolution for volumetric segmentation. The performance is pretty promising with additional tricks in block design. However, when I scale up my kernel sizes (e.g., 5x5x5, 7x7x7), it always pops up the CUDA error as "RuntimeError: CUDA error: an illegal memory access was encountered". Is the c++ code optimized for 3x3x3 kernel size only? If yes, what do you recommend to do for next step about changing the kernel size?
Thank you so much!
We scale up the kernel sizes to 5x5x5, 7x7x7, and can successfully run this code. Details are as follows:
Thank you for your reply. I also can perform the same thing in test.py. However, when I try to use it for semantic segmentation, the code just blows up with the following error.
I have used the following block design with the kernel size of 5 or 7:
Do you mind to give some suggestions about it?
Try to set the groups to 1.
Hi, thank you for the great work and currently I have used the D3D packages to perform deformable convolution for volumetric segmentation. The performance is pretty promising with additional tricks in block design. However, when I scale up my kernel sizes (e.g., 5x5x5, 7x7x7), it always pops up the CUDA error as "RuntimeError: CUDA error: an illegal memory access was encountered". Is the c++ code optimized for 3x3x3 kernel size only? If yes, what do you recommend to do for next step about changing the kernel size?你好,感谢你的出色工作。目前,我已经使用 D3D 软件包执行了用于体积分割的可变形卷积。通过在块设计中使用额外的技巧,性能相当不错。但是,当我扩大内核尺寸(例如 5x5x5、7x7x7)时,总是会弹出 CUDA 错误 "RuntimeError:CUDA error: an illegal memory access was encountered"。c++ 代码是否只针对 3x3x3 内核大小进行了优化?如果是,您建议下一步如何改变内核大小?
Thank you so much!非常感谢!
哥们 你怎么编译成功了 我编译好了 ,然后import不进来