fix bug in 3d group convolution#9860
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@stephenyan1231 could you also add appropriate test cases to cover your fix? |
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Summary: Pull Request resolved: pytorch#9860 For 3D group convolution, in the case of CUDNN 7 and NCHWD order, filter dim is (M, C/group_, k_h, h_w, k_d). According to CUDA doc (https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#grouped-convolutions), the existing implementation is incorrect, and will crash the 3d video model training with group convolution. In the implementation, `filter.dims(1)` is already `C/group_`. So don't need to divide it by `group_` again. Differential Revision: D9008807 fbshipit-source-id: 5c2ebe12a0db875d593e0457136aaab6ecf27660
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xiaomengy
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Jul 31, 2018
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Thanks Zhicheng, I tested it and it should works. I will update the related group conv test so that we can test it in the future.
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Summary: Pull Request resolved: pytorch#9860 For 3D group convolution, in the case of CUDNN 7 and NCHWD order, filter dim is (M, C/group_, k_h, h_w, k_d). According to CUDA doc (https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#grouped-convolutions), the existing implementation is incorrect, and will crash the 3d video model training with group convolution. In the implementation, `filter.dims(1)` is already `C/group_`. So don't need to divide it by `group_` again. Reviewed By: BIT-silence Differential Revision: D9008807 fbshipit-source-id: 2f0d6eb47f4e16d7417a7e3baeba709e3254154f
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Summary:
For 3D group convolution, in the case of CUDNN 7 and NCHWD order, filter dim is (M, C/group_, k_h, h_w, k_d).
According to CUDA doc (https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#grouped-convolutions), the existing implementation is incorrect, and will crash the 3d video model training with group convolution.
In the implementation,
filter.dims(1)is alreadyC/group_. So don't need to divide it bygroup_again.Differential Revision: D9008807