Hello, I replaced the ordinary convolution operation with a graph convolution operation for the skeleton joint point data. The graph convolution operation takes up to 20 seconds in the loss.backward() step, while the ordinary convolution operation only requires 0.2 seconds. Is there any method to accelerate this process?
Versions
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 10 δΈδΈη
GCC version: (i686-posix-sjlj, built by strawberryperl.com project) 4.9.2
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: N/A
Python version: 3.12.2 | packaged by Anaconda, Inc. | (main, Feb 27 2024, 17:28:07) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 2080 Ti
Nvidia driver version: 536.23
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
π Describe the bug
Hello, I replaced the ordinary convolution operation with a graph convolution operation for the skeleton joint point data. The graph convolution operation takes up to 20 seconds in the loss.backward() step, while the ordinary convolution operation only requires 0.2 seconds. Is there any method to accelerate this process?
Versions
PyTorch version: 2.2.1+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A
OS: Microsoft Windows 10 δΈδΈη GCC version: (i686-posix-sjlj, built by strawberryperl.com project) 4.9.2 Clang version: Could not collect CMake version: version 3.26.4 Libc version: N/A
Python version: 3.12.2 | packaged by Anaconda, Inc. | (main, Feb 27 2024, 17:28:07) [MSC v.1916 64 bit (AMD64)] (64-bit runtime) Python platform: Windows-10-10.0.19045-SP0 Is CUDA available: True CUDA runtime version: 10.1.243 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2080 Ti
Nvidia driver version: 536.23 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True
CPU: Revision=21764
Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] torch==2.2.1+cu121 [pip3] torch_cluster==1.6.3+pt22cu121 [pip3] torch_geometric==2.5.2 [pip3] torch_scatter==2.1.2+pt22cu121 [pip3] torch_sparse==0.6.18+pt22cu121 [pip3] torch_spline_conv==1.2.2+pt22cu121 [pip3] torchaudio==2.2.1+cu121 [pip3] torchvision==0.17.1+cu121 [conda] numpy 1.26.4 pypi_0 pypi [conda] torch 2.2.1+cu121 pypi_0 pypi [conda] torch-cluster 1.6.3+pt22cu121 pypi_0 pypi [conda] torch-geometric 2.5.2 pypi_0 pypi [conda] torch-scatter 2.1.2+pt22cu121 pypi_0 pypi [conda] torch-sparse 0.6.18+pt22cu121 pypi_0 pypi [conda] torch-spline-conv 1.2.2+pt22cu121 pypi_0 pypi [conda] torchaudio 2.2.1+cu121 pypi_0 pypi [conda] torchvision 0.17.1+cu121 pypi_0 pypi