With TensorRT 3.0.4, cudnn 7, and CUDA 9 I’ve found that a model using grouped deconvolutions is about twice as slow as the same model with non-grouped deconvolutions. I originally trained my model with grouped deconvolutions with mxnet and then padded the weights with zeroed weight values to approximate the same operation with non-grouped deconvolutions and found that the non-grouped model ran twice as fast. Is this expected? I assumed such an optimization would allow for significantly less operations since I’m just using this to bilinearly upsample my feature maps.
I found another forum post indicating that grouped deconvolution in tensorrt is implemented as a single kernel invocation for each feature channel. This equivalently becomes several hundred kernel invocations per layer instead of one. Is there a timeline for a fix for this from nvidia?
I have the same issue as moodie. Would also appreciate a fix for this.
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Please file a bug here: https://developer.nvidia.com/nvidia-developer-program
Please include the steps/files used to reproduce the problem along with the output of infer_device.