Hi Jeffli,
Thank you for your answer,
Will it works though ? The original layer that does the problematic correllation is a conv2d in pytorch, and a simple conv in the onnx conversion which should be supported by tensorrt according to Support Matrix :: NVIDIA Deep Learning TensorRT Documentation
Before engaging costly and risky developpement I would like to be sure that the custom layer will act better than the original one. Why is the operation not supported when applying convolution using a dynamic kernel ?
If you want to reproduce the problem you can find here the head of the onnx that I am trying to convert using trtexec.
rpn_head_2.onnx (28.6 MB)
The exact command line used to do so is:
trtexec --onnx=<…>/rpn_head_2.onnx --saveEngine=<…>/rpn_head_2.engine --shapes=input_1:3x1x256x7x7,input_2:3x1x256x31x31 --verbose
I tried with part of the model and the full model for the same results, tensorrt converter fails while counting 0 weight for this kernel (which partially make sense since the weight comes from the second input)
Thank you for your answer
By the way this issue is almost identical to the problem mentionned here:
but does not have a solution yet. The cross-convolution layer is in the core of the network and can not be efficiently replaced.