Description
I was able to convert a Pytorch model which uses a custom deformable convolution layer to ONNX.
(model is centernet based on GitHub - xingyizhou/CenterNet: Object detection, 3D detection, and pose estimation using center point detection: with the deformed convolution replaced with GitHub - lbin/DCNv2: Deformable Convolutional Networks v2 with Pytorch)
While I was able to define the operation of the deformable convolution and create an onnx model, when trying to convert it to TensorRT the process fails as it doesn’t recognize the DCN layer:
`In node 135 (parseGraph): UNSUPPORTED_NODE: No importer registered for op: DCNv2_2`
Within these repos a cuda implementation was provided under
but these file are not seem to be complied during the building of the repo.
Can someone please provide a recipe on how to take a cuda implemented layer (.cu) and import it to TensorRT so that the conversion of an onnx model with the layer is processed smoothly?
Thanks in advanced
Environment
TensorRT Version: 0.0.1
GPU Type: Ti2080
Nvidia Driver Version: 440.100
CUDA Version: 10.1
CUDNN Version: 7
Operating System + Version: ubuntu 18.4
Python Version (if applicable): 3.7
TensorFlow Version (if applicable): -
PyTorch Version (if applicable): 1.7
Baremetal or Container (if container which image + tag):
Steps To Reproduce
To reproduce, follow installation instruction on both given repos