(Reposting my question from https://devtalk.nvidia.com/default/topic/998570/jetson-tx1/could-not-parse-layer-type-python/post/5108818/#5108818 as requested by AastaLLL)
I’m trying to train my own DetectNet caffe model and run it using the sample_object_detector software provided in the DriveWorks SDK. I’ve trained my caffe model using the default DetectNet network (https://github.com/NVIDIA/caffe/blob/caffe-0.15/examples/kitti/detectnet_network.prototxt), but commenting out the last 4 python layers (cluster, cluster_gt, score, mAP) because both TensorRT and nvCaffe do not appear to support python layers.
However, after using TensorRT to optimize the model, running the TensorRT binary with sample_object_detector does not return any bounding boxes, whereas using the default TensorRT binary provided in sample_object_detector does.
How should we modify the DetectNet network / sample_object_detector source file to get the object detector to work with our own network?