Thank you.
I tried to generate fp16 on my desktop, but it said unsupported. Perhaps you meant on the Jetson.
However I moved to step 2. I downloaded the models, and copied the maskrcnn model from there to the Jetson. I tried to convert the model after updating the key. I then got the same “Unsupported Operation” error as before, but with MultilevelProposeROI instead of GenerateDetection_TRT. I searched for MultilevelProposeROI, and found this document:
https://docs.nvidia.com/metropolis/TLT/pdf/Transfer-Learning-Toolkit-Getting-Started-Guide-IVA.pdf
I searched for MultilevelProposeROI and found the following sentence in that PDF:
TensorRT OSS build is required for FasterRCNN, SSD, DSSD, YOLOv3, RetinaNet, and MaskRCNN models.
WOW. That’s not what the deepstream_tlt_apps github repo says here:
Instead that README says:
Prerequisites: TensorRT OSS (release/7.x branch) This is ONLY needed when running SSD , DSSD , RetinaNet and YOLOV3 models because BatchTilePlugin required by these models is not supported by TensorRT7.x native package.
So because I saw that TensorRT OSS is only needed for SSD, DSSD, RetinaNet, and YOLOV3, I believed it was not needed for MaskRCNN. Furthermore, I did see a binary for TensorRT OSS but that said version 7.0.0 and my jetson has 7.1.3, so I worried it was not compatible anyway. (however the binary I built from source also says 7.0.0 and it works)
However, now my problem appears solved. I can convert the models on my Jetson (both the one downloaded from box.com and the one from the NGC transfer learning docker container that I trained on COCO).
It seems that the documentation on deepstream_tlt_apps should be updated. I read that multiple times before and if it said “MaskRCNN” I would have tried it.
The jupyter notebook in the NGC also does not mention that the Jetson needs TensorRT OSS, so that file should be updated. It is in the container nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 at “notebooks/examples/maskrcnn/maskrcnn.ipynb”.
Thank you for helping narrow down the issue.