**• Hardware Platform Jetson Xavier AGX
• DeepStream Version: 5.0
• TensorRT Version: 7.1
Hi,
After exporting Retinanet int8 model files (.engine .etlt .bin) using the jupyter example, i’m trying to convert the model for use with the deepstream python app “imagedata-multistream”.
Unfortunately, i’m receiving this error at the final step of the TLT guide:
chmod +x tlt-converter
export API_KEY=################################
export OUTPUT_NODES=NMS
export INPUT_DIMS=3,416,416
export D_TYPE=int8
export ENGINE_PATH=/mnt/Xavier/trt.int8.engine
export MODEL_PATH=/mnt/Xavier/retinanet_resnet_epoch_019.etlt
export CALIB=/mnt/Xavier/cal.bin
./tlt-converter -k $API_KEY -o $OUTPUT_NODES -c $CALIB -d $INPUT_DIMS -e $ENGINE_PATH $MODEL_PATH
[ERROR] UffParser: Validator error: FirstDimTile_4: Unsupported operation _BatchTilePlugin_TRT
[ERROR] Failed to parse the model, please check the encoding key to make sure it’s correct
[ERROR] Network must have at least one output
[ERROR] Network validation failed.
[ERROR] Unable to create engine
Segmentation fault (core dumped)
I’ve tried the solution offered here:
But i’m still getting the same error.
***I’ve downloaded the latest version from GitHub - NVIDIA/TensorRT: NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. · GitHub.
***For some reason these commands don’t produce an “out” folder, but the make is still successful:
/usr/local/bin/cmake .. -DGPU_ARCHS=72 -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=
pwd/out
make nvinfer_plugin -j$(nproc)
Any other solutions, other than the one to do with plugin.so?