Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc) : T4
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc) : RetinaNet
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
when converting the etlt RetineNet model to TenosrRT engine I am getting the error Assertion failed: numPriors * numLocClasses * nbBoxCoordinates == inputDims[param.inputOrder[0]].d[0]
following these instructions and running the tlt_converter via docker image nvcr.io/nvidia/tlt-streamanalytics:v3.0-py3
root@fe4184f4919f:/usr/lib/x86_64-linux-gnu# tlt-converter -k nvidia_tlt
-d 3,384,1248
-o NMS
-c /deepstream_tlt_apps_TRT7.2.1/models/retinanet/cal.bin
-e retinanet_resnet18_trt.int8.engine
-b 8
-m 1
-t int8
-i nchw
/models/retinanet/retinanet_resnet18.etlt
[INFO] Reading Calibration Cache for calibrator: EntropyCalibration2
[INFO] Generated calibration scales using calibration cache. Make sure that calibration cache has latest scales.
[INFO] To regenerate calibration cache, please delete the existing one. TensorRT will generate a new calibration cache.
[INFO] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
[INFO] Detected 1 inputs and 2 output network tensors.
[INTERNAL_ERROR] Assertion failed: numPriors * numLocClasses * nbBoxCoordinates == inputDims[param.inputOrder[0]].d[0]
/home/bcao/code/github/TRT7.2/TensorRT/plugin/nmsPlugin/nmsPlugin.cpp:244
Aborting…
Aborted (core dumped)