How preform inference retinanet using a TLT export .engine file by python

I use TLT V2.0 training retinanet resnet 50 model, and export .engine file.
I don’t need deepstream .
How preform inference by python?
I only search SSD model inference sample and very old sample.

Officially, TLT provides tlt-infer for inference. They can run inference against tlt model.
Some detection networks also provide the command how to run inference against trt engine. Such as , detectnet_v2, faster_rcnn, etc. See tlt user guide or jupyter notebooks for more details.

If end user wants to run inference against trt engine without tlt-infer or deepstream, they need to write their own codes.

Reference topics:
For classification network,

For detectnet_v2 network,

Reference post-processing:
https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/tree/release/tlt2.0.1/nvdsinfer_customparser_ssd_tlt

How run tlt-infer in the jetson nx ?

Reference: https://docs.nvidia.com/metropolis/TLT/archive/tlt-20/tlt-user-guide/text/deploying_to_deepstream.html#id11

Please copy etlt model into Jetson NX, then config the file with deepstream and then run inference with deepstream.

Or
Copy etlt model into Jetson NX, then use tlt-converter to generate trt engine, config the file with deepstream, and then run inference with deepstream.