I made a redaction app using DeepStream and am trying to follow this tutorial to optimize the tutorial’s pre-trained model for deployment in my app, instead of the fp32 one am using currently (from this old redaction repo). I have a couple questions:
- What is the best way to convert the .pth file to .onnx on Xavier, since the retinanet-examples Python package won’t install? Can I do this on a pair of 1080s instead?
- Same as above, to perform calibration for the Xavier, should I do the int8 calibration on the Xavier itself or on x86 Nvidia?
- My app accepts n number of sources and sets the batch-size at runtime to match. When converting to a engine with tensorrt-utils’s onnx_to_tensorrt.py, should I use the --explicit-batch option to generate an optimization profile? Or do I need to do somethign else and have DeepStream do the job for me?