yes, I have extended the swap memory.
but the model loading memory is reduced to 4GB with few solutions,
however the FPS remains the same.
In my laptop with 1650max-Q GPU it gives around 13FPS.
Im using Python with Tensorflow implementation.
we have sucessfully converted the model to ONNX format and inferenced with ONNX runtime in the xavier NX but there is not much improvement in the FPS (3-4FPS)
I have tried to convert the ONNX to Nvidia tensorRT but getting the below error
mirrag@mirrag-desktop:~/Downloads$ python3 createengine.py
Unsupported ONNX data type: UINT8 (2)
Traceback (most recent call last):
File “createengine.py”, line 19, in
engine = eng.build_engine(onnx_path, shape= shape)
File “/home/mirrag/Downloads/engine.py”, line 21, in build_engine
network.get_input(0).shape = shape
AttributeError: ‘NoneType’ object has no attribute ‘shape’
This is the link we have tried to convert Speeding up Deep Learning Inference Using TensorFlow, ONNX, and TensorRT | NVIDIA Developer Blog