TensorRT deployment with engine generated from TLT example

[terry.l.lee]

The following two blob helps. However, did not provide detail.

Use .engine file in python

I used Transfer Learning Toolkit to train resnet18detectnetv2 for custom dataset. I then used the tlt-converter to convert the .etlt model into an .engine file. I am able to deploy both the .etlt and .engine file in DeepStream and it works. But now I need to deploy the model in python and I am not able to find how to load the .engine model in python. Any suggestions?

how to use created .engine file for inference

hi, I created mnist.engine file using this sample to have the understanding of how tensorrt works using this link I am not able to understand hoe to use this mnist.engine file for inference. My ideal point of learning to use tensorrt is I want to create inference engine for object detection using Faster RCNN architecture and Caffe framework with Titan X GPU.how can I use Tensorrt for this? I am not getting intuitive understanding. is dataset needed for inference? what is binaryproto file…

I end up with the following code:
f = open(‘/home/trafficcountuser/sl/testpy/resnet18_detector_baru.engine’, ‘rb’)
runtime = trt.Runtime(TRT_LOGGER)
engine = runtime.deserialize_cuda_engine(f.read())

sourceFileDir = os.path.dirname(os.path.abspath(file))
sourceFileDir = os.path.abspath(os.path.join(sourceFileDir,“…/”))
fileName = os.path.join(sourceFileDir, “runTime”, “Image640x360.bmp”)
thisImage = cv2.imread(fileName)
result = engine.infer(thisImage) # Single function for inference

There is an error on the last line:

Exception has occurred: AttributeError
‘tensorrt.tensorrt.ICudaEngine’ object has no attribute ‘infer’

Any idea about this message?