TrtUniquePtr<nvinfer1::IExecutionContext> context(m_engine->createExecutionContext()); This line of code run normally with TensorRT 7.2.3.4 + CUDA 11.1, takes about 2 ms. But it takes 300 ms with TensorRT 8.0.3.4 + CUDA 11.2. Engines in both environments are converted from ONNX passed n...
"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it.""" def build_engine(max_batch_size, save_engine): """Takes an ONNX file and creates a TensorRT engine to run inference with""" explicit_batch = 1 << (int)(trt.NetworkDefinition...
As the best practice, it's recommended that you use the "modeling machine" environment to create the ONNX model and then move that to the "deployment machine" environment. And then create the TensorRT engine in the "deployment machine" environment from that ONNX model. If that's the case,...
The model can be converted to the TensorRT engine with below GitHub: GitHub - NVIDIA-AI-IOT/trt_pose: Real-time pose estimation accelerated with... Real-time pose estimation accelerated with NVIDIA TensorRT - GitHub - NVIDIA-AI-IOT/trt_pose: ...
[TRT_OUTPUT_NAME, TRT_OUTPUT_NAME + '_1'])30 self.preprocess_fn = preprocess_fn31~/.local/lib/python3.6/site-packages/jetbot-0.4.3-py3.6.egg/jetbot/tensorrt_model.py in __init__(self, engine_path, input_names, output_names, final_shapes)57 with open(engine_path, 'rb') as f:...