DeepStream SDK: How to use Custom Multi-task (Depthmap, Semantics, Detection) tensorrt model for inference in Python?

• Hardware Platform (Jetson / GPU) dGPU (Tesla T4)
• DeepStream Version 6.1.1
• TensorRT Version
• NVIDIA GPU Driver Version (valid for GPU only) 515.65.01
• Issue Type( questions, new requirements, bugs) Question

I have a pre-trained Multi-task(Depthmap, Semantics, Detection) model in ONNX and TensorRT .engine format that can be loaded in a python script for inference.
Input: Cropped, resized fisheye image
Output: Depthmap, Semantic segmentation, Object Detection

From the documentation, it seems like I need to use Preprocessed Tensor Input mode in Gst-nvinfer.
But is there any reference document on how to use tensorrt engine directly in Deepstream SDK using Python bindings? Any suggestions would be appreciated.

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The nvinfer configuration has nothing to do with python. Even with python app deepstream_python_apps/apps/deepstream-test1 at master · NVIDIA-AI-IOT/deepstream_python_apps (, the model is configurated with the nvinfer configuration file deepstream_python_apps/dstest1_pgie_config.txt at master · NVIDIA-AI-IOT/deepstream_python_apps (

For the engine file, you can set “model-engine-file” parameter Gst-nvinfer — DeepStream 6.1.1 Release documentation in the configuration file. Just as in this sample: deepstream_python_apps/dstest1_pgie_config.txt at master · NVIDIA-AI-IOT/deepstream_python_apps (

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