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Seeed J4012 PyTorch/TensorRT Example

This is a sample for running visual inferencing on the Seeed J4012 reComputer (NVIDIA Jetson Orin NX) hardware using balenaOS. See the Getting Started Guide, Jetson-flash, and our Forums if you need more details getting started using the balena platform.

Usage

For one-click deploy, click the button below:

balena deploy button

To test the TensorRT is running correctly on the NVIDIA hardware:

  • Go to the /usr/src/tensorrt/samples folder
  • Run make TARGET=aarch64 - this could take 15 minutes to compile all the samples
  • Go to the /usr/src/tensorrt/bin folder
  • Run ./sample_onnx_mnist

You should see Building and running a GPU inference engine for Onnx MNIST and a bunch of test output, ending with:

&&&& PASSED TensorRT.sample_onnx_mnist [TensorRT v8502] # ./sample_onnx_mnist

More information

This example repo is using the project https://github.com/dusty-nv/jetson-inference, which is based on the NVIDIA l4T PyTorch base image.

However, the "jetson-interface" project fails to build in the container, so you should use the NVIDIA-supplied examples which are located in the container at /usr/src/tensorrt/samples as mentioned above. Below is another NVIDIA example that does run in the container.

Object Detection With The ONNX TensorRT Backend In Python

Here is a quick start to run this example: (full documentation here)

In the container, do the following: (note: /inference-store is simply a persistent volume for storing models, etc...)

cd /usr/src/tensorrt/samples/python/
python3 downloader.py -d /inference-store -f /usr/src/tensorrt/samples/python/yolov3_onnx/download.yml
cd yolov3_onnx
python3 yolov3_to_onnx.py -d /inference-store
python3 onnx_to_tensorrt.py  -d /inference-store

If successful, you should see:

Running inference on image /jetson-inference/python/training/classification/models/samples/python/yolov3_onnx/dog.jpg...
[[134.94005922 219.30816557 184.32604687 324.51474599]
 [ 98.63753808 136.02425953 499.65646314 298.39950069]
 [477.79566252  81.31128895 210.98671105  86.85283442]] [0.9985233  0.99885205 0.93972876] [16  1  7]
Saved image with bounding boxes of detected objects to dog_bboxes.png.

Now you can modify onnx_to_tensorrt.py to run your own inferences!