Attention

As of June 30, 2025, the Isaac ROS Buildfarm for Isaac ROS 2.1 on Ubuntu 20.04 Focal is no longer supported.

Due to an isolated infrastructure event, all ROS 2 Humble Debian packages that were previously built for Ubuntu 20.04 are no longer available in the Isaac Apt Repository. All artifacts for Isaac ROS 3.0 and later are built and maintained with a more robust pipeline.

Users are encouraged to migrate to the latest version of Isaac ROS. The source code for Isaac ROS 2.1 continues to be available on the release-2.1 branches of the Isaac ROS GitHub repositories.

The original documentation for Isaac ROS 2.1 is preserved below.

Tutorial for DOPE Inference with Triton

Overview

This tutorial walks you through a graph to estimate the 6DOF pose of a target object using DOPE using different backends. It uses input monocular images from a rosbag. The different backends show are:

PyTorch and ONNX
TensorRT Plan files with Triton
PyTorch model with Triton

Note

The DOPE converter script only works on x86_64, so the resultant onnx model following these steps must be copied to the Jetson.

Tutorial Walkthrough

Complete steps 1-6 of the quickstart here.
Make a directory called Ketchup inside /tmp/models, which will serve as the model repository. This will be versioned as 1. The downloaded model will be placed here:
```
mkdir -p /tmp/models/Ketchup/1 && \
  mv /tmp/models/Ketchup.pth /tmp/models/Ketchup/
```
Now select a backend. The PyTorch and ONNX options MUST be run on x86_64:
- To run ONNX models with Triton, export the model into an ONNX file using the script provided under /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py:
```
python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py --format onnx --input /tmp/models/Ketchup/Ketchup.pth --output /tmp/models/Ketchup/1/model.onnx --input_name INPUT__0 --output_name OUTPUT__0
```
- To run TensorRT Plan files with Triton, first copy the generated onnx model from the above point to the target platform (e.g. a Jetson or an x86_64 machine). The model will be assumed to be copied to /tmp/models/Ketchup/1/model.onnx inside the Docker container. Then use trtexec to convert the onnx model to a plan model:
```
/usr/src/tensorrt/bin/trtexec --onnx=/tmp/models/Ketchup/1/model.onnx --saveEngine=/tmp/models/Ketchup/1/model.plan
```
- To run PyTorch model with Triton (inferencing PyTorch model is supported for x86_64 platform only), the model needs to be saved using torch.jit.save(). The downloaded DOPE model is saved with torch.save(). Export the DOPE model using the script provided under /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py:
```
python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py --format pytorch --input /tmp/models/Ketchup/Ketchup.pth --output /tmp/models/Ketchup/1/model.pt
```
Create a configuration file for this model at path /tmp/models/Ketchup/config.pbtxt. Note that name has to be the same as the model repository. Depending on the platform selected from a previous step, a slightly different config.pbtxt file must be created: onnxruntime_onnx (.onnx file), tensorrt_plan (.plan file) or pytorch_libtorch (.pt file):
```
name: "Ketchup"
platform: <insert-platform>
max_batch_size: 0
input [
  {
    name: "INPUT__0"
    data_type: TYPE_FP32
    dims: [ 1, 3, 480, 640 ]
  }
]
output [
  {
    name: "OUTPUT__0"
    data_type: TYPE_FP32
    dims: [ 1, 25, 60, 80 ]
  }
]
version_policy: {
  specific {
    versions: [ 1 ]
  }
}
```
The <insert-platform> part should be replaced with onnxruntime_onnx for .onnx files, tensorrt_plan for .plan files and pytorch_libtorch for .pt files.

Note

The DOPE decoder currently works with the output of a DOPE network that has a fixed input size of 640 x 480, which are the default dimensions set in the script. In order to use input images of other sizes, make sure to crop or resize using ROS 2 nodes from Isaac ROS Image Pipeline or similar packages. If another image resolution is desired, please see here.

Note

The model name must be
model.<selected-platform-extension>.

Start isaac_ros_dope using the launch file:

ros2 launch isaac_ros_dope isaac_ros_dope_triton.launch.py model_name:=Ketchup model_repository_paths:=['/tmp/models'] input_binding_names:=['INPUT__0']   output_binding_names:=['OUTPUT__0'] object_name:=Ketchup

Note

object_name should correspond to one of the objects listed in the DOPE configuration file, and the specified model should be a DOPE model that is trained for that specific object.

Open another terminal, and enter the Docker container again:

cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
  ./scripts/run_dev.sh

Then, play the ROS bag:

ros2 bag play -l src/isaac_ros_pose_estimation/resources/rosbags/dope_rosbag/

Open another terminal window and attach to the same container. You should be able to get the poses of the objects in the images through ros2 topic echo:

In a third terminal, enter the Docker container again:
```
cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
  ./scripts/run_dev.sh
```
```
ros2 topic echo /poses
```
Note

We are echoing /poses because we remapped the original topic /dope/pose_array to poses in the launch file.

Now visualize the pose array in RViz2:
```
rviz2
```
Then click on the Add button, select By topic and choose PoseArray under /poses. Finally, change the display to show an axes by updating Shape to be Axes, as shown in the screenshot at the top of this page. Make sure to update the Fixed Frame to tf_camera.

Note

For best results, crop/resize input images to the same dimensions your DNN model is expecting.