Training your own DOPE model
============================

Overview
------------

The DOPE network architecture is intended to be trained on objects of a
specific class, which means that using DOPE for pose estimation of a
custom object class requires training a custom model for that class.

`NVIDIA Isaac Sim <https://developer.nvidia.com/isaac-sim>`__ offers a
convenient workflow for training a custom DOPE model using synthetic
data generation (SDG).

Tutorial Walkthrough
--------------------

1. Clone the `Isaac Sim DOPE Training
   repository <https://github.com/andrewyguo/dope_training#deep-object-pose-estimation-dope---training>`__
   and follow the training instructions to prepare a custom DOPE model.
2. Using the `Isaac Sim DOPE inference
   script <https://github.com/andrewyguo/dope_training/tree/master/inference>`__,
   test the custom DOPE model's inference capability and ensure that the
   quality is acceptable for your use case.

3. Follow steps 1-5 of the main DOPE :doc:`quickstart </repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index>`.

4. At step 6, move the prepared ``.pth`` model output from the Isaac Sim
   DOPE Training script into the ``/tmp/models`` path inside the Docker
   container.
   ``bash  docker cp custom_model.pth isaac_ros_dev-x86_64-container:/tmp/models``
5. At step 7, run the ``dope_converter.py`` script with the custom
   model:

   .. code:: bash

      python3 /workspaces/isaac_ros-dev/src/isaac_ros_pose_estimation/isaac_ros_dope/scripts/dope_converter.py --format onnx --input /tmp/models/custom_model.pth

6. Proceed through steps 8-9.
7. At step 10, launch the ROS 2 launch file with the custom model:

   .. code:: bash

      ros2 launch isaac_ros_dope isaac_ros_dope_tensor_rt.launch.py model_file_path:=/tmp/models/custom_model.onnx engine_file_path:=/tmp/models/custom_model.plan

8. Continue with the rest of the :doc:`quickstart </repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_dope/index>`. You should now be able to
   detect poses of custom objects.