Aidge Export TensorRT API
=========================

MAKE Options
------------

The export provides a Makefile with several options to utilize the export on your machine. You can generate either a C++ export or a Python export.
Additionally, you have the option to compile the export and/or the Python library using Docker if your host machine lacks the necessary packages.

The available commands are summarized in the following table:

.. list-table::
   :widths: 50 50
   :header-rows: 1

   * - Command
     - Description
   * - ``make / make help``
     - Display the different options available
   * - ``make build_cpp``
     - Compile the export on host for C++ apps (generate an executable in build/bin)
   * - ``make build_lib_python``
     - Compile the export on host for Python apps (generate a python lib in build/lib)
   * - ``make build_image_docker``
     - Generate the docker image of the tensorrt compiler
   * - ``make build_cpp_docker``
     - Compile the export in a container for C++ apps (generate an executable in build/bin)
   * - ``make test_cpp_docker``
     - Test the executable for C++ apps in a container
   * - ``make build_lib_python_docker``
     - Compile the export in a container for Python apps (generate a python lib in build/lib)
   * - ``make test_lib_python_docker``
     - Test the lib for Python apps in a container
   * - ``make clean``
     - Clean up the build and bin folders

Graph functions
---------------

.. cpp:function:: device(id)

   :param id: (int) Set the ID of the device.

.. cpp:function:: load(filepath)

   Load a graph from a file, either a `.onnx` file or a `.trt` engine.

   :param filepath: (str) The path to the file containing the graph.

.. cpp:function:: save(filepath)

   Save the current graph as a `.trt` engine.

   :param filepath: (str) The path to save the graph to.

.. cpp:function:: calibrate(calibration_folder_path="./calibration_folder/", cache_file_path="./calibration_cache", batch_size=1)

   Calibrate the graph using the calibration data found inside the `calibration` folder.
   This folder should include a `.info` file containing the dimensions of the calibration data, along with the data stored in a `.batch` file

   :param calibration_folder_path: (str) The path to the calibration folder. Default is "./calibration_folder/".
   :param cache_file_path: (str) The path to the calibration cache file. Default is "./calibration_cache".
   :param batch_size: (int) The batch size for calibration. Default is 1.

.. cpp:function:: initialize()

   Initialize the graph.

.. cpp:function:: profile(nb_iterations, mode=ExecutionMode_T.ASYNC)

   Profile the graph's execution by printing the average profiled tensorRT process time per stimulus.

   :param nb_iterations: (int) The number of iterations to run.
   :param mode: (ExecutionMode_T) The execution mode. Default is ExecutionMode_T.ASYNC.

.. cpp:function:: run_sync(inputs)

   Run the graph synchronously.

   :param inputs: (list) A list of inputs.
   :return: (list) A list of outputs.

Export function
---------------

.. autofunction:: aidge_export_tensorrt.export

Plugin helper
-------------

The TensorRT export allow you to define plugins which will be automatically used when loadig the ONNX file.

The export define an helper command to generate the template of a plugin which you can then fill up.


**Usage example:**

.. code-block::

    python -m aidge_export_tensorrt.generate_plugin -n "test" -f "myExport"

This will create the plugin ``test`` in the folder ``myExport``

..  code-block::

    myExport
    +--- plugins
    |   +--- test
    |   |   +--- test_plugin.hpp
    |   |   +--- test_plugin.cu
    *--- ...