`isaac_ros_tensor_rt`

Source code on GitHub.

Quickstart

Note

This quickstart demonstrates setting up isaac_ros_tensor_rt. It is often used with an encoder and decoder node to perform pre-processing and post-processing respectively to form an application.

To use the packages in useful contexts, please refer here.

Set Up Development Environment

Set up your development environment by following the instructions in getting started.

Clone isaac_ros_common under ${ISAAC_ROS_WS}/src.

cd ${ISAAC_ROS_WS}/src && \
   git clone -b release-3.2 https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_common.git isaac_ros_common

(Optional) Install dependencies for any sensors you want to use by following the sensor-specific guides.

Note

We strongly recommend installing all sensor dependencies before starting any quickstarts. Some sensor dependencies require restarting the Isaac ROS Dev container during installation, which will interrupt the quickstart process.

Build `isaac_ros_tensor_rt`

Launch the Docker container using the run_dev.sh script:

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

Install the prebuilt Debian package:

sudo apt-get update

sudo apt-get install -y ros-humble-isaac-ros-tensor-rt

Clone this repository under ${ISAAC_ROS_WS}/src:

cd ${ISAAC_ROS_WS}/src && \
   git clone -b release-3.2 https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_dnn_inference.git isaac_ros_dnn_inference

Launch the Docker container using the run_dev.sh script:

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

Use rosdep to install the package’s dependencies:

sudo apt-get update

rosdep update && rosdep install --from-paths ${ISAAC_ROS_WS}/src/isaac_ros_dnn_inference/isaac_ros_tensor_rt --ignore-src -y

Build the package from source:

cd ${ISAAC_ROS_WS}/ && \
   colcon build --symlink-install --packages-up-to isaac_ros_tensor_rt --base-paths ${ISAAC_ROS_WS}/src/isaac_ros_dnn_inference/isaac_ros_tensor_rt

Source the ROS workspace:

Note

Make sure to repeat this step in every terminal created inside the Docker container.

Since this package was built from source, the enclosing workspace must be sourced for ROS to be able to find the package’s contents.
```
source install/setup.bash
```

Troubleshooting

Isaac ROS Troubleshooting

For solutions to problems with Isaac ROS, please check here.

Deep Learning Troubleshooting

For solutions to problems with using DNN models, please check here.

API

Usage

This package contains a launch file that solely launches isaac_ros_tensor_rt.

Warning

For your specific application, these launch files may need to be modified. Please consult the available components to see the configurable parameters.

Additionally, for most applications, an encoder node for pre-processing your data source and decoder for post-processing the inference output is required.

Launch File	Components Used
`isaac_ros_tensor_rt.launch.py`	TensorRTNode

TensorRTNode

ROS Parameters

ROS Parameter	Type	Default	Description
`model_file_path`	`string`	`model.onnx`	The absolute path to your model file in the local file system (the model file must be `.onnx`) E.g. `model.onnx`
`engine_file_path`	`string`	`/tmp/trt_engine.plan`	The absolute path to either where you want your TensorRT engine plan to be generated (from your model file) or where your pre-generated engine plan file is located E.g. `model.plan`
`force_engine_update`	`bool`	`true`	If set to true, the node will always try to generate a TensorRT engine plan from your model file and needs to be set to false to use the pre-generated TensorRT engine plan
`input_tensor_names`	`string list`	`['input_tensor']`	A list of tensor names to be bound to specified input bindings names. Bindings occur in sequential order, so the first name here will be mapped to the first name in input_binding_names
`input_binding_names`	`string list`	`['']`	A list of input tensor binding names specified by model E.g. `['input_2:0']`
`input_tensor_formats`	`string list`	`['']`	A list of input tensor NITROS formats. This should be given in sequential order E.g. `['nitros_tensor_list_nchw_rgb_f32']`
`output_tensor_names`	`string list`	`['output_tensor']`	A list of tensor names to be bound to specified output binding names
`output_binding_names`	`string list`	`['']`	A list of tensor names to be bound to specified output binding names E.g. `['argmax_1']`
`output_tensor_formats`	`string list`	`['']`	A list of input tensor NITROS formats. This should be given in sequential order E.g. `[nitros_tensor_list_nchw_rgb_f32]`
`verbose`	`bool`	`true`	If set to true, the node will enable verbose logging to console from the internal TensorRT execution
`max_workspace_size`	`int64_t`	`67108864l`	The size of the working space in bytes
`max_batch_size`	`int32_t`	`1`	The maximum possible batch size in case the first dimension is dynamic and used as the batch size
`dla_core`	`int64_t`	`-1`	The DLA Core to use. Fallback to GPU is always enabled. The default setting is GPU only
`enable_fp16`	`bool`	`true`	Enables building a TensorRT engine plan file which uses FP16 precision for inference. If this setting is false, the plan file will use FP32 precision
`relaxed_dimension_check`	`bool`	`true`	Ignores dimensions of 1 for the input-tensor dimension check
`num_blocks`	`int`	`40`	The number of pre-allocated memory output blocks, should not be less than `40`.

ROS Topics Subscribed

ROS Topic	Type	Description
`tensor_pub`	isaac_ros_tensor_list_interfaces/TensorList	The input tensor stream

ROS Topics Published

ROS Topic	Type	Description
`tensor_sub`	isaac_ros_tensor_list_interfaces/TensorList	The tensor list of output tensors from the model inference

isaac_ros_tensor_rt