This package contains the yolov4_trt_node that performs the inference using NVIDIA's TensorRT engine
This package works for both YOLOv3 and YOLOv4. Do change the commands accordingly, corresponding to the YOLO model used.
- Jetson Xavier AGX
- ROS Melodic
- Ubuntu 18.04
- Jetpack 4.4
- TensorRT 7+
- OpenCV 3.x
- numpy 1.15.1
- Protobuf 3.8.0
- Pycuda 2019.1.2
- onnx 1.4.1 (depends on Protobuf)
Install pycuda (takes awhile)
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/dependencies
$ ./install_pycuda.sh
Install Protobuf (takes awhile)
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/dependencies
$ ./install_protobuf-3.8.0.sh
Install onnx (depends on Protobuf above)
$ sudo pip3 install onnx==1.4.1
- Please also install jetson-inference
- Note: This package uses similar nodes to ros_deep_learning package. Please place a CATKIN_IGNORE in that package to avoid similar node name catkin_make error
- If these scripts do not work for you, do refer to this amazing repository by jefflgaol on installing the above packages and more on Jetson ARM devices.
$ cd ~/catkin_ws && catkin_make
$ source devel/setup.bash
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/plugins
$ make
This will generate a libyolo_layer.so file
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/yolo
** Please name the yolov4.weights and yolov4.cfg file as follows:
- yolov4.weights
- yolov4.cfg
Run the conversion script to convert to TensorRT engine file
$ ./convert_yolo_trt
- Input the appropriate arguments
- This conversion might take awhile
- The optimised TensorRT engine would now be saved as yolov3-416.trt / yolov4-416.trt
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/utils
$ vim yolo_classes.py
- Change the class labels to suit your model
$ cd ${HOME}/catkin_ws/src/yolov4_trt_ros/launch
-
yolov3_trt.launch: change the topic_name -
yolov4_trt.launch: change the topic_name -
video_source.launch: change the input format (refer to this Link- video_source.launch requires jetson-inference to be installed
- Default input is CSI camera
Note: Run the launch files separately in different terminals
# For csi input
$ roslaunch yolov4_trt_ros video_source.launch input:=csi://0
# For video input
$ roslaunch yolov4_trt_ros video_source.launch input:=/path_to_video/video.mp4
# For USB camera
$ roslaunch yolov4_trt_ros video_source.launch input:=v4l2://0
# For YOLOv3 (single input)
$ roslaunch yolov4_trt_ros yolov3_trt.launch
# For YOLOv4 (single input)
$ roslaunch yolov4_trt_ros yolov4_trt.launch
# For YOLOv4 (multiple input)
$ roslaunch yolov4_trt_ros yolov4_trt_batch.launch
$ cd /usr/bin/
$ sudo ./nvpmodel -m 0 # Enable 2 Denver CPU
$ sudo ./jetson_clock # Maximise CPU/GPU performance
- These commands are found/referred in this forum post
- Please ensure the jetson device is cooled appropriately to prevent overheating
-
str model = "yolov3" or "yolov4"
-
str model_path = "/abs_path_to_model/"
-
int input_shape = 288/416/608
-
int category_num = 80
-
double conf_th = 0.5
-
bool show_img = True
-
Default Input FPS from CSI camera = 30.0
- To change this, go to jetson-inference/utils/camera/gstCamera.cpp
# In line 359, change this line
mOptions.frameRate = 15
# To desired frame_rate
mOptions.frameRate = desired_frame_rate
| Model | Hardware | FPS | Inference Time (ms) |
|---|---|---|---|
| Yolov4-416 | Xavier AGX | 40.0 | 0.025 |
| Yolov4-416 | Jetson Tx2 | 16.0 | 0.0625 |
- Will be adding inference tests for YOLOv3/4 and YOLOv3-tiny/YOLOv4-tiny for different Jetson devices and multiple camera inputs inference tests in the future
1. TensorRT samples from jkjung-avt
Many thanks for his project with tensorrt samples. I have referenced his source code and adapted it to ROS for robotics applications.
I also used the pycuda and protobuf installation script from his project
Those code are under MIT License
2. Jetson-inference from dusty-nv
Many thanks for his work on the Jetson Inference with ROS. I have used his video_source input from his project for capturing video inputs.
Those code are under NVIDIA License
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