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true	Detailed guide on deploying trained models on NVIDIA Jetson using TensorRT and DeepStream SDK. Optimize the inference performance on Jetson with Ultralytics.	TensorRT, NVIDIA Jetson, DeepStream SDK, deployment, Ultralytics, YOLO, Machine Learning, AI, Deep Learning, model optimization, inference performance

Deploy on NVIDIA Jetson using TensorRT and DeepStream SDK

📚 This guide explains how to deploy a trained model into NVIDIA Jetson Platform and perform inference using TensorRT and DeepStream SDK. Here we use TensorRT to maximize the inference performance on the Jetson platform. UPDATED 18 November 2022.

Hardware Verification

We have tested and verified this guide on the following Jetson devices

Before You Start

Make sure you have properly installed JetPack SDK with all the SDK Components and DeepStream SDK on the Jetson device as this includes CUDA, TensorRT and DeepStream SDK which are needed for this guide.

JetPack SDK provides a full development environment for hardware-accelerated AI-at-the-edge development. All Jetson modules and developer kits are supported by JetPack SDK.

There are two major installation methods including,

SD Card Image Method
NVIDIA SDK Manager Method

You can find a very detailed installation guide from NVIDIA official website. You can also find guides corresponding to the above-mentioned reComputer J1010 and reComputer J2021.

Install Necessary Packages

Step 1. Access the terminal of Jetson device, install pip and upgrade it

sudo apt update
sudo apt install -y python3-pip
pip3 install --upgrade pip

Step 2. Clone the following repo

git clone https://github.com/ultralytics/yolov5

Step 3. Open requirements.txt

cd yolov5
vi requirements.txt

Step 5. Edit the following lines. Here you need to press i first to enter editing mode. Press ESC, then type :wq to save and quit

# torch>=1.8.0
# torchvision>=0.9.0

Note: torch and torchvision are excluded for now because they will be installed later.

Step 6. install the below dependency

sudo apt install -y libfreetype6-dev

Step 7. Install the necessary packages

pip3 install -r requirements.txt

Install PyTorch and Torchvision

We cannot install PyTorch and Torchvision from pip because they are not compatible to run on Jetson platform which is based on ARM aarch64 architecture. Therefore, we need to manually install pre-built PyTorch pip wheel and compile/ install Torchvision from source.

Visit this page to access all the PyTorch and Torchvision links.

Here are some of the versions supported by JetPack 4.6 and above.

PyTorch v1.10.0

Supported by JetPack 4.4 (L4T R32.4.3) / JetPack 4.4.1 (L4T R32.4.4) / JetPack 4.5 (L4T R32.5.0) / JetPack 4.5.1 (L4T R32.5.1) / JetPack 4.6 (L4T R32.6.1) with Python 3.6

file_name: torch-1.10.0-cp36-cp36m-linux_aarch64.whl URL: https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl

PyTorch v1.12.0

Supported by JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T R34.1.1) / JetPack 5.0.2 (L4T R35.1.0) with Python 3.8

file_name: torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl URL: https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl

Step 1. Install torch according to your JetPack version in the following format

wget <URL> -O <file_name>
pip3 install <file_name>

For example, here we are running JP4.6.1, and therefore we choose PyTorch v1.10.0

cd ~
sudo apt-get install -y libopenblas-base libopenmpi-dev
wget https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl -O torch-1.10.0-cp36-cp36m-linux_aarch64.whl
pip3 install torch-1.10.0-cp36-cp36m-linux_aarch64.whl

Step 2. Install torchvision depending on the version of PyTorch that you have installed. For example, we chose PyTorch v1.10.0, which means, we need to choose Torchvision v0.11.1

sudo apt install -y libjpeg-dev zlib1g-dev
git clone --branch v0.11.1 https://github.com/pytorch/vision torchvision
cd torchvision
sudo python3 setup.py install

Here a list of the corresponding torchvision version that you need to install according to the PyTorch version:

PyTorch v1.10 - torchvision v0.11.1
PyTorch v1.12 - torchvision v0.13.0

DeepStream Configuration for YOLOv5

Step 1. Clone the following repo

cd ~
git clone https://github.com/marcoslucianops/DeepStream-Yolo

Step 2. Copy gen_wts_yoloV5.py from DeepStream-Yolo/utils into yolov5 directory

cp DeepStream-Yolo/utils/gen_wts_yoloV5.py yolov5

Step 3. Inside the yolov5 repo, download pt file from YOLOv5 releases (example for YOLOv5s 6.1)

cd yolov5
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt

Step 4. Generate the cfg and wts files

python3 gen_wts_yoloV5.py -w yolov5s.pt

Note: To change the inference size (default: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280:

-s 1280
or
-s 1280 1280

Step 5. Copy the generated cfg and wts files into the DeepStream-Yolo folder

cp yolov5s.cfg ~/DeepStream-Yolo
cp yolov5s.wts ~/DeepStream-Yolo

Step 6. Open the DeepStream-Yolo folder and compile the library

cd ~/DeepStream-Yolo
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo  # for DeepStream 6.1
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo  # for DeepStream 6.0.1 / 6.0

Step 7. Edit the config_infer_primary_yoloV5.txt file according to your model

[property]
...
custom-network-config=yolov5s.cfg
model-file=yolov5s.wts
...

Step 8. Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_yoloV5.txt

Step 9. Change the video source in deepstream_app_config file. Here a default video file is loaded as you can see below

...
[source0]
...
uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4

Run the Inference

deepstream-app -c deepstream_app_config.txt

The above result is running on Jetson Xavier NX with FP32 and YOLOv5s 640x640. We can see that the FPS is around 30.

INT8 Calibration

If you want to use INT8 precision for inference, you need to follow the steps below

Step 1. Install OpenCV

sudo apt-get install libopencv-dev

Step 2. Compile/recompile the nvdsinfer_custom_impl_Yolo library with OpenCV support

cd ~/DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo  # for DeepStream 6.1
CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo  # for DeepStream 6.0.1 / 6.0

Step 3. For COCO dataset, download the val2017, extract, and move to DeepStream-Yolo folder
Step 4. Make a new directory for calibration images

mkdir calibration

Step 5. Run the following to select 1000 random images from COCO dataset to run calibration

for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do \
    cp ${jpg} calibration/; \
done

Note: NVIDIA recommends at least 500 images to get a good accuracy. On this example, 1000 images are chosen to get better accuracy (more images = more accuracy). Higher INT8_CALIB_BATCH_SIZE values will result in more accuracy and faster calibration speed. Set it according to you GPU memory. You can set it from head -1000. For example, for 2000 images, head -2000. This process can take a long time.

Step 6. Create the calibration.txt file with all selected images

realpath calibration/*jpg > calibration.txt

Step 7. Set environment variables

export INT8_CALIB_IMG_PATH=calibration.txt
export INT8_CALIB_BATCH_SIZE=1

Step 8. Update the config_infer_primary_yoloV5.txt file

From

...
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
...
network-mode=0
...

...
model-engine-file=model_b1_gpu0_int8.engine
int8-calib-file=calib.table
...
network-mode=1
...

Step 9. Run the inference

deepstream-app -c deepstream_app_config.txt

The above result is running on Jetson Xavier NX with INT8 and YOLOv5s 640x640. We can see that the FPS is around 60.

Benchmark results

The following table summarizes how different models perform on Jetson Xavier NX.

Model Name	Precision	Inference Size	Inference Time (ms)	FPS
YOLOv5s	FP32	320x320	16.66	60
	FP32	640x640	33.33	30
	INT8	640x640	16.66	60
YOLOv5n	FP32	640x640	16.66	60

Additional

This tutorial is written by our friends at seeed @lakshanthad and Elaine

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