NVIDIA Jetson Nvidia Jetson Nano, Xavier NX, Orin Deployment tutorial

[AyushExel]

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

• Seeed reComputer J1010 built with Jetson Nano module
• Seeed reComputer J2021 built with Jetson Xavier NX module

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,

1. SD Card Image Method
2. NVIDIA SDK Manager Method

You can find a very detailed installation guide from NVIDIA official website. Also you can 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>=1.7.0
# torchvision>=0.8.1

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 (defaut: 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
...

To

...
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

[glenn-jocher]

@AyushExel awesome! Should this be renamed to something like NVIDIA Jetson Nano deployment tutorial?

[AyushExel]

@glenn-jocher yeah. "Nvidia Jetson Nano deployment tutorial sounds good".
And maybe just pin or add to wikis?

[glenn-jocher]

@AyushExel awesome, added to wiki. I think I'll add to README also. Are those times in the last table right BTW?

[AyushExel]

@glenn-jocher yes. It's also mentioned here - https://wiki.seeedstudio.com/YOLOv5-Object-Detection-Jetson/

[barney2074]

thanks for the great documentation @AyushExel
I'll give it a try in the next day or two

[barney2074]

Hi @AyushExel

For step 4.
python3 gen_wts_yoloV5.py -w yolov5s.pt I get an error Illegal instruction (core dumped)

I'm using a Seeed reComputer J1010 (Jetson Nano) with Jetpack 4.6.2 and I've tried a couple of times with a fresh flash of the Jetson each time.

I noticed that YoloV5 requires Python 3.7, whereas Jetpack 4.6.2 includes Python 3.6.9, so I used YoloV5 v6.0 (and v6.2 initially)

EDIT: also tried JP4.6.1 (same result)

thanks in advance

Andrew

[AyushExel]

@lakshanthad do you know what's causing this?

[barney2074]

thanks @AyushExel

I've found the crash report (which I can send to you or @lakshanthad)
It's pretty big- around 9mb

I also noticed the SeeedStudio article here: similar/the same?
https://wiki.seeedstudio.com/YOLOv5-Object-Detection-Jetson/
I haven't tried this yet- its a bit more complicated

The first 10 lines are:

ProblemType: Crash
Architecture: arm64
CrashCounter: 1
Date: Tue Oct 11 18:06:08 2022
DistroRelease: Ubuntu 18.04
ExecutablePath: /usr/bin/python3.6
ExecutableTimestamp: 1656503157
ProcAttrCurrent: Error: [Errno 22] Invalid argument
ProcCmdline: python3 gen_wts_yoloV5.py -w yolov5s.pt
ProcCwd: /home/nano/yolov5

[lakshanthad]

Hello @barney2074,

Can I know exactly after which command you encounter this crash? And please attach the report here if possible.

I also noticed the SeeedStudio article here: similar/the same?
https://wiki.seeedstudio.com/YOLOv5-Object-Detection-Jetson/

Yes. Most content on this GitHub is based on that wiki. That wiki mainly explains the entire process from labeling to deploying

[barney2074]

Hi @lakshanthad

This occurs after the command **python3 gen_wts_yoloV5.py -w yolov5s.pt**
I've put the crash report here: https://drive.google.com/drive/folders/14bu_dNwQ9VbBLMKDBw92t0vUc3e9Rh00?usp=sharing

I have also tried the Seeed wiki- I'll put outcome in a separate post to avoid confusing the issue

thanks

Andrew

[barney2074]

Hi @lakshanthad

At step 19 of the Seeed wiki (serialising the model) I get the following error:
I've tried a few different models, including https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt and some custom ones

nano@ubuntu:~/tensorrtx/yolov5/build$ sudo ./yolov5 -s best.wts best.engine n6
Loading weights: best.wts
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: (Unnamed Layer* 0) [Convolution]:kernel weights has count 3456 but 1728 was expected
[10/12/2022-09:55:05] [E] [TRT] 4: (Unnamed Layer* 0) [Convolution]: count of 3456 weights in kernel, but kernel dimensions (6,6) with 3 input channels, 16 output channels and 1 groups were specified. Expected Weights count is 3 * 6*6 * 16 / 1 = 1728
[10/12/2022-09:55:05] [E] [TRT] 4: [convolutionNode.cpp::computeOutputExtents::39] Error Code 4: Internal Error ((Unnamed Layer* 0) [Convolution]: number of kernel weights does not match tensor dimensions)
[10/12/2022-09:55:05] [E] [TRT] 3: [network.cpp::addScale::737] Error Code 3: API Usage Error (Parameter check failed at: optimizer/api/network.cpp::addScale::737, condition: shift.count > 0 ? (shift.values != nullptr) : (shift.values == nullptr)
)
yolov5: /home/nano/tensorrtx/yolov5/common.hpp:153: nvinfer1::IScaleLayer* addBatchNorm2d(nvinfer1::INetworkDefinition*, std::map<std::__cxx11::basic_string<char>, nvinfer1::Weights>&, nvinfer1::ITensor&, std::__cxx11::string, float): Assertion `scale_1' failed.
Aborted
nano@ubuntu:~/tensorrtx/yolov5/build$

[dinobei]

@barney2074
I had the same issue too in Jetson-nano b01 dev. It is solved by setting the following environment variable:
export OPENBLAS_CORETYPE=ARMV8

But after that, a problem occurs when building deepstream.

dinobei@dinobei-desktop:~/DeepStream-Yolo$ CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
make: Entering directory '/home/dinobei/DeepStream-Yolo/nvdsinfer_custom_impl_Yolo'
g++ -c  -o yolo.o -Wall -std=c++11 -shared -fPIC -Wno-error=deprecated-declarations -I/opt/nvidia/deepstream/deepstream/sources/includes -I/usr/local/cuda-10.2/include yolo.cpp
In file included from yolo.cpp:26:0:
yolo.h:44:10: fatal error: nvdsinfer_custom_impl.h: No such file or directory
 #include "nvdsinfer_custom_impl.h"
          ^~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
Makefile:70: recipe for target 'yolo.o' failed
make: *** [yolo.o] Error 1
make: Leaving directory '/home/dinobei/DeepStream-Yolo/nvdsinfer_custom_impl_Yolo'

[barney2074]

Thanks @dinobei

One step forward....
I'm not sure if deploying Yolov5 models on Jetson hardware is inherently tricky- but from my perspective, it would be great if there was an easier path.

Andrew

[AyushExel]

@lakshanthad do you know what's happening in this error? I seems like its originating from deepstream-yolo module. Is there a way to run this without that?
@barney2074 I haven't had time to try it out on my nano yet so I'm not of much help here. I'll try it out soon

[lakshanthad]

Hello,
Sorry for the late reply. Can I know how DeepStream was installed in the first place? @dinobei @barney2074. Sometimes improper DeepStream installations can cause errors later on.

It is recommended to choose it inside NVIDIA SDK Manager when installing JetPack. Because this ensures that there will be no compatibility or missing dependency issues.

[lakshanthad]

Thanks @dinobei

One step forward.... I'm not sure if deploying Yolov5 models on Jetson hardware is inherently tricky- but from my perspective, it would be great if there was an easier path.

Andrew

Well. If you just want to deploy, you can use the pre-trained PyTorch model to perform the inference. In this case, follow until and including the Install PyTorch and Torchvision section in the above guide. After that, execute python detect.py --source <video_source>. But the goal of this document is to use TensorRT to increase performance on the Jetson platform. And to use TensorRT with a video stream, DeepStream SDK is used.

So there are 2 ways of deployment on Jetson.

1. Without TensorRT
2. With TensoRT and DeepStream SDK

The first method is the fastest deployment. However, the second method ensures the model performance is better on the Jetson hardware compared with the first method.

[lakshanthad]

I think this document can be divided into two.

1. Without TensorRT (fastest deployment)
2. With TensorRT and DeepStream SDK (takes some time to deploy)

Any suggestions? I can work to reorganize it as above and update this guide.

[dinobei]

@lakshanthad
thank you for reply.
What about TensorRT without DeepStream?
Is using TensorRT and DeepStream SDKs faster than using TensorRT alone? (model performance)

[dinobei]

Hello, Sorry for the late reply. Can I know how DeepStream was installed in the first place? @dinobei @barney2074. Sometimes improper DeepStream installations can cause errors later on.

It is recommended to choose it inside NVIDIA SDK Manager when installing JetPack. Because this ensures that there will be no compatibility or missing dependency issues.

I made a huge mistake. I didn't install DeepStream SDK. I thought DeepStream-Yolo and DeepStream SDK are the same.
Currently, JetPack was installed by SDcard image method, I will try reinstalling it with NVIDIA SDK Manager and share the results.

[barney2074]

Can I know how DeepStream was installed in the first place

Hi @lakshanthad
I installed using SDKManager, and did an OS flash at the same time i.e a completely 'fresh' system.

I'm aiming to get my custom YoloV5 model running on the Jetson, although I tried yolov5s.pt as a test to try to eliminate the problem i.e it is not just my custom model

Just to clarify my understanding: the TensorRT .engine needs to be generated on the same processor architecture as used for inferencing. i.e can't generate it on an x86/RTX machine and run inferencing on an ARM (Jetson) one ??

Andrew

[lakshanthad]

Hello, Sorry for the late reply. Can I know how DeepStream was installed in the first place? @dinobei @barney2074. Sometimes improper DeepStream installations can cause errors later on.
It is recommended to choose it inside NVIDIA SDK Manager when installing JetPack. Because this ensures that there will be no compatibility or missing dependency issues.

I made a huge mistake. I didn't install DeepStream SDK. I thought DeepStream-Yolo and DeepStream SDK are the same. Currently, JetPack was installed by SDcard image method, I will try reinstalling it with NVIDIA SDK Manager and share the results.

Yes. Please try again and share your results.

[lakshanthad]

Can I know how DeepStream was installed in the first place

Hi @lakshanthad I installed using SDKManager, and did an OS flash at the same time i.e a completely 'fresh' system.

I'm aiming to get my custom YoloV5 model running on the Jetson, although I tried yolov5s.pt as a test to try to eliminate the problem i.e it is not just my custom model

Just to clarify my understanding: the TensorRT .engine needs to be generated on the same processor architecture as used for inferencing. i.e can't generate it on an x86/RTX machine and run inferencing on an ARM (Jetson) one ??

Andrew

Yes, you are right. The .engine file should be generated on the same processor architecture as used for inferencing. It also means serializing and deserializing should be done on the same architecture. When you use DeepStream SDK as mentioned in this guide, after you run deepstream-app -c deepstream_app_config.txt, it will first serialize the model (generate .engine) and then after sometime deserialize the model to do the inferencing.

However, the guide that you found out on Seeed wiki that you mentioned earlier, when only TensorRT is used without DeepStream SDK, you need to manually do this serialize and deserialize work.

Coming back to the issues you are still facing, is any of the issues you mentioned before solved, or do they still exist?

Could we debug like this? First try without TensorRT.

1. At the beginning of this GitHub page, go through Install Necessary Packages and Install PyTorch and Torchvision
2. Execute python3 detect.py --source <video_source> that will use yolov5s.pt as the default model for inference

Please let me know whether this works at first.

[lakshanthad]

@lakshanthad thank you for reply. What about TensorRT without DeepStream? Is using TensorRT and DeepStream SDKs faster than using TensorRT alone? (model performance)

There is no big difference. The way to use only TensorRT is this. However, there is no example present to view detection on real-time video. The repo only supports image inferencing at the moment. DeepStream SDK comes with real-time video detection support. However, if you are comfortable with maybe OpenCV, it could be possible to grab the video frames as images using OpenCV and do the inferencing while only using the TensorRT Github mentioned before.

[Alberto1404]

Not sure what's happening but these issues are related to the environment and not yolov5. Try troubleshooting via trt foums

@AyushExel I already asked to nvidia. This is the answer they gave to me.
https://forums.developer.nvidia.com/t/calib-table-not-found-when-running-deepstream/237493/5
@lakshanthad any help?

[arminbiglari]

Hi, @Alberto1404, i had this issue also.

For some reason when you recompile and use OPENCV=1 in cmd, it doesn't actually build with opencv.

Quick fix is to just go into the makefile and change the opencv query to opencv=1.

Then just recompile without the opencv flag.

[Alberto1404]

@arminbiglari Thank you so much for your reply.
Could you please share the modified Makefile? Not sure which line to modify exactly (I guess it is line 33)
Thank you

-----UPDATE-----
After modifying it, I get same error. @arminbiglari How do you create the calibration file calib.table? Where is it pointing to?

[ihaohe]

@arminbiglari Thank you so much for your reply. Could you please share the modified Makefile? Not sure which line to modify exactly (I guess it is line 33) Thank you

-----UPDATE----- After modifying it, I get same error. @arminbiglari How do you create the calibration file calib.table? Where is it pointing to?

just run the inference step 9：“deepstream-app -c deepstream_app_config.txt”. The calib.table will be created when calibrating.

[inyongji]

@Farouq-Mot

I get this error "Illegal instruction", during deployment of the yolov5 network on Jetson xavier nx. Please see the attached image. This error happens when I run this command in this deepstream configuration step:

Try:
sudo OPENBLAS_CORETYPE=ARMV8 python3 setup.py install

[aurelm95]

How should we deploy a instance segmentation model with deepstream ?

[aryanbarik]

I am getting this error when on Step4 of the DeepStream setup:

How do I fix this?

[leeyunhome]

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

• Seeed reComputer J1010 built with Jetson Nano module
• Seeed reComputer J2021 built with Jetson Xavier NX module

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,

1. SD Card Image Method
2. NVIDIA SDK Manager Method

You can find a very detailed installation guide from NVIDIA official website. Also you can 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>=1.7.0
# torchvision>=0.8.1

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 (defaut: 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
...

To

...
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

Hello, @AyushExel

thanks for sharing good information.

Is there any way to use the above without using deepstream?

Thank you.

[aryanbarik]

This is the error I am currently getting. How can I fix this?

[aryanbarik]

This is the error I am currently getting. How can I fix this?

I got it to work by downgrading OpenCV from the default 4.7.0 to 4.5.1!

[ianmharris]

Thanks for the tutorial. Used it to configure and run YoloV5s on an Orin AGX Dev Kit.
Result running in the 30Watt Mode:

Model Name Precision Inference Size FPS
YOLOv5s FP32 640x640 55

[midskymid]

Hey, man. Thank you for your tutorial, but I got the problem with INT8 calibration when I did "deepstream-app -c deepstream_app_config.txt":

i fixed the problem, please remake CUDA_VER......

[midskymid]

i got another problem.the engine model which is produced by "deepstream-app -c deepstream_app_config.txt" and its name is "model_b1_gpu0_int8.engine", i used it on yolov5-6.1 detect.py and the error was as follows:

[junxi-haoyi]

how can I solve this.

when I try this order sudo python3 gen_wts_yoloV5.py -w yolov5s.pt on my jetson nano
and it will feedback this Illegal instruction

[glenn-jocher]

@junxi-haoyi hello! Thank you for reaching out. The "Illegal instruction" error typically means that the executable code being run is not compatible with the CPU architecture of the device.

To resolve this issue, you can try setting the OPENBLAS_CORETYPE environment variable to a compatible architecture for your device. For instance, you could use ARMV8 for most ARM-based devices such as the Jetson Xavier NX.

Here's an example of how you can do this:

sudo OPENBLAS_CORETYPE=ARMV8 python3 setup.py install

This command sets the OPENBLAS_CORETYPE environment variable to ARMV8 for the subsequent python3 setup.py install command, ensuring compatibility with the CPU architecture.

I hope this helps! If you have any further questions or encounter any more issues, feel free to ask.

[vanshdhar999]

There is no file named gen_wts_v5.py in the utils folder of the deepstream-yolo repo? what should I do

[glenn-jocher]

@vanshdhar999 you can create the gen_wts_v5.py file manually by following these steps:

1. Create a new Python file named gen_wts_v5.py.
2. Write the necessary code within this file to generate the required .wts and .cfg files.
3. Place this file in the utils folder of the DeepStream-Yolo repository.

If you have specific requirements or need assistance with the content of the gen_wts_v5.py file, please let me know so I can provide you with further guidance.

[vanshdhar999]

What is the required code to be written? @glenn-jocher

[glenn-jocher]

@vanshdhar999 the gen_wts_v5.py script is used to generate the .wts file from the PyTorch .pt file for YOLOv5 model deployment.

Here's a basic example of what the content of the gen_wts_v5.py file might look like:

import torch
from models.yolo import Model  # Import your YOLOv5 model class here

# Load the PyTorch model
model = Model(pretrained=False)  # Replace with the actual YOLOv5 model class and load the weights

# Convert and save the model to TorchScript format
# Replace 'input_shape' with the actual input shape of the model
ts = torch.jit.script(model.to(torch.device('cpu')).eval())
ts.save('yolov5s.ts')  # Save the TorchScript model

# Convert and save the model to TensorRT engine
import torch2trt
from torch2trt import TRTModule
import pycuda.driver as cuda

# Directories for TensorRT logging
TRT_LOGGER = torch2trt.Logger(torch2trt.LogSeverity.INFO)

model_trt = torch2trt.ts2trt(ts, [torch.randn(1, 3, input_shape, input_shape).to(device)], max_batch_size=1, log_level=torch2trt.LogSeverity.VERBOSE, strict_type_constraints=True)
model_trt.save("yolov5s.trt")  # Save the TensorRT engine

Please replace the placeholders with your actual YOLOv5 model class, input shape, and other specific details.

Remember to adjust the script according to your exact requirements and your trained YOLOv5 model. If you need further assistance or have more specific requirements, feel free to let me know.

[vmukund36]

@glenn-jocher Can we implement Deepstream based trackers like NvDcf or NvSORT by adding the tracker plugin in the config file? I tried but it doesn't seem to work. I want to use Yolov5 along with NvSORT tracker.

[glenn-jocher]

Hello! Integrating NvSORT or other DeepStream trackers with YOLOv5 in a DeepStream framework does involve careful configuration. Ensure that the nvinfer and nvtracker plugins are correctly configured in your DeepStream application config file. For NvSORT, your config may look something like this:

[tracker]
enable=1
tracker-width=640
tracker-height=384
ll-lib-file=/opt/nvidia/deepstream/deepstream/lib/libnvds_nvdcf.so
ll-config-file=config_tracker_NvDCF_perf.yml
gpu-id=0
enable-batch-process=1

Make sure paths and parameters match your setup. Additionally, ensure that the model outputs and tracker configs are compatible (location, detection confidence formats, etc.).

If the issue persists, check for any error messages in the logs which might offer more specific insight into what might be going wrong. 😊

[vmukund36]

@glenn-jocher When I try to run python3 gen_wts_yoloV5.py -w best.pt where "best.pt" is my custom trained model, it gives the error
Traceback (most recent call last): File "gen_wts_yoloV5.py", line 358, in <module> delattr(model.model[-1], 'anchor_grid') File "/home/agx-arka/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1685, in __delattr__ super().__delattr__(name) AttributeError: anchor_grid
Why is it throwing this error?

[glenn-jocher]

Hello! It looks like the error is happening because the attribute anchor_grid does not exist in the last module of your model. This might occur if there have been changes in the model's architecture or the library versions are not matching.

A quick fix could be checking whether the attribute exists before trying to delete it. You could modify your gen_wts_yoloV5.py script to include a conditional check:

if hasattr(model.model[-1], 'anchor_grid'):
    delattr(model.model[-1], 'anchor_grid')

This change ensures that the script only tries to delete the anchor_grid if it exists, potentially circumventing the AttributeError. Make sure your script and your model are compatible in terms of architecture and library versions! 😊

[vmukund36]

Hi @glenn-jocher I was able to convert the model to onnx using python3 export_yoloV5.py. But when I run inference on deepstream, the model's detections falls off by a lot in comparison to Ultralytics inference. Can you let me know why is that the case?

[glenn-jocher]

Hello! The discrepancy in detection performance between the original Ultralytics YOLOv5 inference and the ONNX/DeepStream version might be due to several factors:

1. Precision Differences: ONNX might use different precision settings (e.g., FP16 vs FP32) compared to your original setup.
2. Post-processing: Ensure that all post-processing steps (like non-max suppression and confidence thresholding) are consistently applied in both environments.
3. Model Conversion: Sometimes, model conversion to ONNX can introduce slight discrepancies. Make sure that all model features are supported and correctly translated in ONNX.

To further diagnose, consider comparing the intermediate outputs of both models or checking if the issue persists with different models/samples. A code review might also help ensure settings like input normalization are consistent. 🧐

[vmukund36]

Hello! The discrepancy in detection performance between the original Ultralytics YOLOv5 inference and the ONNX/DeepStream version might be due to several factors:

1. Precision Differences: ONNX might use different precision settings (e.g., FP16 vs FP32) compared to your original setup.
2. Post-processing: Ensure that all post-processing steps (like non-max suppression and confidence thresholding) are consistently applied in both environments.
3. Model Conversion: Sometimes, model conversion to ONNX can introduce slight discrepancies. Make sure that all model features are supported and correctly translated in ONNX.

To further diagnose, consider comparing the intermediate outputs of both models or checking if the issue persists with different models/samples. A code review might also help ensure settings like input normalization are consistent. 🧐

Thanks for reply. I had implemented https://github.com/marcoslucianops/DeepStream-Yolo. All the plugins for post processing are as per the model's requirements. Is there a way to compensate for the loss from the conversion from pt to onnx? Because to run it on deepstream it supports onnx files in this case for TRT inference.

[glenn-jocher]

@vmukund36 hello! Thanks for the update. To address the performance loss after converting your model from .pt to .onnx, you could try the following:

• Optimize the ONNX Model: Use tools like onnx-simplifier to optimize the ONNX model. This can sometimes help in preserving model integrity and performance.

  pip install onnx-simplifier
  python -m onnxsim input_model.onnx output_model.onnx

• Precision Tuning: Experiment with different precision formats during the ONNX export or within the DeepStream configuration. Sometimes setting everything explicitly to FP32 might help, although it will increase resource usage.

• Review Export Settings: Ensure that the export settings in your export.py script match as closely as possible with the inference settings used during training, focusing on details like image sizes and normalizations.

If discrepancies persist, consider further fine-tuning or retraining the model directly in the ONNX format or adjusting the DeepStream pipeline to better accommodate the model's characteristics. 😊

[vmukund36]

Hi @glenn-jocher I tried with onnx-simplifier as well but the results are the same if not worse. I had tried to convert to onnx format using ultralytics api as well and upon inferencing it gave a similar result. I am not really sure how to make sure to not compromise on model's performance while also inferencing on deepstream(which supports onnx only). Do you recommend trying any other alternative approach in order to not face this issue?

[glenn-jocher]

Hello! It sounds like you're facing a challenging issue with the ONNX format in DeepStream. If onnx-simplifier didn't help and direct conversion maintains the performance gap, consider the following approach:

• Model Re-training: Sometimes, re-training the model with an inference-centric focus, particularly targeting the ONNX export, can help. Adjust training parameters slightly to favor features that translate well in ONNX.

• Custom Layer Implementation: If certain layers or operations are not translating well, you might need to implement custom layers in DeepStream that match the behavior of the original PyTorch model.

If you've explored all usual routes, these alternative steps might help in maintaining performance consistency. Keep in mind that slight discrepancies between training frameworks and inference engines are common, and achieving an exact match might require a bit of trial and error. 😊