安裝 Ubuntu 20.04

$ sudo apt-get update

$ sudo apt-get upgrade

$ sudo apt-get install ssh

$ sudo vi /etc/fstab

#中間的空格要使用 tab

ip:/share_folder /mnt/mount_folder nfs defaults,bg 0 0

$ cd /mnt

$ sudo mkdir QNAP_A QNAP_B

$ sudo mount -a

$ mkdir -p ~/.config/autostart

$ cp /usr/share/applications/vino-server.desktop ~/.config/autostart/

$ gsettings set org.gnome.Vino prompt-enabled false

$ gsettings set org.gnome.Vino require-encryption false

$ gsettings set org.gnome.Vino authentication-methods "['vnc']"

$ gsettings set org.gnome.Vino vnc-password $(echo -n 'ChangeToYourPasswd'|base64)

$ sudo vi /etc/gdm3/custom.conf

WaylandEnable=false

AutomaticLoginEnable = true

AutomaticLogin = UserLoginName

$ vi vino.sh

DISP=`ps -u $(id -u) -o pid= | \

    while read pid; do

        cat /proc/$pid/environ 2>/dev/null | tr '\0' '\n' | grep '^DISPLAY=:'

    done | grep -o ':[0-9]*' | sort -u`

echo $DISP

/usr/lib/vino/vino-server --display=$DISP

$ chmod +x vino.sh

依據 使用最新版本的 driver

CUDA Toolkit and Corresponding Driver Versions

https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

dGPU Setup for Ubuntu

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html

Ubuntu 20.04

GStreamer 1.16.3

NVIDIA driver 525.125.06

CUDA 12.1

TensorRT 8.5.3.1

$ sudo ubuntu-drivers devices

$ sudo apt-get install nvidia-driver-535

$ sudo reboot

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb

$ sudo dpkg -i cuda-keyring_1.1-1_all.deb

$ sudo apt-get update

$ sudo apt-get -y install cuda-12-2

$ sudo apt-get -y install cuda-12-1

安裝 cuDNN

參考 https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

到 2.2. Downloading cuDNN for Linux(https://developer.nvidia.com/cudnn)

下載 Local Install for Ubuntu18.04 x86_64(Deb)

$ sudo apt-get install zlib1g

$ sudo dpkg -i cudnn-local-repo-ubuntu2004-8.9.5.29_1.0-1_amd64.deb

sudo cp /var/cudnn-local-repo-ubuntu2004-8.9.5.29/cudnn-local-98C06E99-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ apt list -a libcudnn8

$ sudo apt-get install libcudnn8=8.9.5.29-1+cuda12.2

$ sudo apt-get install libcudnn8-dev=8.9.5.29-1+cuda12.2

$ sudo apt-get install libcudnn8-samples=8.9.5.29-1+cuda12.2

$ update-alternatives --display libcudnn

$ cp -r /usr/src/cudnn_samples_v8/ .

$ cd cudnn_samples_v8/mnistCUDNN/

$ sudo apt-get install libfreeimage3 libfreeimage-dev

$ make clean && make

$ ./mnistCUDNN

...

Test passed!

安裝 TensorRT 8.6.1

https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-861/install-guide/index.html

$ sudo apt-get install python3-pip

$ sudo apt-get install python3.8.venv

$ python3 -m venv envs/tensorrt

$ source envs/tensorrt/bin/activate

$ pip3 install --upgrade pip

$ python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt_libs

$ python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt_bindings

$ python3 -m pip install --upgrade tensorrt

$ python3 -m pip install --upgrade tensorrt_lean

$ python3 -m pip install --upgrade tensorrt_dispatch

測試  TensorRT Python

$ python3

>>> import tensorrt

>>> print(tensorrt.__version__)

>>> assert tensorrt.Builder(tensorrt.Logger())

>>> import tensorrt_lean as trt

>>> print(trt.__version__)

>>> assert trt.Builder(trt.Logger())

>>> import tensorrt_dispatch as trt

>>> print(trt.__version__)

>>> assert trt.Builder(trt.Logger())

連結 https://developer.nvidia.com/tensorrt 按 GET STARTED

連結 https://developer.nvidia.com/tensorrt-getting-started 按 DOWNLOAD NOW

選擇 TensorRT 8

選擇 TensorRT 8.6 GA

TensorRT 8.6 GA for Ubuntu 20.04 and CUDA 12.0 and 12.1 DEB local repo Package

$ sudo dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb

$ sudo cp /var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/nv-tensorrt-local-9A1EDFBA-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ sudo apt-get install tensorrt

$ sudo apt-get install libnvinfer-lean8

$ sudo apt-get install libnvinfer-vc-plugin8

$ sudo apt-get install python3-libnvinfer-lean

$ sudo apt-get install python3-libnvinfer-dispatch

$ python3 -m pip install numpy

$ sudo apt-get install python3-libnvinfer-dev

$ python3 -m pip install protobuf

$ sudo apt-get install uff-converter-tf

$ python3 -m pip install numpy onnx

$ sudo apt-get install onnx-graphsurgeon

確認安裝

$ dpkg-query -W tensorrt

tensorrt        8.6.1.6-1+cuda12.0

安裝 DeepStream

$ sudo apt-get install libssl1.1

$ sudo apt-get install libgstreamer1.0-0

$ sudo apt-get install gstreamer1.0-tools

$ sudo apt-get install gstreamer1.0-plugins-good

$ sudo apt-get install gstreamer1.0-plugins-bad

$ sudo apt-get install gstreamer1.0-plugins-ugly

$ sudo apt-get install gstreamer1.0-libav

$ sudo apt-get install libgstreamer-plugins-base1.0-dev

$ sudo apt-get install libgstrtspserver-1.0-0

$ sudo apt-get install libjansson4

$ sudo apt-get install libyaml-cpp-dev

$ sudo apt-get install libjsoncpp-dev

$ sudo apt-get install protobuf-compiler

$ sudo apt-get install gcc

$ sudo apt-get install make

$ sudo apt-get install git

$ sudo apt-get install python3

$ git clone https://github.com/edenhill/librdkafka.git

$ cd librdkafka

$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

$ ./configure

$ make

$ sudo make install

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.3/lib

$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.3/lib

https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

下載 deepstream-6.3_6.3.0-1_arm64.deb

$ wget --content-disposition 'https://api.ngc.nvidia.com/v2/resources/nvidia/deepstream/versions/6.3/files/deepstream-6.3_6.3.0-1_amd64.deb'

$ sudo apt-get install ./deepstream-6.3_6.3.0-1_amd64.deb

$ cd /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app

$ deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

安裝 Docker

https://docs.docker.com/engine/install/ubuntu/

$ sudo apt-get update

$ sudo apt-get install ca-certificates curl gnupg

$ sudo install -m 0755 -d /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

$ sudo chmod a+r /etc/apt/keyrings/docker.gpg

$ echo \

  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \

  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \

  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update

$ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

$ sudo docker run --rm hello-world

安裝 NVIDIA Container Toolkit

參考 https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

$ curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \

  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \

  && \

    sudo apt-get update

$ sudo apt-get install -y nvidia-container-toolkit

$ sudo nvidia-ctk runtime configure --runtime=docker

$ sudo systemctl restart docker

$ sudo groupadd docker

$ sudo usermod -a -G docker $USER

$ docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

安裝 NGC CLI

參考 https://ngc.nvidia.com/setup/installers/cli

$ wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.30.1/files/ngccli_linux.zip -O ngccli_linux.zip && unzip ngccli_linux.zip

$ find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5

$ sha256sum ngccli_linux.zip

$ chmod u+x ngc-cli/ngc

$ echo "export PATH=\"\$PATH:$(pwd)/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile

$ ngc config set

# 直接 enter 即可

$ docker login nvcr.io

Username: $oauthtoken

Password: <Your API Key>

用 Docker 開發 DeepStream 6.3

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html

$ sudo docker pull nvcr.io/nvidia/deepstream:6.3-gc-triton-devel

$ export DISPLAY=:0

$ xhost +

$ docker run -it --rm --net=host --gpus all -e DISPLAY=$DISPLAY --device /dev/snd -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/deepstream:6.3-gc-triton-devel

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# exit

$ sudo docker ps -a

$ sudo docker stop container_id

$ sudo docker rm container_id

$ sudo docker image list

$ sudo docker image rm image_id

YOLOv8 and TensorRT

參考 YOLOv8 GitHub 官網

參考 YOLOv8 文件

參考 DeepStream-Yolo

1. 下載 DeepStream-Yolo, Ultralytics YOLOv8

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

git clone https://github.com/ultralytics/ultralytics.git /mnt/Data/DeepStream/DeepStream-Yolo/ultralytics

2. 建立 deepstream_yolo docker container

docker_run.sh

xhost +

docker run --name='deepstream_yolo' --gpus all -it --net=host --privileged \

  -v /tmp/.X11-unix:/tmp/.X11-unix \

  -v /etc/localtime:/etc/localtime \

  -v /mnt/Data/DeepStream/DeepStream-Yolo/DeepStream-Yolo:/home/DeepStream-Yolo \

  -v /mnt/Data/DeepStream/DeepStream-Yolo/ultralytics:/home/ultralytics \

  -v /mnt/Data/DeepStream/DeepStream-Yolo/read_me:/home/read_me \

  -v /mnt/Data/DeepStream/DeepStream-Yolo/datasets:/home/datasets \

  -v /mnt/CT1000SSD/ImageData/Light:/home/Light \

  -e DISPLAY=$DISPLAY \

  -w /home/read_me \

  nvcr.io/nvidia/deepstream:6.2-devel

  

3. 在 Docker 內, 安裝 DeepStream-Yolo

apt-get install build-essential

/opt/nvidia/deepstream/deepstream/user_additional_install.sh

cd /home/DeepStream-Yolo

CUDA_VER=11.8 make -C nvdsinfer_custom_impl_Yolo

4. 在 Docker 內, 安裝 Ultralytics YOLOv8

#python3 -m pip install --upgrade pip

pip3 install --upgrade pip

pip3 install protobuf numpy

cd /home/ultralytics

#pip install -e .

pip3 install -r requirements.txt

python3 setup.py install

pip3 install onnx onnxsim onnxruntime

5. 在 Docker 內, 下載，轉換，測試 yolov8s.pt, yolov8s.pt 模型

cd /home/ultralytics

cp /home/DeepStream-Yolo/utils/export_yoloV8.py /home/ultralytics

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt

python3 export_yoloV8.py -w yolov8s.pt --dynamic

python3 export_yoloV8.py -w yolov8n.pt --dynamic

cp yolov8s.onnx labels.txt /home/DeepStream-Yolo

cp yolov8n.onnx labels.txt /home/DeepStream-Yolo

6. 移除 deepstream_yolo container

$ docker container rm deepstream_yolo

7. 重新進入 Docker

docker_attach.sh

xhost +

docker start deepstream_yolo

docker attach deepstream_yolo

8. 轉換模型格式為 onnx

yolov8n.py

from ultralytics import YOLO

# Load a model

model = YOLO("yolov8n.yaml")  # build a new model from scratch

model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model

model.train(data="coco128.yaml", epochs=3)  # train the model

metrics = model.val()  # evaluate model performance on the validation set

results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image

path = model.export(format="onnx")  # export the model to ONNX format

執行 python3 yolov8n.py 出現下列錯誤

ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).

修正方式

$ sudo systemctl stop docker

取得 container id

$ docker inspect deepstream_yolo | grep Id

"Id": "???????"

編輯 container 的 ShmSize

$ sudo vi /var/lib/docker/containers/your_container_id/hostconfig.json

"ShmSize":8589934592

$ sudo systemctl restart docker

$ ./docker_attach.sh

9. 在 DeepStream 中測試 onnx 模型

# cd /home/DeepStream-Yolo

# vi config_infer_primary_yoloV8.txt

onnx-file=yolov8s.onnx

onnx-file=yolov8n.onnx

# vi deepstream_app_config.txt

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

uri=rtsp://root:A1234567@192.168.0.107:554/live1s1.sdp

live-source=0

live-source=1

config-file=config_infer_primary.txt

config-file=config_infer_primary_yoloV8.txt

file-loop=0

file-loop=1

# deepstream-app -c deepstream_app_config.txt

10. 準備自己的圖形資料, PASCAL VOC(LabelImg 產生的 xml) 格式轉換成 txt

參考 Ultralytics 文件之 VOC Dataset 說明

參考 範例程式

prepare_detect.py

import cv2

import os

import random

import re

import xml.etree.ElementTree as ET

import numpy as np

LIGHT_CLASSES_LIST = [

    'forward_right',

    'others',

    'red',

    'red_left',

    'yellow',

    ]

        

def save_false_positives(img_org, iName, xName, tag, classIdx, 

        clip_x0, clip_y0, clip_x1, clip_y1):

    img_new = img_org[clip_y0:clip_y1, clip_x0:clip_x1]

    fPath, fName = os.path.split(iName)

    fName, fExt = os.path.splitext(fName)

    fName = fName + tag + fExt

    rndPaths = ['train', 'val', 'test']

    rndPath = random.choices(rndPaths, weights=(8,1,1))[0]

    iName = os.path.join('/home/datasets/Light/images', rndPath, fName)

    cv2.imwrite(iName, img_new)

        

def convert_box(size, box):

    dw, dh = 1. / size[0], 1. / size[1]

    x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]

    return x * dw, y * dh, w * dw, h * dh

          

def save_file(img_org, iName, xName, tag, classIdx, 

        p0x, p0y, p1x, p1y, p2x, p2y, p3x, p3y, 

        img_w, img_h, xmin, ymin, xmax, ymax,

        clip_x0, clip_y0, clip_x1, clip_y1):

    img_new = img_org[clip_y0:clip_y1, clip_x0:clip_x1]

    fPath, fName = os.path.split(iName)

    fName, fExt = os.path.splitext(fName)

    fName = fName + tag + fExt

    rndPaths = ['train', 'val', 'test']

    rndPath = random.choices(rndPaths, weights=(8,1,1))[0]

    iName = os.path.join('/home/datasets/Light/images', rndPath, fName)

    cv2.imwrite(iName, img_new)

    

    w = clip_x1 - clip_x0

    h = clip_y1 - clip_y0

    xmin = xmin - clip_x0

    ymin = ymin - clip_y0

    xmax = xmax - clip_x0

    ymax = ymax - clip_y0

    bb = convert_box((w, h), (xmin, xmax, ymin, ymax))

    fPath, fName = os.path.split(xName)

    fName, fExt = os.path.splitext(fName)

    fName = fName + tag + '.txt'

    tName = os.path.join('/home/datasets/Light/labels', rndPath, fName)

    with open(tName, 'w') as f:

        f.write(" ".join([str(a) for a in (classIdx, *bb)]) + '\n')

        

def gen_img_yolo(iName, xName):

    tree = ET.parse(open(xName))

    root = tree.getroot()

    img_w = int(root.find('size').find('width').text)

    img_h = int(root.find('size').find('height').text)

    for idx, object in enumerate(root.findall('object')):

        name = object.find('name').text

        classIdx = LIGHT_CLASSES_LIST.index(name)

        #print(classIdx, name)

        bndbox = object.find('bndbox')

        p0x = int(bndbox.find('p0x').text)

        p0y = int(bndbox.find('p0y').text)

        p1x = int(bndbox.find('p1x').text)

        p1y = int(bndbox.find('p1y').text)

        p2x = int(bndbox.find('p2x').text)

        p2y = int(bndbox.find('p2y').text)

        p3x = int(bndbox.find('p3x').text)

        p3y = int(bndbox.find('p3y').text)

        xmin = int(bndbox.find('xmin').text)

        ymin = int(bndbox.find('ymin').text)

        xmax = int(bndbox.find('xmax').text)

        ymax = int(bndbox.find('ymax').text)

        if xmin != p0x or xmin != p3x or ymin != p0y or ymin != p1y or \

                xmax != p1x or xmax != p2x or ymax != p2y or ymax != p3y:

            print('error:bndbox', xName)

            exit()

        if idx > 0:

            print('error:object', xName)

            exit()

    img_org = cv2.imread(iName)

    if img_org.shape[0] != img_h or img_org.shape[1] != img_w:

        print(img_org.shape, (img_h, img_w))

        exit()

    img = np.copy(img_org)

    clip_x0 = random.randrange(0, int(xmin*0.5))

    clip_y0 = random.randrange(0, int(ymin*0.5))

    clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.5), img_w+1)

    clip_y1 = random.randrange(int(ymax + (img_h-ymax)*0.5), img_h+1)

    save_file(img_org, iName, xName, '', classIdx, 

            p0x, p0y, p1x, p1y, p2x, p2y, p3x, p3y, 

            img_w, img_h, xmin, ymin, xmax, ymax,

            clip_x0, clip_y0, clip_x1, clip_y1)

    ratio = (xmax - xmin) / img_w

    if ratio < 0.3:

        clip_x0 = random.randrange(int(xmin*0.3), int(xmin*0.8))

        clip_y0 = random.randrange(int(ymin*0.3), int(ymin*0.8))

        clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.2), int(xmax + (img_w-xmax)*0.7))

        clip_y1 = random.randrange(int(ymax + (img_h-ymax)*0.2), int(ymax + (img_h-ymax)*0.7))

        save_file(img_org, iName, xName, '_a', classIdx, 

                p0x, p0y, p1x, p1y, p2x, p2y, p3x, p3y, 

                img_w, img_h, xmin, ymin, xmax, ymax,

                clip_x0, clip_y0, clip_x1, clip_y1)

        clip_x0 = random.randrange(int(xmin*0.5), int(xmin*0.9))

        clip_y0 = random.randrange(int(ymin*0.5), int(ymin*0.9))

        clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.1), int(xmax + (img_w-xmax)*0.5))

        clip_y1 = random.randrange(int(ymax + (img_h-ymax)*0.1), int(ymax + (img_h-ymax)*0.5))

        save_file(img_org, iName, xName, '_b', classIdx, 

                p0x, p0y, p1x, p1y, p2x, p2y, p3x, p3y, 

                img_w, img_h, xmin, ymin, xmax, ymax,

                clip_x0, clip_y0, clip_x1, clip_y1)

        if xmin > (img_w - xmax):

            if ymin > (img_h - ymax):

                clip_x0 = random.randrange(0, int(xmin*0.8))

                clip_y0 = random.randrange(0, int(ymin*0.8))

                clip_x1 = random.randrange(int(xmin), int(xmin+(xmax-xmin)*0.8))

                clip_y1 = random.randrange(int(ymin), int(ymin+(ymax-ymin)*0.8))

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f0', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

            else:

                clip_x0 = random.randrange(0, int(xmin*0.8))

                clip_y0 = random.randrange(int(ymin+(ymax-ymin)*0.2), int(ymax))

                clip_x1 = random.randrange(int(xmin), int(xmin + (xmax-xmin)*0.8))

                clip_y1 = random.randrange(int(ymax+(img_h-ymax)*0.2), img_h)

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f1', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

        else:

            if ymin > (img_h - ymax):

                clip_x0 = random.randrange(int(xmin+(xmax-xmin)*0.2), int(xmax))

                clip_y0 = random.randrange(0, int(ymin*0.8))

                clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.2), img_w)

                clip_y1 = random.randrange(int(ymin), int(ymin+(ymax-ymin)*0.8))

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f2', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

            else:

                clip_x0 = random.randrange(int(xmin+(xmax-xmin)*0.2), int(xmax))

                clip_y0 = random.randrange(int(ymin+(ymax-ymin)*0.2), int(ymax))

                clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.2), img_w)

                clip_y1 = random.randrange(int(ymax+(img_h-ymax)*0.2), img_h)

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f3', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

    elif ratio < 0.7:

        clip_x0 = random.randrange(int(xmin*0.1), int(xmin*0.7))

        clip_y0 = random.randrange(int(ymin*0.1), int(ymin*0.7))

        clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.3), int(xmax + (img_w-xmax)*0.9))

        clip_y1 = random.randrange(int(ymax + (img_h-ymax)*0.3), int(ymax + (img_h-ymax)*0.9))

        save_file(img_org, iName, xName, '_c', classIdx, 

                p0x, p0y, p1x, p1y, p2x, p2y, p3x, p3y, 

                img_w, img_h, xmin, ymin, xmax, ymax,

                clip_x0, clip_y0, clip_x1, clip_y1)

        if xmin > (img_w - xmax):

            if ymin > (img_h - ymax):

                clip_x0 = random.randrange(0, int(xmin*0.8))

                clip_y0 = random.randrange(0, int(ymin*0.8))

                clip_x1 = random.randrange(int(xmin), int(xmin+(xmax-xmin)*0.8))

                clip_y1 = random.randrange(int(ymin), int(ymin+(ymax-ymin)*0.8))

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f4', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

            else:

                clip_x0 = random.randrange(0, int(xmin*0.8))

                clip_y0 = random.randrange(int(ymin+(ymax-ymin)*0.2), int(ymax))

                clip_x1 = random.randrange(int(xmin), int(xmin + (xmax-xmin)*0.8))

                clip_y1 = random.randrange(int(ymax+(img_h-ymax)*0.2), img_h)

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f5', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

        else:

            if ymin > (img_h - ymax):

                clip_x0 = random.randrange(int(xmin+(xmax-xmin)*0.2), int(xmax))

                clip_y0 = random.randrange(0, int(ymin*0.8))

                clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.2), img_w)

                clip_y1 = random.randrange(int(ymin), int(ymin+(ymax-ymin)*0.8))

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f6', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

            else:

                clip_x0 = random.randrange(int(xmin+(xmax-xmin)*0.2), int(xmax))

                clip_y0 = random.randrange(int(ymin+(ymax-ymin)*0.2), int(ymax))

                clip_x1 = random.randrange(int(xmax + (img_w-xmax)*0.2), img_w)

                clip_y1 = random.randrange(int(ymax+(img_h-ymax)*0.2), img_h)

                root.remove(object)

                save_false_positives(img_org, iName, xName, '_f7', classIdx, 

                        clip_x0, clip_y0, clip_x1, clip_y1)

    elif ratio < 1.0:

        pass

    return

def recursive_folder(path):

    files = os.listdir(path)

    files.sort()

    for file in files:

        fullName = os.path.join(path, file)

        if os.path.isfile(fullName):

            fPath, fName = os.path.split(fullName)

            fName, fExt = os.path.splitext(fName)

            if fExt in ('.jpg'):

                xPath = fPath + '.xml'

                xName = fName + '.xml'

                xFName = os.path.join(xPath, xName)

                if os.path.isfile(xFName):

                    gen_img_yolo(fullName, xFName)

                else:

                    print(xFName)

        else:

            recursive_folder(fullName)

def main():

    recursive_folder('/home/Light')

if __name__ == '__main__':

    main()

11. 訓練自己的模型

from ultralytics import YOLO

# Load a model

model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

# Train the model

model.train(data='VOC.yaml', epochs=100, imgsz=640)

12. 查詢 onnx 模型的輸出輸入層

import onnx

model = onnx.load('yolov8n.onnx')

g_in = model.graph.input

g_out = model.graph.output

Ubuntu 18.04 重灌

因為 Ubuntu 18.04 只能安裝 DeepStream 6.0.1

在 Ubuntu 20.04 才能安裝 DeepStream 6.1

所以在 Ubuntu 18.04 上安裝 Docker Ubuntu 20.04

版本對應如下

===========================

DS 6.1

Ubuntu 20.04

GCC 9.4.0

CUDA 11.6.1

cuDNN 8.4.0.27

TRT 8.2.5.1

Display Driver:R510.47.03

GStreamer 1.16.2

OpenCV 4.2.0

deepstream:6.1

===========================

DS 6.0.1

Ubuntu 18.04

GCC 7.3.0

CUDA 11.4.1

cuDNN 8.2+

TRT 8.0.1

Display Driver:R470.63.01

GStreamer 1.14.5

OpenCV 3.4.0

deepstream:6.0.1

===========================

安裝作業系統

BIOS 選擇開機

Install Ubuntu

Installation type 選 Something else

Create partition/Mount point 選擇 /

Settings/Details/Users

Unlock

Automatic Login: ON

更新系統，安裝一些常用套件

$ sudo apt-get update

$ sudo apt-get upgrade

$ sudo apt-get install ssh

$ sudo apt-get install python3-pip

mount nfs

$ sudo apt-get install nfs-common

$ sudo mount -t nfs ip:/share_folder /mount_folder

$ sudo vi /etc/fstab

#中間的空格要使用 tab

ip:/share_folder /mnt/mount_folder nfs defaults,bg 0 0

$ sudo mount -a

vnc

$ sudo apt-get install x11vnc

$ sudo x11vnc -storepasswd

$ sudo chown user.group ~/.vnc/passwd

安裝顯示卡驅動，CUDA 和 CUDNN

$ sudo ubuntu-drivers devices

$ sudo apt-get install nvidia-driver-510

https://developer.nvidia.com/cuda-downloads

選擇 Archive of Previous CUDA Releases

選擇 CUDA Toolkit 11.4.1

選擇 deb(local), deb(network) 不可使用了

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ wget https://developer.download.nvidia.com/compute/cuda/11.4.1/local_installers/cuda-repo-ubuntu1804-11-4-local_11.4.1-470.57.02-1_amd64.deb

$ sudo dpkg -i cuda-repo-ubuntu1804-11-4-local_11.4.1-470.57.02-1_amd64.deb

$ sudo apt-key add /var/cuda-repo-ubuntu1804-11-4-local/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get -y install cuda-11-4

之後再安裝一遍 CUDA Toolkit 11.3.1(因為 TensorRT 8.0.1 需要 CUDA-11-3)

$ sudo apt-get -y install cuda-11-3

之後再安裝一遍 CUDA Toolkit 11.6.1(因為 DeepStream 6.1 需要 CUDA-11-6)

$ sudo apt-get -y install cuda-11-6

$ update-alternatives --display cuda

$ update-alternatives --config cuda

參考 https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

到 2.2. Downloading cuDNN for Linux

下載 Local Install for Ubuntu18.04 x86_64(Deb)

$ sudo apt-get install zlib1g

$ sudo dpkg -i cudnn-local-repo-ubuntu1804-8.4.1.50_1.0-1_amd64.deb

$ sudo cp /var/cudnn-local-repo-ubuntu1804-8.4.1.50/cudnn-local-BA71F057-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ apt list -a libcudnn8

$ sudo apt-get install libcudnn8=8.4.1.50-1+cuda11.6

$ sudo apt-get install libcudnn8-dev=8.4.1.50-1+cuda11.6

$ sudo apt-get install libcudnn8-samples=8.4.1.50-1+cuda11.6

安裝 TensorRT 8.0.1

因為使用 Docker 安裝 DeepStream 6.1, 所以不用安裝 TensorRT 8.2.5.1

參考 https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html

在 TensorRT Archives 切換安裝文件版本

選擇 8.0.1 的 TensorRT Installation Guide

在 3. Downloading TensorRT

連結 https://developer.nvidia.com/tensorrt 按 GET STARTED

連結 https://developer.nvidia.com/tensorrt-getting-started 按 DOWNLOAD NOW

選擇 TensorRT 8

選擇 TensorRT 8.0 GA

選擇 TensorRT 8.0.1 GA for Ubuntu 18.04 and CUDA 11.3 DEB local repo package

$ sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb

$ sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub

$ sudo apt-get update

$ sudo apt --fix-broken install

$ sudo apt-get upgrade

$ sudo apt-get install tensorrt

安裝 DeepStream

因為不選擇最新的版本

參考 https://docs.nvidia.com/metropolis/deepstream-archive.html

選擇版本 6.0.1 DeepStream SDK Development Guid

到 dGPU Setup for Ubuntu

$ sudo apt install libssl1.0.0

$ sudo apt install libgstreamer1.0-0

$ sudo apt install gstreamer1.0-tools

$ sudo apt install gstreamer1.0-plugins-good

$ sudo apt install gstreamer1.0-plugins-bad

$ sudo apt install gstreamer1.0-plugins-ugly

$ sudo apt install gstreamer1.0-libav

$ sudo apt install libgstrtspserver-1.0-0

$ sudo apt install libjansson4

$ sudo apt install gcc

$ sudo apt install make

$ sudo apt install git

$ sudo apt install python3

$ cd /usr/bin

$ sudo ln -s python3 python

$ git clone https://github.com/edenhill/librdkafka.git

$ cd librdkafka

$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

$ ./configure

$ make

$ sudo make install

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.0/lib

$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.0/lib

下載 Deepstream 6.0 dGPU Debian package

https://developer.nvidia.com/deepstream-6.0_6.0.1-1_amd64deb

$ sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb

$ rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin

$ cd /opt/nvidia/deepstream/deepstream-6.0/samples/configs/deepstream-app/

$ deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt

安裝  Nvidia Docker

參考 https://docs.docker.com/engine/install/ubuntu/

$ sudo apt-get update

$ sudo apt-get install ca-certificates

$ sudo apt-get install curl

$ sudo apt-get install gnupg

$ sudo apt-get install lsb-release

$ sudo mkdir -p /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

$ echo \

  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \

  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update

$ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin

$ sudo docker run --rm hello-world

參考 https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \

      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \

            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

$ sudo apt-get update

$ sudo apt-get install -y nvidia-docker2

$ sudo systemctl restart docker

$ sudo groupadd docker

$ sudo usermod -a -G docker $USER

$ sudo reboot

安裝 NGC CLI

參考 https://ngc.nvidia.com/setup/installers/cli

$ wget --content-disposition https://ngc.nvidia.com/downloads/ngccli_linux.zip && \

  unzip ngccli_linux.zip && \

  chmod u+x ngc-cli/ngc

$ find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5

$ echo "export PATH=\"\$PATH:$(pwd)/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile

$ ngc config set

# 直接 enter 即可

$ docker login nvcr.io

Username: $oauthtoken

Password: <Your API Key>

使用 Docker 安裝 TensorRT OSS

開啟 https://github.com/nvidia/TensorRT

切換至 Tags 8.0.1

$ git clone -b master https://github.com/nvidia/TensorRT TensorRT_OSS-8.0.1

$ cd TensorRT_OSS-8.0.1/

$ git describe --tags

8.2.0-EA-2-g96e2397

$ git tag -l

$ git branch -r

$ git checkout 8.0.1

$ git log -1

$ git describe --tags

8.0.1

$ git submodule update --init --recursive

$ vi docker/ubuntu-18.04.Dockerfile

修改下列一行

RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && \

  unzip ngccli_cat_linux.zip && chmod u+x ngc-cli/ngc && \

  rm ngccli_cat_linux.zip ngc-cli.md5 && echo "no-apikey\nascii\n" | ngc-cli/ngc config set

$ cat docker/ubuntu-18.04.Dockerfile | grep CUDA_VERSION

ARG CUDA_VERSION=11.3.1

$ ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu18.04-cuda11.3 --cuda 11.3.1

$ ./docker/launch.sh --tag tensorrt-ubuntu18.04-cuda11.3 --gpus all

/workspace$ cd $TRT_OSSPATH

/workspace/TensorRT$ mkdir -p build && cd build

/workspace/TensorRT/build$ cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out

/workspace/TensorRT/build$ make -j$(nproc)

/workspace/TensorRT/build$ exit

$ mkdir backup

$ sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.0.1 backup/

$ sudo cp build/out/libnvinfer_plugin.so.8.0.1 /usr/lib/x86_64-linux-gnu/

另外還要再安裝 TensorRT 8.2.1 給 Docker Ubuntu 20.04

將 8.0.1 改成 8.2.1, 18.04 改成 20.04, 11.3.1 改成 11.4.2, 但不需安裝

用 Docker 開發 DeepStream 6.0.1

參考 https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_docker_containers.html

$ docker pull nvcr.io/nvidia/deepstream:6.0.1-devel

$ xhost +

access control disabled, clients can connect from any host

$ sudo docker run --gpus all -it --rm --net=host \

  -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime \

  -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# update-alternatives --display cuda

# cat /etc/os-release

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/apps/sample_apps/deepstream-app

# export CUDA_VER=11.4

# make

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# gst-launch-1.0 uridecodebin uri=$URI ! nvvideoconvert ! nveglglessink

# exit

用 Docker 開發 DeepStream 6.1

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html

$ docker pull nvcr.io/nvidia/deepstream:6.1-devel

$ xhost +

access control disabled, clients can connect from any host

$ sudo docker run --gpus all -it --rm --net=host \

  -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime \

  -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.1 nvcr.io/nvidia/deepstream:6.1-devel

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# cd /opt/nvidia/deepstream/deepstream-6.1/sources/apps/sample_apps/deepstream-app

# export CUDA_VER=11.6

# make

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.1/lib/gst-plugins/

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# gst-launch-1.0 uridecodebin uri=$URI ! nvvideoconvert ! nveglglessink

# exit

用 Docker DeepStream 6.0.1 測試 Integrate TAO model with DeepStream SDK

$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git deepstream_tao_apps-tao3.0_ds6.0.1

$ sudo apt install gitk

$ cd deepstream_tao_apps-tao3.0_ds6.0.1/

$ git branch -r

$ git checkout release/tao3.0_ds6.0.1

$ git log -1

$ sudo docker run --gpus all -it --rm --net=host \

> -v /etc/localtime:/etc/localtime \

> -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \

> -v /home/mark/Data/TensorRT/TensorRT_OSS-8.0.1/:/home/TensorRT \

> -v /home/mark/Data/DeepStream/deepstream_tap_apps/deepstream_tao_apps-tao3.0_ds6.0.1/:/home/deepstream_tao_apps \

> -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# cd /home/deepstream_tao_apps/

# ./download_models.sh 

# export CUDA_VER=11.4

# make

# cp /home/TensorRT/build/out/libnvinfer_plugin.so.8.0.1 /usr/lib/x86_64-linux-gnu/

# ./apps/tao_detection/ds-tao-detection -c configs/frcnn_tao/pgie_frcnn_tao_config.txt -i /opt/nvidia/deepstream/deepstream-6.0/samples/streams/sample_720p.h264 -d

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/gst-plugins/gst-nvdsvideotemplate/

# make

# cp libnvdsgst_videotemplate.so /opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/

# rm -rf ~/.cache/gstreamer-1.0/

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.0/lib/cvcore_libs/

# cd /home/deepstream_tao_apps/apps/tao_others/

# make

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# cd deepstream-bodypose2d-app/

# ./deepstream-bodypose2d-app 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./body2dout

# cd ../deepstream-emotion-app/

# ./deepstream-emotion-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-faciallandmark-app/

# ./deepstream-faciallandmark-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-gaze-app/

# ./deepstream-gaze-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./gazenet

# cd ../deepstream-gesture-app/

# ./deepstream-gesture-app 3 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./gesture

# cd ../deepstream-heartrate-app/

# ./deepstream-heartrate-app 3 $URI ./heartrate

# exit

用 Docker DeepStream 6.1 測試 Integrate TAO model with DeepStream SDK

$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git deepstream_tao_apps-tao3.0_ds6.1ga

$ sudo apt install gitk

$ cd deepstream_tao_apps-tao3.0_ds6.1ga/

$ git branch -r

$ git checkout release/tao3.0_ds6.1ga

$ git log -1

$ sudo docker run --gpus all -it --rm --net=host \

> -v /etc/localtime:/etc/localtime \

> -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \

> -v /home/mark/Data/TensorRT/TensorRT_OSS-8.2.1/:/home/TensorRT \

> -v /home/mark/Data/DeepStream/deepstream_tap_apps/deepstream_tao_apps-tao3.0_ds6.1ga/:/home/deepstream_tao_apps \

> -w /opt/nvidia/deepstream/deepstream-6.1 nvcr.io/nvidia/deepstream:6.1-devel

# cp /home/TensorRT/build/out/libnvinfer_plugin.so.8.2.1 /usr/lib/x86_64-linux-gnu/

# cd /usr/lib/x86_64-linux-gnu/

# rm libnvinfer_plugin.so.8

# ln -s libnvinfer_plugin.so.8.2.1 libnvinfer_plugin.so.8

# cd /home/deepstream_tao_apps/

# ./download_models.sh 

# export CUDA_VER=11.6

# make

# ./apps/tao_detection/ds-tao-detection -c configs/frcnn_tao/pgie_frcnn_tao_config_dgpu.txt -i /opt/nvidia/deepstream/deepstream-6.1/samples/streams/sample_720p.h264 -d

# cd /opt/nvidia/deepstream/deepstream-6.1/sources/gst-plugins/gst-nvdsvideotemplate/

# make

# cp libnvdsgst_videotemplate.so /opt/nvidia/deepstream/deepstream-6.1/lib/gst-plugins/

# rm -rf ~/.cache/gstreamer-1.0/

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.1/lib/cvcore_libs/

# cd /home/deepstream_tao_apps/apps/tao_others/

# make

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# cd deepstream-bodypose2d-app/

# ./deepstream-bodypose2d-app 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt 0 0 $URI ./body2dout

# cd ../deepstream-emotion-app/

# ./deepstream-emotion-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-faciallandmark-app/

# ./deepstream-faciallandmark-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-gaze-app/

# ./deepstream-gaze-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./gazenet

# cd ../deepstream-gesture-app/

# ./deepstream-gesture-app 3 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./gesture

# cd ../deepstream-heartrate-app/

# ./deepstream-heartrate-app 3 $URI ./heartrate

# exit

安裝其他元件

參考 Ubuntu 18.04 重灌 上的 安裝 CMake 和 安裝 OpenCV

Build TensorRT OSS(Open Source Software)

參考 TensorRT Open Source Software

$ git clone -b master https://github.com/nvidia/TensorRT TensorRT_OSS

$ git describe --tags

8.2.0-EA-2-g96e2397

$ cd ..

$ mv TensorRT_OSS TensorRT_OSS-8.2.0 

$ cd TensorRT_OSS-8.2.0/

$ git submodule update --init --recursive

下列三個紅底黑字需一致

$ ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu18.04-cuda11.4 --cuda 11.4.2

Building container:

> docker build -f docker/ubuntu-18.04.Dockerfile --build-arg CUDA_VERSION=11.4.2 --build-arg uid=1000 --build-arg gid=1000 --tag=tensorrt-ubuntu18.04-cuda11.4 .

Sending build context to Docker daemon  95.59MB

Step 1/31 : ARG CUDA_VERSION=11.4.2

Step 2/31 : ARG OS_VERSION=18.04

Step 3/31 : FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION}

11.4.2-cudnn8-devel-ubuntu18.04: Pulling from nvidia/cuda

11323ed2c653: Already exists 

fc717811e279: Pull complete 

ccdba3c8eced: Pull complete 

2e5962ccde0c: Pull complete 

59fd9269dfdb: Pull complete 

ca8ef6b7b26f: Pull complete 

075319d1b973: Pull complete 

a7391b62d0c1: Pull complete 

ef615d2b7b27: Pull complete 

312d7b759a5a: Pull complete 

Digest: sha256:3ccdc2ec6ab45d9e902ac799eb4bfba7a39e2b3057962a95dd2b717493f8e252

Status: Downloaded newer image for nvidia/cuda:11.4.2-cudnn8-devel-ubuntu18.04

 ---> 58c89511016d

Step 4/31 : LABEL maintainer="NVIDIA CORPORATION"

 ---> Running in af3ded1c4476

Removing intermediate container af3ded1c4476

 ---> a72393f203bc

Step 5/31 : ENV TRT_VERSION 8.2.0.6

 ---> Running in 948c6eba13b7

Removing intermediate container 948c6eba13b7

 ---> 67dbe1482cfd

$ ./docker/launch.sh --tag tensorrt-ubuntu18.04-cuda11.4 --gpus all

trtuser@ae7dd87a158f:/workspace$ cd $TRT_OSSPATH

trtuser@ae7dd87a158f:/workspace/TensorRT$ mkdir -p build && cd build

trtuser@ae7dd87a158f:/workspace/TensorRT/build$ cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out

trtuser@ae7dd87a158f:/workspace/TensorRT/build$ make -j$(nproc)

trtuser@ae7dd87a158f:/workspace/TensorRT/build$ exit

$ sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.2.3 backup/

$ sudo cp TensorRT_OSS-8.2.0/build/out/libnvinfer_plugin.so.8.2.0 /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.2.3

$ sudo ldconfig

TensorFlow to TensorRT

參考 Developer Guide

參考 Sample Support Guide

參考 TensorRT Github samples

參考 TensorFlow to ONNX

$ pip install onnxruntime

$ pip install -U tf2onnx

python -m tf2onnx.convert \

--saved-model tensorflow-model-path \

--output output.onnx

yolo v4 to TensorRt

參考: tensorrt_demos

$ cd TensorRT

$ git clone https://github.com/jkjung-avt/tensorrt_demos.git

$ cd tensorrt_demos

安裝環境

$ cd yolo

$ ./install_pycuda.sh

$ pip3 install onnx==1.4.1

$ cd ../plugins

$ make

下載 yolo 的 weights 和 cfg

$ cd ../yolo

$ ./download_yolo.sh

轉換 weights 到 trt

$ python3 yolo_to_onnx.py -m yolov4-tiny-416

$ python3 onnx_to_tensorrt.py -m yolov4-tiny-416

測試

$ cd ..

$ python trt_yolo.py --image doc/dog_trt_yolov4_416.jpg -m yolov4-tiny-416

使用 int8

$ cd yolo

$ ln -s yolov4-tiny-416.cfg yolov4-tiny-int8-416.cfg

$ ln -s yolov4-tiny-416.onnx yolov4-tiny-int8-416.onnx

$ mkdir calib_images

# and copy our image to calib_images

$ python3 onnx_to_tensorrt.py -v --int8 -m yolov4-tiny-int8-416

會產生如下錯誤

[03/02/2022-15:22:07] [TRT] [V] 001_convolutional + 001_convolutional_bn Set Tactic Name: sm70_xmma_fprop_implicit_gemm_f16f16_f16f16_f16_nhwckrsc_nhwc_tilesize128x256x32_stage1_warpsize2x4x1_g1_tensor8x8x4_t1r3s3 Tactic: 46202665595848747

[03/02/2022-15:22:07] [TRT] [V] Deleting timing cache: 2020 entries, 504 hits

[03/02/2022-15:22:07] [TRT] [E] 1: Unexpected exception

ERROR: failed to build the TensorRT engine!

$ vi onnx_to_tensorrt.py

將 from calibrator import YOLOEntropyCalibrator 移到程式開頭

LPD LPR on DeepStream

參考

https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app

https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app

https://blogs.nvidia.com.tw/2021/05/12/creating-a-real-time-license-plate-detection-and-recognition-app/

依據 CUDA cuDNN TensorRT 的版本，下載 tlt-converter

https://developer.nvidia.com/tlt-get-started

完整程序

~/Data/DeepStream$ mkdir deepstream_lpr_app

~/Data/DeepStream$ cd deepstream_lpr_app/

~/Data/DeepStream/deepstream_lpr_app$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app.git

~/Data/DeepStream/deepstream_lpr_app$ cd deepstream_lpr_app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ ./download_us.sh

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ ../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 models/LP/LPR/us_lprnet_baseline18_deployable.etlt -t fp16 -e models/LP/LPR/lpr_us_onnx_b16.engine

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ make

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app$ cd deepstream-lpr-app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ cp dict_us.txt dict.txt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 2 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

以上可以執行，並在第一次執行時產生 engine

也可以利用下列命令 tlt-converter 產生 engine

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ cd ../models/tlt_pretrained_models/trafficcamnet/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ mv trafficnet_int8.txt trafficnet_int8.bin

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ ../../../../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k tlt_encode -o output_bbox/BiasAdd,output_cov/Sigmoid -d 3,544,960 -t int8 -c trafficnet_int8.bin -e resnet18_trafficcamnet_pruned.etlt_b1_gpu0_int8.engine resnet18_trafficcamnet_pruned.etlt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/tlt_pretrained_models/trafficcamnet$ cd ../../LP/LPD/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/LP/LPD$ ../../../../../tlt-converter/cuda11.0_cudnn8.0_trt7.2/tlt-converter -k nvidia_tlt -o output_bbox/BiasAdd,output_cov/Sigmoid -d 3,480,640 -t int8 -c usa_lpd_cal.bin -e usa_pruned.etlt_b16_gpu0_int8.engine usa_pruned.etlt

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/models/LP/LPD$ cd ../../../deepstream-lpr-app/

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ vi lpd_us_config.txt

不顯示

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 2 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

有顯示

~/Data/DeepStream/deepstream_lpr_app/deepstream_lpr_app/deepstream-lpr-app$ ./deepstream-lpr-app 1 3 0 /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.mp4 output.264

重新訓練模型

使用 tlt-launcher

python3 -m venv --system-site-packages tlt

source ~/envs/tlt/bin/activate

(tlt) $ pip3 install nvidia-pyindex

(tlt) $ pip3 install nvidia-tlt

(tlt) $ cat ~/.tlt_mounts.json

{

    "Mounts": [

        {

            "source": "/home/username/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments",

            "destination": "/workspace/tlt-experiments"

        },

        {

            "source": "/home/username/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr",

            "destination": "/workspace/openalpr"

        }

    ]

}

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ git clone https://github.com/openalpr/benchmarks benchmarks

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ mkdir tlt-experiments

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ mkdir openalpr

重新訓練 LPD 模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr$ cd openalpr/

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/lpd_prepare_data.py

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ python3 lpd_prepare_data.py --input_dir ../benchmarks/endtoend/us --output_dir lpd --target_width 640 --target_height 480

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/SPECS_tfrecord.txt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 dataset_convert -d /workspace/openalpr/SPECS_tfrecord.txt -o /workspace/openalpr/lpd_tfrecord/lpd

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpd/SPECS_train.txt

下載預先訓練模型 (tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ ngc registry model download-version "nvidia/tlt_lpdnet:unpruned_v1.0"

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ mv tlt_lpdnet_vunpruned_v1.0/usa_unpruned.tlt .

訓練

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 train -e /workspace/openalpr/SPECS_train.txt -r /workspace/openalpr/exp_unpruned -k nvidia_tlt

修剪

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 prune -m /workspace/openalpr/exp_unpruned/weights/model.tlt -o /workspace/openalpr/exp_pruned/model_pruned_06.tlt -pth 0.6 -k nvidia_tlt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ cp SPECS_train.txt SPECS_retrain.txt

修改 pretrained_model_file: "/workspace/openalpr/exp_pruned/model_pruned_06.tlt"

再次訓練

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 train -e /workspace/openalpr/SPECS_retrain.txt -r /workspace/openalpr/exp_pruned -k nvidia_tlt

匯出

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 export -m /workspace/openalpr/exp_unpruned/weights/model.tlt -o /workspace/openalpr/export/unpruned_model.etlt --cal_cache_file /workspace/openalpr/export/unpruned_calibration.bin -e /workspace/openalpr/SPECS_train.txt -k nvidia_tlt --cal_image_dir /workspace/openalpr/lpd/data/image --data_type int8 --batch_size 4 --batches 10 --engine_file /workspace/openalpr/export/unpruned_int8.trt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 export -m /workspace/openalpr/exp_pruned/weights/model.tlt -o /workspace/openalpr/export/pruned_model.etlt --cal_cache_file /workspace/openalpr/export/pruned_calibration.bin -e /workspace/openalpr/SPECS_train.txt -k nvidia_tlt --cal_image_dir /workspace/openalpr/lpd/data/image --data_type int8 --batch_size 4 --batches 10 --engine_file /workspace/openalpr/export/pruned_int8.trt

評估

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 evaluate -m /workspace/openalpr/exp_unpruned/weights/model.tlt -k nvidia_tlt -e /workspace/openalpr/SPECS_train.txt

(tlt) mark@z390:~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/openalpr$ tlt detectnet_v2 evaluate -m /workspace/openalpr/exp_pruned/weights/model.tlt -k nvidia_tlt -e /workspace/openalpr/SPECS_train.txt

重新訓練 LPR 模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments$ mkdir lprnet

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments$ cd lprnet/

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpr/preprocess_openalpr_benchmark.py

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ python3 preprocess_openalpr_benchmark.py --input_dir=../../benchmarks/endtoend/us --output_dir=../data/openalpr

下載 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/blob/release/tlt3.0/misc/dev_blog/LPDR/lpr/tutorial_spec.txt

修改 tutorial_spec.txt

augmentation_config {

    output_width: 96

    output_height: 48

    output_channel: 3

    keep_original_prob: 0.3

    transform_prob: 0.5

    rotate_degree: 5

}

成為

augmentation_config {

    output_width: 96

    output_height: 48

    output_channel: 3

    keep_original_prob: 0.3

    max_rotate_degree: 5

    rotate_prob: 0.5

    blur_prob: 0.5

}

下載預先訓練模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ ngc registry model download-version "nvidia/tlt_lprnet:trainable_v1.0"

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ cp tlt_lprnet_vtrainable_v1.0/us_lprnet_baseline18_trainable.tlt .

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ mkdir specs

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ cp tlt_lprnet_vtrainable_v1.0/us_lp_characters.txt specs/

訓練模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet train -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt -r /workspace/tlt-experiments/lprnet/ -k nvidia_tlt -m /workspace/tlt-experiments/lprnet/us_lprnet_baseline18_trainable.tlt --use_amp

匯出模型

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet export -m /workspace/tlt-experiments/lprnet/weights/lprnet_epoch-24.tlt -k nvidia_tlt -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ ls weights/lprnet_epoch-24.etlt

評估

(tlt) ~/Data/DeepStream/deepstream_lpr_app/tlt-lpdr/tlt-experiments/lprnet$ tlt lprnet evaluate -m /workspace/tlt-experiments/lprnet/weights/lprnet_epoch-24.tlt -k nvidia_tlt -e /workspace/tlt-experiments/lprnet/tutorial_spec.txt

Ubuntu 18.04 重灌

依據 https://developer.nvidia.com/deepstream-getting-started

選擇各個版本

https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html

sudo apt-get install ssh

sudo apt install python3-pip

sudo ln -s /usr/bin/python3 /usr/bin/python

安裝 NVIDIA Driver

https://www.linuxbabe.com/ubuntu/install-nvidia-driver-ubuntu-18-04

sudo lshw -c display

sudo lshw -c video

sudo ubuntu-drivers devices

sudo ubuntu-drivers autoinstall

sudo reboot

sudo lshw -c display

安裝 CUDA Toolkit

https://developer.nvidia.com/cuda-downloads

請選擇 CUDA Toolkit 10.2

Linux/x86_64/Ubuntu/18.04/deb(local)

vi ~/.bashrc

export PATH=/usr/local/cuda-10.2:$PATH

export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH

安裝 CUDNN

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

https://developer.nvidia.com/cudnn

選擇 cuDNN v8.0.5 for CUDA 10.2

選擇 cuDNN Library for Linux (x86)

tar -xzvf cudnn-10.2-linux-x64-v8.0.5.39.tgz

sudo cp cuda/include/cudnn*.h /usr/local/cuda/include

sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64

sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

sudo ldconfig

/sbin/ldconfig.real: /usr/local/cuda-10.2/targets/x86_64-linux/liblibcudnn.so.8 is not a symbolic link

cd /usr/local/cuda-10.2/targets/x86_64-linux/lib

sudo rm liblibcudnn.so.8 liblibcudnn.so

sudo ln -s libcudnn.so.8.0.5 libcudnn.so.8

sudo ln -s libcudnn.so.8 libcudnn.so

sudo ldconfig

安裝 TensorRT

下載 TensorRT binary

https://developer.nvidia.com/nvidia-tensorrt-7x-download

選擇 TensorRT 7.1.3.4 for Ubuntu 18.04 and CUDA 10.2 TAR package

version="7.1.3.4"

os="Ubuntu-18.04"

arch=$(uname -m)

cuda="cuda-10.2"

cudnn="cudnn8.0"

tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz

vi ~/.bashrc

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/Data/TensorRT/TensorRT-7.1.3.4/lib

source ~/.bashrc

cd TensorRt-7.1.3.4/python

sudo pip3 install tensorrt-7.1.3.4-cp36-none-linux_x86_64.whl

cd ../uff

sudo pip3 install uff-0.6.9-py2.py3-none-any.whl

cd ../graphsurgeon/

sudo pip3 install graphsurgeon-0.4.5-py2.py3-none-any.whl

安裝 GStreamer

https://yingrenn.blogspot.com/2020/09/gstreamer.html

安裝 DeepStream

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#dgpu-setup-for-ubuntu

sudo apt install \

 libssl1.0.0 \

 libgstreamer1.0-0 \

 gstreamer1.0-tools \

 gstreamer1.0-plugins-good \

 gstreamer1.0-plugins-bad \

 gstreamer1.0-plugins-ugly \

 gstreamer1.0-libav \

 libgstrtspserver-1.0-0 \

 libgstrtspserver-1.0-dev \

 libjansson4

git clone https://github.com/edenhill/librdkafka.git

cd librdkafka

git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

./configure

make

sudo make install

sudo mkdir -p /opt/nvidia/deepstream/deepstream-5.0/lib

sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-5.0/lib

下載 DeepStream

https://developer.nvidia.com/assets/Deepstream/5.0/ga/secure/deepstream_sdk_5.0.1_x86_64.tbz2

sudo tar -xvf deepstream_sdk_v5.0.1_x86_64.tbz2 -C /

cd /opt/nvidia/deepstream/deepstream-5.0/

sudo ./install.sh

sudo ldconfig

安裝 CMake v3.13

wget http://www.cmake.org/files/v3.13/cmake-3.13.5.tar.gz

tar xpvf cmake-3.13.5.tar.gz cmake-3.13.5

cd cmake-3.13.5

sudo apt-get install zlib1g-dev

sudo apt-get install curl

sudo apt-get install libcurl3

sudo apt-get install libcurl4 libcurl4-openssl-dev

./bootstrap --system-curl

make -j$(nproc)

sudo make install

安裝 Docker

https://docs.docker.com/engine/install/ubuntu/

sudo apt-get update

sudo apt-get install \

    apt-transport-https \

    ca-certificates \

    curl \

    gnupg-agent \

    software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

sudo apt-key fingerprint 0EBFCD88

sudo add-apt-repository \

   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \

   $(lsb_release -cs) \

   stable"

sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io

安裝 NVIDIA Docker

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker

curl https://get.docker.com | sh \

  && sudo systemctl start docker \

  && sudo systemctl enable docker

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \

   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update

sudo apt-get install -y nvidia-docker2

sudo systemctl restart docker

sudo groupadd docker

sudo usermod -a -G docker $USER

sudo reboot

若 sudo apt update 出現

W: Target CNF (stable/cnf/Commands-all) is configured multiple times in /etc/apt/sources.list:52 and /etc/apt/sources.list.d/docker.list:1

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 4773BD5E130D1D45

sudo rm /etc/apt/sources.list.d/docker.list

安裝 TensorRT 7.1 OSS

https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html

https://github.com/NVIDIA/TensorRT/tree/master

從 master 切換到 release/7.1

下載 TensorRT OSS

TensorRT OSS: 包含 TensorRT plugins, Caffe 和 ONNX parsers 等

git clone -b master https://github.com/nvidia/TensorRT TensorRT

cd TensorRT

git submodule update --init --recursive

參考之前的 TensorRT binary

cd TensorRT-7.1.3.4

export TRT_RELEASE=`pwd`

cd $TRT_SOURCE

./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 10.2

./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE

cd $TRT_SOURCE

mkdir -p build && cd build

cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out

make -j$(nproc)

exit

mkdir backup

mv $TRT_RELEASE/targets/x86_64-linux-gnu/lib/libnvinfer_plugin.so.7.1.3 backup

cp $TRT_SOURCE/build/out/libnvinfer_plugin.so.7.2.1 $TRT_RELEASE/targets/x86_64-linux-gnu/lib/libnvinfer_plugin.so.7.1.3

安裝 OpenCV 4.4

git clone https://github.com/opencv/opencv.git opencv-4.4.0 -b 4.4.0 --depth 1

git clone https://github.com/opencv/opencv_contrib.git opencv_contrib-4.4.0 -b 4.4.0 --depth 1

sudo apt-get update

sudo add-apt-repository ppa:alex-p/tesseract-ocr

sudo apt install tesseract-ocr libtesseract-dev

sudo apt-get install libleptonica-dev

sudo apt-get install qt5-default

sudo apt-get install qtcreator

sudo apt-get install build-essential cmake unzip pkg-config

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev

sudo apt-get install libxvidcore-dev libx264-dev

sudo apt-get install libgtk-3-dev

sudo apt-get install libatlas-base-dev gfortran

sudo apt-get install libhdf5-dev

sudo apt-get install python3-dev

mkdir build; cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \

-D CMAKE_INSTALL_PREFIX=/usr/local \

-D INSTALL_PYTHON_EXAMPLES=ON \

-D INSTALL_C_EXAMPLES=OFF \

-D WITH_TBB=ON \

-D BUILD_opencv_cudacodec=OFF \

-D ENABLE_FAST_MATH=1 \

-D CUDA_FAST_MATH=1 \

-D WITH_CUDA=ON \

-D WITH_CUBLAS=ON \

-D WITH_LIBV4L=ON \

-D BUILD_opencv_python3=ON \

-D BUILD_opencv_python2=OFF \

-D BUILD_opencv_java=OFF \

-D WITH_V4L=ON \

-D WITH_QT=ON \

-D WITH_OPENGL=ON \

-D WITH_GSTREAMER=ON \

-D WITH_GTK=ON \

-D OPENCV_GENERATE_PKGCONFIG=ON \

-D OPENCV_PC_FILE_NAME=opencv.pc \

-D OPENCV_ENABLE_NONFREE=OFF \

-D OPENCV_EXTRA_MODULES_PATH=/your_path_to/opencv/opencv_contrib-4.4.0/modules \

-D BUILD_EXAMPLES=ON \

-D WITH_CUDNN=ON \

-D CUDNN_VERSION="8.0.5" \

-D OPENCV_DNN_CUDA=ON \

-D CUDNN_INCLUDE_DIR=/usr/local/cuda/include \

-D CUDNN_LIBRARY=/usr/local/cuda/lib64/libcudnn.so.8.0.5 \

-D CUDA_ARCH_BIN=7.5 \

../opencv-4.4.0

#-D OPENCV_ENABLE_NONFREE=ON \

#-D OpenGL_GL_PREFERENCE=LEGACY \

make -j$(nproc)

sudo make install

sudo ldconfig

opencv_version -v

下載 Tesseract 的中文字 chi_tra.traineddata, chi_tra_vert.traineddate

https://github.com/tesseract-ocr/tessdata

放至 /usr/share/tesseract-ocr/4.00/tessdata

安裝 Xpra

https://www.xpra.org/trac/wiki/Building/Debian

sudo apt-get install libx11-dev libxtst-dev libxcomposite-dev libxdamage-dev \

 libxkbfile-dev python-all-dev

sudo apt-get install libgtk-3-dev python3-dev python3-cairo-dev python-gi-dev cython3

sudo apt-get install xauth x11-xkb-utils

sudo apt-get install libx264-dev libvpx-dev yasm

sudo apt-get install libnvidia-encode-440

sudo apt-get install libavformat-dev libavcodec-dev libswscale-dev

sudo apt-get install libturbojpeg-dev

sudo apt-get install libwebp-dev

sudo apt-get install uglifyjs brotli libjs-jquery libjs-jquery-ui gnome-backgrounds

sudo apt-get install python3-opengl python3-numpy python3-pil

sudo apt-get install python3-rencode python3-lz4 python3-dbus python3-cryptography \

 python3-netifaces python3-yaml

sudo apt-get install python3-setproctitle python3-xdg python3-pyinotify python3-opencv

sudo apt-get install libpam-dev quilt xserver-xorg-dev xutils-dev xvfb keyboard-configuration

sudo apt-get install python3-kerberos python3-gssapi

sudo apt-get install gstreamer1.0-pulseaudio gstreamer1.0-alsa \

 gstreamer1.0-plugins-base gstreamer1.0-plugins-good \

 gstreamer1.0-plugins-ugly

sudo apt-get install cups-filters cups-common cups-pdf python3-cups

sudo apt-get install openssh-client sshpass python3-paramiko

sudo apt-get install devscripts build-essential lintian debhelper

wget https://www.xpra.org/src/xpra-4.0.4.tar.xz

tar -xf xpra-4.0.6.tar.xz

cd xpra-4.0.6

vi setup.py

#!/usr/bin/env python3

sudo ./setup.py install

安裝 frp

https://yingrenn.blogspot.com/2020/03/frp.html

vi frps.ini

[common]

bind_port = 7000

dashboard_port = 7001

dashboard_user = user

dashboard_pwd = password

vi /etc/systemd/system/frps.service

[Unit]

Description=FRP Server Daemon

[Service]

Type=simple

ExecStartPre=-/usr/sbin/setcap cap_net_bind_service=+ep /home/mark/Data/frp/frp_0.34.3_linux_amd64/frps

ExecStart=/path_to_frp/frp_0.34.3_linux_amd64/frps -c /path_to_frp/frp_0.34.3_linux_amd64/frps.ini

Restart=always

RestartSec=20s

User=nobody

PermissionsStartOnly=true

LimitNOFILE=infinity

[Install]

WantedBy=multi-user.target

sudo systemctl start frps

sudo systemctl enable ftps

安裝 Gitea Tomcat Nginx

https://yingrenn.blogspot.com/2019/11/gitea-in-ubuntu.html

https://yingrenn.blogspot.com/search/label/tomcat

安裝 x11vnc

因為 Ubuntu 18.04 改 LightDM 到 GDM3, 所以要在 console login 後，才能動作

sudo apt install x11vnc

sudo x11vnc -storepasswd

sudo chown mark.mark ~/.vnc/passwd

sudo vi /etc/systemd/system/x11vnc.service

# Description: Custom Service Unit file

# File: /etc/systemd/system/x11vnc.service

[Unit]

Description="x11vnc"

Requires=display-manager.service

After=display-manager.service

[Service]

ExecStart=/usr/bin/x11vnc -loop -nopw -xkb -repeat -noxrecord -noxfixes -noxdamage -forever -rfbport 5900 -display :1 -auth /run/user/1000/gdm/Xauthority -rfbauth /home/mark/.vnc/passwd

ExecStop=/usr/bin/killall x11vnc

Restart=on-failure

RestartSec=2

[Install]

WantedBy=multi-user.target

安裝 VirtualBox

https://www.virtualbox.org/wiki/Downloads

到官網下載

virtualbox-6.1_6.1.16-140961~Ubuntu~bionic_amd64.deb

Oracle_VM_VirtualBox_Extension_Pack-6.1.16.vbox-extpack

sudo dpkg -i virtualbox-6.1_6.1.16-140961~Ubuntu~bionic_amd64.deb

sudo groupadd win10disk

sudo usermod -a -G win10disk youruser

sudo udevadm info /dev/sdX | grep UUID

E: ID_PART_TABLE_UUID=01234567-89ab-cdef-0123-456789abcde

vi /etc/udev/rules.d/99-win10disk.rules

ENV{ID_PART_TABLE_UUID}=="1234567-89ab-cdef-0123-456789abcde", GROUP="win10disk"

ls -l /dev/sdb

brw-rw---- 1 root win10disk 8, 16 Nov 4 23:33 /dev/sdb

VBoxManage internalcommands createrawvmdk -filename .VirtualBox/Crucial1T.vmdk -rawdisk /dev/sda

sudo usermod -a -G vboxusers youruser

拷貝舊的 VM, 修改 win10.vbox, win10.vbox-prev 內的 uuid

與 .VirtualBox/Crucial1T.vmdk 的 uuid 一致

開啟 VirtualBox

File/Preferences...

Extensions 按 +

選擇剛下載的 Oracle_VM_VirtualBox_Extension_Pack-6.1.16.vbox-extpack

只有登入畫面無法使用滑鼠和鍵盤，其餘正常

sudo apt-get instll xserver-xorg-input-all