命令curl

下载文件

下载单个文件

curl http://book.d2l.ai/_images/catdog.jpg -o catdog.jpg

下载多个文件

curl https://download.01.org/opencv/2021/openvinotoolkit/2021.1/open_model_zoo/models_bin/1/face-detection-retail-0004/FP32/face-detection-retail-0004.xml https://download.01.org/opencv/2021/openvinotoolkit/2021.1/open_model_zoo/models_bin/1/face-detection-retail-0004/FP32/face-detection-retail-0004.bin -o face-detection-retail-0004.xml -o face-detection-retail-0004.bin

下载文件并创建目录 curl --create-dirs https://download.01.org/opencv/2021/openvinotoolkit/2021.

curlcommand-linedownloadhttpshell

OpenVINO Benchmark Python Tool

性能指标评测工具

该工具使用卷积网络执行推理。性能可以测量两种推理模式：

  • 同步（面向延迟 Latency）
  • 异步（面向吞吐量 Throughput）

帮助信息 -i PATHS_TO_INPUT [PATHS_TO_INPUT ...], --paths_to_input PATHS_TO_INPUT [PATHS_TO_INPUT ...] Optional. Path to a folder with images and/or binaries or to specific image or binary file.It is also allowed to map files to network inputs: input_1:file_1/dir1,file_2/dir2,input_4:file_4/dir4 input_2:file_3/dir3 -m PATH_TO_MODEL, --path_to_model PATH_TO_MODEL Required. Path to an .xml/.onnx file with a trained model or to a .blob file with a trained compiled model. -d TARGET_DEVICE, --target_device TARGET_DEVICE Optional.

openvinobenchmark_appperformancepythoninferencebenchmarksoptimisation

OpenVINO Cross Check Tool

交叉检查工具 (Cross Check Tool)

可以比较两个连续模型推理的准确性和性能指标，这些推理在两个不同的受支持的英特尔设备上执行或以不同的精度执行。交叉检查工具可以比较每层或整个模型的指标。

查看帮助信息 $ python cross_check_tool.py -h usage: -------------------------------------------------------------- For cross precision check provide two IRs (mapping files may be needed) run: python3 cross_check_tool.py \ --input path/to/file/describing/input \ --model path/to/model/.xml \ --device device_for_model \ --reference_model path/to/reference_model/.

openvinocross-check-toolbenchmarkingvalidationinferenceperformancepython

Deep Learning Accuracy Validation Framework

深度学习准确性验证框架

例子

进入 accuracy_checker 目录

cd open_model_zoo/tools/accuracy_checker

下载数据集

wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar xvf cifar-10-python.tar.gz -C sample

配置文件结构

models:
  - name: model_name

    launchers:
      - framework: openvino
        adapter: adapter_name

    datasets:
      - name: dataset_name

评估 accuracy_check -c sample/sample_config.yml -m data/test_models/ -s sample/ 2022-05-18 11:18:38.663810: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.

openvinoaccuracy-checkervalidationdeep-learningtensorflowpythondatasets

Ubuntu 上将 NVIDIA GPU 切换为 Intel 集成显卡 IGD

IGD（Integrated Graphics Device）

操作系统：Ubuntu 18.04，主机有一张 NVIDIA 的独立显卡 GP106 [GeForce GTX 1060 6GB]，还有 Intel 酷睿处理器 i5 8500 自带的集成显卡（Intel UHD Graphics 630）。为了更充分的使用独立显卡用于深度学习计算，需要把集成显卡用于显示。在这个过程中遇到了各种各样的问题：

  • 鼠标和键盘失灵。
  • 登录 X Window 时，输入正确的密码不能登录。

BIOS 设置

显卡设置

  • IGD 内置图形显示集成显卡
  • PEG 独立PCI Express接口图形显卡

选择 IGD，保存退出。

配置 X Window 显示显卡设备信息 lspci lspci -k | grep -EA3 'VGA|3D|Display' | | | | | - Only VGA is not good enough, | | | | | because Nvidia mobile adapters | | | | | are shown as 3D and some AMD | | | | | adapters are shown as Display. | | | | --------- Print 3 lines after the regexp match.

ubuntugpunvidiaintelx11driverhardwaretroubleshooting

NVIDIA 软件栈搭建

NVIDIA 软件栈

GPU Driver

NVIDIA 驱动程序下载

Ubuntu

  1. 搜索有效的显卡驱动
sudo ubuntu-drivers devices
#搜索匹配
sudo apt search nvidia-
  1. 安装驱动
sudo apt install nvidia-driver-510
  1. 重启系统
sudo reboot
  1. 查看
nvidia-smi
  1. 卸载驱动
sudo apt purge nvidia*

CUDA Toolkit

CUDA Toolkit 自带驱动。

下载

这里下载 run 格式安装包。

CUDA Toolkit 下载

安装

$ sudo sh cuda_xx.x.x_xxx.xx.xx_linux.run

deviceQuery $ ./deviceQuery ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA GeForce GTX 1060 6GB" CUDA Driver Version / Runtime Version 11.6 / 11.

nvidiacudacudnntensorrtncclhpcgpudriverinstallationdeep-learning

Get Started YOLOv5

Quick Start Examples

Install

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

# create virtual python environments
python -m venv yolov5_env
source yolov5_env/bin/activate
python -m pip install --upgrade pip

# install
pip install -r requirements.txt

Inference import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .

yolov5yoloobject-detectionpytorchcomputer-visionwandbtensorboarddeep-learning