[Docs] Fixes in readme files: (#750)
- change repo name to openvino - update driver version - fix path to samples data - remove section about Movidius driver installation - change latest release to 2020.3 - merge fixes in install_dependencies.sh from 2020 branch
This commit is contained in:
Alexey Suhov
GitHub
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1ffada0b23
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@ -1,5 +1,5 @@
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# [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Deep Learning Deployment Toolkit repository
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[](https://github.com/openvinotoolkit/openvino/releases/tag/2020.2)
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[](https://github.com/openvinotoolkit/openvino/releases/tag/2020.3.0)
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[](LICENSE)
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This toolkit allows developers to deploy pre-trained deep learning models
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@ -28,7 +28,6 @@
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- [Add Inference Engine to Your Project](#add-inference-engine-to-your-project)
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- [(Optional) Additional Installation Steps for the Intel® Movidius™ Neural Compute Stick and Neural Compute Stick 2](#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2)
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- [For Linux, Raspbian Stretch* OS](#for-linux-raspbian-stretch-os)
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- [For Windows](#for-windows-1)
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- [Next Steps](#next-steps)
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- [Additional Resources](#additional-resources)
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@ -60,12 +59,12 @@ The software was validated on:
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- [CMake]\* 3.11 or higher
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- GCC\* 4.8 or higher to build the Inference Engine
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- Python 2.7 or higher for Inference Engine Python API wrapper
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- (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441].
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- (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352].
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### Build Steps
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1. Clone submodules:
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```sh
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cd dldt
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cd openvino
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git submodule update --init --recursive
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```
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2. Install build dependencies using the `install_dependencies.sh` script in the
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@ -78,7 +77,7 @@ The software was validated on:
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```
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3. By default, the build enables the Inference Engine GPU plugin to infer models
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on your Intel® Processor Graphics. This requires you to
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[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441]
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[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352]
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before running the build. If you don't want to use the GPU plugin, use the
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`-DENABLE_CLDNN=OFF` CMake build option and skip the installation of the
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Intel® Graphics Compute Runtime for OpenCL™ Driver.
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@ -172,10 +171,10 @@ Native compilation of the Inference Engine is the most straightforward solution.
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sudo apt-get install -y git cmake libusb-1.0-0-dev
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```
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2. Go to the cloned `dldt` repository:
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2. Go to the cloned `openvino` repository:
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```bash
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cd dldt
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cd openvino
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```
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3. Initialize submodules:
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@ -262,15 +261,15 @@ with the following content:
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5. Run Docker\* container with mounted source code folder from host:
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```bash
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docker run -it -v /absolute/path/to/dldt:/dldt ie_cross_armhf /bin/bash
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docker run -it -v /absolute/path/to/openvino:/openvino ie_cross_armhf /bin/bash
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```
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6. While in the container:
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1. Go to the cloned `dldt` repository:
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1. Go to the cloned `openvino` repository:
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```bash
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cd dldt
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cd openvino
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```
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2. Create a build folder:
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@ -291,8 +290,8 @@ with the following content:
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```
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7. Press **Ctrl+D** to exit from Docker. You can find the resulting binaries
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in the `dldt/bin/armv7l/` directory and the OpenCV*
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installation in the `dldt/inference-engine/temp`.
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in the `openvino/bin/armv7l/` directory and the OpenCV*
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installation in the `openvino/inference-engine/temp`.
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>**NOTE**: Native applications that link to cross-compiled Inference Engine
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library require an extra compilation flag `-march=armv7-a`.
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@ -381,8 +380,8 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
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6. Before running the samples, add paths to the TBB and OpenCV binaries used for
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the build to the `%PATH%` environment variable. By default, TBB binaries are
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downloaded by the CMake-based script to the `<dldt_repo>/inference-engine/temp/tbb/bin`
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folder, OpenCV binaries to the `<dldt_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
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downloaded by the CMake-based script to the `<openvino_repo>/inference-engine/temp/tbb/bin`
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folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
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folder.
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### Additional Build Options
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@ -437,7 +436,7 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
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call "C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\bin\ipsxe-comp-vars.bat" intel64 vs2017
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set CXX=icl
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set CC=icl
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:: clean TBBROOT value set by ipsxe-comp-vars.bat, required TBB package will be downloaded by dldt cmake script
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:: clean TBBROOT value set by ipsxe-comp-vars.bat, required TBB package will be downloaded by openvino cmake script
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set TBBROOT=
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cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Release ..
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cmake --build . --config Release
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@ -461,7 +460,7 @@ The software was validated on:
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1. Clone submodules:
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```sh
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cd dldt
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cd openvino
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git submodule update --init --recursive
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```
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2. Install build dependencies using the `install_dependencies.sh` script in the
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@ -545,7 +544,7 @@ This section describes how to build Inference Engine for Android x86 (64-bit) op
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2. Clone submodules
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```sh
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cd dldt
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cd openvino
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git submodule update --init --recursive
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```
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@ -610,7 +609,7 @@ before running the Inference Engine build:
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For CMake projects, set the `InferenceEngine_DIR` environment variable:
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```sh
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export InferenceEngine_DIR=/path/to/dldt/build/
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export InferenceEngine_DIR=/path/to/openvino/build/
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```
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Then you can find Inference Engine by `find_package`:
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@ -660,20 +659,6 @@ sudo ldconfig
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rm 97-myriad-usbboot.rules
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```
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### For Windows
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For Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2,
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install the Movidius™ VSC driver:
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1. Go to the `<DLDT_ROOT_DIR>/inference-engine/thirdparty/movidius/MovidiusDriver`
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directory, where the `DLDT_ROOT_DIR` is the directory to which the DLDT
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repository was cloned.
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2. Right click on the `Movidius_VSC_Device.inf` file and choose **Install** from
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the pop-up menu.
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You have installed the driver for your Intel® Movidius™ Neural Compute Stick
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or Intel® Neural Compute Stick 2.
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## Next Steps
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Congratulations, you have built the Inference Engine. To get started with the
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@ -706,7 +691,7 @@ This target collects all dependencies, prepares the nGraph package and copies it
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[Intel® Distribution of OpenVINO™]:https://software.intel.com/en-us/openvino-toolkit
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[CMake]:https://cmake.org/download/
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[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441]:https://github.com/intel/compute-runtime/releases/tag/19.41.14441
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[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352]:https://github.com/intel/compute-runtime/releases/tag/20.13.16352
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[MKL-DNN repository]:https://github.com/intel/mkl-dnn/releases/download/v0.19/mklml_lnx_2019.0.5.20190502.tgz
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[MKL-DNN repository for Windows]:(https://github.com/intel/mkl-dnn/releases/download/v0.19/mklml_win_2019.0.5.20190502.zip)
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[OpenBLAS]:https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download
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@ -1,7 +1,7 @@
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# Get Started with OpenVINO™ Deep Learning Deployment Toolkit (DLDT) on Linux*
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# Get Started with OpenVINO™ Toolkit on Linux*
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This guide provides you with the information that will help you to start using
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the DLDT on Linux\*. With this guide, you will learn how to:
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the OpenVINO™ Toolkit on Linux\*. With this guide, you will learn how to:
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1. [Configure the Model Optimizer](#configure-the-model-optimizer)
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2. [Prepare a model for sample inference](#prepare-a-model-for-sample-inference)
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@ -10,13 +10,13 @@ the DLDT on Linux\*. With this guide, you will learn how to:
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3. [Run the Image Classification Sample Application with the model](#run-the-image-classification-sample-application)
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## Prerequisites
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1. This guide assumes that you have already cloned the `dldt` repo and
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1. This guide assumes that you have already cloned the `openvino` repo and
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successfully built the Inference Engine and Samples using the
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[build instructions](inference-engine/README.md).
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2. The original structure of the repository directories remains unchanged.
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> **NOTE**: Below, the directory to which the `dldt` repository is cloned is
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referred to as `<DLDT_DIR>`.
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> **NOTE**: Below, the directory to which the `openvino` repository is cloned is
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referred to as `<OPENVINO_DIR>`.
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## Configure the Model Optimizer
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@ -53,7 +53,7 @@ If you see error messages, check for any missing dependencies.
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1. Go to the Model Optimizer prerequisites directory:
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```sh
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cd <DLDT_DIR>/model_optimizer/install_prerequisites
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cd <OPENVINO_DIR>/model_optimizer/install_prerequisites
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```
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2. Run the script to configure the Model Optimizer for Caffe,
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TensorFlow, MXNet, Kaldi\*, and ONNX:
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@ -68,7 +68,7 @@ Configure individual frameworks separately **ONLY** if you did not select
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1. Go to the Model Optimizer prerequisites directory:
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```sh
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cd <DLDT_DIR>/model_optimizer/install_prerequisites
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cd <OPENVINO_DIR>/model_optimizer/install_prerequisites
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```
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2. Run the script for your model framework. You can run more than one script:
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@ -162,20 +162,20 @@ as `<models_dir>` below) with the Model Downloader:
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**For CPU (FP32):**
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```sh
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python3 <DLDT_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP32 --output_dir <ir_dir>
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python3 <OPENVINO_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP32 --output_dir <ir_dir>
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```
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**For GPU and MYRIAD (FP16):**
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```sh
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python3 <DLDT_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir <ir_dir>
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python3 <OPENVINO_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir <ir_dir>
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```
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After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `<ir_dir>` directory.
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3. Copy the `squeezenet1.1.labels` file from the `<DLDT_DIR>/inference-engine/samples/sample_data/`
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3. Copy the `squeezenet1.1.labels` file from the `<OPENVINO_DIR>/scripts/demo/`
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folder to the model IR directory. This file contains the classes that ImageNet
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uses so that the inference results show text instead of classification numbers:
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```sh
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cp <DLDT_DIR>/inference-engine/samples/sample_data/squeezenet1.1.labels <ir_dir>
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cp <OPENVINO_DIR>/scripts/demo/squeezenet1.1.labels <ir_dir>
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```
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Now you are ready to run the Image Classification Sample Application.
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@ -184,28 +184,28 @@ Now you are ready to run the Image Classification Sample Application.
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The Inference Engine sample applications are automatically compiled when you
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built the Inference Engine using the [build instructions](inference-engine/README.md).
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The binary files are located in the `<DLDT_DIR>/inference-engine/bin/intel64/Release`
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The binary files are located in the `<OPENVINO_DIR>/inference-engine/bin/intel64/Release`
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directory.
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To run the Image Classification sample application with an input image on the prepared IR:
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1. Go to the samples build directory:
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```sh
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cd <DLDT_DIR>/inference-engine/bin/intel64/Release
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cd <OPENVINO_DIR>/inference-engine/bin/intel64/Release
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2. Run the sample executable with specifying the `car.png` file from the
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`<DLDT_DIR>/inference-engine/samples/sample_data/` directory as an input
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`<OPENVINO_DIR>/scripts/demo/` directory as an input
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image, the IR of your model and a plugin for a hardware device to perform
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inference on:
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**For CPU:**
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```sh
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./classification_sample -i <DLDT_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d CPU
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./classification_sample -i <OPENVINO_DIR>/scripts/demo/car.png -m <ir_dir>/squeezenet1.1.xml -d CPU
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```
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**For GPU:**
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```sh
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./classification_sample -i <DLDT_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d GPU
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./classification_sample -i <OPENVINO_DIR>/scripts/demo/car.png -m <ir_dir>/squeezenet1.1.xml -d GPU
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```
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**For MYRIAD:**
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@ -214,14 +214,14 @@ To run the Image Classification sample application with an input image on the pr
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Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires
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performing [additional hardware configuration steps](inference-engine/README.md#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2).
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```sh
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./classification_sample -i <DLDT_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d MYRIAD
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./classification_sample -i <OPENVINO_DIR>/scripts/demo/car.png -m <ir_dir>/squeezenet1.1.xml -d MYRIAD
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```
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When the Sample Application completes, you will have the label and confidence for the top-10 categories printed on the screen. Below is a sample output with inference results on CPU:
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```sh
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Top 10 results:
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Image /home/user/dldt/inference-engine/samples/sample_data/car.png
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Image /home/user/openvino/scripts/demo/car.png
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classid probability label
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------- ----------- -----
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# install dependencies
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if [ -f /etc/lsb-release ]; then
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# Ubuntu
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host_cpu=$(uname -m)
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if [ $host_cpu = x86_64 ]; then
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x86_64_specific_packages="gcc-multilib g++-multilib"
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else
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x86_64_specific_packages=""
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fi
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sudo -E apt update
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sudo -E apt-get install -y \
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build-essential \
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ca-certificates \
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git \
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libboost-regex-dev \
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gcc-multilib \
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g++-multilib \
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$x86_64_specific_packages \
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libgtk2.0-dev \
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pkg-config \
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unzip \
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