Documentation updates for 2020.4 (#1672) (#1729)

* Doc updates

* 2020.4 doc updates

* Removed </br> tag

* Minor fix

* Minor fixes

* Updated documentation for 2020.4 (#1434)

* Updated documentation for 2020.4

* Updated Core::ReadNetwork documentation (#1178)

* Fixed docs

Co-authored-by: Ilya Churaev <ilya.churaev@intel.com>

Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
Co-authored-by: Ilya Churaev <ilya.churaev@intel.com>

Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
Co-authored-by: Ilya Churaev <ilya.churaev@intel.com>

.ci/azure/linux.yml

@@ -1,118 +0,0 @@
-jobs:
-- job: Lin
-  # About 150% of total time
-  timeoutInMinutes: 85
-  pool:
-    name: LIN_VMSS_VENV_F8S_WU2
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 8
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)/_w
-    BUILD_DIR: $(WORK_DIR)/build
-    BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      curl -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/instance?api-version=2019-06-01"
-      whoami
-      uname -a
-      which python3
-      python3 --version
-      gcc --version
-      lsb_release
-      env
-      cat /proc/cpuinfo
-      cat /proc/meminfo
-      vmstat -s
-      df
-    displayName: 'System info'
-  - script: |
-      rm -rf $(WORK_DIR) ; mkdir $(WORK_DIR)
-      rm -rf $(BUILD_DIR) ; mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - script: |
-      sudo apt --assume-yes install libusb-1.0-0-dev
-      python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
-      # For running Python API tests
-      python3 -m pip install -r ./inference-engine/ie_bridges/python/src/requirements-dev.txt
-    displayName: 'Install dependencies'
-  - script: |
-      wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
-      unzip ninja-linux.zip
-      sudo cp -v ninja /usr/local/bin/
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Install Ninja'
-  - task: CMake@1
-    inputs:
-      # CMake must get Python 3.x version by default
-      cmakeArgs: -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE=/usr/bin/python3.6 -DENABLE_TESTS=ON $(REPO_DIR)
-      workingDirectory: $(BUILD_DIR)
-  - script: ninja
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Lin'
-  - script: ls -alR $(REPO_DIR)/bin/
-    displayName: 'List files'
-  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/gnaUnitTests
-    displayName: 'GNA UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      python3 $(WORK_DIR)/gtest-parallel/gtest-parallel $(BIN_DIR)/MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json -- --gtest_print_time=1
-    workingDirectory: $(WORK_DIR)
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      $(BIN_DIR)/InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      export LD_LIBRARY_PATH=$(BIN_DIR)/lib
-      export PYTHONPATH=$(BIN_DIR)/lib/python_api/python3.6
-      env
-      cd $(REPO_DIR)/inference-engine/ie_bridges/python/tests
-      pytest
-    displayName: 'Python API Tests'
-    continueOnError: false
-    enabled: false
-

.ci/azure/mac.yml

@@ -1,102 +0,0 @@
-jobs:
-- job: Mac
-  # About 200% of total time (perfomace of Mac hosts is unstable)
-  timeoutInMinutes: 180
-  pool:
-    vmImage: 'macOS-10.15'
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 3
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)/_w
-    BUILD_DIR: $(WORK_DIR)/build
-    BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      whoami
-      uname -a
-      which python3
-      python3 --version
-      gcc --version
-      xcrun --sdk macosx --show-sdk-version
-      env
-      sysctl -a
-    displayName: 'System info'
-  - script: |
-      rm -rf $(WORK_DIR) ; mkdir $(WORK_DIR)
-      rm -rf $(BUILD_DIR) ; mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - task: UsePythonVersion@0
-    inputs:
-      versionSpec: '3.7'
-  - script: |
-      brew install cython
-      brew install automake
-    displayName: 'Install dependencies'
-  - script: brew install ninja
-    displayName: 'Install Ninja'
-  - script: |
-      export PATH="/usr/local/opt/cython/bin:$PATH"
-      export CC=gcc
-      export CXX=g++
-      # Disable errors with Ninja
-      export CXXFLAGS="-Wno-error=unused-command-line-argument"
-      export CFLAGS="-Wno-error=unused-command-line-argument"
-      cmake -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON $(REPO_DIR)
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'CMake'
-  - script: ninja
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Mac'
-  - script: ls -alR $(REPO_DIR)/bin/
-    displayName: 'List files'
-  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: $(BIN_DIR)/ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: $(BIN_DIR)/MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(WORK_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      python3 $(WORK_DIR)/gtest-parallel/gtest-parallel $(BIN_DIR)/MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json --gtest_filter=-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric* -- --gtest_print_time=1
-    workingDirectory: $(WORK_DIR)
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      export DATA_PATH=$(WORK_DIR)/testdata
-      export MODELS_PATH=$(WORK_DIR)/testdata
-      $(BIN_DIR)/InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false
-

.ci/azure/windows.yml

@@ -1,133 +0,0 @@
-jobs:
-- job: Win
-  # About 150% of total time
-  timeoutInMinutes: 120
-  pool:
-    name: WIN_VMSS_VENV_F8S_WU2
-  variables:
-    system.debug: true
-    WORKERS_NUMBER: 8
-    BUILD_TYPE: Release
-    REPO_DIR: $(Build.Repository.LocalPath)
-    WORK_DIR: $(Pipeline.Workspace)\_w
-    BUILD_DIR: D:\build
-    BIN_DIR: $(REPO_DIR)\bin\intel64
-    MSVS_VARS_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat
-    MSVC_COMPILER_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Tools\MSVC\14.24.28314\bin\Hostx64\x64\cl.exe
-  steps:
-  - checkout: self
-    clean: true
-    fetchDepth: 1
-    lfs: false
-    submodules: recursive
-    path: openvino
-  - script: |
-      powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
-      where python3
-      where python
-      python --version
-      wmic computersystem get TotalPhysicalMemory
-      wmic cpu list
-      wmic logicaldisk get description,name
-      wmic VOLUME list
-      set
-    displayName: 'System info'
-  - script: |
-      rd /Q /S $(WORK_DIR) & mkdir $(WORK_DIR)
-      rd /Q /S $(BUILD_DIR) & mkdir $(BUILD_DIR)
-    displayName: 'Make dir'
-  - script: |
-      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-win.zip ninja-win.zip
-      powershell -command "Expand-Archive -Force ninja-win.zip"
-    workingDirectory: $(WORK_DIR)
-    displayName: Install Ninja
-  - script: |
-      certutil -urlcache -split -f https://incredibuilddiag1wu2.blob.core.windows.net/incredibuild/IBSetupConsole_9_5_0.exe IBSetupConsole_9_5_0.exe
-      call IBSetupConsole_9_5_0.exe /Install /Components=Agent,oneuse /Coordinator=11.1.0.4 /AGENT:OPENFIREWALL=ON /AGENT:AUTOSELECTPORTS=ON /ADDTOPATH=ON /AGENT:INSTALLADDINS=OFF
-    workingDirectory: $(WORK_DIR)
-    displayName: Install IncrediBuild
-  - script: |
-      echo Stop IncrediBuild_Agent && net stop IncrediBuild_Agent
-      reg add HKEY_LOCAL_MACHINE\SOFTWARE\Wow6432Node\Xoreax\IncrediBuild\Builder /f /v LastEnabled /d 0 && echo Start IncrediBuild_Agent && net start IncrediBuild_Agent
-    displayName: Start IncrediBuild
-  - script: |
-      set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'CMake'
-  - script: |
-      set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && "C:\Program Files (x86)\IncrediBuild\BuildConsole.exe" /COMMAND="ninja" /MaxCPUS=40
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Build Win'
-  - script: echo Stop IncrediBuild_Agent && net stop IncrediBuild_Agent
-    displayName: Stop IncrediBuild
-    continueOnError: true
-  - script: dir $(REPO_DIR)\bin\ /s /b
-    displayName: 'List files'
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
-    displayName: 'nGraph UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\InferenceEngineUnitTests --gtest_print_time=1
-    displayName: 'IE UT old'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\ieUnitTests
-    displayName: 'IE UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\cpuUnitTests
-    displayName: 'CPU UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\gnaUnitTests
-    displayName: 'GNA UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\vpuUnitTests
-    displayName: 'VPU UT'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\ieFuncTests
-    displayName: 'IE FuncTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\cpuFuncTests --gtest_print_time=1
-    displayName: 'CPU FuncTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;%PATH%
-      $(BIN_DIR)\MklDnnBehaviorTests
-    displayName: 'MklDnnBehaviorTests'
-    continueOnError: false
-  - script: |
-      git clone https://github.com/openvinotoolkit/testdata.git
-      git clone https://github.com/google/gtest-parallel.git
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Clone testdata & gtest-parallel'
-    # Add for gtest-parallel, it hangs now (CVS-33386)
-    #python $(BUILD_DIR)\gtest-parallel\gtest-parallel $(BIN_DIR)\MklDnnFunctionalTests --workers=$(WORKERS_NUMBER) --print_test_times --dump_json_test_results=MklDnnFunctionalTests.json -- --gtest_print_time=1
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;$(REPO_DIR)\inference-engine\temp\opencv_4.5.0\opencv\bin;%PATH%
-      set DATA_PATH=$(BUILD_DIR)\testdata
-      set MODELS_PATH=$(BUILD_DIR)\testdata
-      $(BIN_DIR)\MklDnnFunctionalTests --gtest_print_time=1
-    displayName: 'MklDnnFunctionalTests'
-    continueOnError: false
-  - script: |
-      set PATH=$(REPO_DIR)\inference-engine\temp\tbb\bin;$(REPO_DIR)\inference-engine\temp\opencv_4.5.0\opencv\bin;%PATH%
-      set DATA_PATH=$(BUILD_DIR)\testdata
-      set MODELS_PATH=$(BUILD_DIR)\testdata
-      $(BIN_DIR)\InferenceEngineCAPITests
-    displayName: 'IE CAPITests'
-    continueOnError: false

.ci/openvino-onnx/Dockerfile

@@ -1,83 +0,0 @@
-FROM ubuntu:20.04
-
-LABEL version=2020.07.09.1
-
-ARG http_proxy
-ARG https_proxy
-ENV http_proxy ${http_proxy}
-ENV https_proxy ${https_proxy}
-
-ENV CI=true
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED 1
-
-# Install base dependencies
-RUN apt-get update && apt-get install -y locales && apt-get clean autoclean && apt-get autoremove -y
-
-# Set the locale to en_US.UTF-8
-RUN locale-gen en_US.UTF-8
-ENV LANG en_US.UTF-8
-ENV LANGUAGE en_US:en
-ENV LC_ALL en_US.UTF-8
-
-RUN apt-get update && apt-get -y --no-install-recommends install \
-# OpenVINO dependencies
-        autoconf \
-        automake \
-        build-essential \
-        cmake \
-        curl \
-        git \
-        libtool \
-        ocl-icd-opencl-dev \
-        pkg-config \
-        unzip \
-        wget \
-# Python dependencies
-        python3 \
-        python3-pip \
-        python3-dev \
-        python3-virtualenv \
-        cython3 \
-        tox \
-# ONNX dependencies
-        git-lfs \
-        protobuf-compiler \
-        libprotobuf-dev && \
-    apt-get clean autoclean && \
-    apt-get autoremove -y
-
-# Build OpenVINO
-COPY . /openvino/
-WORKDIR /openvino/build
-RUN cmake .. \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DENABLE_VPU=OFF \
-    -DENABLE_GNA=OFF \
-    -DENABLE_OPENCV=OFF \
-    -DENABLE_CPPLINT=OFF \
-    -DENABLE_TESTS=OFF \
-    -DENABLE_BEH_TESTS=OFF \
-    -DENABLE_FUNCTIONAL_TESTS=OFF \
-    -DENABLE_MKL_DNN=ON \
-    -DENABLE_CLDNN=OFF \
-    -DENABLE_PROFILING_ITT=OFF \
-    -DENABLE_SAMPLES=OFF \
-    -DENABLE_SPEECH_DEMO=OFF \
-    -DENABLE_PYTHON=ON \
-    -DPYTHON_EXECUTABLE=/usr/bin/python3 \
-    -DNGRAPH_ONNX_IMPORT_ENABLE=ON \
-    -DNGRAPH_INTERPRETER_ENABLE=ON \
-    -DNGRAPH_DEBUG_ENABLE=OFF \
-    -DNGRAPH_DYNAMIC_COMPONENTS_ENABLE=ON \
-    -DCMAKE_INSTALL_PREFIX=/openvino/dist
-RUN make -j $(nproc) install
-
-# Run tests via tox
-WORKDIR /openvino/ngraph/python
-ENV NGRAPH_CPP_BUILD_PATH=/openvino/dist
-ENV LD_LIBRARY_PATH=/openvino/dist/lib
-ENV NGRAPH_ONNX_IMPORT_ENABLE=TRUE
-ENV PYTHONPATH=/openvino/bin/intel64/Release/lib/python_api/python3.8:${PYTHONPATH}
-RUN git clone --recursive https://github.com/pybind/pybind11.git -b v2.5.0 --depth 1
-CMD tox

.ci/openvino-onnx/Jenkinsfile

@@ -1,129 +0,0 @@
-// Copyright (C) 2018-2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-DOCKER_CONTAINER_NAME= "openvino-onnx-ci-container"
-DOCKER_IMAGE_TAG = "openvino-onnx-ci-image"
-
-def getGitPrInfo(String project) {
-    def gitPrInfo = [
-        prAuthorEmail : "",
-        commitAuthorEmail : "",
-        commitHash : "",
-        commitSubject : ""
-    ]
-    try {
-        dir ("${WORKDIR}/${project}") {
-            gitPrInfo.prAuthorEmail = sh (script: 'git log -1 --pretty="format:%ae" ', returnStdout: true).trim()
-            gitPrInfo.commitAuthorEmail = sh (script: 'git log -1 --pretty="format:%ce" ', returnStdout: true).trim()
-            gitPrInfo.commitSubject = sh (script: 'git log -1 --pretty="format:%H" ', returnStdout: true).trim()
-            gitPrInfo.commitHash = sh (script: 'git log -1 --pretty="format:%s" ', returnStdout: true).trim()
-        }
-    }
-    catch(e) {
-        echo "Failed to retrieve ${project} git repository information!"
-        echo "ERROR: ${e}"
-    }
-    return gitPrInfo
-}
-
-def notifyByEmail(def gitPrInfo) {
-    stage('Notify') {
-        String notifyPeople = "${gitPrInfo.prAuthorEmail}, ${gitPrInfo.commitAuthorEmail}"
-        emailext (
-            subject: "OpenVino CI: PR ${CHANGE_ID} ${currentBuild.result}!",
-            body: """
-                    Status: ${currentBuild.result}
-                    Pull Request Title: ${CHANGE_TITLE}
-                    Pull Request: ${CHANGE_URL}
-                    Branch: ${CHANGE_BRANCH}
-                    Commit Hash: ${gitPrInfo.commitSubject}
-                    Commit Subject: ${gitPrInfo.commitHash}
-                    Jenkins Build: ${RUN_DISPLAY_URL}
-            """,
-            to: "${notifyPeople}"
-        )
-    }
-}
-
-def gitSubmoduleUpdate(String repository_name) {
-    dir ("${WORKDIR}/${repository_name}") {
-        sh  label: "Init ${repository_name} submodules",
-            script:
-        """
-            git submodule init && git submodule update \
-                --init \
-                --no-fetch \
-                --recursive 
-        """
-    }
-}
-
-def buildDockerImage() {
-    sh """
-        docker build --tag=${DOCKER_IMAGE_TAG}  --file=.ci/openvino-onnx/Dockerfile \
-        --build-arg http_proxy=http://proxy-chain.intel.com:911/ \
-        --build-arg https_proxy=http://proxy-chain.intel.com:912/ .
-    """
-}
-
-def runTests() {
-    sh """
-        docker run --name ${DOCKER_CONTAINER_NAME} \
-        --volume ${HOME}/ONNX_CI/onnx_models/.onnx:/root/.onnx ${DOCKER_IMAGE_TAG}
-    """
-}
-
-pipeline {
-    agent {
-        label "OpenVino"
-    }
-    environment {
-        PROJECT_NAME = "openvino"
-        WORKDIR = "${WORKSPACE}/${BUILD_NUMBER}"
-    }
-    options {
-        skipDefaultCheckout true
-    }
-    stages {
-        stage("Clone repository") {
-            steps{
-                dir("${WORKDIR}") {
-                    checkout scm
-                }
-                gitSubmoduleUpdate(PROJECT_NAME)
-            }
-        }
-        stage("Prepare Docker environment") {
-            steps{
-                dir("${WORKDIR}") {
-                    buildDockerImage()
-                }
-            }
-        }
-        stage("Run tests") {
-            options {
-                  timeout(time: 10, unit: 'MINUTES')
-              }
-            steps{
-                runTests()
-            }
-        }
-    }
-    post {
-        failure {
-            script {
-                gitPrInfo = getGitPrInfo(PROJECT_NAME)
-                notifyByEmail(gitPrInfo)
-            }
-        }
-        cleanup {
-            dir("${WORKDIR}") {
-                deleteDir()
-                sh """
-                    docker image prune -f
-                    docker rm -f ${DOCKER_CONTAINER_NAME}
-                """
-            }
-        }
-    }
-}

.github/ISSUE_TEMPLATE/bug.md

@@ -1,58 +0,0 @@
----
-name: Bug
-about: Create a report to help us improve
-title: "[Bug]"
-labels: bug, support_request
-assignees: ''
-
----
-
-##### System information (version)
-<!-- Example
-- OpenVINO => 2020.4
-- Operating System / Platform => Windows 64 Bit
-- Compiler => Visual Studio 2017
-- Problem classification: Model Conversion
-- Framework: TensorFlow (if applicable)
-- Model name: ResNet50 (if applicable)
--->
-
-- OpenVINO=> :grey_question:
-- Operating System / Platform => :grey_question:
-- Compiler => :grey_question:
-- Problem classification => :grey_question:
-
-##### Detailed description
-<!-- your description -->
-
-##### Steps to reproduce
-
-<!--
-   Describe your problem and steps you've done before you got to this point.    
-   to add code example fence it with triple backticks and optional file extension
-    ```.cpp
-    // C++ code example
-    ```
- or attach as .txt or .zip file
--->
-
-##### Issue submission checklist
-
- - [ ] I report the issue, it's not a question
-   <!--
-   OpenVINO team works with support forum, Stack Overflow and other communities
-   to discuss problems. Tickets with question without real issue statement will be
-   closed.
-   -->
- - [ ] I checked the problem with documentation, FAQ, open issues, Stack Overflow, etc and have not found solution
-   <!--
-   Places to check:
-   * OpenVINO documentation: https://docs.openvinotoolkit.org/
-   * OpenVINO forum: https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit
-   * OpenVINO issue tracker: https://github.com/openvinotoolkit/openvino/issues?q=is%3Aissue
-   * Stack Overflow branch: https://stackoverflow.com/questions/tagged/openvino
-   -->
- - [ ] There is reproducer code and related data files: images, videos, models, etc.
-   <!--
-   The best reproducer -- test case for OpenVINO that we can add to the library.
-   -->

.github/org_control/check_org.py

@@ -1,51 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Check GitHub organization and invite members
-"""
-
-# pylint: disable=fixme,no-member
-
-from argparse import ArgumentParser
-
-import github_api
-from configs import Config
-
-
-def main():
-    """The main entry point function"""
-    arg_parser = ArgumentParser()
-    arg_parser.add_argument("--cfg-file", metavar="PATH", default=Config.default_cfg_path,
-                            help=f"Path to json configuration file, e.g. {Config.default_cfg_path}")
-    arg_parser.add_argument("--teams", action="store_true", help="Check GitHub teams")
-    args, unknown_args = arg_parser.parse_known_args()
-
-    Config(args.cfg_file, unknown_args)
-    gh_api = github_api.GithubOrgApi()
-
-    if args.teams:
-        gh_api.get_org_teams()
-    else:
-        dev_emails = github_api.get_dev_emails()
-        print(f'\nDeveloper emails {len(dev_emails)}:', '; '.join(dev_emails))
-
-        org_emails = gh_api.get_org_emails()
-        print(f'\nOrg emails {len(org_emails)}:', '; '.join(org_emails))
-
-        org_pendig_invitation_emails = gh_api.get_org_invitation_emails()
-
-        invite_emails = dev_emails.difference(org_emails).difference(org_pendig_invitation_emails)
-        print(f'\nInvite emails {len(invite_emails)}:', '; '.join(invite_emails))
-
-        no_in_dev_emails = org_emails.difference(dev_emails)
-        print(f'\nOrg members - no in developers list {len(no_in_dev_emails)}:',
-              '; '.join(no_in_dev_emails))
-
-        valid_github_users = gh_api.get_valid_github_users(invite_emails)
-
-        gh_api.invite_users(valid_github_users)
-
-
-if __name__ == '__main__':
-    main()

.github/org_control/check_pr.py

@@ -1,149 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Check GitHub PRs and set labels by type and categories, e.g. 'ExternalPR', 'category: ci'
-"""
-
-# pylint: disable=fixme,no-member
-
-import re
-import datetime
-from argparse import ArgumentParser
-from enum import Enum
-
-import github_api
-from configs import Config
-
-
-class PrType(Enum):
-    """Constants for type of GitHub pull request by author membership"""
-    EXTERNAL = 'ExternalPR'
-    INTEL = 'ExternalIntelPR'
-    ORG = 'OpenvinoPR'
-    BAD = 'BadPR'
-
-
-def get_pr_labels(pull):
-    """Gets PR labels as set"""
-    pr_lables = set()
-    for label in pull.labels:
-        pr_lables.add(label.name)
-    return pr_lables
-
-
-def set_pr_labels(pull, labels):
-    """Sets PR labels"""
-    if not labels or Config().DRY_RUN:
-        return
-    print(f'Set PR labels:', labels)
-    pull.set_labels(labels)
-
-
-def get_pr_type_by_labels(pull):
-    """Gets PR type using labels"""
-    pr_lables = get_pr_labels(pull)
-    pr_types = set(type.value for type in PrType)
-    pr_types_labels = pr_lables & pr_types
-    if not pr_types_labels:
-        return None
-    if len(pr_types_labels) > 1:
-        print(f'Duplicated labels: {pr_types_labels}')
-        return PrType.BAD
-    return PrType(PrType(pr_types_labels.pop()))
-
-
-def get_label_by_team_name_re(team_name):
-    """Generates label by PR reviwer team name using regular expressions"""
-    if 'admins' in team_name:
-        return 'category: ci'
-    re_compile_label = re.compile(rf'{Config().GITHUB_REPO}-(.+)-maintainers')
-    re_label = re_compile_label.match(team_name)
-    if re_label:
-        return f'category: {re_label.group(1).strip()}'
-    return None
-
-
-def get_label_by_team_name_map(team_name):
-    """Generates label by PR reviwer team name using config map"""
-    return Config().TEAM_TO_LABEL.get(team_name)
-
-
-def get_category_labels(pull):
-    """Gets list of category labels by all PR reviwer teams"""
-    labels = []
-    pr_lables = get_pr_labels(pull)
-    for reviewer_team in pull.get_review_requests()[1]:
-        reviewer_label = get_label_by_team_name_map(reviewer_team.name)
-        if reviewer_label and reviewer_label not in pr_lables:
-            labels.append(reviewer_label)
-    return labels
-
-
-def main():
-    """The main entry point function"""
-    arg_parser = ArgumentParser()
-    arg_parser.add_argument("--cfg-file", metavar="PATH", default=Config.default_cfg_path,
-                            help=f"Path to json configuration file, e.g. {Config.default_cfg_path}")
-    arg_parser.add_argument("--pr", metavar="NUMBER",
-                            help="Get GitHub pull request with the number")
-    arg_parser.add_argument("--pr-state", default="open", choices=["open", "closed"],
-                            help="Set GitHub pull request state")
-    arg_parser.add_argument("--newer", metavar="MINUTES",
-                            help="Get newly created GitHub pull request only")
-    args, unknown_args = arg_parser.parse_known_args()
-
-    Config(args.cfg_file, unknown_args)
-    gh_api = github_api.GithubOrgApi()
-
-    if args.pr:
-        pulls = [gh_api.repo.get_pull(int(args.pr))]
-    else:
-        pulls = gh_api.repo.get_pulls(state=args.pr_state)
-        print(f'\nPRs count ({args.pr_state}):', pulls.totalCount)
-
-    if args.newer:
-        pr_created_after = datetime.datetime.now() - datetime.timedelta(minutes=int(args.newer))
-        print('PRs created after:', pr_created_after)
-    non_org_intel_pr_users = set()
-    non_org_pr_users = set()
-    for pull in pulls:
-        if args.newer and pull.created_at <= pr_created_after:
-            print(f'\nIGNORE: {pull} - Created: {pull.created_at}')
-            continue
-        pr_lables = get_pr_labels(pull)
-        pr_type_by_labels = get_pr_type_by_labels(pull)
-        set_labels = []
-        print(f'\n{pull} - Created: {pull.created_at} - Labels: {pr_lables} -',
-              f'Type: {pr_type_by_labels}', end='')
-
-        # Checks PR source type
-        if gh_api.is_org_user(pull.user):
-            print(' - Org user')
-        elif github_api.is_intel_email(pull.user.email) or \
-             github_api.is_intel_company(pull.user.company):
-            print(' - Non org user with Intel email or company')
-            non_org_intel_pr_users.add(pull.user)
-            if pr_type_by_labels is not PrType.INTEL:
-                print(f'NO "{PrType.INTEL.value}" label: ', end='')
-                github_api.print_users(pull.user)
-                set_labels.append(PrType.INTEL.value)
-        else:
-            print(f' - Non org user with NO Intel email or company')
-            non_org_pr_users.add(pull.user)
-            if pr_type_by_labels is not PrType.EXTERNAL:
-                print(f'NO "{PrType.EXTERNAL.value}" label: ', end='')
-                github_api.print_users(pull.user)
-                set_labels.append(PrType.EXTERNAL.value)
-
-        set_labels += get_category_labels(pull)
-        set_pr_labels(pull, set_labels)
-
-    print(f'\nNon org user with Intel email or company:')
-    github_api.print_users(non_org_intel_pr_users)
-    print(f'\nNon org user with NO Intel email or company:')
-    github_api.print_users(non_org_pr_users)
-
-
-if __name__ == '__main__':
-    main()

.github/org_control/config.json

@@ -1,36 +0,0 @@
-{
-    "GITHUB_TOKEN": "<Put token here or set as arg or as env variable>",
-    "GITHUB_ORGANIZATION": "openvinotoolkit",
-    "GITHUB_REPO": "openvino",
-    "IGNORE_LOGINS": [
-        "openvino-ci",
-        "openvino-pushbot",
-        "lab-nerval",
-        "lab-nerval-onnx-ci"
-    ],
-    "EMAILS_FILE_PATH": "dev_emails-test.txt",
-    "PROXIES": {
-        "HTTP_PROXY": null,
-        "HTTPS_PROXY": null,
-        "NO_PROXY": "localhost,127.0.0.1,.intel.com"
-    },
-    "DRY_RUN": false,
-    "TEAM_TO_LABEL": {
-        "openvino-admins": "category: CI",
-        "openvino-maintainers": "category: IE common",
-        "openvino-docs-maintainers": "category: docs",
-        "openvino-ie-maintainers": "category: IE common",
-        "openvino-ie-cpu-maintainers": "category: CPU",
-        "openvino-ie-gna-maintainers": "category: GNA",
-        "openvino-ie-gpu-maintainers": "category: GPU",
-        "openvino-ie-lpt-maintainers": "category: LP transformations",
-        "openvino-ie-multi-maintainers": "category: MULTI",
-        "openvino-ie-python-api-maintainers": "category: python api",
-        "openvino-ie-tests-maintainers": "category: IE Tests",
-        "openvino-ie-vpu-maintainers": "category: VPU",
-        "openvino-mo-maintainers": "category: MO",
-        "openvino-ngraph-maintainers": "category: nGraph",
-        "openvino-tests-maintainers": "category: IE Tests",
-        "openvino-tools-maintainers": "category: tools"
-    }
-}

.github/org_control/configs.py

@@ -1,113 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-Configurations management
-"""
-
-# pylint: disable=fixme,broad-except
-
-import os
-import sys
-import ast
-import json
-from pathlib import Path
-
-
-if sys.hexversion < 0x3060000:
-    raise Exception('Python version must be >= 3.6')
-
-
-class ConfigException(Exception):
-    """Base configuration exception"""
-
-
-class Config:
-    """Configuration wrapper"""
-    _instance = None
-    properties = None
-    default_cfg_path = Path(__file__).resolve().parent / 'config.json'
-
-    def __new__(cls, *_args, **_kwargs):
-        if not Config._instance:
-            Config._instance = super(Config, cls).__new__(cls)
-        return Config._instance
-
-    def __init__(self, file_path=None, cli_args=None):
-        """
-        :param file_path: Path to json configuration file
-        :type file_path: String
-
-        :param args: List of argparse arguments with patterns: 'name=value' or 'name'
-        :type args: list
-        """
-        if Config.properties:
-            return
-
-        self._file_path = file_path or Config.default_cfg_path
-        self._cli_args = cli_args or []
-
-        self._json_cfg = {}
-        self._args = {}
-
-        self._load_cfg()
-        self._parse_cli_args()
-
-        Config.properties = {}
-        for name, value in self._json_cfg.items():
-            if hasattr(self, name):
-                raise ConfigException(f'Duplicating prosperity: {name}')
-            prosperity_value = self._args.get(name) or os.getenv(name)
-            if prosperity_value:
-                # Try to set prosperity_value as Python literal structures, e.g. DRY_RUN=False
-                try:
-                    prosperity_value = ast.literal_eval(prosperity_value)
-                except Exception:
-                    pass
-                if not isinstance(prosperity_value, type(value)):
-                    raise ConfigException(f'Python type of {name} parameter must be {type(value)}')
-            else:
-                prosperity_value = value
-            setattr(self, name, prosperity_value)
-            Config.properties[name] = prosperity_value
-
-        self.set_proxy()
-
-    def _load_cfg(self):
-        """Load the json configuration file"""
-        try:
-            with open(self._file_path) as conf:
-                self._json_cfg = json.load(conf)
-        except:
-            print('Failed to load configuration from:', self._file_path)
-            raise
-
-    def _parse_cli_args(self):
-        """Parse argparse arguments with patterns: 'name=value' or 'name'"""
-        for cli_arg in self._cli_args:
-            arg = cli_arg.split('=')
-            if arg[0] not in self._json_cfg:
-                raise ConfigException(f'Unsupported argument: {arg}')
-            self._args[arg[0]] = True if len(arg) == 1 else '='.join(arg[1:])
-
-    def get_properties(self):
-        """Get all properties as Dict"""
-        return self.properties
-
-    def set_proxy(self):
-        """Set proxies"""
-        for proxy_name, url in self.properties['PROXIES'].items():
-            if url is not None:
-                print(f'Set proxy: {proxy_name}={url}')
-                os.environ[proxy_name] = url
-
-
-def _test():
-    """Test and debug"""
-    print('Config.default_cfg_path:', Config.default_cfg_path)
-    cfg = Config(cli_args=['DRY_RUN=True'])
-    print('Config.properties:', cfg.get_properties())
-
-
-if __name__ == '__main__':
-    _test()

.github/org_control/dev_emails-test.txt

@@ -1,9 +0,0 @@
-# good comment
-Last_name, First_name <first_name.last_name@intel.com>
-first_name.last_name@intel.com
-openvino_pushbot@intel.com
-
-# Wrong emails
-foo@foo.com
-  foo1  foo2
-foo1 foo2@intel.com

.github/org_control/github_api.py

@@ -1,287 +0,0 @@
-# Copyright (C) 2020 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-GitHub API for controlling organization
-"""
-
-# pylint: disable=fixme,no-member
-
-import re
-import time
-
-from github import Github, GithubException, RateLimitExceededException, IncompletableObject
-from github import UnknownObjectException
-from github.PaginatedList import PaginatedList
-
-from configs import Config
-
-
-def is_valid_user(user):
-    """Checks that user is valid github.Github object"""
-    try:
-        return user and user.login
-    except IncompletableObject:
-        return False
-
-
-def is_user_ignored(user):
-    """Checks that user should be ignored"""
-    cfg = Config()
-    if is_valid_user(user) and user.login.lower() not in cfg.properties['IGNORE_LOGINS']:
-        return False
-    return True
-
-
-def is_valid_name(name):
-    """Checks that GitHub user's name is valid"""
-    return name and len(name) >= 3 and ' ' in name
-
-
-def is_intel_email(email):
-    """Checks that email is valid Intel email"""
-    return email and len(email) > 10 and ' ' not in email and email.lower().endswith('@intel.com')
-
-
-def is_intel_company(company):
-    """Checks that company contains intel"""
-    return company and 'intel' in company.lower()
-
-
-def is_valid_intel_user(user):
-    """Checks that user is valid GitHub and Intel user"""
-    return is_valid_user(user) and (is_valid_name(user.name) and is_intel_email(user.email) or
-                                    is_user_ignored(user))
-
-
-def print_users(users):
-    """Print list of users in different formats: list, set, PaginatedList"""
-    if isinstance(users, (list, set, PaginatedList)):
-        users_count = users.totalCount if isinstance(users, PaginatedList) else len(users)
-        print(f'\nGitHub users {users_count} (login - name - company - email - valid):')
-    else:
-        users = [users]
-    for user in users:
-        if not is_valid_user(user):
-            print('WRONG GitHub user: ???')
-            continue
-        valid_check = 'OK' if is_valid_intel_user(user) else 'FIX'
-        if not is_intel_email(user.email):
-            valid_check += ' email'
-        if not is_valid_name(user.name):
-            valid_check += ' name'
-        print(f'{user.login} - "{user.name}" - "{user.company}" - {user.email} - {valid_check}')
-
-
-def get_dev_emails():
-    """
-    Read a file with developer emails. Supported email formats
-    first_name.last_name@intel.com
-    Import from Outlook: Last_name, First_name <first_name.last_name@intel.com>
-    """
-    re_email = re.compile(r'.+<(.+)>')
-    emails = set()
-    cfg = Config()
-    with open(cfg.properties['EMAILS_FILE_PATH']) as file_obj:
-        for line in file_obj:
-            line = line.strip().lower()
-            if not line or line.startswith('#'):
-                continue
-            re_outlook_email = re_email.match(line)
-            if re_outlook_email:
-                line = re_outlook_email.group(1).strip()
-            if not is_intel_email(line):
-                print(f'Wrong email in {cfg.properties["EMAILS_FILE_PATH"]}: {line}')
-                continue
-            emails.add(line)
-    return emails
-
-
-class GithubOrgApi:
-    """Common API for GitHub organization"""
-
-    def __init__(self):
-        self._cfg = Config()
-        self.github = Github(self._cfg.GITHUB_TOKEN)
-        self.github_org = self.github.get_organization(self._cfg.GITHUB_ORGANIZATION)
-        self.repo = self.github.get_repo(f'{self._cfg.GITHUB_ORGANIZATION}/'
-                                         f'{self._cfg.GITHUB_REPO}')
-
-    def is_org_user(self, user):
-        """Checks that user is a member of GitHub organization"""
-        if is_valid_user(user):
-            try:
-                membership = user.get_organization_membership(self.github_org)
-                # membership.role can be 'member' or 'admin'
-                if membership.state == 'active' and membership.role:
-                    return True
-            except UnknownObjectException:
-                pass
-        return False
-
-    def get_org_emails(self):
-        """Gets and prints all emails of GitHub organization members"""
-        org_members = self.github_org.get_members()
-        org_emails = set()
-        org_members_fix = set()
-        org_emails_fix_name = set()
-        org_logins_fix_intel_email = set()
-
-        print(f'\nOrg members {org_members.totalCount} (login - name - company - email - valid):')
-        for org_member in org_members:
-            print_users(org_member)
-            if is_user_ignored(org_member):
-                continue
-            if is_intel_email(org_member.email):
-                org_emails.add(org_member.email.lower())
-                if not is_valid_name(org_member.name):
-                    org_members_fix.add(org_member)
-                    org_emails_fix_name.add(org_member.email.lower())
-            else:
-                org_members_fix.add(org_member)
-                org_logins_fix_intel_email.add(org_member.login.lower())
-
-        print_users(org_members_fix)
-        print(f'\nOrg members - no Intel emails {len(org_logins_fix_intel_email)}:',
-              '; '.join(org_logins_fix_intel_email))
-        print(f'\nOrg members - no real name {len(org_emails_fix_name)}:',
-              '; '.join(org_emails_fix_name))
-        return org_emails
-
-    def get_org_invitation_emails(self):
-        """Gets GitHub organization teams prints info"""
-        org_invitations = self.github_org.invitations()
-        org_invitation_emails = set()
-
-        print(f'\nOrg invitations {org_invitations.totalCount} (login - name - email - valid):')
-        for org_invitation in org_invitations:
-            # TODO: investigate GithubException while access to user name and enable print_users()
-            # github.GithubException.IncompletableObject: 400 "Returned object contains no URL"
-            #print_users(org_invitation)
-            print(f'{org_invitation.login} - ??? - {org_invitation.email} - ???')
-            if is_user_ignored(org_invitation):
-                continue
-            if is_intel_email(org_invitation.email):
-                org_invitation_emails.add(org_invitation.email.lower())
-            else:
-                print('Strange org invitation:', org_invitation)
-
-        print(f'\nOrg invitation emails {len(org_invitation_emails)}:',
-              '; '.join(org_invitation_emails))
-        return org_invitation_emails
-
-    def get_org_teams(self):
-        """Gets GitHub organization teams prints info"""
-        teams = []
-        org_teams = self.github_org.get_teams()
-        print('\nOrg teams count:', org_teams.totalCount)
-        for team in org_teams:
-            teams.append(team.name)
-            print(f'\nTeam: {team.name} - parent: {team.parent}')
-
-            repos = team.get_repos()
-            print('Repos:')
-            for repo in repos:
-                print(f'    {repo.name} -', team.get_repo_permission(repo))
-
-            team_maintainers = team.get_members(role='maintainer')
-            team_maintainer_logins = set()
-            for maintainer in team_maintainers:
-                team_maintainer_logins.add(maintainer.login)
-            team_members = team.get_members(role='member')
-            team_member_logins = set()
-            for member in team_members:
-                team_member_logins.add(member.login)
-            members = team.get_members(role='all')
-            member_emails = []
-            print('Members (role - login - name - company - email - valid):')
-            for user in members:
-                if user.login in team_maintainer_logins:
-                    print('    Maintainer - ', end='')
-                elif user.login in team_member_logins:
-                    print('    Member - ', end='')
-                else:
-                    # It is not possible to check child teams members
-                    print('    ??? - ', end='')
-                print_users(user)
-                if is_intel_email(user.email) and not is_user_ignored(user):
-                    member_emails.append(user.email.lower())
-            print(f'Intel emails {len(member_emails)}:', '; '.join(member_emails))
-        return teams
-
-    def get_valid_github_users(self, emails):
-        """Gets valid GitHub users by email and prints status"""
-        valid_users = set()
-        no_account_emails = set()
-        print(f'\nGitHub users from {len(emails)} invite emails (email - status):')
-        for email in emails:
-            if not is_intel_email(email):
-                print(f'{email} - Non Intel email')
-                continue
-
-            # You can make up to 30 requests per minute; https://developer.github.com/v3/search/
-            # Sleep 2.4 sec is about 25 requests per minute
-            time.sleep(2.4)
-            try:
-                users = self.github.search_users(f'{email} in:email')
-            except RateLimitExceededException:
-                time.sleep(5)
-                users = self.github.search_users(f'{email} in:email')
-
-            if users.totalCount == 0:
-                print(f'{email} - No valid GitHub account')
-                no_account_emails.add(email)
-                continue
-            if users.totalCount > 1:
-                print(f'{email} - Found {users.totalCount} GitHub accounts')
-            for user in users:
-                if user.email and user.email.lower() == email:
-                    print(f'{email} - OK')
-                    valid_users.add(user)
-                else:
-                    print(f'{email} - Non public or wrong email - login: {user.login} - '
-                          f'email: {user.email}')
-        print('Valid users count:', len(valid_users))
-        print_users(valid_users)
-        print(f'\nIntel emails - No valid GitHub account {len(no_account_emails)}:',
-              '; '.join(no_account_emails))
-        return valid_users
-
-    def invite_users(self, users):
-        """Invites users and prints status"""
-        if isinstance(users, (list, set)):
-            print(f'\nInvite {len(users)} users:')
-        else:
-            users = [users]
-
-        for user in users:
-            if isinstance(user, str):
-                print(f'Email: {user}')
-                self.github_org.invite_user(email=user)
-            else:
-                print(f'{user.login} - "{user.name}" - {user.email} - ', end='')
-                try:
-                    if is_user_ignored(user):
-                        print('Ignored')
-                        continue
-                    if not self._cfg.DRY_RUN:
-                        self.github_org.invite_user(user=user)
-                        print('OK')
-                    else:
-                        print('Dry run')
-                except GithubException as exc:
-                    print(f'FAIL: {exc.data["errors"][0]["message"]}')
-
-
-def _test():
-    """Test and debug"""
-    Config(cli_args=['DRY_RUN=True'])
-    dev_emails = get_dev_emails()
-    print('dev_emails:', dev_emails)
-
-    gh_api = GithubOrgApi()
-    gh_api.get_org_emails()
-
-
-if __name__ == '__main__':
-    _test()

.github/org_control/requirements.txt

@@ -1 +0,0 @@
-PyGithub==1.51

.github/org_control/requirements_dev.txt

@@ -1 +0,0 @@
-pylint==2.3.0

.github/workflows/code_style.yml

@@ -1,67 +0,0 @@
-name: Code Style
-on: [push, pull_request]
-
-jobs:
-  nGraph:
-    runs-on: ubuntu-18.04
-    steps:
-      - uses: actions/checkout@v2
-        with:
-          submodules: recursive
-
-      - name: Install clang-format-3.9
-        run: sudo apt --assume-yes install clang-format-3.9
-
-      - name: Install dependencies
-        run: |
-          sudo apt --assume-yes install libusb-1.0-0-dev
-          python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
-
-      - name: CMake
-        run: |
-          mkdir build
-          cd build
-          cmake ..
-
-      - name: Check code style
-        run: make style-check
-        working-directory: build
-
-      - name: Create code style diff
-        if: failure()
-        run: |
-          ngraph/maint/apply-code-format.sh
-          git diff >ngraph_code_style_diff.patch
-
-      - uses: actions/upload-artifact@v2
-        if: failure()
-        with:
-          name: ngraph_code_style_diff
-          path: ngraph_code_style_diff.patch
-          
-  Java:
-    runs-on: ubuntu-18.04
-    steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-java@v1
-        with:
-          java-version: '11'
-
-      - name: Install dependencies
-        run: |
-          wget -nc https://github.com/google/google-java-format/releases/download/google-java-format-1.9/google-java-format-1.9-all-deps.jar
-
-      - name: Check code style
-        run: |
-          java -jar google-java-format-1.9-all-deps.jar --set-exit-if-changed -a -i $(find . -type f -name "*.java")
-
-      - name: Create code style diff
-        if: failure()
-        run: |
-          git diff >java_code_style_diff.patch
-
-      - uses: actions/upload-artifact@v2
-        if: failure()
-        with:
-          name: java_code_style_diff
-          path: java_code_style_diff.patch

.github/workflows/mo.yml

@@ -32,6 +32,8 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip setuptools
+          # For Pylint
+          pip install tensorflow==1.14.0 tensorboard==1.14.0 tensorflow-estimator==1.14.0
           # For UT
           pip install unittest-xml-reporting==3.0.2
           # MO requirements

CMakeLists.txt

@@ -8,7 +8,17 @@ cmake_policy(SET CMP0054 NEW)
 # it allows to install targets created outside of current projects
 # See https://blog.kitware.com/cmake-3-13-0-available-for-download/
 
-cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
+if (APPLE)
+    if(CMAKE_GENERATOR STREQUAL "Xcode")
+        # due to https://gitlab.kitware.com/cmake/cmake/issues/14254
+        cmake_minimum_required(VERSION 3.12.0 FATAL_ERROR)
+    else()
+        # due to https://cmake.org/cmake/help/v3.12/policy/CMP0068.html
+        cmake_minimum_required(VERSION 3.9 FATAL_ERROR)
+    endif()
+else()
+    cmake_minimum_required(VERSION 3.7.2 FATAL_ERROR)
+endif()
 
 project(OpenVINO)
 
@@ -56,16 +66,19 @@ function(build_ngraph)
         ngraph_set(NGRAPH_ADDRESS_SANITIZER FALSE)
     endif ()
     ngraph_set(NGRAPH_PYTHON_BUILD_ENABLE FALSE)
-
-    if(ENABLE_TESTS AND NOT ANDROID)
-        ngraph_set(NGRAPH_UNIT_TEST_ENABLE TRUE)
-    else()
-        ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
-    endif()
-
-    if(NOT ANDROID)
+    if (NOT ANDROID)
+        if(ENABLE_TESTS)
+            ngraph_set(NGRAPH_UNIT_TEST_ENABLE TRUE)
+            ngraph_set(NGRAPH_IE_ENABLE TRUE)
+        else()
+            ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
+            ngraph_set(NGRAPH_IE_ENABLE FALSE)
+        endif()
         ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE TRUE)
     else()
+        ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE)
+        ngraph_set(NGRAPH_TEST_UTIL_ENABLE FALSE)
+        ngraph_set(NGRAPH_IE_ENABLE FALSE)
         ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE FALSE)
     endif()
     ngraph_set(NGRAPH_INTERPRETER_ENABLE TRUE)
@@ -85,13 +98,6 @@ function(build_ngraph)
     elseif(WIN32)
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4308 /wd4146 /wd4703 /wd4244 /wd4819")
     endif()
-    
-    # Preserve the original flags for further use
-    set(CMAKE_ORIGINAL_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE}")
-    set(CMAKE_ORIGINAL_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE}")
 
     if(ENABLE_LTO)
         ie_enable_lto()
@@ -105,34 +111,6 @@ function(build_ngraph)
     set(NGRAPH_LIBRARIES ngraph PARENT_SCOPE)
 endfunction()
 
-file(REMOVE "${CMAKE_BINARY_DIR}/openvino_targets_developer.cmake")
-
-unset(OpenVINODeveloperPackageTargets CACHE)
-
-function(openvino_developer_export_targets)
-    set(OpenVINODeveloperPackageTargets "${OpenVINODeveloperPackageTargets};${ARGV}")
-
-    # to allow exporting of aliased targets with the original names
-    foreach(target_name ${OpenVINODeveloperPackageTargets})
-        if(TARGET "${target_name}")
-            get_target_property(original_name ${target_name} ALIASED_TARGET)
-            if(TARGET "${original_name}")
-                message(STATUS "The name ${target_name} is an ALIAS for ${original_name}. "
-                        "It will be exported to the InferenceEngineDeveloperPackage with the original name.")
-                list(REMOVE_ITEM OpenVINODeveloperPackageTargets ${target_name})
-                list(APPEND OpenVINODeveloperPackageTargets ${original_name})
-            endif()
-        endif()
-    endforeach()
-
-    list(REMOVE_DUPLICATES OpenVINODeveloperPackageTargets)
-    set(OpenVINODeveloperPackageTargets "${OpenVINODeveloperPackageTargets}" CACHE INTERNAL
-        "Paths to extra Inference Engine plugins" FORCE)
-endfunction()
-
-
-add_subdirectory(openvino)
-
 build_ngraph()
 
 add_subdirectory(inference-engine)

CODEOWNERS

@@ -8,18 +8,13 @@ CODEOWNERS  @openvinotoolkit/openvino-admins @openvinotoolkit/openvino-maintaine
 Jenkinsfile  @openvinotoolkit/openvino-admins
 azure-pipelines.yml  @openvinotoolkit/openvino-admins
 /.github/  @openvinotoolkit/openvino-admins
-/.ci/  @openvinotoolkit/openvino-admins
 
 # QA Tests:
 /tests/  @openvinotoolkit/openvino-tests-maintainers
 
-# OpenVINO Scripts
-/scripts/ @openvinotoolkit/openvino-admins @openvinotoolkit/openvino-scripts-maintainers
-
 # IE Core:
 /inference-engine/  @openvinotoolkit/openvino-ie-maintainers
-/inference-engine/ie_bridges/python @openvinotoolkit/openvino-ie-python-api-maintainers
-/inference-engine/src/transformations/  @GlebKazantaev @ilyachur
+/inference-engine/src/transformations/  @GlebKazantaev @ichuraev
 /inference-engine/src/legacy_api/  @openvinotoolkit/openvino-ngraph-maintainers
 /inference-engine/src/readers/  @openvinotoolkit/openvino-ngraph-maintainers
 
@@ -69,7 +64,3 @@ azure-pipelines.yml  @openvinotoolkit/openvino-admins
 
 # Tools
 /tools/  @openvinotoolkit/openvino-tools-maintainers
-
-# Documentation
-/docs/  @openvinotoolkit/openvino-docs-maintainers
-*.md  @openvinotoolkit/openvino-docs-maintainers

CONTRIBUTING_DOCS.md

@@ -1,58 +0,0 @@
-# Contribute to Documentation
-
-If you want to contribute to a project documentation and make it better, your help is very welcome.
-This guide puts together the guidelines to help you figure out how you can offer your feedback and contribute to the documentation.
-
-## Contribute in Multiple ways
-
-There are multiple ways to help improve our documentation:
-* [Log an issue](https://jira.devtools.intel.com/projects/CVS/issues): Enter an issue for the OpenVINO™ documentation component for minor issues such as typos.
-* Make a suggestion: Send your documentation suggestion to the mailing list.
-* Contribute via GitHub: Submit pull requests in the [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/docs) documentation repository.
-
-## Contribute via GitHub
-
-Use the following steps to contribute in the OpenVINO™ Toolkit documentation
-
-### Use Documentation Guidelines
-The documentation for our project is written using Markdown. Use our [guidelines](./docs/documentation_guidelines.md) and best practices to write consistent, readable documentation:
-
-* **[Authoring Guidelines](./docs/documentation_guidelines.md#authoring-guidelines)**
-* **[Structure Guidelines](./docs/documentation_guidelines.md#structure-guidelines)**
-* **[Formatting Guidelines](./docs/documentation_guidelines.md#structure-guidelines)**
-* **[Graphics Guidelines](./docs/documentation_guidelines.md#graphics-guidelines)**
-
-### Add New Document to the Documentation
-> **NOTE**: Please check if that information can be added to existing documents instead of creating a new one.
-
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Create a new markdown file in an appropriate folder.
-    > **REQUIRED**: The document title must contain a document label in a form: `{#openvino_docs_<name>}`. For example: `Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™ {#openvino_docs_MO_DG_IR_and_opsets}`.
-4. Add your file to the documentation structure. Open the documentation structure file [docs/doxygen/ie_docs.xml](./docs/doxygen/ie_docs.xml) and add your file path to the appropriate section.
-5. Commit changes to your branch.
-6. Create a pull request.
-7. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-8. Discuss, review, and update your contributions.
-9. Get merged once the maintainer approves.
-
-### Edit Existing Document
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Edit the documentation markdown file and commit changes to the branch.
-4. Create a pull request.
-5. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-6. Discuss, review, and update your contributions.
-7. Get merged once the maintainer approves.
-
-### Delete Document from the Documentation
-1. Fork the [OpenVINO™ Toolkit](https://github.com/openvinotoolkit/openvino) repository.
-2. Create a new branch.
-3. Remove the documentation file.
-4. Remove your file from the documentation structure. Open the documentation structure file [docs/doxygen/ie_docs.xml](./docs/doxygen/ie_docs.xml) and remove all occurences of your file path.
-5. Remove all references to that file from other documents or replace with links to alternatives topics (if any).
-6. Commit changes to your branch.
-7. Create a pull request.
-8. Once the pull request is created, automatic checks are started. All checks must pass to continue.
-9. Discuss, review, and update your contributions.
-10. Get merged once the maintainer approves.

README.md

@@ -2,15 +2,15 @@
 [![Stable release](https://img.shields.io/badge/version-2020.4-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2020.4.0)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 
-This toolkit allows developers to deploy pre-trained deep learning models
-through a high-level C++ Inference Engine API integrated with application logic.
+This toolkit allows developers to deploy pre-trained deep learning models 
+through a high-level C++ Inference Engine API integrated with application logic. 
 
-This open source version includes two components: namely [Model Optimizer] and
-[Inference Engine], as well as CPU, GPU and heterogeneous plugins to accelerate
-deep learning inferencing on Intel® CPUs and Intel® Processor Graphics.
-It supports pre-trained models from the [Open Model Zoo], along with 100+ open
-source and public models in popular formats such as Caffe\*, TensorFlow\*,
-MXNet\* and ONNX\*.
+This open source version includes two components: namely [Model Optimizer] and 
+[Inference Engine], as well as CPU, GPU and heterogeneous plugins to accelerate 
+deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. 
+It supports pre-trained models from the [Open Model Zoo], along with 100+ open 
+source and public models in popular formats such as Caffe\*, TensorFlow\*, 
+MXNet\* and ONNX\*. 
 
 ## Repository components:
 * [Inference Engine]
@@ -18,7 +18,7 @@ MXNet\* and ONNX\*.
 
 ## License
 Deep Learning Deployment Toolkit is licensed under [Apache License Version 2.0](LICENSE).
-By contributing to the project, you agree to the license and copyright terms therein
+By contributing to the project, you agree to the license and copyright terms therein 
 and release your contribution under these terms.
 
 ## Documentation
@@ -30,15 +30,13 @@ and release your contribution under these terms.
 * [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 
 ## How to Contribute
-See [CONTRIBUTING](./CONTRIBUTING.md) for contribution to the code.
-See [CONTRIBUTING_DOCS](./CONTRIBUTING_DOCS.md) for contribution to the documentation.
-Thank you!
+See [CONTRIBUTING](./CONTRIBUTING.md) for details. Thank you!
 
 ## Support
 Please report questions, issues and suggestions using:
 
 * The `openvino` [tag on StackOverflow]\*
-* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues)
+* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues) 
 * [Forum](https://software.intel.com/en-us/forums/computer-vision)
 
 ---

azure-pipelines.yml

@@ -0,0 +1,333 @@
+jobs:
+- job: Lin
+  # About 150% of total time
+  timeoutInMinutes: 75
+  pool:
+    #vmImage: 'ubuntu-18.04'
+    name: LIN_VMSS_VENV_F8S_WU2
+  variables:
+    BUILD_TYPE: Release
+    BIN_DIR: ../bin/intel64/$(BUILD_TYPE)
+  steps:
+  - script: |
+      whoami
+      uname -a
+      which python3
+      gcc --version
+      lsb_release
+      env
+      cat /proc/cpuinfo
+      cat /proc/meminfo
+      vmstat -s
+      df
+    displayName: 'System properties'
+  - script: |
+      sudo apt --assume-yes install libusb-1.0-0-dev
+      python3 -m pip install -r ./inference-engine/ie_bridges/python/requirements.txt
+      # For running Python API tests
+      python3 -m pip install -r ./inference-engine/ie_bridges/python/src/requirements-dev.txt
+    displayName: 'Install dependencies'
+  - script: |
+      wget https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-linux.zip
+      unzip ninja-linux.zip
+      sudo cp -v ninja /usr/local/bin/
+    displayName: 'Install Ninja'
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      mkdir dldt-build
+      cd dldt-build
+    displayName: 'Create build directory'
+  - task: CMake@1
+    inputs:
+      workingDirectory: dldt-build
+      # CMake must get Python 3.x version by default
+      cmakeArgs: .. -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE=/usr/bin/python3.6 -DENABLE_TESTS=ON
+  - script: ninja
+    workingDirectory: dldt-build
+    displayName: 'Build Lin'
+  - script: ls -alR ../bin/
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/gnaUnitTests
+    workingDirectory: dldt-build
+    displayName: 'GNA UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    displayName: 'Clone testdata'
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      export LD_LIBRARY_PATH=`pwd`/$(BIN_DIR)/lib
+      export PYTHONPATH=`pwd`/$(BIN_DIR)/lib/python_api/python3.6
+      env
+      cd ../inference-engine/ie_bridges/python/tests
+      pytest
+    workingDirectory: dldt-build
+    displayName: 'Python API Tests'
+    continueOnError: false
+    enabled: false
+
+- job: Mac
+  # About 200% of total time (perfomace of Mac hosts is unstable)
+  timeoutInMinutes: 180
+  pool:
+    vmImage: 'macOS-10.15'
+  variables:
+    BUILD_TYPE: Release
+    BIN_DIR: ../bin/intel64/$(BUILD_TYPE)
+  steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '3.7'
+  - script: |
+      whoami
+      uname -a
+      which python3
+      gcc --version
+      xcrun --sdk macosx --show-sdk-version
+      env
+      sysctl -a
+    displayName: 'System properties'
+  - script: |
+      brew install cython
+      brew install automake
+    displayName: 'Install dependencies'
+  - script: brew install ninja
+    displayName: 'Install Ninja'
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      mkdir dldt-build
+      cd dldt-build
+    displayName: 'Create build directory'
+  - script: |
+      export PATH="/usr/local/opt/cython/bin:$PATH"
+      export CC=gcc
+      export CXX=g++
+      # Disable errors with Ninja
+      export CXXFLAGS="-Wno-error=unused-command-line-argument"
+      export CFLAGS="-Wno-error=unused-command-line-argument"
+      cmake .. -GNinja -DVERBOSE_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=ON
+    workingDirectory: dldt-build
+    displayName: 'CMake'
+  - script: ninja
+    workingDirectory: dldt-build
+    displayName: 'Build Mac'
+  - script: ls -alR ../bin/
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: $(BIN_DIR)/ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: $(BIN_DIR)/MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    displayName: 'Clone testdata'
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      export DATA_PATH=`pwd`/../testdata
+      export MODELS_PATH=`pwd`/../testdata
+      $(BIN_DIR)/InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false
+
+- job: Win
+  # About 150% of total time
+  timeoutInMinutes: 120
+  pool:
+    #vmImage: 'vs2017-win2016'
+    name: WIN_VMSS_VENV_F8S_WU2
+  variables:
+    BUILD_TYPE: Release
+    BUILD_DIR: D:\dldt-build
+    BIN_DIR: ..\bin\intel64
+    MSVS_VARS_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat
+    MSVC_COMPILER_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Tools\MSVC\14.24.28314\bin\Hostx64\x64\cl.exe
+  steps:
+  - script: |
+      where python3
+      wmic computersystem get TotalPhysicalMemory
+      wmic cpu list
+      wmic logicaldisk get description,name
+      wmic VOLUME list
+      set
+    displayName: 'System properties'
+  - script: |
+      certutil -urlcache -split -f https://github.com/ninja-build/ninja/releases/download/v1.10.0/ninja-win.zip ninja-win.zip
+      powershell -command "Expand-Archive -Force ninja-win.zip"
+    displayName: Install Ninja
+  - script: git submodule update --init --recursive --jobs 8
+    displayName: 'Clone submodules'
+  - script: |
+      rd /Q /S $(BUILD_DIR)
+      mkdir $(BUILD_DIR)\bin
+      rd /Q /S dldt-build
+      mkdir dldt-build
+    displayName: 'Create build directory'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\ninja-win;%PATH%
+      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(Build.Repository.LocalPath)
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'CMake'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\ninja-win;%PATH%
+      call "$(MSVS_VARS_PATH)" && ninja
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'Build Win'
+  - script: dir ..\bin\ /s /b
+    workingDirectory: dldt-build
+    displayName: 'List files'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*
+    workingDirectory: dldt-build
+    displayName: 'nGraph UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\InferenceEngineUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT old'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\ieUnitTests
+    workingDirectory: dldt-build
+    displayName: 'IE UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\cpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'CPU UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\gnaUnitTests
+    workingDirectory: dldt-build
+    displayName: 'GNA UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\vpuUnitTests
+    workingDirectory: dldt-build
+    displayName: 'VPU UT'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\ieFuncTests
+    workingDirectory: dldt-build
+    displayName: 'IE FuncTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\cpuFuncTests
+    workingDirectory: dldt-build
+    displayName: 'CPU FuncTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;%PATH%
+      $(BIN_DIR)\MklDnnBehaviorTests
+    workingDirectory: dldt-build
+    displayName: 'MklDnnBehaviorTests'
+    continueOnError: false
+  - script: git clone https://github.com/openvinotoolkit/testdata.git
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'Clone testdata'
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;$(Build.Repository.LocalPath)\inference-engine\temp\opencv_4.3.0\opencv\bin;%PATH%
+      set DATA_PATH=$(BUILD_DIR)\testdata
+      set MODELS_PATH=$(BUILD_DIR)\testdata
+      $(BIN_DIR)\MklDnnFunctionalTests --gtest_filter=*smoke*:-smoke_MobileNet/ModelTransformationsTest.LPT/mobilenet_v2_tf_depthwise_batch1_inPluginDisabled_inTestDisabled_asymmetric*
+    workingDirectory: dldt-build
+    displayName: 'MklDnnFunctionalTests'
+    continueOnError: false
+  - script: |
+      set PATH=$(Build.Repository.LocalPath)\inference-engine\temp\tbb\bin;$(Build.Repository.LocalPath)\inference-engine\temp\opencv_4.3.0\opencv\bin;%PATH%
+      set DATA_PATH=$(BUILD_DIR)\testdata
+      set MODELS_PATH=$(BUILD_DIR)\testdata
+      $(BIN_DIR)\InferenceEngineCAPITests
+    workingDirectory: dldt-build
+    displayName: 'IE CAPITests'
+    continueOnError: false

build-instruction.md

@@ -57,11 +57,10 @@ The software was validated on:
 - CentOS\* 7.4 (64-bit) with default GCC\* 4.8.5
 
 ### Software Requirements
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - GCC\* 4.8 or higher to build the Inference Engine
-- Python 3.6 or higher for Inference Engine Python API wrapper
+- Python 3.5 or higher for Inference Engine Python API wrapper
 - (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441].
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
 
 ### Build Steps
 1. Clone submodules:
@@ -147,6 +146,7 @@ You can use the following additional build options:
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build for Raspbian Stretch* OS
@@ -243,9 +243,7 @@ with the following content:
       libgstreamer1.0-dev:armhf \
       libgstreamer-plugins-base1.0-dev:armhf \
       libpython3-dev:armhf \
-      python3-pip \
-      python-minimal \
-      python-argparse
+      python3-pip
 
   RUN wget https://www.cmake.org/files/v3.14/cmake-3.14.3.tar.gz && \
       tar xf cmake-3.14.3.tar.gz && \
@@ -327,6 +325,7 @@ You can use the following additional build options:
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build on Windows* Systems
@@ -336,11 +335,10 @@ The software was validated on:
   Compiler 2018 Update 3
 
 ### Software Requirements
-- [CMake]\*3.13 or higher
+- [CMake]\*3.11 or higher
 - Microsoft\* Visual Studio 2017, 2019 or [Intel® C++ Compiler] 18.0
 - (Optional) Intel® Graphics Driver for Windows* (26.20) [driver package].
-- Python 3.6 or higher for Inference Engine Python API wrapper
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
+- Python 3.5 or higher for Inference Engine Python API wrapper
 
 ### Build Steps
 
@@ -384,7 +382,7 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
 6. Before running the samples, add paths to the TBB and OpenCV binaries used for
    the build to the `%PATH%` environment variable. By default, TBB binaries are
    downloaded by the CMake-based script to the `<openvino_repo>/inference-engine/temp/tbb/bin`
-   folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.5.0/opencv/bin`
+   folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
    folder.
 
 ### Additional Build Options
@@ -430,6 +428,7 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ### Building Inference Engine with Ninja* Build System
@@ -454,10 +453,9 @@ The software was validated on:
 
 ### Software Requirements
 
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - Clang\* compiler from Xcode\* 10.1 or higher
-- Python\* 3.6 or higher for the Inference Engine Python API wrapper
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
+- Python\* 3.5 or higher for the Inference Engine Python API wrapper
 
 ### Build Steps
 
@@ -522,6 +520,7 @@ You can use the following additional build options:
 
 - nGraph-specific compilation options:
   `-DNGRAPH_ONNX_IMPORT_ENABLE=ON` enables the building of the nGraph ONNX importer.
+  `-DNGRAPH_JSON_ENABLE=ON` enables nGraph JSON-based serialization.
   `-DNGRAPH_DEBUG_ENABLE=ON` enables additional debug prints.
 
 ## Build on Android* Systems
@@ -530,9 +529,8 @@ This section describes how to build Inference Engine for Android x86 (64-bit) op
 
 ### Software Requirements
 
-- [CMake]\* 3.13 or higher
+- [CMake]\* 3.11 or higher
 - Android NDK (this guide has been validated with r20 release)
-> **NOTE**: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake\* 3.10 or higher. 
 
 ### Build Steps
 

cmake/developer_package.cmake

@@ -122,9 +122,9 @@ include(debug)
 include(whole_archive)
 
 string(TOLOWER ${CMAKE_SYSTEM_PROCESSOR} ARCH_FOLDER)
-if(X86_64)
+if(ARCH_FOLDER STREQUAL "x86_64" OR ARCH_FOLDER STREQUAL "amd64") # Windows detects Intel's 64-bit CPU as AMD64
     set(ARCH_FOLDER intel64)
-elseif(X86)
+elseif(ARCH_FOLDER STREQUAL "i386")
     set(ARCH_FOLDER ia32)
 endif()
 

cmake/features.cmake

@@ -28,8 +28,6 @@ ie_option (OS_FOLDER "create OS dedicated folder in output" OFF)
 # FIXME: ARM cross-compiler generates several "false positive" warnings regarding __builtin_memcpy buffer overflow
 ie_dependent_option (TREAT_WARNING_AS_ERROR "Treat build warnings as errors" ON "X86 OR X86_64" OFF)
 
-ie_option (ENABLE_INTEGRITYCHECK "build DLLs with /INTEGRITYCHECK flag" OFF)
-
 ie_option (ENABLE_SANITIZER "enable checking memory errors via AddressSanitizer" OFF)
 
 ie_option (ENABLE_THREAD_SANITIZER "enable checking data races via ThreadSanitizer" OFF)
@@ -43,8 +41,3 @@ ie_dependent_option (ENABLE_SSE42 "Enable SSE4.2 optimizations" ON "X86_64 OR X8
 ie_dependent_option (ENABLE_AVX2 "Enable AVX2 optimizations" ON "X86_64 OR X86" OFF)
 
 ie_dependent_option (ENABLE_AVX512F "Enable AVX512 optimizations" ON "X86_64 OR X86" OFF)
-
-ie_option (ENABLE_PROFILING_ITT "Build with ITT tracing. Optionally configure pre-built ittnotify library though INTEL_VTUNE_DIR variable." OFF)
-
-# Documentation build
-ie_option (ENABLE_DOCS "build docs using Doxygen" OFF)

cmake/os_flags.cmake

@@ -238,17 +238,16 @@ if(WIN32)
     endif()
 
     if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
-        # 161: unrecognized pragma
-        # 177: variable was declared but never referenced
-        # 556: not matched type of assigned function pointer
+        # 161 unrecognized pragma
+        # 177 variable was declared but never referenced
+        # 556 not matched type of assigned function pointer
         # 1744: field of class type without a DLL interface used in a class with a DLL interface
-        # 1879: unimplemented pragma ignored
-        # 2586: decorated name length exceeded, name was truncated
+        # 2586 decorated name length exceeded, name was truncated
         # 2651: attribute does not apply to any entity
-        # 3180: unrecognized OpenMP pragma
+        # 3180 unrecognized OpenMP pragma
         # 11075: To get full report use -Qopt-report:4 -Qopt-report-phase ipo
-        # 15335: was not vectorized: vectorization possible but seems inefficient. Use vector always directive or /Qvec-threshold0 to override
-        ie_add_compiler_flags(/Qdiag-disable:161,177,556,1744,1879,2586,2651,3180,11075,15335)
+        # 15335 was not vectorized: vectorization possible but seems inefficient. Use vector always directive or /Qvec-threshold0 to override
+        ie_add_compiler_flags(/Qdiag-disable:161,177,556,1744,2586,2651,3180,11075,15335)
     endif()
 
     # Debug information flags
@@ -265,7 +264,6 @@ else()
     ie_add_compiler_flags(-ffunction-sections -fdata-sections)
     ie_add_compiler_flags(-fdiagnostics-show-option)
     ie_add_compiler_flags(-Wundef)
-    ie_add_compiler_flags(-Wreturn-type)
 
     # Disable noisy warnings
 

cmake/sanitizer.cmake

@@ -15,9 +15,7 @@ if (ENABLE_SANITIZER)
     if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
         set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=gold")
     elseif(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$" AND NOT WIN32)
-        if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
-            set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld")
-        endif()
+        set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld")
     endif()
 
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SANITIZER_COMPILER_FLAGS}")

cmake/sdl.cmake

@@ -14,7 +14,9 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release")
         endif()
 
         if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
+            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
             if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
                 set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
             else()
@@ -30,21 +32,14 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release")
                 set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wl,--strip-all")
             endif()
             set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
-        endif()
-    else()
-        if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl")
-        endif()
-        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /guard:cf")
-        if(ENABLE_INTEGRITYCHECK)
-            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} /INTEGRITYCHECK")
+            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
+            set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now")
         endif()
+    elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl /guard:cf")
     endif()
 
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${IE_C_CXX_FLAGS}")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${IE_C_CXX_FLAGS}")
-    set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
 endif()

cmake/target_flags.cmake

@@ -30,13 +30,6 @@ elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64.*|AARCH64.*)")
   set(AARCH64 ON)
 endif()
 
-# in case of cross-compilation (or -m32) CMAKE_SYSTEM_PROCESSOR is equal to
-# CMAKE_HOST_SYSTEM_PROCESSOR which is X86_64; patch this until a better solution
-if(CMAKE_SIZEOF_VOID_P EQUAL 4 AND X86_64)
-  unset(X86_64)
-  set(X86 ON)
-endif()
-
 if(UNIX AND NOT APPLE)
     set(LINUX ON)
 endif()

docs/CMakeLists.txt

@@ -2,186 +2,59 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-if(NOT ENABLE_DOCKER)
-    add_subdirectory(examples)
+add_subdirectory(examples)
 
-    # Detect nGraph
-    find_package(ngraph QUIET)
-    if(NOT ngraph_FOUND)
-        set(ngraph_DIR ${CMAKE_BINARY_DIR}/ngraph)
-    endif()
-
-    # Detect InferenceEngine
-    find_package(InferenceEngine QUIET)
-    if(NOT InferenceEngine_FOUND)
-        set(InferenceEngine_DIR ${CMAKE_BINARY_DIR})
-    endif()
-
-    add_subdirectory(template_extension)
-
-    set(all_docs_targets
-        ie_docs_examples
-        template_extension
-        templatePlugin TemplateBehaviorTests TemplateFunctionalTests)
-    foreach(target_name IN LISTS all_docs_targets)
-        if (TARGET ${target_name})
-            set_target_properties(${target_name} PROPERTIES FOLDER docs)
-        endif()
-    endforeach()
+# Detect nGraph
+find_package(ngraph QUIET)
+if(NOT ngraph_FOUND)
+    set(ngraph_DIR ${CMAKE_BINARY_DIR}/ngraph)
 endif()
 
-function(build_docs)
-
-    find_package(Doxygen REQUIRED dot)
-    find_package(Python3 COMPONENTS Interpreter)
-    find_package(LATEX)
-
-    if(NOT DOXYGEN_FOUND)
-        message(FATAL_ERROR "Doxygen is required to build the documentation")
-    endif()
-
-    if(NOT Python3_FOUND)
-        message(FATAL_ERROR "Python3 is required to build the documentation")
-    endif()
-
-    if(NOT LATEX_FOUND)
-        message(FATAL_ERROR "LATEX is required to build the documentation")
-    endif()
-
-    set(DOCS_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
-    set(DOXYGEN_DIR ${OpenVINO_MAIN_SOURCE_DIR}/docs/doxygen)
-    set(IE_SOURCE_DIR ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine)
-    set(PYTHON_API_IN ${IE_SOURCE_DIR}/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx)
-    set(PYTHON_API_OUT ${DOCS_BINARY_DIR}/python_api/ie_api.pyx)
-    set(C_API ${IE_SOURCE_DIR}/ie_bridges/c/include)
-    set(PLUGIN_API_DIR ${DOCS_BINARY_DIR}/IE_PLUGIN_DG)
-
-    # Preprocessing scripts
-    set(DOXY_MD_FILTER ${DOXYGEN_DIR}/doxy_md_filter.py)
-    set(PYX_FILTER ${DOXYGEN_DIR}/pyx_filter.py)
-
-    file(GLOB_RECURSE doc_source_files
-        LIST_DIRECTORIES true
-        RELATIVE ${OpenVINO_MAIN_SOURCE_DIR}
-        ${OpenVINO_MAIN_SOURCE_DIR}/docs/*.md
-        ${OpenVINO_MAIN_SOURCE_DIR}/docs/*.png
-        ${OpenVINO_MAIN_SOURCE_DIR}/docs/*.gif
-        ${OpenVINO_MAIN_SOURCE_DIR}/docs/*.jpg
-        ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.md
-        ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.png
-        ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.gif
-        ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/*.jpg
-    )
-
-    configure_file(${PYTHON_API_IN} ${PYTHON_API_OUT} @ONLY)
-
-    set(IE_CONFIG_SOURCE ${DOXYGEN_DIR}/ie_docs.config)
-    set(C_CONFIG_SOURCE ${DOXYGEN_DIR}/ie_c_api.config)
-    set(PY_CONFIG_SOURCE ${DOXYGEN_DIR}/ie_py_api.config)
-    set(PLUGIN_CONFIG_SOURCE ${DOXYGEN_DIR}/ie_plugin_api.config)
-
-    set(IE_CONFIG_BINARY ${DOCS_BINARY_DIR}/ie_docs.config)
-    set(C_CONFIG_BINARY ${DOCS_BINARY_DIR}/ie_c_api.config)
-    set(PY_CONFIG_BINARY ${DOCS_BINARY_DIR}/ie_py_api.config)
-    set(PLUGIN_CONFIG_BINARY ${DOCS_BINARY_DIR}/ie_plugin_api.config)
-
-    set(IE_LAYOUT_SOURCE ${DOXYGEN_DIR}/ie_docs.xml)
-    set(C_LAYOUT_SOURCE ${DOXYGEN_DIR}/ie_c_api.xml)
-    set(PY_LAYOUT_SOURCE ${DOXYGEN_DIR}/ie_py_api.xml)
-    set(PLUGIN_LAYOUT_SOURCE ${DOXYGEN_DIR}/ie_plugin_api.xml)
-
-    set(IE_LAYOUT_BINARY ${DOCS_BINARY_DIR}/ie_docs.xml)
-    set(C_LAYOUT_BINARY ${DOCS_BINARY_DIR}/ie_c_api.xml)
-    set(PY_LAYOUT_BINARY ${DOCS_BINARY_DIR}/ie_py_api.xml)
-    set(PLUGIN_LAYOUT_BINARY ${DOCS_BINARY_DIR}/ie_plugin_api.xml)
-
-    # Tables of contents
-    configure_file(${IE_LAYOUT_SOURCE} ${IE_LAYOUT_BINARY} @ONLY)
-    configure_file(${C_LAYOUT_SOURCE} ${C_LAYOUT_BINARY} @ONLY)
-    configure_file(${PY_LAYOUT_SOURCE} ${PY_LAYOUT_BINARY} @ONLY)
-    configure_file(${PLUGIN_LAYOUT_SOURCE} ${PLUGIN_LAYOUT_BINARY} @ONLY)
-
-    # Doxygen config files
-    configure_file(${IE_CONFIG_SOURCE} ${IE_CONFIG_BINARY} @ONLY)
-    configure_file(${C_CONFIG_SOURCE} ${C_CONFIG_BINARY} @ONLY)
-    configure_file(${PY_CONFIG_SOURCE} ${PY_CONFIG_BINARY} @ONLY)
-    configure_file(${PLUGIN_CONFIG_SOURCE} ${PLUGIN_CONFIG_BINARY} @ONLY)
-
-    # Preprocessing scripts
-    set(DOXY_MD_FILTER ${DOXYGEN_DIR}/doxy_md_filter.py)
-    set(PYX_FILTER ${DOXYGEN_DIR}/pyx_filter.py)
-
-    add_custom_target(c_api
-                    COMMAND ${DOXYGEN_EXECUTABLE} ${C_CONFIG_BINARY}
-                    WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                    COMMENT "Generating C API Reference"
-                    VERBATIM
-    )
-
-    add_custom_target(py_api
-                    COMMAND ${DOXYGEN_EXECUTABLE} ${PY_CONFIG_BINARY}
-                    WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                    COMMENT "Generating Python API Reference"
-                    VERBATIM
-    )
-
-    add_custom_target(plugin_api
-                    COMMAND ${DOXYGEN_EXECUTABLE} ${PLUGIN_CONFIG_BINARY}
-                    WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                    COMMENT "Generating Plugin API Reference"
-                    VERBATIM
-    )
-
-    add_custom_target(preprocess_docs
-                    COMMENT "Pre-process docs"
-                    VERBATIM
-    )
-
-    add_custom_target(ie_docs
-                    DEPENDS preprocess_docs
-                    COMMAND ${DOXYGEN_EXECUTABLE} ${IE_CONFIG_BINARY}
-                    WORKING_DIRECTORY ${DOCS_BINARY_DIR}
-                    VERBATIM
-    )
-
-    add_custom_target(openvino_docs
-                    DEPENDS c_api py_api ie_docs plugin_api
-                    COMMENT "Generating OpenVINO documentation"
-                    VERBATIM
-    )
-
-    add_custom_command(TARGET py_api
-                    PRE_BUILD
-                    COMMAND ${Python3_EXECUTABLE} ${PYX_FILTER} ${PYTHON_API_OUT}
-                    COMMENT "Pre-process Python API."
-    )
-
-    foreach(source_file ${doc_source_files})
-    add_custom_command(TARGET preprocess_docs
-                        PRE_BUILD
-                        COMMAND ${CMAKE_COMMAND} -E copy ${OpenVINO_MAIN_SOURCE_DIR}/${source_file} ${DOCS_BINARY_DIR}/${source_file})
-    endforeach()
-
-    add_custom_command(TARGET preprocess_docs
-                    PRE_BUILD
-                    COMMAND ${Python3_EXECUTABLE} ${DOXY_MD_FILTER} ${DOCS_BINARY_DIR}
-                    COMMENT "Pre-process markdown and image links."
-    )
-
-    set_target_properties(ie_docs PROPERTIES FOLDER docs)
-
-    find_program(browser NAMES xdg-open)
-    if(browser)
-        add_custom_target(ie_docs_open
-                        COMMAND ${browser} "${OpenVINO_MAIN_SOURCE_DIR}/doc/html/index.html"
-                        DEPENDS ie_docs
-                        COMMENT "Open OpenVINO documentation"
-                        VERBATIM)
-        set_target_properties(ie_docs_open PROPERTIES FOLDER docs)
-    endif()
-
-endfunction()
-
-if (ENABLE_DOCS)
-    build_docs()
+# Detect InferenceEngine
+find_package(InferenceEngine QUIET)
+if(NOT InferenceEngine_FOUND)
+    set(InferenceEngine_DIR ${CMAKE_BINARY_DIR})
+endif()
+
+add_subdirectory(template_extension)
+
+set(all_docs_targets
+    ie_docs_examples
+    template_extension
+    templatePlugin TemplateBehaviorTests TemplateFunctionalTests)
+foreach(target_name IN LISTS all_docs_targets)
+    if (TARGET ${target_name})
+        set_target_properties(${target_name} PROPERTIES FOLDER docs)
+    endif()
+endforeach()
+
+# OpenVINO docs
+
+set(OPENVINO_DOCS_PATH "" CACHE PATH "Path to openvino-documentation local repository")
+set(args "")
+
+if(OPENVINO_DOCS_PATH)
+    set(args "${args} ovinodoc_path:${OPENVINO_DOCS_PATH}")
+endif()
+
+file(GLOB_RECURSE docs_files "${OpenVINO_MAIN_SOURCE_DIR}/docs")
+file(GLOB_RECURSE include_files "${OpenVINO_MAIN_SOURCE_DIR}/inference-engine/include")
+file(GLOB_RECURSE ovino_files "${OPENVINO_DOCS_PATH}")
+
+add_custom_target(ie_docs
+                  COMMAND ./build_docs.sh ${args}
+                  WORKING_DIRECTORY "${OpenVINO_MAIN_SOURCE_DIR}/docs/build_documentation"
+                  COMMENT "Generating OpenVINO documentation"
+                  SOURCES ${docs_files} ${include_files} ${ovino_files}
+                  VERBATIM)
+set_target_properties(ie_docs PROPERTIES FOLDER docs)
+
+find_program(browser NAMES xdg-open)
+if(browser)
+    add_custom_target(ie_docs_open
+                      COMMAND ${browser} "${OpenVINO_MAIN_SOURCE_DIR}/doc/html/index.html"
+                      DEPENDS ie_docs
+                      COMMENT "Open OpenVINO documentation"
+                      VERBATIM)
+    set_target_properties(ie_docs_open PROPERTIES FOLDER docs)
 endif()

docs/IE_DG/API_Changes.md

@@ -19,122 +19,6 @@ Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previo
 
 Therefore, ONNX RT Execution Provider for nGraph will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.
 
-## 2021.1
-
-### Deprecated API
-
- **Utility functions to convert Unicode paths**
-
- * InferenceEngine::stringToFileName - use OS-specific native conversion functions
- * InferenceEngine::fileNameToString - use OS-specific native conversion functions
-
-### Removed API
-
- **Plugin API:**
-
- * InferenceEngine::InferencePlugin C++ plugin wrapper class
- * InferenceEngine::IInferencePlugin plugin interface
- * InferenceEngine::PluginDispatcher class
- * InferenceEngine::InferenceEnginePluginPtr typedef
- * InferenceEngine::ICNNNetReader reader interface
- * InferenceEngine::CNNNetReader class
-
- **Extensibility API:**
-
- * InferenceEngine::ILayerImplFactory class
- * InferenceEngine::IShapeInferImpl class
- * InferenceEngine::IShapeInferExtension class
- * InferenceEngine::IExtension::getFactoryFor(ILayerImplFactory\*& factory, const CNNLayer\* cnnLayer, ResponseDesc\* resp) noexcept method
- * InferenceEngine::IExtension::getPrimitiveTypes(char\*\*& types, unsigned int& size, ResponseDesc\* resp) noexcept method
- * InferenceEngine::ShapeInferImpl class
- * InferenceEngine::Extension::getFactoryFor(ILayerImplFactory\*& factory, const CNNLayer\* cnnLayer, ResponseDesc\* resp) noexcept method
- * InferenceEngine::Extension::getPrimitiveTypes(char\*\*& types, unsigned int& size, ResponseDesc\* resp) noexcept method
-
- **Network API:**
-
- * InferenceEngine::details::CNNNetworkIterator class
- * InferenceEngine::CNNNetwork::getPrecision() const method
- * InferenceEngine::CNNNetwork::getLayerByName(const char\* layerName) const method
- * InferenceEngine::CNNNetwork::size() const method
- * InferenceEngine::CNNNetwork::begin() const method
- * InferenceEngine::CNNNetwork::end() const method
- * InferenceEngine::CNNNetwork::AddExtension(const IShapeInferExtensionPtr& extension) method
- * InferenceEngine::ICNNNetwork::getPrecision() const noexcept method
- * InferenceEngine::ICNNNetwork::getName(char\* pName, size_t len) const noexcept method
- * InferenceEngine::ICNNNetwork::getData(const char\* dname) noexcept method
- * InferenceEngine::ICNNNetwork::addLayer(const CNNLayerPtr& layer) noexcept method
- * InferenceEngine::ICNNNetwork::getLayerByName(const char\* layerName, CNNLayerPtr& out, ResponseDesc\* resp) const noexcept method
- * InferenceEngine::ICNNNetwork::AddExtension(const IShapeInferExtensionPtr& extension, ResponseDesc\* resp) noexcept method
- * InferenceEngine::ICNNNetwork::getStats(ICNNNetworkStats\*\* stats, ResponseDesc\* resp) const noexcept method
- * InferenceEngine::ICNNNetworkStats class
- * InferenceEngine::NetworkNodeStats class
- * InferenceEngine::Data::getCreatorLayer() method
- * InferenceEngine::Data::getInputTo() method
- * InferenceEngine::LayerParams class
-
- **Layer API:**
-
- * InferenceEngine::CNNLayer class
- * InferenceEngine::WeightableLayer class
- * InferenceEngine::BatchNormalizationLayer class
- * InferenceEngine::BatchToSpaceLayer class
- * InferenceEngine::BinaryConvolutionLayer class
- * InferenceEngine::BroadcastLayer class
- * InferenceEngine::BucketizeLayer class
- * InferenceEngine::ClampLayer class
- * InferenceEngine::ConcatLayer class
- * InferenceEngine::ConvolutionLayer class
- * InferenceEngine::CropLayer class
- * InferenceEngine::DeconvolutionLayer class
- * InferenceEngine::DeformableConvolutionLayer class
- * InferenceEngine::DepthToSpaceLayer class
- * InferenceEngine::EltwiseLayer class
- * InferenceEngine::ExperimentalDetectronPriorGridGenerator class
- * InferenceEngine::ExperimentalDetectronPriorGridGeneratorLayer class
- * InferenceEngine::ExperimentalSparseWeightedReduceLayer class
- * InferenceEngine::FillLayer class
- * InferenceEngine::FullyConnectedLayer class
- * InferenceEngine::GRNLayer class
- * InferenceEngine::GRUCell class
- * InferenceEngine::GatherLayer class
- * InferenceEngine::GemmLayer class
- * InferenceEngine::LSTMCell class
- * InferenceEngine::MVNLayer class
- * InferenceEngine::MathLayer class
- * InferenceEngine::NonMaxSuppression class
- * InferenceEngine::NormLayer class
- * InferenceEngine::OneHotLayer class
- * InferenceEngine::PReLULayer class
- * InferenceEngine::PadLayer class
- * InferenceEngine::PoolingLayer class
- * InferenceEngine::PowerLayer class
- * InferenceEngine::QuantizeLayer class
- * InferenceEngine::RNNCell class
- * InferenceEngine::RNNCellBase class
- * InferenceEngine::RNNSequenceLayer class
- * InferenceEngine::RangeLayer class
- * InferenceEngine::ReLU6Layer class
- * InferenceEngine::ReLULayer class
- * InferenceEngine::ReduceLayer class
- * InferenceEngine::ReshapeLayer class
- * InferenceEngine::ReverseSequenceLayer class
- * InferenceEngine::ScaleShiftLayer class
- * InferenceEngine::ScatterLayer class
- * InferenceEngine::SelectLayer class
- * InferenceEngine::ShuffleChannelsLayer class
- * InferenceEngine::SoftMaxLayer class
- * InferenceEngine::SpaceToBatchLayer class
- * InferenceEngine::SpaceToDepthLayer class
- * InferenceEngine::SparseFillEmptyRowsLayer class
- * InferenceEngine::SparseSegmentReduceLayer class
- * InferenceEngine::SparseToDenseLayer class
- * InferenceEngine::SplitLayer class
- * InferenceEngine::StridedSliceLayer class
- * InferenceEngine::TensorIterator class
- * InferenceEngine::TileLayer class
- * InferenceEngine::TopKLayer class
- * InferenceEngine::UniqueLayer class
-
 ## 2020.4
 
 ### New API

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -58,7 +58,7 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 
 * [Integrating Inference Engine in Your Application](Integrate_with_customer_application_new_API.md)
 
-* [[DEPRECATED] Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md)
+* [Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md)
 
 * [Introduction to Performance Topics](Intro_to_Performance.md)
 
@@ -84,7 +84,7 @@ inference of a pre-trained and optimized deep learning model and a set of sample
       * [HDDL](supported_plugins/HDDL.md)
     * [Heterogeneous execution](supported_plugins/HETERO.md)
     * [GNA](supported_plugins/GNA.md)
-    * [MULTI](supported_plugins/MULTI.md)
+    * **NEW!** [MULTI](supported_plugins/MULTI.md)
 
 * [Pre-Trained Models](@ref omz_models_intel_index)
 

docs/IE_DG/Extensibility_DG/AddingNGraphOps.md

@@ -12,12 +12,10 @@ To add your custom nGraph operation, create a new class that extends `ngraph::Op
 
 3. Override the shape inference method `validate_and_infer_types`. This method is called multiple times during graph manipulations to determine the shapes and element types of the outputs of the operations. You can access the input shapes through the `get_input_partial_shape()` method and input element types through the `get_input_element_type()` method of `ngraph::Node`. Set the inferred shape and element type of the output using `set_output_type`.
 
-4. Override the `clone_with_new_inputs` method, which allows graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.
+4. Override the `copy_with_new_args` method, which allows graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.
 
 5. Override the `visit_attributes` method, which allows serialization and deserialization of attributes. An `AttributeVisitor` is passed to the method, and the implementation is expected to walk over all the attributes in the op using the type-aware `on_attribute` helper. Helpers are already implemented for standard C++ types like `int64_t`, `float`, `bool`, `vector` and for existing nGraph defined types.
 
-6. Override `evaluate`, which is an optional method that enables the application of constant folding if there is a custom operation on the constant branch.
-
 Based on that, declaration of a operation class can look as follows:
 
 @snippet op.hpp op:header
@@ -41,9 +39,9 @@ nGraph operation contains two constructors: a default constructor, which allows
 
 @snippet op.cpp op:validate
 
-### `clone_with_new_inputs()`
+### `copy_with_new_args()`
 
-`ngraph::Node::clone_with_new_inputs` method creates a copy of nGraph operation with new inputs.
+`ngraph::Node::copy_with_new_args` method creates a copy of nGraph operation with new inputs.
 
 @snippet op.cpp op:copy
 
@@ -53,12 +51,6 @@ nGraph operation contains two constructors: a default constructor, which allows
 
 @snippet op.cpp op:visit_attributes
 
-### `evaluate()`
-
-`ngraph::Node::evaluate` method allows to apply constant folding to an operation.
-
-@snippet op.cpp op:evaluate
-
 ## Register Custom Operations in Extension Class
 
 To add custom operations to the [Extension](Extension.md) class, create an operation set with custom operations and implement the `InferenceEngine::IExtension::getOpSets` method:

docs/IE_DG/Extensibility_DG/Intro.md

@@ -40,6 +40,13 @@ The following pages describe how to integrate custom _kernels_ into the Inferenc
  * [Introduction to development of custom GPU kernels](GPU_Kernel.md)
  * [Introduction to development of custom VPU kernels](VPU_Kernel.md)
 
+## Deprecated Extensibility API
+
+Shape Inference API and some methods of extensibility mechanism was deprecated and will be removed soon.
+Old Extensibility mechanism contains two parts shape inference and execution kernel.
+ * [Shape Inference](deprecated/ShapeInfer.md)
+ * [Execution Kernel](deprecated/Factory.md)
+
 ## Additional Resources
 
 * [Build an extension library using CMake*](Building.md)

docs/IE_DG/Extensibility_DG/deprecated/Factory.md

@@ -0,0 +1,96 @@
+# Deprecated API for CPU kernels creation {#openvino_docs_IE_DG_Extensibility_DG_deprecated_Factory}
+
+List of deprecated API for kernels development:
+ * `InferenceEngine::IExtension::getPrimitiveTypes(char**& types, unsigned int& size, ResponseDesc* resp)` method
+ * `InferenceEngine::IExtension::getFactoryFor(ILayerImplFactory *&factory, const CNNLayer *cnnLayer, ResponseDesc *resp)` method
+ * `InferenceEngine::ILayerImplFactory` class
+
+>**NOTE**: This guide demonstrates how to use deprecated API for kernels creation. However, keep in mind that this API will be deleted soon.
+
+1. Create your custom layer factory `CustomLayerFactory` class:
+```cpp
+// custom_layer.h
+// A CustomLayerFactory class is an example layer, which makes exponentiation by 2 for the input and does not change dimensions
+class CustomLayerFactory {
+
+};
+```
+2. Inherit it from the abstract `InferenceEngine::ILayerImplFactory` class: 
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+
+};
+```
+
+3. Create a constructor, a virtual destructor, and a data member to keep the layer info:
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+public:
+    explicit CustomLayerFactory(const CNNLayer *layer): cnnLayer(*layer) {}
+private:
+  CNNLayer cnnLayer;
+};
+```
+
+4. Overload and implement the abstract methods `getShapes` and `getImplementations` of the `InferenceEngine::ILayerImplFactory` class:
+```cpp
+// custom_layer.h
+class CustomLayerFactory: public InferenceEngine::ILayerImplFactory {
+public:
+    // ... constructor and destructor
+
+    StatusCode getShapes(const std::vector<TensorDesc>& inShapes, std::vector<TensorDesc>& outShapes, ResponseDesc *resp) noexcept override {
+        if (cnnLayer == nullptr) {
+            std::string errorMsg = "Cannot get cnn layer!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return GENERAL_ERROR;
+        }
+        if (inShapes.size() != 1) {
+            std::string errorMsg = "Incorrect input shapes!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return GENERAL_ERROR;
+        }
+        outShapes.clear();
+        outShapes.emplace_back(inShapes[0]);
+        return OK;
+    }
+
+    StatusCode getImplementations(std::vector<ILayerImpl::Ptr>& impls, ResponseDesc *resp) noexcept override {
+        // You can add cnnLayer to implementation if it is necessary
+        impls.push_back(ILayerImpl::Ptr(new CustomLayerImpl()));
+        return OK;
+    }
+};
+```
+5. Create your custom layer implementation `CustomLayerImpl` class using the [instruction](../CPU_Kernel.md).
+
+6. Implement methods in the `Extension` class:
+```cpp
+// custom_extension.h
+class CustomExtention : public InferenceEngine::IExtension {
+public:
+    // ... utility methods
+    // Retruns the list of supported kernels/layers
+    StatusCode getPrimitiveTypes(char**& types, unsigned int& size, ResponseDesc* resp) noexcept override {
+        std::string type_name = "CustomLayer";
+        types = new char *[1];
+        size = 1;
+        types[0] = new char[type_name.size() + 1];
+        std::copy(type_name.begin(), type_name.end(), types[0]);
+        types[0][type_name.size()] = '\0';
+        return OK;
+    }
+    // Main function
+    StatusCode getFactoryFor(ILayerImplFactory *&factory, const CNNLayer *cnnLayer, ResponseDesc *resp) noexcept override {
+        if (cnnLayer->type != "CustomLayer") {
+            std::string errorMsg = std::string("Factory for ") + cnnLayer->type + " wasn't found!";
+            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
+            return NOT_FOUND;
+        }
+        factory = new CustomLayerFactory(cnnLayer);
+        return OK;
+    }
+};
+```

docs/IE_DG/Extensibility_DG/deprecated/ShapeInfer.md

@@ -0,0 +1,18 @@
+# Old ShapeInference Extensibility API {#openvino_docs_IE_DG_Extensibility_DG_deprecated_ShapeInfer}
+
+The new approach to shape inference suggests a creation of a custom nGraph operation that contains a special method for shape inference. 
+The following classes and methods were deprecated:
+
+ * `InferenceEngine::IShapeInferExtension` class
+ * `InferenceEngine::IShapeInferExtension::getShapeInferTypes(char**&, unsigned int&, ResponseDesc*)` method
+ * `InferenceEngine::IShapeInferExtension::getShapeInferImpl(IShapeInferImpl::Ptr&, const char*, ResponseDesc*)` method
+
+However, the old approach with the `InferenceEngine::IShapeInferExtension` method still works for already existing custom layers.
+Custom Shape Inference functions are registered by calling `InferenceEngine::ICNNNetwork::AddExtension` with the implemented `InferenceEngine::IShapeInferExtension` method, which is a holder of custom implementations. 
+The holder requires to implement two key methods:
+* `InferenceEngine::IShapeInferExtension::getShapeInferImpl` - Returns custom shape inference implementation for the given type. 
+* `InferenceEngine::IShapeInferExtension::getShapeInferTypes` - Provides all custom types.
+
+Custom shape inference implementation is represented by the `InferenceEngine::IShapeInferImpl::inferShapes` method.
+
+It is impossible to overwrite built-in shape inference functions. Custom type must be different from the supported ones. 

docs/IE_DG/Glossary.md

@@ -68,11 +68,13 @@ Glossary of terms used in the Inference Engine
 | Extensibility mechanism, Custom layers | The mechanism that provides you with capabilities to extend the Inference Engine and Model Optimizer so that they can work with topologies containing layers that are not yet supported |
 | <code>ICNNNetwork</code> | An Interface of the Convolutional Neural Network that Inference Engine reads from IR. Consists of topology, weights and biases |
 | <code>IExecutableNetwork</code> | An instance of the loaded network which allows the Inference Engine to request (several) infer requests and perform inference synchronously or asynchronously |
+| <code>IHeteroInferencePlugin</code> |  Interface that is implemented by the heterogeneity plugin to allow the Inference Engine to set the default affinities for layers by devices before loading the network to the heterogeneous plugin. You can modify affinities manually before loading to the plugin. |
+| <code>IInferencePlugin</code> | Interface provided by each plugin to allow the Inference Engine to load <code>ICNNNetwork</code> to the plugin, create Executable network and set special dedicated options for the plugin |
 | <code>IInferRequest</code> | Interface that represents the end point of inference on the model loaded to the plugin and represented by executable network. Inputs are set here, outputs should be requested from this interface as well |
 | <code>InferenceEngineProfileInfo</code> | Represents basic inference profiling information per layer |
 | Inference Engine | A C++ library with a set of classes that you can use in your application to infer input data (images) and get the result |
 | Inference Engine API | The basic default API for all supported devices, which allows you to load a model from Intermediate Representation, set input and output formats and execute the model on various devices |
-| Inference Engine <code>Core<code> | Inference Engine Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, MYRIAD, GNA, etc. |
+| Inference Engine Plugin | Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel(R) hardware device: CPU, GPU, VPU, FPGA, etc. Each plugin implements the unified API and provides additional hardware-specific APIs. |
 | Layer catalog or Operations specification | A list of supported layers or operations and its parameters. Sets of supported layers are different for different plugins, please check the documentation on plugins to verify if the Inference Engine supports certain layer on the dedicated hardware |
 | <code>Layout</code> | Image data layout refers to the representation of images batch. Layout shows a sequence of 4D or 5D tensor data in memory. A typical NCHW format represents pixel in horizontal direction, rows by vertical dimension, planes by channel and images into batch |
 | <code>OutputsDataMap</code> | Structure which contains information about output precisions and layouts |

docs/IE_DG/Graph_debug_capabilities.md

@@ -8,6 +8,20 @@ CNNNetwork. Both representations provide an API to get detailed information abou
 To receive additional messages about applied graph modifications, rebuild the nGraph library with 
 the `-DNGRAPH_DEBUG_ENABLE=ON` option.
 
+To enable serialization and deserialization of the nGraph function to a JSON file, rebuild the 
+nGraph library with the `-DNGRAPH_JSON_ENABLE=ON` option. To serialize or deserialize the nGraph
+function, call the nGraph function as follows:
+
+```cpp
+#include <ngraph/serializer.hpp>
+
+std::shared_ptr<ngraph::Function> nGraph;
+...
+ngraph::serialize("test_json.json", nGraph);        // For graph serialization
+std::ifstream file("test_json.json");               // Open a JSON file
+nGraph = ngraph::deserialize(file);                 // For graph deserialization
+```
+
 To visualize the nGraph function to the xDot format or to an image file, use the 
 `ngraph::pass::VisualizeTree` graph transformation pass:
 ```cpp
@@ -15,7 +29,8 @@ To visualize the nGraph function to the xDot format or to an image file, use the
 
 std::shared_ptr<ngraph::Function> nGraph;
 ...
-ngraph::pass::VisualizeTree("after.png").run_on_function(nGraph);     // Visualize the nGraph function to an image
+std::vector<std::shared_ptr<ngraph::Function>> g2{nGraph};
+ngraph::pass::VisualizeTree("after.png").run_on_module(g2);     // Visualize the nGraph function to an image
 ```
 
 ## CNNNetwork

docs/IE_DG/Integrate_with_customer_application_new_API.md

@@ -5,6 +5,9 @@ This section provides a high-level description of the process of integrating the
 Refer to the [Hello Classification Sample](../../inference-engine/samples/hello_classification/README.md) sources
 for example of using the Inference Engine in applications.
 
+> **NOTE**: For 2019 R2 Release, the new Inference Engine Core API is introduced. This guide is updated to reflect the new API approach.
+> The Inference Engine Plugin API is still supported, but is going to be deprecated in future releases. Please, refer to [Migration from Inference Engine Plugin API to Core API](Migration_CoreAPI.md) guide to update your application.
+
 ## Use the Inference Engine API in Your Code
 
 The core `libinference_engine.so` library implements loading and parsing a model Intermediate Representation (IR), and triggers inference using a specified device. The core library has the following API:
@@ -36,7 +39,7 @@ InferenceEngine::Core core;
 ```cpp
 auto network = core.ReadNetwork("Model.xml");
 ```
-**Or read the model from ONNX format** (.onnx and .prototxt are supported formats). You can find more information about the ONNX format support in the document [ONNX format support in the OpenVINO™](./ONNX_Support.md).
+**Or read the model from ONNX format** (.onnx and .prototxt are supported formats)
 ```cpp
 auto network = core.ReadNetwork("model.onnx");
 ```

docs/IE_DG/Introduction.md

@@ -117,7 +117,7 @@ Please refer to the [Overview of nGraph Flow](nGraph_Flow.md) describing the det
 
 Inference Engine is a runtime that delivers a unified API to integrate the inference with application logic:
 
-* Takes a model as an input. The model can be presented in [the native ONNX format](./ONNX_Support.md) or in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
+* Takes as input the model. The model presented in the specific form of [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md)
 produced by Model Optimizer.
 * Optimizes inference execution for target hardware.
 * Delivers inference solution with reduced footprint on embedded inference platforms.

docs/IE_DG/Migration_CoreAPI.md

@@ -1,4 +1,4 @@
-[DEPRECATED] Migration from Inference Engine Plugin API to Core API {#openvino_docs_IE_DG_Migration_CoreAPI}
+Migration from Inference Engine Plugin API to Core API {#openvino_docs_IE_DG_Migration_CoreAPI}
 ===============================
 
 For 2019 R2 Release, the new Inference Engine Core API is introduced. This guide is updated to reflect the new API approach. The Inference Engine Plugin API is still supported, but is going to be deprecated in future releases.
@@ -45,7 +45,7 @@ read networks using the Core class:
 ```cpp
 CNNNetwork network = core.ReadNetwork(input_model);
 ```
-The Core class also allows reading models from the ONNX format (more information is [here](./ONNX_Support.md)):
+The Core class also allows reading models from ONNX format:
 ```cpp
 CNNNetwork network = core.ReadNetwork("model.onnx");
 ```

docs/IE_DG/ONNX_Support.md

@@ -1,19 +0,0 @@
-# ONNX format support in the OpenVINO™ {#openvino_docs_IE_DG_ONNX_Support}
-
-Starting from the 2020.4 release, OpenVINO™ supports reading native ONNX models.
-`Core::ReadNetwork()` method provides a uniform way to read models from IR or ONNX format, it is a recommended approach to reading models.
-Example:
-
-```cpp
-InferenceEngine::Core core;
-auto network = core.ReadNetwork("model.onnx");
-```
-
-OpenVINO™ doesn't provide a mechanism to specify pre-processing (like mean values subtraction, reverse input channels) for the ONNX format.
-If an ONNX model contains dynamic shapes for input, please use the `CNNNetwork::reshape` method for shape specialization.
-
-Unsupported types of tensors:
-
-* `string`,
-* `complex64`,
-* `complex128`.

docs/IE_DG/OnnxImporterTutorial.md

@@ -2,7 +2,7 @@
 
 > **NOTE**: This tutorial is deprecated. Since OpenVINO™ 2020.4 version, Inference Engine enables reading ONNX models via the Inference Engine Core API
 > and there is no need to use directly the low-level ONNX* Importer API anymore. 
-> To read ONNX\* models, it's recommended to use the `Core::ReadNetwork()` method that provide a uniform way to read models from IR or ONNX format.
+> To read ONNX\* models, it's recommended to use the InferenceEngine::Core::ReadNetwork method that provide a uniform way to read models from IR or ONNX format.
 
 This tutorial demonstrates how to use the ONNX\* Importer API.
 This API makes it possible to create an nGraph `Function` object from an imported ONNX model.

docs/IE_DG/ShapeInference.md

@@ -1,61 +1,38 @@
 Using Shape Inference {#openvino_docs_IE_DG_ShapeInference}
 ==========================================
 
-Inference Engine takes three kinds of a model description as an input, which are converted into an `InferenceEngine::CNNNetwork` object:
-1. [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) through `InferenceEngine::Core::ReadNetwork`
-2. [ONNX model](../IE_DG/OnnxImporterTutorial.md) through `InferenceEngine::Core::ReadNetwork`
-3. [nGraph::Function](../IE_DG/nGraph_Flow.md) through the constructor of `InferenceEngine::CNNNetwork`
+Inference Engine takes two kinds of model description as an input: [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) and [nGraph::Function](nGraph_Flow.md) objects. 
+Both should have fixed input shapes to be successfully loaded to the Inference Engine.
+To feed input data of a shape that is different from the model input shape, resize the model first.
 
-`InferenceEngine::CNNNetwork` keeps an `ngraph::Function` object with the model description internally.
-The object should have fully defined input shapes to be successfully loaded to the Inference Engine plugins.
-To resolve undefined input dimensions of a model, call the `CNNNetwork::reshape` method providing new input shapes before loading to the Inference Engine plugin.
+Model resizing on the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](nGraphTutorial.md) is the recommended approach. 
+OpenVINO™ provides the following experimental methods for runtime model reshaping:
 
-Run the following code right after `InferenceEngine::CNNNetwork` creation to explicitly check for model input names and shapes:
-```cpp
-CNNNetwork network = ... // read IR / ONNX model or create from nGraph::Function explicitly
-const auto parameters = network.getFunction()->get_parameters();
-for (const auto & parameter : parameters) {
-    std::cout << "name: " << parameter->get_friendly_name() << " shape: " << parameter->get_partial_shape() << std::endl;
-    if (parameter->get_partial_shape().is_dynamic())
-        std::cout << "ATTENTION: Input shape is not fully defined. Use the CNNNetwork::reshape method to resolve it." << std::endl;
-}
-```
+1.  Setting a new input shape with the `InferenceEngine::CNNNetwork::reshape` method
+ 
+	`InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.
+    
+    Shape propagation for `InferenceEngine::CNNNetwork` objects created from `nGraph::Function` or IR of the version 10 works through the `nGraph` shape inference mechanism. 
+    `InferenceEngine::CNNNetwork` objects created from lower IR versions are considered deprecated and may be reshaped incorrectly or give unexpected results.
+ 
+	To keep the v10 IR resizable by the `InferenceEngine::CNNNetwork::reshape` method, convert the model with the additional Model Optimizer key `--keep_shape_ops`.
+ 
+2.  Setting a new batch dimension value with the `InferenceEngine::CNNNetwork::setBatchSize` method
+    
+    The meaning of a model batch may vary depending on choices you made during the model designing. 
+    The `InferenceEngine::CNNNetwork::setBatchSize` method deduces index of batch dimension relying only on the input rank. 
+    This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
 
-To feed input data of a shape that is different from the model input shape, reshape the model first.
+    Batch-setting algorithm does not involve shape inference mechanism.
+    Batch of input and output shapes for all layers is set to a new batch value without layer validation.
+    It may cause both positive and negative side effects.
+ 
+    Due to the limitations described above, the current method is recommended for simple image processing models only.
 
-OpenVINO™ provides the following methods for runtime model reshaping:
 
-* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
-   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
-   You can reshape a model multiple times like in this application scheme:
-   ```
-   ReadNetwork -> reshape(input_1_shape) -> LoadNetwork -> infer(input_1)
-              \
-               -> reshape(input_2_shape) -> LoadNetwork -> infer(input_2)
-   ```
-   > **NOTES**:
-   > - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping.
-   > - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
-   > - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
-* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
-   The meaning of a model batch may vary depending on the model design.
-   The `InferenceEngine::CNNNetwork::setBatchSize` method deduces the index of a batch dimension based only on the input rank. 
-   This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
-   The batch-setting algorithm does not involve the shape inference mechanism.
-   Batch of input and output shapes for all layers is set to a new batch value without layer validation.
-   It may cause both positive and negative side effects.
-   Due to the limitations described above, the current method is not recommended to use.
-   If you need to set a new batch size for the model, use the `CNNNetwork::reshape` method instead.
+Practically, some models are not ready to be resized. In this case, a new input shape cannot be set with the Model Optimizer or the `InferenceEngine::CNNNetwork::reshape` method.
 
-Do not use runtime reshaping methods simultaneously, especially do not call the `CNNNetwork::reshape` method after you use `InferenceEngine::CNNNetwork::setBatchSize`.
-The `InferenceEngine::CNNNetwork::setBatchSize` method causes irreversible conversion of the internal model representation into the legacy model representation.
-The method does not use nGraph for shape inference which leads to reduced reshape opportunities and may affect the performance of the model.
-
-There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../IE_DG/nGraphTutorial.md).
-
-Practically, some models are not ready to be reshaped. In this case, a new input shape cannot be set with the Model Optimizer or the `InferenceEngine::CNNNetwork::reshape` method.
-
-## Troubleshooting Reshape Errors
+## Troubleshooting Resize Errors
 
 Operation semantics may impose restrictions on input shapes of the operation. 
 Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. 
@@ -65,7 +42,7 @@ Examples of such operations:
 - <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#Reshape">`Reshape` operation</a> with a hard-coded output shape value
 - <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#MatMul">`MatMul` operation</a> with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
 
-Model structure and logic should not change significantly after model reshaping.
+Model structure and logic should not change significantly after resizing.
 - The Global Pooling operation is commonly used to reduce output feature map of classification models output.
 Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1].
 Model architects usually express Global Pooling with the help of the `Pooling` operation with the fixed kernel size [H, W].
@@ -73,12 +50,12 @@ During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling wi
 It breaks the classification model structure.
 For example, [publicly available Inception family models from TensorFlow*](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models) have this issue.
 
-- Changing the model input shape may significantly affect its accuracy.
+- Resizing the model input shape may significantly affect its accuracy.
 For example, Object Detection models from TensorFlow have resizing restrictions by design. 
 To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the `pipeline.config` file. 
 For details, refer to the <a href="_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html#tf_od_custom_input_shape">Tensorflow Object Detection API models resizing techniques</a>.
 
-## Usage of Reshape Method <a name="usage_of_reshape_method"></a>
+## Usage of Reshape Method
 
 The primary method of the feature is `InferenceEngine::CNNNetwork::reshape`.
 It gets new input shapes and propagates it from input to output for all intermediates layers of the given network.

docs/IE_DG/nGraph_Flow.md

@@ -35,7 +35,7 @@ The rest of the document describes the coexistence of legacy and new flows showe
 ## Read the Intermediate Representation to `CNNNetwork`
 
 As the new operation set is introduced, the Model Optimizer generates the IR version 10 using the new operations by default.
-Each layer generated in the IR has a semantics matching to the corresponding operation from the nGraph namespaces `opset1`, `opset2` etc.
+Each layer generated in the IR has a semantics matching to the corresponding operation from the nGraph namespace `opset3`.
 The IR version 10 automatically triggers the nGraph flow inside the Inference Engine.
 When such IR is read in an application, the Inference Engine IR reader produces `CNNNetwork` that encapsulates the `ngraph::Function` instance underneath.
 Thus the OpenVINO IR becomes a new serialization format for the nGraph IR, and it can be deserialized reading the `CNNNetwork`.
@@ -43,6 +43,7 @@ Thus the OpenVINO IR becomes a new serialization format for the nGraph IR, and i
 > **IMPORTANT**: Conventional interfaces are used (`CNNNetwork`, the reader), so no changes required in most applications.
 
 > **NOTE**: While you still can use old APIs, there is an independent process of continuous improvements in the Inference Engine API.
+> For example, the Core::Read API is recommended to use instead of `CNNNetworkReader`.
 > These changes are independent of nGraph integration and do not enable or disable new features.
 
 Interpretation of the IR version 10 differs from the old IR version.
@@ -51,7 +52,14 @@ Both shapes and types are reinferred while loading to the Inference Engine using
 
 ### Legacy IR Versions
 
-Starting from the OpenVINO™ release 2021.1 you cannot read IR version 7 and lower in the Inference Engine.
+You can read old versions of the IR in the Inference Engine.
+Each version below or equal to 7 is treated as an old one.
+When the Inference Engine reader reads an old version of the IR, it does not use the nGraph representation.
+There is no way to activate nGraph flow with an old IR version.
+The rest of this document is not applied in this case.
+
+Model Optimizer generates the IR version 10 by default, and there is the command line key `--generate_deprecated_IR_V7` which switches generation to the legacy IR version 7.
+It is useful when the new nGraph flow does not work for some reason.
 
 ## Build a Model in the Application
 

docs/IE_DG/supported_plugins/CPU.md

@@ -114,11 +114,9 @@ CPU-specific settings:
 | :---                        | :---                  | :---               | :--- |
 | KEY_CPU_THREADS_NUM         | positive integer values| 0                 | Specifies the number of threads that CPU plugin should use for inference. Zero (default) means using all (logical) cores|
 | KEY_CPU_BIND_THREAD         | YES/NUMA/NO           | YES                | Binds inference threads to CPU cores. 'YES' (default) binding option maps threads to cores - this works best for static/synthetic scenarios like benchmarks. The 'NUMA' binding is more relaxed, binding inference threads only to NUMA nodes, leaving further scheduling to specific cores to the OS. This option might perform better in the real-life/contended scenarios. Note that for the latency-oriented cases (single execution stream, see below) both YES and NUMA options limit number of inference threads to the number of hardware cores (ignoring hyper-threading) on the multi-socket machines. |
-| KEY_CPU_THROUGHPUT_STREAMS  | KEY_CPU_THROUGHPUT_NUMA, KEY_CPU_THROUGHPUT_AUTO, or positive integer values| 1 | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior with all available cores processing requests one by one.<br>KEY_CPU_THROUGHPUT_NUMA creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>KEY_CPU_THROUGHPUT_AUTO creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> Non-negative integer value creates the requested number of streams. If a number of streams is 0, no internal streams are created and user threads are interpreted as stream master threads.|
+| KEY_CPU_THROUGHPUT_STREAMS  | KEY_CPU_THROUGHPUT_NUMA, KEY_CPU_THROUGHPUT_AUTO, or positive integer values| 1 | Specifies number of CPU "execution" streams for the throughput mode. Upper bound for the number of inference requests that can be executed simultaneously. All available CPU cores are evenly distributed between the streams. The default value is 1, which implies latency-oriented behavior with all available cores processing requests one by one.<br>KEY_CPU_THROUGHPUT_NUMA creates as many streams as needed to accommodate NUMA and avoid associated penalties.<br>KEY_CPU_THROUGHPUT_AUTO creates bare minimum of streams to improve the performance; this is the most portable option if you don't know how many cores your target machine has (and what would be the optimal number of streams). Note that your application should provide enough parallel slack (for example, run many inference requests) to leverage the throughput mode. <br> A positive integer value creates the requested number of streams. |
 | KEY_ENFORCE_BF16            | YES/NO| YES | The name for setting to execute in bfloat16 precision whenever it is possible. This option lets plugin know to downscale the precision where it sees performance benefits from bfloat16 execution. Such option does not guarantee accuracy of the network, you need to verify the accuracy in this mode separately, based on performance and accuracy results. It should be your decision whether to use this option or not. |
 
-> **NOTE**: To disable all internal threading, use the following set of configuration parameters: `KEY_CPU_THROUGHPUT_STREAMS=0`, `KEY_CPU_THREADS_NUM=1`, `KEY_CPU_BIND_THREAD=NO`.
-
 ## See Also
 * [Supported Devices](Supported_Devices.md)
 

docs/IE_DG/supported_plugins/HETERO.md

@@ -9,13 +9,12 @@ Purposes to execute networks in heterogeneous mode
 * To utilize all available hardware more efficiently during one inference
 
 The execution through heterogeneous plugin can be divided to two independent steps:
-* Setting of affinity to layers
+* Setting of affinity to layers (binding them to devices in <code>InferenceEngine::ICNNNetwork</code>)
 * Loading a network to the Heterogeneous plugin, splitting the network to parts, and executing them through the plugin
 
 These steps are decoupled. The setting of affinity can be done automatically using fallback policy or in manual mode.
 
 The fallback automatic policy means greedy behavior and assigns all layers which can be executed on certain device on that device follow priorities.
-Automatic policy does not take into account such plugin peculiarities as inability to infer some layers without other special layers placed before of after that layers. It is plugin responsibility to solve such cases. If device plugin does not support subgraph topology constructed by Hetero plugin affinity should be set manually.
 
 Some of the topologies are not friendly to heterogeneous execution on some devices or cannot be executed in such mode at all.
 Example of such networks might be networks having activation layers which are not supported on primary device.
@@ -26,12 +25,7 @@ In this case you can define heaviest part manually and set affinity thus way to
 ## Annotation of Layers per Device and Default Fallback Policy
 Default fallback policy decides which layer goes to which device automatically according to the support in dedicated plugins (FPGA, GPU, CPU, MYRIAD).
 
-Another way to annotate a network is to set affinity manually using <code>ngraph::Node::get_rt_info</code> with key `"affinity"`:
-
-```cpp
-for (auto && op : function->get_ops())
-    op->get_rt_info()["affinity"] = std::shared_ptr<ngraph::VariantWrapper<std::string>>("CPU");
-```
+Another way to annotate a network is setting affinity manually using <code>CNNLayer::affinity</code> field. This field accepts string values of devices like "CPU" or "FPGA".
 
 The fallback policy does not work if even one layer has an initialized affinity. The sequence should be calling of automating affinity settings and then fix manually.
 ```cpp
@@ -49,10 +43,8 @@ InferenceEngine::QueryNetworkResult res = core.QueryNetwork(network, device, { }
 res.supportedLayersMap["layerName"] = "CPU";
 
 // set affinities to network
-for (auto&& node : function->get_ops()) {
-    auto& affinity = res.supportedLayersMap[node->get_friendly_name()];
-    // Store affinity mapping using node runtime information
-    node->get_rt_info()["affinity"] = std::make_shared<ngraph::VariantWrapper<std::string>>(affinity);
+for (auto && layer : res.supportedLayersMap) {
+	network.getLayerByName(layer->first)->affinity = layer->second;
 }
 
 // load network with affinities set before
@@ -66,8 +58,6 @@ auto network = core.ReadNetwork("Model.xml");
 auto executable_network = core.LoadNetwork(network, "HETERO:FPGA,CPU");
 ```
 
-> **NOTE**: `InferenceEngine::Core::QueryNetwork` does not depend on affinities set by a user, but queries for layer support based on device capabilities.
-
 
 ## Details of Splitting Network and Execution
 During loading of the network to heterogeneous plugin, network is divided to separate parts and loaded to dedicated plugins.
@@ -80,7 +70,9 @@ Precision for inference in heterogeneous plugin is defined by
 
 Examples:
 * If you want to execute GPU with CPU fallback with FP16 on GPU, you need to use only FP16 IR.
-* If you want to execute on FPGA with CPU fallback, you can use any precision for IR. The execution on FPGA is defined by bitstream, the execution on CPU happens in FP32.
+Weight are converted from FP16 to FP32 automatically for execution on CPU by heterogeneous plugin automatically.
+* If you want to execute on FPGA with CPU fallback, you can use any precision for IR. The execution on FPGA is defined by bitstream,
+the execution on CPU happens in FP32.
 
 Samples can be used with the following command:
 

docs/IE_PLUGIN_DG/Building.md

@@ -16,24 +16,22 @@ $ cmake -DCMAKE_BUILD_TYPE=Release ../dldt
 Once the commands above are executed, the Inference Engine Developer Package is generated in the `dldt-release-build` folder. It consists of several files:
  - `InferenceEngineDeveloperPackageConfig.cmake` - the main CMake script which imports targets and provides compilation flags and CMake options.
  - `InferenceEngineDeveloperPackageConfig-version.cmake` - a file with a package version.
- - `targets_developer.cmake` - an automatically generated file which contains all targets exported from the OpenVINO build tree. This file is included by `InferenceEngineDeveloperPackageConfig.cmake` to import the following targets:
+ - `targets_developer.cmake` - an automatically generated file which contains all targets exported from the Deep Learning Deployment Toolkit (DLDT) build tree. This file is included by `InferenceEngineDeveloperPackageConfig.cmake` to import the following targets:
    - Libraries for plugin development:
-       * `IE::ngraph` - shared nGraph library
-       * `IE::inference_engine` - shared Inference Engine library
-       * `IE::inference_engine_transformations` - shared library with Inference Engine ngraph-based Transformations
-       * `IE::inference_engine_preproc` - shared library with Inference Engine preprocessing plugin
-       * `IE::inference_engine_plugin_api` - interface library with Inference Engine Plugin API headers
-       * `IE::inference_engine_lp_transformations` - shared library with low-precision transformations
-       * `IE::pugixml` - static Pugixml library
-       * `IE::xbyak` - interface library with Xbyak headers
-       * `IE::itt` - static library with tools for performance measurement using Intel ITT
+	   * `IE::ngraph` - shared nGraph library
+	   * `IE::inference_engine` - shared Inference Engine library
+	   * `IE::inference_engine_preproc` - shared library with Inference Engine preprocessing plugin
+	   * `IE::inference_engine_plugin_api` - interface library with Inference Engine Plugin API headers
+	   * `IE::inference_engine_lp_transformations` - shared library with low-precision transformations
+	   * `IE::pugixml` - static Pugixml library
+	   * `IE::xbyak` - interface library with Xbyak headers
    - Libraries for tests development:
-       * `IE::gtest`, `IE::gtest_main`, `IE::gmock` - Google Tests framework libraries
-       * `IE::commonTestUtils` - static library with common tests utilities 
-       * `IE::funcTestUtils` - static library with functional tests utilities 
-       * `IE::unitTestUtils` - static library with unit tests utilities 
-       * `IE::ngraphFunctions` - static library with the set of `ngraph::Function` builders
-       * `IE::funcSharedTests` - static library with common functional tests
+	   * `IE::gtest`, `IE::gtest_main`, `IE::gmock` - Google Tests framework libraries
+	   * `IE::commonTestUtils` - static library with common tests utilities 
+	   * `IE::funcTestUtils` - static library with functional tests utilities 
+	   * `IE::unitTestUtils` - static library with unit tests utilities 
+	   * `IE::ngraphFunctions` - static library with the set of Ngraph Functions builders
+	   * `IE::funcSharedTests` - static library with common functional tests
 
 > **Note:** it's enough just to run `cmake --build . --target ie_dev_targets` command to build only targets from the
 > Inference Engine Developer package.
@@ -70,20 +68,23 @@ find_package(InferenceEngineDeveloperPackage REQUIRED)
 add_subdirectory(src)
 
 if(ENABLE_TESTS)
-    include(CTest)
-    enable_testing()
+	include(CTest)
+	enable_testing()
 
-    if(ENABLE_FUNCTIONAL_TESTS)
-        add_subdirectory(tests/functional)
-    endif()
+	if(ENABLE_FUNCTIONAL_TESTS)
+	    add_subdirectory(tests/functional)
+	endif()
+
+	if(ENABLE_BEH_TESTS)
+	    add_subdirectory(tests/behavior)
+	endif()
 endif()
 ```
 
-> **NOTE**: The default values of the `ENABLE_TESTS`, `ENABLE_FUNCTIONAL_TESTS` options are shared via the Inference Engine Developer Package and they are the same as for the main DLDT build tree. You can override them during plugin build using the command below:
-
-    ```bash
-    $ cmake -DENABLE_FUNCTIONAL_TESTS=OFF -DInferenceEngineDeveloperPackage_DIR=../dldt-release-build ../template-plugin
-    ``` 
+> **NOTE**: The default values of the `ENABLE_TESTS`, `ENABLE_FUNCTIONAL_TESTS`, `ENABLE_BEH_TESTS` options are shared via the Inference Engine Developer Package and they are the same as for the main DLDT build tree. You can override them during plugin build using the command below:  
+	```bash
+	$ cmake -DENABLE_FUNCTIONAL_TESTS=OFF -DInferenceEngineDeveloperPackage_DIR=../dldt-release-build ../template-plugin
+	``` 
 
 - `src/CMakeLists.txt` to build a plugin shared library from sources:
 

docs/IE_PLUGIN_DG/Doxyfile

@@ -781,9 +781,7 @@ WARN_LOGFILE           =
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = . \
-						 ../../inference-engine/src/transformations/include/transformations \
-						 ../../inference-engine/src/plugin_api \
-						 ../../openvino/itt/include/openvino
+						 ../../inference-engine/src/plugin_api
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -848,9 +846,8 @@ EXCLUDE_PATTERNS       = cnn_network_ngraph_impl.hpp \
                          ie_imemory_state_internal.hpp \
                          ie_memory_state_internal.hpp \
                          ie_memory_state_base.hpp \
-                         generic_ie.hpp \
-                         function_name.hpp \
-                         macro_overload.hpp
+                         convert_function_to_cnn_network.hpp \
+                         generic_ie.hpp
 
 # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
 # (namespaces, classes, functions, etc.) that should be excluded from the
@@ -871,9 +868,6 @@ EXAMPLE_PATH           = ../template_plugin/src \
 						 ../template_plugin/include \
                          ../template_plugin/src/CMakeLists.txt \
                          ../template_plugin/tests/functional/CMakeLists.txt \
-                         ../template_plugin/tests/functional/transformations \
-                         ../template_plugin/tests/functional/shared_tests_instances/ \
-                         ../../inference-engine/tests/functional/plugin/shared/include \
                          ../examples
 
 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
@@ -882,8 +876,7 @@ EXAMPLE_PATH           = ../template_plugin/src \
 # files are included.
 
 EXAMPLE_PATTERNS       = *.cpp \
-                         *.hpp \
-                         *.txt
+						 *.hpp
 
 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
 # searched for input files to be used with the \include or \dontinclude commands
@@ -896,7 +889,7 @@ EXAMPLE_RECURSIVE      = YES
 # that contain images that are to be included in the documentation (see the
 # \image command).
 
-IMAGE_PATH             = ./images
+IMAGE_PATH             =
 
 # The INPUT_FILTER tag can be used to specify a program that doxygen should
 # invoke to filter for each input file. Doxygen will invoke the filter program
@@ -2050,8 +2043,7 @@ INCLUDE_FILE_PATTERNS  =
 # recursively expanded use the := operator instead of the = operator.
 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
 
-PREDEFINED             = INFERENCE_PLUGIN_API \
-                         INFERENCE_ENGINE_API \
+PREDEFINED             = INFERENCE_ENGINE_API \
                          INFERENCE_ENGINE_API_CPP \
                          INFERENCE_ENGINE_API_CLASS \
                          INFERENCE_ENGINE_DEPRECATED \
@@ -2059,8 +2051,7 @@ PREDEFINED             = INFERENCE_PLUGIN_API \
                          IE_SUPPRESS_DEPRECATED_END \
                          IE_SUPPRESS_DEPRECATED_START_WIN \
                          IE_SUPPRESS_DEPRECATED_END_WIN \
-                         IE_THREAD=IE_THREAD_TBB \
-                         TRANSFORMATIONS_API
+                         IE_THREAD=IE_THREAD_TBB
 
 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
 # tag can be used to specify a list of macro names that should be expanded. The

docs/IE_PLUGIN_DG/ExecutableNetwork.md

@@ -1,7 +1,7 @@
 # Executable Network {#executable_network}
 
 `ExecutableNetwork` class functionality:
-- Compile an InferenceEngine::ICNNNetwork instance to a backend specific graph representation
+- Compile an InferenceEngine::ICNNNetwork instance to a hardware-specific graph representation
 - Create an arbitrary number of `InferRequest` objects
 - Hold some common resources shared between different instances of `InferRequest`. For example:
 	- InferenceEngine::ExecutableNetworkInternal::_taskExecutor task executor to implement asynchronous execution
@@ -19,37 +19,36 @@ Inference Engine Plugin API provides the helper InferenceEngine::ExecutableNetwo
 The example class has several fields:
 
 - `_requestId` - Tracks a number of created inference requests, which is used to distinguish different inference requests during profiling via the Intel® Instrumentation and Tracing Technology (ITT) library.
+- `_name` - Provides a network name.
 - `_cfg` - Defines a configuration an executable network was compiled with.
 - `_plugin` - Refers to a plugin instance.
-- `_function` - Keeps a reference to transformed `ngraph::Function` which is used in ngraph reference backend computations. Note, in case of other backends with backend specific graph representation `_function` has different type and represents backend specific graph or just a set of computational kernels to perform an inference.
-- `_inputIndex` - maps a name of input with its index among all network inputs.
-- `_outputIndex` - maps a name of output with its index among all network outputs.
 
 ### `ExecutableNetwork` Constructor with `ICNNNetwork`
 
-This constructor accepts a generic representation of a neural network as an InferenceEngine::ICNNNetwork reference and is compiled into a backend specific device graph:
+This constructor accepts a generic representation of a neural network as an InferenceEngine::ICNNNetwork reference and is compiled into a hardware-specific device graph:
 
 @snippet src/template_executable_network.cpp executable_network:ctor_cnnnetwork
 
-The implementation `CompileNetwork` is fully device-specific.
+The implementation `CompileGraph` is fully device-specific.
 
-### `CompileNetwork()`
+### `CompileGraph()`
 
-The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:
+The function accepts a const shared pointer to `const ngraph::Function` object and performs the following steps:
 
-1. Applies ngraph passes using `TransformNetwork` function, which defines plugin-specific conversion pipeline. 
-2. Maps the transformed graph to a backend specific graph representation (for example, to MKLDNN graph for Intel CPU).
-3. Allocates and fills memory for graph weights, backend specific memory handles and so on.
+1. Deep copies a const object to a local object, which can later be modified.
+2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations.
+3. Maps the transformed graph to a plugin-specific graph representation (for example, to MKLDNN graph for CPU). See [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html) for details about supported operations.
+4. Allocates and fills memory for graph weights.
 
-@snippet src/template_executable_network.cpp executable_network:map_graph
+@snippet src/template_executable_network.cpp executable_network:compile_graph
 
-> **NOTE**: After all these steps, the backend specific graph is ready to create inference requests and perform inference.
+> **NOTE**: After all these steps, the hardware-specific graph is ready to create inference requests and perform inference.
 
 ### `ExecutableNetwork` Constructor Importing from Stream
 
-This constructor creates a backend specific graph by importing from a stream object:
+This constructor creates a hardware-specific graph by importing from a stream object:
 
-> **NOTE**: The export of backend specific graph is done in the `ExportImpl` method, and data formats must be the same for both import and export.
+> **NOTE**: The export of hardware-specific graph is done in the `ExportImpl` method, and data formats must be the same for both import and export.
 
 @snippet src/template_executable_network.cpp executable_network:ctor_import_stream
 
@@ -58,9 +57,9 @@ This constructor creates a backend specific graph by importing from a stream obj
 **Implementation details:**   
 Base InferenceEngine::ExecutableNetworkThreadSafeDefault class implements the public InferenceEngine::ExecutableNetworkThreadSafeDefault::Export method as following:
 - Writes `_plugin->GetName()` to the `model` stream.
-- Calls the `ExportImpl` method defined in a derived class to dump a backend specific graph.
+- Calls the `ExportImpl` method defined in a derived class to dump a hardware-specific graph.
 
-The implementation of the method should write all data to the `model` stream, which is required to import a backend specific graph later in the `Plugin::Import` method:
+The implementation of the method should write all data to the `model` stream, which is required to import a hardware-specific graph later in the `Plugin::Import` method:
 
 @snippet src/template_executable_network.cpp executable_network:export_impl
 
@@ -72,6 +71,7 @@ The method creates an asynchronous inference request and returns it. While the p
 - [Asynchronous inference request](@ref async_infer_request), which is a wrapper for a synchronous inference request and can run a pipeline asynchronously. Depending on a device pipeline structure, it can has one or several stages:
    - For single-stage pipelines, there is no need to define this method and create a class derived from InferenceEngine::AsyncInferRequestThreadSafeDefault. For single stage pipelines, a default implementation of this method creates InferenceEngine::AsyncInferRequestThreadSafeDefault wrapping a synchronous inference request and runs it asynchronously in the `_taskExecutor` executor.
    - For pipelines with multiple stages, such as performing some preprocessing on host, uploading input data to a device, running inference on a device, or downloading and postprocessing output data, schedule stages on several task executors to achieve better device use and performance. You can do it by creating a sufficient number of inference requests running in parallel. In this case, device stages of different inference requests are overlapped with preprocessing and postprocessing stage giving better performance.
+
    > **IMPORTANT**: It is up to you to decide how many task executors you need to optimally execute a device pipeline.
 
 @snippet src/template_executable_network.cpp executable_network:create_infer_request

docs/IE_PLUGIN_DG/InferRequest.md

@@ -1,7 +1,7 @@
 # Synchronous Inference Request {#infer_request}
 
 `InferRequest` class functionality:
-- Allocate input and output blobs needed for a backend-dependent network inference.
+- Allocate input and output blobs needed for a hardware-dependent network inference.
 - Define functions for inference process stages (for example, `preprocess`, `upload`, `infer`, `download`, `postprocess`). These functions can later be used to define an execution pipeline during [Asynchronous Inference Request](@ref async_infer_request) implementation.
 - Call inference stages one by one synchronously.
 
@@ -20,15 +20,9 @@ The example class has several fields:
 
 - `_executableNetwork` - reference to an executable network instance. From this reference, an inference request instance can take a task executor, use counter for a number of created inference requests, and so on.
 - `_profilingTask` - array of the `std::array<InferenceEngine::ProfilingTask, numOfStages>` type. Defines names for pipeline stages. Used to profile an inference pipeline execution with the Intel® instrumentation and tracing technology (ITT).
-- `_durations` - array of durations of each pipeline stage.
-- `_networkInputBlobs` - input blob map.
-- `_networkOutputBlobs` - output blob map.
-- `_parameters` - `ngraph::Function` parameter operations.
-- `_results` - `ngraph::Function` result operations.
-- backend specific fields:
-	- `_inputTensors` - inputs tensors which wrap `_networkInputBlobs` blobs. They are used as inputs to backend `_executable` computational graph.
-	- `_outputTensors` - output tensors which wrap `_networkOutputBlobs` blobs. They are used as outputs from backend `_executable` computational graph.
-	- `_executable` - an executable object / backend computational graph.
+- `_inputsNCHW` - input blob map
+- `_outputsNCHW` - output blob map
+- Several double values to hold an execution time for pipeline stages.
 
 ### `InferRequest` Constructor
 
@@ -36,6 +30,11 @@ The constructor initializes helper fields and calls methods which allocate blobs
 
 @snippet src/template_infer_request.cpp infer_request:ctor
 
+The implementation of function allocating device buffers is fully device-specific and not provided in the guide. 
+The implementation of function allocating host buffers assumes that the `Template` device works 
+natively only with the InferenceEngine::NCHW input and output layout, while the user can specify the InferenceEngine::NHWC as a layout 
+of InferenceEngine::CNNNetwork inputs and outputs and set InferenceEngine::NHWC blobs via the InferenceEngine::InferRequest::SetBlob method.
+
 > **NOTE**: Call InferenceEngine::CNNNetwork::getInputsInfo and InferenceEngine::CNNNetwork::getOutputsInfo to specify both layout and precision of blobs, which you can set with InferenceEngine::InferRequest::SetBlob and get with InferenceEngine::InferRequest::GetBlob. A plugin uses these hints to determine its internal layouts and precisions for input and output blobs if needed. 
 
 ### `~InferRequest` Destructor
@@ -52,27 +51,14 @@ Decrements a number of created inference requests:
 
 @snippet src/template_infer_request.cpp infer_request:infer_impl
 
-#### 1. `inferPreprocess`
-
 Below is the code of the the `inferPreprocess` method to demonstrate Inference Engine common preprocessing step handling:
 
 @snippet src/template_infer_request.cpp infer_request:infer_preprocess
 
 **Details:**
 * `InferImpl` must call the InferenceEngine::InferRequestInternal::execDataPreprocessing function, which executes common Inference Engine preprocessing step (for example, applies resize or color conversion operations) if it is set by the user. The output dimensions, layout and precision matches the input information set via InferenceEngine::CNNNetwork::getInputsInfo.
-* If `inputBlob` passed by user differs in terms of precisions from precision expected by plugin, `blobCopy` is performed which does actual precision conversion.
-
-#### 2. `startPipeline`
-
-Executes a pipeline synchronously using `_executable` object:
-
-@snippet src/template_infer_request.cpp infer_request:start_pipeline
-
-#### 3. `inferPostprocess`
-
-Converts output blobs if precisions of backend output blobs and blobs passed by user are different:
-
-@snippet src/template_infer_request.cpp infer_request:infer_postprocess
+* To handle both InferenceEngine::NCHW and InferenceEngine::NHWC input layouts, the `TemplateInferRequest` class has the `_inputsNCHW` field, which holds blobs in the InferenceEngine::NCHW layout. During Inference Request execution, `InferImpl` copies from the input InferenceEngine::NHWC layout to `_inputsNCHW` if needed.
+* The next logic of `InferImpl` works with `_inputsNCHW`.
 
 ### `GetPerformanceCounts()`
 

docs/IE_PLUGIN_DG/Intro.md

@@ -12,36 +12,33 @@ Inference Engine plugin dynamic library consists of several main components:
 1. [Plugin class](@ref plugin):
 	- Provides information about devices of a specific type.
 	- Can create an [executable network](@ref executable_network) instance which represents a Neural 
-	Network backend specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork 
-	interface which is backend-independent.
+	Network hardware-specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork 
+	interface which is hardware-independent.
 	- Can import an already compiled graph structure from an input stream to an 
 	[executable network](@ref executable_network) object.
 2. [Executable Network class](@ref executable_network):
 	- Is an execution configuration compiled for a particular device and takes into account its capabilities.
 	- Holds a reference to a particular device and a task executor for this device.
 	- Can create several instances of [Inference Request](@ref infer_request).
-	- Can export an internal backend specific graph structure to an output stream.
-3. [Inference Request class](@ref infer_request):
+	- Can export an internal hardware-specific graph structure to an output stream.
+3. [Inference Request class](@ref infer_request) :
     - Runs an inference pipeline serially.
     - Can extract performance counters for an inference pipeline execution profiling.
-4. [Asynchronous Inference Request class](@ref async_infer_request):
+4. [Asynchronous Inference Request class](@ref async_infer_request) :
     - Wraps the [Inference Request](@ref infer_request) class and runs pipeline stages in parallel 
 	on several task executors based on a device-specific pipeline structure.
 
 > **NOTE**: This documentation is written based on the `Template` plugin, which demonstrates plugin 
 development details. Find the complete code of the `Template`, which is fully compilable and up-to-date,
-at `<dldt source dir>/docs/template_plugin`.
+at `<dldt source dir>/docs_developer/template_plugin`.
 
-Detailed guides
+Additional Resources
 -----------------------
 
 * [Build](@ref plugin_build) a plugin library using CMake\*
 * Plugin and its components [testing](@ref plugin_testing)
-* [Quantized networks](@ref quantized_networks)
-* [Writing ngraph transformations](@ref new_ngraph_transformation) guide
 
 API References
 -----------------------
 
 * [Inference Engine Plugin API](group__ie__dev__api.html)
-* [Inference Engine Transformation API](group__ie__transformation__api.html)

docs/IE_PLUGIN_DG/LowPrecisionModelRepresentation.md

@@ -1,18 +0,0 @@
-# Representation of low-precision models
-The goal of this document is to describe how optimized models are represented in OpenVINO Intermediate Representation (IR) and provide guidance on interpretation rules for such models at runtime. 
-Currently, there are two groups of optimization methods that can influence on the IR after applying them to the full-precision model:
-- **Sparsity**. It is represented by zeros inside the weights and this is up to the hardware plugin how to interpret these zeros (use weights as is or apply special compression algorithms and sparse arithmetic). No additional mask is provided with the model.
-- **Quantization**. The rest of this document is dedicated to the representation of quantized models.
-
-## Representation of quantized models
-The OpenVINO Toolkit represents all the quantized models using the so-called [FakeQuantize](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Legacy_IR_Layers_Catalog_Spec.html#FakeQuantize) operation. This operation is very expressive and allows mapping values from arbitrary input and output ranges. The whole idea behind that is quite simple: we project (discretize) the input values to the low-precision data type using affine transformation (with clamp and rounding) and then reproject discrete values back to the original range and data type. It can be considered as an emulation of the quantization process which happens at runtime.
-In order to be able to execute a particular DL operation in low-precision all its inputs should be quantized i.e. should have FakeQuantize between operation and data blobs.  The figure below shows an example of quantized Convolution which contains two FakeQuantize nodes: one for weights and one for activations (bias is quantized using the same parameters).
-![quantized_convolution]
-<div align="center">Figure 1. Example of quantized Convolution operation.</div>
-
-Starting from OpenVINO 2020.2 release all the quantized models are represented in the compressed form. It means that the weights of low-precision operations are converted into the target precision (e.g. INT8). It helps to substantially reduce the model size. The rest of the parameters can be represented in FLOAT32 or FLOAT16 precision depending on the input full-precision model used in the quantization process. Fig. 2 below shows an example of the part of the compressed IR.
-![quantized_model_example]
-<div align="center">Figure 2. Example of compressed quantized model.</div>  
-
-[quantized_convolution]: ../images/quantized_convolution.png
-[quantized_model_example]: ../images/quantized_model_example.png

docs/IE_PLUGIN_DG/NewTransformation.md

@@ -1,455 +0,0 @@
-# Writing ngraph transformations {#new_ngraph_transformation}
-
-This guide contains all necessary information that could help you to start writing nGraph transformations.
-
-First of all before writing transformation make sure that there is no transformation with the same functionality
-in [Transformation Library](group__ie__transformation__api.html). To start writing transformation it's good to know
-how [Transformation Library](group__ie__transformation__api.html) is structured, how transformations are organized
-and where to put your transformation code.
-
-Let's start from reviewing transformations library structure.
-Transformations library is independent from InferenceEngine target library named as `inference_engine_transformations`
-and located in `inference-engine/src/transformations` directory.
-
-Transformations root directory contains two folders:
-1. ngraph_ops - legacy opset operations needed for nGraph to CNNNetwork conversion.
-> **Note**: this operation are prohibited to use inside new plugins until they are not moved to separate directory with allowed operations.
-2. transformations - includes all transformations, utils, runtime info attributes and pass managers.
-> **Note**: do not use transformation that belongs to `ngraph::pass::ConvertOpSet1ToLegacy` transformations until they are not moved to separate directory with allowed transformations.
-
-Transformation flow in transformation library has several layers:
-1. Pass managers - executes any type of transformations and provides additional debug capabilities.
-2. Transformations - performs particular transformation algorithm on `ngraph::Function`.
-3. Low level functions that takes set of nodes and performs some transformation action. 
-They are not mandatory and all transformation code can be located inside transformation.
-But if some transformation parts can potentially be reused in other transformations we suggest to keep them as a separate functions.
-
-To decide where to store your transformation code please follow these rules:
-1. If it's plugin specific transformation and can't be reused by other plugins keep source code inside plugin.
-2. If this transformation relates to OpSetXToOpSetY conversion or it's common optimization then keep sources inside transformation library.
-
-After you decided where to store your transformation code you can start develop your own nGraph transformation.
-
-## Table of Contents:
-
-### 1. [`ngraph::Function` and graph representation](#ngraph_function) 
-### 2. [Transformations types](#transformations_types)
-### 2.1 [Function pass](#function_pass)
-### 2.2 [Matcher pass](#matcher_pass)
-### 2.3 [GraphRewrite pass](#graph_rewrite_pass) 
-### 3. [Pattern matching](#pattern_matching)
-### 4. [Working with ngraph::Function](#working_with_ngraph_function)
-### 5. [Transformation writing essentials](#transformation_writing_essentials)
-### 6. [Common mistakes in transformations](#common_mistakes)
-### 7. [Using pass manager](#using_pass_manager)
-### 8. [How to debug transformations](#how_to_debug_transformations)
-### 9. [Disabling/Enabling specific transformations for plugin X](#disabling_transformation)
-### 10. [Transformations testing](#transformations_testing)
-
-## ngraph::Function and graph representation <a name="ngraph_function"></a>
-
-nGraph function is a very simple thing: it stores shared pointers to `ngraph::op::Result` and `ngraph::op::Parameter` operations that are inputs and outputs of the graph. 
-All other operations hold each other via shared pointers: child operation holds its parent (hard link). If operation has no consumers and it's not Result operation
-(shared pointer counter is zero) then it will be destructed and won't be accessible anymore. Each operation in `ngraph::Function` has a `std::shared_ptr<ngraph::Node>` type.
-
-Below you can find examples how `ngraph::Function` can be created:
-
-@snippet example_ngraph_utils.cpp ngraph_utils:simple_function
-
-@snippet example_ngraph_utils.cpp ngraph_utils:advanced_function
-
-## Transformations types <a name="transformations_types"></a>
-
-nGraph has tree main transformation types: `ngraph::pass::FunctionPass` - strait forward way to work with `ngraph::Function` directly;
-`ngraph::pass::MatcherPass` - pattern based transformation approach; `ngraph::pass::GraphRewrite` - container for matcher passes.
-
-![transformations_structure]
-
-###1. ngraph::pass::FunctionPass <a name="function_pass"></a>
-
-`ngraph::pass::FunctionPass` is used for transformations that take entire `ngraph::Function` as input and process it.
-
-Template for FunctionPass transformation class
-
-@snippet src/template_function_transformation.hpp function_pass:template_transformation_hpp
-
-@snippet src/template_function_transformation.cpp function_pass:template_transformation_cpp
-
-Using `ngraph::FunctionPass` you need to override `run_on_function` method where you will write transformation code.
-Return value must be `true` if original function has changed during transformation (new operation were added or operations replacement was made or node attributes were changed) otherwise it must be `false`.
-For transformation API please follow [working with ngraph::Function](#working_with_ngraph_function) section.
-Also `ngraph::FunctionPass` based transformations can be executed via `pass::Manager`. See examples in [Using pass manager](#using_pass_manager) section.
-
-###2. ngraph::pass::MatcherPass <a name="matcher_pass"></a>
-
-`ngraph::pass::MatcherPass` is used for pattern based transformations.
-
-Template for MatcherPass transformation class
-@snippet src/template_pattern_transformation.hpp graph_rewrite:template_transformation_hpp
-
-@snippet src/template_pattern_transformation.cpp graph_rewrite:template_transformation_cpp
-
-Using `ngraph::pass::MatcherPass` you need to complete these steps:
-1. Create pattern
-2. Implement callback 
-3. Register pattern and Matcher
-4. MatcherPass execution
-
-So let's go though each of this steps.
-
-### Create pattern
-Pattern is a single root `ngraph::Function`. But the only difference is that you don't need to create function object, you just create and connect nGraph or special pattern operations.
-And then take the last created operation and put it as a root of the pattern. This root node will be used as a root node in pattern matching.
-> **Note**: any nodes in pattern that have no consumers and not registered as root won't be used in pattern matching. 
-
-@snippet example_ngraph_utils.cpp pattern:simple_example
-
-You may have noticed that `Parameter` operation in example has type and shape specified. These attributes are needed only to create Parameter operation class and won't be used in pattern matching. 
-
-But what if we want to match pattern where `ShapeOf` takes any operation as input? To find an answer please follow [pattern matching](#pattern_matching) section.
-
-### Implement callback
-Callback is an action applied to every pattern entrance. In general callback is lambda function that takes Matcher object with detected sub-graph.
-
-@snippet example_ngraph_utils.cpp pattern:callback_example
-
-Example above shows callback structure and how Matcher can be used for accessing nodes detected by pattern.
-Callback return value must be `true` if root node was replaced and another pattern can't be applied to the same root node otherwise it must be `false`.
-> **Note**: it's not recommended to manipulate with nodes that are under root node. This may affect GraphRewrite execution as it's expected that all nodes that comes after root node in topological order are valid and can be used in pattern matching. 
-
-MatcherPass also provides functionality that allows to report which newly created nodes can be used in additional pattern matching.
-If MatcherPass was registered in `pass::Manager` or `pass::GraphRewrite` then this registered nodes will be added for additional pattern matching.
-That means that matcher passes registered in `pass::GraphRewrite` will be applied to this nodes.
-
-Example below shows how single MatcherPass can fuse sequence of operations using `register_new_node` method.
-
-@snippet src/template_pattern_transformation.cpp matcher_pass:relu_fusion
-
-> **Note**: if you register multiple nodes please add them in topological order. We do not topologically sort this nodes as it's time consuming operation.
-
-### Register pattern and Matcher
-The last step is to register Matcher and callback inside MatcherPass pass. And to do this you need to call `register_matcher` method.
-> **Note**: Only one matcher can be registered for single MatcherPass class.
-
-```cpp
-// Register matcher and callback
-register_matcher(m, callback);
-```
-### Matcher pass execution
-MatcherPass has multiple ways to be executed:
-1. Run on a single node - it can be useful if you want to run MatcherPass inside another transformation.
-@snippet src/template_pattern_transformation.cpp matcher_pass:run_on_node
-2. Run on `ngraph::Function` using GraphRewrite - this approach gives ability to run MatcherPass on whole `ngraph::Functoin`. Moreover multiple MatcherPass transformation can be registered in a single GraphRewite to be executed in a single graph traversal.
-@snippet src/template_pattern_transformation.cpp matcher_pass:graph_rewrite
-3. Run on `ngraph::Function` using `pass::Manager` - this approach helps you to register MatcherPass for execution on `ngraph::Function` as another transformation types.
-@snippet src/template_pattern_transformation.cpp matcher_pass:manager
-
-
-###3. ngraph::pass::GraphRewrite <a name="graph_rewrite_pass"></a>
-
-GraphRewrite pass serves for running multiple matcher passes on `ngraph::Function` in a single graph traversal. 
-Example:
-
-@snippet src/template_pattern_transformation.cpp matcher_pass:graph_rewrite
-
-In addition GraphRewrite handles nodes that were registered by MatcherPasses during their execution. This nodes will be added to the beginning of sequence with nodes for pattern matching.
-
-> **Note**: when using `pass::Manager` temporary GraphRewrite is used to execute single MatcherPass. 
-
-GraphRewrite has two algorithms for MatcherPasses execution. First algorithm is a straight-forward. It applies each MatcherPass in registraion order to current node.
-
-![graph_rewrite_execution]
-
-But it is nor really efficient when you have a lot of registered passes. So first of all GraphRewrite check that all MatcherPass patterns has type based root node (it means that type of this node is not hidden into predicate).
-And then creates map from registered MatcherPases. That helps to avoid additional cost of applying each MatcherPass for each node.
-
-![graph_rewrite_efficient_search] 
-
-## Pattern matching <a name="pattern_matching"></a>
-
-Sometimes patterns can't be expressed via regular nGraph operations or it is too complicated. 
-For example if you want to detect Convolution->Add sub-graph without specifying particular input type for Convolution operation or you want to create pattern where some of operations can have different types.
-And for these cases nGraph provides additional helpers to construct patterns for GraphRewrite transformations. 
-
-There are two main helpers:
-1. `ngraph::pattern::any_input` - helps to express inputs if their types are undefined.
-2. `ngraph::pattern::wrap_type<T>` - helps to express nodes of pattern without specifying node attributes.
-
-Let's go through example to have better understanding how it works:
-
-> **Note**: node attributes do not participate in pattern matching and needed only for operations creation. Only operation types participate in pattern matching.
-
-Example below shows basic usage of `pattern::any_input`.
-Here we construct Multiply pattern with arbitrary first input and Constant as a second input. 
-Also as Multiply is commutative operation it does not matter in which order we set inputs (any_input/Constant or Constant/any_input) because both cases will be matched.
-
-@snippet example_ngraph_utils.cpp pattern:label_example
-
-This example show how we can construct pattern when operation has arbitrary number of inputs.
-
-@snippet example_ngraph_utils.cpp pattern:concat_example
-
-This example shows how to use predicate to construct pattern. Also it shows how to match pattern manually on given node.
-
-@snippet example_ngraph_utils.cpp pattern:predicate_example
-
-> **Note**: be careful with manual matching because Matcher object holds matched nodes. To clear match use m->clear_state() method.
-
-## Working with ngraph::Function <a name="working_with_ngraph_function"></a>
-
-In this chapter we will review nGraph API that allows us to manipulate with `ngraph::Function`.
-
-###1. ngraph::Node input and output ports
-
-First of all let's talk about `ngraph::Node` input/output ports. Each nGraph operation has input and output ports except cases when operation has `Result`, `Parameter` or `Constant` type.
-
-Every port belongs to its node so using port we can access parent node, get shape and type for particular input/output, get all consumers in case of output port and get producer node in case of input port.
-With output port we can set inputs for newly created operations. 
-
-Lets look at code example.
-
-@snippet example_ngraph_utils.cpp ngraph:ports_example
-
-You may notice that we usually construct operations in this way:
-```cpp
-std::shared_ptr<Node> neg_const = opset1::Constant::create(sub->get_input_element_type(1), Shape{1}, {-1}));
-Output<Node> data = node->input_value(0);
-auto neg = std::make_shared<ngraph::opset1::Multiply>(data, neg_const);
-```
-In this example `opset3::Multiply` operation takes `Output<Node>` and `std::shared_ptr<Node>` as inputs. But constructor takes both as `Output<Node>`. 
-In this case `std::shared_ptr<Node>` will be automatically converted to `Output<Node>` if node has exactly one output port otherwise conversion will raise an exception.   
-
-###2. ngraph::Node replacement
-
-nGraph provides two ways for node replacement: via nGraph helper function and directly via port methods. We are going to review both of them.
-
-Let's start with nGraph helper functions. The most popular function is `ngraph::replace_node(old_node, new_node)`.
-
-We will review real replacement case where Negative operation replaces with Multiply.
-
-![ngraph_replace_node]
-
-@snippet example_ngraph_utils.cpp ngraph:replace_node
-
-`ngraph::replace_node` has a constraint that number of output ports for both of ops must be the same otherwise it will raise an exception.
-
-
-The alternative way to do the same replacement is next:
-```cpp
-// All neg->output(0) consumers will be moved to mul->output(0) port
-neg->output(0).replace(mul->output(0));
-```
-
-Another transformation example is insertion.
-
-![ngraph_insert_node]
-
-@snippet example_ngraph_utils.cpp ngraph:insert_node
-
-The alternative way to insert operation is to make a node copy and use `replace_node`:
-
-@snippet example_ngraph_utils.cpp ngraph:insert_node_with_copy
-
-###3. ngraph::Node elimination
-
-Another type of node replacement is its elimination.
-
-To eliminate operation nGraph has special method that consider all limitations related to InferenceEngine.
-
-@snippet example_ngraph_utils.cpp ngraph:eliminate_node
-
-`replace_output_update_name` in case of successful replacement it automatically preserves friendly name and runtime info.
-  
-
-## Transformation writing essentials <a name="transformation_writing_essentials"></a>
-
-When developing transformation we need to follow next transformation rules:
-
-###1. Operation Set (OpSet)
-
-Which OpSet to use in your transformation? The right answer is latest that exists at the moment. An exception is ConvertOpSetXToOpSetY transformations where operations from OpSetX and OpSetY are required to use.
-
-@snippet example_ngraph_utils.cpp ngraph:include
-
-###2. Dynamic Shape and Rank
-
-nGraph has two types for shape representation: 
-`ngraph::Shape` - represents static shape.
-`ngraph::PartialShape` - represents dynamic shape. That means that rank or some of dimensions are dynamic (undefined).
-`ngraph::PartialShape` can be converted to `ngraph::Shape` using `get_shape()` method if all dimensions are static otherwise conversion will raise an exception.
-
-@snippet example_ngraph_utils.cpp ngraph:shape
-
-But in most cases before getting static shape using `get_shape()` method you need to check that shape is static.  
-
-Also if your transformation requires only input shape rank or particular dimension value for some reason please do not use `get_shape()` method. See example below how not to use `get_shape()`
-
-@snippet example_ngraph_utils.cpp ngraph:shape_check
-
-Not using `get_shape()` method makes your transformation more flexible and applicable for more cases.
-
-###3. Friendly Names
-
-Each `ngraph::Node` has unique name (is used for nGraph internals) and friendly name. In transformations we care only about friendly name because it represents name from IR. 
-Also friendly name is used as output tensor name (until we do not have other way to represent output tensor name) and user code that requests intermediate outputs based on this names.
-So not to loose friendly name when replacing node with other node or sub-graph we need to set original friendly name to the latest node in replacing sub-garph. See example below. 
-
-```cpp
-// Replace Div operation with Power and Multiply sub-graph and set original friendly name to Multiply operation
-auto pow = std::make_shared<ngraph::opset1::Power>(div->input(1).get_source_output(),
-                                                           op::Constant::create(div->get_input_element_type(1), Shape{1}, {-1}));
-auto mul = std::make_shared<ngraph::opset1::Multiply>(div->input(0).get_source_output(), pow);
-mul->set_friendly_name(div->get_friendly_name());
-ngraph::replace_node(div, mul);
-```
-
-In more advanced cases when replaced operation has several outputs and we add additional consumers to its outputs we make decision how to set friendly name by arrangement.
-
-###4. Runtime Info
-
-Runtime info is a map `std::map<std::string, std::shared_ptr<Variant>>` located inside `ngraph::Node` class. It represents additional attributes in `ngraph::Node`.
-These attributes can be set by users or by plugins and when executing transformation that changes `ngraph::Function` we need to preserve this attributes as they won't be automatically propagated.
-In most cases transformations has next types: 1:1 (replace node with another node), 1:N (replace node with a sub-graph), N:1 (fuse sub-graph into a single node), N:M (any other transformation).
-Currently there is no mechanism that automatically detects transformation types so we need to propagate this runtime information manually. See examples below.
-
-```cpp
-// Replace Transpose with Reshape operation (1:1)
-ngraph::copy_runtime_info(transpose, reshape);
-```
-
-```cpp
-// Replace Div operation with Power and Multiply sub-graph (1:N)
-ngraph::copy_runtime_info(div, {pow, mul});
-```
-
-```cpp
-// Fuse Convolution with Add operation (N:1)
-ngraph::copy_runtime_info({conv, bias}, {conv_ie});
-```
-
-```cpp
-// Any other transformation that replaces one sub-graph with another sub-graph (N:M)
-ngraph::copy_runtime_info({a, b, c}, {e, f});
-```
-
-When transformation has multiple fusions or decompositions `ngraph::copy_runtime_info` must be called multiple times for each case. 
-
-> **Note**: copy_runtime_info removes rt_info from destination nodes. If you want to keep it you need to specify them in source nodes like this: copy_runtime_info({a, b, c}, {a, b})
-
-###5. Constant Folding
-
-If your transformation inserts constant sub-graphs that needs to be folded do not forget to use `ngraph::pass::ConstantFolding()` after your transformation or call constant folding directly for operation.
-Example below shows how constant sub-graph can be constructed.
-
-```cpp
-// After ConstantFolding pass Power will be replaced with Constant 
-auto pow = std::make_shared<ngraph::opset3::Power>(
-                    opset3::Constant::create(element::f32, Shape{1}, {2})
-                    opset3::Constant::create(element::f32, Shape{1}, {3}));
-auto mul = std::make_shared<ngraph::opset3::Multiply>(input /* not constant input */, pow);
-``` 
-
-Manual constant folding is more preferable than `ngraph::pass::ConstantFolding()` because it is much faster.
-
-Below you can find an example of manual constant folding:
-
-@snippet src/template_pattern_transformation.cpp manual_constant_folding
-
-## Common mistakes in transformations <a name="common_mistakes"></a>
-
-In transformation development process 
-
-* Do not use deprecated nGraph API. Deprecated methods has `NGRAPH_DEPRECATED` macros in its definition. 
-* Do not pass `shared_ptr<Node>` as input for other node if type of node is unknown or it has multiple outputs. Use explicit output port.
-* If you replace node with another node that produce different shape you need to remember that new shape won't be propagated until first `validate_nodes_and_infer_types` call for `ngraph::Function`. If you are using `pass::Manager` it will automatically call this method after each transformation execution.
-* Do not forget to call `ngraph::ConstantFolding` pass if your transformation creates constant sub-graphs.
-* Use latest OpSet if you are not developing downgrade transformation pass.
-* When developing callback for `ngraph::pass::MatcherPass` do not change nodes that comes after root node in topological order. 
-
-## Using pass manager <a name="using_pass_manager"></a>
-
-`ngraph::pass::Manager` is a container class that can store list of transformations and execute them. The main idea of this class is to have high-level representation for grouped list of transformations.
-It can register and apply any [transformation types](#transformations_types) on function.
-In addition `ngraph::pass::Manager` has extended debug capabilities (find more information in [how to debug transformations](#how_to_debug_transformations) section). 
-
-Example below shows basic usage of `ngraph::pass::Manager`
-
-@snippet src/template_pattern_transformation.cpp matcher_pass:manager3
-
-Another example how multiple matcher passes can be united into single GraphRewrite.
-
-@snippet src/template_pattern_transformation.cpp matcher_pass:manager2
-
-## How to debug transformations <a name="how_to_debug_transformations"></a>
-
-The most popular tool for transformations debugging is `ngraph::pass::VisualizeTree` transformation that visualize ngraph::Function.
-
-Usage example:
-
-@snippet example_ngraph_utils.cpp ngraph:visualize
-
-`ngraph::pass::VisualizeTree` can be parametrized via environment variables:
-
-```
-NGRAPH_VISUALIZE_TREE_OUTPUT_SHAPES=1 - visualize shapes
-NGRAPH_VISUALIZE_TREE_OUTPUT_TYPES=1  - visualize types
-```
-
-> **Note**: current VisualTree has not user friendly interface and it will be changed in nearest future. The intention is to move visualize abilities inside transformations.
-
-If you are using `ngraph::pass::Manager` to run sequence of transformations you can get additional debug capabilities by using next environment variables:
-
-```
-NGRAPH_PROFILE_PASS_ENABLE=1 - enables performance measurement for each transformation and prints execution status
-NGRAPH_ENABLE_VISUALIZE_TRACING=1 -  enables visualization after each transformation. By default it saves dot and svg files.
-```
-
-> **Note**: make sure that you have dot installed on your machine otherwise it will silently save only dot file without svg file.
-
-## Disabling/Enabling specific transformations for plugin X	 <a name="disabling_transformation"></a>
-
-This topic mostly related to conversion to legacy opset and plugins that based on CNNNetwork but still this mechanism can be applied for other cases.
-Let's suppose that plugin X enabled `opset3::StridedSlice` operation support and you want to disable `ngraph::pass::ConvertStridedSliceToCrop` transformation for plugin X.
-To do this you need to create callback on plugin side and pass it to transformation. And also you need to update particular transformation to use this callback.  
-
-```cpp
-// Update callback to be able to use m_transformation_callback if this transformation based on GraphRewrite.
-ngraph::graph_rewrite_callback callback = [this](pattern::Matcher &m) {
-    ...
-}
-
-// Use transformation_callback not to execute transformation if callback returns true for given node
-if (m_transformation_callback(node)) {
-    return false;
-}
-
-// Implement transformation callback and pass it directly to transformation or pass::Manager
-const auto transformations_callback = [](const std::shared_ptr<const ::ngraph::Node> &node) -> bool {
-    return std::dynamic_pointer_cast<const ::ngraph::opset3::StridedSlice>(node) != nullptr;
-};
-
-// Register transformation and pass callback to pass::Manager
-ngraph::pass::Manager manager;
-manager.register_pass<ngraph::pass::ConvertStridedSliceToCrop>();
-// pass::Manager will set callback to all reistered transformations automatically
-manager.set_callback(transformations_callback);
-manager.run_passes(f);
-```
-
-## Transformations testing <a name="transformations_testing"></a>
-
-If you are developing new transformation inside plugin you need to add test into `template_plugin/tests/functional/transformations` folder.
-We have two types of tests: nGraph reader tests located in `inference-engine/tests/functional/inference_engine/ngraph_reader` and transformation tests located  in `inference-engine/tests/functional/inference_engine/transformations`
-Reader tests are IR based and test end to end conversion from IR to CNNNetwork. Transformation tests test single ngraph transformations or low level functiont that are used inside transformations.
-
-The basic transformation test looks like this:
-
-@snippet tests/functional/transformations/template_transformations_test.cpp transformation:test
-
-
-[ngraph_replace_node]: ../images/ngraph_replace_node.png
-[ngraph_insert_node]: ../images/ngraph_insert_node.png
-[transformations_structure]: ../images/transformations_structure.png
-[register_new_node]: ../images/register_new_node.png
-[graph_rewrite_execution]: ../images/graph_rewrite_execution.png
-[graph_rewrite_efficient_search]: ../images/graph_rewrite_efficient_search.png

docs/IE_PLUGIN_DG/Plugin.md

@@ -1,15 +1,5 @@
 # Plugin {#plugin}
 
-Inference Engine Plugin usually represents a wrapper around a backend. Backends can be:
-- OpenCL-like backend (e.g. clDNN library) for GPU devices.
-- MKLDNN backend for Intel CPU devices.
-- NVIDIA cuDNN for NVIDIA GPUs.
-
-The responsibility of Inference Engine Plugin:
-- Initializes a backend and throw exception in `Engine` constructor if backend cannot be initialized.
-- Provides information about devices enabled by a particular backend, e.g. how many devices, their properties and so on.
-- Loads or imports [executable network](@ref executable_network) objects.
-
 In addition to the Inference Engine Public API, the Inference Engine provides the Plugin API, which is a set of functions and helper classes that simplify new plugin development:
 
 - header files in the `inference_engine/src/plugin_api` directory
@@ -28,10 +18,8 @@ Based on that, declaration of a plugin class can look as follows:
 
 #### Class Fields
 
-The provided plugin class also has several fields:
+The provided plugin class also has a single field:
 
-* `_backend` - a backend engine that is used to perform actual computations for network inference. For `Template` plugin `ngraph::runtime::Backend` is used which performs computations using ngraph reference implementations.
-* `_waitExecutor` - a task executor that waits for a response from a device about device tasks completion.
 * `_cfg` of type `Configuration`:
 
 @snippet src/template_config.hpp configuration:header
@@ -40,7 +28,6 @@ As an example, a plugin configuration has three value parameters:
 
 - `deviceId` - particular device ID to work with. Applicable if a plugin supports more than one `Template` device. In this case, some plugin methods, like `SetConfig`, `QueryNetwork`, and `LoadNetwork`, must support the CONFIG_KEY(KEY_DEVICE_ID) parameter. 
 - `perfCounts` - boolean value to identify whether to collect performance counters during [Inference Request](@ref infer_request) execution.
-- `_streamsExecutorConfig` - configuration of `InferenceEngine::IStreamsExecutor` to handle settings of multi-threaded context.
 
 ### Engine Constructor
 
@@ -60,45 +47,25 @@ A plugin must define a device name enabled via the `_pluginName` field of a base
 of the public InferenceEngine::InferencePluginInternal::LoadNetwork method that calls plugin-specific `LoadExeNetworkImpl`, which is defined in a derived class.
 
 This is the most important function of the `Plugin` class and creates an instance of compiled `ExecutableNetwork`,
-which holds a backend-dependent compiled graph in an internal representation:
+which holds a hardware-dependent compiled graph in an internal representation:
 
 @snippet src/template_plugin.cpp plugin:load_exe_network_impl
 
 Before a creation of an `ExecutableNetwork` instance via a constructor, a plugin may check if a provided 
 InferenceEngine::ICNNNetwork object is supported by a device. In the example above, the plugin checks precision information.
 
-The very important part before creation of `ExecutableNetwork` instance is to call `TransformNetwork` method which applies ngraph transformation passes.
-
 Actual graph compilation is done in the `ExecutableNetwork` constructor. Refer to the [ExecutableNetwork Implementation Guide](@ref executable_network) for details.
 
 > **NOTE**: Actual configuration map used in `ExecutableNetwork` is constructed as a base plugin 
 > configuration set via `Plugin::SetConfig`, where some values are overwritten with `config` passed to `Plugin::LoadExeNetworkImpl`. 
 > Therefore, the config of  `Plugin::LoadExeNetworkImpl` has a higher priority.
 
-### `TransformNetwork()`
-
-The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:
-
-1. Deep copies a const object to a local object, which can later be modified.
-2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations. For details how to write custom plugin-specific transformation, please, refer to [Writing ngraph transformations](@ref new_ngraph_transformation) guide. See detailed topics about network representation:
-    * [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html)
-    * [Quantized networks](@ref quantized_networks).
-
-@snippet src/template_plugin.cpp plugin:transform_network
-
-> **NOTE**: After all these transformations, a `ngraph::Function` object cointains operations which can be perfectly mapped to backend kernels. E.g. if backend has kernel computing `A + B` operations at once, the `TransformNetwork` function should contain a pass which fuses operations `A` and `B` into a single custom operation `A + B` which fits backend kernels set. 
-
 ### `QueryNetwork()`
 
 Use the method with the `HETERO` mode, which allows to distribute network execution between different 
 devices based on the `ngraph::Node::get_rt_info()` map, which can contain the `"affinity"` key.
 The `QueryNetwork` method analyzes operations of provided `network` and returns a list of supported
-operations via the InferenceEngine::QueryNetworkResult structure. The `QueryNetwork` firstly applies `TransformNetwork` passes to input `ngraph::Function` argument. After this, the transformed network in ideal case contains only operations are 1:1 mapped to kernels in computational backend. In this case, it's very easy to analyze which operations is supposed (`_backend` has a kernel for such operation or extensions for the operation is provided) and not supported (kernel is missed in `_backend`):
-
-1. Store original names of all operations in input `ngraph::Function`
-2. Apply `TransformNetwork` passes. Note, the names of operations in a transformed network can be different and we need to restore the mapping in the steps below.
-3. Construct `supported` and `unsupported` maps which contains names of original operations. Note, that since the inference is performed using ngraph reference backend, the decision whether the operation is supported or not depends on whether the latest OpenVINO opset contains such operation.
-4. `QueryNetworkResult.supportedLayersMap` contains only operations which are fully supported by `_backend`.
+operations via the InferenceEngine::QueryNetworkResult structure:
 
 @snippet src/template_plugin.cpp plugin:query_network
 
@@ -116,7 +83,7 @@ Sets new values for plugin configuration keys:
 @snippet src/template_plugin.cpp plugin:set_config
 
 In the snippet above, the `Configuration` class overrides previous configuration values with the new 
-ones. All these values are used during backend specific graph compilation and execution of inference requests.
+ones. All these values are used during hardware-specific graph compilation and execution of inference requests.
 
 > **NOTE**: The function must throw an exception if it receives an unsupported configuration key.
 
@@ -144,7 +111,7 @@ all devices of the same `Template` type with automatic logic of the `MULTI` devi
 in the `option` parameter as `{ CONFIG_KEY(KEY_DEVICE_ID), "deviceID" }`.
 - METRIC_KEY(SUPPORTED_METRICS) - list of metrics supported by a plugin
 - METRIC_KEY(SUPPORTED_CONFIG_KEYS) - list of configuration keys supported by a plugin that
-affects their behavior during a backend specific graph compilation or an inference requests execution
+affects their behavior during a hardware-specific graph compilation or an inference requests execution
 - METRIC_KEY(OPTIMIZATION_CAPABILITIES) - list of optimization capabilities of a device.
 For example, supported data types and special optimizations for them.
 - Any other device-specific metrics. In this case, place metrics declaration and possible values to 
@@ -161,9 +128,9 @@ The snippet below provides an example of the implementation for `GetMetric`:
 
 ### `ImportNetworkImpl()`
 
-The importing network mechanism allows to import a previously exported backend specific graph and wrap it 
+The importing network mechanism allows to import a previously exported hardware-specific graph and wrap it 
 using an [ExecutableNetwork](@ref executable_network) object. This functionality is useful if 
-backend specific graph compilation takes significant time and/or cannot be done on a target host 
+hardware-specific graph compilation takes significant time and/or cannot be done on a target host 
 device due to other reasons.
 
 **Implementation details:** The base plugin class InferenceEngine::InferencePluginInternal implements InferenceEngine::InferencePluginInternal::ImportNetwork 
@@ -174,7 +141,7 @@ implementation and define an output blob structure up to its needs. This
 can be useful if a plugin exports a blob in a special format for integration with other frameworks 
 where a common Inference Engine header from a base class implementation is not appropriate. 
 
-During export of backend specific graph using `ExecutableNetwork::Export`, a plugin may export any 
+During export of hardware-specific graph using `ExecutableNetwork::Export`, a plugin may export any 
 type of information it needs to import a compiled graph properly and check its correctness. 
 For example, the export information may include:
 
@@ -183,7 +150,7 @@ For example, the export information may include:
 throw an exception if the `model` stream contains wrong data. For example, if devices have different 
 capabilities and a graph compiled for a particular device cannot be used for another, such type of 
 information must be stored and checked during the import. 
-- Compiled backend specific graph itself
+- Compiled hardware-specific graph itself
 - Information about precisions and shapes set by the user
 
 @snippet src/template_plugin.cpp plugin:import_network_impl
@@ -191,7 +158,7 @@ information must be stored and checked during the import.
 Create Instance of Plugin Class
 ------------------------
 
-Inference Engine plugin library must export only one function creating a plugin instance using IE_DEFINE_PLUGIN_CREATE_FUNCTION macro:
+Inference Engine plugin library must export only one function creating a plugin instance:
 
 @snippet src/template_plugin.cpp plugin:create_plugin_engine
 

docs/IE_PLUGIN_DG/PluginTesting.md

@@ -1,58 +1,40 @@
 # Plugin Testing {#plugin_testing}
 
-Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities. They are used to verify a plugin using the Inference Engine public API.
+Inference Engine (IE) tests infrastructure provides a predefined set of functional tests and utilities exported via the Inference
+Engine developer package. They are used to verify a plugin using the Inference Engine public API.
 All the tests are written in the [Google Test C++ framework](https://github.com/google/googletest).
 
-Inference Engine Plugin tests are included in the `IE::funcSharedTests` CMake target which is built within the OpenVINO repository
-(see [Build Plugin Using CMake](@ref plugin_build) guide). This library contains tests definitions (the tests bodies) which can be parametrized and instantiated in plugins depending on whether a plugin supports a particular feature, specific sets of parameters for test on supported operation set and so on.
-
-Test definitions are splitted into tests class declaration (see `inference_engine/tests/functional/plugin/shared/include`) and tests class implementation (see `inference_engine/tests/functional/plugin/shared/src`) and include the following scopes of plugin conformance tests:
-
-1. **Behavior tests** (`behavior` sub-folder), which are a separate test group to check that a plugin satisfies basic Inference
-Engine concepts: plugin creation, multiple executable networks support, multiple synchronous and asynchronous inference requests support, and so on. See the next section with details how to instantiate the tests definition class with plugin-specific parameters.
-
-2. **Single layer tests** (`single_layer_tests` sub-folder). This groups of tests checks that a particular single layer can be inferenced on a device. An example of test instantiation based on test definition from `IE::funcSharedTests` library:
-
-    - From the declaration of convolution test class we can see that it's a parametrized GoogleTest based class with the `convLayerTestParamsSet` tuple of parameters:
-
-    @snippet single_layer_tests/convolution.hpp test_convolution:definition
-
-    - Based on that, define a set of parameters for `Template` plugin functional test instantiation:
-
-    @snippet single_layer_tests/convolution.cpp test_convolution:declare_parameters
-
-    - Instantiate the test itself using standard GoogleTest macro `INSTANTIATE_TEST_CASE_P`:
-
-    @snippet single_layer_tests/convolution.cpp test_convolution:instantiate
-
-3. **Sub-graph tests** (`subgraph_tests` sub-folder). This group of tests is designed to tests small patterns or combination of layers. E.g. when a particular topology is being enabled in a plugin e.g. TF ResNet-50, there is no need to add the whole topology to test tests. In opposite way, a particular repetative subgraph or pattern can be extracted from `ResNet-50` and added to the tests. The instantiation of the sub-graph tests is done in the same way as for single layer tests.  
-> **Note**, such sub-graphs or patterns for sub-graph tests should be added to `IE::ngraphFunctions` library first (this library is a pre-defined set of small `ngraph::Function`) and re-used in sub-graph tests after.
-
-4. **HETERO tests** (`subgraph_tests` sub-folder) contains tests for `HETERO` scenario (manual or automatic affinities settings, tests for `QueryNetwork`).
-
-5. **Other tests**, which contain tests for other scenarios and has the following types of tests:
-    - Tests for execution graph
-    - Etc.
-
-To use these tests for your own plugin development, link the `IE::funcSharedTests` library to your test binary and instantiate required test cases with desired parameters values.
-
-> **NOTE**: A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively tested.
-
 To build test binaries together with other build artifacts, use the `make all` command. For details, see
 [Build Plugin Using CMake*](@ref plugin_build).
 
-### Tests for plugin-specific ngraph transformations
+Inference Engine Plugin tests are included in the `funcSharedTests` CMake target which is built within the  Deep Learning Deployment Toolkit (DLDT) repository
+(see [Build Plugin Using CMake](@ref plugin_build) guide).
 
-Please, refer to [Transformation testing](@ref new_ngraph_transformation) guide.
+Test definitions:
 
-### How to Extend Inference Engine Plugin Tests
+1. **Conformance tests**, which are a separate test group to check that a plugin satisfies basic Inference
+Engine concepts: plugin creation, multiple executable networks support, multiple synchronous and asynchronous inference requests support, and so on.
+2. **Other API tests**, which contain the following types of tests:
+    - Per-layer tests. Located in the `single_layer_tests`and `subgraph_tests` folders.
+    - Tests for integration with the `InferenceEngine::Core` class. Located in the the `ie_class` folder.
+    - Tests to check that IE common preprocessing works with your plugin. The `io_blob_tests` folder.
+
+To use these tests for your own plugin development, link the `funcSharedTests` library to your test binary and
+instantiate required test cases with desired parameters values.
+
+> **NOTE**: A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively
+> tested. Depending on your device positioning, you can implement more specific tests for your device. Such tests can
+> be defined both for conformance and other API tests groups within your own test binary.
+
+How to Extend Inference Engine Plugin Tests
+========================
 
 Inference Engine Plugin tests are open for contribution.
-Add common test case definitions applicable for all plugins to the `IE::funcSharedTests` target within the DLDT repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.
+Add common test case definitions applicable for all plugins to the `funcSharedTests` target within the DLDT repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.
 
 All Inference Engine per-layer tests check test layers functionality. They are developed using nGraph functions
 as input graphs used by tests. In this case, to test a new layer with layer tests, extend
-the `IE::ngraphFunctions` library, which is also included in the Inference Engine Developer package, with a new nGraph function
+the `ngraphFunctions` CMake target, which is also included in the Inference Engine Developer package, with a new nGraph function
 including the corresponding operation.
 
-> **NOTE**: When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph if such test does not exist.
+> **NOTE**: When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph.

docs/IE_PLUGIN_DG/QuantizedNetworks.md

@@ -1,53 +0,0 @@
-# Quantized networks compute and restrictions {#quantized_networks}
-
-One of the feature of Inference Engine is the support of quantized networks with different precisions: INT8, INT4, etc.
-However, it is up to the plugin to define what exact precisions are supported by the particular HW.
-All quantized networks which can be expressed in IR have a unified representation by means of *FakeQuantize* operation. 
-For more details about low-precision model representation please refer to this [document](LowPrecisionModelRepresentation.md).
-
-### Interpreting FakeQuantize at runtime
-During the model load each plugin can interpret quantization rules expressed in *FakeQuantize* operations:
-- Independently based on the definition of *FakeQuantize* operation.
-- Using a special library of low-precision transformations (LPT) which applies common rules for generic operations,
-such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Int8Inference.html). 
-
-Here we provide only a high-level overview of the interpretation rules of FakeQuantize. 
-At runtime each FakeQuantize can be split into two independent operations: **Quantize** and **Dequantize**. 
-The former one is aimed to transform the input data into the target precision while the latter transforms the resulting values back to the original range and precision. 
-In practice *Dequantize* operations can be propagated forward through the linear operations, such as *Convolution* or *Fully-Connected*, 
-and in some cases fused with the following *Quantize* operation for the next layer into the so-called *Requantize* operation (see Fig. 1).
-
-![qdq_propagation]
-<div align="center">Figure 1. Quantization operations propagation at runtime. Q, DQ, RQ stand for Quantize, Dequantize, and Requantize correspondingly.</div>
-
-From the calculation standpoint, the FakeQuantize formula also is split into two parts accordingly:  
-`output = round((x - input_low) / (input_high - input_low) * (levels-1)) / (levels-1) * (output_high - output_low) + output_low`  
-The first part of this formula represents *Quantize* operation:  
-`q = round((x - input_low) / (input_high - input_low) * (levels-1))`  
-The second is responsible for the dequantization:  
-`r = q / (levels-1) * (output_high - output_low) + output_low`  
-From the scale/zero-point notation standpoint the latter formula can be written as follows:  
-`r = (output_high - output_low) / (levels-1) * (q + output_low / (output_high - output_low) * (levels-1))`  
-Thus we can define:
-- **Scale** as `(output_high - output_low) / (levels-1)`
-- **Zero-point** as `-output_low / (output_high - output_low) * (levels-1)`
-
-**Note**: During the quantization process the values `input_low`, `input_high`, `output_low`, `output_high` are selected so that to map a floating-point zero exactly to an integer value (zero-point) and vice versa.
-
-## Quantization specifics and restrictions
-In general, OpenVINO can represent and execute quantized models from different sources. However, the Post-training Optimization Toolkit (POT)
-is considered the default way to get optimized models. Since the POT supports HW-aware quantization it means that specific rules can be implemented in it for 
-the particular HW. However, it is reasonable to have compatibility with general-purpose HW such as CPU and GPU and support their quantization schemes.
-Below we define these rules as follows:
-- Support of mixed-precision models where some layers can be kept in the floating-point precision.
-- Per-channel quantization of weights of Convolutional and Fully-Connected layers.
-- Per-channel quantization of activations for channel-wise and element-wise operations, e.g. Depthwise Convolution, Eltwise Add/Mul, ScaleShift.
-- Symmetric and asymmetric quantization of weights and activations with the support of per-channel scales and zero-points.
-- Non-unified quantization parameters for Eltwise and Concat operations.  
-- Non-quantized network output, i.e. there are no quantization parameters for it.
-
-## Quantized model inference
-
-!!! Need details from the runtime team.
-
-[qdq_propagation]: ../images/qdq_propagation.png

docs/IE_PLUGIN_DG/images/graph_rewrite_efficient_search.png

@@ -1,3 +0,0 @@
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             </tab>
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         <!-- Additional resources -->
-         <tab type="usergroup" visibile="no" title="DETAILED GUIDES">
+         <tab type="usergroup" visibile="no" title="ADDITIONAL RESOURCES">
             <tab type="user" url="@ref plugin_build" visibile="yes" title="Build Your Plugin with CMake*"/>
             <tab type="user" url="@ref plugin_testing" visibile="yes" title="Test Your Plugin"/>
-            <tab type="user" url="@ref quantized_networks" visibile="yes" title="Quantized networks guide"/>
-            <tab type="user" url="@ref new_ngraph_transformation" visibile="yes" title="Writing ngraph transformations"/>
         </tab>
         <!-- API References -->
         <tab type="usergroup" title="API REFERENCE">

docs/MO_DG/prepare_model/Config_Model_Optimizer.md

@@ -48,22 +48,14 @@ install_prerequisites_caffe.sh
 ```
 install_prerequisites_caffe.bat
 ```
-*   For TensorFlow\* 1.x on Linux:
+*   For TensorFlow\* on Linux:
 ```
 install_prerequisites_tf.sh
 ```
-*   For TensorFlow 1.x on Windows:
+*   For TensorFlow on Windows:
 ```
 install_prerequisites_tf.bat
 ```
-*   For TensorFlow\* 2.x on Linux:
-```
-install_prerequisites_tf2.sh
-```
-*   For TensorFlow 2.x on Windows:
-```
-install_prerequisites_tf2.bat
-```
 *   For MXNet\* on Linux:
 ```
 install_prerequisites_mxnet.sh
@@ -122,7 +114,7 @@ virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
 virtualenv -p /usr/bin/python3.6 .env3/bin/activate
 ```
 3.  Install all dependencies or only the dependencies for a specific framework:
-    *   To install dependencies for all frameworks except TensorFlow* 2.x:
+    *   To install dependencies for all frameworks:
 ```shell
 pip3 install -r requirements.txt
 ```
@@ -130,13 +122,9 @@ pip3 install -r requirements.txt
 ```shell
 pip3 install -r requirements_caffe.txt
 ```
-    *   To install dependencies only for TensorFlow 1.x:
+    *   To install dependencies only for TensorFlow:
 ```shell
 pip3 install -r requirements_tf.txt
-```
-    *   To install dependencies only for TensorFlow 2.x:
-```shell
-pip3 install -r requirements_tf2.txt
 ```
     *   To install dependencies only for MXNet:
 ```shell

docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md

@@ -2,7 +2,7 @@
 
 If your question is not covered by the topics below, use the [OpenVINO™ Support page](https://software.intel.com/en-us/openvino-toolkit/documentation/get-started), where you can participate on a free forum.
 
-#### 1. What does the message "[ ERROR ]: Current caffe.proto does not contain field" mean? <a name="question-1"></a>
+#### 1. What does the message "[ ERROR ]: Current caffe.proto does not contain field" mean? <a name="FAQ1"></a>
 
 Internally, the Model Optimizer uses a protobuf library to parse and load Caffe\* models. This library requires a file grammar and a generated parser. For a Caffe fallback, the Model Optimizer uses a Caffe-generated parser for a Caffe-specific `.proto` file (which is usually located in the `src/caffe/proto` directory). So, if you have Caffe installed on your machine with Python* interface available, make sure that this is exactly the version of Caffe that was used to create the model.
 
@@ -37,7 +37,7 @@ where `PATH_TO_CUSTOM_CAFFE` is the path to the root directory of custom Caffe\*
 
 However, because your model has custom layers, you must register your custom layers as custom. To learn more about it, refer to the section [Custom Layers in Model Optimizer](customize_model_optimizer/Customize_Model_Optimizer.md). 
 
-#### 2. How do I create a bare caffemodel, if I have only prototxt? <a name="question-2"></a>
+#### 2. How do I create a bare caffemodel, if I have only prototxt? <a name="FAQ2"></a>
 
 You need the Caffe\* Python\* interface. In this case, do the following:
 ```shell
@@ -46,25 +46,25 @@ import caffe
 net = caffe.Net('<PATH_TO_PROTOTXT>/my_net.prototxt', caffe.TEST)
 net.save('<PATH_TO_PROTOTXT>/my_net.caffemodel')
 ```
-#### 3. What does the message "[ ERROR ]: Unable to create ports for node with id" mean? <a name="question-3"></a>
+#### 3. What does the message "[ ERROR ]: Unable to create ports for node with id" mean? <a name="FAQ3"></a>
 
 Most likely, the Model Optimizer does not know how to infer output shapes of some layers in the given topology. 
 To lessen the scope, compile the list of layers that are custom for the Model Optimizer: present in the topology, 
 absent in [list of supported layers](Supported_Frameworks_Layers.md) for the target framework. Then refer to available options in the corresponding section in [Custom Layers in Model Optimizer](customize_model_optimizer/Customize_Model_Optimizer.md).
 
-#### 4. What does the message "Input image of shape is larger than mean image from file" mean? <a name="question-4"></a>
+#### 4. What does the message "Input image of shape is larger than mean image from file" mean? <a name="FAQ4"></a>
 
 Your model input shapes must be smaller than or equal to the shapes of the mean image file you provide. The idea behind the mean file is to subtract its values from the input image in an element-wise manner. When the mean file is smaller than the input image, there are not enough values to perform element-wise subtraction. Also, make sure that you use the mean file that was used during the network training phase. Note that the mean file is dataset dependent.
 
-#### 5. What does the message "Mean file is empty" mean? <a name="question-5"></a>
+#### 5. What does the message "Mean file is empty" mean? <a name="FAQ5"></a>
 
 Most likely, the mean file that you have is specified with `--mean_file` flag, while launching the Model Optimizer is empty. Make sure that this is exactly the required mean file and try to regenerate it from the given dataset if possible.
 
-#### 6. What does the message "Probably mean file has incorrect format" mean? <a name="question-6"></a>
+#### 6. What does the message "Probably mean file has incorrect format" mean? <a name="FAQ6"></a>
 
 The mean file that you provide for the Model Optimizer must be in a `.binaryproto` format. You can try to check the content using recommendations from the BVLC Caffe\* ([#290](https://github.com/BVLC/caffe/issues/290)).
 
-#### 7. What does the message "Invalid proto file: there is neither 'layer' nor 'layers' top-level messages" mean? <a name="question-7"></a>
+#### 7. What does the message "Invalid proto file: there is neither 'layer' nor 'layers' top-level messages" mean? <a name="FAQ7"></a>
 
 The structure of any Caffe\* topology is described in the `caffe.proto` file of any Caffe version. For example, in the Model Optimizer, you can find the following proto file, used by default: `<INSTALL_DIR>/deployment_tools/model_optimizer/mo/front/caffe/proto/my_caffe.proto`. There you can find the structure:
 ```
@@ -79,7 +79,7 @@ message NetParameter {
 ```
 This means that any topology should contain layers as top-level structures in `prototxt`. For example, see the [LeNet topology](https://github.com/BVLC/caffe/blob/master/examples/mnist/lenet.prototxt).
 
-#### 8. What does the message "Old-style inputs (via 'input_dims') are not supported. Please specify inputs via 'input_shape'" mean? <a name="question-8"></a>
+#### 8. What does the message "Old-style inputs (via 'input_dims') are not supported. Please specify inputs via 'input_shape'" mean? <a name="FAQ8"></a>
 
 The structure of any Caffe\* topology is described in the `caffe.proto` file for any Caffe version. For example, in the Model Optimizer you can find the following `.proto` file, used by default: `<INSTALL_DIR>/deployment_tools/model_optimizer/mo/front/caffe/proto/my_caffe.proto`. There you can find the structure:
 ```sh
@@ -156,195 +156,195 @@ input_dim: 500
 
 However, if your model contains more than one input, the Model Optimizer is able to convert the model with inputs specified in a form of 1, 2, 3 of the list above. The last form is not supported for multi-input topologies.
 
-#### 9. What does the message "Mean file for topologies with multiple inputs is not supported" mean? <a name="question-9"></a>
+#### 9. What does the message "Mean file for topologies with multiple inputs is not supported" mean? <a name="FAQ9"></a>
 
 Model Optimizer does not support mean file processing for topologies with more than one input. In this case, you need to perform preprocessing of the inputs for a generated Intermediate Representation in the Inference Engine to perform subtraction for every input of your multi-input model.
 
-#### 10. What does the message "Cannot load or process mean file: value error" mean? <a name="question-10"></a>
+#### 10. What does the message "Cannot load or process mean file: value error" mean? <a name="FAQ10"></a>
 
-There are multiple reasons why the Model Optimizer does not accept the mean file. See FAQs [#4](#question-4), [#5](#question-5), and [#6](#question-6).
+There are multiple reasons why the Model Optimizer does not accept the mean file. See FAQs [#4](#FAQ4), [#5](#FAQ5), and [#6](#FAQ6).
 
-#### 11. What does the message "Invalid prototxt file: value error" mean? <a name="question-11"></a>
+#### 11. What does the message "Invalid prototxt file: value error" mean? <a name="FAQ11"></a>
 
-There are multiple reasons why the Model Optimizer does not accept a Caffe* topology. See FAQs [#7](#question-7) and [#20](#question-20).
+There are multiple reasons why the Model Optimizer does not accept a Caffe* topology. See FAQs [#7](#FAQ7) and [#20](#FAQ20).
 
-#### 12. What does the message "Error happened while constructing caffe.Net in the Caffe fallback function" mean? <a name="question-12"></a>
+#### 12. What does the message "Error happened while constructing caffe.Net in the Caffe fallback function" mean? <a name="FAQ12"></a>
 
-Model Optimizer tried to infer a specified layer via the Caffe\* framework, however it cannot construct a net using the Caffe Python* interface. Make sure that your `caffemodel` and `prototxt` files are correct. To prove that the problem is not in the `prototxt` file, see FAQ [#2](#question-2).
+Model Optimizer tried to infer a specified layer via the Caffe\* framework, however it cannot construct a net using the Caffe Python* interface. Make sure that your `caffemodel` and `prototxt` files are correct. To prove that the problem is not in the `prototxt` file, see FAQ [#2](#FAQ2).
 
-#### 13. What does the message "Cannot infer shapes due to exception in Caffe" mean? <a name="question-13"></a>
+#### 13. What does the message "Cannot infer shapes due to exception in Caffe" mean? <a name="FAQ13"></a>
 
 Model Optimizer tried to infer a custom layer via the Caffe\* framework, however an error occurred, meaning that the model could not be inferred using the Caffe. It might happen if you try to convert the model with some noise weights and biases resulting in problems with layers with dynamic shapes. You should write your own extension for every custom layer you topology might have. For more details, refer to [Extending Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 14. What does the message "Cannot infer shape for node {} because there is no Caffe available. Please register python infer function for op or use Caffe for shape inference" mean? <a name="question-14"></a>
+#### 14. What does the message "Cannot infer shape for node {} because there is no Caffe available. Please register python infer function for op or use Caffe for shape inference" mean? <a name="FAQ14"></a>
 
-Your model contains a custom layer and you have correctly registered it with the `CustomLayersMapping.xml` file. These steps are required to offload shape inference of the custom layer with the help of the system Caffe\*. However, the Model Optimizer could not import a Caffe package. Make sure that you have built Caffe with a `pycaffe` target and added it into the `PYTHONPATH` environment variable. For more information, please refer to the [Configuring the Model Optimizer](customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md). At the same time, it is highly recommend to avoid dependency on Caffe and write your own Model Optimizer extension for your custom layer. For more information, refer to the FAQ [#45](#question-45).
+Your model contains a custom layer and you have correctly registered it with the `CustomLayersMapping.xml` file. These steps are required to offload shape inference of the custom layer with the help of the system Caffe\*. However, the Model Optimizer could not import a Caffe package. Make sure that you have built Caffe with a `pycaffe` target and added it into the `PYTHONPATH` environment variable. For more information, please refer to the [Configuring the Model Optimizer](customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md). At the same time, it is highly recommend to avoid dependency on Caffe and write your own Model Optimizer extension for your custom layer. For more information, refer to the FAQ [#45](#FAQ45).
 
-#### 15. What does the message "Framework name can not be deduced from the given options. Use --framework to choose one of Caffe, TensorFlow, MXNet" mean? <a name="question-15"></a>
+#### 15. What does the message "Framework name can not be deduced from the given options. Use --framework to choose one of Caffe, TensorFlow, MXNet" mean? <a name="FAQ15"></a>
 
 You have run the Model Optimizer without a flag `--framework caffe|tf|mxnet`. Model Optimizer tries to deduce the framework by the input model file extension (`.pb` for TensorFlow\*, `.caffemodel` for Caffe\*, `.params` for MXNet\*). Your input model might have a different extension and you need to explicitly set the source framework. For example, use `--framework caffe`.
 
-#### 16. What does the message "Input shape is required to convert MXNet model. Please provide it with --input_shape" mean? <a name="question-16"></a>
+#### 16. What does the message "Input shape is required to convert MXNet model. Please provide it with --input_shape" mean? <a name="FAQ16"></a>
 
-Input shape was not provided. That is mandatory for converting an MXNet\* model to the Intermediate Representation, because MXNet models do not contain information about input shapes. Please, use the `--input_shape` flag to specify it. For more information about using the `--input_shape`, refer to the FAQ [#57](#question-57).
+Input shape was not provided. That is mandatory for converting an MXNet\* model to the Intermediate Representation, because MXNet models do not contain information about input shapes. Please, use the `--input_shape` flag to specify it. For more information about using the `--input_shape`, refer to the FAQ [#57](#FAQ57).
 
-#### 17. What does the message "Both --mean_file and mean_values are specified. Specify either mean file or mean values" mean? <a name="question-17"></a>
+#### 17. What does the message "Both --mean_file and mean_values are specified. Specify either mean file or mean values" mean? <a name="FAQ17"></a>
 
 `--mean_file` and `--mean_values` are two ways of specifying preprocessing for the input. However, they cannot be used together, as it would mean double subtraction and lead to ambiguity. Choose one of these options and pass it using the corresponding CLI option.
 
-#### 18. What does the message "Negative value specified for --mean_file_offsets option. Please specify positive integer values in format '(x,y)'" mean? <a name="question-18"></a>
+#### 18. What does the message "Negative value specified for --mean_file_offsets option. Please specify positive integer values in format '(x,y)'" mean? <a name="FAQ18"></a>
 
 You might have specified negative values with `--mean_file_offsets`. Only positive integer values in format '(x,y)' must be used.
 
-#### 19. What does the message "Both --scale and --scale_values are defined. Specify either scale factor or scale values per input channels" mean? <a name="question-19"></a>
+#### 19. What does the message "Both --scale and --scale_values are defined. Specify either scale factor or scale values per input channels" mean? <a name="FAQ19"></a>
 
 `--scale` sets a scaling factor for all channels. `--scale_values` sets a scaling factor per each channel. Using both of them simultaneously produces ambiguity, so you must use only one of them. For more information, refer to the Using Framework-Agnostic Conversion Parameters: for <a href="ConvertFromCaffe.html#using-framework-agnostic-conv-param">Converting a Caffe* Model</a>, <a href="ConvertFromTensorFlow.html#using-framework-agnostic-conv-param">Converting a TensorFlow* Model</a>, <a href="ConvertFromMXNet.html#using-framework-agnostic-conv-param">Converting an MXNet* Model</a>.
 
-#### 20. What does the message "Cannot find prototxt file: for Caffe please specify --input_proto - a protobuf file that stores topology and --input_model that stores pretrained weights" mean? <a name="question-20"></a>
+#### 20. What does the message "Cannot find prototxt file: for Caffe please specify --input_proto - a protobuf file that stores topology and --input_model that stores pretrained weights" mean? <a name="FAQ20"></a>
 
 Model Optimizer cannot find a `.prototxt` file for a specified model. By default, it must be located in the same directory as the input model with the same name (except extension). If any of these conditions is not satisfied, use `--input_proto` to specify the path to the `.prototxt` file.
 
-#### 22. What does the message "Failed to create directory .. . Permission denied!" mean? <a name="question-22"></a>
+#### 22. What does the message "Failed to create directory .. . Permission denied!" mean? <a name="FAQ22"></a>
 
 Model Optimizer cannot create a directory specified via `--output_dir`. Make sure that you have enough permissions to create the specified directory.
 
-#### 23. What does the message "Discovered data node without inputs and value" mean? <a name="question-23"></a>
+#### 23. What does the message "Discovered data node without inputs and value" mean? <a name="FAQ23"></a>
 
 One of the layers in the specified topology might not have inputs or values. Please make sure that the provided `caffemodel` and `protobuf` files are correct.
 
-#### 24. What does the message "Part of the nodes was not translated to IE. Stopped" mean? <a name="question-24"></a>
+#### 24. What does the message "Part of the nodes was not translated to IE. Stopped" mean? <a name="FAQ24"></a>
 
 Some of the layers are not supported by the Inference Engine and cannot be translated to an Intermediate Representation. You can extend the Model Optimizer by allowing generation of new types of layers and implement these layers in the dedicated Inference Engine plugins. For more information, refer to [Extending the Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md) page and [Inference Engine Extensibility Mechanism](../../IE_DG/Extensibility_DG/Intro.md)
 
-#### 25. What does the message "While creating an edge from .. to .. : node name is undefined in the graph. Check correctness of the input model" mean? <a name="question-25"></a>
+#### 25. What does the message "While creating an edge from .. to .. : node name is undefined in the graph. Check correctness of the input model" mean? <a name="FAQ25"></a>
 
 Model Optimizer cannot build a graph based on a specified model. Most likely, it is incorrect.
 
-#### 26. What does the message "Node does not exist in the graph" mean? <a name="question-26"></a>
+#### 26. What does the message "Node does not exist in the graph" mean? <a name="FAQ26"></a>
 
 You might have specified an output node via the `--output` flag that does not exist in a provided model. Make sure that the specified output is correct and this node exists in the current model.
 
-#### 27. What does the message "--input parameter was provided. Other inputs are needed for output computation. Provide more inputs or choose another place to cut the net" mean? <a name="question-27"></a>
+#### 27. What does the message "--input parameter was provided. Other inputs are needed for output computation. Provide more inputs or choose another place to cut the net" mean? <a name="FAQ27"></a>
 
 Most likely, the Model Optimizer tried to cut the model by a specified input. However, other inputs are needed.
 
-#### 28. What does the message "Placeholder node does not have an input port, but input port was provided" mean?  <a name="question-28"></a>
+#### 28. What does the message "Placeholder node does not have an input port, but input port was provided" mean?  <a name="FAQ28"></a>
 
 You might have specified a placeholder node with an input node, while the placeholder node does not have it the model.
 
-#### 29. What does the message "Port index is out of number of available input ports for node" mean? <a name="question-29"></a>
+#### 29. What does the message "Port index is out of number of available input ports for node" mean? <a name="FAQ29"></a>
 
 This error occurs when an incorrect input port is specified with the `--input` command line argument. When using `--input`, you can optionally specify an input port in the form: `X:node_name`, where `X` is an integer index of the input port starting from 0 and `node_name` is the name of a node in the model. This error occurs when the specified input port `X` is not in the range 0..(n-1), where n is the number of input ports for the node. Please, specify a correct port index, or do not use it if it is not needed.
 
-#### 30. What does the message "Node has more than 1 input and input shapes were provided. Try not to provide input shapes or specify input port with PORT:NODE notation, where PORT is an integer" mean? <a name="question-30"></a>
+#### 30. What does the message "Node has more than 1 input and input shapes were provided. Try not to provide input shapes or specify input port with PORT:NODE notation, where PORT is an integer" mean? <a name="FAQ30"></a>
 
 This error occurs when an incorrect combination of the `--input` and `--input_shape` command line options is used. Using both `--input` and `--input_shape` is valid only if `--input` points to the `Placeholder` node, a node with one input port or `--input` has the form `PORT:NODE`, where `PORT` is an integer port index of input for node `NODE`. Otherwise, the combination of `--input` and `--input_shape` is incorrect.
 
-#### 31. What does the message "Input port > 0 in --input is not supported if --input_shape is not provided. Node: NAME_OF_THE_NODE. Omit port index and all input ports will be replaced by placeholders. Or provide --input_shape" mean? <a name="question-31"></a>
+#### 31. What does the message "Input port > 0 in --input is not supported if --input_shape is not provided. Node: NAME_OF_THE_NODE. Omit port index and all input ports will be replaced by placeholders. Or provide --input_shape" mean? <a name="FAQ31"></a>
 
 When using the `PORT:NODE` notation for the `--input` command line argument and `PORT` > 0, you should specify `--input_shape` for this input. This is a limitation of the current Model Optimizer implementation.
 
-#### 32. What does the message "No or multiple placeholders in the model, but only one shape is provided, cannot set it" mean? <a name="question-32"></a>
+#### 32. What does the message "No or multiple placeholders in the model, but only one shape is provided, cannot set it" mean? <a name="FAQ32"></a>
 
 Looks like you have provided only one shape for the placeholder, however there are no or multiple inputs in the model. Please, make sure that you have provided correct data for placeholder nodes.
 
-#### 33. What does the message "The amount of input nodes for port is not equal to 1" mean? <a name="question-33"></a>
+#### 33. What does the message "The amount of input nodes for port is not equal to 1" mean? <a name="FAQ33"></a>
 
 This error occurs when the `SubgraphMatch.single_input_node` function is used for an input port that supplies more than one node in a sub-graph. The `single_input_node` function can be used only for ports that has a single consumer inside the matching sub-graph. When multiple nodes are connected to the port, use the `input_nodes` function or `node_by_pattern` function instead of `single_input_node`. Please, refer to [Sub-Graph Replacement in the Model Optimizer](customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md) for more details.
 
-#### 34. What does the message "Output node for port has already been specified" mean? <a name="question-34"></a>
+#### 34. What does the message "Output node for port has already been specified" mean? <a name="FAQ34"></a>
 
 This error occurs when the `SubgraphMatch._add_output_node` function is called manually from user's extension code. This is an internal function, and you should not call it directly.
 
-#### 35. What does the message "Unsupported match kind.... Match kinds "points" or "scope" are supported only" mean? <a name="question-35"></a>
+#### 35. What does the message "Unsupported match kind.... Match kinds "points" or "scope" are supported only" mean? <a name="FAQ35"></a>
 
 While using configuration file to implement a TensorFlow\* front replacement extension, an incorrect match kind was used. Only `points` or `scope` match kinds are supported. Please, refer to [Sub-Graph Replacement in the Model Optimizer](customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md) for more details.
 
-#### 36. What does the message "Cannot write an event file for the TensorBoard to directory" mean? <a name="question-36"></a>
+#### 36. What does the message "Cannot write an event file for the TensorBoard to directory" mean? <a name="FAQ36"></a>
 
 Model Optimizer tried to write an event file in the specified directory but failed to do that. That could happen because the specified directory does not exist or you do not have enough permissions to write in it.
 
-#### 37. What does the message "There is no registered 'infer' function for node  with op = .. . Please implement this function in the extensions" mean? <a name="question-37"></a>
+#### 37. What does the message "There is no registered 'infer' function for node  with op = .. . Please implement this function in the extensions" mean? <a name="FAQ37"></a>
 
 Most likely, you tried to extend Model Optimizer with a new primitive, but did not specify an infer function. For more information on extensions, see [Extending the Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 38. What does the message "Stopped shape/value propagation at node" mean? <a name="question-38"></a>
+#### 38. What does the message "Stopped shape/value propagation at node" mean? <a name="FAQ38"></a>
 
 Model Optimizer cannot infer shapes or values for the specified node. It can happen because of a bug in the custom shape infer function, because the node inputs have incorrect values/shapes, or because the input shapes are incorrect.
 
-#### 39. What does the message "The input with shape .. does not have the batch dimension" mean? <a name="question-39"></a>
+#### 39. What does the message "The input with shape .. does not have the batch dimension" mean? <a name="FAQ39"></a>
 
 Batch dimension is the first dimension in the shape and it should be equal to 1 or undefined. In your case, it is not equal to either 1 or undefined, which is why the `-b` shortcut produces undefined and unspecified behavior. To resolve the issue, specify full shapes for each input with the `--input_shape` option. Run Model Optimizer with the `--help` option to learn more about the notation for input shapes.
 
-#### 40. What does the message "Not all output shapes were inferred or fully defined for node" mean? <a name="question-40"></a>
+#### 40. What does the message "Not all output shapes were inferred or fully defined for node" mean? <a name="FAQ40"></a>
 
 Most likely, the shape is not defined (partially or fully) for the specified node. You can use `--input_shape` with positive integers to override model input shapes.
 
-#### 41. What does the message "Shape for tensor is not defined. Can not proceed" mean? <a name="question-41"></a>
+#### 41. What does the message "Shape for tensor is not defined. Can not proceed" mean? <a name="FAQ41"></a>
 
 This error occurs when the `--input` command line option is used to cut a model and `--input_shape` is not used to override shapes for a node and a shape for the node cannot be inferred by Model Optimizer. You need to help Model Optimizer and specify shapes with `--input_shape` for each node that is specified with the `--input` command line option.
 
-#### 42. What does the message "Module TensorFlow was not found. Please install TensorFlow 1.2 or higher" mean? <a name="question-42"></a>
+#### 42. What does the message "Module TensorFlow was not found. Please install TensorFlow 1.2 or higher" mean? <a name="FAQ42"></a>
 
 To convert TensorFlow\* models with Model Optimizer, TensorFlow 1.2 or newer must be installed. For more information on prerequisites, see [Configuring the Model Optimizer](Config_Model_Optimizer.md).
 
-#### 43. What does the message "Cannot read the model file: it is incorrect TensorFlow model file or missing" mean? <a name="question-43"></a>
+#### 43. What does the message "Cannot read the model file: it is incorrect TensorFlow model file or missing" mean? <a name="FAQ43"></a>
 
 The model file should contain a frozen TensorFlow\* graph in the text or binary format. Make sure that `--input_model_is_text` is provided for a model in the text format. By default, a model is interpreted as binary file.
 
-#### 44. What does the message "Cannot pre-process TensorFlow graph after reading from model file. File is corrupt or has unsupported format" mean? <a name="question-44"></a>
+#### 44. What does the message "Cannot pre-process TensorFlow graph after reading from model file. File is corrupt or has unsupported format" mean? <a name="FAQ44"></a>
 
 Most likely, there is a problem with the specified file for model. The file exists, but it has bad formatting or is corrupted.
 
-#### 45. What does the message "Found custom layer. Model Optimizer does not support this layer. Please, register it in CustomLayersMapping.xml or implement extension" mean? <a name="question-45"></a>
+#### 45. What does the message "Found custom layer. Model Optimizer does not support this layer. Please, register it in CustomLayersMapping.xml or implement extension" mean? <a name="FAQ45"></a>
 
 This means that the layer `{layer_name}` is not supported in the Model Optimizer. You can find a list of all unsupported layers in the corresponding section. You should add this layer to `CustomLayersMapping.xml` ([Legacy Mode for Caffe* Custom Layers](customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md)) or implement the extensions for this layer ([Extending Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md)).
 
-#### 46. What does the message "Custom replacement configuration file does not exist" mean? <a name="question-46"></a>
+#### 46. What does the message "Custom replacement configuration file does not exist" mean? <a name="FAQ46"></a>
 
 Path to the custom replacement configuration file was provided with the `--transformations_config` flag, but the file could not be found. Please, make sure that the specified path is correct and the file exists.
 
-#### 47. What does the message "Extractors collection have case insensitive duplicates" mean? <a name="question-47"></a>
+#### 47. What does the message "Extractors collection have case insensitive duplicates" mean? <a name="FAQ47"></a>
 
 When extending Model Optimizer with new primitives keep in mind that their names are case insensitive. Most likely, another operation with the same name is already defined. For more information, see [Extending the Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 48. What does the message "Input model name is not in an expected format, cannot extract iteration number" mean? <a name="question-48"></a>
+#### 48. What does the message "Input model name is not in an expected format, cannot extract iteration number" mean? <a name="FAQ48"></a>
 
 Model Optimizer can not load an MXNet\* model in the specified file format. Please, use the `.json` or `.param` format.
 
-#### 49. What does the message "Cannot convert type of placeholder because not all of its outputs are 'Cast' to float operations" mean? <a name="question-49"></a>
+#### 49. What does the message "Cannot convert type of placeholder because not all of its outputs are 'Cast' to float operations" mean? <a name="FAQ49"></a>
 
 There are models where `Placeholder` has the UINT8 type and the first operation after it is 'Cast', which casts the input to FP32. Model Optimizer detected that the `Placeholder` has the UINT8 type, but the next operation is not 'Cast' to float. Model Optimizer does not support such a case. Please, change the model to have placeholder FP32 data type.
 
-#### 50. What does the message "Data type is unsupported" mean? <a name="question-50"></a>
+#### 50. What does the message "Data type is unsupported" mean? <a name="FAQ50"></a>
 
 Model Optimizer cannot convert the model to the specified data type. Currently, FP16 and FP32 are supported. Please, specify the data type with the `--data_type` flag. The available values are: FP16, FP32, half, float.
 
-#### 51. What does the message "No node with name ..." mean? <a name="question-51"></a>
+#### 51. What does the message "No node with name ..." mean? <a name="FAQ51"></a>
 
 Model Optimizer tried to access a node that does not exist. This could happen if you have incorrectly specified placeholder, input or output node name.
 
-#### 52. What does the message "Module mxnet was not found. Please install MXNet 1.0.0" mean? <a name="question-52"></a>
+#### 52. What does the message "Module mxnet was not found. Please install MXNet 1.0.0" mean? <a name="FAQ52"></a>
 
 To convert MXNet\* models with Model Optimizer, MXNet 1.0.0 must be installed. For more information about prerequisites, see [Configuring the Model Optimizer](Config_Model_Optimizer.md).
 
-#### 53. What does the message "The following error happened while loading MXNet model .." mean? <a name="question-53"></a>
+#### 53. What does the message "The following error happened while loading MXNet model .." mean? <a name="FAQ53"></a>
 
 Most likely, there is a problem with loading of the MXNet\* model. Please, make sure that the specified path is correct, the model exists, it is not corrupted, and you have sufficient permissions to work with it.
 
-#### 54. What does the message "The following error happened while processing input shapes: .." mean? <a name="question-54"></a>
+#### 54. What does the message "The following error happened while processing input shapes: .." mean? <a name="FAQ54"></a>
 
 Please, make sure that inputs are defined and have correct shapes. You can use `--input_shape` with positive integers to override model input shapes.
 
-#### 55. What does the message "Attempt to register of custom name for the second time as class. Note that custom names are case-insensitive" mean? <a name="question-55"></a>
+#### 55. What does the message "Attempt to register of custom name for the second time as class. Note that custom names are case-insensitive" mean? <a name="FAQ55"></a>
 
 When extending Model Optimizer with new primitives keep in mind that their names are case insensitive. Most likely, another operation with the same name is already defined. For more information, see [Extending the Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md) .
 
-#### 56. What does the message "Both --input_shape and --batch were provided. Please, provide only one of them" mean? <a name="question-56"></a>
+#### 56. What does the message "Both --input_shape and --batch were provided. Please, provide only one of them" mean? <a name="FAQ56"></a>
 
 You cannot specify the batch and the input shape at the same time. You should specify a desired batch as the first value of the input shape.
 
-#### 57. What does the message "Input shape .. cannot be parsed" mean? <a name="question-57"></a>
+#### 57. What does the message "Input shape .. cannot be parsed" mean? <a name="FAQ57"></a>
 
 The specified input shape cannot be parsed. Please, define it in one of the following ways:
 
@@ -363,99 +363,99 @@ python3 mo.py --input_model /path-to/your-model.caffemodel --input data,rois --i
 
 Keep in mind that there is no space between and inside the brackets for input shapes.
 
-#### 58. What does the message "Please provide input layer names for input layer shapes" mean? <a name="question-58"></a>
+#### 58. What does the message "Please provide input layer names for input layer shapes" mean? <a name="FAQ58"></a>
 
-When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe\* Model](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe.html). Additional information for `--input_shape` is in FAQ [#57](#question-57).
+When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe\* Model](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe.html). Additional information for `--input_shape` is in FAQ [#57](#FAQ57).
 
-#### 59. What does the message "Values cannot be parsed" mean? <a name="question-59"></a>
+#### 59. What does the message "Values cannot be parsed" mean? <a name="FAQ59"></a>
 
 Mean values for the given parameter cannot be parsed. It should be a string with a list of mean values. For example, in '(1,2,3)', 1 stands for the RED channel, 2 for the GREEN channel, 3 for the BLUE channel.
 
-#### 60. What does the message ".. channels are expected for given values" mean? <a name="question-60"></a>
+#### 60. What does the message ".. channels are expected for given values" mean? <a name="FAQ60"></a>
 
 The number of channels and the number of given values for mean values do not match. The shape should be defined as '(R,G,B)' or '[R,G,B]'. The shape should not contain undefined dimensions (? or -1). The order of values is as follows: (value for a RED channel, value for a GREEN channel, value for a BLUE channel).
 
-#### 61. What does the message "You should specify input for each mean value" mean? <a name="question-61"></a>
+#### 61. What does the message "You should specify input for each mean value" mean? <a name="FAQ61"></a>
 
-Most likely, you have not specified inputs using `--mean_values`. Please, specify inputs with the `--input` flag. For usage examples, please, refer to FAQ [#63](#question-63).
+Most likely, you have not specified inputs using `--mean_values`. Please, specify inputs with the `--input` flag. For usage examples, please, refer to FAQ [#63](#FAQ63).
 
-#### 62. What does the message "You should specify input for each scale value" mean? <a name="question-62"></a>
+#### 62. What does the message "You should specify input for each scale value" mean? <a name="FAQ62"></a>
 
-Most likely, you have not specified inputs using `--scale_values`. Please, specify inputs with the `--input` flag. For usage examples, please, refer to FAQ [#64](#question-64).
+Most likely, you have not specified inputs using `--scale_values`. Please, specify inputs with the `--input` flag. For usage examples, please, refer to FAQ [#64](#FAQ64).
 
-#### 63. What does the message "Number of inputs and mean values does not match" mean? <a name="question-63"></a>
+#### 63. What does the message "Number of inputs and mean values does not match" mean? <a name="FAQ63"></a>
 
 The number of specified mean values and the number of inputs must be equal. Please, refer to [Converting a Caffe* Model](convert_model/Convert_Model_From_Caffe.md) for a usage example.
 
-#### 64. What does the message "Number of inputs and scale values does not match" mean? <a name="question-64"></a>
+#### 64. What does the message "Number of inputs and scale values does not match" mean? <a name="FAQ64"></a>
 
 The number of specified scale values and the number of inputs must be equal. Please, refer to [Converting a Caffe* Model](convert_model/Convert_Model_From_Caffe.md) for a usage example.
 
-#### 65. What does the message "No class registered for match kind ... Supported match kinds are .. " mean? <a name="question-65"></a>
+#### 65. What does the message "No class registered for match kind ... Supported match kinds are .. " mean? <a name="FAQ65"></a>
 
 A replacement defined in the configuration file for sub-graph replacement using node names patterns or start/end nodes has the `match_kind` attribute. The attribute may have only one of the values: `scope` or `points`. If a different value is provided, this error is displayed.
 
-#### 66. What does the message "No instance(s) is(are) defined for the custom replacement" mean? <a name="question-66"></a>
+#### 66. What does the message "No instance(s) is(are) defined for the custom replacement" mean? <a name="FAQ66"></a>
 
 A replacement defined in the configuration file for sub-graph replacement using node names patterns or start/end nodes has the `instances` attribute. This attribute is mandatory, and it causes this error if it is missing. Refer to documentation with a description of the sub-graph replacement feature.
 
-#### 67. What does the message "The instance must be a single dictionary for the custom replacement with id .." mean? <a name="question-67"></a>
+#### 67. What does the message "The instance must be a single dictionary for the custom replacement with id .." mean? <a name="FAQ67"></a>
 
 A replacement defined in the configuration file for sub-graph replacement using start/end nodes has the `instances` attribute. For this type of replacement, the instance must be defined with a dictionary with two keys `start_points` and `end_points`. Values for these keys are lists with the start and end node names, respectively. Refer to documentation with a description of the sub-graph replacement feature.
 
-#### 68. What does the message "No instances are defined for replacement with id .. " mean? <a name="question-68"></a>
+#### 68. What does the message "No instances are defined for replacement with id .. " mean? <a name="FAQ68"></a>
 
-A replacement for the specified id is not defined in the configuration file. Please, refer to FAQ [#66](#question-66) for more information.
+A replacement for the specified id is not defined in the configuration file. Please, refer to FAQ [#66](#FAQ66) for more information.
 
-#### 69. What does the message "Custom replacements configuration file .. does not exist" mean? <a name="question-69"></a>
+#### 69. What does the message "Custom replacements configuration file .. does not exist" mean? <a name="FAQ69"></a>
 
 Path to a custom replacement configuration file was provided with the `--transformations_config` flag, but it cannot be found. Please, make sure that the specified path is correct and the file exists.
 
-#### 70. What does the message "Failed to parse custom replacements configuration file .." mean? <a name="question-70"></a>
+#### 70. What does the message "Failed to parse custom replacements configuration file .." mean? <a name="FAQ70"></a>
 
 The file for custom replacement configuration provided with the `--transformations_config` flag cannot be parsed. In particular, it should have a valid JSON structure. For more details, refer to [JSON Schema Reference](https://spacetelescope.github.io/understanding-json-schema/reference/index.html).
 
-#### 71. What does the message "One of the custom replacements in the configuration file .. does not contain attribute 'id'" mean? <a name="question-71"></a>
+#### 71. What does the message "One of the custom replacements in the configuration file .. does not contain attribute 'id'" mean? <a name="FAQ71"></a>
 
-Every custom replacement should declare a set of mandatory attributes and their values. For more details, refer to FAQ [#72](#question-72).
+Every custom replacement should declare a set of mandatory attributes and their values. For more details, refer to FAQ [#72](#FAQ72).
 
-#### 72. What does the message "File .. validation failed" mean? <a name="question-72"></a>
+#### 72. What does the message "File .. validation failed" mean? <a name="FAQ72"></a>
 
 The file for custom replacement configuration provided with the `--transformations_config` flag cannot pass validation. Make sure that you have specified `id`, `instances` and `match_kind` for all the patterns.
 
-#### 73. What does the message "Cannot update the file .. because it is broken" mean? <a name="question-73"></a>
+#### 73. What does the message "Cannot update the file .. because it is broken" mean? <a name="FAQ73"></a>
 
-The custom replacement configuration file provided with the `--tensorflow_custom_operations_config_update` cannot be parsed. Please, make sure that the file is correct and refer to FAQs [#69](#question-69), [#70](#question-70), [#71](#question-71), and [#72](#question-72).
+The custom replacement configuration file provided with the `--tensorflow_custom_operations_config_update` cannot be parsed. Please, make sure that the file is correct and refer to FAQs [#69](#FAQ69), [#70](#FAQ70), [#71](#FAQ71), and [#72](#FAQ72).
 
-#### 74. What does the message "End node .. is not reachable from start nodes: .." mean? <a name="question-74"></a>
+#### 74. What does the message "End node .. is not reachable from start nodes: .." mean? <a name="FAQ74"></a>
 
 This error occurs when you try to make a sub-graph match. It is detected that between the start and end nodes that were specified as inputs/outputs of the subgraph to find, there are nodes that are marked as outputs but there is no path from them to the input nodes. Make sure that the subgraph you want to match does actually contain all the specified output nodes.
 
-#### 75. What does the message "Sub-graph contains network input node .." mean? <a name="question-75"></a>
+#### 75. What does the message "Sub-graph contains network input node .." mean? <a name="FAQ75"></a>
 
 Start or end node for the sub-graph replacement using start/end nodes is specified incorrectly. Model Optimizer finds internal nodes of the sub-graph strictly "between" the start and end nodes. Then it adds all input nodes to the sub-graph (and inputs of their inputs and so on) for these "internal" nodes. The error reports, that the Model Optimizer reached input node during this phase. This means that the start/end points are specified incorrectly in the configuration file. Refer to documentation with a description of the sub-graph replacement feature.
 
-#### 76. What does the message "... elements of ... were clipped to infinity while converting a blob for node [...] to ..." mean? <a name="question-76"></a>
+#### 76. What does the message "... elements of ... were clipped to infinity while converting a blob for node [...] to ..." mean? <a name="FAQ76"></a>
 
 This message may appear when the `--data_type=FP16` command line option is used. This option implies conversion of all the blobs in the node to FP16. If a value in a blob is out of the range of valid FP16 values, the value is converted to positive or negative infinity. It may lead to incorrect results of inference or may not be a problem, depending on the model. The number of such elements and the total number of elements in the blob is printed out together with the name of the node, where this blob is used.
 
-#### 77. What does the message "... elements of ... were clipped to zero while converting a blob for node [...] to ..." mean? <a name="question-77"></a>
+#### 77. What does the message "... elements of ... were clipped to zero while converting a blob for node [...] to ..." mean? <a name="FAQ77"></a>
 
 This message may appear when the `--data_type=FP16` command line option is used. This option implies conversion of all blobs in the mode to FP16. If a value in the blob is so close to zero that it cannot be represented as a valid FP16 value, it is converted to a true zero FP16 value. Depending on the model, it may lead to incorrect results of inference or may not be a problem. The number of such elements and the total number of elements in the blob are printed out together with a name of the node, where this blob is used.
 
-#### 78. What does the message "The amount of nodes matched pattern ... is not equal to 1" mean? <a name="question-78"></a>
+#### 78. What does the message "The amount of nodes matched pattern ... is not equal to 1" mean? <a name="FAQ78"></a>
 
 This error occurs when the `SubgraphMatch.node_by_pattern` function is used with a pattern that does not uniquely identify a single node in a sub-graph. Try to extend the pattern string to make unambiguous match to a single sub-graph node. For more details, refer to [Sub-graph Replacement in the Model Optimizer](customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md).
 
-#### 79. What does the message "The topology contains no "input" layers" mean? <a name="question-79"></a>
+#### 79. What does the message "The topology contains no "input" layers" mean? <a name="FAQ79"></a>
 
 Your Caffe\* topology `.prototxt` file is intended for training. Model Optimizer expects a deployment-ready `.prototxt` file. To fix the problem, prepare a deployment-ready `.prototxt` file. Usually, preparation of a deploy-ready topology results in removing `data` layer(s), adding `input` layer(s), and removing loss layer(s).
 
-#### 80. What does the message "Warning: please expect that Model Optimizer conversion might be slow" mean? <a name="question-80"></a>
+#### 80. What does the message "Warning: please expect that Model Optimizer conversion might be slow" mean? <a name="FAQ80"></a>
 
 You are using an unsupported Python\* version. Use only versions 3.4 - 3.6 for the C++ `protobuf` implementation that is supplied with the OpenVINO Toolkit. You can still boost conversion speed by building protobuf library from sources. For complete instructions about building `protobuf` from sources, see the appropriate section in [Converting a Model to Intermediate Representation](Config_Model_Optimizer.md).
 
-#### 81. What does the message "Arguments --nd_prefix_name, --pretrained_model_name and --input_symbol should be provided. Please provide all or do not use any." mean? <a name="question-81"></a>
+#### 81. What does the message "Arguments --nd_prefix_name, --pretrained_model_name and --input_symbol should be provided. Please provide all or do not use any." mean? <a name="FAQ81"></a>
 
 This error occurs if you do not provide `--nd_prefix_name`, `--pretrained_model_name` and `--input_symbol` parameters. 
 Model Optimizer requires both `.params` and `.nd` model files to merge into the result file (`.params`). Topology 
@@ -466,33 +466,33 @@ from one `.params` file and two additional `.nd` files (`*_args.nd`, `*_auxs.nd`
 To do that, provide both CLI options or do not pass them if you want to convert an MXNet model without additional weights.
 For more information, refer to [Converting a MXNet* Model](convert_model/Convert_Model_From_MxNet.md).
 
-#### 82. What does the message "You should specify input for mean/scale values" mean? <a name="question-82"></a>
+#### 82. What does the message "You should specify input for mean/scale values" mean? <a name="FAQ82"></a>
 
 In case when the model has multiple inputs and you want to provide mean/scale values, you need to pass those values for each input. More specifically, a number of passed values should be the same as the number of inputs of the model. 
 For more information, refer to [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md).
 
-#### 83. What does the message "Input with name ... not found!" mean? <a name="question-83"></a>
+#### 83. What does the message "Input with name ... not found!" mean? <a name="FAQ83"></a>
 
 When you passed the mean/scale values and specify names of input layers of the model, you might have used the name that does not correspond to any input layer. Make sure that by passing values with `--input` option, you list only names of the input layers of your model.
 For more information, refer to the [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md).
 
-#### 84. What does the message "Specified input json ... does not exist" mean? <a name="question-84"></a>
+#### 84. What does the message "Specified input json ... does not exist" mean? <a name="FAQ84"></a>
 
 Most likely, `.json` file does not exist or has a name that does not match the notation of MXNet. Make sure that the file exists and it has a correct name.
 For more information, refer to [Converting a MXNet\* Model](convert_model/Convert_Model_From_MxNet.md).
 
-#### 85. What does the message "Unsupported Input model file type ... Model Optimizer support only .params and .nd files format" mean? <a name="question-85"></a>
+#### 85. What does the message "Unsupported Input model file type ... Model Optimizer support only .params and .nd files format" mean? <a name="FAQ85"></a>
 
 Model Optimizer for MXNet supports only `.params` and `.nd` files formats. Most likely, you specified some unsupported file format in `--input_model`.
 For more information, refer to [Converting a MXNet* Model](convert_model/Convert_Model_From_MxNet.md).
 
-#### 86. What does the message "Operation ... not supported. Please register it as custom op" mean? <a name="question-86"></a>
+#### 86. What does the message "Operation ... not supported. Please register it as custom op" mean? <a name="FAQ86"></a>
 
 Model Optimizer tried to load the model that contains some unsupported operations. 
 If you want to convert model that contains unsupported operations you need to prepare extension for all such operations.
 For more information, refer to [Extending Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 87. What does the message "Can not register Op ... Please, call function 'register_caffe_python_extractor' with parameter 'name'" mean? <a name="question-87"></a>
+#### 87. What does the message "Can not register Op ... Please, call function 'register_caffe_python_extractor' with parameter 'name'" mean? <a name="FAQ87"></a>
 
 This error appears if the class of implementation of op for Python Caffe layer could not be used by Model Optimizer. Python layers should be handled differently compared to ordinary Caffe layers.
 
@@ -538,42 +538,42 @@ The second call prevents Model Optimizer from using this extension as if it is a
 a layer with type `Proposal`. Otherwise, this layer can be chosen as an implementation of extension that can lead to potential issues.
 For more information, refer to the [Extending Model Optimizer with New Primitives](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 88. What does the message "Model Optimizer is unable to calculate output shape of Memory node .." mean? <a name="question-88"></a>
+#### 88. What does the message "Model Optimizer is unable to calculate output shape of Memory node .." mean? <a name="FAQ88"></a>
 
 Model Optimizer supports only `Memory` layers, in which `input_memory` goes before `ScaleShift` or `FullyConnected` layer.  
 This error message means that in your model the layer after input memory is not of type `ScaleShift` or `FullyConnected`.
 This is a known limitation.
 
-#### 89. What do the messages "File ...  does not appear to be a Kaldi file (magic number does not match)", "Kaldi model should start with <Nnet> tag" mean? <a name="question-89"></a>
+#### 89. What do the messages "File ...  does not appear to be a Kaldi file (magic number does not match)", "Kaldi model should start with <Nnet> tag" mean? <a name="FAQ89"></a>
 
 These error messages mean that the Model Optimizer does not support your Kaldi\* model, because check sum of the model is not 
 16896 (the model should start with this number) or model file does not contain tag `<Net>` as a starting one.
 Double check that you provide a path to a true Kaldi model and try again.
 
-#### 90. What do the messages "Expect counts file to be one-line file." or "Expect counts file to contain list of integers" mean? <a name="question-90"></a>
+#### 90. What do the messages "Expect counts file to be one-line file." or "Expect counts file to contain list of integers" mean? <a name="FAQ90"></a>
 
 These messages mean that you passed the file counts containing not one line. The count file should start with 
 `[` and end with  `]`,  and integer values should be separated by space between those signs.
 
-#### 91. What does the message "Model Optimizer is not able to read Kaldi model .." mean? <a name="question-91"></a>
+#### 91. What does the message "Model Optimizer is not able to read Kaldi model .." mean? <a name="FAQ91"></a>
 
 There are multiple reasons why the Model Optimizer does not accept a Kaldi topology:
-file is not available or does not exist. Refer to FAQ [#89](#question-89).
+file is not available or does not exist. Refer to FAQ [#89](#FAQ89).
 
-#### 92. What does the message "Model Optimizer is not able to read counts file  .." mean? <a name="question-92"></a>
+#### 92. What does the message "Model Optimizer is not able to read counts file  .." mean? <a name="FAQ92"></a>
 
 There are multiple reasons why the Model Optimizer does not accept a counts file:
-file is not available or does not exist. Also refer to FAQ [#90](#question-90).
+file is not available or does not exist. Also refer to FAQ [#90](#FAQ90).
 
-#### 93. What does the message "For legacy MXNet models Model Optimizer does not support conversion of old MXNet models (trained with 1.0.0 version of MXNet and lower) with custom layers." mean? <a name="question-93"></a>
+#### 93. What does the message "For legacy MXNet models Model Optimizer does not support conversion of old MXNet models (trained with 1.0.0 version of MXNet and lower) with custom layers." mean? <a name="FAQ93"></a>
 
 This message means that if you have model with custom layers and its json file has been generated with MXNet version
-lower than 1.0.0, Model Optimizer does not support such topologies. If you want to convert it you have to rebuild 
+lower than 1.0.0, Model Optimizer does not support such topologies. If you want to convert it you have to rebuld 
 MXNet with unsupported layers or generate new json with MXNet version 1.0.0 and higher. Also you need to implement 
 Inference Engine extension for used custom layers.
 For more information, refer to the [appropriate section of Model Optimizer configuration](customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md).
 
-#### 97. What does the message "Graph contains a cycle. Can not proceed .." mean?  <a name="question-97"></a>
+#### 97. What does the message "Graph contains a cycle. Can not proceed .." mean?  <a name="FAQ97"></a>
 
 Model Optimizer supports only straightforward models without cycles.
 
@@ -589,7 +589,7 @@ For all frameworks:
 or
 * Edit network in original framework to exclude cycle.
 
-#### 98. What does the message "Can not transpose attribute '..' with value .. for node '..' .." mean?  <a name="question-98"></a>
+#### 98. What does the message "Can not transpose attribute '..' with value .. for node '..' .." mean?  <a name="FAQ98"></a>
 
 This message means that model is not supported. It may be caused by using shapes larger than 4-D.
 There are two ways to avoid such message:
@@ -597,21 +597,21 @@ There are two ways to avoid such message:
 1. [Cut model part containing such layers in Model Optimizer](convert_model/Cutting_Model.md) 
 2. Edit network in original framework to exclude such layers.
 
-#### 99. What does the message "Expected token `</ParallelComponent>`, has `...`" mean?  <a name="question-99"></a>
+#### 99. What does the message "Expected token `</ParallelComponent>`, has `...`" mean?  <a name="FAQ99"></a>
 
 This error messages mean that Model Optimizer does not support your Kaldi model, because the Net contains `ParallelComponent` that does not end by tag `</ParallelComponent>`.
 Double check that you provide a path to a true Kaldi model and try again.
 
-#### 100. What does the message "Interp layer shape inference function may be wrong, please, try to update layer shape inference function in the file (extensions/ops/interp.op at the line ...)." mean?  <a name="question-100"></a>
+#### 100. What does the message "Interp layer shape inference function may be wrong, please, try to update layer shape inference function in the file (extensions/ops/interp.op at the line ...)." mean?  <a name="FAQ100"></a>
 
 There are many flavors of Caffe framework, and most layers in them are implemented identically.
 But there are exceptions. For example, output value of layer Interp is calculated differently in Deeplab-Caffe and classic Caffe. So if your model contain layer Interp and converting of your model has failed, please modify the 'interp_infer' function in the file extensions/ops/interp.op according to the comments of the file.
 
-#### 101. What does the message "Mean/scale values should ..." mean? <a name="question-101"></a>
+#### 101. What does the message "Mean/scale values should ..." mean? <a name="FAQ101"></a>
 
 It means that your mean/scale values have wrong format. Specify mean/scale values using the form `layer_name(val1,val2,val3)`. 
 You need to specify values for each input of the model. For more information, refer to [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md).
 
-#### 102. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="question-102"></a>
+#### 102. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="FAQ102"></a>
 
 It means that you trying to convert the topology which contains '_contrib_box_nms' operation which is not supported directly. However the sub-graph of operations including the '_contrib_box_nms' could be replaced with DetectionOutput layer if your topology is one of the gluoncv topologies. Specify '--enable_ssd_gluoncv' command line parameter for the Model Optimizer to enable this transformation.

docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md

@@ -40,46 +40,6 @@ Standard MXNet\* symbols:
 
 | Symbol Name in MXNet\*| Limitations|
 | :----------| :----------|
-| _Plus | No |
-| _contrib_MultiBoxDetection | "force_suppress" = 1 is not supported, non-default variances are not supported |
-| _contrib_MultiBoxPrior | No |
-| _contrib_Proposal | No |
-| _copy | Not needed for inference |
-| _minus_scalar | No |
-| _mul_scalar | No |
-| _arange | No |
-| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings |
-| _maximum | No |
-| _minimum | No |
-| add_n | No |
-| arccosh | No |
-| arcsinh | No |
-| arctanh | No |
-| broadcast_add | No |
-| broadcast_mul | No |
-| cumsum | No |
-| div_scalar | No |
-| elementwise_sub | No |
-| elemwise_add | No |
-| elemwise_mul | No |
-| exp | No |
-| expand_dims | No |
-| greater_scalar | No |
-| minus_scalar | No |
-| null | Not needed for inference |
-| repeat | No |
-| rnn | No |
-| rnn_param_concat | No |
-| sigmoid | No |
-| slice | No |
-| slice_axis | No |
-| slice_channel | No |
-| slice_like | No |
-| stack | No |
-| swapaxis | No |
-| tile | No |
-| transpose | No |
-| zeros | No |
 | Activation | supported "act_type" = "relu", "sigmoid", "softrelu" or "tanh" |
 | BatchNorm | No |
 | Concat | No |
@@ -110,6 +70,43 @@ Standard MXNet\* symbols:
 | Tile | No |
 | UpSampling | No |
 | Where | No |
+| _Plus | No |
+| _contrib_MultiBoxDetection | "force_suppress" = 1 is not supported, non-default variances are not supported |
+| _contrib_MultiBoxPrior | No |
+| _contrib_Proposal | No |
+| _copy | Not needed for inference |
+| _minus_scalar | No |
+| _mul_scalar | No |
+| _arange | No |
+| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings |
+| _maximum | No |
+| _minimum | No |
+| add_n | No |
+| broadcast_add | No |
+| broadcast_mul | No |
+| cumsum | No |
+| div_scalar | No |
+| elementwise_sub | No |
+| elemwise_add | No |
+| elemwise_mul | No |
+| exp | No |
+| expand_dims | No |
+| greater_scalar | No |
+| minus_scalar | No |
+| null | Not needed for inference |
+| repeat | No |
+| rnn | No |
+| rnn_param_concat | No |
+| sigmoid | No |
+| slice | No |
+| slice_axis | No |
+| slice_channel | No |
+| slice_like | No |
+| stack | No |
+| swapaxis | No |
+| tile | No |
+| transpose | No |
+| zeros | No |
 
 
 ## TensorFlow\* Supported Operations
@@ -120,12 +117,9 @@ Standard TensorFlow\* operations:
 
 | Operation Name in TensorFlow\* | Limitations|
 | :----------| :----------|
-| Acosh | No |
 | Add | No |
 | AddN | No |
 | ArgMax | No |
-| Asinh | No |
-| Atanh | No |
 | AvgPool | No |
 | BatchToSpaceND | No |
 | BiasAdd | No |
@@ -150,10 +144,8 @@ Standard TensorFlow\* operations:
 | ExpandDims | No |
 | ExperimentalSparseWeightedSum | CPU only |
 | ExtractImagePatches | No |
-| EuclideanNorm | No |
 | Fill | No |
 | Floor | No |
-| FloorDiv | No |
 | FusedBatchNorm | No |
 | Gather | No |
 | GatherNd | Supported if it can be replaced with Gather |
@@ -293,14 +285,11 @@ Standard ONNX\* operators:
 | :----------| :----------|
 | Abs | No |
 | Acos | No |
-| Acosh | No |
 | Add | No |
 | Affine | No |
 | ArgMax | No |
 | Asin | No |
-| Asinh | No |
 | Atan | No |
-| Atanh | No |
 | AveragePool | No |
 | BatchMatMul | No |
 | BatchNormalization | No |
@@ -317,7 +306,7 @@ Standard ONNX\* operators:
 | Cosh | No |
 | Crop | No |
 | CumSum | No |
-| DequantizeLinear | No |
+| DequantizeLinear | Only in combination with QuantizeLinear, refer to the desc of the latter |
 | DetectionOutput (Intel experimental) | No |
 | Div | No |
 | Dropout | Not needed for inference |
@@ -362,13 +351,11 @@ Standard ONNX\* operators:
 | Pad | No |
 | Pow | No |
 | PriorBox (Intel experimental) | No |
-| QuantizeLinear | No |
+| QuantizeLinear | Only in combination with DequantizeLinear. When the ops following each other in the graph and the scale and zero-point values for these operations are the same (or explicitly shared), the combination is fused into a 'FakeQuantization'|
 | RNN | No |
 | ROIAlign | No |
 | Range | No |
 | Reciprocal | No |
-| ReduceL1 | No |
-| ReduceL2 | No |
 | ReduceMax | No |
 | ReduceMean | No |
 | ReduceMin | No |
@@ -376,10 +363,9 @@ Standard ONNX\* operators:
 | ReduceSum | No |
 | Relu | No |
 | Reshape | No |
-| Resize |  transformation mode `tf_crop_and_resize` is not supported, mode `nearest` is not supported for 5D+ inputs. |
+| Resize | Opset-10 version is supported |
 | ReverseSequence | No |
 | Scatter | Supported if fuse-able to ScatterUpdate. MYRIAD only |
-| ScatterND | No |
 | ScatterElements | Supported if fuse-able to ScatterUpdate. MYRIAD only |
 | Select | No |
 | Shape | No |

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -38,8 +38,7 @@ A summary of the steps for optimizing and deploying a model that was trained wit
 
 **Other supported topologies**
 
-* [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) can be converted using the following [instructions](mxnet_specific/Convert_GluonCV_Models.md).
-* [Style transfer model](https://github.com/zhaw/neural_style) can be converted using the following [instructions](mxnet_specific/Convert_Style_Transfer_From_MXNet.md).
+* Style transfer [model](https://github.com/zhaw/neural_style) can be converted using [instruction](mxnet_specific/Convert_Style_Transfer_From_MXNet.md),
 
 ## Convert an MXNet* Model <a name="ConvertMxNet"></a>
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -295,7 +295,6 @@ python3 mo_tf.py --input_model custom_model.pb --tensorflow_custom_layer_librari
 
 ## Convert TensorFlow* 2 Models <a name="Convert_From_TF2X"></a>
 
-In order to convert TensorFlow* 2 models, installation of dependencies from `requirements_tf2.txt` is required.
 TensorFlow* 2.X officially supports two model formats: SavedModel and Keras H5 (or HDF5).    
 Below are the instructions on how to convert each of them.
 
@@ -332,15 +331,6 @@ model = tf.keras.models.load_model('model.h5')
 tf.saved_model.save(model,'model')
 ```
 
-The Keras H5 model with a custom layer has specifics to be converted into SavedModel format.
-For example, the model with a custom layer `CustomLayer` from `custom_layer.py` is converted as follows:
-```python
-import tensorflow as tf
-from custom_layer import CustomLayer
-model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})
-tf.saved_model.save(model,'model')
-```
-
 Then follow the above instructions for the SavedModel format. 
 
 > **NOTE:** Do not use other hacks to resave TensorFlow* 2 models into TensorFlow* 1 formats.    

docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md

@@ -109,6 +109,7 @@ Framework-agnostic parameters:
   --disable_gfusing     Turn off fusing of grouped convolutions
   --enable_concat_optimization
                         Turn on Concat optimization.
+  --move_to_preprocess  Move mean values to IR preprocess section
   --extensions EXTENSIONS
                         Directory or a comma separated list of directories
                         with extensions. To disable all extensions including
@@ -126,10 +127,12 @@ Framework-agnostic parameters:
                         value, for example: "node_name->True". It will be
                         DEPRECATED in future releases. Use --input option to
                         specify a value for freezing.
-  --static_shape        Enables IR generation for fixed input shape (folding
-                        `ShapeOf` operations and shape-calculating sub-graphs
-                        to `Constant`). Changing model input shape using
-                        the Inference Engine API in runtime may fail for such an IR.
+  --generate_deprecated_IR_V7
+                        Force to generate old deprecated IR V7 with layers
+                        from old IR specification.
+  --keep_shape_ops      [ Experimental feature ] Enables `Shape` operation
+                        with all children keeping. This feature makes model
+                        reshapable in Inference Engine
   --disable_weights_compression
                         Disable compression and store weights with original
                         precision.
@@ -165,9 +168,12 @@ There are situations when the input data shape for the model is not fixed, like
 ## When to Reverse Input Channels <a name="when_to_reverse_input_channels"></a>
 Input data for your application can be of RGB or BRG color input order. For example, Inference Engine samples load input images in the BGR channels order. However, the model may be trained on images loaded with the opposite order (for example, most TensorFlow\* models are trained with images in RGB order). In this case, inference results using the Inference Engine samples may be incorrect. The solution is to provide `--reverse_input_channels` command line parameter. Taking this parameter, the Model Optimizer performs first convolution or other channel dependent operation weights modification so these operations output will be like the image is passed with RGB channels order.
 
-## When to Specify `--static_shape` Command Line Parameter
-If the `--static_shape` command line parameter is specified the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
-Resulting Intermediate Representation will not be resizable with the help of Inference Engine.
+## When to Specify `--keep_shape_ops` Command Line Parameter
+The `--keep_shape_ops` is an **experimental** command line parameter, so the model conversion may fail if it is specified.
+
+By default, the Model Optimizer evaluates shapes of all operations in the model (shape propagation) for a fixed input(s) shape(s). During the shape propagation the Model Optimizer evaluates operations *Shape* and removes them from the computation graph. With that approach, the initial model which can consume inputs of different shapes may be converted to IR working with the input of one fixed shape only. For example, consider the case when some blob is reshaped from 4D of a shape *[N, C, H, W]* to a shape *[N, C, H \* W]*. During the model conversion the Model Optimize calculates output shape as a constant 1D blob with values *[N, C, H \* W]*. So if the input shape changes to some other value *[N,C,H1,W1]* (it is possible scenario for a fully convolutional model) then the reshape layer becomes invalid.
+
+If the `--keep_shape_ops` command line parameter is specified then the Model Optimizer keeps *Shape* operations in the model and inserts additional layers to convert the graph layout from NHWC to NCHW layout if necessary.
 
 ## Examples of CLI Commands
 

docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_GluonCV_Models.md

@@ -1,26 +0,0 @@
-# Converting GluonCV* Models {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models}
-
-This document provides the instructions and examples on how to use Model Optimizer to convert [GluonCV SSD and YOLO-v3 models](https://gluon-cv.mxnet.io/model_zoo/detection.html) to IR.
-
-1. Choose the topology available from the [GluonCV Moodel Zoo](https://gluon-cv.mxnet.io/model_zoo/detection.html) and export to the MXNet format using the GluonCV API. For example, for the `ssd_512_mobilenet1.0` topology: 
-```python
-from gluoncv import model_zoo, data, utils
-from gluoncv.utils import export_block
-net = model_zoo.get_model('ssd_512_mobilenet1.0_voc', pretrained=True)
-export_block('ssd_512_mobilenet1.0_voc', net, preprocess=True, layout='HWC')
-```
-As a result, you will get an MXNet model representation in `ssd_512_mobilenet1.0.params` and `ssd_512_mobilenet1.0.json` files generated in the current directory. 
-2. Run the Model Optimizer tool specifying the `--enable_ssd_gluoncv` option. Make sure the `--input_shape` parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrates running the Model Optimizer for the SSD and YOLO-v3 models trained with the NHWC layout and located in the `<model_directory>`:
-* **For GluonCV SSD topologies:**
-```sh
-python3 mo_mxnet.py --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data
-```
-* **For YOLO-v3 topology:**
-   * To convert the model:
-   ```sh
-   python3 mo_mxnet.py --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3]
-   ```
-   * To convert the model with replacing the subgraph with RegionYolo layers:
-   ```sh
-   python3 mo_mxnet.py --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "mo/extensions/front/mxnet/yolo_v3_mobilenet1_voc.json"
-   ```

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_BERT_From_Tensorflow.md

@@ -114,6 +114,7 @@ python3 ./mo_tf.py
 --input_model inference_graph.pb 
 --input "IteratorGetNext:0{i32}[1 128],IteratorGetNext:1{i32}[1 128],IteratorGetNext:4{i32}[1 128]" 
 --disable_nhwc_to_nchw 
+--keep_shape_ops
 ```
 For other applicable parameters, refer to [Convert Model from TensorFlow](../Convert_Model_From_TensorFlow.md).
 

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md

@@ -1,7 +1,9 @@
 # Converting TensorFlow* Object Detection API Models {#openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models}
 
 > **NOTES**:
-> * Starting with the 2021.1 release, the Model Optimizer converts the TensorFlow\* Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
+>
+> * Starting with the 2019 R1 release, the Model Optimizer supports the `--keep_shape_ops` command line parameter that allows you to convert the TensorFlow\* Object Detection API Faster and Mask RCNNs topologies so they can be re-shaped in the Inference Engine using dedicated reshape API. Refer to [Using Shape Inference](../../../../IE_DG/ShapeInference.md) for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
+> * Starting with the 2018 R4 release, the Model Optimizer supports the `--input_shape` command line parameter for the TensorFlow\* Object Detection API topologies. Refer to the [Custom Input Shape](#tf_od_custom_input_shape) for more information.
 > * To generate IRs for SSD topologies, the Model Optimizer creates a number of `PriorBoxClustered` layers instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the Inference Engine using dedicated Inference Engine API. The reshaping is supported for all SSD topologies except FPNs which contain hardcoded shapes for some operations preventing from changing topology input shape.  
 
 ## How to Convert a Model

docs/documentation_guidelines.md

@@ -1,207 +0,0 @@
-# Documentation Guidelines
-
-## Authoring Guidelines
-
-We want our documentation to be easy to read and understand. This section describes guidelines for writing documentation that is clear, concise, confident, and courteous.
-
-For details on organizing content and how we use Markdown, refer to [Structure and Formatting](#structure-and-formatting-guidelines).
-
-###	Use Simple English
-
-* **Be brief.** Use short sentences and paragraphs. Stick to the principle of one main idea per sentence, plus one additional point if needed. Each paragraph should address one main idea. Remember the basic structure of a paragraph: Introduction, body, and conclusion.
-* **Use simple words.** Use simple words to increase reader comprehension and reduce ambiguity. Follow our tips for making good word choices:
-   * **Avoid jargon:** Write for your audience, using everyday language where possible, and technical terms where appropriate. Avoid clichés, idioms, and metaphors.
-   * **Be consistent:** Use one term for each concept or action and use it consistently.
-   * **Avoid “fancy” words and phrases:** If there is a simpler word or phrase, use it.
-
-###	Make Content Scannable
-
-Organize your content to make it scannable for the reader, which helps them find what they need quickly, and to understand the information more efficiently.
-
-* **Put the most important content first.** Make sure your introduction clearly communicates what the reader can find on the page. Present the point of the document first, and organize supporting information towards the end of the page.
-* **Write scannable headings.** Expect readers of documentation to skim and scan the content, and to leave if they don’t find what they need quickly. Good headings add organization to your content and help the reader to find and understand content more effectively. Follow our guidelines for writing effective Headings.
-* **Write great link text.** Great link text tells the reader what they can expect when they click on a link. It also helps make the page more scannable. Follow our guidelines for writing Link text.
-
-###	Use Strong Verbs
-
-* **Passive verbs make writing stuffy and formal**. Use strong verbs to get to the point and avoid unnecessary words and phrases.
-* **Commands**, also called imperatives, are the fastest and most direct way of giving someone instructions.
-* **Use simple present tense** instead of future tense for most text. Search for the words “will” or “shall” to find future tense instances. Future tense is acceptable for conditional statements, such as in a caution or a warning.
-
-###	Parallelism
-
-Parallelism refers to the practice of using similar patterns of grammar, and sometimes length, to coordinate words, phrases, and clauses.  
-Use parallel construction in lists. The table below shows some unparallel structures and how they can be made parallel with a little rewording.
-
-| Parallel (do)           | Unparallel (don’t)        |
-|-------------------------|---------------------------|
-| 1. Mount the panel.     | 1. Mount the panel.       |
-| 2. Install the battery. | 2. Battery installation.  |
-| 3. Wire the keypad.     | 3. Wiring the keypad.     |
-
-## Structure Guidelines
-
-Content should be organized to support scanning. Consistent organization, formatting, and writing style helps readers quickly find what they need and to understand the content more effectively. This section describes our organization.
-
-### Website Structure
-The documentation website is organized this way:
-
-1. **Getting Started**: installation guides, prerequisites, how to set working environment
-2. **How To-s**: videos and guides, for some reason not included in **Guides** section
-3. **Guides**: developer guides for all OpenVINO Toolkit components
-4. **Resources**: samples, demos, pre-trained models and tools (Accuracy Checker, Post-Training Optimization etc)
-5. **Performance Information**: benchmark tests information, ways to improve Performance
-6. **API References**: in-built wiki with class structures
-
-### Page Structures
-Each page in the documentation should follow the basic format of:
-
-* **Overview**: 1-2 sentences describing what this page shows and why it matters
-* **Prerequisites**: Describe any pre-work necessary to the content (if appropriate)
-* **Content**
-* **Next steps**: List links to next steps (if appropriate)
-* **Related topics**: List links to related content (if appropriate)
-
-## Formatting Guidelines
-We apply the formatting using Markdown.
-
-### Inline Text Formatting
-Use our quick reference for the most commonly used inline text elements:
-
-| Element                 | Markdown Examples            |   Notes |
-|---------------          |---------------------| -------- |
-| Notes                   | `> **NOTE**: text`   | The space between `>` and `**NOTE**` is required  |
-| Code elements (variables, methods, classes)      | ``` `text` ```      |              |
-| Code snippets           | ` ```cpp`<br>text<br>` ``` `      |                         |
-| Console commands, output| ` ```sh`<br>text<br>` ``` ` |                               |
-| Product name	          | OpenVINO™,<br>Intel® Distribution of OpenVINO™ toolkit  |   |
-
-### Headings
-Use headings to section and organize your content for better readability and clarity.
-
-* **Use strong verbs.** Strong, active verbs get to the point. Avoid **-ing** verbs, such as Running, Testing, etc.
-* **Be concise and descriptive.** Use only the words necessary to describe the section.
-* **Use sentence case.** Capitalize first letter every word except articles, prepositions: **Build Inference Engine on Linux**
-* **Avoid punctuation.** Unless your heading is a question, don’t use sentence punctuation in headings.
-* **Use parallel structure.** Headings at the same level should use the same grammatical pattern. This provides structure to the document and helps users find information more easily. See [Parallelism](#parallelism).
-
-Use following rules to write headings in your documentation:
-* All files must have a top level heading, which is the title for the page.
-  ```
-  # First Level Heading
-  ```
-* Up to three additional levels of headings are allowed under the title heading.
-  ```
-  ## Second Level Heading
-  ### Third Level Heading
-  #### Fourth Level Heading
-  ```
-* Each heading should be followed by at least one paragraph of content. Avoid two or more consecutive headings.
-* To add a link to a heading in the current page, do the following:  
-    * Add the `<a>` HTML tag and set a unique name to your heading with `name` argument:
-    ```XML
-    ## <a name="set-the-environment-variables"></a> Set the Environment Variables
-    ```
-    * Place the link to the heading using the set name:
-    ```XML
-        Refer <a href="#set-the-environment-variables">here</a>
-    ```
-
-### Code Snippets and Examples
-You can create fenced code snippets by placing triple backticks ` ``` `  before and after the code block and add an optional language identifier to enable syntax highlighting in your fenced code block. For example, to syntax highlight C++ code:
-```
-```cpp
-#include <inference_engine.hpp>
-void main(int argc, char *argv[])
-{
-    InferenceEngine::Core ie;
-}
-```
-
-```cpp
-#include <inference_engine.hpp>
-void main(int argc, char *argv[])
-{
-    InferenceEngine::Core ie;
-}
-```
-
-For full list with supported languages refer [here](https://github.com/github/linguist/blob/master/lib/linguist/languages.yml).
-
-### Lists and Instructions
-This section provides information about formatting numbered and bulleted lists.
-
-#### Numbered Lists
-Use a numbered list when the order or priority of the items is important, such as step-by-step instructions:
-```
-1. **Initialize core** to..
-2. **Prepare input** for..
-3. ...
-```
-Numbered lists are most frequently used for procedures. Use numbered lists to show sequence for the items. Follow our guidelines for numbered lists:
-* Make few first words describe whole step and bold it:  
-  6) **Prepare input**. You can use one of the following options to prepare input
-* Introduce a numbered list with a sentence. End the setup text with a colon. Example:
-  "To configure the unit, perform the following steps:"
-* Each item in the list should be [parallel](#parallelism).
-* Treat numbered list items as full sentences with correct ending punctuation.
-* You may interrupt numbered lists with other content, if relevant, e.g. explanatory text, commands, or code.
-
-#### Bulleted lists
-Use a bulleted list when the order of the items is not important:
-```
-* Option X
-* Option Y
-* Option Z
-```
-* Introduce a bulleted list with a sentence. End the setup text with a colon. Example:  
-  “To repair the unit, you will need the following items:”
-* Make few first words describe whole step and bold it:  
-  * **Prepare input**. You can use one of the following options to prepare input
-* Each item in the list should be parallel.
-* Avoid interrupting bulleted lists with other paragraph styles.
-* Second-level bullets are acceptable; avoid third-level bullets.
-
-For both list types, keep all items in the list parallel. See [Parallelism](#parallelism):
-
-### Links
-All links in content should follow these guidelines:
-
-* **Avoid generic text:** Don’t use generic, uninformative link text such as “click here” or “read more”.
-* **Write descriptive link text:** Link text should describe where the link goes, without having to read the surrounding text.
-* **Use unique link text:** Each link text on a page should be unique. If users see the same link text twice on a page, they’ll assume it goes to the same place.
-* **Start link text with keywords:** Frontload the link text with the most important words to help users scan the text.
-
-Use following examples to write links in your documentation:
-* To add a cross-reference to another documentation page, use file path from [DoxygenLayout.xml](./openvino-documentation/blob/master/docs/doxygen/DoxygenLayout.xml) (don't use direct links to docs.openvinotoolkit.org):
-```
-[OpenVINO Installation on Linux](./docs/install_guides/installing-openvino-linux.md)
-```
-Currently links to headings in other markdown files are not supported. To refer to a specific section, you may use the following example:  
-```
-"Use **Set Environment Variables** section in the [OpenVINO Installation on Linux](./docs/install_guides/installing-openvino-linux.md) document.
-```
-* To add URL link to any software, link to the latest version, for example:
-```
-For more details, see [CMake page](https://cmake.org/cmake/help/latest/manual/cmake.1.html#manual:cmake(1))
-```
-
-## Graphics Guidelines
-Use images or figures to convey information that may be difficult to explain using words alone. Well-planned graphics reduce the amount of text required to explain a topic or example.
-
-Follow these guidelines when using graphics in support of your documentation:
-
-* Keep it simple. Use images that serve a specific purpose in your document, and contain only the information the reader needs.
-* Avoid graphics that will need frequent updating. Don’t include information in a graphic that might change with each release, such as product versions.
-* Use either PNG or JPEG bitmap files for screenshots and SVG files for vector graphics.
-* Place the image immediately after the text it helps clarify, or as close as possible.
-* Use the Markdown directives to insert images and figures into the document. Include both alt text, and the title.
-* Include at least one direct reference to an image from the main text, using the figure number.
-
-Images should follow these naming and location conventions:
-
-* Save the image files in a figures folder at the same level as the file that will reference the image.
-* Name image files according to the following rules:
-   * Use only lower case letters.
-   * Separate multiple words in filenames using dashes.
-   * Name images using the filename of the file they appear on and add a number to indicate their place in the file. For example, the third figure added to the `welcome.md` file must be named `welcome-3.png`.

docs/doxygen/doxy_md_filter.py

@@ -1,94 +0,0 @@
-import os
-import re
-import glob
-import argparse
-
-
-def get_label(file):
-    """
-    Read lines of a file and try to find a doxygen label.
-    If the label is not found return None.
-    """
-    for line in file:
-        label = re.search(r'\{\#(.+)\}', line)
-        if label:
-            return label.group(1)
-    return
-
-
-def replace_links(content, items, folder, labels, docs_folder):
-    """
-    Replace markdown links with doxygen labels.
-    """
-    for item in items:
-        link = item[0]
-        ext = item[1]
-        link_path = os.path.abspath(os.path.join(folder, link))
-        if os.path.exists(link_path):
-            if ext == 'md':
-                if link_path in labels:
-                    label = labels.get(link_path)
-                else:
-                    with open(link_path, 'r', encoding='utf-8') as file:
-                        lines = []
-                        i = 0
-                        while i < 5:
-                            try:
-                                lines.append(next(file))
-                            except StopIteration:
-                                break
-                            i += 1
-                        label = get_label(lines)
-                        labels[link_path] = label
-                if label:
-                    content = content.replace(link, '@ref ' + label)
-            else:
-                rel_path = os.path.relpath(link_path, docs_folder).replace('\\', '/')
-                content = content.replace(link, rel_path)
-    return content
-
-
-def process_github_md_links(content, items):
-    """
-    This is a workaround to support github markdown links in doxygen 1.8.12.
-    """
-    for item in items:
-        orig = item[0]
-        link_name = item[1]
-        link_url = item[2]
-        html_link = '<a href="{}">{}</a>'.format(link_url, link_name)
-        content = content.replace(orig, html_link)
-    return content
-
-
-def process(docs_folder):
-    """
-    Recursively find markdown files in docs_folder and
-    replace links to markdown files with doxygen labels (ex. @ref label_name).
-    """
-    labels = dict()  # store labels in dictionary
-    md_files = glob.glob(os.path.join(docs_folder, '**/*.md'), recursive=True)
-    for md_file in md_files:
-        md_folder = os.path.dirname(md_file)
-        with open(md_file, 'r', encoding='utf-8') as f:
-            content = f.read()
-        inline_links = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.(md|png|jpg|gif))\)', content, flags=re.IGNORECASE))
-        github_md_links = set(re.findall(r'(\[(.+?)\]\((https:[\w\.\/-]+?\.md)\))', content, flags=re.IGNORECASE))
-        reference_links = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.(md|png|jpg|gif))', content, flags=re.IGNORECASE))
-        content = replace_links(content, inline_links, md_folder, labels, docs_folder)
-        content = replace_links(content, reference_links, md_folder, labels, docs_folder)
-        content = process_github_md_links(content, github_md_links)
-        if inline_links or reference_links or github_md_links:
-            with open(md_file, 'w', encoding='utf-8') as f:
-                f.write(content)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('docs', type=str, help='Path to a folder containing .md files.')
-    args = parser.parse_args()
-    process(args.docs)
-
-
-if __name__ == '__main__':
-    main()

docs/doxygen/ie_c_api.config

@@ -1,25 +0,0 @@
-@INCLUDE = @IE_CONFIG_BINARY@
-
-EXCLUDE_SYMBOLS        = INFERENCE_ENGINE_C_API_EXTERN \
-                         INFERENCE_ENGINE_C_API \
-                         IE_NODISCARD
-
-PREDEFINED             = "__attribute__(x)=" \
-                         "__VA_ARGS__=" \
-                         "INFERENCE_ENGINE_C_API_EXTERN=" \
-                         "INFERENCE_ENGINE_C_API=" \
-                         "IE_NODISCARD=" \
-                         "__cdecl=" \
-                         "__declspec(x)=" \
-                         "__GNUC__=" \
-                         "_WIN32"
-
-FILE_PATTERNS          = *.h
-
-LAYOUT_FILE            = @C_LAYOUT_BINARY@
-
-INPUT                  = @C_API@
-
-HTML_OUTPUT            = ie_c_api
-
-GENERATE_TAGFILE       = @DOCS_BINARY_DIR@/ie_c_api.tag

docs/doxygen/ie_c_api.xml

@@ -29,8 +29,6 @@
       </tab>
       <tab type="user" title="Inference Engine С++ API Reference" url="../annotated.html"/>
       <tab type="user" title="Inference Engine Python API Reference" url="../ie_python_api/annotated.html"/>
-      <!-- DL Streamer -->
-      <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
     </tab>
     <!-- Chinese docs -->
     <tab type="user" title="中文文件" url="https://docs.openvinotoolkit.org/cn/index.html"/>

docs/doxygen/ie_docs.xml

@@ -62,7 +62,6 @@
                             </tab>
                             <tab type="usergroup" title="Converting a MXNet* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet">
                                 <tab type="user" title="Converting a Style Transfer Model from MXNet" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet"/>
-                                <tab type="user" title="Converting GluonCV* Models" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models"/>
                             </tab>
                             <tab type="usergroup" title="Converting Your Kaldi* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi">
                                 <tab type="user" title="Convert Kaldi* ASpIRE Chain Time Delay Neural Network (TDNN) Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model"/>
@@ -94,7 +93,6 @@
                 <!-- IR -->
                 <tab type="usergroup" title="Intermediate Representation and Operations Sets" url="@ref openvino_docs_MO_DG_IR_and_opsets">
                     <tab type="usergroup" title="Available Operations Sets" url="@ref openvino_docs_ops_opset">
-                        <tab type="user" title="opset4 Specification" url="@ref openvino_docs_ops_opset4"/>
                         <tab type="user" title="opset3 Specification" url="@ref openvino_docs_ops_opset3"/>
                         <tab type="user" title="opset2 Specification" url="@ref openvino_docs_ops_opset2"/>
                         <tab type="user" title="opset1 Specification" url="@ref openvino_docs_ops_opset1"/>
@@ -102,13 +100,13 @@
                     <tab type="usergroup" title="Operations Specifications" url="">
                         <tab type="user" title="Abs-1" url="@ref openvino_docs_ops_arithmetic_Abs_1"/>
                         <tab type="user" title="Acos-1" url="@ref openvino_docs_ops_arithmetic_Acos_1"/>
-                        <tab type="user" title="Acosh-3" url="@ref openvino_docs_ops_arithmetic_Acosh_3"/>
+                        <tab type="user" title="Acosh-1" url="@ref openvino_docs_ops_arithmetic_Acosh_1"/>
                         <tab type="user" title="Add-1" url="@ref openvino_docs_ops_arithmetic_Add_1"/>
                         <tab type="user" title="Asin-1" url="@ref openvino_docs_ops_arithmetic_Asin_1"/>
-                        <tab type="user" title="Asinh-3" url="@ref openvino_docs_ops_arithmetic_Asinh_3"/>
+                        <tab type="user" title="Asinh-1" url="@ref openvino_docs_ops_arithmetic_Asinh_1"/>
                         <tab type="user" title="Assign-3" url="@ref openvino_docs_ops_infrastructure_Assign_3"/>
                         <tab type="user" title="Atan-1" url="@ref openvino_docs_ops_arithmetic_Atan_1"/>
-                        <tab type="user" title="Atanh-3" url="@ref openvino_docs_ops_arithmetic_Atanh_3"/>
+                        <tab type="user" title="Atanh-1" url="@ref openvino_docs_ops_arithmetic_Atanh_1"/>
                         <tab type="user" title="AvgPool-1" url="@ref openvino_docs_ops_pooling_AvgPool_1"/>
                         <tab type="user" title="BatchNormInference-1" url="@ref openvino_docs_ops_normalization_BatchNormInference_1"/>
                         <tab type="user" title="BatchToSpace-2" url="@ref openvino_docs_ops_movement_BatchToSpace_2"/>
@@ -127,7 +125,6 @@
                         <tab type="user" title="Convolution-1" url="@ref openvino_docs_ops_convolution_Convolution_1"/>
                         <tab type="user" title="Cos-1" url="@ref openvino_docs_ops_arithmetic_Cos_1"/>
                         <tab type="user" title="Cosh-1" url="@ref openvino_docs_ops_arithmetic_Cosh_1"/>
-                        <tab type="user" title="CTCLoss-4" url="@ref openvino_docs_ops_sequence_CTCLoss_4"/>
                         <tab type="user" title="CumSum" url="@ref openvino_docs_ops_arithmetic_CumSum_3"/>
                         <tab type="user" title="DeformableConvolution-1" url="@ref openvino_docs_ops_convolution_DeformableConvolution_1"/>
                         <tab type="user" title="DeformablePSROIPooling-1" url="@ref openvino_docs_ops_detection_DeformablePSROIPooling_1"/>
@@ -155,9 +152,7 @@
                         <tab type="user" title="GroupConvolutionBackpropData-1" url="@ref openvino_docs_ops_convolution_GroupConvolutionBackpropData_1"/>
                         <tab type="user" title="GroupConvolution-1" url="@ref openvino_docs_ops_convolution_GroupConvolution_1"/>
                         <tab type="user" title="HardSigmoid-1" url="@ref openvino_docs_ops_activation_HardSigmoid_1"/>
-                        <tab type="user" title="HSwish-4" url="@ref openvino_docs_ops_activation_HSwish_4"/>
                         <tab type="user" title="Interpolate-1" url="@ref openvino_docs_ops_image_Interpolate_1"/>
-                        <tab type="user" title="Interpolate-4" url="@ref openvino_docs_ops_image_Interpolate_4"/>
                         <tab type="user" title="LRN-1" url="@ref openvino_docs_ops_normalization_LRN_1"/>
                         <tab type="user" title="LSTMCell-1" url="@ref openvino_docs_ops_sequence_LSTMCell_1"/>
                         <tab type="user" title="LSTMSequence-1" url="@ref openvino_docs_ops_sequence_LSTMSequence_1"/>
@@ -173,13 +168,11 @@
                         <tab type="user" title="MaxPool-1" url="@ref openvino_docs_ops_pooling_MaxPool_1"/>
                         <tab type="user" title="Maximum-1" url="@ref openvino_docs_ops_arithmetic_Maximum_1"/>
                         <tab type="user" title="Minimum-1" url="@ref openvino_docs_ops_arithmetic_Minimum_1"/>
-                        <tab type="user" title="Mish-4" url="@ref openvino_docs_ops_activation_Mish_4"/>
                         <tab type="user" title="Mod-1" url="@ref openvino_docs_ops_arithmetic_Mod_1"/>
                         <tab type="user" title="Multiply-1" url="@ref openvino_docs_ops_arithmetic_Multiply_1"/>
                         <tab type="user" title="Negative-1" url="@ref openvino_docs_ops_arithmetic_Negative_1"/>
                         <tab type="user" title="NonMaxSuppression-1" url="@ref openvino_docs_ops_sort_NonMaxSuppression_1"/>
                         <tab type="user" title="NonMaxSuppression-3" url="@ref openvino_docs_ops_sort_NonMaxSuppression_3"/>
-                        <tab type="user" title="NonMaxSuppression-4" url="@ref openvino_docs_ops_sort_NonMaxSuppression_4"/>
                         <tab type="user" title="NonZero-3" url="@ref openvino_docs_ops_condition_NonZero_3"/>
                         <tab type="user" title="NormalizeL2-1" url="@ref openvino_docs_ops_normalization_NormalizeL2_1"/>
                         <tab type="user" title="NotEqual-1" url="@ref openvino_docs_ops_comparison_NotEqual_1"/>
@@ -192,13 +185,9 @@
                         <tab type="user" title="PriorBoxClustered-1" url="@ref openvino_docs_ops_detection_PriorBoxClustered_1"/>
                         <tab type="user" title="PriorBox-1" url="@ref openvino_docs_ops_detection_PriorBox_1"/>
                         <tab type="user" title="Proposal-1" url="@ref openvino_docs_ops_detection_Proposal_1"/>
-                        <tab type="user" title="Proposal-4" url="@ref openvino_docs_ops_detection_Proposal_4"/>
                         <tab type="user" title="Range-1" url="@ref openvino_docs_ops_generation_Range_1"/>
-                        <tab type="user" title="Range-4" url="@ref openvino_docs_ops_generation_Range_4"/>
                         <tab type="user" title="ReadValue-3" url="@ref openvino_docs_ops_infrastructure_ReadValue_3"/>
                         <tab type="user" title="ReLU-1" url="@ref openvino_docs_ops_activation_ReLU_1"/>
-                        <tab type="user" title="ReduceL1-4" url="@ref openvino_docs_ops_reduction_ReduceL1_4"/>
-                        <tab type="user" title="ReduceL2-4" url="@ref openvino_docs_ops_reduction_ReduceL2_4"/>
                         <tab type="user" title="ReduceLogicalAnd-1" url="@ref openvino_docs_ops_reduction_ReduceLogicalAnd_1"/>
                         <tab type="user" title="ReduceLogicalOr-1" url="@ref openvino_docs_ops_reduction_ReduceLogicalOr_1"/>
                         <tab type="user" title="ReduceMax-1" url="@ref openvino_docs_ops_reduction_ReduceMax_1"/>
@@ -228,7 +217,6 @@
                         <tab type="user" title="Sin-1" url="@ref openvino_docs_ops_arithmetic_Sin_1"/>
                         <tab type="user" title="Sinh-1" url="@ref openvino_docs_ops_arithmetic_Sinh_1"/>
                         <tab type="user" title="SoftMax-1" url="@ref openvino_docs_ops_activation_SoftMax_1"/>
-                        <tab type="user" title="SoftPlus-4" url="@ref openvino_docs_ops_activation_SoftPlus_4"/>
                         <tab type="user" title="SpaceToBatch-2" url="@ref openvino_docs_ops_movement_SpaceToBatch_2"/>
                         <tab type="user" title="SpaceToDepth-1" url="@ref openvino_docs_ops_movement_SpaceToDepth_1"/>
                         <tab type="user" title="Split-1" url="@ref openvino_docs_ops_movement_Split_1"/>
@@ -237,7 +225,6 @@
                         <tab type="user" title="Squeeze-1" url="@ref openvino_docs_ops_shape_Squeeze_1"/>
                         <tab type="user" title="StridedSlice-1" url="@ref openvino_docs_ops_movement_StridedSlice_1"/>
                         <tab type="user" title="Subtract-1" url="@ref openvino_docs_ops_arithmetic_Subtract_1"/>
-                        <tab type="user" title="Swish-4" url="@ref openvino_docs_ops_activation_Swish_4"/>
                         <tab type="user" title="Tan-1" url="@ref openvino_docs_ops_arithmetic_Tan_1"/>
                         <tab type="user" title="Tanh-1" url="@ref openvino_docs_ops_arithmetic_Tanh_1"/>
                         <tab type="user" title="TensorIterator-1" url="@ref openvino_docs_ops_infrastructure_TensorIterator_1"/>
@@ -263,13 +250,14 @@
                         <tab type="user" title="GPU Kernels Extensibility" url="@ref openvino_docs_IE_DG_Extensibility_DG_GPU_Kernel"/>
                         <tab type="user" title="VPU Kernels Extensibility" url="@ref openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel"/>
                         <tab type="user" title="Build Extension Library Using CMake" url="@ref openvino_docs_IE_DG_Extensibility_DG_Building"/>
+                        <tab type="user" title="[Deprecated] Shape Infer API" url="@ref openvino_docs_IE_DG_Extensibility_DG_deprecated_ShapeInfer"/>
+                        <tab type="user" title="[Deprecated] CPU Kernels Extensibility API" url="@ref openvino_docs_IE_DG_Extensibility_DG_deprecated_Factory"/>
                     </tab>
                     <tab type="user" title="Integrate the Inference Engine with Your Application" url="@ref openvino_docs_IE_DG_Integrate_with_customer_application_new_API"/>
-                    <tab type="user" title="[DEPRECATED] Migration from Inference Engine Plugin API to Core API" url="@ref openvino_docs_IE_DG_Migration_CoreAPI"/>
+                    <tab type="user" title="Migration from Inference Engine Plugin API to Core API" url="@ref openvino_docs_IE_DG_Migration_CoreAPI"/>
                     <tab type="user" title="Introduction to Performance Topics" url="@ref openvino_docs_IE_DG_Intro_to_Performance"/>
                     <tab type="user" title="Inference Engine Python* API Overview" url="@ref openvino_inference_engine_ie_bridges_python_docs_api_overview"/>
                     <tab type="user" title="Build a Model with nGraph" url="@ref openvino_docs_IE_DG_nGraphTutorial"/>
-                    <tab type="user" title="Read an ONNX model" url="@ref openvino_docs_IE_DG_ONNX_Support"/>
                     <tab type="user" title="[DEPRECATED] Import an ONNX model" url="@ref openvino_docs_IE_DG_OnnxImporterTutorial"/>
                     <tab type="user" title="Graph Debug Capabilities" url="@ref openvino_docs_IE_DG_Graph_debug_capabilities"/>
                     <tab type="user" title="Using Dynamic Batching Feature" url="@ref openvino_docs_IE_DG_DynamicBatching"/>
@@ -798,7 +786,7 @@
             <!-- OpenCV -->
             <tab type="user" title="OpenCV Developer Guide" url="https://docs.opencv.org/master/"/>
             <!-- IE C -->
-            <tab type="user" title="Inference Engine C API Reference" url="ie_c_api/modules.html"/>
+            <tab type="user" title="Inference Engine C API Reference" url="ie_c_api/groups.html"/>
             <!-- IE C++-->
             <tab type="classes" visible="yes" title="Inference Engine С++ API Reference">
                 <tab type="classlist" visible="yes" title=""/>
@@ -812,7 +800,7 @@
             <!-- IE Python -->
             <tab type="user" title="Inference Engine Python API Reference" url="ie_python_api/annotated.html"/>
             <!-- DL Streamer -->
-            <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
+            <tab type="user" title="DL Streamer API Reference" url="https://opencv.github.io/gst-video-analytics/"/>
 
         </tab>
         <!-- Chinese docs -->

docs/doxygen/ie_plugin_api.config

@@ -1,51 +0,0 @@
-@INCLUDE = @IE_CONFIG_BINARY@
-
-LAYOUT_FILE            = @PLUGIN_LAYOUT_BINARY@
-
-HTML_OUTPUT            = ie_plugin_api
-
-GENERATE_TAGFILE       = @DOCS_BINARY_DIR@/ie_plugin_api.tag
-
-EXTRACT_LOCAL_CLASSES  = NO
-
-INPUT                  = @DOCS_BINARY_DIR@/docs/IE_PLUGIN_DG \
-						             @IE_SOURCE_DIR@/src/plugin_api
-
-FILE_PATTERNS          = *.c \
-                         *.cpp \
-                         *.c++ \
-                         *.h \
-                         *.hpp \
-                         *.md
-
-EXCLUDE_PATTERNS       = cnn_network_ngraph_impl.hpp \
-                         ie_imemory_state_internal.hpp \
-                         ie_memory_state_internal.hpp \
-                         ie_memory_state_base.hpp \
-                         convert_function_to_cnn_network.hpp \
-                         generic_ie.hpp
-
-EXCLUDE_SYMBOLS        =
-
-EXAMPLE_PATH           = @CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src \
-						             @CMAKE_CURRENT_SOURCE_DIR@/template_plugin/include \
-                         @CMAKE_CURRENT_SOURCE_DIR@/template_plugin/src/CMakeLists.txt \
-                         @CMAKE_CURRENT_SOURCE_DIR@/template_plugin/tests/functional/CMakeLists.txt \
-                         @CMAKE_CURRENT_SOURCE_DIR@/examples
-
-EXAMPLE_PATTERNS       = *.cpp \
-						             *.hpp
-
-ENUM_VALUES_PER_LINE   = 1
-
-EXPAND_ONLY_PREDEF     = YES
-
-PREDEFINED             = INFERENCE_ENGINE_API \
-                         INFERENCE_ENGINE_API_CPP \
-                         INFERENCE_ENGINE_API_CLASS \
-                         INFERENCE_ENGINE_DEPRECATED \
-                         IE_SUPPRESS_DEPRECATED_START \
-                         IE_SUPPRESS_DEPRECATED_END \
-                         IE_SUPPRESS_DEPRECATED_START_WIN \
-                         IE_SUPPRESS_DEPRECATED_END_WIN \
-                         IE_THREAD=IE_THREAD_TBB

docs/doxygen/ie_py_api.config

@@ -1,35 +0,0 @@
-@INCLUDE = @IE_CONFIG_BINARY@
-
-EXCLUDE_SYMBOLS        = ie_api::BlobBuffer \
-                         *impl* \
-                         *device_name* \
-                         *num_requests* \
-                         *exec_net* \
-                         *c_config* \
-                         *ie_core_impl* \
-                         *plugin_impl* \
-                         *extension_str* \
-                         *buffer* \
-                         *__cinit__*
-
-PREDEFINED             = "__attribute__(x)=" \
-                         "__VA_ARGS__=" \
-                         "INFERENCE_ENGINE_C_API_EXTERN=" \
-                         "INFERENCE_ENGINE_C_API=" \
-                         "IE_NODISCARD=" \
-                         "__cdecl=" \
-                         "__declspec(x)=" \
-                         "__GNUC__=" \
-                         "_WIN32"
-
-EXTENSION_MAPPING      = pyx=Python
-
-FILE_PATTERNS          = *.pyx
-
-LAYOUT_FILE            = @PY_LAYOUT_BINARY@
-
-INPUT                  = @PYTHON_API_OUT@
-
-HTML_OUTPUT            = ie_python_api
-
-GENERATE_TAGFILE       = @DOCS_BINARY_DIR@/ie_python_api.tag

docs/doxygen/ie_py_api.xml

@@ -14,7 +14,7 @@
       <!-- OpenCV -->
       <tab type="user" title="OpenCV Developer Guide" url="https://docs.opencv.org/master/"/>
       <!-- IE C -->
-      <tab type="usergroup" title="Inference Engine C API Reference" url="../ie_c_api/modules.html"/>
+      <tab type="usergroup" title="Inference Engine C API Reference" url="../ie_c_api/groups.html"/>
       <!-- IE C++-->
       <tab type="user" title="Inference Engine С++ API Reference" url="../annotated.html"/>
       <!-- IE Python -->
@@ -27,8 +27,6 @@
         <tab type="filelist" visible="no"/>
         <tab type="globals" visible="no"/>
       </tab>
-      <!-- DL Streamer -->
-      <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
     </tab>
     <!-- Chinese docs -->
     <tab type="user" title="中文文件" url="https://docs.openvinotoolkit.org/cn/index.html"/>

docs/doxygen/pyx_filter.py

@@ -1,129 +0,0 @@
-import re
-import argparse
-
-
-def process_pyx(pyx_file):
-    """
-    Convert .pyx file to a more readable format for doxygen.
-    """
-    with open(pyx_file, 'r') as f:
-        source = f.readlines()
-    idx = 0
-    while idx < len(source):
-        line = source[idx]
-        striped_line = line.lstrip()
-        tabs = ' ' * (len(line) - len(striped_line))  # Keep indentation
-        striped_line = striped_line.rstrip()
-        if striped_line == '@property':  # Python functions wrapped with @property decorator
-            new_getter = convert_getter(source, idx)
-            if new_getter:
-                indent = tabs + ' ' * 4
-                new_func, comments, shift = new_getter
-                func_name = re.search(r'def\s+?([A-Za-z0-9_]+)\s*?\(', new_func).group(1)
-                source[idx + 1] = tabs + new_func + '\n'
-                for i in range(shift):
-                    source.pop(idx + 2)
-                # This is a workaround to help Doxygen understand "@property" functions as class properties.
-                for comm in comments:
-                    source.insert(idx + 2, '{indent}{comment}\n'.format(indent=indent, comment=comm))
-                    idx += 1
-                source.insert(idx + 2, '{indent}self.{func_name} = {func_name}\n'.format(
-                    indent=indent,
-                    func_name=func_name
-                ))
-                idx += 1
-        if re.search(r'c?p?def.+\(', striped_line):  # Convert cython functions to python format
-            new_sign = get_signature(source, idx)
-            if new_sign:
-                new_func, shift = new_sign
-                args = re.search(r'\((.+)\)', new_func)
-                if args:
-                    new_func = new_func.replace(args.group(1), process_args(args.group(1))).replace('cpdef', 'def')
-                source[idx] = tabs + new_func + '\n'
-                for i in range(shift):
-                    source.pop(idx + 1)
-        if '__cinit__' in striped_line:  # Doxygen only interprets "__init__" constructors
-            source[idx] = source[idx].replace('__cinit__', '__init__')
-        idx += 1
-
-    with open(pyx_file, 'w') as f:
-        f.writelines(source)
-
-
-def process_args(str_args):
-    """
-    Convert function arguments to the doxygen readable format.
-    """
-    args = re.sub(r'\[.*?\]', r'', str_args)
-    args = re.sub(r'\(.*?\)', r'', args)
-    args = args.split(',')
-    for idx, arg in enumerate(args):
-        arg = arg.replace('&', '').strip()
-        if arg.startswith('const'):
-            arg = arg.replace('const', '').strip()
-        if ':' in arg:
-            arg = arg.split(':')[0]
-        match = re.match(r'^[\w\.]+\s+(\w.+)', arg)
-        if match:
-            arg = match.group(1)
-        args[idx] = arg.strip()
-    return ', '.join(args)
-
-
-def convert_getter(source, start):
-    """
-    Process a function that is wrapped with @property decorator
-    """
-    current = source[start + 1].strip()
-    if not current.startswith('def'):  # Base Case
-        return
-    new_sign = get_signature(source, start + 1)
-    if new_sign:
-        new_func, shift = new_sign
-        new_func += ':'
-        # get comments
-        comments = []
-        if start > 1:
-            idx = start - 1
-            while source[idx].lstrip().startswith('#') and idx >= 0:
-                comments.append(source[idx].strip())
-                idx -= 1
-            comments.reverse()
-        return new_func, comments, shift
-
-
-def get_signature(source, start):
-    """
-    Get function signature and process it
-    """
-    match = re.search(r'c?p?def.+\(', source[start].strip())
-    if not match:
-        return
-    start_j = match.span()[1]
-    open_brackets = 1
-    new_sign = match.group()
-
-    for i in range(start, len(source)):
-        line = source[i].strip()
-        for j in range(start_j, len(line)):
-            char = line[j]
-            if char == ')':
-                open_brackets -= 1
-            if char == '(':
-                open_brackets += 1
-            new_sign += char
-            if not open_brackets:
-                return new_sign + ':\n', i - start
-        start_j = 0
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('pyx_file', type=str, nargs='+', help='Path to a .pyx file.')
-    args = parser.parse_args()
-    for pyx in args.pyx_file:
-        process_pyx(pyx)
-
-
-if __name__ == '__main__':
-    main()

docs/examples/CMakeLists.txt

@@ -8,6 +8,6 @@ file(GLOB SOURCES *.cpp)
 
 add_library(ie_docs_examples STATIC ${SOURCES})
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api ngraph)
+target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api)
 
 #add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})

docs/examples/example_ngraph_utils.cpp

@@ -1,251 +0,0 @@
-// Copyright (C) 2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <memory>
-
-#include <ngraph/pattern/op/wrap_type.hpp>
-
-// ! [ngraph:include]
-#include <ngraph/ngraph.hpp>
-#include <ngraph/opsets/opset3.hpp>
-// ! [ngraph:include]
-
-using namespace ngraph;
-
-// ! [ngraph_utils:simple_function]
-std::shared_ptr<ngraph::Function> create_simple_function() {
-    // This example shows how to create ngraph::Function
-    //
-    // Parameter--->Multiply--->Add--->Result
-    //    Constant---'          /
-    //              Constant---'
-
-    // Create opset3::Parameter operation with static shape
-    auto data = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
-
-    auto mul_constant = ngraph::opset3::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {1.5});
-    auto mul = std::make_shared<ngraph::opset3::Multiply>(data, mul_constant);
-
-    auto add_constant = ngraph::opset3::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {0.5});
-    auto add = std::make_shared<ngraph::opset3::Add>(mul, add_constant);
-
-    // Create opset3::Result operation
-    auto res = std::make_shared<ngraph::opset3::Result>(mul);
-
-    // Create nGraph function
-    return std::make_shared<ngraph::Function>(ngraph::ResultVector{res}, ngraph::ParameterVector{data});
-}
-// ! [ngraph_utils:simple_function]
-
-// ! [ngraph_utils:advanced_function]
-std::shared_ptr<ngraph::Function> create_advanced_function() {
-    // Advanced example with multi output operation
-    //
-    // Parameter->Split---0-->Result
-    //               | `--1-->Relu-->Result
-    //               `----2-->Result
-
-    auto data = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::f32, ngraph::Shape{1, 3, 64, 64});
-
-    // Create Constant for axis value
-    auto axis_const = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}/*scalar shape*/, {1});
-
-    // Create opset3::Split operation that splits input to three slices across 1st dimension
-    auto split = std::make_shared<ngraph::opset3::Split>(data, axis_const, 3);
-
-    // Create opset3::Relu operation that takes 1st Split output as input
-    auto relu = std::make_shared<ngraph::opset3::Relu>(split->output(1)/*specify explicit output*/);
-
-    // Results operations will be created automatically based on provided OutputVector
-    return std::make_shared<ngraph::Function>(ngraph::OutputVector{split->output(0), relu, split->output(2)}, ngraph::ParameterVector{data});
-}
-// ! [ngraph_utils:advanced_function]
-
-void pattern_matcher_examples(std::shared_ptr<Node> node) {
-{
-// ! [pattern:simple_example]
-// Pattern example
-auto input = std::make_shared<ngraph::opset3::Parameter>(element::i64, Shape{1});
-auto shapeof = std::make_shared<ngraph::opset3::ShapeOf>(input);
-
-// Create Matcher with Parameter->ShapeOf pattern
-auto m = std::make_shared<ngraph::pattern::Matcher>(shapeof, "MyPatternBasedTransformation");
-// ! [pattern:simple_example]
-
-// ! [pattern:callback_example]
-ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) {
-    // Get root node
-    std::shared_ptr<Node> root_node = m.get_match_root();
-
-    // Get all nodes matched by pattern
-    NodeVector nodes = m.get_matched_nodes();
-
-    // Transformation code
-    return false;
-};
-// ! [pattern:callback_example]
-}
-
-{
-// ! [pattern:label_example]
-// Detect Multiply with arbitrary first input and second as Constant
-// ngraph::pattern::op::Label - represent arbitrary input
-auto input = ngraph::pattern::any_input();
-auto value = ngraph::opset3::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {0.5});
-auto mul = std::make_shared<ngraph::opset3::Multiply>(input, value);
-auto m = std::make_shared<ngraph::pattern::Matcher>(mul, "MultiplyMatcher");
-// ! [pattern:label_example]
-}
-
-{
-// ! [pattern:concat_example]
-// Detect Concat operation with arbitrary number of inputs
-auto concat = ngraph::pattern::wrap_type<ngraph::opset3::Concat>();
-auto m = std::make_shared<ngraph::pattern::Matcher>(concat, "ConcatMatcher");
-// ! [pattern:concat_example]
-}
-
-{
-// ! [pattern:predicate_example]
-// Detect Multiply->Add sequence where mul has exactly one consumer
-auto mul = ngraph::pattern::wrap_type<ngraph::opset3::Multiply>(ngraph::pattern::consumers_count(1)/*сheck consumers count*/);
-auto add = ngraph::pattern::wrap_type<ngraph::opset3::Add>({mul, ngraph::pattern::any_input()});
-auto m = std::make_shared<ngraph::pattern::Matcher>(add, "MultiplyAddMatcher");
-// Matcher can be used to match pattern manually on given node
-if (m->match(node->output(0))) {
-    // Successfully matched
-}
-// ! [pattern:predicate_example]
-}
-
-}
-
-bool ngraph_api_examples(std::shared_ptr<Node> node) {
-{
-// ! [ngraph:ports_example]
-// Let's suppose that node is opset3::Convolution operation
-// as we know opset3::Convolution has two input ports (data, weights) and one output port
-Input <Node> data = node->input(0);
-Input <Node> weights = node->input(1);
-Output <Node> output = node->output(0);
-
-// Getting shape and type
-auto pshape = data.get_partial_shape();
-auto el_type = data.get_element_type();
-
-// Ggetting parent for input port
-Output <Node> parent_output;
-parent_output = data.get_source_output();
-
-// Another short way to get partent for output port
-parent_output = node->input_value(0);
-
-// Getting all consumers for output port
-auto consumers = output.get_target_inputs();
-// ! [ngraph:ports_example]
-}
-
-{
-// ! [ngraph:shape]
-auto partial_shape = node->input(0).get_partial_shape(); // get zero input partial shape
-if (partial_shape.is_dynamic() /* or !partial_shape.is_staic() */) {
-    return false;
-}
-auto static_shape = partial_shape.get_shape();
-// ! [ngraph:shape]
-}
-
-{
-// ! [ngraph:shape_check]
-auto partial_shape = node->input(0).get_partial_shape(); // get zero input partial shape
-
-// Check that input shape rank is static
-if (!partial_shape.rank().is_static()) {
-    return false;
-}
-auto rank_size = partial_shape.rank().get_length();
-
-// Check that second dimension is not dynamic
-if (rank_size < 2 || partial_shape[1].is_dynamic()) {
-    return false;
-}
-auto dim = partial_shape[1].get_length();
-// ! [ngraph:shape_check]
-}
-
-return true;
-}
-
-// ! [ngraph:replace_node]
-bool ngraph_replace_node(std::shared_ptr<Node> node) {
-    // Step 1. Verify that node has opset3::Negative type
-    auto neg = std::dynamic_pointer_cast<ngraph::opset3::Negative>(node);
-    if (!neg) {
-        return false;
-    }
-
-    // Step 2. Create opset3::Multiply operation where the first input is negative operation input and second as Constant with -1 value
-    auto mul = std::make_shared<ngraph::opset3::Multiply>(neg->input_value(0),
-                                                          opset3::Constant::create(neg->get_element_type(), Shape{1}, {-1}));
-
-    mul->set_friendly_name(neg->get_friendly_name());
-    ngraph::copy_runtime_info(neg, mul);
-
-    // Step 3. Replace Negative operation with Multiply operation
-    ngraph::replace_node(neg, mul);
-    return true;
-
-    // Step 4. Negative operation will be removed automatically because all consumers was moved to Multiply operation
-}
-// ! [ngraph:replace_node]
-
-// ! [ngraph:insert_node]
-// Step 1. Lets suppose that we have a node with single output port and we want to insert additional operation new_node after it
-void insert_example(std::shared_ptr<ngraph::Node> node) {
-    // Get all consumers for node
-    auto consumers = node->output(0).get_target_inputs();
-
-    // Step 2. Create new node. Let it be opset1::Relu.
-    auto new_node = std::make_shared<ngraph::opset3::Relu>(node);
-
-    // Step 3. Reconnect all consumers to new_node
-    for (auto input : consumers) {
-        input.replace_source_output(new_node);
-    }
-}
-// ! [ngraph:insert_node]
-
-// ! [ngraph:insert_node_with_copy]
-void insert_example_with_copy(std::shared_ptr<ngraph::Node> node) {
-    // Make a node copy
-    auto node_copy = node->clone_with_new_inputs(node->input_values());
-    // Create new node
-    auto new_node = std::make_shared<ngraph::opset3::Relu>(node_copy);
-    ngraph::replace_node(node, new_node);
-}
-// ! [ngraph:insert_node_with_copy]
-
-void eliminate_example(std::shared_ptr<ngraph::Node> node) {
-// ! [ngraph:eliminate_node]
-// Suppose we have a node that we want to remove
-bool success = replace_output_update_name(node->output(0), node->input_value(0));
-// ! [ngraph:eliminate_node]
-}
-
-// ! [ngraph:visualize]
-void visualization_example(std::shared_ptr<ngraph::Function> f) {
-    ngraph::pass::Manager manager;
-
-    // Serialize ngraph::Function to before.svg file before transformation
-    manager.register_pass<ngraph::pass::VisualizeTree>("/path/to/file/before.svg");
-
-    // Run your transformation
-    // manager.register_pass<ngraph::pass::MyTransformation>();
-
-    // Serialize ngraph::Function to after.svg file after transformation
-    manager.register_pass<ngraph::pass::VisualizeTree>("/path/to/file/after.svg");
-
-    manager.run_passes(f);
-}
-// ! [ngraph:visualize]

docs/get_started/get_started_windows.md

@@ -1,541 +0,0 @@
-# Get Started with OpenVINO™ Toolkit on Windows* {#openvino_docs_get_started_get_started_windows}
-
-The OpenVINO™ toolkit optimizes and runs Deep Learning Neural Network models on Intel® hardware. This guide helps you get started with the OpenVINO™ toolkit you installed on Windows* OS.
-
-In this guide, you will:
-* Learn the OpenVINO™ inference workflow
-* Run demo scripts that illustrate the workflow and perform the steps for you
-* Run the workflow steps yourself, using detailed instructions with a code sample and demo application
-
-## <a name="openvino-components"></a>OpenVINO™ toolkit Components
-The toolkit consists of three primary components:
-* **Model Optimizer:** Optimizes models for Intel® architecture, converting models into a format compatible with the Inference Engine. This format is called an Intermediate Representation (IR).
-* **Intermediate Representation:** The Model Optimizer output. A model converted to a format that has been optimized for Intel® architecture and is usable by the Inference Engine.
-* **Inference Engine:** The software libraries that run inference against the IR (optimized model) to produce inference results.
-
-
-In addition, demo scripts, code samples and demo applications are provided to help you get up and running with the toolkit:
-* **Demo Scripts** - Batch scripts that automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios.  
-* [**Code Samples**](../IE_DG/Samples_Overview.md) - Small console applications that show you how to:
-    * Utilize specific OpenVINO capabilities in an application.
-    * Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
-* [**Demo Applications**](@ref omz_demos_README) - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
-
-## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
-This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Windows*](../install_guides/installing-openvino-windows.md).
-
-By default, the installation directory is `C:\Program Files (x86)\IntelSWTools\openvino`, referred to as `<INSTALL_DIR>`. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `C:\Program Files (x86)\IntelSWTools` with the directory in which you installed the software.
-
-The primary tools for deploying your models and applications are installed to the `<INSTALL_DIR>\deployment_tools` directory.
-<details>
-    <summary><strong>Click for the <code>deployment_tools</code> directory structure</strong></summary>
-   
-
-| Directory&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Description                                                                           |  
-|:----------------------------------------|:--------------------------------------------------------------------------------------|
-| `demo\`                                 | Demo scripts. Demonstrate pipelines for inference scenarios, automatically perform steps and print detailed output to the console. For more information, see the [Use OpenVINO: Demo Scripts](#use-openvino-demo-scripts) section.|
-| `inference_engine\`                     | Inference Engine directory. Contains Inference Engine API binaries and source files, samples and extensions source files, and resources like hardware drivers.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`bin\`          | Inference Engine binaries.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`external\`     | Third-party dependencies and drivers.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`include\`      | Inference Engine header files. For API documentation, see the [Inference Engine API Reference](./annotated.html). |
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`lib\`          | Inference Engine static libraries.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`samples\`      | Inference Engine samples. Contains source code for C++ and Python* samples and build scripts. See the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md). |
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`share\`        | CMake configuration files for linking with Inference Engine.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`src\`          | Source files for CPU extensions.|
-| `~intel_models\` | Symbolic link to the `intel_models` subfolder of the `open_model-zoo` folder |
-| `model_optimizer\`                      | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). |
-| `ngraph\`                               | nGraph directory. Includes the nGraph header and library files. |   
-| `open_model_zoo\`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`demos\`        | Demo applications for inference scenarios. Also includes documentation and build scripts.| 
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models\` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`models`        | Intel's trained and public models that can be obtained with Model Downloader.|
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools\`        | Model Downloader and Accuracy Checker tools. |
-| `tools\`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
-
-</details>
-
-## <a name="workflow-overview"></a>OpenVINO™ Workflow Overview
-
-The simplified OpenVINO™ workflow is:
-1. **Get a trained model** for your inference task. Example inference tasks: pedestrian detection, face detection, vehicle detection, license plate recognition, head pose.
-2. **Run the trained model through the Model Optimizer** to convert the model to an IR, which consists of a pair of `.xml` and `.bin` files that are used as the input for Inference Engine.
-3. **Use the Inference Engine API in the application** to run inference against the IR (optimized model) and output inference results. The application can be an OpenVINO™ sample, demo, or your own application.
-
-## Use the Demo Scripts to Learn the Workflow
-
-The demo scripts in `<INSTALL_DIR>\deployment_tools\demo` give you a starting point to learn the OpenVINO workflow. These scripts automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios. The demo steps demonstrate how to: 
-* Compile several samples from the source files delivered as part of the OpenVINO toolkit
-* Download trained models
-* Perform pipeline steps and see the output on the console
-
-> **REQUIRED**: You must have Internet access to run the demo scripts. If your Internet access is through a proxy server, make sure the operating system environment proxy information is configured.
-
-The demo scripts can run inference on any [supported target device](https://software.intel.com/en-us/openvino-toolkit/hardware). Although the default inference device is CPU, you can use the `-d` parameter to change the inference device. The general command to run the scripts looks as follows:
-
-```bat
-.\<script_name> -d [CPU, GPU, MYRIAD, HDDL]
-```
-
-Before running the demo applications on Intel® Processor Graphics, you must complete the  [Steps for Intel® Processor Graphics (GPU)](../install_guides/installing-openvino-windows.md#Install-GPU).
-
-Before running the demo applications on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, you must complete the [Steps for [Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs](../install_guides/installing-openvino-windows.md#hddl-myriad).
-
-The following paragraphs describe each demo script.
-
-### Image Classification Demo Script
-The `demo_squeezenet_download_convert_run` script illustrates the image classification pipeline.
-
-The script: 
-1. Downloads a SqueezeNet model. 
-2. Runs the Model Optimizer to convert the model to the IR.
-3. Builds the Image Classification Sample Async application.
-4. Runs the compiled sample with the `car.png` image located in the `demo` directory.
-
-<details>
-    <summary><strong>Click for an example of running the Image Classification demo script</strong></summary>
-
-To run the script to perform inference on a CPU:
-
-```bat
-.\demo_squeezenet_download_convert_run.bat
-```
-
-When the script completes, you see the label and confidence for the top-10 categories:
-
-```bat
-
-Top 10 results:
-
-Image C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png
-
-classid probability label
-------- ----------- -----
-817     0.6853030   sports car, sport car
-479     0.1835197   car wheel
-511     0.0917197   convertible
-436     0.0200694   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
-751     0.0069604   racer, race car, racing car
-656     0.0044177   minivan
-717     0.0024739   pickup, pickup truck
-581     0.0017788   grille, radiator grille
-468     0.0013083   cab, hack, taxi, taxicab
-661     0.0007443   Model T
-
-[ INFO ] Execution successful
-```
-
-</details>
-
-### Inference Pipeline Demo Script
-The `demo_security_barrier_camera` uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.
-
-The script:
-1. Downloads three pre-trained model IRs.
-2. Builds the Security Barrier Camera Demo application.
-3. Runs the application with the downloaded models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline.
-
-This application:
-
-1. Identifies an object identified as a vehicle.
-2. Uses the vehicle identification as input to the second model, which identifies specific vehicle attributes, including the license plate.
-3. Uses the the license plate as input to the third model, which recognizes specific characters in the license plate.
-
-<details>
-    <summary><strong>Click for an example of Running the Pipeline demo script</strong></summary>
-    
-To run the script performing inference on Intel® Processor Graphics:
-
-```bat
-.\demo_security_barrier_camera.bat -d GPU
-```
-
-When the verification script completes, you see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
-
-![](../img/inference_pipeline_script_win.png) 
-
-</details>
-
-### Benchmark Demo Script
-The `demo_benchmark_app` script illustrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices. 
-
-The script: 
-1. Downloads a SqueezeNet model.
-2. Runs the Model Optimizer to convert the model to the IR.
-3. Builds the Inference Engine Benchmark tool.
-4. Runs the tool with the `car.png` image located in the `demo` directory.
-
-<details>
-    <summary><strong>Click for an example of running the Benchmark demo script</strong></summary>
-
-To run the script that performs inference on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs:
-
-```bat
-.\demo_squeezenet_download_convert_run.bat -d HDDL
-```
-When the verification script completes, you see the performance counters, resulting latency, and throughput values displayed on the screen.
-</details>
-
-## <a name="using-sample-application"></a>Use Code Samples and Demo Applications to Learn the Workflow
-
-This section guides you through a simplified workflow for the Intel® Distribution of OpenVINO™ toolkit using code samples and demo applications. 
-
-You will perform the following steps: 
-
-1. <a href="#download-models">Use the Model Downloader to download suitable models.</a>
-2. <a href="#convert-models-to-intermediate-representation">Convert the models with the Model Optimizer.</a> 
-3. <a href="#download-media">Download media files to run inference on.</a>
-4. <a href="#run-image-classification">Run inference on the Image Classification Code Sample and see the results</a>.
-5. <a href="#run-security-barrier">Run inference on the Security Barrier Camera Demo application and see the results</a>.
-
-Each demo and code sample is a separate application, but they use the same behavior and components.
- 
-Inputs you need to specify when using a code sample or demo application:
-- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
-- **One or more models** in the IR format. Each model is trained for a specific task. Examples include pedestrian detection, face detection, vehicle detection, license plate recognition, head pose, and others. Different models are used for different applications. Models can be chained together to provide multiple features; for example, vehicle + make/model + license plate recognition.
-- **One or more media files**. The media is typically a video file, but can be a still photo.
-- **One or more target device** on which you run inference. The target device can be the CPU, GPU, FPGA, or VPU accelerator.
-
-### Build the Code Samples and Demo Applications
-
-To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release` and `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_demos_build\intel64\Release` directories, respectively.
-
-You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md#build_samples_linux) and the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) sections.
-
-### <a name="download-models"></a> Step 1: Download the Models
-
-You must have a model that is specific for you inference task. Example model types are:
-- Classification (AlexNet, GoogleNet, SqueezeNet, others) - Detects one type of element in a frame.
-- Object Detection (SSD, YOLO) - Draws bounding boxes around multiple types of objects.
-- Custom (Often based on SSD)
-
-Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README#model_downloader_usage).
-- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
-- Train your own model.
-        
-This guide uses the Model Downloader to get pre-trained models. You can use one of the following options to find a model:
-
-* **List the models available in the downloader**: 
-```bat
-cd <INSTALL_DIR>\deployment_tools\tools\model_downloader\
-```
-```bat
-python info_dumper.py --print_all
-```
-
-* **Use `grep` to list models that have a specific name pattern**: 
-```bat
-python info_dumper.py --print_all | grep <model_name>
-```
-
-Use the Model Downloader to download the models to a models directory. This guide uses `<models_dir>` as the models directory and `<models_name>` as the model name:
-```bat
-python .\downloader.py --name <model_name> --output_dir <models_dir>
-```
-
-Download the following models if you want to run the Image Classification Sample and Security Barrier Camera Demo application:
-
-|Model Name                                     | Code Sample or Demo App                             |
-|-----------------------------------------------|-----------------------------------------------------|
-|`squeezenet1.1`                                | Image Classification Sample                         |
-|`vehicle-license-plate-detection-barrier-0106` | Security Barrier Camera Demo application            |
-|`vehicle-attributes-recognition-barrier-0039`  | Security Barrier Camera Demo application            |
-|`license-plate-recognition-barrier-0001`       | Security Barrier Camera Demo application            |
-
-<details>
-    <summary><strong>Click for an example of downloading the SqueezeNet Caffe* model</strong></summary>
-
-To download the SqueezeNet 1.1 Caffe* model to the `C:\Users\<USER_ID>\Documents\models` folder:
-
-```bat
-python .\downloader.py --name squeezenet1.1 --output_dir C:\Users\username\Documents\models
-```
-
-Your screen looks similar to this after the download:
-```
-################|| Downloading models ||################
-
-========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
-... 100%, 9 KB, ? KB/s, 0 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel
-... 100%, 4834 KB, 571 KB/s, 8 seconds passed
-
-################|| Post-processing ||################
-
-========== Replacing text in C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.prototxt
-```
-</details>
-
-<details>
-    <summary><strong>Click for an example of downloading models for the Security Barrier Camera Demo application</strong></summary>
-
-To download all three pre-trained models in FP16 precision to the `C:\Users\<USER_ID>\Documents\models` folder:   
-
-```bat
-python .\downloader.py --name vehicle-license-plate-detection-barrier-0106,vehicle-attributes-recognition-barrier-0039,license-plate-recognition-barrier-0001 --output_dir C:\Users\username\Documents\models --precisions FP16
-```   
-Your screen looks similar to this after the download:
-```
-################|| Downloading models ||################
-
-========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml
-... 100%, 207 KB, 13810 KB/s, 0 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.bin
-... 100%, 1256 KB, 70 KB/s, 17 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml
-... 100%, 32 KB, ? KB/s, 0 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.bin
-... 100%, 1222 KB, 277 KB/s, 4 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml
-... 100%, 47 KB, ? KB/s, 0 seconds passed
-
-========== Downloading C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.bin
-... 100%, 2378 KB, 120 KB/s, 19 seconds passed
-
-################|| Post-processing ||################
-```
-
-</details>   
-
-### <a name="convert-models-to-intermediate-representation"></a> Step 2: Convert the Models to the Intermediate Representation
-
-In this step, your trained models are ready to run through the Model Optimizer to convert them to the Intermediate Representation (IR) format. This is required before using the Inference Engine with the model.
-
-Models in the Intermediate Representation format always include a pair of `.xml` and `.bin` files. Make sure you have these files for the Inference Engine to find them.
-- **REQUIRED:** `model_name.xml`
-- **REQUIRED:** `model_name.bin`
-
-This guide uses the public SqueezeNet 1.1 Caffe\* model to run the Image Classification Sample. See the example to download a model in the <a href="#download-models">Download Models</a> section to learn how to download this model.
-
-The `squeezenet1.1` model is downloaded in the Caffe* format. You must use the Model Optimizer to convert the model to the IR. 
-The `vehicle-license-plate-detection-barrier-0106`, `vehicle-attributes-recognition-barrier-0039`, `license-plate-recognition-barrier-0001` models are downloaded in the IR format. You do not need to use the Model Optimizer to convert these models.
-
-1. Create an `<ir_dir>` directory to contain the model's IR. 
-
-2. The Inference Engine can perform inference on different precision formats, such as `FP32`, `FP16`, `INT8`. To prepare an IR with specific precision, run the Model Optimizer with the appropriate `--data_type` option.
-
-3. Run the Model Optimizer script:
-   ```bat
-   cd <INSTALL_DIR>\deployment_tools\model_optimizer
-   ```
-   ```bat 
-   python .\mo.py --input_model <model_dir>\<model_file> --data_type <model_precision> --output_dir <ir_dir>
-   ```
-   The produced IR files are in the `<ir_dir>` directory.
-
-<details>
-    <summary><strong>Click for an example of converting the SqueezeNet Caffe* model</strong></summary>
-
-The following command converts the public SqueezeNet 1.1 Caffe\* model to the FP16 IR and saves to the `C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir` output directory:
-
-```bat
-   cd <INSTALL_DIR>\deployment_tools\model_optimizer
-   ```
-   ```bat  
-   python .\mo.py --input_model C:\Users\username\Documents\models\public\squeezenet1.1\squeezenet1.1.caffemodel --data_type FP16 --output_dir C:\Users\username\Documents\models\public\squeezenet1.1\ir
-   ```
-
-After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir` directory.
-
-Copy the `squeezenet1.1.labels` file from the `<INSTALL_DIR>\deployment_tools\demo\` to `<ir_dir>`. This file contains the classes that ImageNet uses. Therefore, the inference results show text instead of classification numbers:
-   ```batch   
-   cp <INSTALL_DIR>\deployment_tools\demo\squeezenet1.1.labels <ir_dir>
-   ```
-</details>
-
-### <a name="download-media"></a> Step 3: Download a Video or a Still Photo as Media
-
-Many sources are available from which you can download video media to use the code samples and demo applications. Possibilities include: 
-- https://videos.pexels.com
-- https://images.google.com
-
-As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes two sample images that you can use for running code samples and demo applications:
-* `<INSTALL_DIR>\deployment_tools\demo\car.png`
-* `<INSTALL_DIR>\deployment_tools\demo\car_1.bmp`
-
-### <a name="run-image-classification"></a>Step 4: Run the Image Classification Code Sample
-
-> **NOTE**: The Image Classification code sample is automatically compiled when you run the Image Classification demo script. If you want to compile it manually, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md#build_samples_windows) section.
-
-To run the **Image Classification** code sample with an input image on the IR: 
-
-1. Set up the OpenVINO environment variables:
-   ```bat
-   <INSTALL_DIR>\openvino\bin\setupvars.sh
-   ``` 
-2. Go to the code samples build directory:
-   ```bat
-   cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release
-   ```
-3. Run the code sample executable, specifying the input media file, the IR of your model, and a target device on which you want to perform inference:
-   ```bat
-   classification_sample_async.exe -i <path_to_media> -m <path_to_model> -d <target_device>
-   ```
-<details>
-    <summary><strong>Click for examples of running the Image Classification code sample on different devices</strong></summary>
-
-The following commands run the Image Classification Code Sample using the `car.png` file from the `<INSTALL_DIR>\deployment_tools\demo` directory as an input image, the IR of your model from `C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir` and on different hardware devices:
-
-**CPU:**
-   ```bat
-   .\classification_sample_async -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\Documents\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d CPU
-   ```
-
-   **GPU:**
-   
-   > **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires     [additional hardware configuration steps](../install_guides/installing-openvino-windows.md#Install-GPU).
-   ```bat
-   .\classification_sample_async -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d GPU
-   ```
-   
-   **MYRIAD:** 
-
-  ```bat   
-   .\classification_sample_async -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d MYRIAD
-   ```
-
-When the Sample Application completes, you see the label and confidence for the top-10 categories on the display. Below is a sample output with inference results on CPU:    
-```bat
-Top 10 results:
-
-Image C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png
-
-classid probability label
-------- ----------- -----
-817     0.8364177   sports car, sport car
-511     0.0945683   convertible
-479     0.0419195   car wheel
-751     0.0091233   racer, race car, racing car
-436     0.0068038   beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
-656     0.0037315   minivan
-586     0.0025940   half track
-717     0.0016044   pickup, pickup truck
-864     0.0012045   tow truck, tow car, wrecker
-581     0.0005833   grille, radiator grille
-
-[ INFO ] Execution successful
-```
-
-</details>
-
-### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
-
-> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) section.
-
-To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
-
-1. Set up the OpenVINO environment variables:
-   ```bat
-   <INSTALL_DIR>\bin\setupvars.bat
-   ``` 
-2. Go to the demo application build directory:
-   ```bat
-   cd C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release
-   ```
-3. Run the demo executable, specifying the input media file, list of model IRs, and a target device on which to perform inference:
-   ```bat
-   .\security_barrier_camera_demo -i <path_to_media> -m <path_to_vehicle-license-plate-detection_model_xml> -m_va <path_to_vehicle_attributes_model_xml> -m_lpr <path_to_license_plate_recognition_model_xml> -d <target_device>
-   ```
-
-<details>
-    <summary><strong>Click for examples of running the Security Barrier Camera demo application on different devices</strong></summary>
-
-**CPU:**
-
-```bat
-.\security_barrier_camera_demo -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m C:\Users\username\Documents\models\intel\vehicle-license-plate-detection-barrier-0106\FP16\vehicle-license-plate-detection-barrier-0106.xml -m_va C:\Users\username\Documents\models\intel\vehicle-attributes-recognition-barrier-0039\FP16\vehicle-attributes-recognition-barrier-0039.xml -m_lpr C:\Users\username\Documents\models\intel\license-plate-recognition-barrier-0001\FP16\license-plate-recognition-barrier-0001.xml -d CPU
-```
-
-**GPU:**
-   
-> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires [additional hardware configuration steps](../install_guides/installing-openvino-windows.md#Install-GPU).
-```bat
-.\security_barrier_camera_demo -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
-```
-
-**MYRIAD:** 
-   
-```bat   
-.\classification_sample_async -i <INSTALL_DIR>\inference-engine\samples\sample_data\car.png -m <ir_dir>\squeezenet1.1.xml -d MYRIAD
-```
-
-</details>
-
-## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples and Demo Applications
-
-Below you can find basic guidelines for executing the OpenVINO™ workflow using the code samples and demo applications:
-
-1. Before using the OpenVINO™ samples, always set up the environment: 
-```bat
-<INSTALL_DIR>\bin\setupvars.bat
-``` 
-2. Make sure to have the directory path for the following:
-- Code Sample binaries located in `C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release`
-- Demo Application binaries located in `C:\Users\<USER_ID>\Documents\Intel\OpenVINO\inference_engine_demos_build\intel64\Release`
-- Media: Video or image. See <a href="#download-media">Download Media</a>.
-- Model: Neural Network topology converted with the Model Optimizer to the IR format (.bin and .xml files). See <a href="#download-models">Download Models</a> for more information.
-
-## <a name="syntax-examples"></a> Typical Code Sample and Demo Application Syntax Examples
-
-Template to call sample code or a demo application:
-
-```bat
-<path_to_app> -i <path_to_media> -m <path_to_model> -d <target_device>
-```
-
-With the sample information specified, the command might look like this:
-
-```bat
-.\object_detection_demo_ssd_async -i C:\Users\<USER_ID>\Documents\Videos\catshow.mp4 \
--m C:\Users\<USER_ID>\Documents\ir\fp32\mobilenet-ssd.xml -d CPU
-```
-
-## <a name="advanced-samples"></a> Advanced Demo Use 
-
-Some demo applications let you use multiple models for different purposes. In these cases, the output of the first model is usually used as the input for later models.
-
-For example, an SSD detects a variety of objects in a frame, then age, gender, head pose, emotion recognition and similar models target the objects classified by the SSD to perform their functions.
-
-In these cases, the use pattern in the last part of the template above is usually:
-
-`-m_<acronym> … -d_<acronym> …`
-
-For head pose:
-
-`-m_hp <headpose model> -d_hp <headpose hardware target>`
-
-**Example of an Entire Command (object_detection + head pose):**
-
-```bat
-.\object_detection_demo_ssd_async -i C:\Users\<USER_ID>\Documents\Videos\catshow.mp4 \
--m C:\Users\<USER_ID>\Documents\ir\fp32\mobilenet-ssd.xml -d CPU -m_hp headpose.xml \
--d_hp CPU
-``` 
-
-**Example of an Entire Command (object_detection + head pose + age-gender):**
-
-```bat
-.\object_detection_demo_ssd_async -i C:\Users\<USER_ID>\Documents\Videos\catshow.mp4 \
--m C:\Users\<USER_ID>\Documents\ir\fp32\mobilenet-ssd.xml -d CPU -m_hp headpose.xml \
--d_hp CPU -m_ag age-gender.xml -d_ag CPU
-```
-
-You can see all the sample application’s parameters by adding the `-h` or `--help` option at the command line.
-
-
-## Additional Resources
-
-Use these resources to learn more about the OpenVINO™ toolkit:
-
-* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
-* [Introduction to Intel® Deep Learning Deployment Toolkit](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Introduction.html)
-* [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
-* [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
-* [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
-* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

docs/index.md

@@ -35,7 +35,7 @@ OpenVINO™ toolkit includes the following components:
 - [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) -  A web-based graphical environment that allows you to easily use various sophisticated OpenVINO™ toolkit components
 - Deep Learning Streamer (DL Streamer) – Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open source version is available on [GitHub](https://github.com/opencv/gst-video-analytics). For the DL Streamer documentation, see:
     - [DL Streamer Samples](IE_DG/Tools_Overview.md)
-    - [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/)
+    - [API Reference](https://opencv.github.io/gst-video-analytics/)
     - [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements)
     - [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial)
 - [OpenCV](https://docs.opencv.org/master/) - OpenCV* community version compiled for Intel® hardware

docs/install_guides/installing-openvino-conda.md

@@ -10,7 +10,6 @@ This guide provides installation steps for Intel® Distribution of OpenVINO™ t
 **Operating Systems**
 
 - Ubuntu* 18.04 long-term support (LTS), 64-bit
-- CentOS* 7.4, 64-bit
 - macOS* 10.14.x versions. 
 - Windows 10*, 64-bit Pro, Enterprise or Education (1607 Anniversary Update, Build 14393 or higher) editions
 - Windows Server* 2016 or higher
@@ -30,10 +29,6 @@ This guide provides installation steps for Intel® Distribution of OpenVINO™ t
    ```sh
    conda install openvino-ie4py-ubuntu18 -c intel
    ```
- - CentOS* 7.4 
-   ```sh
-   conda install openvino-ie4py-centos7 -c intel
-   ```
  - Windows* 10 and macOS*
    ```sh
    conda install openvino-ie4py -c intel

docs/install_guides/installing-openvino-linux-fpga.md

@@ -215,7 +215,7 @@ You can choose to either configure all supported frameworks at once **OR** confi
 cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites 
 ```
 2.  Run the script to configure the Model Optimizer for Caffe,
-    TensorFlow 1.x, MXNet, Kaldi\*, and ONNX:
+    TensorFlow, MXNet, Kaldi\*, and ONNX:
 ```sh
 sudo ./install_prerequisites.sh
 ```
@@ -233,14 +233,10 @@ cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites
 ```sh
 sudo ./install_prerequisites_caffe.sh
 ```
--   For **TensorFlow 1.x**:
+-   For **TensorFlow**:
 ```sh
 sudo ./install_prerequisites_tf.sh
 ```
--   For **TensorFlow 2.x**:
-```sh
-sudo ./install_prerequisites_tf2.sh
-```
 -   For **MXNet**:
 ```sh
 sudo ./install_prerequisites_mxnet.sh

docs/install_guides/installing-openvino-linux.md

@@ -215,7 +215,7 @@ You can choose to either configure all supported frameworks at once **OR** confi
 cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites
 ```
 2.  Run the script to configure the Model Optimizer for Caffe,
-    TensorFlow 1.x, MXNet, Kaldi\*, and ONNX:
+    TensorFlow, MXNet, Kaldi\*, and ONNX:
 ```sh
 sudo ./install_prerequisites.sh
 ```
@@ -235,16 +235,11 @@ cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites
    sudo ./install_prerequisites_caffe.sh
    ```
 
-   - For **TensorFlow 1.x**:
+   - For **TensorFlow**:
    ```sh
    sudo ./install_prerequisites_tf.sh
    ```
 
-   - For **TensorFlow 2.x**:
-   ```sh
-   sudo ./install_prerequisites_tf2.sh
-   ```
-
    - For **MXNet**:
    ```sh
    sudo ./install_prerequisites_mxnet.sh

docs/install_guides/installing-openvino-macos.md

@@ -181,7 +181,7 @@ You can choose to either configure the Model Optimizer for all supported framewo
    cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites
    ```
 
-2. Run the script to configure the Model Optimizer for Caffe, TensorFlow 1.x, MXNet, Kaldi\*, and ONNX:
+2. Run the script to configure the Model Optimizer for Caffe, TensorFlow, MXNet, Kaldi\*, and ONNX:
    ```sh
    sudo ./install_prerequisites.sh
    ```
@@ -202,16 +202,11 @@ Configure individual frameworks separately **ONLY** if you did not select **Opti
    sudo ./install_prerequisites_caffe.sh
    ```
 
-   - For **TensorFlow 1.x**:
+   - For **TensorFlow**:
    ```sh
    sudo ./install_prerequisites_tf.sh
    ```
 
-   - For **TensorFlow 2.x**:
-   ```sh
-   sudo ./install_prerequisites_tf2.sh
-   ```
-
    - For **MXNet**:
    ```sh
    sudo ./install_prerequisites_mxnet.sh

docs/install_guides/installing-openvino-windows-fpga.md

@@ -208,7 +208,7 @@ Type commands in the opened window:
 cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\install_prerequisites
 ```
 
-3. Run the following batch file to configure the Model Optimizer for Caffe\*, TensorFlow\* 1.x, MXNet\*, Kaldi\*, and ONNX\*:<br>
+3. Run the following batch file to configure the Model Optimizer for Caffe\*, TensorFlow\*, MXNet\*, Kaldi\*, and ONNX\*:<br>
 ```sh
 install_prerequisites.bat
 ```
@@ -227,16 +227,11 @@ cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer
    install_prerequisites_caffe.bat
    ```
 
-   * For **TensorFlow 1.x**:<br>
+   * For **TensorFlow**:<br>
    ```sh
    install_prerequisites_tf.bat
    ```
 
-   * For **TensorFlow 2.x**:<br>
-   ```sh
-   install_prerequisites_tf2.bat
-   ```
-
    * For **MXNet**:<br>
    ```sh
    install_prerequisites_mxnet.bat

docs/install_guides/installing-openvino-windows.md

@@ -186,7 +186,7 @@ Type commands in the opened window:
 cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\install_prerequisites
 ```
 
-3. Run the following batch file to configure the Model Optimizer for Caffe\*, TensorFlow\* 1.x, MXNet\*, Kaldi\*, and ONNX\*:<br>
+3. Run the following batch file to configure the Model Optimizer for Caffe\*, TensorFlow\*, MXNet\*, Kaldi\*, and ONNX\*:<br>
 ```sh
 install_prerequisites.bat
 ```
@@ -205,16 +205,11 @@ cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer
    install_prerequisites_caffe.bat
    ```
 
-   * For **TensorFlow 1.x**:<br>
+   * For **TensorFlow**:<br>
    ```sh
    install_prerequisites_tf.bat
    ```
 
-   * For **TensorFlow 2.x**:<br>
-   ```sh
-   install_prerequisites_tf2.bat
-   ```
-
    * For **MXNet**:<br>
    ```sh
    install_prerequisites_mxnet.bat

docs/ops/activation/GELU_2.md

@@ -15,15 +15,13 @@ Gelu(x)=x*Φ(x), where Φ(x) is the Cumulative Distribution Function for Gaussia
 The following equivalent combination is recognized and fused into single Gelu op: 
 
 \f[
-    Gelu(x) = 0.5*x*(1.0 + erf((x) / \sqrt{2})
+    Gelu(x) = 0.5*x*(1 + erf((x) / sqrt(2) )
 \f]
 
-Similarly, the following Gelu approximation (typical for the TensorFlow*) is recognized and fused into single Gelu op 
-
+Similarly, the following Gelu approximation (typical for the TensorFlow*) is  recognized and fused into single Gelu op
 \f[
-    Gelu(x) \approx 0.5x(1.0 + tanh(\sqrt{2.0/pi} * (x + 0.044715 * x ^ 3))
+    Gelu(x) \approx  0.5*x*(1 + tanh((sqrt(2/pi)) * (x + 0.044715 * x ^ 3))
 \f]
-
 **Inputs**:
 
 *   **1**: Multidimensional input tensor. Required.