Revert "Fix experimental detectron do ref impl (#10621)" (#12683) (#13009)

* Revert "Fix experimental detectron do ref impl (#10621)"

This reverts commit d87233863d.

* Disabled Experimental Detectron per agreement with GPU team. Ticket to fix it: 90209

.ci/azure/android_arm64.yml

@@ -13,7 +13,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: android_arm64
@@ -114,6 +114,7 @@ jobs:
         -DENABLE_SAMPLES=ON
         -DENABLE_INTEL_MYRIAD=OFF
         -DBUILD_java_api=ON
+        -DBUILD_cuda_plugin=OFF
         -DTHREADING=SEQ
         -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache

.ci/azure/linux.yml

@@ -13,13 +13,13 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
   - repository: testdata
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/testdata
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: Lin
@@ -111,7 +111,8 @@ jobs:
       set -e
       $(REPO_DIR)/install_build_dependencies.sh
       # Move jdk into contrib
-      sudo apt --assume-yes install openjdk-11-jdk
+      # 'clang' compiler is to check that samples can be built using it
+      sudo apt --assume-yes install openjdk-11-jdk clang
       # For opencv-python: python3-setuptools and pip upgrade
       python3 -m pip install --upgrade pip
       python3 -m pip install -r $(REPO_DIR)/src/bindings/python/src/compatibility/openvino/requirements.txt
@@ -157,10 +158,10 @@ jobs:
         -DENABLE_FASTER_BUILD=ON
         -DENABLE_STRICT_DEPENDENCIES=OFF
         -DENABLE_REQUIREMENTS_INSTALL=OFF
-        -DENABLE_OPENCV=ON
         -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules
         -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
         -DCMAKE_C_COMPILER_LAUNCHER=ccache
+        -DBUILD_cuda_plugin=OFF
         $(REPO_DIR)
       workingDirectory: $(BUILD_DIR)
 
@@ -214,7 +215,6 @@ jobs:
       set -e
       mkdir -p $(INSTALL_DIR)/opencv/
       cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -DCOMPONENT=tests -P cmake_install.cmake
-      cp -R $(REPO_DIR)/temp/opencv_4.5.2_ubuntu20/opencv/* $(INSTALL_DIR)/opencv/
     workingDirectory: $(BUILD_DIR)
     displayName: 'Install tests'
 
@@ -226,6 +226,14 @@ jobs:
     displayName: 'Build cpp samples'
     continueOnError: false
 
+  - script: |
+      export CC=clang
+      export CXX=clang++
+      $(INSTALL_DIR)/samples/cpp/build_samples.sh -i $(INSTALL_DIR)
+    workingDirectory: $(BUILD_SAMPLES_DIR)
+    displayName: 'Build cpp samples - clang'
+    continueOnError: false
+
   - script: $(INSTALL_DIR)/samples/c/build_samples.sh -i $(INSTALL_DIR)
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build c samples'
@@ -247,7 +255,7 @@ jobs:
   - script: |
       export DATA_PATH=$(MODELS_PATH)
       export MODELS_PATH=$(MODELS_PATH)
-      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_TEST_DIR)/pyopenvino $(PYTHON_STATIC_ARGS) --junitxml=TEST-Pyngraph.xml --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_utils/test_utils.py --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_onnx/test_zoo_models.py --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_onnx/test_backend.py
+      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_TEST_DIR)/pyopenvino $(PYTHON_STATIC_ARGS) --junitxml=TEST-Pyngraph.xml --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_utils/test_utils.py --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_onnx/test_zoo_models.py --ignore=$(INSTALL_TEST_DIR)/pyopenvino/tests/test_onnx/test_backend.py -v
     displayName: 'Python API 2.0 Tests'
     continueOnError: false
 
@@ -255,7 +263,6 @@ jobs:
       export MO_ROOT=$(INSTALL_DIR)/tools/mo
       . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_DIR)/tests/mo/unit_tests --junitxml=TEST-ModelOptimizer.xml
     displayName: 'Model Optimizer UT'
-    condition: false
     continueOnError: false
 
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ov_core_unit_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
@@ -263,10 +270,20 @@ jobs:
     displayName: 'OV Core UT'
     continueOnError: false
 
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/onnx_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU* --gtest_output=xml:TEST-ONNXImportUT.xml
+    workingDirectory: $(INSTALL_TEST_DIR)
+    displayName: 'ONNX Frontend UT'
+    continueOnError: false
+
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/paddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-Paddle.xml
     displayName: 'Paddle Frontend UT'
     continueOnError: false
 
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/onnx_frontend_tests --gtest_print_time=1 --gtest_output=xml:TEST-Paddle.xml
+    workingDirectory: $(INSTALL_TEST_DIR)
+    displayName: 'ONNX Frontend UT'
+    continueOnError: false
+
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
     displayName: 'Tensorflow Frontend UT'
     continueOnError: false
@@ -292,6 +309,10 @@ jobs:
     displayName: 'VPU UT'
     continueOnError: false
 
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/XLinkTests --gtest_output=xml:TEST-XLinkTests.xml
+    displayName: 'XLink Tests'
+    continueOnError: false
+
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ieMultiPluginUnitTests --gtest_output=xml:TEST-ieMultiPluginUnitTests.xml
     displayName: 'MULTI UT'
     continueOnError: false
@@ -311,7 +332,7 @@ jobs:
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/cpuFuncTests --gtest_filter=*smoke* --gtest_print_time=1 --gtest_output=xml:TEST-cpuFuncTests.xml
     displayName: 'CPU FuncTests'
     continueOnError: false
-    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF')
+    condition: and(succeeded(), eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF'))
 
   - script: |
       export DATA_PATH=$(MODELS_PATH)

.ci/azure/linux_arm64.yml

@@ -13,7 +13,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: linux_arm64
@@ -142,6 +142,7 @@ jobs:
         -DCMAKE_BUILD_TYPE=$(BUILD_TYPE)
         -DENABLE_SAMPLES=ON
         -DBUILD_java_api=OFF
+        -DBUILD_cuda_plugin=OFF
         -DENABLE_INTEL_MYRIAD=OFF
         -DTHREADING=SEQ
         -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules

.ci/azure/linux_conditional_compilation.yml

@@ -21,7 +21,6 @@ jobs:
     VSTS_HTTP_TIMEOUT: 200
     BUILD_TYPE: Release
     REPO_DIR: $(Build.Repository.LocalPath)
-    OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)/../openvino_contrib
     MODELS_PATH: $(REPO_DIR)/../testdata
     WORK_DIR: $(Pipeline.Workspace)/_w
     BUILD_DIR: $(WORK_DIR)/build

.ci/azure/linux_coverity.yml

@@ -4,7 +4,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: Lin

.ci/azure/linux_lohika.yml

@@ -4,6 +4,7 @@
 #    type: github
 #    endpoint: openvinotoolkit
 #    name: openvinotoolkit/testdata
+#    ref: releases/2022/2
 
 jobs:
 - job: Lin_lohika

.ci/azure/mac.yml

@@ -13,13 +13,13 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
   - repository: testdata
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/testdata
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: Mac
@@ -101,7 +101,7 @@ jobs:
       export PATH="/usr/local/opt/cython/bin:$PATH"
       export CC=gcc
       export CXX=g++
-      cmake -GNinja -DVERBOSE_BUILD=ON -DENABLE_REQUIREMENTS_INSTALL=OFF -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=OFF -DENABLE_STRICT_DEPENDENCIES=OFF -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_C_COMPILER_LAUNCHER=ccache $(REPO_DIR)
+      cmake -GNinja -DVERBOSE_BUILD=ON -DENABLE_REQUIREMENTS_INSTALL=OFF -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_PYTHON=ON -DENABLE_TESTS=OFF -DENABLE_STRICT_DEPENDENCIES=OFF -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)/modules -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_C_COMPILER_LAUNCHER=ccache -DBUILD_cuda_plugin=OFF $(REPO_DIR)
     workingDirectory: $(BUILD_DIR)
     displayName: 'CMake'
 
@@ -151,12 +151,18 @@ jobs:
   - script: ls -alR $(INSTALL_DIR)
     displayName: 'List install files'
 
-  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ov_core_unit_tests --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid:IE_CPU/GRUSequenceOp.onnx_model_gru* --gtest_output=xml:TEST-NGraphUT.xml
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/ov_core_unit_tests --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
     workingDirectory: $(INSTALL_TEST_DIR)
     displayName: 'OV Core UT'
     continueOnError: false
     enabled: false
 
+  - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/onnx_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU*:IE_CPU.onnx_model_sigmoid:IE_CPU/GRUSequenceOp.onnx_model_gru* --gtest_output=xml:TEST-ONNXImportUT.xml
+    workingDirectory: $(INSTALL_TEST_DIR)
+    displayName: 'ONNX Frontend UT'
+    continueOnError: false
+    enabled: false
+
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/InferenceEngineUnitTests --gtest_print_time=1 --gtest_filter=-MKLDNNGraphStructureTests.TestNoRedundantReordersBeforeDWConvolution:TestConvolution/MKLDNNGraphConvolutionTests.TestsConvolution/0:TestConvolutionDefaultPrimitivesPriority/MKLDNNGraphConvolutionTests.TestsConvolution/0 --gtest_output=xml:TEST-InferenceEngineUnitTests.xml
     displayName: 'IE UT old'
     continueOnError: false

.ci/azure/windows.yml

@@ -13,13 +13,13 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: releases/2022/1
+    ref: releases/2022/2
 
   - repository: testdata
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/testdata
-    ref: releases/2022/1
+    ref: releases/2022/2
 
 jobs:
 - job: Win
@@ -32,7 +32,7 @@ jobs:
     maxParallel: 2
 
   # About 150% of total time
-  timeoutInMinutes: 180
+  timeoutInMinutes: 270 #Temporary change
 
   pool:
     name: WIN_VMSS_VENV_D8S_WU2
@@ -135,7 +135,7 @@ jobs:
 
   - script: |
       set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -G "Ninja Multi-Config" -DENABLE_WHEEL=ON -DENABLE_ONEDNN_FOR_GPU=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_GAPI_PREPROCESSING=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
+      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -G "Ninja Multi-Config" -DENABLE_WHEEL=ON -DENABLE_ONEDNN_FOR_GPU=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DBUILD_cuda_plugin=OFF $(REPO_DIR)
     workingDirectory: $(BUILD_DIR)
     displayName: 'CMake'
 
@@ -169,13 +169,6 @@ jobs:
     workingDirectory: $(BUILD_SAMPLES_TESTS_DIR)
     displayName: 'Install Samples Tests'
 
-  - script: $(CMAKE_CMD) -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -DCOMPONENT=tests -P cmake_install.cmake
-    workingDirectory: $(BUILD_DIR)
-    displayName: 'Install tests'
-
-  - script: dir $(INSTALL_DIR) /s
-    displayName: 'List install files'
-
   - script: $(INSTALL_DIR)\samples\cpp\build_samples_msvc.bat -i $(INSTALL_DIR)
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build cpp samples'
@@ -200,9 +193,15 @@ jobs:
       python -m pytest $(INSTALL_DIR)\tests\smoke_tests\  --env_conf $(INSTALL_DIR)\tests\smoke_tests\env_config.yml -s --junitxml=TEST-SamplesSmokeTests.xml
     workingDirectory: $(INSTALL_DIR)
     displayName: 'Samples Smoke Tests'
-    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
     continueOnError: false
 
+  - script: $(CMAKE_CMD) -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -DCOMPONENT=tests -P cmake_install.cmake
+    workingDirectory: $(BUILD_DIR)
+    displayName: 'Install tests'
+
+  - script: dir $(INSTALL_DIR) /s
+    displayName: 'List install files'
+
   - script: rd /Q /S $(BUILD_DIR)
     displayName: 'Clean build dir'
     continueOnError: false
@@ -212,10 +211,20 @@ jobs:
     displayName: 'OV Core UT'
     continueOnError: false
 
+  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\onnx_tests --gtest_print_time=1 --gtest_filter=-*IE_GPU* --gtest_output=xml:TEST-ONNXImportUT.xml
+    workingDirectory: $(INSTALL_TEST_DIR)
+    displayName: 'ONNX Frontend UT'
+    continueOnError: false
+
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-Paddle.xml
     displayName: 'Paddle Frontend UT'
     continueOnError: false
 
+  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\onnx_frontend_tests --gtest_print_time=1 --gtest_output=xml:TEST-ONNX.xml
+    workingDirectory: $(INSTALL_TEST_DIR)
+    displayName: 'ONNX Frontend UT'
+    continueOnError: false
+
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
     displayName: 'Tensorflow Frontend UT'
     continueOnError: false
@@ -242,6 +251,10 @@ jobs:
     displayName: 'VPU UT'
     continueOnError: false
 
+  - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\XLinkTests --gtest_output=xml:TEST-XLinkTests.xml
+    displayName: 'XLink Tests'
+    continueOnError: false
+
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\onnxImporterUnitTests --gtest_output=xml:TEST-onnxImporterUnitTests.xml
     displayName: 'ONNX Importer UT'
     continueOnError: false
@@ -263,7 +276,7 @@ jobs:
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\cpuFuncTests --gtest_filter=*smoke* --gtest_output=xml:TEST-cpuFuncTests.xml
     displayName: 'CPU FuncTests'
     continueOnError: false
-    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF')
+    condition: and(succeeded(), eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF'))
 
   - script: |
       set DATA_PATH=$(MODELS_PATH)

.ci/azure/windows_conditional_compilation.yml

@@ -21,7 +21,6 @@ jobs:
     VSTS_HTTP_TIMEOUT: 200
     BUILD_TYPE: Release
     REPO_DIR: $(Build.Repository.LocalPath)
-    OPENVINO_CONTRIB_REPO_DIR: $(REPO_DIR)\..\openvino_contrib
     MODELS_PATH: $(REPO_DIR)\..\testdata
     WORK_DIR: $(Pipeline.Workspace)\_w
     BUILD_DIR: $(WORK_DIR)\build

.ci/openvino-onnx/Jenkinsfile

@@ -1,213 +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"
-ONNX_MODEL_ZOO_SHA = "d58213534f2a4d1c4b19ba62b3bb5f544353256e"
-
-BACKEND_CONFIGURATIONS = [
-    [ name: "Release", build_type: "Release" ],
-    [ name: "Debug", build_type: "Debug" ],
-]
-
-// workaround for aborting previous builds on PR update
-@NonCPS
- def stopPreviousRunningBuilds() {
-     def jobname = env.JOB_NAME
-     if (jobname.startsWith("onnx-ci/openvino onnx ci/openvino/PR")){
-         def buildnum = env.BUILD_NUMBER.toInteger()
-         def job = Jenkins.instance.getItemByFullName(jobname)
-         def job_newest = job.builds.first()
-         for (build in job.builds.reverse()[0..<-1]) {
-             if (build.isBuilding()){
-                 echo "Stop task = ${build} because newest #${job_newest} is on the way"
-                 build.doStop();
-                 continue;
-             }
-         }
-     }
-
- }
-
-def getGitPrInfo(String project, String workdir) {
-    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, String workdir) {
-    dir ("${workdir}/${repository_name}") {
-        sh  label: "Init ${repository_name} submodules",
-            script:
-        """
-            git submodule init && git submodule update \
-                --init \
-                --no-fetch \
-                --recursive
-        """
-    }
-}
-
-def prepare_repository(String workdir) {
-    dir("${workdir}") {
-        println "Preparing repository in directory: ${workdir}"
-        checkout scm
-        gitSubmoduleUpdate(PROJECT_NAME, workdir)
-    }
-}
-
-def updateModels() {
-    sh """
-        ./src/bindings/python/tests/test_onnx/model_zoo_preprocess.sh -d ${HOME}/ONNX_CI/models_data -o -s ${ONNX_MODEL_ZOO_SHA}
-    """
-}
-
-def get_docker_container_name(Map configuration){
-    println "RUN get_docker_container_name for ${configuration.name}"
-    String docker_container_name = "${DOCKER_CONTAINER_NAME}_${BUILD_NUMBER}_${env.CHANGE_ID}_${configuration.name}"
-    return docker_container_name
-}
-
-def buildDockerImage(Map configuration, String workdir) {
-    String docker_image_tag = "${DOCKER_IMAGE_TAG}_${BUILD_NUMBER}_${env.CHANGE_ID}_${configuration.name}".toLowerCase()
-    println "docker_image_tag: ${docker_image_tag}"
-    updateModels()
-    sh """
-        docker build --tag=${docker_image_tag} \
-        --build-arg BUILD_TYPE=${configuration.build_type} \
-        --file=.ci/openvino-onnx/Dockerfile \
-        --build-arg http_proxy=${HTTP_PROXY}  \
-        --build-arg https_proxy=${HTTPS_PROXY} .
-    """
-}
-
-def runTests(Map configuration, String workdir) {
-    println "Run tests for ${configuration.name}"
-    String docker_image_tag = "${DOCKER_IMAGE_TAG}_${BUILD_NUMBER}_${env.CHANGE_ID}_${configuration.name}".toLowerCase()
-
-    String docker_container_name = get_docker_container_name(configuration)
-
-    // Run only basic unit tests in Debug configuration
-    if (configuration.build_type == "Debug") {
-        sh """
-            docker run --name ${docker_container_name} ${docker_image_tag}
-        """
-    }
-
-    // Run unit-tests AND large model tests by default
-    else {
-        sh """
-            docker run --name ${docker_container_name} \
-            --volume ${HOME}/ONNX_CI/models_data/model_zoo/onnx_model_zoo_${ONNX_MODEL_ZOO_SHA}:/root/.onnx/model_zoo/onnx_model_zoo \
-            --volume ${HOME}/ONNX_CI/data/model_zoo/MSFT:/root/.onnx/model_zoo/MSFT \
-            ${docker_image_tag} /bin/bash -c "tox && tox -e zoo_models"
-        """
-    }
-}
-
-def getConfigurationsMap() {
-    def configurationsMap = [:]
-    for (backend in BACKEND_CONFIGURATIONS) {
-        def configuration = backend.clone()
-        configurationsMap[configuration.name] = {
-            stage(configuration.name) { CONFIGURATION_WORKFLOW(configuration) }
-        }
-    }
-    return configurationsMap
-}
-
-CONFIGURATION_WORKFLOW = { configuration ->
-    node("OpenVINO") {
-        String workdir = "${HOME}/workspace/${BUILD_NUMBER}_${env.CHANGE_ID}_${configuration.name}"
-        try {
-            PROJECT_NAME = "openvino"
-            stage("Clone repository") {
-                    prepare_repository(workdir)
-            }
-            stage("Prepare Docker environment") {
-              dir("${workdir}") {
-                    buildDockerImage(configuration, workdir)
-                }
-            }
-            stage("Run tests") {
-                timeout(time: 60, unit: 'MINUTES') {
-                    runTests(configuration, workdir)
-                }
-            }
-        }
-        catch(e) {
-                // Set result to ABORTED if exception contains exit code of a process interrupted by SIGTERM
-                if ("$e".contains("143")) {
-                    currentBuild.result = "ABORTED"
-                } else {
-                    currentBuild.result = "FAILURE"
-                }
-                def gitPrInfo = getGitPrInfo(PROJECT_NAME, workdir)
-                notifyByEmail(gitPrInfo)
-        }
-        finally {
-            stage("Cleanup") {
-                String docker_container_name = get_docker_container_name(configuration)
-                sh """
-                    docker rm -f ${docker_container_name}
-                    rm -rf ${workdir}
-                """
-            }
-        }
-    }
-}
-
-pipeline {
-    agent none
-    options {
-        skipDefaultCheckout true
-        timeout(activity: true, time: 120, unit: 'MINUTES')
-    }
-    stages {
-        stage('Parallel CI') {
-            steps {
-                stopPreviousRunningBuilds()
-                script {
-                    parallelStagesMap = getConfigurationsMap()
-                    parallel parallelStagesMap
-                }
-            }
-
-        }
-    }
-}

.ci/openvino-onnx/watchdog/Jenkinsfile

@@ -1,65 +0,0 @@
-// Copyright (C) 2018-2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-timeout(30)
-{
-    node(LABEL) {
-
-        BUILD_WORKSPACE = "$WORKSPACE/$BUILD_NUMBER"
-        WATCHDOG_ROOT = "$BUILD_WORKSPACE/.ci/openvino-onnx/watchdog"
-        VENV_PATH = "${BUILD_WORKSPACE}/.wdvenv"
-
-        try {
-                stage("Clone repository") {
-                    dir ("$BUILD_WORKSPACE") {
-                        checkout([$class: 'GitSCM', branches: [[name: "*/$BRANCH"]],
-                                doGenerateSubmoduleConfigurations: false, extensions: [[$class: 'CloneOption', timeout: 30]],  submoduleCfg: [],
-                                userRemoteConfigs: [[credentialsId: "${GITHUB_KEY}", url: "${OPEN_VINO_URL}"]]])
-                    }
-                }
-                stage("Prepare environment") {
-                    sh """#!/bin/bash
-                        if [ ! -d ${VENV_PATH} ]; then
-                            python3 -m venv ${VENV_PATH}
-                            source ${VENV_PATH}/bin/activate
-                            pip install -r ${WATCHDOG_ROOT}/requirements.txt
-                        fi
-                    """
-                }
-                stage("Run script") {
-                    withCredentials([
-                        usernamePassword(credentialsId: '7157091e-bc04-42f0-99fd-dc4da2922a55',
-                        usernameVariable: 'username',
-                        passwordVariable: 'password')])
-                    {
-                        dir ("$BUILD_WORKSPACE") {
-                            sh """#!/bin/bash
-                                source ${VENV_PATH}/bin/activate
-                                export PYTHONHTTPSVERIFY=0
-                                python ${WATCHDOG_ROOT}/src/main.py \
-                                    --msteams-url=${MSTEAMS_URL_FILE} \
-                                    --github-credentials '${username}' '${password}' \
-                                    --github-org=${GITHUB_ORG} \
-                                    --github-project=${GITHUB_PROJECT} \
-                                    --jenkins-token=${JENKINS_TOKEN_FILE} \
-                                    --jenkins-server=${JENKINS_SERVER} \
-                                    --jenkins-user=${JENKINS_USER} \
-                                    --ci-job=${CI_JOB_NAME} \
-                                    --watchdog-job=${WATCHDOG_JOB_NAME}
-                            """
-                        }
-                    }
-                }
-        } catch (e) {
-            echo "$e"
-            currentBuild.result = "FAILURE"
-        } finally {
-            stage("Cleanup") {
-                sh """
-                    cd $BUILD_WORKSPACE
-                    rm -rf ..?* .[!.]* *
-                """
-            }
-        }
-    }
-}

.ci/openvino-onnx/watchdog/requirements.txt

@@ -1,6 +0,0 @@
-python-jenkins==1.7.0
-retrying==1.3.3
-pygithub==1.51
-timeout-decorator==0.4.1
-requests==2.23.0
-wheel

.ci/openvino-onnx/watchdog/src/git_wrapper.py

@@ -1,108 +0,0 @@
-#!/usr/bin/python3
-
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import logging
-import timeout_decorator
-from datetime import datetime
-from retrying import retry
-from github import Github, GithubException
-
-# Logging
-logging.basicConfig(format='%(name)s - %(levelname)s - %(message)s')
-log = logging.getLogger(__name__)
-log.setLevel(logging.INFO)
-
-_RETRY_LIMIT = 3
-_RETRY_COOLDOWN_MS = 2000
-_REQUEST_TIMEOUT_S = 10
-
-
-class GitWrapper:
-    """Class wrapping PyGithub API.
-
-    The purpose of this class is to wrap methods from PyGithub API used in Watchdog, for less error-prone and
-    more convenient use. Docs for used API, including wrapped methods can be found at:
-    https://pygithub.readthedocs.io/en/latest/introduction.html
-
-    :param github_credentials:       Credentials used for GitHub
-    :param repository:            GitHub repository name
-    :param project:               GitHub project name
-    :type github_credentials:        String
-    :type repository:             String
-    :type project:                String
-    """
-
-    def __init__(self, github_credentials, repository, project):
-        self.git = Github(*github_credentials)
-        self.repository = repository
-        self.project = project
-        self.github_credentials = github_credentials
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_git_time(self):
-        """Retrieve time from GitHub.
-
-        Used to reliably determine time during Watchdog run.
-
-        :return:                    Datetime object describing current time
-        :rtype:                     datetime
-        """
-        try:
-            datetime_object = self._get_git_time()
-        except ValueError as e:
-            raise GitWrapperError(str(e))
-        except GithubException as e:
-            message = 'GitHub Exception during API status retrieval. Exception: {}'.format(str(e))
-            raise GitWrapperError(message)
-        except timeout_decorator.TimeoutError:
-            message = 'GitHub Exception during API status retrieval. Timeout during API request.'
-            raise GitWrapperError(message)
-        return datetime_object
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_pull_requests(self):
-        """Retrieve paginated list of pull requests from GitHub.
-
-        :return:                    Paginated list of Pull Requests in GitHub repo
-        :rtype:                     github.PaginatedList.PaginatedList of github.PullRequest.PullRequest
-        """
-        try:
-            prs = self._get_pull_requests()
-        except GithubException as e:
-            message = 'GitHub Exception during API status retrieval. Exception: {}'.format(str(e))
-            raise GitWrapperError(message)
-        return prs
-
-    @timeout_decorator.timeout(_REQUEST_TIMEOUT_S)
-    def _get_git_time(self):
-        """Private method retrieving time from GitHub.
-
-        :return:                    Datetime object describing current time
-        :rtype:                     datetime
-        """
-        datetime_string = self.git.get_api_status().raw_headers.get('date', '')
-        datetime_format = '%a, %d %b %Y %H:%M:%S %Z'
-        datetime_object = datetime.strptime(datetime_string, datetime_format)
-        return datetime_object
-
-    @timeout_decorator.timeout(_REQUEST_TIMEOUT_S)
-    def _get_pull_requests(self):
-        """Private method retrieving pull requests from GitHub.
-
-        :return:                    Paginated list of Pull Requests in GitHub repo
-        :rtype:                     github.PaginatedList.PaginatedList of github.PullRequest.PullRequest
-        """
-        return self.git.get_organization(self.repository).get_repo(self.project).get_pulls()
-
-
-class GitWrapperError(Exception):
-    """Base class for exceptions raised in GitWrapper.
-
-    :param message                   Explanation of the error
-    """
-
-    def __init__(self, message):
-        self.message = message
-        log.exception(message)

.ci/openvino-onnx/watchdog/src/jenkins_wrapper.py

@@ -1,91 +0,0 @@
-#!/usr/bin/python3
-
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import requests
-import jenkins
-import logging
-from retrying import retry
-
-# Logging
-logging.basicConfig(format='%(name)s - %(levelname)s - %(message)s')
-log = logging.getLogger(__name__)
-log.setLevel(logging.INFO)
-
-_RETRY_LIMIT = 3
-_RETRY_COOLDOWN_MS = 5000
-
-
-class JenkinsWrapper:
-    """Class wrapping Python-Jenkins API.
-
-    The purpose of this class is to wrap methods from Python-Jenkins API used in Watchdog, for less error-prone and
-    more convenient use. Docs for used API, including wrapped methods can be found at:
-    https://python-jenkins.readthedocs.io/en/latest/
-
-        :param jenkins_token:       Token used for Jenkins
-        :param jenkins_user:        Username used to connect to Jenkins
-        :param jenkins_server:      Jenkins server address
-        :type jenkins_token:        String
-        :type jenkins_user:         String
-        :type jenkins_server:       String
-    """
-
-    def __init__(self, jenkins_token, jenkins_user, jenkins_server):
-        self.jenkins_server = jenkins_server
-        self.jenkins = jenkins.Jenkins(jenkins_server, username=jenkins_user,
-                                       password=jenkins_token)
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_build_console_output(self, job_name, build_number):
-        return self.jenkins.get_build_console_output(job_name, build_number)
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_job_info(self, job_name):
-        return self.jenkins.get_job_info(job_name)
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_build_info(self, job_name, build_number):
-        return self.jenkins.get_build_info(job_name, build_number)
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_queue_item(self, queue_id):
-        """Attempt to retrieve Jenkins job queue item.
-
-        Exception communicating queue doesn't exist is expected,
-        in that case method returns empty dict.
-
-            :param queue_id:            Jenkins job queue ID number
-            :type queue_id:             int
-            :return:                    Dictionary representing Jenkins job queue item
-            :rtype:                     dict
-        """
-        try:
-            return self.jenkins.get_queue_item(queue_id)
-        except Exception as e:
-            # Exception 'queue does not exist' is expected behaviour when job is running
-            if 'queue' in str(e) and 'does not exist' in str(e):
-                return {}
-            else:
-                raise
-
-    @retry(stop_max_attempt_number=_RETRY_LIMIT, wait_fixed=_RETRY_COOLDOWN_MS)
-    def get_idle_ci_hosts(self):
-        """Query Jenkins for idle servers.
-
-        Send GET request to Jenkins server, querying for idle servers labeled
-        for OpenVino-ONNX CI job.
-
-            :return:     Number of idle hosts delegated to OpenVino-ONNX CI
-            :rtype:      int
-        """
-        jenkins_request_url = self.jenkins_server + 'label/ci&&onnx/api/json?pretty=true'
-        try:
-            log.info('Sending request to Jenkins: %s', jenkins_request_url)
-            r = requests.Request(method='GET', url=jenkins_request_url, verify=False)
-            response = self.jenkins.jenkins_request(r).json()
-            return int(response['totalExecutors']) - int(response['busyExecutors'])
-        except Exception as e:
-            log.exception('Failed to send request to Jenkins!\nException message: %s', str(e))
-            raise

.ci/openvino-onnx/watchdog/src/main.py

@@ -1,89 +0,0 @@
-#!/usr/bin/python3
-
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import argparse
-import sys
-from watchdog import Watchdog
-
-DEFAULT_MSTEAMS_URL_FILE = '/home/lab_nerval/tokens/msteams_url'
-DEFAULT_GITHUB_ORGANIZATION = 'openvinotoolkit'
-DEFAULT_GITHUB_PROJECT = 'openvino'
-DEFAULT_JENKINS_TOKEN_FILE = '/home/lab_nerval/tokens/crackerjack'
-DEFAULT_JENKINS_SERVER = 'https://crackerjack.intel.com/'
-DEFAULT_JENKINS_USER = 'lab_nerval'
-DEFAULT_CI_JOB_NAME = 'onnx/OpenVino_CI'
-DEFAULT_WATCHDOG_JOB_NAME = 'onnx/ci_watchdog'
-
-
-def main(args):
-    """
-    Read args passed to script, load tokens and run watchdog.
-
-    Keyword arguments:
-    :param args:    arguments parsed by argparse ArgumentParser
-
-    :return:        returns status code 0 on successful completion
-
-    """
-    jenkins_server = args.jenkins_server.strip()
-    jenkins_user = args.jenkins_user.strip()
-    jenkins_token = open(args.jenkins_token).read().replace('\n', '').strip()
-    msteams_url = open(args.msteams_url).read().replace('\n', '').strip()
-    github_credentials = args.github_credentials
-    github_org = args.github_org
-    github_project = args.github_project
-    ci_job = args.ci_job.strip()
-    watchdog_job = args.watchdog_job.strip()
-    quiet = args.quiet
-
-    wd = Watchdog(jenkins_token=jenkins_token,
-                  jenkins_server=jenkins_server,
-                  jenkins_user=jenkins_user,
-                  github_credentials=github_credentials,
-                  git_org=github_org,
-                  git_project=github_project,
-                  msteams_url=msteams_url,
-                  ci_job_name=ci_job,
-                  watchdog_job_name=watchdog_job)
-    wd.run(quiet=quiet)
-
-    return 0
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--msteams-url', help='Path to MS Teams channel url to communicate messages.',
-                        default=DEFAULT_MSTEAMS_URL_FILE, action='store', required=False)
-
-    parser.add_argument('--github-credentials', help='GitHub user credentials to access repo.',
-                        nargs="+", required=True)
-
-    parser.add_argument('--github-org', help='Name of organization on GitHub.',
-                        default=DEFAULT_GITHUB_ORGANIZATION, action='store', required=False)
-
-    parser.add_argument('--github-project', help='Name of project on GitHub.',
-                        default=DEFAULT_GITHUB_PROJECT, action='store', required=False)
-
-    parser.add_argument('--jenkins-token', help='Path to Jenkins user token to access build info.',
-                        default=DEFAULT_JENKINS_TOKEN_FILE, action='store', required=False)
-
-    parser.add_argument('--jenkins-server', help='Jenkins server address.',
-                        default=DEFAULT_JENKINS_SERVER, action='store', required=False)
-
-    parser.add_argument('--jenkins-user', help='Jenkins user used to log in.',
-                        default=DEFAULT_JENKINS_USER, action='store', required=False)
-
-    parser.add_argument('--ci-job', help='Jenkins CI job name.',
-                        default=DEFAULT_CI_JOB_NAME, action='store', required=False)
-
-    parser.add_argument('--watchdog-job', help='Jenkins CI Watchdog job name.',
-                        default=DEFAULT_WATCHDOG_JOB_NAME, action='store', required=False)
-
-    parser.add_argument('--quiet', help="Quiet mode - doesn\'t send message to communicator.",
-                        action='store_true', required=False)
-
-    args = parser.parse_args()
-    sys.exit(main(args))

.ci/openvino-onnx/watchdog/src/ms_teams_communicator.py

@@ -1,128 +0,0 @@
-#!/usr/bin/python3
-
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import requests
-
-
-class MSTeamsCommunicator:
-    """Class communicating with MSTeams using Incoming Webhook.
-
-    The purpose of this class is to use MSTeams API to send message.
-    Docs for used API, including wrapped methods can be found at:
-    https://docs.microsoft.com/en-us/outlook/actionable-messages/send-via-connectors
-    """
-
-    def __init__(self, _ci_alerts_channel_url):
-        self._ci_alerts_channel_url = _ci_alerts_channel_url
-        self._queued_messages = {
-            self._ci_alerts_channel_url: [],
-        }
-
-    @property
-    def messages(self):
-        """
-        Get list of queued messages.
-
-            :return:           List of queued messages
-            :return type:      List[String]
-        """
-        return self._queued_messages.values()
-
-    def queue_message(self, message):
-        """
-        Queue message to be sent later.
-
-            :param message:     Message content
-            :type message:      String
-        """
-        self._queued_messages[self._ci_alerts_channel_url].append(message)
-
-    def _parse_text(self, watchdog_log, message):
-        """
-        Parse text to display as alert.
-
-            :param watchdog_log:     Watchdog log content
-            :param message:          Unparsed message content
-            :type watchdog_log:      String
-            :type message:           String
-        """
-        message_split = message.split('\n')
-        log_url = None
-        if len(message_split) == 3:
-            log_url = message_split[-1]
-        title = message_split[0]
-        text = message_split[1]
-        header = watchdog_log.split(' - ')
-        header_formatted = '{} - [Watchdog Log]({})'.format(header[0], header[1])
-        return title, log_url, '{}\n\n{}'.format(header_formatted, text)
-
-    def _json_request_content(self, title, log_url, text_formatted):
-        """
-        Create final json request to send message to MS Teams channel.
-
-            :param title:            Title of alert
-            :param log_url:          URL to PR
-            :param text_formatted:   General content of alert - finally formatted
-            :type title:             String
-            :type title:             String
-            :type title:             String
-        """
-        data = {
-            '@context': 'https://schema.org/extensions',
-            '@type': 'MessageCard',
-            'themeColor': '0072C6',
-            'title': title,
-            'text': text_formatted,
-            'potentialAction':
-                [
-                    {
-                        '@type': 'OpenUri',
-                        'name': 'Open PR',
-                        'targets':
-                            [
-                                {
-                                    'os': 'default',
-                                    'uri': log_url,
-                                },
-                            ],
-                    },
-                ],
-        }
-        return data
-
-    def _send_to_channel(self, watchdog_log, message_queue, channel_url):
-        """
-        Send MSTeams message to specified channel.
-
-            :param watchdog_log:            Watchdog log content
-            :param message_queue:           Queued messages to send
-            :param channel_url:             Channel url
-            :type watchdog_log:             String
-            :type message_queue:            String
-            :type channel_url:              String
-
-        """
-        for message in message_queue:
-            title, log_url, text_formatted = self._parse_text(watchdog_log, message)
-            data = self._json_request_content(title, log_url, text_formatted)
-
-            try:
-                requests.post(url=channel_url, json=data)
-            except Exception as ex:
-                raise Exception('!!CRITICAL!! MSTeamsCommunicator: Could not send message '
-                                'due to {}'.format(ex))
-
-    def send_message(self, watchdog_log, quiet=False):
-        """
-        Send queued messages as single communication.
-
-            :param watchdog_log:     Watchdog log content
-            :param quiet:            Flag for disabling sending report through MS Teams
-            :type watchdog_log:      String
-            :type quiet:             Boolean
-        """
-        for channel, message_queue in self._queued_messages.items():
-            if not quiet and message_queue:
-                self._send_to_channel(watchdog_log, message_queue, channel)

.ci/openvino-onnx/watchdog/src/watchdog.py

@@ -1,505 +0,0 @@
-#!/usr/bin/python3
-
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import datetime
-import time
-import re
-import logging
-import requests
-from ms_teams_communicator import MSTeamsCommunicator
-from jenkins_wrapper import JenkinsWrapper
-from jenkins import NotFoundException
-from git_wrapper import GitWrapper, GitWrapperError
-import os
-import json
-
-# Logging
-logging.basicConfig(format='%(name)s - %(levelname)s - %(message)s')
-log = logging.getLogger(__name__)
-log.setLevel(logging.INFO)
-
-# Watchdog static constant variables
-_SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
-_BUILD_DURATION_THRESHOLD = datetime.timedelta(minutes=60)
-_CI_START_THRESHOLD = datetime.timedelta(minutes=30)
-_AWAITING_JENKINS_THRESHOLD = datetime.timedelta(minutes=5)
-_WATCHDOG_DIR = os.path.expanduser('~')
-_PR_REPORTS_CONFIG_KEY = 'pr_reports'
-_CI_BUILD_FAIL_MESSAGE = 'ERROR:   py3: commands failed'
-_CI_BUILD_SUCCESS_MESSAGE = 'py3: commands succeeded'
-_GITHUB_CI_CHECK_NAME = 'OpenVINO-ONNX'
-
-INTERNAL_ERROR_MESSAGE_HEADER = '!!! --- !!! INTERNAL WATCHDOG ERROR !!! --- !!!'
-ERROR_MESSAGE_HEADER = '!!! OpenVino-ONNX CI Error !!!'
-WARNING_MESSAGE_HEADER = 'OpenVino-ONNX CI WARNING'
-INFO_MESSAGE_HEADER = 'OpenVino-ONNX CI INFO'
-
-
-class Watchdog:
-    """Class describing OpenVino-ONNX-CI Watchdog.
-
-    Watchdog connects to GitHub and retrieves the list of current pull requests (PRs) in
-    OpenVino repository. Then it connects to specified Jenkins server to
-    check CI jobs associated with every PR. Watchdog verifies time durations for Jenkins
-    initial response, job queue and execution against time treshold constants. Every fail
-    is logged and reported through MS Teams communicators.
-
-    :param jenkins_token:       Token used for Jenkins
-    :param jenkins_server:      Jenkins server address
-    :param jenkins_user:        Username used to connect to Jenkins
-    :param github_credentials:  Credentials used to connect to GitHub
-    :param msteams_url:         URL used to connect to MS Teams channel
-    :param ci_job_name:         OpenVino-ONNX CI job name used in Jenkins
-    :param watchdog_job_name:   Watchdog job name used in Jenkins
-    :type jenkins_token:        String
-    :type jenkins_server:       String
-    :type jenkins_user:         String
-    :type github_credentials:   String
-    :type msteams_url:          String
-    :type ci_job_name:          String
-    :type watchdog_job_name:    String
-
-    .. note::
-        Watchdog and OpenVino-ONNX CI job must be placed on the same Jenkins server.
-    """
-
-    def __init__(self, jenkins_token, jenkins_server, jenkins_user, github_credentials, git_org,
-                 git_project, msteams_url, ci_job_name, watchdog_job_name):
-        self._config_path = os.path.join(_WATCHDOG_DIR, '{}/.{}_ci_watchdog.json'.format(_WATCHDOG_DIR, git_project))
-        # Jenkins Wrapper object for CI job
-        self._jenkins = JenkinsWrapper(jenkins_token,
-                                       jenkins_user=jenkins_user,
-                                       jenkins_server=jenkins_server)
-        # Load GitHub token and log in, retrieve pull requests
-        self._git = GitWrapper(github_credentials, repository=git_org, project=git_project)
-        # Create MS Teams api object
-        self._msteams_hook = MSTeamsCommunicator(msteams_url)
-        self._ci_job_name = ci_job_name.lower()
-        self._watchdog_job_name = watchdog_job_name
-        # Read config file
-        self._config = self._read_config_file()
-        # Time at Watchdog initiation
-        self._now_time = datetime.datetime.now()
-        self._current_prs = {}
-        self._ms_teams_enabled = True
-
-    def run(self, quiet=False):
-        """Run main watchdog logic.
-
-        Retrieve list of pull requests and pass it to the method responsible for checking them.
-
-        :param quiet:   Flag for disabling sending report through communicator
-        :type quiet:    Boolean
-        """
-        try:
-            pull_requests = self._git.get_pull_requests()
-        except GitWrapperError:
-            message = 'Failed to retrieve Pull Requests!'
-            log.exception(message)
-            self._queue_message(message, message_severity='internal')
-        # Check all pull requests
-        for pr in pull_requests:
-            try:
-                self._check_pr(pr)
-            except Exception as e:
-                log.exception(str(e))
-                self._queue_message(str(e), message_severity='internal', pr=pr)
-        self._update_config()
-        self._send_message(quiet=quiet)
-
-    def _read_config_file(self):
-        """Read Watchdog config file stored on the system.
-
-        The file stores every fail already reported along with timestamp. This
-        mechanism is used to prevent Watchdog from reporting same failure
-        multiple times. In case there's no config under the expected path,
-        appropriate data structure is created and returned.
-
-        :return:            Returns dict of dicts with reported fails with their timestamps
-        :rtype:             dict of dicts
-        """
-        if os.path.isfile(self._config_path):
-            log.info('Reading config file in: {}'.format(self._config_path))
-            file = open(self._config_path, 'r')
-            data = json.load(file)
-        else:
-            log.info('No config file found in: {}'.format(self._config_path))
-            data = {_PR_REPORTS_CONFIG_KEY: {}}
-        return data
-
-    def _check_pr(self, pr):
-        """Check pull request (if there's no reason to skip).
-
-        Retrieve list of statuses for every PR's last commit and interpret them. Filters out statuses
-        unrelated to OpenVino-ONNX Jenkins CI and passes relevant statuses to method that interprets them.
-        If no commit statuses related to Jenkins are available after time defined by
-        **_AWAITING_JENKINS_THRESHOLD** calls appropriate method to check for builds waiting in queue.
-
-        :param pr:       GitHub Pull Requests
-        :type pr:        github.PullRequest.PullRequest
-        """
-        log.info('===============================================')
-        log.info('Checking PR#{}'.format(pr.number))
-        # Get last Jenkins status
-        last_status = self._get_last_status(pr)
-        # Append PR checked in current run for Watchdog config
-        self._current_prs[str(pr.number)] = self._get_pr_timestamps(pr, last_status)
-        if self._should_ignore(pr) or self._updated_since_last_run(pr):
-            log.info('Ignoring PR#{}'.format(pr.number))
-
-            return
-
-        # Calculate time passed since PR update (any commit, merge or comment)
-        pr_time_delta = self._now_time - pr.updated_at
-        if last_status:
-            # Interpret found CI statuses
-            log.info('Last status: {} at {}'.format(last_status.description, last_status.updated_at))
-            self._interpret_status(last_status, pr)
-        elif pr_time_delta > _CI_START_THRESHOLD:
-            # If there's no status after assumed time - check if build is waiting in queue
-            log.info('CI for PR {}: NO JENKINS STATUS YET'.format(pr.number))
-            self._check_missing_status(pr)
-
-    @staticmethod
-    def _get_pr_timestamps(pr, last_status):
-        """Get dict containing PR timestamp and last status timestamp.
-
-        :param pr:          Single PR being currently checked
-        :type pr:           github.PullRequest.PullRequest
-
-        :return:            Dictionary with PR and last status update timestamps
-        :rtype:             dict
-        """
-        pr_timestamp = time.mktime(pr.updated_at.timetuple())
-        if last_status:
-            status_timestamp = time.mktime(last_status.updated_at.timetuple())
-        else:
-            status_timestamp = None
-        pr_dict = {'pr_timestamp': pr_timestamp,
-                   'status_timestamp': status_timestamp}
-        return pr_dict
-
-    @staticmethod
-    def _get_last_status(pr):
-        """Get last commit status posted from Jenkins.
-
-        :param pr:          Single PR being currently checked
-        :type pr:           github.PullRequest.PullRequest
-
-        :return:            Either last PR status posted from Jenkins or None
-        :rtype:             github.CommitStatus.CommitStatus
-        """
-        # Find last commit in PR
-        last_commit = pr.get_commits().reversed[0]
-        # Get statuses and filter them to contain only those related to Jenkins CI
-        # and check if CI in Jenkins started
-        statuses = last_commit.get_statuses()
-        jenk_statuses = [stat for stat in statuses if
-                         _GITHUB_CI_CHECK_NAME in stat.context]
-        try:
-            last_status = jenk_statuses[0]
-        except IndexError:
-            last_status = None
-        return last_status
-
-    @staticmethod
-    def _should_ignore(pr):
-        """Determine if PR should be ignored.
-
-        :param pr:          Single PR being currently checked
-        :type pr:           github.PullRequest.PullRequest
-
-        :return:            Returns True if PR should be ignored
-        :rtype:             Bool
-        """
-        # Ignore PR if it has WIP label or WIP in title
-        if 'WIP' in pr.title:
-            log.info('PR#{} should be ignored. WIP tag in title.'.format(pr.number))
-            return True
-
-        label_names = [label.name for label in pr.labels]
-        if 'WIP' in label_names:
-            log.info('PR#{} should be ignored. WIP label present.'.format(pr.number))
-            return True
-
-        # Ignore PR if base ref is not master
-        if 'master' not in pr.base.ref:
-            log.info('PR#{} should be ignored. Base ref is not master'.format(pr.number))
-            return True
-
-        # Ignore PR if mergeable state is 'dirty' or 'behind'.
-        # Practically this ignores PR in case of merge conflicts
-        ignored_mergeable_states = ['behind', 'dirty', 'draft']
-        if pr.mergeable_state in ignored_mergeable_states:
-            log.info('PR#{} should be ignored. Mergeable state is {}. '.format(pr.number, pr.mergeable_state))
-            return True
-
-        # If no criteria for ignoring PR are met - return false
-        return False
-
-    def _updated_since_last_run(self, pr):
-        # Ignore if PR was already checked and there was no update in meantime
-        pr_number = str(pr.number)
-        current_pr_timestamps = self._current_prs.get(pr_number)
-        last_pr_timestamps = self._config[_PR_REPORTS_CONFIG_KEY].get(pr_number)
-        if current_pr_timestamps == last_pr_timestamps:
-            log.info('PR#{} - No update since last check'.format(pr.number))
-            return True
-        else:
-            return False
-
-    def _check_missing_status(self, pr):
-        """Verify if missing status is expected.
-
-        This method checks if CI build for last was scheduled and still waits in queue for
-        executor.
-
-        :param pr:                  Single PR being currently checked
-        :type pr:                   github.PullRequest.PullRequest
-        """
-        pr_time_delta = self._now_time - pr.updated_at
-        try:
-            build_number = self._build_scheduled(pr)
-            if self._build_in_queue(pr, build_number):
-                message = ('PR# {}: build waiting in queue after {} minutes.'
-                           .format(pr.number, pr_time_delta.seconds / 60))
-                severity = 'warning'
-            else:
-                message = ('PR# {}: missing status on GitHub after {} minutes.'
-                           .format(pr.number, pr_time_delta.seconds / 60))
-                severity = 'error'
-            self._queue_message(message, message_severity=severity, pr=pr)
-        except TypeError:
-            log.info('Committer outside of OpenVino organization')
-
-    def _build_scheduled(self, pr):
-        """Check if Jenkins build corresponding to PR was scheduled.
-
-        This method takes last Jenkins build for given PR and compares hash from Jenkins console output
-        and sha from PR object to determine if CI build for appropriate commit was scheduled.
-
-        :param pr:          Single PR being currently checked
-        :type pr:           github.PullRequest.PullRequest
-
-        :return:            Returns build number or -1 if no build found
-        :rtype:             int
-        """
-        pr_number = str(pr.number)
-        project_name_full = self._ci_job_name + '/PR-' + pr_number
-
-        try:
-            # Retrieve console output from last Jenkins build for job corresponding to this PR
-            last_build_number = self._jenkins.get_job_info(project_name_full)['lastBuild']['number']
-            console_output = self._jenkins.get_build_console_output(project_name_full, last_build_number)
-            # Check if CI build was scheduled - commit hash on GH must match hash in last Jenkins build console output
-            # Retrieve hash from Jenkins output
-            match_string = '(?:Obtained .ci/[a-zA-Z/]+Jenkinsfile from ([a-z0-9]{40}))'
-            retrieved_sha = re.search(match_string, console_output).group(1)
-            if retrieved_sha == pr.get_commits().reversed[0].sha:
-                return last_build_number
-            else:
-                return -1
-        except (NotFoundException, AttributeError, requests.exceptions.HTTPError):
-            message = ('PR #{}: Jenkins build corresponding to commit {} not found!'
-                       .format(pr_number, pr.get_commits().reversed[0].sha))
-            self._queue_message(message, message_severity='error', pr=pr)
-            return -1
-
-    def _build_in_queue(self, pr, build_number):
-        """Check if Jenkins build waits in queue.
-
-        This method verifies if CI build is waiting in queue based on console output.
-
-        :param pr:                  Single PR being currently checked
-        :param build_number:        Jenkins build number to retrieve console output from
-        :type pr:                   github.PullRequest.PullRequest
-        :type build_number:         int
-
-        :return:            Returns True if CI build is waiting in queue
-        :rtype:             Bool
-        """
-        pr_number = str(pr.number)
-        project_name_full = self._ci_job_name + '/PR-' + pr_number
-        # Retrieve console output
-        try:
-            console_output = self._jenkins.get_build_console_output(project_name_full, build_number)
-        except NotFoundException:
-            return False
-        # Check if build is waiting in queue (and not already running on an executor)
-        if 'Waiting for next available executor on' in console_output \
-                and 'Running on' not in console_output:
-            log.info('CI for PR %s: WAITING IN QUEUE', pr_number)
-            return True
-        else:
-            return False
-
-    def _interpret_status(self, status, pr):
-        """
-        Verify GitHub status passed to the method.
-
-        This method verifies last commit status for given PR, calling appropriate methods
-        to further validate the status.
-
-        :param status:              GitHub commit status
-        :param pr:                  Single PR being currently checked
-        :type status:               github.CommitStatus.CommitStatus
-        :type pr:                   github.PullRequest.PullRequest
-        """
-        try:
-            # Retrieve build number for Jenkins build related to this PR
-            build_number = self._retrieve_build_number(status.target_url)
-            # CI build finished - verify if expected output is present
-            finished_statuses = ['Build finished', 'This commit cannot be built', 'This commit looks good']
-            pending_statuses = ['This commit is being built', 'Testing in progress',
-                                'This commit is scheduled to be built']
-            if any(phrase in status.description for phrase in finished_statuses):
-                self._check_finished(pr, build_number)
-            # CI build in progress - verify timeouts for build queue and duration
-            elif any(phrase in status.description for phrase in pending_statuses):
-                self._check_in_progress(pr, build_number)
-            else:
-                message = 'ONNX CI job for PR# {}: unrecognized status: {}'.format(pr.number, status.description)
-                self._queue_message(message, message_severity='error', pr=pr)
-        except Exception:
-            # Log Watchdog internal error in case any status can't be properly verified
-            message = 'Failed to verify status "{}" for PR# {}'.format(status.description, pr.number)
-            log.exception(message)
-            self._queue_message(message, message_severity='internal', pr=pr)
-
-    def _retrieve_build_number(self, url):
-        """Retrieve Jenkins CI job build number from URL address coming from GitHub commit status.
-
-        :param url:         URL address from GitHub commit status
-        :type url:          String
-
-        :return:            Returns build number
-        :rtype:             int
-        """
-        # Retrieve the build number from url string
-        match_obj = re.search('(?:/PR-[0-9]+/)([0-9]+)', url)
-        try:
-            number = int(match_obj.group(1))
-            return number
-        except Exception:
-            log.exception('Failed to retrieve build number from url link: %s', url)
-            raise
-
-    def _queue_message(self, message, message_severity='info', pr=None):
-        """Add a message to message queue in communicator object.
-
-        The queued message is constructed based on message string passed as
-        a method argument and message header. Message header is mapped to message severity
-        also passed as an argument.
-
-        :param message:                 Message content
-        :param message_severity:        Message severity level
-        :type message:                  String
-        :type message_severity:         int
-        """
-        log.info(message)
-        internal = False
-        if 'internal' in message_severity:
-            message_header = INTERNAL_ERROR_MESSAGE_HEADER
-            internal = True
-        elif 'error' in message_severity:
-            message_header = ERROR_MESSAGE_HEADER
-        elif 'warning' in message_severity:
-            message_header = WARNING_MESSAGE_HEADER
-        else:
-            message_header = INFO_MESSAGE_HEADER
-        # If message is related to PR attatch url
-        if pr:
-            message = message + '\n' + pr.html_url
-
-        send = message_header + '\n' + message
-        if self._ms_teams_enabled:
-            self._msteams_hook.queue_message(send)
-
-    def _check_finished(self, pr, build_number):
-        """Verify if finished build output contains expected string for either fail or success.
-
-        :param pr:                  Single PR being currently checked
-        :param build_number:        Jenkins CI job build number
-        :type pr:                   github.PullRequest.PullRequest
-        :type build_number:         int
-        """
-        pr_number = str(pr.number)
-        log.info('CI for PR %s: FINISHED', pr_number)
-        # Check if FINISH was valid FAIL / SUCCESS
-        project_name_full = self._ci_job_name + '/PR-' + pr_number
-        build_output = self._jenkins.get_build_console_output(project_name_full, build_number)
-        if _CI_BUILD_FAIL_MESSAGE not in build_output \
-                and _CI_BUILD_SUCCESS_MESSAGE not in build_output:
-            message = ('ONNX CI job for PR #{}: finished but no tests success or fail '
-                       'confirmation is present in console output!'.format(pr_number))
-            self._queue_message(message, message_severity='error', pr=pr)
-
-    def _send_message(self, quiet=False):
-        """Send messages queued in MS Teams objects to designated channel.
-
-        Queued messages are being sent as a single communication.
-
-        :param quiet:   Flag for disabling sending report through communicator
-        :type quiet:    Boolean
-        """
-        if any(messages for messages in self._msteams_hook.messages):
-            try:
-                watchdog_build = self._jenkins.get_job_info(self._watchdog_job_name)['lastBuild']
-                watchdog_build_number = watchdog_build['number']
-                watchdog_build_link = watchdog_build['url']
-            except Exception:
-                watchdog_build_number = 'UNKNOWN'
-                watchdog_build_link = self._jenkins.jenkins_server
-            send = self._watchdog_job_name + '- build ' + str(
-                watchdog_build_number) + ' - ' + watchdog_build_link
-
-            if self._ms_teams_enabled:
-                self._msteams_hook.send_message(send, quiet=quiet)
-        else:
-            log.info('Nothing to report.')
-
-    def _check_in_progress(self, pr, build_number):
-        """Check if CI build succesfully started.
-
-        Checks if build started within designated time threshold, and job is
-        currently running - it didn't cross the time threshold.
-
-        :param pr:                  Single PR being currently checked
-        :param build_number:        Jenkins CI job build number
-        :type pr:                   github.PullRequest.PullRequest
-        :type build_number:         int
-        """
-        pr_number = str(pr.number)
-        log.info('CI for PR %s: TESTING IN PROGRESS', pr_number)
-        project_name_full = self._ci_job_name + '/PR-' + pr_number
-        build_info = self._jenkins.get_build_info(project_name_full, build_number)
-        build_datetime = datetime.datetime.fromtimestamp(build_info['timestamp'] / 1000.0)
-        build_delta = self._now_time - build_datetime
-        log.info('Build %s: IN PROGRESS, started: %s minutes ago', str(build_number),
-                 str(build_delta))
-        # If build still waiting in queue
-        if build_delta > _CI_START_THRESHOLD and self._build_in_queue(pr, build_number):
-            message = ('ONNX CI job build #{}, for PR #{} waiting in queue after {} '
-                       'minutes'.format(build_number, pr_number, str(build_delta.seconds / 60)))
-            self._queue_message(message, message_severity='warning', pr=pr)
-        elif build_delta > _BUILD_DURATION_THRESHOLD:
-            # CI job take too long, possibly froze - communicate failure
-            message = ('ONNX CI job build #{}, for PR #{} started, '
-                       'but did not finish in designated time of {} '
-                       'minutes!'.format(build_number, pr_number,
-                                         str(_BUILD_DURATION_THRESHOLD.seconds / 60)))
-            self._queue_message(message, message_severity='error', pr=pr)
-
-    def _update_config(self):
-        """Update Watchdog config file with PRs checked in current Watchdog run, remove old entries.
-
-        :param current_prs:        List of PR numbers checked during current Watchdog run
-        :type current_prs:         list of ints
-        """
-        # Cleanup config of old reports
-        log.info('Writing to config file at: {}'.format(self._config_path))
-        new_config = {_PR_REPORTS_CONFIG_KEY: self._current_prs}
-        file = open(self._config_path, 'w+')
-        json.dump(new_config, file)

.github/dependabot.yml

@@ -7,7 +7,7 @@ updates:
     directory: "/src/bindings/python"
     schedule:
       interval: weekly
-      day: monday
+      day: sunday
       time: "13:00"
     open-pull-requests-limit: 0
     reviewers:
@@ -15,4 +15,3 @@ updates:
       - akuporos
     labels:
       - "category: dependencies"
-      

.github/github_org_control/config.json

@@ -5,11 +5,10 @@
     "IGNORE_LOGINS": [
         "openvino-ci",
         "openvino-pushbot",
-        "lab-nerval",
-        "lab-nerval-onnx-ci",
-        "onnx-watchdog-agent",
         "workbench-ci-bot",
-        "openvino-pot-ci"
+        "openvino-pot-ci",
+        "sysicvvpux",
+        "ote-ci-bot"
     ],
     "MAX_MEMBERS_TO_REMOVE": 15,
     "EMAILS_FILE_PATH": "dev_emails-test.txt",
@@ -28,7 +27,7 @@
         "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-auto-multi-maintainers": "category: MULTI",
         "openvino-ie-python-api-maintainers": "category: python api",
         "openvino-ie-template-maintainers": "category: TEMPLATE",
         "openvino-ie-tests-maintainers": "category: IE Tests",

.github/github_org_control/github_api.py

@@ -157,7 +157,7 @@ class GithubOrgApi:
                 self.github_users_by_email[email] = org_member
                 if not is_valid_name(org_member.name):
                     self.members_to_fix_name.add(org_member)
-            elif not is_user_ignored(org_member):
+            else:
                 self.members_to_remove.add(org_member)
 
         print("\nOrg members - no Intel emails:")

.github/workflows/build_doc.yml

@@ -4,7 +4,7 @@ on: [push, pull_request]
 jobs:
   Build_Doc:
     if: github.repository == 'openvinotoolkit/openvino'
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     steps:
       - name: Clone OpenVINO
         uses: actions/checkout@v2
@@ -17,11 +17,11 @@ jobs:
           set -e
           # install doc dependencies
           sudo apt update
-          sudo apt --assume-yes install libusb-1.0-0-dev graphviz texlive
+          sudo apt --assume-yes install libusb-1.0-0-dev graphviz texlive liblua5.2-0
           cd docs
-          python -m pip install -r requirements.txt --user
+          python3 -m pip install -r requirements.txt --user
           cd openvino_sphinx_theme
-          python setup.py install --user
+          python3 setup.py install --user
           cd ../..
           # install doxyrest
           wget https://github.com/vovkos/doxyrest/releases/download/doxyrest-2.1.3/doxyrest-2.1.3-linux-amd64.tar.xz
@@ -43,7 +43,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake -DENABLE_DOCS=ON -DENABLE_PYTHON=ON -DNGRAPH_PYTHON_BUILD_ENABLE=ON -DCMAKE_BUILD_TYPE=Release ..
+          cmake -DENABLE_DOCS=ON -DENABLE_PYTHON=ON -DCMAKE_BUILD_TYPE=Release ..
 
       - name: Build doc
         run: |

.github/workflows/check_pr_commits.yml

@@ -3,7 +3,7 @@ on: [pull_request]
 
 jobs:
   Checks:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     steps:
       - name: Clone OpenVINO
         uses: actions/checkout@v2

.github/workflows/code_style.yml

@@ -48,7 +48,7 @@ jobs:
           path: build/code_style_diff.diff
 
   ShellCheck:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v2
         with:
@@ -73,7 +73,7 @@ jobs:
         working-directory: build
 
   NamingConventionCheck:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v2
         with:
@@ -82,8 +82,8 @@ jobs:
       - name: Install Clang dependency
         run: |
           sudo apt update
-          sudo apt --assume-yes remove clang-7 clang-8 clang-9 clang-10 clang-11
-          sudo apt --assume-yes install libclang-12-dev
+          sudo apt --assume-yes remove clang-7 clang-8 clang-9 clang-10 clang-11 clang-12 clang-13
+          sudo apt --assume-yes install libclang-14-dev
 
       - name: Install Python-based dependencies
         run: python3 -m pip install -r cmake/developer_package/ncc_naming_style/requirements_dev.txt

.github/workflows/files_size.yml

@@ -3,7 +3,7 @@ on: [push, pull_request]
 
 jobs:
   Check_Files_Size:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v2
 

.github/workflows/mo.yml

@@ -9,7 +9,7 @@ on:
 
 jobs:
   Pylint-UT:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-22.04
     steps:
       - uses: actions/checkout@v2
         with:

.github/workflows/py_checks.yml

@@ -1,4 +1,4 @@
-name: IE Python Checks
+name: Python API Checks
 
 on:
   workflow_dispatch:
@@ -6,13 +6,15 @@ on:
     paths:
       - 'src/bindings/python/**'
       - 'samples/python/**'
+      - '.github/workflows/py_checks.yml'
   pull_request:
     paths:
       - 'src/bindings/python/**'
       - 'samples/python/**'
+      - '.github/workflows/py_checks.yml'
 jobs:
   linters:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     steps:
       - name: Code checkout
         uses: actions/checkout@v2
@@ -23,8 +25,9 @@ jobs:
         with:
           python-version: '3.6'
       - name: Install dependencies
-        run: python -m pip install -r src/bindings/python/src/compatibility/openvino/requirements_dev.txt
-      - name: Run Flake on samples
+        run: python -m pip install -r src/bindings/python/requirements_test.txt
+      # samples code-style
+      - name: Run flake8 on samples
         run: python -m flake8 ./ --config=setup.cfg
         working-directory: samples/python
       - name: Create code style diff for samples
@@ -38,21 +41,53 @@ jobs:
         with:
           name: samples_diff
           path: samples_diff.diff
-      - name: Run Flake on src
+      # IE Python API Flake code-style
+      - name: Run flake8 on IE Python API
         run: python -m flake8 ./ --config=setup.cfg
         working-directory: src/bindings/python/src/compatibility/openvino
-      - name: Create code style diff for Python src
+      - name: Create code style diff for IE Python API
         if: failure()
         run: |
           python -m black -l 160 -S ./
-          git diff > src_diff.diff
+          git diff > ie_python_diff.diff
         working-directory: src/bindings/python/src/compatibility/openvino
       - uses: actions/upload-artifact@v2
         if: failure()
         with:
-          name: src_diff
-          path: src_diff.diff
-      - name: Run Flake on wheel
+          name: ie_python_diff
+          path: ie_python_diff.diff
+      # nGraph Python API Flake code-style
+      - name: Run flake8 on nGraph Python API
+        run: python -m flake8 ./src/compatibility/ngraph --config=setup.cfg
+        working-directory: src/bindings/python
+      - name: Create code style diff for nGraph Python API
+        if: failure()
+        run: |
+          python -m black -l 160 -S ./
+          git diff > pyngraph_diff.diff
+        working-directory: src/bindings/python/src/compatibility/ngraph
+      - uses: actions/upload-artifact@v2
+        if: failure()
+        with:
+          name: pyngraph_diff
+          path: pyngraph_diff.diff
+      # Python API 2.0 Flake code-style
+      - name: Run flake8 on Python API 2.0
+        run: python -m flake8 ./src/openvino --config=setup.cfg
+        working-directory: src/bindings/python
+      - name: Create code style diff for Python API 2.0
+        if: failure()
+        run: |
+          python -m black -l 160 -S ./
+          git diff > pyopenvino_diff.diff
+        working-directory: src/bindings/python/src/openvino
+      - uses: actions/upload-artifact@v2
+        if: failure()
+        with:
+          name: pyopenvino_diff
+          path: pyopenvino_diff.diff
+      # wheel Flake code-style
+      - name: Run flake8 on wheel
         run: python -m flake8 ./ --config=../setup.cfg
         working-directory: src/bindings/python/wheel
       - name: Create code style diff for wheel
@@ -66,12 +101,26 @@ jobs:
         with:
           name: wheel_diff
           path: wheel_diff.diff
-      
-      - name: Run MyPy
+      # Python API 2.0 tests Flake code-style
+      - name: Run flake8 on python tests
+        # ignore lack of docs in tests
+        run: python -m flake8 tests/ --config=setup.cfg
+        working-directory: src/bindings/python
+      # IE Python API mypy check
+      - name: Run mypy on IE Python API
         run: python -m mypy ./ --config-file ./setup.cfg
         working-directory: src/bindings/python/src/compatibility/openvino
+      # nGraph Python API mypy check
+      - name: Run mypy on nGraph Python API
+        run: python -m mypy ./src/compatibility/ngraph --config-file ./setup.cfg
+        working-directory: src/bindings/python
+      # Python API 2.0 mypy check
+      - name: Run mypy on Python API 2.0
+        run: python -m mypy ./src/openvino --config-file ./setup.cfg
+        working-directory: src/bindings/python
+
       - name: Run Bandit
         run: python -m bandit -r ./ -f screen
         working-directory: src/bindings/python/src/compatibility/openvino
 
-      
+      

.gitignore

@@ -12,12 +12,10 @@ cmake-build*
 # developer tools
 *.idea
 .vscode
-cmake-build-*
 .DS_Store
 **/tags
 compile_commands.json
 bin/
-build/
 .local_vimrc
 .gdb_history
 .vimspector.json
@@ -37,14 +35,13 @@ docs/IE_PLUGIN_DG/html/
 *.pydevproject
 *.settings
 */gen/
-__pycache__
 *.swp
 /config.xml
 
 # Python-specific
-*.env3
+*.?env*
 *.pyc
-
+__pycache__
 # Tests-specific
 *.coverage
 *htmlcov

.gitmodules

@@ -1,5 +1,5 @@
-[submodule "src/plugins/intel_cpu/thirdparty/mkl-dnn"]
-	path = src/plugins/intel_cpu/thirdparty/mkl-dnn
+[submodule "src/plugins/intel_cpu/thirdparty/onednn"]
+	path = src/plugins/intel_cpu/thirdparty/onednn
 	url = https://github.com/openvinotoolkit/oneDNN.git
 	ignore = dirty
 [submodule "thirdparty/xbyak"]

CMakeLists.txt

@@ -17,6 +17,10 @@ if(NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type" FORCE)
 endif()
 
+if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+    add_compile_options(-fno-strict-overflow -fno-delete-null-pointer-checks -fwrapv)
+endif()
+
 find_package(IEDevScripts REQUIRED
              PATHS "${OpenVINO_SOURCE_DIR}/cmake/developer_package"
              NO_CMAKE_FIND_ROOT_PATH
@@ -51,6 +55,7 @@ file(REMOVE "${CMAKE_BINARY_DIR}/InferenceEngineTargets.cmake")
 file(REMOVE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
 foreach(component IN LISTS openvino_export_components)
     file(REMOVE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
+    file(REMOVE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
     unset(${component} CACHE)
 endforeach()
 unset(openvino_export_components CACHE)

CODEOWNERS

@@ -30,7 +30,7 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 # IE Core:
 /inference-engine/  @openvinotoolkit/openvino-ie-maintainers
 /src/bindings/python/ @openvinotoolkit/openvino-ie-python-api-maintainers
-/src/common/transformations/  @GlebKazantaev @ilyachur
+/src/common/transformations/  @openvinotoolkit/openvino-ie-transformations-maintainers
 /src/common/legacy/  @openvinotoolkit/openvino-ngraph-maintainers
 /src/common/  @openvinotoolkit/openvino-ie-maintainers
 /src/core/  @openvinotoolkit/openvino-ngraph-maintainers
@@ -39,8 +39,10 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 
 # IE CPU:
 /src/plugins/intel_cpu/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
-/src/common/low_precision_transformations/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
-/src/plugins/intel_cpu/thirdparty/mkl-dnn/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
+/src/plugins/intel_cpu/thirdparty/onednn/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
+
+#IE LPT
+/src/common/low_precision_transformations/  @openvinotoolkit/openvino-ie-lpt-maintainers
 
 # IE GPU:
 /src/inference/include/ie/gpu/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
@@ -77,8 +79,8 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 /src/frontends/paddle/  @openvinotoolkit/openvino-ie-paddle-maintainers
 
 # IE Tests:
-/src/tests/  @openvinotoolkit/openvino-ie-tests-maintainers
-/src/tests_deprecated/  @openvinotoolkit/openvino-ie-tests-maintainers
+/src/tests/  @openvinotoolkit/openvino-ie-tests-maintainers @openvinotoolkit/openvino-ie-test-developers
+/src/tests_deprecated/  @openvinotoolkit/openvino-ie-tests-maintainers @openvinotoolkit/openvino-ie-test-developers
 /src/tests/functional/inference_engine/ngraph_reader/  @openvinotoolkit/openvino-ie-tests-maintainers @openvinotoolkit/openvino-ngraph-maintainers
 /src/tests/functional/inference_engine/transformations/  @openvinotoolkit/openvino-ie-tests-maintainers @openvinotoolkit/openvino-ngraph-maintainers
 
@@ -88,6 +90,6 @@ Jenkinsfile  @openvinotoolkit/openvino-admins
 *.md  @openvinotoolkit/openvino-docs-maintainers
 
 # Control 3d party dependencies
-**/*requirements*.*  @openvino-configuration-mgmt
-**/setup.py  @openvino-configuration-mgmt
-/scripts/install_dependencies/  @openvino-configuration-mgmt
+**/*requirements*.*  @openvinotoolkit/openvino-configuration-mgmt
+**/setup.py  @openvinotoolkit/openvino-configuration-mgmt
+/scripts/install_dependencies/  @openvinotoolkit/openvino-configuration-mgmt

CONTRIBUTING.md

@@ -1,68 +1,55 @@
 # How to contribute to the OpenVINO repository
 
-We suppose that you are an enthusiastic coder, want to contribute some code. For that purpose OpenVINO project now has a repository on the GitHub, to simplify everybody's life! All the bug fixes, new functionality, new tutorials etc. should be submitted via the GitHub's mechanism of pull requests.
+We welcome community contributions to OpenVINO™. Please read the following guide to learn how to find ideas for contribution, practices for good pull requests, checking your changes with our tests and more.
 
-If you are not familiar with the mechanism - do not worry, it's very simple. Keep reading.
 
 ## Before you start contributing you should
 
--   Make sure you agree to contribute your code under  [OpenVINO (Apache 2.0)](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE)  license.
--   If you are submitting a new module, you should go into  [openvino_contrib](https://github.com/openvinotoolkit/openvino_contrib)  repository by default.
--   If you are going to fix a bug, check that it's still exists. This can be done by building the latest  [releases/2020/3](https://github.com/openvinotoolkit/openvino/tree/releases/2020/3)  branch (LTS release) or the latest master branch, and make sure that the error is still reproducible there. We do not fix bugs that only affect older non-LTS releases like 2020.2 for example (more details about  [branching strategy](https://github.com/openvinotoolkit/openvino/wiki/Branches))
--   Make sure that nobody beat you into fixing or reporting the issue by doing a search on the  [Github OpenVINO issues](https://github.com/openvinotoolkit/openvino/issues)  page, and making sure that there isn't someone working on it. In the latter case you might provide support or suggestion in the issue or in the linked pull request.
--   If you have a question about the software, then this is  **NOT**  the right place. You should open up a question at the  [OpenVINO forum](https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit). In order to post a decent question from the start, feel free to read the official forum guidelines.
+-   Make sure you agree to contribute your code under  [OpenVINO™ (Apache 2.0)](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE)  license.
+-   Figure out what you’re going to contribute. If you don’t know what you are going to work on, navigate to the   [Github "Issues" tab](https://github.com/openvinotoolkit/openvino/issues). Make sure that there isn't someone working on it. In the latter case you might provide support or suggestion in the issue or in the linked pull request.
+-   If you are going to fix a bug, check that it's still exists in the latest release. This can be done by building the latest master branch, and make sure that the error is still reproducible there. We do not fix bugs that only affect older non-LTS releases like 2020.2 for example (more details about  [branching strategy](https://github.com/openvinotoolkit/openvino/wiki/Branches)).
 
-Before you open up anything on the OpenVINO GitHub page, be sure that you are at the right place with your problem.
 
 ## "Fork & Pull Request model" for code contribution
 
-### [](https://github.com/openvinotoolkit/openvino/wiki/Contribute#the-instruction-in-brief)The instruction in brief
+### [](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING.md#the-instruction-in-brief)The instruction in brief
 
--   Register at GitHub. Create your fork of OpenVINO repository  [https://github.com/openvinotoolkit/openvino](https://github.com/openvinotoolkit/openvino)  (see  [https://help.github.com/articles/fork-a-repo](https://help.github.com/articles/fork-a-repo)  for details).
+-   Register at GitHub. Create your fork of OpenVINO™ repository  [https://github.com/openvinotoolkit/openvino](https://github.com/openvinotoolkit/openvino)  (see  [https://help.github.com/articles/fork-a-repo](https://help.github.com/articles/fork-a-repo)  for details).
 -   Install Git.
     -   Set your user name and email address in a Git configuration according to GitHub account (see  [https://git-scm.com/book/en/v2/Getting-Started-First-Time-Git-Setup](https://git-scm.com/book/en/v2/Getting-Started-First-Time-Git-Setup)  for details).
 -   Choose a task for yourself. It could be a bugfix or some new code.
 -   Choose a base branch for your work. More details about branches and policies are here:  [Branches](https://github.com/openvinotoolkit/openvino/wiki/Branches)
 -   Clone your fork to your computer.
 -   Create a new branch (with a meaningful name) from the base branch you chose.
--   Modify / add the code following our  [Coding Style Guide](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLines)  and  [Documentation guidelines](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLinesDocumentation).
+-   Modify / add the code following our  [Coding Style Guide](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLines).
 -   If you want to add a new sample, please look at this  [Guide for contributing to C++/C/Python IE samples](https://github.com/openvinotoolkit/openvino/wiki/SampleContribute)
+-   If you want to contribute to the documentation and want to add a new guide, follow that instruction [Documentation guidelines](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLinesDocumentation)
 -   Run testsuite locally:
     -   execute each test binary from the artifacts directory, e.g.  `<source dir>/bin/intel64/Release/ieFuncTests`
--   If you contribute to the documentation and want to add a new guide:
-    -   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}`.
-    -   Add your file to the documentation structure. Open the documentation structure file  [`docs/doxygen/ie_docs.xml`](https://github.com/openvinotoolkit/openvino/blob/master/docs/doxygen/ie_docs.xml)  and add your file path to the appropriate section.
 -   When you are done, make sure that your branch is to date with latest state of the branch you want to contribute to (e.g.  `git fetch upstream && git merge upstream/master`), push your branch to your GitHub fork; then create a pull request from your branch to the base branch (see  [https://help.github.com/articles/using-pull-requests](https://help.github.com/articles/using-pull-requests)  for details).
 
 ## Making a good pull request
 
 Following these guidelines will increase the likelihood of your pull request being accepted:
 
--   Before pushing your PR to the repository, make sure that it builds perfectly fine on your local system.
--   Add enough information, like a meaningful title, the reason why you made the commit and a link to the issue page if you opened one for this PR.
--   Scope your PR to one issue. Before submitting, make sure the diff contains no unrelated changes. If you want to cover more than one issue, submit your changes for each as separate pull requests.
--   If you have added new functionality, you should update/create the relevant documentation, as well as add tests for it to the testsuite.
--   Try not to include "oops" commits - ones that just fix an error in the previous commit. If you have those, then before submitting  [squash](https://github.com/openvinotoolkit/openvino/wiki/Contribute#https://git-scm.com/book/en/v2/Git-Tools-Rewriting-History#Squashing-Commits)  those fixes directly into the commits where they belong.
--   Make sure to choose the right base branch and to follow the  [Coding Style Guide](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLines)  for your code or  [Documentation guidelines](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLinesDocumentation)  you are changing documentation files.
--   Make sure to add test for new functionality or test that reproduces fixed bug with related test data. Please do not add extra images or videos, if some of existing media files are suitable.
+-   One PR – one issue.
+-   Build perfectly on your local system.
+-   Choose the right base branch [Branches](https://github.com/openvinotoolkit/openvino/wiki/Branches).
+-   Follow the  [Coding Style Guide](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLines) for your code.
+-   Update documentation using [Documentation guidelines](https://github.com/openvinotoolkit/openvino/wiki/CodingStyleGuideLinesDocumentation) if needed.
+-   Cover your changes with test. 
+-   Add license at the top of new files [C++ example](https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/classification_sample_async/main.cpp#L1-L2), [Python example](https://github.com/openvinotoolkit/openvino/blob/master/samples/python/hello_classification/hello_classification.py#L3-L4). 
+-   Add enough information: a meaningful title, the reason why you made the commit and a link to the issue page if exists.
+-   Remove unrelated to PR changes.
+-   If it is still WIP and you want to check CI test results early then use  _Draft_  PR.
+-   Submit your PR and become an OpenVINO™ contributor! 
+
 
 ## Testing and merging pull requests
 
--   Your pull request will be automatically tested by OpenVINO's precommit (testing status are automatically reported as "green" or "red" circles in precommit steps on PR's page). If any builders have failed, you should fix the issue. To rerun the automatic builds just push changes to your branch on GitHub. No need to close pull request and open a new one!
--   Once all the builders are "green", one of OpenVINO developers will review your code. Reviewer could ask you to modify your pull request. Please provide timely response for reviewers (within weeks, not months), otherwise you submission could be postponed or even rejected.
+Your pull request will be automatically tested by OpenVINO™'s precommit (testing status are automatically reported as "green" or "red" circles in precommit steps on PR's page). If any builders have failed, you need fix the issue. To rerun the automatic builds just push changes to your branch on GitHub. No need to close pull request and open a new one!
 
-## PR review good practices
-
--   Originator is responsible for driving the review of changes and should ping reviewers periodically.
--   Originator should close comments from the Reviewer when it is resolved. The Reviewer may re-open the comment if he does not agree with the resolution.
--   Originator should request re-review from the Reviewer when all comments are resolved by pushing the button in the “Reviewers” section.
--   If it is still WIP and you want to check CI test results early then use  _Draft_  PR.
--   Do  **NOT**  rewrite history (push -f) once you converted draft PR into regular one, add new commits instead. Looking at diffs makes review easier.
--   Write meaningful description of commits resulting from review.  _"Addressing review comments"_  is  **NOT**  a good description! Having a quick look at good descriptions can tell you much what is going on in PR without a need to go through all of resolved comments.
 
 ## Merging PR
 
-As soon as the reviewer is fine with the pull request and Precommit likes your code and shows "green" status, the "Approved" review status is put, which signals OpenVINO maintainers that they can merge your pull request.
-
-© Copyright 2018-2022, OpenVINO team
+As soon as the reviewer is fine with the pull request and precommit shows "green" status, the "Approved" review status is put, which signals OpenVINO™ maintainers that they can merge your pull request.

README.md

@@ -1,43 +1,203 @@
-# OpenVINO™ Toolkit
+<div align="center">
+
+<img src="docs/img/openvino-logo-purple-black.png" width="400px">
+
 [![Stable release](https://img.shields.io/badge/version-2022.1-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2022.1)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 ![GitHub branch checks state](https://img.shields.io/github/checks-status/openvinotoolkit/openvino/master?label=GitHub%20checks)
 ![Azure DevOps builds (branch)](https://img.shields.io/azure-devops/build/openvinoci/b2bab62f-ab2f-4871-a538-86ea1be7d20f/13?label=Public%20CI)
+[![PyPI Status](https://badge.fury.io/py/openvino.svg)](https://badge.fury.io/py/openvino)
 [![PyPI Downloads](https://pepy.tech/badge/openvino)](https://pepy.tech/project/openvino)
+ 
+ </div>
 
-This toolkit allows developers to deploy pre-trained deep learning models
-through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.
+## Contents:
 
-This open source version includes several components: namely [Model Optimizer], [OpenVINO™ Runtime], [Post-Training Optimization Tool], as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics.
+ - [What is OpenVINO?](#what-is-openvino-toolkit)
+    - [Components](#components)
+ - [Supported Hardware matrix](#supported-hardware-matrix)
+ - [License](#license)
+ - [Documentation](#documentation)
+ - [Tutorials](#tutorials)
+ - [Products which use OpenVINO](#products-which-use-openvino)
+ - [System requirements](#system-requirements)
+ - [How to build](#how-to-build)
+ - [How to contribute](#how-to-contribute)
+ - [Get a support](#get-a-support)
+ - [See also](#see-also)
+
+## What is OpenVINO toolkit?
+
+OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
+ - Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
+ - Use models trained with popular frameworks like TensorFlow, PyTorch and more
+ - Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud
+
+
+This open-source version includes several components: namely [Model Optimizer], [OpenVINO™ Runtime], [Post-Training Optimization Tool], as well as CPU, GPU, MYRIAD, multi device 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 TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
 
-## Repository components
-* [OpenVINO™ Runtime]
-* [Model Optimizer]
-* [Post-Training Optimization Tool]
-* [Samples]
+### Components
+* [OpenVINO™ Runtime] - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
+    * [core](https://github.com/openvinotoolkit/openvino/tree/master/src/core) - provides the base API for model representation and modification.
+    * [inference](https://github.com/openvinotoolkit/openvino/tree/master/src/inference) - provides an API to infer models on device.
+    * [transformations](https://github.com/openvinotoolkit/openvino/tree/master/src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
+    * [low precision transformations](https://github.com/openvinotoolkit/openvino/tree/master/src/common/low_precision_transformations) - contains the set of transformations which are used in low precision models
+    * [bindings](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings) - contains all awailable OpenVINO bindings which are maintained by OpenVINO team.
+        * [c](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings/c) - provides C API for OpenVINO™ Runtime
+        * [python](https://github.com/openvinotoolkit/openvino/tree/master/src/bindings/python) - Python API for OpenVINO™ Runtime
+* [Plugins](https://github.com/openvinotoolkit/openvino/tree/master/src/plugins) - contains OpenVINO plugins which are maintained in open-source by OpenVINO team. For more information please taje a look to the [list of supported devices](#supported-hardware-matrix).
+* [Frontends](https://github.com/openvinotoolkit/openvino/tree/master/src/frontends) - contains available OpenVINO frontends which allow to read model from native framework format.
+* [Model Optimizer] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
+* [Post-Training Optimization Tool] - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization. 
+* [Samples] - applications on C, C++ and Python languages which shows basic use cases of OpenVINO usages.
+
+## Supported Hardware matrix
+
+The OpenVINO™ Runtime can infer models on different hardware devices. This section provides the list of supported devices.
+
+<table>
+    <thead>
+        <tr>
+            <th>Device</th>
+            <th>Plugin</th>
+            <th>Library</th>
+            <th>ShortDescription</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan=2>CPU</td>
+            <td> <a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_CPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-c-p-u">Intel CPU</a></tb>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_cpu">openvino_intel_cpu_plugin</a></i></b></td>
+            <td>Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE)</td>
+        </tr>
+        <tr>
+            <td> <a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_ARM_CPU.html">ARM CPU</a></tb>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/arm_plugin">openvino_arm_cpu_plugin</a></i></b></td>
+            <td>Raspberry Pi™ 4 Model B, Apple® Mac mini with M1 chip, NVIDIA® Jetson Nano™, Android™ devices
+        </tr>
+        <tr>
+            <td>GPU</td>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_GPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-p-u">Intel GPU</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_gpu">openvino_intel_gpu_plugin</a></i></b></td>
+            <td>Intel Processor Graphics, including Intel HD Graphics and Intel Iris Graphics</td>
+        </tr>
+        <tr>
+            <td>GNA</td>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_supported_plugins_GNA.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-n-a">Intel GNA</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_gna">openvino_intel_gna_plugin</a></i></b></td>
+            <td>Intel Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel Pentium Silver J5005 Processor, Intel Pentium Silver N5000 Processor, Intel Celeron J4005 Processor, Intel Celeron J4105 Processor, Intel Celeron Processor N4100, Intel Celeron Processor N4000, Intel Core i3-8121U Processor, Intel Core i7-1065G7 Processor, Intel Core i7-1060G7 Processor, Intel Core i5-1035G4 Processor, Intel Core i5-1035G7 Processor, Intel Core i5-1035G1 Processor, Intel Core i5-1030G7 Processor, Intel Core i5-1030G4 Processor, Intel Core i3-1005G1 Processor, Intel Core i3-1000G1 Processor, Intel Core i3-1000G4 Processor</td>
+        </tr>
+        <tr>
+            <td>VPU</td>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_IE_DG_supported_plugins_VPU.html#doxid-openvino-docs-i-e-d-g-supported-plugins-v-p-u">Myriad plugin</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_myriad">openvino_intel_myriad_plugin</a></i></b></td>
+            <td>Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X</td>
+        </tr>
+    </tbody>
+</table>
+
+Also OpenVINO™ Toolkit contains several plugins which should simplify to load model on several hardware devices:
+<table>
+    <thead>
+        <tr>
+            <th>Plugin</th>
+            <th>Library</th>
+            <th>ShortDescription</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_IE_DG_supported_plugins_AUTO.html#doxid-openvino-docs-i-e-d-g-supported-plugins-a-u-t-o">Auto</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/auto">openvino_auto_plugin</a></i></b></td>
+            <td>Auto plugin enables selecting Intel device for inference automatically</td>
+        </tr>
+        <tr>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_Automatic_Batching.html">Auto Batch</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/auto_batch">openvino_auto_batch_plugin</a></i></b></td>
+            <td>Auto batch plugin performs on-the-fly automatic batching (i.e. grouping inference requests together) to improve device utilization, with no programming effort from the user</td>
+        </tr>
+        <tr>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_Hetero_execution.html#doxid-openvino-docs-o-v-u-g-hetero-execution">Hetero</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/hetero">openvino_hetero_plugin</a></i></b></td>
+            <td>Heterogeneous execution enables automatic inference splitting between several devices</td>
+        </tr>
+        <tr>
+            <td><a href="https://docs.openvino.ai/nightly/openvino_docs_OV_UG_Running_on_multiple_devices.html#doxid-openvino-docs-o-v-u-g-running-on-multiple-devices">Multi</a></td>
+            <td><b><i><a href="https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/auto">openvino_auto_plugin</a></i></b></td>
+            <td>Multi plugin enables simultaneous inference of the same model on several devices in parallel</td>
+        </tr>
+    </tbody>
+</table>
 
 ## License
 OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE).
 By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
 
-## Resources
-* Docs: https://docs.openvino.ai/
-* Wiki: https://github.com/openvinotoolkit/openvino/wiki
-* Issue tracking: https://github.com/openvinotoolkit/openvino/issues
-* Storage: https://storage.openvinotoolkit.org/
-* Additional OpenVINO™ toolkit modules: https://github.com/openvinotoolkit/openvino_contrib
-* [Intel® Distribution of OpenVINO™ toolkit Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
-* [Intel® Distribution of OpenVINO™ toolkit Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
+## Documentation
+
+### User documentation
+
+The latest documentation for OpenVINO™ Toolkit is availabe [here](https://docs.openvino.ai/). This documentation contains detailed information about all OpenVINO components and provides all important information which could be needed if you create an application which is based on binary OpenVINO distribution or own OpenVINO version without source code modification.
+
+### Developer documentation
+
+[Developer documentation](#todo-add) contains information about architectural decisions which are applied inside the OpenVINO components. This documentation has all necessary information which could be needed in order to contribute to OpenVINO.
+
+## Tutorials
+
+The list of OpenVINO tutorials:
+
+- [Jupiter notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
+
+## Products which use OpenVINO
+
+- [OpenCV](https://opencv.org/)
+- [ONNX Runtime](https://onnxruntime.ai/)
+- [OpenVINO™ Integration with TensorFlow](https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/build/ovtfoverview.html)
+- [TNN](https://github.com/Tencent/TNN/tree/master)
+
+## System requirements
+
+The full information about system requirements depends on platform and is available on dedicated pages:
+- [Linux](https://docs.openvino.ai/latest/openvino_docs_install_guides_installing_openvino_linux_header.html)
+- [Windows](https://docs.openvino.ai/latest/openvino_docs_install_guides_installing_openvino_windows_header.html)
+- [macOS](https://docs.openvino.ai/latest/openvino_docs_install_guides_installing_openvino_macos_header.html)
+- [Raspbian](https://docs.openvino.ai/latest/openvino_docs_install_guides_installing_openvino_raspbian.html)
+
+## How to build
+
+Please take a look to [OpenVINO Wiki](https://github.com/openvinotoolkit/openvino/wiki#how-to-build) to get more information about OpenVINO build process.
+
+## How to contribute
+
+See [CONTRIBUTING](./CONTRIBUTING.md) for details. Thank you!
+
+## Get a support
 
-## Support
 Please report questions, issues and suggestions using:
 
-* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on StackOverflow\*
 * [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues)
+* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on StackOverflow\*
 * [Forum](https://software.intel.com/en-us/forums/computer-vision)
 
+## See also
+
+* [OpenVINO Wiki](https://github.com/openvinotoolkit/openvino/wiki)
+* [OpenVINO Storage](https://storage.openvinotoolkit.org/)
+* Additional OpenVINO™ toolkit modules: 
+    * [openvino_contrib](https://github.com/openvinotoolkit/openvino_contrib)
+* [Intel® Distribution of OpenVINO™ toolkit Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
+* [Intel® Distribution of OpenVINO™ toolkit Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
+* [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - a suite of advanced algorithms for model inference optimization including quantization, filter pruning, binarization and sparsity
+* [OpenVINO™ Training Extensions (OTE)](https://github.com/openvinotoolkit/training_extensions) - convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
+* [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving deep learning models optimized for Intel architectures
+* [DL Workbench](https://docs.openvino.ai/nightly/workbench_docs_Workbench_DG_Introduction.html) - An alternative, web-based version of OpenVINO designed to make production of pretrained deep learning models significantly easier.
+* [Computer Vision Annotation Tool (CVAT)](https://github.com/openvinotoolkit/cvat) - an online, interactive video and image annotation tool for computer vision purposes.
+* [Dataset Management Framework (Datumaro)](https://github.com/openvinotoolkit/datumaro) - a framework and CLI tool to build, transform, and analyze datasets.
+
 ---
 \* Other names and brands may be claimed as the property of others.
 
@@ -46,5 +206,3 @@ Please report questions, issues and suggestions using:
 [Model Optimizer]:https://docs.openvino.ai/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html
 [Post-Training Optimization Tool]:https://docs.openvino.ai/latest/pot_introduction.html
 [Samples]:https://github.com/openvinotoolkit/openvino/tree/master/samples
-[tag on StackOverflow]:https://stackoverflow.com/search?q=%23openvino
-

cmake/arm.toolchain.cmake

@@ -7,6 +7,7 @@ set(CMAKE_SYSTEM_PROCESSOR armv7l)
 
 set(CMAKE_C_COMPILER arm-linux-gnueabihf-gcc)
 set(CMAKE_CXX_COMPILER arm-linux-gnueabihf-g++)
+set(PKG_CONFIG_EXECUTABLE arm-linux-gnueabihf-pkg-config CACHE PATH "Path to ARM pkg-config")
 
 set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
 set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)

cmake/arm64.toolchain.cmake

@@ -7,6 +7,7 @@ set(CMAKE_SYSTEM_PROCESSOR aarch64)
 
 set(CMAKE_C_COMPILER aarch64-linux-gnu-gcc)
 set(CMAKE_CXX_COMPILER aarch64-linux-gnu-g++)
+set(PKG_CONFIG_EXECUTABLE aarch64-linux-gnu-pkg-config CACHE PATH "Path to ARM64 pkg-config")
 
 set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
 set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)

cmake/coverage.cmake

@@ -84,6 +84,11 @@ ie_coverage_extract(INPUT "openvino" OUTPUT "core"
 ie_coverage_genhtml(INFO_FILE "core"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
+ie_coverage_extract(INPUT "openvino" OUTPUT "openvino_all"
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/*" "${OV_COVERAGE_BASE_DIRECTORY}/docs/template_plugin/*")
+ie_coverage_genhtml(INFO_FILE "openvino_all"
+                    PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
+
 if(ENABLE_OV_ONNX_FRONTEND)
     ie_coverage_extract(INPUT "openvino" OUTPUT "onnx"
         PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/frontends/onnx/*"

cmake/dependencies.cmake

@@ -317,8 +317,8 @@ if(ENABLE_INTEL_GNA)
             GNA_LIB_DIR
             libGNA_INCLUDE_DIRS
             libGNA_LIBRARIES_BASE_PATH)
-        set(GNA_VERSION "03.00.00.1455.0")
-        set(GNA_HASH "99891696269d8fa10116c96e6b7bda4362736881f0df8df8b56c751ee18e5820")
+        set(GNA_VERSION "03.00.00.1455.2")
+        set(GNA_HASH "e52785d3f730fefb4e794bb7ab40c8676537ef2f7c69c5b4bb89a5d3cc0bbe60")
 
         set(FILES_TO_EXTRACT_LIST gna_${GNA_VERSION}/include)
         if(WIN32)

cmake/developer_package/IEDevScriptsConfig.cmake

@@ -14,8 +14,8 @@ set(CMAKE_MODULE_PATH "${IEDevScripts_DIR}")
 function(set_ci_build_number)
     set(repo_root "${CMAKE_SOURCE_DIR}")
     include(version)
-    foreach(var CI_BUILD_NUMBER IE_VERSION IE_VERSION_BUILD
-                IE_VERSION_MAJOR IE_VERSION_MINOR IE_VERSION_PATCH)
+    foreach(var CI_BUILD_NUMBER OpenVINO_VERSION OpenVINO_VERSION_BUILD
+                OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH)
         if(NOT DEFINED ${var})
             message(FATAL_ERROR "${var} version component is not defined")
         endif()
@@ -186,6 +186,8 @@ endif()
 # Use solution folders
 set_property(GLOBAL PROPERTY USE_FOLDERS ON)
 
+# cmake_dependent_option() supports full Condition Syntax
+set(CMAKE_POLICY_DEFAULT_CMP0127 NEW)
 # Enable CMAKE_<LANG>_COMPILER_ID AppleClang
 set(CMAKE_POLICY_DEFAULT_CMP0025 NEW)
 

cmake/developer_package/add_ie_target.cmake

@@ -76,8 +76,8 @@ function(addIeTarget)
 
     # remove unnecessary directories
     foreach(excludedDir ${ARG_EXCLUDED_SOURCE_PATHS})
-        list(FILTER includes EXCLUDE REGEX "${excludedDir}*")
-        list(FILTER sources EXCLUDE REGEX "${excludedDir}*")
+        list(FILTER includes EXCLUDE REGEX "${excludedDir}.*")
+        list(FILTER sources EXCLUDE REGEX "${excludedDir}.*")
     endforeach()
 
     source_group("include" FILES ${includes})

cmake/developer_package/clang_format/clang_format.cmake

@@ -28,7 +28,6 @@ if(ENABLE_CLANG_FORMAT AND NOT TARGET clang_format_check_all)
     add_custom_target(clang_format_fix_all)
     set_target_properties(clang_format_check_all clang_format_fix_all
                           PROPERTIES FOLDER clang_format)
-    set(CLANG_FORMAT_ALL_OUTPUT_FILES "" CACHE INTERNAL "All clang-format output files")
 endif()
 
 function(add_clang_format_target TARGET_NAME)
@@ -88,14 +87,10 @@ function(add_clang_format_target TARGET_NAME)
             "[clang-format] ${source_file}"
             VERBATIM)
 
+        list(APPEND all_input_sources "${source_file}")
         list(APPEND all_output_files "${output_file}")
     endforeach()
 
-    set(CLANG_FORMAT_ALL_OUTPUT_FILES
-        ${CLANG_FORMAT_ALL_OUTPUT_FILES} ${all_output_files}
-        CACHE INTERNAL
-        "All clang-format output files")
-
     add_custom_target(${TARGET_NAME}
         DEPENDS ${all_output_files}
         COMMENT "[clang-format] ${TARGET_NAME}")
@@ -104,11 +99,11 @@ function(add_clang_format_target TARGET_NAME)
         COMMAND
         "${CMAKE_COMMAND}"
         -D "CLANG_FORMAT=${CLANG_FORMAT}"
-        -D "INPUT_FILES=${CLANG_FORMAT_FOR_SOURCES}"
+        -D "INPUT_FILES=${all_input_sources}"
         -D "EXCLUDE_PATTERNS=${CLANG_FORMAT_EXCLUDE_PATTERNS}"
         -P "${IEDevScripts_DIR}/clang_format/clang_format_fix.cmake"
         DEPENDS
-        "${CLANG_FORMAT_FOR_SOURCES}"
+        "${all_input_sources}"
         "${IEDevScripts_DIR}/clang_format/clang_format_fix.cmake"
         COMMENT
         "[clang-format] ${TARGET_NAME}_fix"

cmake/developer_package/frontends/frontends.cmake

@@ -82,10 +82,11 @@ unset(protobuf_installed CACHE)
 
 #
 # ov_add_frontend(NAME <IR|ONNX|...>
-#                 FILEDESCRIPTION <description>
-#                 [LINKABLE_FRONTEND]
-#                 [SKIP_INSTALL]
-#                 [PROTOBUF_LITE]
+#                 FILEDESCRIPTION <description> # used on Windows to describe DLL file
+#                 [LINKABLE_FRONTEND] # whether we can use FE API directly or via FEM only
+#                 [SKIP_INSTALL] # private frontend, not for end users
+#                 [PROTOBUF_LITE] # requires only libprotobuf-lite
+#                 [SKIP_NCC_STYLE] # use custom NCC rules
 #                 [LINK_LIBRARIES <lib1 lib2 ...>])
 #
 macro(ov_add_frontend)
@@ -106,6 +107,17 @@ macro(ov_add_frontend)
     set(FRONTEND_NAMES "${FRONTEND_NAMES}" CACHE INTERNAL "" FORCE)
 
     file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+    if (WIN32)
+        # Remove linux specific files
+        file(GLOB_RECURSE LIN_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/*.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/src/os/lin/*.hpp)
+        list(REMOVE_ITEM LIBRARY_SRC "${LIN_FILES}")
+    else()
+        # Remove windows specific files
+        file(GLOB_RECURSE WIN_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/os/win/*.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/src/os/win/*.hpp)
+        list(REMOVE_ITEM LIBRARY_SRC "${WIN_FILES}")
+    endif()
     file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
     file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 
@@ -231,7 +243,7 @@ macro(ov_add_frontend)
         endif()
 
         if(OV_FRONTEND_LINKABLE_FRONTEND)
-            # install -dev part
+            # install library development files
             install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/openvino
                     DESTINATION ${FRONTEND_INSTALL_INCLUDE}/
                     COMPONENT core_dev

cmake/developer_package/ncc_naming_style/ncc_naming_style.cmake

@@ -9,26 +9,46 @@ endif()
 set(ncc_style_dir "${IEDevScripts_DIR}/ncc_naming_style")
 set(ncc_style_bin_dir "${CMAKE_CURRENT_BINARY_DIR}/ncc_naming_style")
 
-# try to find_package(Clang QUIET)
-# ClangConfig.cmake contains bug that if libclang-XX-dev is not
-# installed, then find_package fails with errors even in QUIET mode
-configure_file("${ncc_style_dir}/try_find_clang.cmake"
-               "${ncc_style_bin_dir}/source/CMakeLists.txt" COPYONLY)
-execute_process(
-    COMMAND
-        "${CMAKE_COMMAND}" -S "${ncc_style_bin_dir}/source"
-                           -B "${ncc_style_bin_dir}/build"
-    RESULT_VARIABLE clang_find_result
-    OUTPUT_VARIABLE output_var
-    ERROR_VARIABLE error_var)
+# find python3
 
-if(NOT clang_find_result EQUAL "0")
-    message(WARNING "Please, install clang-[N] libclang-[N]-dev package (required for ncc naming style check)")
-    message(WARNING "find_package(Clang) output: ${output_var}")
-    message(WARNING "find_package(Clang) error: ${error_var}")
+find_package(PythonInterp 3 QUIET)
+if(NOT PYTHONINTERP_FOUND)
+    message(WARNING "Python3 interpreter was not found (required for ncc naming style check)")
     set(ENABLE_NCC_STYLE OFF)
 endif()
 
+if(PYTHON_VERSION_MINOR EQUAL 6)
+    set(clang_version 10)
+elseif(PYTHON_VERSION_MINOR EQUAL 8)
+    set(clang_version 12)
+elseif(PYTHON_VERSION_MINOR EQUAL 9)
+    set(clang_version 12)
+elseif(PYTHON_VERSION_MINOR EQUAL 10)
+    set(clang_version 14)
+endif()
+
+
+if(ENABLE_NCC_STYLE)
+    # try to find_package(Clang QUIET)
+    # ClangConfig.cmake contains bug that if libclang-XX-dev is not
+    # installed, then find_package fails with errors even in QUIET mode
+    configure_file("${ncc_style_dir}/try_find_clang.cmake"
+                   "${ncc_style_bin_dir}/source/CMakeLists.txt" COPYONLY)
+    execute_process(
+        COMMAND "${CMAKE_COMMAND}" -S "${ncc_style_bin_dir}/source"
+                                   -B "${ncc_style_bin_dir}/build"
+        RESULT_VARIABLE clang_find_result
+        OUTPUT_VARIABLE output_var
+        ERROR_VARIABLE error_var)
+
+    if(NOT clang_find_result EQUAL "0")
+        message(WARNING "Please, install `apt-get install clang-${clang_version} libclang-${clang_version}-dev` package (required for ncc naming style check)")
+        message(TRACE "find_package(Clang) output: ${output_var}")
+        message(TRACE "find_package(Clang) error: ${error_var}")
+        set(ENABLE_NCC_STYLE OFF)
+    endif()
+endif()
+
 # Since we were able to find_package(Clang) in a separate process
 # let's try to find in current process
 if(ENABLE_NCC_STYLE)
@@ -37,19 +57,11 @@ if(ENABLE_NCC_STYLE)
         get_target_property(libclang_location libclang LOCATION)
         message(STATUS "Found libclang: ${libclang_location}")
     else()
-        message(WARNING "libclang is not found (required for ncc naming style check)")
+        message(WARNING "libclang-${clang_version} is not found (required for ncc naming style check)")
         set(ENABLE_NCC_STYLE OFF)
     endif()
 endif()
 
-# find python3
-
-find_package(PythonInterp 3 QUIET)
-if(NOT PYTHONINTERP_FOUND)
-    message(WARNING "Python3 interpreter was not found (required for ncc naming style check)")
-    set(ENABLE_NCC_STYLE OFF)
-endif()
-
 # check python requirements_dev.txt
 
 set(ncc_script_py "${ncc_style_dir}/ncc/ncc.py")
@@ -106,7 +118,6 @@ function(ov_ncc_naming_style)
          "${NCC_STYLE_SOURCE_DIRECTORY}/*.cpp")
 
     list(APPEND NCC_STYLE_ADDITIONAL_INCLUDE_DIRECTORIES "${NCC_STYLE_SOURCE_DIRECTORY}")
-
     # without it sources with same name from different directories will map to same .ncc_style target
     file(RELATIVE_PATH source_dir_rel ${CMAKE_SOURCE_DIR} ${NCC_STYLE_SOURCE_DIRECTORY})
 

cmake/developer_package/ncc_naming_style/openvino.style

@@ -1,5 +1,5 @@
 # custom OpenVINO values
-CppMethod: '^(operator\W+|[a-z_\d]+|signaling_NaN|quiet_NaN|OPENVINO_OP)$'
+CppMethod: '^(operator\W+|[a-z_\d]+|signaling_NaN|quiet_NaN)$'
 ClassName: '^([A-Z][\w]+|b?float16|numeric_limits|ngraph_error|stopwatch|unsupported_op)$'
 StructName: '^([A-Z][\w]+|element_type_traits|hash|oi_pair)$'
 FunctionName: '^(operator\W+|[a-z_\d]+)|PrintTo$'

cmake/developer_package/ncc_naming_style/requirements_dev.txt

@@ -1,2 +1,5 @@
-clang==11.0
+clang==10.0.1; python_version == '3.6'
+clang==12.0.1; python_version == '3.8'
+clang==12.0.1; python_version == '3.9'
+clang==14.0; python_version == '3.10'
 pyyaml

cmake/developer_package/options.cmake

@@ -11,6 +11,11 @@ macro (ie_option variable description value)
     list(APPEND IE_OPTIONS ${variable})
 endmacro()
 
+# Usage: ov_option(<option_variable> "description" <initial value or boolean expression> [IF <condition>])
+macro (ov_option variable description value)
+    ie_option(${variable} "${description}" ${value})
+endmacro()
+
 macro (ie_dependent_option variable description def_value condition fallback_value)
     cmake_dependent_option(${variable} "${description}" ${def_value} "${condition}" ${fallback_value})
     list(APPEND IE_OPTIONS ${variable})

cmake/developer_package/packaging.cmake

@@ -69,8 +69,8 @@ macro(ie_cpack)
     endif()
 
     foreach(ver IN LISTS MAJOR MINOR PATCH)
-        if(DEFINED IE_VERSION_${ver})
-            set(CPACK_PACKAGE_VERSION_${ver} ${IE_VERSION_${ver}})
+        if(DEFINED OpenVINO_VERSION_${ver})
+            set(CPACK_PACKAGE_VERSION_${ver} ${OpenVINO_VERSION_${ver}})
         endif()
     endforeach()
 

cmake/developer_package/plugins/plugins.cmake

@@ -13,8 +13,8 @@ function(ie_plugin_get_file_name target_name library_name)
     set("${library_name}" "${LIB_PREFIX}${target_name}${LIB_SUFFIX}" PARENT_SCOPE)
 endfunction()
 
-if(NOT TARGET ie_plugins)
-    add_custom_target(ie_plugins)
+if(NOT TARGET ov_plugins)
+    add_custom_target(ov_plugins)
 endif()
 
 #
@@ -27,11 +27,12 @@ endif()
 #               [OBJECT_LIBRARIES <object_libs>]
 #               [VERSION_DEFINES_FOR <source>]
 #               [SKIP_INSTALL]
+#               [SKIP_REGISTRATION] Skip creation of <device>.xml
 #               [ADD_CLANG_FORMAT]
 #               )
 #
 function(ie_add_plugin)
-    set(options SKIP_INSTALL ADD_CLANG_FORMAT AS_EXTENSION)
+    set(options SKIP_INSTALL ADD_CLANG_FORMAT AS_EXTENSION SKIP_REGISTRATION)
     set(oneValueArgs NAME DEVICE_NAME VERSION_DEFINES_FOR PSEUDO_PLUGIN_FOR)
     set(multiValueArgs DEFAULT_CONFIG SOURCES OBJECT_LIBRARIES CPPLINT_FILTERS)
     cmake_parse_arguments(IE_PLUGIN "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -101,7 +102,7 @@ function(ie_add_plugin)
             add_cpplint_target(${IE_PLUGIN_NAME}_cpplint FOR_TARGETS ${IE_PLUGIN_NAME} CUSTOM_FILTERS ${custom_filter})
         endif()
 
-        add_dependencies(ie_plugins ${IE_PLUGIN_NAME})
+        add_dependencies(ov_plugins ${IE_PLUGIN_NAME})
         if(TARGET openvino_gapi_preproc)
             if(BUILD_SHARED_LIBS)
                 add_dependencies(${IE_PLUGIN_NAME} openvino_gapi_preproc)
@@ -146,25 +147,27 @@ function(ie_add_plugin)
         endif()
     endif()
 
-    # check that plugin with such name is not registered
+    # Enable for static build to generate correct plugins.hpp
+    if(NOT IE_PLUGIN_SKIP_REGISTRATION OR NOT BUILD_SHARED_LIBS)
+        # check that plugin with such name is not registered
+        foreach(plugin_entry IN LISTS PLUGIN_FILES)
+            string(REPLACE ":" ";" plugin_entry "${plugin_entry}")
+            list(GET plugin_entry -1 library_name)
+            list(GET plugin_entry 0 plugin_name)
+            if(plugin_name STREQUAL "${IE_PLUGIN_DEVICE_NAME}" AND
+                    NOT library_name STREQUAL ${IE_PLUGIN_NAME})
+                message(FATAL_ERROR "${IE_PLUGIN_NAME} and ${library_name} are both registered as ${plugin_name}")
+            endif()
+        endforeach()
 
-    foreach(plugin_entry IN LISTS PLUGIN_FILES)
-        string(REPLACE ":" ";" plugin_entry "${plugin_entry}")
-        list(GET plugin_entry -1 library_name)
-        list(GET plugin_entry 0 plugin_name)
-        if(plugin_name STREQUAL "${IE_PLUGIN_DEVICE_NAME}" AND
-           NOT library_name STREQUAL ${IE_PLUGIN_NAME})
-            message(FATAL_ERROR "${IE_PLUGIN_NAME} and ${library_name} are both registered as ${plugin_name}")
-        endif()
-    endforeach()
+        # append plugin to the list to register
 
-    # append plugin to the list to register
-
-    list(APPEND PLUGIN_FILES "${IE_PLUGIN_DEVICE_NAME}:${IE_PLUGIN_NAME}")
-    set(PLUGIN_FILES "${PLUGIN_FILES}" CACHE INTERNAL "" FORCE)
-    set(${IE_PLUGIN_DEVICE_NAME}_CONFIG "${IE_PLUGIN_DEFAULT_CONFIG}" CACHE INTERNAL "" FORCE)
-    set(${IE_PLUGIN_DEVICE_NAME}_PSEUDO_PLUGIN_FOR "${IE_PLUGIN_PSEUDO_PLUGIN_FOR}" CACHE INTERNAL "" FORCE)
-    set(${IE_PLUGIN_DEVICE_NAME}_AS_EXTENSION "${IE_PLUGIN_AS_EXTENSION}" CACHE INTERNAL "" FORCE)
+        list(APPEND PLUGIN_FILES "${IE_PLUGIN_DEVICE_NAME}:${IE_PLUGIN_NAME}")
+        set(PLUGIN_FILES "${PLUGIN_FILES}" CACHE INTERNAL "" FORCE)
+        set(${IE_PLUGIN_DEVICE_NAME}_CONFIG "${IE_PLUGIN_DEFAULT_CONFIG}" CACHE INTERNAL "" FORCE)
+        set(${IE_PLUGIN_DEVICE_NAME}_PSEUDO_PLUGIN_FOR "${IE_PLUGIN_PSEUDO_PLUGIN_FOR}" CACHE INTERNAL "" FORCE)
+        set(${IE_PLUGIN_DEVICE_NAME}_AS_EXTENSION "${IE_PLUGIN_AS_EXTENSION}" CACHE INTERNAL "" FORCE)
+    endif()
 endfunction()
 
 function(ov_add_plugin)
@@ -172,13 +175,12 @@ function(ov_add_plugin)
 endfunction()
 
 #
-# ie_register_plugins_dynamic(MAIN_TARGET <main target name>
-#                             POSSIBLE_PLUGINS <list of plugins which can be build by this repo>)
+# ie_register_plugins_dynamic(MAIN_TARGET <main target name>)
 #
 macro(ie_register_plugins_dynamic)
     set(options)
     set(oneValueArgs MAIN_TARGET)
-    set(multiValueArgs POSSIBLE_PLUGINS)
+    set(multiValueArgs)
     cmake_parse_arguments(IE_REGISTER "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
     if(NOT IE_REGISTER_MAIN_TARGET)
@@ -261,6 +263,15 @@ macro(ie_register_plugins)
     endif()
 endmacro()
 
+#
+# ov_register_plugins()
+#
+macro(ov_register_plugins)
+    if(BUILD_SHARED_LIBS)
+        ie_register_plugins_dynamic(${ARGN})
+    endif()
+endmacro()
+
 #
 # ie_target_link_plugins(<TARGET_NAME>)
 #

cmake/developer_package/shellcheck/shellcheck.cmake

@@ -6,6 +6,17 @@ include(CMakeParseArguments)
 
 find_host_program(shellcheck_PROGRAM NAMES shellcheck DOC "Path to shellcheck tool")
 
+if(shellcheck_PROGRAM)
+    execute_process(COMMAND "${shellcheck_PROGRAM}" --version
+        RESULT_VARIABLE shellcheck_EXIT_CODE
+        OUTPUT_VARIABLE shellcheck_VERSION_STRING)
+    if(shellcheck_EXIT_CODE EQUAL 0)
+        if(shellcheck_VERSION_STRING MATCHES "version: ([0-9]+)\.([0-9]+).([0-9]+)")
+            set(shellcheck_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}" CACHE STRING "shellcheck version")
+        endif()
+    endif()
+endif()
+
 function(ie_shellcheck_process)
     if(NOT shellcheck_PROGRAM)
         message(WARNING "shellcheck tool is not found")
@@ -33,7 +44,7 @@ function(ie_shellcheck_process)
         set(output_file "${output_file}.txt")
         get_filename_component(script_name "${script}" NAME)
 
-        add_custom_command(OUTPUT ${output_file} 
+        add_custom_command(OUTPUT ${output_file}
                            COMMAND ${CMAKE_COMMAND}
                              -D IE_SHELLCHECK_PROGRAM=${shellcheck_PROGRAM}
                              -D IE_SHELL_SCRIPT=${script}

cmake/developer_package/version.cmake

@@ -19,27 +19,35 @@ function (commitHash VAR)
         message(FATAL_ERROR "repo_root is not defined")
     endif()
     execute_process(
-            COMMAND git rev-parse HEAD
+            COMMAND git rev-parse --short=11 HEAD
             WORKING_DIRECTORY ${repo_root}
             OUTPUT_VARIABLE GIT_COMMIT_HASH
             OUTPUT_STRIP_TRAILING_WHITESPACE)
     set (${VAR} ${GIT_COMMIT_HASH} PARENT_SCOPE)
 endfunction()
 
-macro(ie_parse_ci_build_number)
-    set(IE_VERSION_BUILD 000)
+macro(ov_parse_ci_build_number)
+    set(OpenVINO_VERSION_BUILD 000)
+
     if(CI_BUILD_NUMBER MATCHES "^([0-9]+)\.([0-9]+)\.([0-9]+)\-([0-9]+)\-.*")
-        set(IE_VERSION_MAJOR ${CMAKE_MATCH_1})
-        set(IE_VERSION_MINOR ${CMAKE_MATCH_2})
-        set(IE_VERSION_PATCH ${CMAKE_MATCH_3})
-        set(IE_VERSION_BUILD ${CMAKE_MATCH_4})
+        set(OpenVINO_VERSION_MAJOR ${CMAKE_MATCH_1})
+        set(OpenVINO_VERSION_MINOR ${CMAKE_MATCH_2})
+        set(OpenVINO_VERSION_PATCH ${CMAKE_MATCH_3})
+        set(OpenVINO_VERSION_BUILD ${CMAKE_MATCH_4})
+        set(the_whole_version_is_defined_by_ci ON)
+    elseif(CI_BUILD_NUMBER MATCHES "^[0-9]+$")
+        set(OpenVINO_VERSION_BUILD ${CI_BUILD_NUMBER})
+        # only build number is defined by CI
+        set(the_whole_version_is_defined_by_ci OFF)
+    elseif(CI_BUILD_NUMBER)
+        message(FATAL_ERROR "Failed to parse CI_BUILD_NUMBER which is ${CI_BUILD_NUMBER}")
     endif()
 
     if(NOT DEFINED repo_root)
         message(FATAL_ERROR "repo_root is not defined")
     endif()
 
-    macro(ie_get_hpp_version)
+    macro(ov_get_hpp_version)
         if(NOT DEFINED OpenVINO_SOURCE_DIR)
             return()
         endif()
@@ -59,11 +67,12 @@ macro(ie_parse_ci_build_number)
 
         foreach(suffix MAJOR MINOR PATCH)
             set(ie_version_name "IE_VERSION_${suffix}")
-            set(ov_version_name "OPENVINO_VERSION_${suffix}")
+            set(ov_version_name "OpenVINO_VERSION_${suffix}")
+            set(ov_version_name_hpp "OPENVINO_VERSION_${suffix}")
 
             string(REGEX REPLACE ".+${ie_version_name}[ ]+([0-9]+).*" "\\1"
                     ${ie_version_name}_HPP "${IE_VERSION_PARTS}")
-            string(REGEX REPLACE ".+${ov_version_name}[ ]+([0-9]+).*" "\\1"
+            string(REGEX REPLACE ".+${ov_version_name_hpp}[ ]+([0-9]+).*" "\\1"
                     ${ov_version_name}_HPP "${OV_VERSION_PARTS}")
 
             if(NOT ${ie_version_name}_HPP EQUAL ${ov_version_name}_HPP)
@@ -72,42 +81,54 @@ macro(ie_parse_ci_build_number)
             endif()
         endforeach()
 
-        set(ie_hpp_version_is_found ON)
+        set(ov_hpp_version_is_found ON)
     endmacro()
 
-    # detect OpenVINO version via ie_version.hpp
-    ie_get_hpp_version()
+    # detect OpenVINO version via openvino/core/version.hpp and ie_version.hpp
+    ov_get_hpp_version()
 
-    if(ie_hpp_version_is_found)
-        foreach(var IE_VERSION_MAJOR IE_VERSION_MINOR IE_VERSION_PATCH)
+    if(ov_hpp_version_is_found)
+        foreach(var OpenVINO_VERSION_MAJOR OpenVINO_VERSION_MINOR OpenVINO_VERSION_PATCH)
             if(DEFINED ${var} AND NOT ${var} EQUAL ${var}_HPP)
                 message(FATAL_ERROR "${var} parsed from CI_BUILD_NUMBER (${${var}}) \
-                    and from ie_version.hpp (${${var}_HPP}) are different")
+                    and from openvino/core/version.hpp (${${var}_HPP}) are different")
             else()
-                # CI_BUILD_NUMBER is not defined well, take info from ie_verison.hpp as a baseline
+                # CI_BUILD_NUMBER is not defined well, take info from openvino/core/version.hpp as a baseline
                 set(${var} ${${var}_HPP})
             endif()
         endforeach()
     endif()
 
-    set(IE_VERSION "${IE_VERSION_MAJOR}.${IE_VERSION_MINOR}.${IE_VERSION_PATCH}")
-    message(STATUS "OpenVINO version is ${IE_VERSION}")
+    set(OpenVINO_VERSION "${OpenVINO_VERSION_MAJOR}.${OpenVINO_VERSION_MINOR}.${OpenVINO_VERSION_PATCH}")
+    message(STATUS "OpenVINO version is ${OpenVINO_VERSION} (Build ${OpenVINO_VERSION_BUILD})")
+
+    if(NOT the_whole_version_is_defined_by_ci)
+        # create CI_BUILD_NUMBER
+
+        branchName(GIT_BRANCH)
+        commitHash(GIT_COMMIT_HASH)
+
+        if(NOT GIT_BRANCH STREQUAL "master")
+            set(GIT_BRANCH_POSTFIX "-${GIT_BRANCH}")
+        endif()
+
+        set(CI_BUILD_NUMBER "${OpenVINO_VERSION}-${OpenVINO_VERSION_BUILD}-${GIT_COMMIT_HASH}${GIT_BRANCH_POSTFIX}")
+
+        unset(GIT_BRANCH_POSTFIX)
+        unset(GIT_BRANCH)
+        unset(GIT_COMMIT_HASH)
+    else()
+        unset(the_whole_version_is_defined_by_ci)
+    endif()
 endmacro()
 
+# provides OpenVINO version
+# 1. If CI_BUILD_NUMBER is defined, parses this information
+# 2. Otherwise, parses openvino/core/version.hpp
 if (DEFINED ENV{CI_BUILD_NUMBER})
     set(CI_BUILD_NUMBER $ENV{CI_BUILD_NUMBER})
-else()
-    branchName(GIT_BRANCH)
-    commitHash(GIT_COMMIT_HASH)
-
-    set(custom_build "custom_${GIT_BRANCH}_${GIT_COMMIT_HASH}")
-    set(CI_BUILD_NUMBER "${custom_build}")
 endif()
-
-# provides Inference Engine version
-# 1. If CI_BUILD_NUMBER is defined, parses this information
-# 2. Otherwise, parses ie_version.hpp
-ie_parse_ci_build_number()
+ov_parse_ci_build_number()
 
 macro (addVersionDefines FILE)
     set(__version_file ${FILE})

cmake/developer_package/vs_version/vs_version.cmake

@@ -2,9 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(IE_VS_VER_FILEVERSION_QUAD "${IE_VERSION_MAJOR},${IE_VERSION_MINOR},${IE_VERSION_PATCH},0")
-set(IE_VS_VER_PRODUCTVERSION_QUAD "${IE_VERSION_MAJOR},${IE_VERSION_MINOR},${IE_VERSION_PATCH},0")
-set(IE_VS_VER_FILEVERSION_STR "${IE_VERSION_MAJOR}.${IE_VERSION_MINOR}.${IE_VERSION_PATCH}.0")
+set(IE_VS_VER_FILEVERSION_QUAD "${OpenVINO_VERSION_MAJOR},${OpenVINO_VERSION_MINOR},${OpenVINO_VERSION_PATCH},${OpenVINO_VERSION_BUILD}")
+set(IE_VS_VER_PRODUCTVERSION_QUAD "${OpenVINO_VERSION_MAJOR},${OpenVINO_VERSION_MINOR},${OpenVINO_VERSION_PATCH},${OpenVINO_VERSION_BUILD}")
+set(IE_VS_VER_FILEVERSION_STR "${OpenVINO_VERSION_MAJOR}.${OpenVINO_VERSION_MINOR}.${OpenVINO_VERSION_PATCH}.${OpenVINO_VERSION_BUILD}")
 
 set(IE_VS_VER_COMPANY_NAME_STR "Intel Corporation")
 set(IE_VS_VER_PRODUCTVERSION_STR "${CI_BUILD_NUMBER}")

cmake/extra_modules.cmake

@@ -6,15 +6,19 @@ function(ie_generate_dev_package_config)
     # dummy check that OpenCV is here
     find_package(OpenCV QUIET)
 
-    set(all_dev_targets gflags ov_runtime_libraries)
     foreach(component IN LISTS openvino_export_components)
         # export all targets with prefix and use them during extra modules build
         export(TARGETS ${${component}} NAMESPACE IE::
-            APPEND FILE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
+               APPEND FILE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
         list(APPEND all_dev_targets ${${component}})
     endforeach()
     add_custom_target(ie_dev_targets DEPENDS ${all_dev_targets})
 
+    # if we've found system gflags
+    if(gflags_DIR)
+        set(gflags_BINARY_DIR "${gflags_DIR}")
+    endif()
+
     configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in"
                                   "${CMAKE_BINARY_DIR}/InferenceEngineDeveloperPackageConfig.cmake"
                                   INSTALL_DESTINATION share # not used
@@ -30,18 +34,22 @@ function(ov_generate_dev_package_config)
     # dummy check that OpenCV is here
     find_package(OpenCV QUIET)
 
-    set(all_dev_targets gflags ov_runtime_libraries)
     foreach(component IN LISTS openvino_export_components)
         string(FIND "${component}" "_legacy" index)
-        if (index EQUAL -1)
+        if(index EQUAL -1)
             # export all targets with prefix and use them during extra modules build
             export(TARGETS ${${component}} NAMESPACE openvino::
-                APPEND FILE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
+                   APPEND FILE "${CMAKE_BINARY_DIR}/ov_${component}_dev_targets.cmake")
             list(APPEND all_dev_targets ${${component}})
         endif()
     endforeach()
     add_custom_target(ov_dev_targets DEPENDS ${all_dev_targets})
 
+    # if we've found system gflags
+    if(gflags_DIR)
+        set(gflags_BINARY_DIR "${gflags_DIR}")
+    endif()
+
     configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in"
                                   "${CMAKE_BINARY_DIR}/OpenVINODeveloperPackageConfig.cmake"
                                   INSTALL_DESTINATION share # not used
@@ -59,14 +67,14 @@ endfunction()
 
 function(register_extra_modules)
     # post export
-    openvino_developer_export_targets(COMPONENT core TARGETS inference_engine)
-    openvino_developer_export_targets(COMPONENT core TARGETS ngraph)
+    openvino_developer_export_targets(COMPONENT core_legacy TARGETS inference_engine)
+    openvino_developer_export_targets(COMPONENT core_legacy TARGETS ngraph)
 
     set(InferenceEngineDeveloperPackage_DIR "${CMAKE_CURRENT_BINARY_DIR}/runtime")
     set(OpenVINODeveloperPackage_DIR "${CMAKE_BINARY_DIR}/runtime")
 
     function(generate_fake_dev_package NS)
-        if (NS STREQUAL "openvino")
+        if(NS STREQUAL "openvino")
             set(devconfig_file "${OpenVINODeveloperPackage_DIR}/OpenVINODeveloperPackageConfig.cmake")
         else()
             set(devconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
@@ -81,10 +89,6 @@ function(register_extra_modules)
                 file(APPEND "${devconfig_file}" "add_library(${NS}::${target} ALIAS ${target})\n")
             endif()
         endforeach()
-        if ("${NS}" STREQUAL "openvino")
-            file(APPEND "${devconfig_file}" "add_library(${NS}::runtime ALIAS openvino)\n")
-            file(APPEND "${devconfig_file}" "add_library(${NS}::runtime::dev ALIAS openvino_dev)\n")
-        endif()
     endfunction()
 
     generate_fake_dev_package("openvino")
@@ -137,7 +141,7 @@ ie_generate_dev_package_config()
 ov_generate_dev_package_config()
 
 # extra modules must be registered after inference_engine library
-# and all other IE common libraries (ov_runtime_libraries) are creared
+# and all other OpenVINO Core libraries are creared
 # because 'register_extra_modules' creates fake InferenceEngineDeveloperPackageConfig.cmake
 # with all imported developer targets
 register_extra_modules()

cmake/features.cmake

@@ -59,7 +59,7 @@ cmake_dependent_option (ENABLE_WHEEL "Build wheel packages for PyPi" OFF
 # Inference Engine specific options
 #
 
-# "MKL-DNN library based on OMP or TBB or Sequential implementation: TBB|OMP|SEQ"
+# "OneDNN library based on OMP or TBB or Sequential implementation: TBB|OMP|SEQ"
 if(X86 OR ARM OR (MSVC AND (ARM OR AARCH64)) )
     set(THREADING_DEFAULT "SEQ")
 else()

cmake/templates/InferenceEngineConfig-version.cmake.in

@@ -2,9 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(PACKAGE_VERSION_MAJOR @IE_VERSION_MAJOR@)
-set(PACKAGE_VERSION_MINOR @IE_VERSION_MINOR@)
-set(PACKAGE_VERSION_PATCH @IE_VERSION_PATCH@)
+set(PACKAGE_VERSION_MAJOR @OpenVINO_VERSION_MAJOR@)
+set(PACKAGE_VERSION_MINOR @OpenVINO_VERSION_MINOR@)
+set(PACKAGE_VERSION_PATCH @OpenVINO_VERSION_PATCH@)
 set(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
 
 set(PACKAGE_VERSION_EXACT False)

cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in

@@ -12,19 +12,20 @@ set_and_check(OpenVINO_MAIN_SOURCE_DIR "@OpenVINO_SOURCE_DIR@") # KMB
 
 # Variables to export in plugin's projects
 
-set(ie_options "@IE_OPTIONS@;CMAKE_BUILD_TYPE;CMAKE_SKIP_RPATH")
-list(APPEND ie_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER)
+set(ie_options "@IE_OPTIONS@")
+list(APPEND ie_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER
+                       CMAKE_BUILD_TYPE CMAKE_SKIP_RPATH CMAKE_INSTALL_PREFIX)
 file(TO_CMAKE_PATH "${CMAKE_CURRENT_LIST_DIR}" cache_path)
 
 message(STATUS "The following CMake options are exported from Inference Engine Developer package")
-message("")
+message(" ")
 foreach(option IN LISTS ie_options)
     if(NOT DEFINED "${option}")
         load_cache("${cache_path}" READ_WITH_PREFIX "" ${option})
     endif()
     message("    ${option}: ${${option}}")
 endforeach()
-message("")
+message(" ")
 
 # for samples in 3rd party projects
 set_and_check(gflags_DIR "@gflags_BINARY_DIR@")
@@ -48,11 +49,6 @@ find_dependency(ngraph
                 NO_CMAKE_FIND_ROOT_PATH
                 NO_DEFAULT_PATH)
 
-find_dependency(OpenVINODeveloperPackage
-                PATHS "${CMAKE_CURRENT_LIST_DIR}"
-                NO_CMAKE_FIND_ROOT_PATH
-                NO_DEFAULT_PATH)
-
 if(TARGET openvino::runtime AND NOT TARGET IE::runtime)
     add_library(IE::runtime INTERFACE IMPORTED)
     set_target_properties(IE::runtime PROPERTIES
@@ -70,6 +66,18 @@ foreach(component @openvino_export_components@)
     include("${CMAKE_CURRENT_LIST_DIR}/${component}_dev_targets.cmake")
 endforeach()
 
+if(TARGET IE::ov_core_dev AND NOT TARGET openvino::core::dev)
+    add_library(openvino::core::dev INTERFACE IMPORTED)
+    set_target_properties(openvino::core::dev PROPERTIES
+        INTERFACE_LINK_LIBRARIES IE::ov_core_dev)
+endif()
+
+if(TARGET IE::runtime::dev AND NOT TARGET openvino::runtime::dev)
+    add_library(openvino::runtime::dev INTERFACE IMPORTED)
+    set_target_properties(openvino::runtime::dev PROPERTIES
+        INTERFACE_LINK_LIBRARIES IE::runtime::dev)
+endif()
+
 if(ENABLE_SYSTEM_PUGIXML)
     find_dependency(PugiXML)
     set_property(TARGET pugixml PROPERTY IMPORTED_GLOBAL TRUE)
@@ -86,13 +94,11 @@ endif()
 # Extra Compile Flags
 #
 
-if(NOT MSVC)
+if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wno-error=unused-variable)
-    if(CMAKE_COMPILER_IS_GNUCXX)
-        ie_add_compiler_flags(-Wno-error=unused-but-set-variable)
-        if(SUGGEST_OVERRIDE_SUPPORTED)
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
-        endif()
+    ie_add_compiler_flags(-Wno-error=unused-but-set-variable)
+    if(SUGGEST_OVERRIDE_SUPPORTED)
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
     endif()
 endif()
 

cmake/templates/OpenVINOConfig-version.cmake.in

@@ -2,9 +2,9 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(PACKAGE_VERSION_MAJOR @IE_VERSION_MAJOR@)
-set(PACKAGE_VERSION_MINOR @IE_VERSION_MINOR@)
-set(PACKAGE_VERSION_PATCH @IE_VERSION_PATCH@)
+set(PACKAGE_VERSION_MAJOR @OpenVINO_VERSION_MAJOR@)
+set(PACKAGE_VERSION_MINOR @OpenVINO_VERSION_MINOR@)
+set(PACKAGE_VERSION_PATCH @OpenVINO_VERSION_PATCH@)
 set(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
 
 set(PACKAGE_VERSION_EXACT False)

cmake/templates/OpenVINOConfig.cmake.in

@@ -150,10 +150,15 @@ if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND NOT TBB_FOUND
     set(enable_system_tbb "@ENABLE_SYSTEM_TBB@")
     if(NOT enable_system_tbb)
         set_and_check(_tbb_dir "@PACKAGE_IE_TBB_DIR@")
+
+        # see https://stackoverflow.com/questions/28070810/cmake-generate-error-on-windows-as-it-uses-as-escape-seq
         if(DEFINED ENV{TBBROOT})
-            # see https://stackoverflow.com/questions/28070810/cmake-generate-error-on-windows-as-it-uses-as-escape-seq
             file(TO_CMAKE_PATH $ENV{TBBROOT} ENV_TBBROOT)
         endif()
+        if(DEFINED ENV{TBB_DIR})
+            file(TO_CMAKE_PATH $ENV{TBB_DIR} ENV_TBB_DIR)
+        endif()
+
         set(find_package_tbb_extra_args
             CONFIG
             PATHS
@@ -161,7 +166,7 @@ if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND NOT TBB_FOUND
                 "${ENV_TBBROOT}/lib64/cmake/TBB"
                 "${ENV_TBBROOT}/lib/cmake/TBB"
                 "${ENV_TBBROOT}/lib/cmake/tbb"
-                # "$ENV{TBB_DIR}"
+                "${ENV_TBB_DIR}"
                 # for custom TBB exposed via cmake -DTBBROOT=<custom TBB root>
                 "${TBBROOT}/cmake"
                 # _tbb_dir points to TBB_DIR (custom | temp | system) used to build OpenVINO
@@ -206,7 +211,8 @@ if(NOT TARGET openvino)
     set(_ov_as_external_package ON)
     include("${CMAKE_CURRENT_LIST_DIR}/OpenVINOTargets.cmake")
 
-    # TODO: WA for cmake version < 3.16
+    # WA for cmake version < 3.16 which does not export
+    # IMPORTED_LINK_DEPENDENT_LIBRARIES_** properties if no PUBLIC dependencies for the library
     if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND TBB_FOUND)
         foreach (type RELEASE DEBUG RELWITHDEBINFO MINSIZEREL)
             set_property(TARGET openvino::runtime APPEND PROPERTY IMPORTED_LINK_DEPENDENT_LIBRARIES_${type} "TBB::tbb;TBB::tbbmalloc")

cmake/templates/OpenVINODeveloperPackageConfig.cmake.in

@@ -10,19 +10,20 @@ set_and_check(OpenVINO_SOURCE_DIR "@OpenVINO_SOURCE_DIR@")
 
 # Variables to export in plugin's projects
 
-set(ie_options "@IE_OPTIONS@;CMAKE_BUILD_TYPE;CMAKE_SKIP_RPATH")
-list(APPEND ie_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER)
+set(ov_options "@IE_OPTIONS@")
+list(APPEND ov_options CMAKE_CXX_COMPILER_LAUNCHER CMAKE_C_COMPILER_LAUNCHER
+                       CMAKE_BUILD_TYPE CMAKE_SKIP_RPATH CMAKE_INSTALL_PREFIX)
 file(TO_CMAKE_PATH "${CMAKE_CURRENT_LIST_DIR}" cache_path)
 
 message(STATUS "The following CMake options are exported from OpenVINO Developer package")
-message("")
-foreach(option IN LISTS ie_options)
+message(" ")
+foreach(option IN LISTS ov_options)
     if(NOT DEFINED "${option}")
         load_cache("${cache_path}" READ_WITH_PREFIX "" ${option})
     endif()
     message("    ${option}: ${${option}}")
 endforeach()
-message("")
+message(" ")
 
 # for samples in 3rd party projects
 set_and_check(gflags_DIR "@gflags_BINARY_DIR@")
@@ -51,10 +52,10 @@ endforeach()
 if(ENABLE_SYSTEM_PUGIXML)
     find_dependency(PugiXML)
     set_property(TARGET pugixml PROPERTY IMPORTED_GLOBAL TRUE)
-    add_library(IE::pugixml ALIAS pugixml)
+    add_library(openvino::pugixml ALIAS pugixml)
 endif()
 
-# inherit OpenCV from main IE project if enabled
+# inherit OpenCV from main OpenVINO project if enabled
 if ("@OpenCV_FOUND@")
     load_cache("${cache_path}" READ_WITH_PREFIX "" OpenCV_DIR)
     find_dependency(OpenCV)
@@ -64,13 +65,11 @@ endif()
 # Extra Compile Flags
 #
 
-if(NOT MSVC)
+if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wno-error=unused-variable)
-    if(CMAKE_COMPILER_IS_GNUCXX)
-        ie_add_compiler_flags(-Wno-error=unused-but-set-variable)
-        if(SUGGEST_OVERRIDE_SUPPORTED)
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
-        endif()
+    ie_add_compiler_flags(-Wno-error=unused-but-set-variable)
+    if(SUGGEST_OVERRIDE_SUPPORTED)
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
     endif()
 endif()
 

cmake/test_model_zoo.cmake

@@ -108,7 +108,7 @@ if(ENABLE_TESTS)
 
         message(STATUS "pip version is ${pip3_version}")
         set(args --quiet)
-        if(pip3_version VERSION_GREATER 20.2.2)
+        if(pip3_version VERSION_GREATER 20.2.2 AND pip3_version VERSION_LESS 20.3.0)
             list(APPEND args --use-feature=2020-resolver)
         endif()
 

docs/Documentation/deployment_guide_introduction.md

@@ -0,0 +1,8 @@
+# Introduction to OpenVINO™ Deployment {#openvino_docs_deployment_guide_introduction}
+
+
+Once you have a model that meets both OpenVINO™ and your requirements, you can choose among several ways of deploying it with your application: 
+* [Run inference and develop your app with OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md).
+* [Deploy your application locally](../OV_Runtime_UG/deployment/deployment_intro.md).
+* [Deploy your model online with the OpenVINO Model Server](@ref ovms_what_is_openvino_model_server).
+

docs/Documentation/dl_workbench_overview.md

@@ -0,0 +1,111 @@
+# OpenVINO™ Deep Learning Workbench Overview {#workbench_docs_Workbench_DG_Introduction}
+
+@sphinxdirective
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   workbench_docs_Workbench_DG_Install
+   workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets
+   Tutorials <workbench_docs_Workbench_DG_Tutorials>
+   User Guide <workbench_docs_Workbench_DG_User_Guide>
+   workbench_docs_Workbench_DG_Troubleshooting
+
+@endsphinxdirective
+
+
+
+
+Deep Learning Workbench (DL Workbench) is an official OpenVINO™ graphical interface designed to make the production of pretrained deep learning Computer Vision and Natural Language Processing models significantly easier. 
+
+Minimize the inference-to-deployment workflow timing for neural models right in your browser: import a model, analyze its performance and accuracy, visualize the outputs, optimize and make the final model deployment-ready in a matter of minutes. DL Workbench takes you through the full OpenVINO™ workflow, providing the opportunity to learn about various toolkit components.
+ 
+![](../img/openvino_dl_wb.png)
+
+
+@sphinxdirective
+
+.. link-button:: workbench_docs_Workbench_DG_Start_DL_Workbench_in_DevCloud
+    :type: ref
+    :text: Run DL Workbench in Intel® DevCloud
+    :classes: btn-primary btn-block
+
+@endsphinxdirective
+
+DL Workbench enables you to get a detailed performance assessment, explore inference configurations, and obtain an optimized model ready to be deployed on various Intel® configurations, such as client and server CPU, Intel® Processor Graphics (GPU), Intel® Movidius™ Neural Compute Stick 2 (NCS 2), and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs.
+
+DL Workbench also provides the [JupyterLab environment](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Jupyter_Notebooks.html#doxid-workbench-docs-workbench-d-g-jupyter-notebooks) that helps you quick start with OpenVINO™ API and command-line interface (CLI). Follow the full OpenVINO workflow created for your model and learn about different toolkit components. 
+
+
+## Video
+
+@sphinxdirective
+
+.. list-table::
+
+   * - .. raw:: html
+
+           <iframe  allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen  height="315" width="560"
+           src="https://www.youtube.com/embed/on8xSSTKCt8">
+           </iframe>
+   * - **DL Workbench Introduction**. Duration: 1:31
+     
+@endsphinxdirective
+
+
+## User Goals
+
+DL Workbench helps achieve your goals depending on the stage of your deep learning journey. 
+
+If you are a beginner in the deep learning field, the DL Workbench provides you with
+learning opportunities:
+* Learn what neural networks are, how they work, and how to examine their architectures. 
+* Learn the basics of neural network analysis and optimization before production.
+* Get familiar with the OpenVINO™ ecosystem and its main components without installing it on your system.
+
+If you have enough experience with neural networks, DL Workbench provides you with a
+convenient web interface to optimize your model and prepare it for production:
+* Measure and interpret model performance.
+* Tune the model for enhanced performance.
+* Analyze the quality of your model and visualize output.
+
+## General Workflow
+
+The diagram below illustrates the typical DL Workbench workflow. Click to see the full-size image:
+
+![](../img/openvino_dl_wb_diagram_overview.svg)
+
+Get a quick overview of the workflow in the DL Workbench User Interface:
+
+![](../img/openvino_dl_wb_workflow.gif)
+
+## OpenVINO™ Toolkit Components
+
+The intuitive web-based interface of the DL Workbench enables you to easily use various
+OpenVINO™ toolkit components:
+
+Component  |                 Description 
+|------------------|------------------|
+| [Open Model Zoo](https://docs.openvinotoolkit.org/latest/omz_tools_downloader.html)| Get access to the collection of high-quality pre-trained deep learning [public](https://docs.openvinotoolkit.org/latest/omz_models_group_public.html) and [Intel-trained](https://docs.openvinotoolkit.org/latest/omz_models_group_intel.html) models trained to resolve a variety of different tasks. 
+| [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) |Optimize and transform models trained in supported frameworks to the IR format. <br>Supported frameworks include TensorFlow\*, Caffe\*, Kaldi\*, MXNet\*, and ONNX\* format.  
+| [Benchmark Tool](https://docs.openvinotoolkit.org/latest/openvino_inference_engine_tools_benchmark_tool_README.html)| Estimate deep learning model inference performance on supported devices.   
+| [Accuracy Checker](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html)| Evaluate the accuracy of a model by collecting one or several metric values. 
+| [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)| Optimize pretrained models with lowering the precision of a model from floating-point precision(FP32 or FP16) to integer precision (INT8), without the need to retrain or fine-tune models.                               |
+
+
+@sphinxdirective
+
+.. link-button:: workbench_docs_Workbench_DG_Start_DL_Workbench_in_DevCloud
+    :type: ref
+    :text: Run DL Workbench in Intel® DevCloud
+    :classes: btn-outline-primary 
+
+@endsphinxdirective
+
+## Contact Us
+
+* [DL Workbench GitHub Repository](https://github.com/openvinotoolkit/workbench)
+
+* [DL Workbench on Intel Community Forum](https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit)
+
+* [DL Workbench Gitter Chat](https://gitter.im/dl-workbench/general?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&content=body)

docs/Documentation/model_introduction.md

@@ -0,0 +1,12 @@
+# Introduction to Model Processing {#openvino_docs_model_processing_introduction}
+
+Every deep learning workflow begins with obtaining a model. You can choose to prepare a custom one, use a ready-made solution and adjust it to your needs, or even download and run a pre-trained network from an online database, such as OpenVINO's [Open Model Zoo](../model_zoo.md).  
+
+This section describes how to obtain and prepare your model for work with OpenVINO to get the best inference results:
+* [Browse a database of models for use in your projects](../model_zoo.md).
+* [Convert different model formats to the OpenVINO IR format](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+* [Automate model-related tasks with Model Downloader and additional OMZ Tools](https://docs.openvino.ai/latest/omz_tools_downloader.html).
+
+
+
+

docs/Documentation/openvino_ecosystem.md

@@ -0,0 +1,76 @@
+# OpenVINO™ Ecosystem Overview {#openvino_ecosystem}
+
+
+OpenVINO™ is not just one tool. It is an expansive ecosystem of utilities, providing a comprehensive workflow for deep learning solution development. Learn more about each of them to reach the full potential of OpenVINO™ Toolkit.
+
+### OpenVINO™ Model Server (OVMS)	   
+OpenVINO Model Server is a scalable, high-performance solution for serving deep learning models optimized for Intel® architectures. The server uses Inference Engine libraries as a backend and exposes gRPC and HTTP/REST interfaces for inference that are fully compatible with TensorFlow Serving.
+
+More resources:
+* [OpenVINO documentation](https://docs.openvino.ai/latest/openvino_docs_ovms.html)
+* [Docker Hub](https://hub.docker.com/r/openvino/model_server)
+* [GitHub](https://github.com/openvinotoolkit/model_server)
+* [Red Hat Ecosystem Catalog](https://catalog.redhat.com/software/container-stacks/detail/60649e41ccfb383fe395a167)
+
+### Neural Network Compression Framework (NNCF)
+A suite of advanced algorithms for Neural Network inference optimization with minimal accuracy drop. NNCF applies quantization, filter pruning, binarization and sparsity algorithms to PyTorch and TensorFlow models during training.
+
+More resources:
+* [Documentation](@ref docs_nncf_introduction)
+* [GitHub](https://github.com/openvinotoolkit/nncf)
+* [PyPI](https://pypi.org/project/nncf/)
+
+### OpenVINO™ Security Add-on	 
+A solution for Model Developers and Independent Software Vendors to use secure packaging and secure model execution.	 
+
+More resources:
+* [documentation](https://docs.openvino.ai/latest/ovsa_get_started.html)
+* [GitHub]https://github.com/openvinotoolkit/security_addon)
+
+
+### OpenVINO™ integration with TensorFlow (OVTF)
+A solution empowering TensorFlow developers with OpenVINO's optimization capabilities. With just two lines of code in your application, you can offload inference to OpenVINO, while keeping the TensorFlow API.
+
+More resources:
+* [documentation](https://github.com/openvinotoolkit/openvino_tensorflow)	 
+* [PyPI](https://pypi.org/project/openvino-tensorflow/) 
+* [GitHub](https://github.com/openvinotoolkit/openvino_tensorflow)
+
+### DL Streamer		
+A streaming media analytics framework, based on the GStreamer multimedia framework, for creating complex media analytics pipelines.
+
+More resources:
+* [documentation on GitHub](https://openvinotoolkit.github.io/dlstreamer_gst/)
+* [installation Guide on GitHub](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Install-Guide)
+
+### DL Workbench
+A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting [Intel® DevCloud for the Edge](https://software.intel.com/content/www/us/en/develop/tools/devcloud.html) and launching DL Workbench on-line.
+
+More resources:
+* [documentation](dl_workbench_overview.md)
+* [Docker Hub](https://hub.docker.com/r/openvino/workbench)
+* [PyPI](https://pypi.org/project/openvino-workbench/)
+
+### OpenVINO™ Training Extensions (OTE)
+A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
+
+More resources:
+* [GitHub](https://github.com/openvinotoolkit/training_extensions)
+
+### Computer Vision Annotation Tool (CVAT)
+An online, interactive video and image annotation tool for computer vision purposes.
+
+More resources:
+* [documentation on GitHub](https://openvinotoolkit.github.io/cvat/docs/)	
+* [web application](https://cvat.org/) 
+* [Docker Hub](https://hub.docker.com/r/openvino/cvat_server) 
+* [GitHub](https://github.com/openvinotoolkit/cvat)
+
+### Dataset Management Framework (Datumaro)
+A framework and CLI tool to build, transform, and analyze datasets.
+
+More resources:
+* [documentation on GitHub](https://openvinotoolkit.github.io/datumaro/docs/)
+* [PyPI](https://pypi.org/project/datumaro/)
+* [GitHub](https://github.com/openvinotoolkit/datumaro)
+

docs/Documentation/openvino_ecosystem_ovtf.md

@@ -0,0 +1,44 @@
+# OpenVINO™ integration with TensorFlow {#ovtf_integration}
+
+**OpenVINO™ integration with TensorFlow** is a solution for TensorFlow developers who want to get started with OpenVINO™ in their inferencing applications. By adding just two lines of code you can now take advantage of OpenVINO™ toolkit optimizations with TensorFlow inference applications across a range of Intel® computation devices.
+
+This is all you need:
+```bash
+import openvino_tensorflow
+openvino_tensorflow.set_backend('<backend_name>')
+```
+
+**OpenVINO™ integration with TensorFlow** accelerates inference across many AI models on a variety of Intel® technologies, such as:
+- Intel® CPUs
+- Intel® integrated GPUs
+- Intel® Movidius™ Vision Processing Units - referred to as VPU
+- Intel® Vision Accelerator Design with 8 Intel Movidius™ MyriadX VPUs - referred to as VAD-M or HDDL
+
+> **NOTE**: For maximum performance, efficiency, tooling customization, and hardware control, we recommend developers to adopt native OpenVINO™ solutions.
+To find out more about the product itself, as well as learn how to use it in your project, check its dedicated [GitHub repository](https://github.com/openvinotoolkit/openvino_tensorflow/tree/master/docs). 
+
+
+To see what you can do with **OpenVINO™ integration with TensorFlow**, explore the demos located in the [examples folder](https://github.com/openvinotoolkit/openvino_tensorflow/tree/master/examples) in our GitHub repository.  
+
+Sample tutorials are also hosted on [Intel® DevCloud](https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/build/ovtfoverview.html). The demo applications are implemented using Jupyter Notebooks. You can interactively execute them on Intel® DevCloud nodes, compare the results of **OpenVINO™ integration with TensorFlow**, native TensorFlow, and OpenVINO™. 
+
+## License
+**OpenVINO™ integration with TensorFlow** is licensed under [Apache License Version 2.0](https://github.com/openvinotoolkit/openvino_tensorflow/blob/master/LICENSE).
+By contributing to the project, you agree to the license and copyright terms therein
+and release your contribution under these terms.
+
+## Support
+
+Submit your questions, feature requests and bug reports via [GitHub issues](https://github.com/openvinotoolkit/openvino_tensorflow/issues).
+
+## How to Contribute
+
+We welcome community contributions to **OpenVINO™ integration with TensorFlow**. If you have an idea for improvement:
+
+* Share your proposal via [GitHub issues](https://github.com/openvinotoolkit/openvino_tensorflow/issues).
+* Submit a [pull request](https://github.com/openvinotoolkit/openvino_tensorflow/pulls).
+
+We will review your contribution as soon as possible. If any additional fixes or modifications are necessary, we will guide you and provide feedback. Before you make your contribution, make sure you can build **OpenVINO™ integration with TensorFlow** and run all the examples with your fix/patch. If you want to introduce a large feature, create test cases for your feature. Upon our verification of your pull request, we will merge it to the repository provided that the pull request has met the above mentioned requirements and proved acceptable.
+
+---
+\* Other names and brands may be claimed as the property of others.

docs/Extensibility_UG/Intro.md

@@ -9,13 +9,13 @@
    openvino_docs_Extensibility_UG_add_openvino_ops
    openvino_docs_Extensibility_UG_Frontend_Extensions
    openvino_docs_Extensibility_UG_GPU
-   openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel
+   openvino_docs_Extensibility_UG_VPU_Kernel
    openvino_docs_MO_DG_prepare_model_customize_model_optimizer_Customize_Model_Optimizer
 
 @endsphinxdirective
 
 The Intel® Distribution of OpenVINO™ toolkit supports neural network models trained with various frameworks, including
-TensorFlow, PyTorch, ONNX, PaddlePaddle, MXNet, Caffe, and Kaldi. The list of supported operations is different for
+TensorFlow, PyTorch, ONNX, PaddlePaddle, Apache MXNet, Caffe, and Kaldi. The list of supported operations is different for
 each of the supported frameworks. To see the operations supported by your framework, refer to
 [Supported Framework Operations](../MO_DG/prepare_model/Supported_Frameworks_Layers.md).
 
@@ -52,7 +52,7 @@ Depending on model format used for import, mapping of custom operation is implem
 
 2. If model is represented in TensorFlow, Caffe, Kaldi or MXNet formats, then [Model Optimizer Extensions](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) should be used. This approach is available for model conversion in Model Optimizer only.
 
-Existing of two approaches simultaneously is explained by two different types of frontends used for model conversion in OpenVINO: new frontends (ONNX, PaddlePaddle) and legacy frontends (TensorFlow, Caffe, Kaldi and MXNet). Model Optimizer can use both front-ends in contrast to the direct import of model with `read_model` method which can use new frontends only. Follow one of the appropriate guides referenced above to implement mappings depending on framework frontend.
+Existing of two approaches simultaneously is explained by two different types of frontends used for model conversion in OpenVINO: new frontends (ONNX, PaddlePaddle) and legacy frontends (TensorFlow, Caffe, Kaldi and Apache MXNet). Model Optimizer can use both front-ends in contrast to the direct import of model with `read_model` method which can use new frontends only. Follow one of the appropriate guides referenced above to implement mappings depending on framework frontend.
 
 If you are implementing extensions for ONNX or PaddlePaddle new frontends and plan to use Model Optimizer `--extension` option for model conversion, then the extensions should be
 

docs/Extensibility_UG/VPU_Extensibility.md

@@ -1,4 +1,4 @@
-# How to Implement Custom Layers for VPU (Intel® Neural Compute Stick 2) {#openvino_docs_IE_DG_Extensibility_DG_VPU_Kernel}
+# How to Implement Custom Layers for VPU (Intel® Neural Compute Stick 2) {#openvino_docs_Extensibility_UG_VPU_Kernel}
 
 To enable operations not supported by OpenVINO™ out of the box, you need a custom extension for Model Optimizer, a custom nGraph operation set, and a custom kernel for the device you will target. This page describes custom kernel support for one the VPU, the Intel® Neural Compute Stick 2 device, which uses the MYRIAD device plugin.
 

docs/IE_PLUGIN_DG/ExecutableNetwork.md

@@ -37,8 +37,8 @@ The implementation `CompileNetwork` is fully device-specific.
 
 The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:
 
-1. Applies ngraph passes using `TransformNetwork` function, which defines plugin-specific conversion pipeline. To support low precision inference, the pipeline can include Low Precision Transformations. These transformations are usually hardware specific. You can find how to use and configure Low Precisions Transformations in [Low Precision Transformations](@ref openvino_docs_OV_UG_lpt) guide.
-2. Maps the transformed graph to a backend specific graph representation (for example, to MKLDNN graph for Intel CPU).
+1. Applies nGraph passes using `TransformNetwork` function, which defines plugin-specific conversion pipeline. To support low precision inference, the pipeline can include Low Precision Transformations. These transformations are usually hardware specific. You can find how to use and configure Low Precisions Transformations in [Low Precision Transformations](@ref openvino_docs_OV_UG_lpt) guide.
+2. Maps the transformed graph to a backend specific graph representation (for example, to CPU plugin internal graph representation).
 3. Allocates and fills memory for graph weights, backend specific memory handles and so on.
 
 @snippet src/template_executable_network.cpp executable_network:map_graph

docs/IE_PLUGIN_DG/Plugin.md

@@ -2,7 +2,7 @@
 
 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.
+- oneDNN backend for Intel CPU devices.
 - NVIDIA cuDNN for NVIDIA GPUs.
 
 The responsibility of Inference Engine Plugin:

docs/IE_PLUGIN_DG/layout.xml

@@ -9,10 +9,10 @@
                         <tab type="user" title="Attributes" url="@ref openvino_docs_OV_UG_lpt_attributes">
                             <tab type="user" title="AvgPoolPrecisionPreserved" url="@ref openvino_docs_OV_UG_lpt_AvgPoolPrecisionPreserved"/>
                             <tab type="user" title="IntervalsAlignment" url="@ref openvino_docs_OV_UG_lpt_IntervalsAlignment"/>
-                            <tab type="user" title="PerTensorQuantization" url="@ref openvino_docs_OV_UG_lpt_PerTensorQuantization"/>
                             <tab type="user" title="PrecisionPreserved" url="@ref openvino_docs_OV_UG_lpt_PrecisionPreserved"/>
                             <tab type="user" title="Precisions" url="@ref openvino_docs_OV_UG_lpt_Precisions"/>
                             <tab type="user" title="QuantizationAlignment" url="@ref openvino_docs_OV_UG_lpt_QuantizationAlignment"/>
+                            <tab type="user" title="QuantizationGranularity" url="@ref openvino_docs_OV_UG_lpt_QuantizationGranularity"/>
                         </tab>
                         <tab type="user" title="Step 1. Prerequisites transformations" url="@ref openvino_docs_OV_UG_lpt_step1_prerequisites">
                             <tab type="user" title="LinOpSequenceFusion" url="@ref openvino_docs_OV_UG_lpt_LinOpSequenceFusion"/>

docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/attributes/per_tensor_quantization.md

@@ -1,11 +0,0 @@
-# PerTensorQuantization attribute {#openvino_docs_OV_UG_lpt_PerTensorQuantization}
-
-ngraph::PerTensorQuantizationAttribute class represents the `PerTensorQuantization` attribute.
-
-The attribute defines if the operation input port requires per-tensor quantization.
-
-| Property name | Values                                       |
-|---------------|----------------------------------------------|
-| Required      | Yes                                          |
-| Defined       | Operation, input ports                        |
-| Properties    |                                              |

docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/attributes/quantization_granularity.md

@@ -0,0 +1,11 @@
+# QuantizationGranularity attribute {#openvino_docs_OV_UG_lpt_QuantizationGranularity}
+
+ngraph::QuantizationAttribute class represents the `QuantizationGranularity` attribute.
+
+The attribute defines quantization granularity of operation inputs.
+
+| Property name | Values                                       |
+|---------------|----------------------------------------------|
+| Required      | No                                           |
+| Defined       | Input ports                                  |
+| Properties    | Quantization granularity                     |

docs/IE_PLUGIN_DG/plugin_transformation_pipeline/low_precision_transformations/lpt_attributes.md

@@ -8,29 +8,30 @@
    :hidden:
 
    AvgPoolPrecisionPreserved <openvino_docs_OV_UG_lpt_AvgPoolPrecisionPreserved>
-   IntervalsAlignment <openvino_docs_OV_UG_lpt_IntervalsAlignment>
-   PerTensorQuantization <openvino_docs_OV_UG_lpt_PerTensorQuantization>
+   IntervalsAlignment <openvino_docs_OV_UG_lpt_IntervalsAlignment>   
    PrecisionPreserved <openvino_docs_OV_UG_lpt_PrecisionPreserved>
    Precisions <openvino_docs_OV_UG_lpt_Precisions>
    QuantizationAlignment <openvino_docs_OV_UG_lpt_QuantizationAlignment>
+   QuantizationGranularity <openvino_docs_OV_UG_lpt_QuantizationGranularity>
 
 @endsphinxdirective
 
 ## Introduction
 
-| Name                                                                                | Target                 | Required | Mutable |
-|-------------------------------------------------------------------------------------|------------------------|----------|---------|
-| [AvgPoolPrecisionPreserved](@ref openvino_docs_OV_UG_lpt_AvgPoolPrecisionPreserved) | Precision              | No       | Yes     |
-| [IntervalsAlignment](@ref openvino_docs_OV_UG_lpt_IntervalsAlignment)               | Quantization interval  | Yes      | Yes     |
-| [PerTensorQuantization](@ref openvino_docs_OV_UG_lpt_PerTensorQuantization)         | Precision              | Yes      | No      |
-| [PrecisionPreserved](@ref openvino_docs_OV_UG_lpt_PrecisionPreserved)               | Precision              | Yes      | Yes     |
-| [Precisions](@ref openvino_docs_OV_UG_lpt_Precisions)                               | Precision              | Yes      | Yes     |
-| [QuantizationAlignment](@ref openvino_docs_OV_UG_lpt_QuantizationAlignment)         | Quantization alignment | Yes      | Yes     |
+| Name                                                                                | Target                   | Required | Mutable |
+|-------------------------------------------------------------------------------------|--------------------------|----------|---------|
+| [AvgPoolPrecisionPreserved](@ref openvino_docs_OV_UG_lpt_AvgPoolPrecisionPreserved) | Precision                | No       | Yes     |
+| [IntervalsAlignment](@ref openvino_docs_OV_UG_lpt_IntervalsAlignment)               | Quantization interval    | Yes      | Yes     |
+| [PrecisionPreserved](@ref openvino_docs_OV_UG_lpt_PrecisionPreserved)               | Precision                | Yes      | Yes     |
+| [Precisions](@ref openvino_docs_OV_UG_lpt_Precisions)                               | Precision                | Yes      | Yes     |
+| [QuantizationAlignment](@ref openvino_docs_OV_UG_lpt_QuantizationAlignment)         | Quantization granularity | Yes      | Yes     |
+| [QuantizationGranularity](@ref openvino_docs_OV_UG_lpt_QuantizationGranularity)     | Quantization granularity | Yes      | No      |
 
 > `Target` attribute group defines attribute usage during model transformation for the best performance:
 >  - `Precision` - the attribute defines the most optimal output port precision.
 >  - `Quantization interval` - the attribute defines quantization interval.
->  - `Quantization alignment` - the attribute defines quantization alignment: per-channel or per-tensor quantization.
+>  - `Quantization alignment` - the attribute defines quantization granularity in runtime: per-channel or per-tensor quantization.
+>  - `Quantization granularity` - the attribute is set by plugin to define quantization granularity: per-channel or per-tensor quantization.
 >
 > `Required` attribute group defines if attribute usage is required to get an optimal model during transformation:
 >  - `Yes` - the attribute is used by all OpenVINO plugins for low-precision optimization.

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -1,4 +1,4 @@
-# Convert model with Model Optimizer {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide}
+# Converting Models with Model Optimizer {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide}
 
 @sphinxdirective
 
@@ -9,6 +9,7 @@
    :hidden:
 
    openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model
+   openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques
    openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model
    openvino_docs_MO_DG_Additional_Optimization_Use_Cases
    openvino_docs_MO_DG_FP16_Compression
@@ -24,54 +25,49 @@
 
 @endsphinxdirective
 
-## Introduction
-
 Model Optimizer is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
 
-Using Model Optimizer tool assumes you already have a deep learning model trained using one of the supported frameworks: TensorFlow, PyTorch, PaddlePaddle, MXNet, Caffe, Kaldi, or represented in ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the model, which can be inferred with [OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md).
+To use it, you need a pre-trained deep learning model in one of the supported formats: TensorFlow, PyTorch, PaddlePaddle, MXNet, Caffe, Kaldi, or ONNX. Model Optimizer converts the model to the OpenVINO Intermediate Representation format (IR), which you can infer later with [OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md).
 
-> **NOTE**: Model Optimizer does not infer models. Model Optimizer is an offline tool that converts a model into IR and optimizes before the inference takes place.
+Note that Model Optimizer does not infer models.
 
-The scheme below illustrates the typical workflow for deploying a trained deep learning model:
+The figure below illustrates the typical workflow for deploying a trained deep learning model:
 
 ![](img/BASIC_FLOW_MO_simplified.svg)
 
-The IR is a pair of files describing the model:
+where IR is a pair of files describing the model:
 
-*  <code>.xml</code> - Describes the network topology
+*  <code>.xml</code> - Describes the network topology.
 
 *  <code>.bin</code> - Contains the weights and biases binary data.
 
-> **NOTE**: The generated IR can be additionally optimized for inference by [Post-training optimization](../../tools/pot/docs/Introduction.md)
+The generated IR can be additionally optimized for inference by [Post-training optimization](../../tools/pot/docs/Introduction.md)
 > that applies post-training quantization methods.
 
-> **TIP**: You also can work with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Introduction.html) (DL Workbench).
-> [DL Workbench](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Introduction.html) is a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models.
+> **TIP**: You can also work with Model Optimizer in OpenVINO™ [Deep Learning Workbench (DL Workbench)](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Introduction.html), which is a web-based tool with GUI for optimizing, fine-tuning, analyzing, visualizing, and comparing performance of deep learning models.
 
-## Run Model Optimizer
+## How to Run Model Optimizer
 
-To convert the model to IR, run Model Optimizer:
+To convert a model to IR, you can run Model Optimizer by using the following command:
 
 ```sh
 mo --input_model INPUT_MODEL
 ```
 
-If out-of-the-box conversion (only the `--input_model` parameter is specified) is not succeed,
-try to use parameters for overriding input shapes and cutting the model, mentioned below.
+If the out-of-the-box conversion (only the `--input_model` parameter is specified) is not successful, use the parameters mentioned below to override input shapes and cut the model:
 
-To override original input shapes for model conversion, Model Optimizer provides two parameters: `--input` and `--input_shape`.
-For more information about these parameters, refer to [Setting Input Shapes](prepare_model/convert_model/Converting_Model.md).
+- Model Optimizer provides two parameters to override original input shapes for model conversion: `--input` and `--input_shape`.
+For more information about these parameters, refer to the [Setting Input Shapes](prepare_model/convert_model/Converting_Model.md) guide.
 
-To cut off unwanted parts of a model, such as unsupported operations and training sub-graphs,
-the `--input` and `--output` parameters can be used, defining new inputs and outputs of the converted model.
-For a more detailed description, refer to [Cutting Off Parts of a Model](prepare_model/convert_model/Cutting_Model.md).
+- To cut off unwanted parts of a model (such as unsupported operations and training sub-graphs),
+use the `--input` and `--output` parameters to define new inputs and outputs of the converted model.
+For a more detailed description, refer to the [Cutting Off Parts of a Model](prepare_model/convert_model/Cutting_Model.md) guide.
 
-Also, you can insert additional input pre-processing sub-graphs into the converted model using
+You can also insert additional input pre-processing sub-graphs into the converted model by using
 the `--mean_values`, `scales_values`, `--layout`, and other parameters described
-in [Embedding Preprocessing Computation](prepare_model/Additional_Optimizations.md).
+in the [Embedding Preprocessing Computation](prepare_model/Additional_Optimizations.md) article.
 
-Model Optimizer's compression parameter `--data_type` allows to generate IR of the `FP16` data type. For more details,
-please refer to [Compression of a Model to FP16](prepare_model/FP16_Compression.md).
+The `--data_type` compression parameter in Model Optimizer allows generating IR of the `FP16` data type. For more details, refer to the [Compression of a Model to FP16](prepare_model/FP16_Compression.md) guide.
 
 To get the full list of conversion parameters available in Model Optimizer, run the following command:
 
@@ -81,54 +77,51 @@ mo --help
 
 ## Examples of CLI Commands
 
-Below is a list of separate examples for different frameworks and Model Optimizer parameters.
+Below is a list of separate examples for different frameworks and Model Optimizer parameters:
 
-1. Launch Model Optimizer for a TensorFlow MobileNet model in the binary protobuf format.
+1. Launch Model Optimizer for a TensorFlow MobileNet model in the binary protobuf format:
 ```sh
 mo --input_model MobileNet.pb
 ```
-Launch Model Optimizer for a TensorFlow BERT model in the SavedModel format, with three inputs. Explicitly specify input shapes
-where the batch size and the sequence length equal 2 and 30 respectively.
+Launch Model Optimizer for a TensorFlow BERT model in the SavedModel format with three inputs. Specify input shapes explicitly
+where the batch size and the sequence length equal 2 and 30 respectively:
 ```sh
 mo --saved_model_dir BERT --input mask,word_ids,type_ids --input_shape [2,30],[2,30],[2,30]
 ```
-For more information on TensorFlow model conversion,
-refer to [Converting a TensorFlow Model](prepare_model/convert_model/Convert_Model_From_TensorFlow.md).
+For more information, refer to the [Converting a TensorFlow Model](prepare_model/convert_model/Convert_Model_From_TensorFlow.md) guide.
 
-2. Launch Model Optimizer for an ONNX OCR model and explicitly specify new output.
+2. Launch Model Optimizer for an ONNX OCR model and specify new output explicitly:
 ```sh
 mo --input_model ocr.onnx --output probabilities
 ```
-For more information on ONNX model conversion,
-please refer to [Converting an ONNX Model](prepare_model/convert_model/Convert_Model_From_ONNX.md).
-Note that PyTorch models must be exported to the ONNX format before its conversion into IR.
-More details can be found in [Converting a PyTorch Model](prepare_model/convert_model/Convert_Model_From_PyTorch.md).
+For more information, refer to the [Converting an ONNX Model (prepare_model/convert_model/Convert_Model_From_ONNX.md) guide.
 
-3. Launch Model Optimizer for a PaddlePaddle UNet model and apply mean-scale normalization to the input.
+> **NOTE**: PyTorch models must be exported to the ONNX format before conversion into IR. More information can be found in [Converting a PyTorch Model](prepare_model/convert_model/Convert_Model_From_PyTorch.md).
+
+3. Launch Model Optimizer for a PaddlePaddle UNet model and apply mean-scale normalization to the input:
 ```sh
 mo --input_model unet.pdmodel --mean_values [123,117,104] --scale 255
 ```
-For more information on PaddlePaddle model conversion, please refer to
-[Converting a PaddlePaddle Model](prepare_model/convert_model/Convert_Model_From_Paddle.md).
+For more information, refer to the [Converting a PaddlePaddle Model](prepare_model/convert_model/Convert_Model_From_Paddle.md) guide.
 
-4. Launch Model Optimizer for an MXNet SSD Inception V3 model and specify first-channel layout for the input.
+4. Launch Model Optimizer for an Apache MXNet SSD Inception V3 model and specify first-channel layout for the input:
 ```sh
 mo --input_model ssd_inception_v3-0000.params --layout NCHW
 ```
-For more information on MXNet models conversion, please refer to [Converting an MXNet Model](prepare_model/convert_model/Convert_Model_From_MxNet.md).
+For more information, refer to the [Converting an Apache MXNet Model](prepare_model/convert_model/Convert_Model_From_MxNet.md) guide.
 
-5. Launch Model Optimizer for a Caffe AlexNet model with input channels in the RGB format, which needs to be reversed.
+5. Launch Model Optimizer for a Caffe AlexNet model with input channels in the RGB format which needs to be reversed:
 ```sh
 mo --input_model alexnet.caffemodel --reverse_input_channels
 ```
-For more information on Caffe model conversion, please refer to [Converting a Caffe Model](prepare_model/convert_model/Convert_Model_From_Caffe.md).
+For more information, refer to the [Converting a Caffe Model](prepare_model/convert_model/Convert_Model_From_Caffe.md) guide.
 
-6. Launch Model Optimizer for a Kaldi LibriSpeech nnet2 model.
+6. Launch Model Optimizer for a Kaldi LibriSpeech nnet2 model:
 ```sh
 mo --input_model librispeech_nnet2.mdl --input_shape [1,140]
 ```
-For more information on Kaldi model conversion,
-refer to [Converting a Kaldi Model](prepare_model/convert_model/Convert_Model_From_Kaldi.md).
+For more information, refer to the [Converting a Kaldi Model](prepare_model/convert_model/Convert_Model_From_Kaldi.md) guide.
 
-To get conversion recipes for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models,
-refer to [Model Conversion Tutorials](prepare_model/convert_model/Convert_Model_Tutorials.md).
+- To get conversion recipes for specific TensorFlow, ONNX, PyTorch, Apache MXNet, and Kaldi models,
+refer to the [Model Conversion Tutorials](prepare_model/convert_model/Convert_Model_Tutorials.md).
+- For more information about IR, see [Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™](IR_and_opsets.md).

docs/MO_DG/IR_and_opsets.md

@@ -1,46 +1,44 @@
 # Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™ {#openvino_docs_MO_DG_IR_and_opsets}
 
-This document provides essential information on the format used for representation of deep learning models in OpenVINO™ toolkit and supported operation sets.  
+This article provides essential information on the format used for representation of deep learning models in OpenVINO toolkit and supported operation sets.  
 
 ## Overview of Artificial Neural Networks Representation
 
-This paragraph provides an overview of how a deep learning network is represented in various deep learning frameworks.
-
 A deep learning network is usually represented as a directed graph describing the flow of data from the network input data to the inference results.
-Input data can be represented as a photograph, video, audio information or some preprocessed data that represent object from the target area of interest in a convenient way.
+Input data can be in the form of images, video, audio, or preprocessed information representing objects from the target area of interest.
 
 Here is an illustration of a small graph representing a model that consists of a single Convolutional layer and activation function:
 
 ![](img/small_IR_graph_demonstration.png)
 
-Vertices in the graph represent layers or operation instances, like convolution, pooling or element-wise operations with tensors.
-Layer and operation terms are used interchangeably along the OpenVINO™ documentation and define how input data is processed to produce output data for a node in a graph.
+Vertices in the graph represent layers or operation instances such as convolution, pooling, and element-wise operations with tensors.
+The terms of "layer" and "operation" are used interchangeably within OpenVINO documentation and define how input data is processed to produce output data for a node in a graph.
 An operation node in a graph may consume data at one or multiple input ports.
-For example, element-wise addition operation has two input ports which accepts tensors that are added together.
-Some operations don't have any input ports, for example Const operation which knowns the data to be produced without any input.
-An edge between operations represent data flow or data dependency implied from one operation node to another operation node.
+For example, an element-wise addition operation has two input ports which accept tensors that are to be summed.
+Some operations do not have any input ports, for example the `Const` operation, which knows the data to be produced without any input.
+An edge between operations represents data flow or data dependency implied from one operation node to another.
 
-Each operation produces data on one or multiple output ports. For example, convolution produces output tensor with activations at a single output port. Split operation usually has multiple output ports each producing part of an input tensor.
+Each operation produces data on one or multiple output ports. For example, convolution produces output tensor with activations at a single output port. Split operation usually has multiple output ports, each producing part of an input tensor.
 
 Depending on a deep learning framework, the graph can also contain extra nodes that explicitly represent tensors between operations.
-In such representations, operation nodes are not connected directly to each other, rather using data nodes as intermediate stops for data flow.
+In such representations, operation nodes are not connected to each other directly. They are rather using data nodes as intermediate stops for data flow.
 If data nodes are not used, the produced data is associated with an output port of a corresponding operation node that produces the data.
 
 A set of various operations used in a network is usually fixed for each deep learning framework.
 It determines expressiveness and level of representation available in that framework.
-It may happen that a network that can be represented in one framework is hard or impossible to be represented in another one or should use significantly different graph because operation sets used in those two frameworks do not match.
+Sometimes, a network that can be represented in one framework is hard or impossible to be represented in another one or should use significantly different graph, because operation sets used in those two frameworks do not match.
 
-## Intermediate Representation Used in OpenVINO™
+## Intermediate Representation Used in OpenVINO
 
-OpenVINO™ toolkit introduces its own format of graph representation and its own operation set.
+OpenVINO toolkit introduces its own format of graph representation and its own operation set.
 A graph is represented with two files: an XML file and a binary file.
 This representation is commonly referred to as the *Intermediate Representation* or *IR*.
 
 The XML file describes a network topology using a `<layer>` tag for an operation node and an `<edge>` tag for a data-flow connection.
 Each operation has a fixed number of attributes that define operation flavor used for a node.
-For example, `Convolution` operation has such attributes as `dilation`, `stride`, `pads_begin` and `pads_end`.
+For example, the `Convolution` operation has such attributes as `dilation`, `stride`, `pads_begin`, and `pads_end`.
 
-The XML file doesn't have big constant values, like convolution weights.
+The XML file does not have big constant values like convolution weights.
 Instead, it refers to a part of the accompanying binary file that stores such values in a binary format.
 
 Here is an example of a small IR XML file that corresponds to a graph from the previous section:
@@ -151,48 +149,48 @@ Here is an example of a small IR XML file that corresponds to a graph from the p
 </net>
 ```
 
-The IR doesn't use explicit data nodes described in the previous section.
+The IR does not use explicit data nodes described in the previous section.
 In contrast, properties of data such as tensor dimensions and their data types are described as properties of input and output ports of operations.
 
-## Operation Set
+## Operation Sets
 
-Operations in the OpenVINO™ Operation Set are selected based on capabilities of supported deep learning frameworks and hardware capabilities of the target inference device.
+Operations in OpenVINO Operation Sets are selected based on capabilities of supported deep learning frameworks and hardware capabilities of the target inference device.
 It consists of several groups of operations:
 
- * Conventional deep learning layers like Convolution, MaxPool, MatMul (also known as FullyConnected).
+ * Conventional deep learning layers such as `Convolution`, `MaxPool`, and `MatMul` (also known as `FullyConnected`).
 
- * Various activation functions, e.g. ReLU, Tanh, PReLU.
+ * Various activation functions such as `ReLU`, `Tanh`, and `PReLU`.
 
- * Generic element-wise arithmetic tensor operations like Add, Subtract, Multiply.
+ * Generic element-wise arithmetic tensor operations such as `Add`, `Subtract`, and `Multiply`.
 
- * Comparison operations that compare two numeric tensors and produce boolean tensors, for example Less, Equeal, Greater.
+ * Comparison operations that compare two numeric tensors and produce boolean tensors, for example, `Less`, `Equeal`, `Greater`.
 
- * Logical operations that are dealing with boolean tensors, like And, Xor, Not.
+ * Logical operations that are dealing with boolean tensors, for example, `And`, `Xor`, `Not`.
 
- * Data movement operations which are dealing with parts of tensors: Concat, Split, StridedSlice, Select.
+ * Data movement operations which are dealing with parts of tensors, for example, `Concat`, `Split`, `StridedSlice`, `Select`.
 
- * Specialized operations that implement complex algorithms dedicated for models of specific type: DetectionOutput, RegionYolo, PriorBox.
+ * Specialized operations that implement complex algorithms dedicated for models of specific type, for example, `DetectionOutput`, `RegionYolo`, `PriorBox`.
 
-Refer to the complete description of the supported operation sets in the [Available Operation Sets](../ops/opset.md) document.
+For more information, refer to the complete description of the supported operation sets in the [Available Operation Sets](../ops/opset.md) article.
 
 ## IR Versions vs Operation Set Versions
 
-The expressiveness of operations in OpenVINO™ is highly dependent on the supported frameworks and target hardware capabilities.
+The expressiveness of operations in OpenVINO is highly dependent on the supported frameworks and target hardware capabilities.
 As the frameworks and hardware capabilities grow over time, the operation set is constantly evolving to support new models.
 To maintain backward compatibility and growing demands, both IR format and operation set have versioning.
 
 Version of IR specifies the rules which are used to read the XML and binary files that represent a model. It defines an XML schema and compatible operation set that can be used to describe operations.
 
-Historically, there are two major IR version epochs. 
+Historically, there are two major IR version epochs:
 
 1. The older one includes IR versions from version 1 to version 7 without versioning of the operation set. During that epoch, the operation set has been growing evolutionally accumulating more layer types and extending existing layer semantics. Changing of the operation set for those versions meant increasing of IR version. 
 
-2. OpenVINO™ 2020.1 is the starting point of the next epoch. With IR version 10 introduced in OpenVINO™ 2020.1, the versioning of the operation set is tracked separately from the IR versioning. Also, the operation set was significantly reworked as the result of nGraph integration to the OpenVINO.
+2. OpenVINO 2020.1 is the starting point of the next epoch. With IR version 10 introduced in OpenVINO 2020.1, the versioning of the operation set is tracked separately from the IR versioning. Also, the operation set was significantly reworked as the result of nGraph integration to the OpenVINO.
 
 The first supported operation set in the new epoch is `opset1`.
-The number after `opset` is going to be increased each time when new operations are added or old operations deleted at the release cadence.
+The number after `opset` is going to be increased each time new operations are added or old operations deleted at the release cadence.
 
-The operations from the new epoch cover more TensorFlow* and ONNX* operators in a form that is closer to the original operation semantics from the frameworks in comparison to the operation set used in former versions of IR (7 and lower).
+The operations from the new epoch cover more TensorFlow and ONNX operators in a form that is closer to the original operation semantics from the frameworks in comparison to the operation set used in former versions of IR (7 and lower).
 
 The name of the opset is specified for each operation in IR.
 The IR version is specified once per whole IR.
@@ -215,31 +213,30 @@ Here is an example from the IR snippet:
                     ...
 ```
 
-The attributes `type="Parameter"` and `version="opset1"` in the example above mean "use that version of operation `Parameter` that is included into the operation set `opset1`".
+The `type="Parameter"` and `version="opset1"` attributes in the example above mean "use that version of the `Parameter` operation that is included in the `opset1` operation set. "
 
-When a new operation set is introduced, the significant part of the operations remains unchanged and it is just aliased from the previous operation set within a new one.
-The goal of operation set versions evolution is adding new operations, and probably changing of small fraction of existing operations (fixing bugs and extending semantics).
-However such changes affect only new versions of operations from a new operation set, while old operations are used by specifying an appropriate `version`.
-When the old `version` is specified, the behavior is kept unchanged from that specified version to provide the backward compatibility with older IRs.
+When a new operation set is introduced, most of the operations remain unchanged and are just aliased from the previous operation set within a new one.
+The goal of operation set version evolution is to add new operations, and probably change small fractions of existing operations (fixing bugs and extending semantics).
+However, such changes affect only new versions of operations from a new operation set, while old operations are used by specifying an appropriate `version`.
+When an old `version` is specified, the behavior will be kept unchanged from that specified version to provide backward compatibility with older IRs.
 
 A single `xml` file with IR may contain operations from different opsets.
-An operation that is included into several opsets may be referred to with `version` which points to any opset that includes that operation.
-For example, the same `Convolution` can be used with `version="opset1"` and `version="opset2"` because both opsets have the same operations `Convolution`.
+An operation that is included in several opsets may be referred to with `version` which points to any opset that includes that operation.
+For example, the same `Convolution` can be used with `version="opset1"` and `version="opset2"` because both opsets have the same `Convolution` operations.
 
-## How to Read the Specification
+## How to Read Opset Specification
 
 In the [Available Operation Sets](../ops/opset.md) there are opsets and there are operations.
 Each opset specification has a list of links to operations descriptions that are included into that specific opset.
 Two or more opsets may refer to the same operation.
 That means an operation is kept unchanged from one operation set to another.
 
-Each operation description has a field `Versioned name`.
-For example, `ReLU` entry point in [`opset1`](../ops/opset1.md) refers to [`ReLU-1`](../ops/activation/ReLU_1.md) as the versioned name.
-And `ReLU` in `opset2` refers to the same `ReLU-1` and both `ReLU` operations are the same operation and it has a single [description](../ops/activation/ReLU_1.md).
-So `opset1` and `opset2` share the same operation `ReLU`.
+The description of each operation has a `Versioned name` field.
+For example, the `ReLU` entry point in [`opset1`](../ops/opset1.md) refers to [`ReLU-1`](../ops/activation/ReLU_1.md) as the versioned name.
+Meanwhile, `ReLU` in `opset2` refers to the same `ReLU-1` and both `ReLU` operations are the same operation and it has a single [description](../ops/activation/ReLU_1.md), which means that `opset1` and `opset2` share the same operation `ReLU`.
 
-To differentiate versions of the same operation type, like `ReLU`, the suffix `-N` is used in a versioned name of the operation.
-`N` usually refers to the first `opsetN` where this version of the operation is introduced.
-It is not guaranteed that new operations will be named according to that rule, the naming convention might be changed, but not for old operations which are frozen completely.
+To differentiate versions of the same operation type such as `ReLU`, the `-N` suffix is used in a versioned name of the operation.
+The `N` suffix usually refers to the first occurrence of `opsetN` where this version of the operation is introduced.
+There is no guarantee that new operations will be named according to that rule. The naming convention might be changed, but not for old operations which are frozen completely.
 
 

docs/MO_DG/prepare_model/Additional_Optimizations.md

@@ -2,100 +2,101 @@
 
 Input data for inference can be different from the training dataset and requires additional preprocessing before inference.
 To accelerate the whole pipeline including preprocessing and inference, Model Optimizer provides special parameters such as `--mean_values`,
-`--scale_values`, `--reverse_input_channels`, and `--layout`. Based on these parameters, Model Optimizer generates IR with additionally
-inserted sub-graph that performs the defined preprocessing. This preprocessing block can perform mean-scale normalization of input data,
-reverting data along channel dimension, and changing the data layout. For more details about these parameters, refer to the paragraphs below.
-The same functionality is also available in runtime, please refer to [Overview of Preprocessing API](../../OV_Runtime_UG/preprocessing_overview.md)
-for more information.
 
-## When to Specify Layout
+`--scale_values`, `--reverse_input_channels`, and `--layout`. Based on these parameters, Model Optimizer generates OpenVINO IR with additionally
+inserted sub-graphs to perform the defined preprocessing. This preprocessing block can perform mean-scale normalization of input data,
+reverting data along channel dimension, and changing the data layout. 
+See the following sections for details on the parameters, or the [Overview of Preprocessing API](../../OV_Runtime_UG/preprocessing_overview.md) for the same functionality in OpenVINO Runtime.
 
-You may need to set input layouts, as it is required by some preprocessing, for example, setting a batch,
-applying mean or scales, and reversing input channels (BGR<->RGB).
+## Specifying Layout
+
+You may need to set input layouts, as it is required by some preprocessing, for example, setting a batch, applying mean or scales, and reversing input channels (BGR<->RGB).
+
+Layout defines the meaning of dimensions in shape and can be specified for both inputs and outputs. Some preprocessing requires to set input layouts, for example, setting a batch, applying mean or scales, and reversing input channels (BGR<->RGB).
 
-Layout defines the meaning of dimensions in shape and can be specified for both inputs and outputs.
 For the layout syntax, check the [Layout API overview](../../OV_Runtime_UG/layout_overview.md). 
-To specify the layout, you can use `--layout` option followed by the layout value. 
+To specify the layout, you can use the `--layout` option followed by the layout value. 
 
-For example, for Tensorflow\* `nasnet_large` model that was exported to ONNX format and thus has input with `NHWC` layout:
+For example, the following command specifies the `NHWC` layout for a Tensorflow `nasnet_large` model that was exported to the ONNX format:
 
 ```
 mo --input_model tf_nasnet_large.onnx --layout nhwc
 ```
 
-Additionally, if a model has more than one input or needs both input and output layouts specified,
-you need to provide the name of each input or output to which you apply the layout.
+Additionally, if a model has more than one input or needs both input and output layouts specified, you need to provide the name of each input or output to apply the layout.
 
-For example, for ONNX\* `Yolo v3 Tiny` model that has first input `input_1` in `NCHW` layout and second input `image_shape`
-with 2 dimensions: batch and size of the image which can be expressed as `N?` layout:
+For example, the following command specifies the layout for an ONNX `Yolo v3 Tiny` model with its first input `input_1` in `NCHW` layout and second input `image_shape` having two dimensions: batch and size of the image expressed as the `N?` layout:
 
 ```
 mo --input_model yolov3-tiny.onnx --layout input_1(nchw),image_shape(n?)
 ```
 
-## How to Change Layout of a Model Inputs and Outputs
+## Changing Model Layout
 
 Changing the model layout may be necessary if it differs from the one presented by input data. 
-To change the layout, you can use either `--layout` or `--source_layout` with `--target_layout`.
+Use either `--layout` or `--source_layout` with `--target_layout` to change the layout.
 
-For example, for the same `nasnet_large` that were mentioned previously we may want to provide data in `NCHW` layout:
+For example, for the same `nasnet_large` model mentioned previously, you can use the following commands to provide data in the `NCHW` layout:
 
 ```
 mo --input_model tf_nasnet_large.onnx --source_layout nhwc --target_layout nchw
 mo --input_model tf_nasnet_large.onnx --layout "nhwc->nchw"
 ```
 
-Again, if a model has more than one input or needs both input and output layouts specified, you need to provide the name of each input or output to which you apply the layout.
+Again, if a model has more than one input or needs both input and output layouts specified, you need to provide the name of each input or output to apply the layout.
 
-For example, to provide data in the `NHWC` layout for the `Yolo v3 Tiny` model mentioned earlier:
+For example, to provide data in the `NHWC` layout for the `Yolo v3 Tiny` model mentioned earlier, use the following commands:
 
 ```
 mo --input_model yolov3-tiny.onnx --source_layout "input_1(nchw),image_shape(n?)" --target_layout "input_1(nhwc)"
 mo --input_model yolov3-tiny.onnx --layout "input_1(nchw->nhwc),image_shape(n?)"
 ```
 
-## When to Specify Mean and Scale Values
-Usually neural network models are trained with the normalized input data. This means that the input data values are converted to be in a specific range,
-for example, `[0, 1]` or `[-1, 1]`. Sometimes the mean values (mean images) are subtracted from the input data values as part of the pre-processing.
-There are two cases of how the input data pre-processing is implemented.
- * The input pre-processing operations are a part of a model. In this case, the application does not pre-process the input data as a separate step: everything is embedded into the model itself.
- * The input pre-processing operations are not a part of a model and the pre-processing is performed within the application which feeds the model with input data.
+## Specifying Mean and Scale Values
+Neural network models are usually trained with the normalized input data. This means that the input data values are converted to be in a specific range,
+for example, `[0, 1]` or `[-1, 1]`. Sometimes, the mean values (mean images) are subtracted from the input data values as part of the preprocessing.
 
-In the first case, the Model Optimizer generates the IR with required pre-processing operations and no `mean` and `scale` parameters are required.
+There are two cases of how the input data preprocessing is implemented.
+ * The input preprocessing operations are a part of a model. 
+
+  In this case, the application does not perform a separate preprocessing step: everything is embedded into the model itself. Model Optimizer will generate the OpenVINO IR format with required preprocessing operations, and no `mean` and `scale` parameters are required.
+ * The input preprocessing operations are not a part of a model and the preprocessing is performed within the application which feeds the model with input data.
+
+  In this case, information about mean/scale values should be provided to Model Optimizer to embed it to the generated OpenVINO IR format.
 
-In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR.
 Model Optimizer provides command-line parameters to specify the values: `--mean_values`, `--scale_values`, `--scale`.
 Using these parameters, Model Optimizer embeds the corresponding preprocessing block for mean-value normalization of the input data
 and optimizes this block so that the preprocessing takes negligible time for inference.
 
-For example, run the Model Optimizer for the PaddlePaddle* UNet model and apply mean-scale normalization to the input data.
+For example, the following command runs Model Optimizer for the PaddlePaddle UNet model and applies mean-scale normalization to the input data:
 
 ```sh
 mo --input_model unet.pdmodel --mean_values [123,117,104] --scale 255
 ```
 
-## When to Reverse Input Channels <a name="when_to_reverse_input_channels"></a>
-Sometimes input images for your application can be of the RGB (BGR) format and the model is trained on images of the BGR (RGB) format,
-the opposite color channel order. In this case, it is important to preprocess the input images by reverting the color channels before inference.
-To embed this preprocessing step into IR, Model Optimizer provides the `--reverse_input_channels` command-line parameter to shuffle the color channels.
+## Reversing Input Channels <a name="when_to_reverse_input_channels"></a>
+Sometimes, input images for your application can be of the RGB (or BGR) format and the model is trained on images of the BGR (or RGB) format,
+which is in the opposite order of color channels. In this case, it is important to preprocess the input images by reverting the color channels before inference.
 
-The `--reverse_input_channels` parameter applies to an input of the model in two cases.
+To embed this preprocessing step into OpenVINO IR, Model Optimizer provides the `--reverse_input_channels` command-line parameter to shuffle the color channels.
+
+The `--reverse_input_channels` parameter can be used to preprocess the model input in the following cases:
  * Only one dimension in the input shape has a size equal to 3.
  * One dimension has an undefined size and is marked as `C` channel using `layout` parameters.
 
 Using the `--reverse_input_channels` parameter, Model Optimizer embeds the corresponding preprocessing block for reverting
-the input data along channel dimension and optimizes this block so that the preprocessing takes negligible time for inference.
+the input data along channel dimension and optimizes this block so that the preprocessing takes only negligible time for inference.
 
-For example, launch the Model Optimizer for the TensorFlow* AlexNet model and embed `reverse_input_channel` preprocessing block into IR.
+For example, the following command launches Model Optimizer for the TensorFlow AlexNet model and embeds the `reverse_input_channel` preprocessing block into OpenVINO IR:
 
 ```sh
 mo --input_model alexnet.pb --reverse_input_channels
 ```
 
 > **NOTE**: If both mean and scale values are specified, the mean is subtracted first and then the scale is applied regardless of the order of options
-in the command line. Input values are *divided* by the scale value(s). If also `--reverse_input_channels` option is used, the `reverse_input_channels`
+in the command-line. Input values are *divided* by the scale value(s). If the `--reverse_input_channels` option is also used, `reverse_input_channels`
 will be applied first, then `mean` and after that `scale`. The data flow in the model looks as follows:
 `Parameter -> ReverseInputChannels -> Mean apply-> Scale apply -> the original body of the model`.
 
-## See Also
+## Additional Resources
 * [Overview of Preprocessing API](../../OV_Runtime_UG/preprocessing_overview.md)

docs/MO_DG/prepare_model/FP16_Compression.md

@@ -1,4 +1,4 @@
-# Compression of a Model to FP16 {#openvino_docs_MO_DG_FP16_Compression}
+# Compressing a Model to FP16 {#openvino_docs_MO_DG_FP16_Compression}
 
 Model Optimizer can convert all floating-point weights to `FP16` data type. The resulting IR is called
 compressed `FP16` model.
@@ -10,11 +10,12 @@ To compress the model, use the `--data_type` option:
 ```
 
 > **NOTE**: Using `--data_type FP32` will give no result and will not force `FP32` 
-> precision in the model. If the model was `FP16` it will have `FP16` precision in IR as well.
+> precision in the model. If the model was `FP16`, it will have `FP16` precision in IR as well.
 
-The resulting model will occupy about twice as less space in the file system, but it may have some accuracy drop,
-although for the majority of models accuracy degradation is negligible. For details on how plugins handle
-compressed `FP16` models refer to [Working with devices](../../OV_Runtime_UG/supported_plugins/Device_Plugins.md) page.
+The resulting model will occupy about twice as less space in the file system, but it may have some accuracy drop.
+The resulting model will occupy about half of the previous space in the file system, but lose some of its accuracy.
+For most models, the accuracy drop is negligible. 
+For details on how plugins handle compressed `FP16` models, see [Working with devices](../../OV_Runtime_UG/supported_plugins/Device_Plugins.md).
 
-> **NOTE**: `FP16` compression is sometimes used as initial step for `INT8` quantization, please refer to
-> [Post-training optimization](../../../tools/pot/docs/Introduction.md) for more information about that.
+> **NOTE**: `FP16` compression is sometimes used as the initial step for `INT8` quantization.
+> Refer to the [Post-training optimization](../../../tools/pot/docs/Introduction.md) guide for more information about that.

docs/MO_DG/prepare_model/Getting_performance_numbers.md

@@ -1,28 +1,28 @@
 # Getting Performance Numbers {#openvino_docs_MO_DG_Getting_Performance_Numbers}
 
+This guide introduces things to notice and how to use the benchmark_app to get performance numbers. It also explains how the performance numbers are reflected through internal inference performance counters and execution graphs. In the last section, it includes information on using ITT and Intel® VTune™ Profiler to get performance insights.
 
-## Tip 1. Measure the Proper Set of Operations 
+## Tip 1: Select Proper Set of Operations to Measure
 
-When evaluating performance of your model with the OpenVINO Runtime, you must measure the proper set of operations. To do so, consider the following tips: 
+When evaluating the performance of a model with OpenVINO Runtime, it is required to measure proper set of operations. Remember the following tips:
+- Avoid including one-time costs such as model loading.
 
-- Avoid including one-time costs like model loading.
+- Track operations that occur outside OpenVINO Runtime (such as video decoding) separately. 
 
-- Track separately the operations that happen outside the OpenVINO Runtime, like video decoding. 
+> **NOTE**: Some image pre-processing can be baked into OpenVINO IR and accelerated accordingly. For more information, refer to [Embedding the Pre-processing](Additional_Optimizations.md) and [General Runtime Optimizations](../../optimization_guide/dldt_deployment_optimization_common).
 
-> **NOTE**: Some image pre-processing can be baked into the IR and accelerated accordingly. For more information, refer to [Embedding the Preprocessing](Additional_Optimizations.md). Also consider [Runtime Optimizations of the Preprocessing](../../optimization_guide/dldt_deployment_optimization_common).
+## Tip 2: Try to Get Credible Data
 
-## Tip 2. Getting Credible Performance Numbers 
-
-You need to build your performance conclusions on reproducible data. Do the performance measurements with a large number of invocations of the same routine. Since the first iteration is almost always significantly slower than the subsequent ones, you can use an aggregated value for the execution time for final projections:
+Performance conclusions should be build upon reproducible data. As for the performance measurements, they should be done with a large number of invocations of the same routine. Since the first iteration is almost always significantly slower than the subsequent ones, an aggregated value can be used for the execution time for final projections:
 
 -	If the warm-up run does not help or execution time still varies, you can try running a large number of iterations and then average or find a mean of the results.
--	For time values that range too much, consider geomean.
--   Beware of the throttling and other power oddities. A device can exist in one of several different power states. When optimizing your model, for better performance data reproducibility consider fixing the device frequency. However the end to end (application) benchmarking should be also performed under real operational conditions.
+-	If the time values range too much, consider geomean.
+-  Be aware of the throttling and other power oddities. A device can exist in one of several different power states. When optimizing your model, consider fixing the device frequency for better performance data reproducibility. However, the end-to-end (application) benchmarking should also be performed under real operational conditions.
 
-## Tip 3. Measure Reference Performance Numbers with OpenVINO's benchmark_app 
+## Using benchmark_app to Measure Reference Performance Numbers 
 
-To get performance numbers, use the dedicated [Benchmark App](../../../samples/cpp/benchmark_app/README.md) sample which is the best way to produce the performance reference.
-It has a lot of device-specific knobs, but the primary usage is as simple as: 
+To get performance numbers, use the dedicated [OpenVINO Benchmark app](../../../samples/cpp/benchmark_app/README.md) sample, which is the most-recommended solution to produce performance reference.
+It includes a lot of device-specific knobs, but the primary usage is as simple as in the following command to measure the performance of the model on GPU: 
 ```bash
 $ ./benchmark_app –d GPU –m <model> -i <input>
 ```
@@ -33,28 +33,28 @@ $ ./benchmark_app –d CPU –m <model> -i <input>
 ```
 to execute on the CPU instead.
 
-Each of the [OpenVINO supported devices](../../OV_Runtime_UG/supported_plugins/Supported_Devices.md) offers performance settings that have command-line equivalents in the [Benchmark App](../../../samples/cpp/benchmark_app/README.md).
-While these settings provide really low-level control and allow to leverage the optimal model performance on the _specific_ device, we suggest always starting the performance evaluation with the [OpenVINO High-Level Performance Hints](../../OV_Runtime_UG/performance_hints.md) first:
+Each of the [OpenVINO supported devices](../../OV_Runtime_UG/supported_plugins/Supported_Devices.md) offers performance settings that contain command-line equivalents in the [Benchmark app](../../../samples/cpp/benchmark_app/README.md).
+While these settings provide really low-level control and allow leveraging the optimal model performance on the _specific_ device, it is recommended to always start the performance evaluation with the [OpenVINO High-Level Performance Hints](../../OV_Runtime_UG/performance_hints.md) first:
  - benchmark_app **-hint tput** -d 'device' -m 'path to your model'
  - benchmark_app **-hint latency** -d 'device' -m 'path to your model'
 
-## Comparing Performance with Native/Framework Code 
+## Notes for Comparing Performance with Native/Framework Code 
 
 When comparing the OpenVINO Runtime performance with the framework or another reference code, make sure that both versions are as similar as possible:
 
--	Wrap exactly the inference execution (refer to the  [Benchmark App](../../../samples/cpp/benchmark_app/README.md) for examples).
+-	Wrap the exact inference execution (refer to the [Benchmark app](../../../samples/cpp/benchmark_app/README.md) for examples).
 -	Do not include model loading time.
--	Ensure the inputs are identical for the OpenVINO Runtime and the framework. For example, beware of random values that can be used to populate the inputs.
--	Consider [Image Pre-processing and Conversion](../../OV_Runtime_UG/preprocessing_overview.md), while any user-side pre-processing should be tracked separately.
--   When applicable, leverage the [Dynamic Shapes support](../../OV_Runtime_UG/ov_dynamic_shapes.md)
+-	Ensure that the inputs are identical for OpenVINO Runtime and the framework. For example, watch out for random values that can be used to populate the inputs.
+-	In situations when any user-side pre-processing should be tracked separately, consider [image pre-processing and conversion](../../OV_Runtime_UG/preprocessing_overview.md).
+-  When applicable, leverage the [Dynamic Shapes support](../../OV_Runtime_UG/ov_dynamic_shapes.md).
 -	If possible, demand the same accuracy. For example, TensorFlow allows `FP16` execution, so when comparing to that, make sure to test the OpenVINO Runtime with the `FP16` as well.
 
-## Internal Inference Performance Counters and Execution Graphs <a name="performance-counters"></a>
-Further, finer-grained insights into inference performance breakdown can be achieved with device-specific performance counters and/or execution graphs.
-Both [C++](../../../samples/cpp/benchmark_app/README.md) and [Python](../../../tools/benchmark_tool/README.md) versions of the `benchmark_app` supports a `-pc` command-line parameter that outputs internal execution breakdown.
+## Data from Internal Inference Performance Counters and Execution Graphs <a name="performance-counters"></a>
+More detailed insights into inference performance breakdown can be achieved with device-specific performance counters and/or execution graphs.
+Both [C++](../../../samples/cpp/benchmark_app/README.md) and [Python](../../../tools/benchmark_tool/README.md) versions of the `benchmark_app` support a `-pc` command-line parameter that outputs internal execution breakdown.
 
-For example, below is the part of performance counters for quantized [TensorFlow* implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model inference on [CPU Plugin](../../OV_Runtime_UG/supported_plugins/CPU.md).
-Notice that since the device is CPU, the layers wall clock `realTime` and the `cpu` time are the same. Information about layer precision is also stored in the performance counters. 
+For example, the table shown below is the part of performance counters for quantized [TensorFlow implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model inference on [CPU Plugin](../../OV_Runtime_UG/supported_plugins/CPU.md).
+Keep in mind that since the device is CPU, the `realTime` wall clock and the `cpu` time layers are the same. Information about layer precision is also stored in the performance counters. 
 
 | layerName                                                 | execStatus | layerType    | execType             | realTime (ms) | cpuTime (ms) |
 | --------------------------------------------------------- | ---------- | ------------ | -------------------- | ------------- | ------------ |
@@ -68,22 +68,23 @@ Notice that since the device is CPU, the layers wall clock `realTime` and the `c
 | resnet\_model/add\_5/fq\_input\_1                         | NOT\_RUN   | FakeQuantize | undef                | 0             | 0            |
 
 
-   The `exeStatus` column of the table includes possible values:
-   - `EXECUTED` - layer was executed by standalone primitive,
-   - `NOT_RUN` - layer was not executed by standalone primitive or was fused with another operation and executed in another layer primitive.  
+   The `exeStatus` column of the table includes the following possible values:
+   - `EXECUTED` - the layer was executed by standalone primitive.
+   - `NOT_RUN` - the layer was not executed by standalone primitive or was fused with another operation and executed in another layer primitive.  
    
-   The `execType` column of the table includes inference primitives with specific suffixes. The layers have the following marks:
-   * Suffix `I8` for layers that had 8-bit data type input and were computed in 8-bit precision
-   * Suffix `FP32` for layers computed in 32-bit precision 
+   The `execType` column of the table includes inference primitives with specific suffixes. The layers could have the following marks:
+   * The `I8` suffix is for layers that had 8-bit data type input and were computed in 8-bit precision.
+   * The `FP32` suffix is for layers computed in 32-bit precision.
 
-   All `Convolution` layers are executed in int8 precision. Rest layers are fused into Convolutions using post operations optimization technique, which is described in [Internal CPU Plugin Optimizations](../../OV_Runtime_UG/supported_plugins/CPU.md).
-   This contains layers name (as seen in IR), layers type and execution statistics.
+   All `Convolution` layers are executed in `int8` precision. The rest of the layers are fused into Convolutions using post-operation optimization, as described in [CPU Device](../../OV_Runtime_UG/supported_plugins/CPU.md).
+   This contains layer names (as seen in OpenVINO IR), type of the layer, and execution statistics.
 
-Both benchmark_app versions also support "exec_graph_path" command-line option governing the OpenVINO to output the same per-layer execution statistics, but in the form of the plugin-specific [Netron-viewable](https://netron.app/) graph to the specified file.
+Both `benchmark_app` versions also support the `exec_graph_path` command-line option. It requires OpenVINO to output the same execution statistics per layer, but in the form of plugin-specific [Netron-viewable](https://netron.app/) graph to the specified file.
 
-Notice that on some devices, the execution graphs/counters may be pretty intrusive overhead-wise. 
-Also, especially when performance-debugging the [latency case](../../optimization_guide/dldt_deployment_optimization_latency.md) notice that  the counters do not reflect the time spent in the plugin/device/driver/etc queues. If the sum of the counters is too different from the latency of an inference request, consider testing with less inference requests. For example running single [OpenVINO stream](../../optimization_guide/dldt_deployment_optimization_tput.md) with multiple requests would produce nearly identical counters as running single inference request, yet the actual latency can be quite different.
+Especially when performance-debugging the [latency](../../optimization_guide/dldt_deployment_optimization_latency.md), note that the counters do not reflect the time spent in the `plugin/device/driver/etc` queues. If the sum of the counters is too different from the latency of an inference request, consider testing with less inference requests. For example, running single [OpenVINO stream](../../optimization_guide/dldt_deployment_optimization_tput.md) with multiple requests would produce nearly identical counters as running a single inference request, while the actual latency can be quite different.
 
-Finally, the performance statistics with both performance counters and execution graphs is averaged, so such a data for the [dynamically-shaped inputs](../../OV_Runtime_UG/ov_dynamic_shapes.md) should be measured carefully (ideally by isolating the specific shape and executing multiple times in a loop, to gather the reliable data).
+Lastly, the performance statistics with both performance counters and execution graphs are averaged, so such data for the [inputs of dynamic shapes](../../OV_Runtime_UG/ov_dynamic_shapes.md) should be measured carefully, preferably by isolating the specific shape and executing multiple times in a loop, to gather the reliable data.
 
-OpenVINO in general and individual plugins are heavily instrumented with Intel® instrumentation and tracing technology (ITT), so another option is to compile the OpenVINO from the source code with the ITT enabled and using tools like [Intel® VTune™ Profiler](https://software.intel.com/en-us/vtune) to get detailed inference performance breakdown and additional insights in the application-level performance on the timeline view.
+## Using ITT to Get Performance Insights
+
+In general, OpenVINO and its individual plugins are heavily instrumented with Intel® Instrumentation and Tracing Technology (ITT). Therefore, you can also compile OpenVINO from the source code with ITT enabled and use tools like [Intel® VTune™ Profiler](https://software.intel.com/en-us/vtune) to get detailed inference performance breakdown and additional insights in the application-level performance on the timeline view.

docs/MO_DG/prepare_model/Model_Optimization_Techniques.md

@@ -0,0 +1,65 @@
+# Model Optimization Techniques  {#openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques}
+
+Optimization offers methods to accelerate inference with the convolution neural networks (CNN) that do not require model retraining.
+
+* * *
+
+## Linear Operations Fusing
+
+Many convolution neural networks includes `BatchNormalization` and `ScaleShift` layers (for example, Resnet\*, Inception\*) that can be presented as a sequence of linear operations: additions and multiplications. For example ScaleShift layer can be presented as Mul → Add sequence. These layers can be fused into previous `Convolution` or `FullyConnected` layers, except when Convolution comes after an Add operation (due to Convolution paddings).
+
+### Usage
+
+In the Model Optimizer, this optimization is turned on by default. To disable it, you can pass `--disable_fusing` parameter to the Model Optimizer.
+
+### Optimization Description
+
+This optimization method consists of three stages:
+
+1. `BatchNormalization` and `ScaleShift` decomposition: in this stage, `BatchNormalization` layer is decomposed to `Mul → Add → Mul → Add` sequence, and `ScaleShift` layer is decomposed to `Mul → Add` layers sequence.
+
+2. Linear operations merge: in this stage, the `Mul` and `Add` operations are merged into a single `Mul → Add` instance. 
+    For example, if there is a `BatchNormalization → ScaleShift` sequence in the topology, it is replaced with `Mul → Add` in the first stage. In the next stage, the latter is replaced with a `ScaleShift` layer if there is no available `Convolution` or `FullyConnected` layer to fuse into next.
+3. Linear operations fusion: in this stage, the tool fuses `Mul` and `Add` operations to `Convolution` or `FullyConnected` layers.   Note that it searches for `Convolution` and `FullyConnected` layers both backward and forward in the graph (except for `Add` operation that cannot be fused to `Convolution` layer in forward direction).
+
+### Usage Examples
+
+The picture below shows the depicted part of Caffe Resnet269 topology where `BatchNorm` and `ScaleShift` layers will be fused to `Convolution` layers.
+
+![Caffe ResNet269 block before and after optimization generated with Netscope*](../img/optimizations/resnet_269.png)
+
+* * *
+
+## ResNet optimization (stride optimization)
+
+ResNet optimization is a specific optimization that applies to Caffe ResNet topologies such as ResNet50, ResNet101, ResNet152 and to ResNet-based topologies. This optimization is turned on by default, and can be disabled with the `--disable_resnet_optimization` key.
+
+### Optimization Description
+
+In the picture below, you can see the original and optimized parts of a Caffe ResNet50 model. The main idea of this optimization is to move the stride that is greater than 1 from Convolution layers with the kernel size = 1 to upper Convolution layers. In addition, the Model Optimizer adds a Pooling layer to align the input shape for a Eltwise layer, if it was changed during the optimization. 
+
+![ResNet50 blocks (original and optimized) from Netscope](../img/optimizations/resnet_optimization.png)
+
+In this example, the stride from the `res3a_branch1` and `res3a_branch2a` Convolution layers moves to the `res2c_branch2b` Convolution layer. In addition, to align the input shape for `res2c` Eltwise, the optimization inserts the Pooling layer with kernel size = 1 and stride = 2.
+
+* * *
+
+## Grouped Convolution Fusing
+
+Grouped convolution fusing is a specific optimization that applies for TensorFlow topologies. The main idea of this optimization is to combine convolutions results for the `Split` outputs and then recombine them using `Concat` operation in the same order as they were out from `Split`.
+
+![Split→Convolutions→Concat block from TensorBoard*](../img/optimizations/groups.png)
+
+* * *
+
+## Disabling Fusing
+
+Model Optimizer allows to disable optimizations for specified nodes via `--finegrain_fusing <node_name1>,<node_name2>,...` (regex is also supported). Using this key, you mark nodes that will noy be touched by any optimizations.
+
+### Examples of usage
+
+On the picture below you can see two visualized Intermediate Representations (IR) of TensorFlow InceptionV4 topology.
+The first one is original IR that will be produced by the Model Optimizer.
+The second one will be produced by the Model Optimizer with key `--finegrain_fusing InceptionV4/InceptionV4/Conv2d_1a_3x3/Conv2D`, where you can see that `Convolution` was not fused with `Mul1_3752` and `Mul1_4061/Fused_Mul_5096/FusedScaleShift_5987` operations.
+
+![TF InceptionV4 block without/with key --finegrain_fusing (from IR visualizer)](../img/optimizations/inception_v4.png)

docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md

@@ -4,25 +4,25 @@ If your question is not covered by the topics below, use the [OpenVINO™ Su
 
 #### 1. What does the message "[ ERROR ]: Current caffe.proto does not contain field" mean? <a name="question-1"></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.
+Internally, 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, Model Optimizer uses a Caffe-generated parser for a Caffe-specific `.proto` file (which is usually located in the `src/caffe/proto` directory). Make sure that you install exactly the same version of Caffe (with Python interface) as that was used to create the model.
 
-If you just want to experiment with the Model Optimizer and test a Python extension for working with your custom
+If you just want to experiment with Model Optimizer and test a Python extension for working with your custom
 layers without building Caffe, add the layer description to the `caffe.proto` file and generate a parser for it.
 
 For example, to add the description of the `CustomReshape` layer, which is an artificial layer not present in any `caffe.proto` files:
 
-1.  Add the following lines to of the `caffe.proto` file:
+1.  Add the following lines to the `caffe.proto` file:
 ```shell
-    package mo_caffe; // to avoid conflict with system Caffe* it is highly recommended to specify different package name
+    package mo_caffe; // To avoid conflict with Caffe system, it is highly recommended to specify different package name.
     ...
     message LayerParameter {
-      // other layers parameters description
+      // Other layers parameters description.
       ...
-      optional CustomReshapeParameter custom_reshape_param = 546; // 546 - ID is any number not present in caffe.proto
+      optional CustomReshapeParameter custom_reshape_param = 546; // 546 - ID is any number not present in caffe.proto.
     }
-    // these lines to end of the file - describing contents of this parameter
+    // The lines from here to the end of the file are describing contents of this parameter.
     message CustomReshapeParameter {
-      optional BlobShape shape = 1; // we just use the same parameter type as some other Caffe layers
+      optional BlobShape shape = 1; // Just use the same parameter type as some other Caffe layers.
     }
 ```
 
@@ -31,15 +31,15 @@ For example, to add the description of the `CustomReshape` layer, which is an ar
 cd <SITE_PACKAGES_WITH_INSTALLED_OPENVINO>/openvino/tools/mo/front/caffe/proto
 python3 generate_caffe_pb2.py --input_proto <PATH_TO_CUSTOM_CAFFE>/src/caffe/proto/caffe.proto
 ```
-where `PATH_TO_CUSTOM_CAFFE` is the path to the root directory of custom Caffe\*.
+where `PATH_TO_CUSTOM_CAFFE` is the path to the root directory of custom Caffe.
 
-3.  Now, the Model Optimizer is able to load the model into memory and start working with your extensions if there are any.
+3.  Now, Model Optimizer is able to load the model into memory and start working with your extensions if there are any.
 
-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).
+However, since your model has custom layers, you must register them as custom. To learn more about it, refer to [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>
 
-You need the Caffe\* Python\* interface. In this case, do the following:
+You need the Caffe Python interface. In this case, do the following:
 ```shell
 python3
 import caffe
@@ -48,25 +48,25 @@ 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>
 
-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).
+Most likely, 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 Model Optimizer: present in the topology,
+absent in the [list of supported layers](Supported_Frameworks_Layers.md) for the target framework. Then, refer to available options in the corresponding section in the  [Custom Layers in Model Optimizer](customize_model_optimizer/Customize_Model_Optimizer.md) page.
 
 #### 4. What does the message "Input image of shape is larger than mean image from file" mean? <a name="question-4"></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.
+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 you use the mean file that was used during the network training phase. Note that the mean file is dependent on dataset.
 
 #### 5. What does the message "Mean file is empty" mean? <a name="question-5"></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.
+Most likely, the mean file specified with the `--mean_file` flag is empty while Model Optimizer is launched. 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>
 
-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)).
+The mean file that you provide for Model Optimizer must be in the `.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>
 
-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: `mo/front/caffe/proto/my_caffe.proto`. There you can find the structure:
+The structure of any Caffe topology is described in the `caffe.proto` file of any Caffe version. For example, the following `.proto` file in Model Optimizer is used by default: `mo/front/caffe/proto/my_caffe.proto`, with the structure:
 ```
 message NetParameter {
   // ... some other parameters
@@ -81,7 +81,7 @@ This means that any topology should contain layers as top-level structures in `p
 
 #### 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>
 
-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: `mo/front/caffe/proto/my_caffe.proto`. There you can find the structure:
+The structure of any Caffe topology is described in the `caffe.proto` file for any Caffe version. For example, the following `.proto` file in Model Optimizer is used by default: `mo/front/caffe/proto/my_caffe.proto`, with the structure:
 ```sh
 message NetParameter {
 
@@ -98,7 +98,7 @@ message NetParameter {
   // ... other parameters
 }
 ```
-So, the input layer of the provided model must be specified in one of the following styles:
+Therefore, the input layer of the provided model must be specified in one of the following styles:
 
 *
 ```sh
@@ -154,43 +154,43 @@ input_dim: 3
 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.
+However, if your model contains more than one input, Model Optimizer is able to convert the model with inputs specified in one of the first three forms in the above list. The 4th 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>
 
-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 OpenVINO Runtime to perform subtraction for every input of your multi-input model, see [Overview of Preprocessing](../../OV_Runtime_UG/preprocessing_overview.md) for details.
+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 OpenVINO Runtime to perform subtraction for every input of your multi-input model. See the [Overview of Preprocessing](../../OV_Runtime_UG/preprocessing_overview.md) for details.
 
 #### 10. What does the message "Cannot load or process mean file: value error" mean? <a name="question-10"></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 Model Optimizer does not accept the mean file. See FAQs [#4](#question-4), [#5](#question-5), and [#6](#question-6).
 
 #### 11. What does the message "Invalid prototxt file: value error" mean? <a name="question-11"></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 Model Optimizer does not accept a Caffe topology. See FAQs [#7](#question-7) and [#20](#question-20).
 
 #### 12. What does the message "Error happened while constructing caffe.Net in the Caffe fallback function" mean? <a name="question-12"></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 ensure that the problem is not in the `prototxt` file, see FAQ [#2](#question-2).
 
 #### 13. What does the message "Cannot infer shapes due to exception in Caffe" mean? <a name="question-13"></a>
 
-Model Optimizer tried to infer a custom layer via the Caffe\* framework, but an error occurred, meaning that the model could not be inferred using Caffe. This might happen if you try to convert the model with some noise weights and biases that result in problems with layers that have dynamic shapes. You should write your own extension for every custom layer you topology might have. For more details, refer to [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md).
+Model Optimizer tried to infer a custom layer via the Caffe framework, but the model could not be inferred using Caffe. This might happen if you try to convert the model with some noise weights and biases, which conflict with layers that have dynamic shapes. You should write your own extension for every custom layer your topology might have. For more details, refer to the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) page.
 
 #### 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>
 
-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. 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, Model Optimizer could not import a Caffe package. Make sure that you have built Caffe with a `pycaffe` target and added it to the `PYTHONPATH` environment variable. At the same time, it is highly recommended to avoid dependency on Caffe and write your own Model Optimizer extension for your custom layer. For more information, refer to FAQ [#44](#question-44).
 
 #### 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>
 
-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`.
+You have run Model Optimizer without a flag `--framework caffe|tf|mxnet`. Model Optimizer tries to deduce the framework by the extension of input model file (`.pb` for TensorFlow, `.caffemodel` for Caffe, `.params` for Apache 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>
 
-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 OpenVINO Intermediate Representation, because MXNet models do not contain information about input shapes. Use the `--input_shape` flag to specify it. For more information about using the `--input_shape`, refer to FAQ [#56](#question-56).
 
 #### 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>
 
-`--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.
+The `--mean_file` and `--mean_values` options 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 with 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>
 
@@ -198,157 +198,157 @@ You might have specified negative values with `--mean_file_offsets`. Only positi
 
 #### 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>
 
-`--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>.
+The `--scale` option sets a scaling factor for all channels, while `--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** section: 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 pre-trained weights" mean? <a name="question-20"></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>
+#### 21. What does the message "Failed to create directory .. . Permission denied!" mean? <a name="question-21"></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>
+#### 22. What does the message "Discovered data node without inputs and value" mean? <a name="question-22"></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.
+One of the layers in the specified topology might not have inputs or values. 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>
+#### 23. What does the message "Part of the nodes was not translated to IE. Stopped" mean? <a name="question-23"></a>
 
-Some of the operations are not supported by the OpenVINO Runtime and cannot be translated to an Intermediate Representation. You can extend the Model Optimizer by allowing generation of new types of operations and implement these operations in the dedicated OpenVINO plugins. For more information, refer to the [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md)
+Some of the operations are not supported by OpenVINO Runtime and cannot be translated to OpenVINO Intermediate Representation. You can extend Model Optimizer by allowing generation of new types of operations and implement these operations in the dedicated OpenVINO plugins. For more information, refer to the [OpenVINO Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 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>
+#### 24. 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-24"></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>
+#### 25. What does the message "Node does not exist in the graph" mean? <a name="question-25"></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>
+#### 26. 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-26"></a>
 
-Most likely, the Model Optimizer tried to cut the model by a specified input. However, other inputs are needed.
+Most likely, 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>
+#### 27. What does the message "Placeholder node does not have an input port, but input port was provided" mean?  <a name="question-27"></a>
 
-You might have specified a placeholder node with an input node, while the placeholder node does not have it the model.
+You might have specified a placeholder node with an input node, while the placeholder node does not have it in 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>
+#### 28. What does the message "Port index is out of number of available input ports for node" mean? <a name="question-28"></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.
+This error occurs when an incorrect input port is specified with the `--input` command line argument. When using `--input`, you may 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. 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>
+#### 29. 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-29"></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>
+#### 30. 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-30"></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.
 
 **NOTE**: It is no longer relevant message since the limitation on input port index for model truncation has been resolved.
 
-#### 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>
+#### 31. What does the message "No or multiple placeholders in the model, but only one shape is provided, cannot set it" mean? <a name="question-31"></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.
+You might have provided only one shape for the placeholder, while there are none or multiple inputs in the model. Make sure that you have provided the 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>
+#### 32. What does the message "The amount of input nodes for port is not equal to 1" mean? <a name="question-32"></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 **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) documentation for more details.
+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`. For more details, refer to the **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide.
 
-#### 34. What does the message "Output node for port has already been specified" mean? <a name="question-34"></a>
+#### 33. What does the message "Output node for port has already been specified" mean? <a name="question-33"></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>
+#### 34. What does the message "Unsupported match kind.... Match kinds "points" or "scope" are supported only" mean? <a name="question-34"></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 [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) for more details.
+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.  For more details, refer to the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide.
 
-#### 36. What does the message "Cannot write an event file for the TensorBoard to directory" mean? <a name="question-36"></a>
+#### 35. What does the message "Cannot write an event file for the TensorBoard to directory" mean? <a name="question-35"></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.
+Model Optimizer tried to write an event file in the specified directory but failed to do that. That could happen when the specified directory does not exist or you do not have 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>
+#### 36. 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-36"></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 [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+Most likely, you tried to extend Model Optimizer with a new primitive, but you did not specify an infer function. For more information on extensions, see the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 38. What does the message "Stopped shape/value propagation at node" mean? <a name="question-38"></a>
+#### 37. What does the message "Stopped shape/value propagation at node" mean? <a name="question-37"></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.
+Model Optimizer cannot infer shapes or values for the specified node. It can happen because of the following reasons: a bug exists in the custom shape infer function, the node inputs have incorrect values/shapes, or 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>
+#### 38. What does the message "The input with shape .. does not have the batch dimension" mean? <a name="question-38"></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.
+Batch dimension is the first dimension in the shape and it should be equal to 1 or undefined. In your case, it is not either equal to 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>
+#### 39. What does the message "Not all output shapes were inferred or fully defined for node" mean? <a name="question-39"></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>
+#### 40. What does the message "Shape for tensor is not defined. Can not proceed" mean? <a name="question-40"></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.
+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, so a shape for the node cannot be inferred by Model Optimizer. You need to help Model Optimizer by specifying shapes with `--input_shape` for each node 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>
+#### 41. What does the message "Module TensorFlow was not found. Please install TensorFlow 1.2 or higher" mean? <a name="question-41"></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](../Deep_Learning_Model_Optimizer_DevGuide.md).
+To convert TensorFlow models with Model Optimizer, TensorFlow 1.2 or newer must be installed. For more information on prerequisites, see the [Configuring Model Optimizer](../Deep_Learning_Model_Optimizer_DevGuide.md) guide.
 
-#### 43. What does the message "Cannot read the model file: it is incorrect TensorFlow model file or missing" mean? <a name="question-43"></a>
+#### 42. What does the message "Cannot read the model file: it is incorrect TensorFlow model file or missing" mean? <a name="question-42"></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.
+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>
+#### 43. 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-43"></a>
 
-Most likely, there is a problem with the specified file for model. The file exists, but it has bad formatting or is corrupted.
+Most likely, there is a problem with the specified file for the model. The file exists, but it has an invalid format 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>
+#### 44. 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-44"></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 implement the extensions for this layer ([OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md)).
+This means that the layer `{layer_name}` is not supported in Model Optimizer. You will find a list of all unsupported layers in the corresponding section. You should implement the extensions for this layer. See [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) for more information.
 
-#### 46. What does the message "Custom replacement configuration file does not exist" mean? <a name="question-46"></a>
+#### 45. What does the message "Custom replacement configuration file does not exist" mean? <a name="question-45"></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.
+A path to the custom replacement configuration file was provided with the `--transformations_config` flag, but the file could not be found. Make sure 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>
+#### 46. What does the message "Extractors collection have case insensitive duplicates" mean? <a name="question-46"></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 [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+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 the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 48. What does the message "Input model name is not in an expected format, cannot extract iteration number" mean? <a name="question-48"></a>
+#### 47. What does the message "Input model name is not in an expected format, cannot extract iteration number" mean? <a name="question-47"></a>
 
-Model Optimizer can not load an MXNet\* model in the specified file format. Please, use the `.json` or `.param` format.
+Model Optimizer cannot load an MXNet model in the specified file format. Make sure you 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>
+#### 48. What does the message "Cannot convert type of placeholder because not all of its outputs are 'Cast' to float operations" mean? <a name="question-48"></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.
+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. Make sure you change the model to have `Placeholder` for FP32.
 
-#### 50. What does the message "Data type is unsupported" mean? <a name="question-50"></a>
+#### 49. What does the message "Data type is unsupported" mean? <a name="question-49"></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.
+Model Optimizer cannot convert the model to the specified data type. Currently, FP16 and FP32 are supported. Make sure you 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>
+#### 50. What does the message "No node with name ..." mean? <a name="question-50"></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>
+#### 51. What does the message "Module MXNet was not found. Please install MXNet 1.0.0" mean? <a name="question-51"></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](../Deep_Learning_Model_Optimizer_DevGuide.md).
+To convert MXNet models with Model Optimizer, Apache MXNet 1.0.0 must be installed. For more information about prerequisites, see the[Configuring Model Optimizer](../Deep_Learning_Model_Optimizer_DevGuide.md) guide.
 
-#### 53. What does the message "The following error happened while loading MXNet model .." mean? <a name="question-53"></a>
+#### 52. What does the message "The following error happened while loading MXNet model .." mean? <a name="question-52"></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.
+Most likely, there is a problem with loading of the MXNet model. Make sure the specified path is correct, the model exists and 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>
+#### 53. What does the message "The following error happened while processing input shapes: .." mean? <a name="question-53"></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.
+Make sure 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>
+#### 54. 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-54"></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 [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+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 the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 56. What does the message "Both --input_shape and --batch were provided. Please, provide only one of them" mean? <a name="question-56"></a>
+#### 55. What does the message "Both --input_shape and --batch were provided. Please, provide only one of them" mean? <a name="question-55"></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.
+Specifying the batch and the input shapes at the same time is not supported. You must 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>
+#### 56. What does the message "Input shape .. cannot be parsed" mean? <a name="question-56"></a>
 
-The specified input shape cannot be parsed. Please, define it in one of the following ways:
+The specified input shape cannot be parsed. Define it in one of the following ways:
 
 *
 ```shell
@@ -365,141 +365,141 @@ The specified input shape cannot be parsed. Please, define it in one of the foll
 
 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>
+#### 57. What does the message "Please provide input layer names for input layer shapes" mean? <a name="question-57"></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](convert_model/Convert_Model_From_Caffe.md. 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 the [Converting a Caffe Model](convert_model/Convert_Model_From_Caffe.md). Additional information for `--input_shape` is in FAQ [#56](#question-56).
 
-#### 59. What does the message "Values cannot be parsed" mean? <a name="question-59"></a>
+#### 58. What does the message "Values cannot be parsed" mean? <a name="question-58"></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>
+#### 59. What does the message ".. channels are expected for given values" mean? <a name="question-59"></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>
+#### 60. What does the message "You should specify input for each mean value" mean? <a name="question-60"></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 didn't specify inputs using `--mean_values`. Specify inputs with the `--input` flag. For usage examples, refer to the FAQ [#62](#question-62).
 
-#### 62. What does the message "You should specify input for each scale value" mean? <a name="question-62"></a>
+#### 61. What does the message "You should specify input for each scale value" mean? <a name="question-61"></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 didn't specify inputs using `--scale_values`. Specify inputs with the `--input` flag. For usage examples, refer to the FAQ [#63](#question-63).
 
-#### 63. What does the message "Number of inputs and mean values does not match" mean? <a name="question-63"></a>
+#### 62. What does the message "Number of inputs and mean values does not match" mean? <a name="question-62"></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.
+The number of specified mean values and the number of inputs must be equal. For a usage example, refer to the [Converting a Caffe Model](convert_model/Convert_Model_From_Caffe.md) guide.
 
-#### 64. What does the message "Number of inputs and scale values does not match" mean? <a name="question-64"></a>
+#### 63. What does the message "Number of inputs and scale values does not match" mean? <a name="question-63"></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.
+The number of specified scale values and the number of inputs must be equal.  For a usage example, refer to the [Converting a Caffe Model](convert_model/Convert_Model_From_Caffe.md) guide.
 
-#### 65. What does the message "No class registered for match kind ... Supported match kinds are .. " mean? <a name="question-65"></a>
+#### 64. What does the message "No class registered for match kind ... Supported match kinds are .. " mean? <a name="question-64"></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.
+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>
+#### 65. What does the message "No instance(s) is(are) defined for the custom replacement" mean? <a name="question-65"></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.
+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. This error will occur if the attribute is missing. For more details, refer to the **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide.
 
-#### 67. What does the message "The instance must be a single dictionary for the custom replacement with id .." mean? <a name="question-67"></a>
+#### 66. What does the message "The instance must be a single dictionary for the custom replacement with id .." mean? <a name="question-66"></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.
+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. For more details, refer to the **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide.
 
-#### 68. What does the message "No instances are defined for replacement with id .. " mean? <a name="question-68"></a>
+#### 67. What does the message "No instances are defined for replacement with id .. " mean? <a name="question-67"></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. For more information, refer to the FAQ [#65](#question-65).
 
-#### 69. What does the message "Custom replacements configuration file .. does not exist" mean? <a name="question-69"></a>
+#### 68. What does the message "Custom replacements configuration file .. does not exist" mean? <a name="question-68"></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.
+The path to a custom replacement configuration file was provided with the `--transformations_config` flag, but it cannot be found. Make sure 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>
+#### 69. What does the message "Failed to parse custom replacements configuration file .." mean? <a name="question-69"></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).
+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 the [JSON Schema Reference](https://spacetelescope.github.io/understanding-json-schema/reference/index.html) page.
 
-#### 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>
+#### 70. What does the message "One of the custom replacements in the configuration file .. does not contain attribute 'id'" mean? <a name="question-70"></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 [#71](#question-71).
 
-#### 72. What does the message "File .. validation failed" mean? <a name="question-72"></a>
+#### 71. What does the message "File .. validation failed" mean? <a name="question-71"></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.
+The file for custom replacement configuration provided with the `--transformations_config` flag cannot pass validation. Make sure 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>
+#### 72. What does the message "Cannot update the file .. because it is broken" mean? <a name="question-72"></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. Make sure that the file is correct and refer to FAQ [#68](#question-68), [#69](#question-69), [#70](#question-70), and [#71](#question-71).
 
-#### 74. What does the message "End node .. is not reachable from start nodes: .." mean? <a name="question-74"></a>
+#### 73. What does the message "End node .. is not reachable from start nodes: .." mean? <a name="question-73"></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.
+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 for the subgraph to find, there are nodes marked as outputs but there is no path from them to the input nodes. Make sure 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>
+#### 74. What does the message "Sub-graph contains network input node .." mean? <a name="question-74"></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.
+The 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, and then adds all input nodes to the sub-graph (and the inputs of their inputs, etc.) for these "internal" nodes. This error reports that Model Optimizer reached input node during this phase. This means that the start/end points are specified incorrectly in the configuration file. For more details, refer to the **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide. 
 
-#### 76. What does the message "... elements of ... were clipped to infinity while converting a blob for node [...] to ..." mean? <a name="question-76"></a>
+#### 75. What does the message "... elements of ... were clipped to infinity while converting a blob for node [...] to ..." mean? <a name="question-75"></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.
+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>
+#### 76. What does the message "... elements of ... were clipped to zero while converting a blob for node [...] to ..." mean? <a name="question-76"></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.
+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>
+#### 77. What does the message "The amount of nodes matched pattern ... is not equal to 1" mean? <a name="question-77"></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 **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) documentation.
+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 the **Graph Transformation Extensions** section in the [Model Optimizer Extensibility](customize_model_optimizer/Customize_Model_Optimizer.md) guide.
 
-#### 79. What does the message "The topology contains no "input" layers" mean? <a name="question-79"></a>
+#### 78. What does the message "The topology contains no "input" layers" mean? <a name="question-78"></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).
+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. Preparation of a deploy-ready topology usually 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>
+#### 79. What does the message "Warning: please expect that Model Optimizer conversion might be slow" mean? <a name="question-79"></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](../Deep_Learning_Model_Optimizer_DevGuide.md).
+You are using an unsupported Python version. Use only versions 3.4 - 3.6 for the C++ `protobuf` implementation that is supplied with OpenVINO toolkit. You can still boost the conversion speed by building the protobuf library from sources. For complete instructions about building `protobuf` from sources, see the appropriate section in the[Converting a Model to Intermediate Representation](../Deep_Learning_Model_Optimizer_DevGuide.md) guide.
 
-#### 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>
+#### 80. 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-80"></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
-description (`.json` file) should be prepared (merged) in advance and provided with `--input_symbol` parameter.
+This error occurs if you did not provide the `--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 description (`.json` file) should be prepared (merged) in advance and provided with the `--input_symbol` parameter.
 
-If you add to your model additional layers and weights that are in `.nd` files, the Model Optimizer can build a model
+If you add additional layers and weights that are in `.nd` files to your model, Model Optimizer can build a model
 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).
+For more information, refer to the [Converting an MXNet Model](convert_model/Convert_Model_From_MxNet.md) guide.
 
-#### 82. What does the message "You should specify input for mean/scale values" mean? <a name="question-82"></a>
+#### 81. What does the message "You should specify input for mean/scale values" mean? <a name="question-81"></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).
+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, the number of passed values should be the same as the number of inputs of the model.
+For more information, refer to the [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md) guide.
 
-#### 83. What does the message "Input with name ... not found!" mean? <a name="question-83"></a>
+#### 82. What does the message "Input with name ... not found!" mean? <a name="question-82"></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).
+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 you list only names of the input layers of your model when passing values with the `--input` option. 
+For more information, refer to the [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md) guide.
 
-#### 84. What does the message "Specified input json ... does not exist" mean? <a name="question-84"></a>
+#### 83. What does the message "Specified input json ... does not exist" mean? <a name="question-83"></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).
+Most likely, `.json` file does not exist or has a name that does not match the notation of Apache MXNet. Make sure the file exists and has a correct name.
+For more information, refer to the [Converting an MXNet Model](convert_model/Convert_Model_From_MxNet.md) guide.
 
-#### 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>
+#### 84. What does the message "Unsupported Input model file type ... Model Optimizer support only .params and .nd files format" mean? <a name="question-84"></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).
+Model Optimizer for Apache MXNet supports only `.params` and `.nd` files formats. Most likely, you specified an unsupported file format in `--input_model`.
+For more information, refer to [Converting an 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>
+#### 85. What does the message "Operation ... not supported. Please register it as custom op" mean? <a name="question-85"></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 [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+If you want to convert model that contains unsupported operations, you need to prepare extension for all such operations.
+For more information, refer to the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 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>
+#### 86. What does the message "Can not register Op ... Please, call function 'register_caffe_python_extractor' with parameter 'name'" mean? <a name="question-86"></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.
+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 comparing to ordinary Caffe layers.
 
 In particular, you need to call the function `register_caffe_python_extractor` and pass `name` as the second argument of the function.
-The name should be the compilation of the layer name and the module name separated by a dot.
+The name should be the compilation of the layer name with the module name separated by a dot.
 
 For example, your topology contains this layer with type `Python`:
 
@@ -516,7 +516,7 @@ layer {
 }
 ```
 
-What you do first is implementing an extension for this layer in the Model Optimizer as an ancestor of `Op` class.
+The first step is to implement an extension for this layer in Model Optimizer as an ancestor of `Op` class:
 ```
 class ProposalPythonExampleOp(Op):
        op = 'Proposal'
@@ -534,48 +534,48 @@ register_caffe_python_extractor(ProposalPythonExampleOp, 'rpn.proposal_layer.Pro
 Op.excluded_classes.append(ProposalPythonExampleOp)
 ```
 
-Note that the first call <code>register_caffe_python_extractor(ProposalPythonExampleOp, 'rpn.proposal_layer.ProposalLayer')</code> registers extension of the layer in the Model Optimizer that will be found by the specific name (mandatory to join module name and layer name): <code>rpn.proposal_layer.ProposalLayer</code>.
+Note that the first call <code>register_caffe_python_extractor(ProposalPythonExampleOp, 'rpn.proposal_layer.ProposalLayer')</code> registers an extension of the layer in Model Optimizer, which will be found by the specific name (mandatory to join module name and layer name): <code>rpn.proposal_layer.ProposalLayer</code>.
 
 The second call prevents Model Optimizer from using this extension as if it is an extension for
 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 [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+For more information, refer to the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 88. What does the message "Model Optimizer is unable to calculate output shape of Memory node .." mean? <a name="question-88"></a>
+#### 87. What does the message "Model Optimizer is unable to calculate output shape of Memory node .." mean? <a name="question-87"></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`.
+Model Optimizer supports only `Memory` layers, in which `input_memory` goes before `ScaleShift` or the `FullyConnected` layer.
+This error message means that in your model the layer after input memory is not of the `ScaleShift` or `FullyConnected` type.
 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>
+#### 88. 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-88"></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.
+These error messages mean that Model Optimizer does not support your Kaldi model, because the `checksum` of the model is not
+16896 (the model should start with this number), or the model file does not contain the `<Net>` tag as a starting one.
+Make sure 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>
+#### 89. 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-89"></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.
+These messages mean that the file counts you passed contain not one line. The count file should start with
+`[` and end with  `]`,  and integer values should be separated by spaces between those brackets.
 
-#### 91. What does the message "Model Optimizer is not able to read Kaldi model .." mean? <a name="question-91"></a>
+#### 90. What does the message "Model Optimizer is not able to read Kaldi model .." mean? <a name="question-90"></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).
+There are multiple reasons why Model Optimizer does not accept a Kaldi topology, including: 
+the file is not available or does not exist. Refer to FAQ [#88](#question-88).
 
-#### 92. What does the message "Model Optimizer is not able to read counts file  .." mean? <a name="question-92"></a>
+#### 91. What does the message "Model Optimizer is not able to read counts file  .." mean? <a name="question-91"></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).
+There are multiple reasons why Model Optimizer does not accept a counts file, including: 
+the file is not available or does not exist. Refer to FAQ [#89](#question-89).
 
-#### 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>
+#### 92. 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-92"></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
-MXNet with unsupported layers or generate new json with MXNet version 1.0.0 and higher. Also you need to implement
-OpenVINO extension for used custom layers.
-For more information, refer to the [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md).
+This message means that if you have a model with custom layers and its JSON file has been generated with Apache MXNet version
+lower than 1.0.0, Model Optimizer does not support such topologies. If you want to convert it, you have to rebuild
+MXNet with unsupported layers or generate a new JSON file with Apache MXNet version 1.0.0 or higher. You also need to implement
+OpenVINO extension to use custom layers.
+For more information, refer to the [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md) guide.
 
-#### 97. What does the message "Graph contains a cycle. Can not proceed .." mean?  <a name="question-97"></a>
+#### 93. What does the message "Graph contains a cycle. Can not proceed .." mean?  <a name="question-93"></a>
 
 Model Optimizer supports only straightforward models without cycles.
 
@@ -586,56 +586,56 @@ For Tensorflow:
 
 For all frameworks:
 1. [Replace cycle containing Sub-graph in Model Optimizer](customize_model_optimizer/Customize_Model_Optimizer.md)
-2. [OpenVINO™ Extensibility Mechanism](../../Extensibility_UG/Intro.md)
+2. See [OpenVINO&trade; Extensibility Mechanism](../../Extensibility_UG/Intro.md)
 
 or
-* Edit model in original framework to exclude cycle.
+* Edit the model in its original framework to exclude cycle.
 
-#### 98. What does the message "Can not transpose attribute '..' with value .. for node '..' .." mean?  <a name="question-98"></a>
+#### 94. What does the message "Can not transpose attribute '..' with value .. for node '..' .." mean?  <a name="question-94"></a>
 
-This message means that model is not supported. It may be caused by using shapes larger than 4-D.
+This message means that the model is not supported. It may be caused by using shapes larger than 4-D.
 There are two ways to avoid such message:
 
-1. [Cutting Off Parts of a Model](convert_model/Cutting_Model.md)
-2. Edit network in original framework to exclude such layers.
+* [Cut off parts of the model](convert_model/Cutting_Model.md).
+* Edit the network in its original framework to exclude such layers.
 
-#### 99. What does the message "Expected token `</ParallelComponent>`, has `...`" mean?  <a name="question-99"></a>
+#### 95. What does the message "Expected token `</ParallelComponent>`, has `...`" mean?  <a name="question-95"></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.
+This error messages mean that Model Optimizer does not support your Kaldi model, because the Net contains `ParallelComponent` that does not end with the `</ParallelComponent>` tag.
+Make sure 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>
+#### 96. 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-96"></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.
+However, there are exceptions. For example, the output value of layer Interp is calculated differently in Deeplab-Caffe and classic Caffe. Therefore, if your model contains layer Interp and the conversion of your model has failed, modify the `interp_infer` function in the `extensions/ops/interp.op` file according to the comments in the file.
 
-#### 101. What does the message "Mean/scale values should ..." mean? <a name="question-101"></a>
+#### 97. What does the message "Mean/scale values should ..." mean? <a name="question-97"></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).
+It means that your mean/scale values have a wrong format. Specify mean/scale values in the form of `layer_name(val1,val2,val3)`.
+You need to specify values for each input of the model. For more information, refer to the [Converting a Model to Intermediate Representation](convert_model/Converting_Model.md) guide.
 
-#### 102. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="question-102"></a>
+#### 98. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="question-98"></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.
+It means that you are trying to convert a topology contains the `_contrib_box_nms` operation which is not supported directly. However, the sub-graph of operations including `_contrib_box_nms` could be replaced with the DetectionOutput layer if your topology is one of the `gluoncv` topologies. Specify the `--enable_ssd_gluoncv` command-line parameter for Model Optimizer to enable this transformation.
 
-#### 103. What does the message "ModelOptimizer is not able to parse *.caffemodel" mean? <a name="question-103"></a>
+#### 99. What does the message "ModelOptimizer is not able to parse *.caffemodel" mean? <a name="question-99"></a>
 
-If a '*.caffemodel' file exists and it is correct, the error possibly occured due to the use of Python protobuf implementation. In some cases, it shows error message during model parsing, for example: "'utf-8' codec can't decode byte 0xe0 in position 4: invalid continuation byte in field: mo_caffe.SpatialTransformerParameter.transform_type". You can either use Python 3.6/3.7 or build 'cpp' implementation of protobuf yourself for your version of Python. For the complete instructions about building `protobuf` from sources, see the appropriate section in [Converting a Model to Intermediate Representation](../Deep_Learning_Model_Optimizer_DevGuide.md).
+If a `*.caffemodel` file exists and is correct, the error occurred possibly because of the use of Python protobuf implementation. In some cases, error messages may appear during model parsing, for example: "`utf-8` codec can't decode byte 0xe0 in position 4: invalid continuation byte in field: mo_caffe.SpatialTransformerParameter.transform_type". You can either use Python 3.6/3.7 or build the `cpp` implementation of `protobuf` yourself for your version of Python. For the complete instructions about building `protobuf` from sources, see the appropriate section in the [Converting Models with Model Optimizer](../Deep_Learning_Model_Optimizer_DevGuide.md) guide.
 
-#### 104. What does the message "SyntaxError: 'yield' inside list comprehension" during MxNet\* model conversion mean? <a name="question-104"></a>
+#### 100. What does the message "SyntaxError: 'yield' inside list comprehension" during MxNet model conversion mean? <a name="question-100"></a>
 
-The issue "SyntaxError: 'yield' inside list comprehension" might occur during converting MXNet\* models (mobilefacedet-v1-mxnet, brain-tumor-segmentation-0001) on Windows* platform with Python* 3.8 environment. This issue is caused by API changes for `yield expression` in Python 3.8.
+The issue "SyntaxError: `yield` inside list comprehension" might occur during converting MXNet models (`mobilefacedet-v1-mxnet`, `brain-tumor-segmentation-0001`) on Windows platform with Python 3.8 environment. This issue is caused by the API changes for `yield expression` in Python 3.8.
 The following workarounds are suggested to resolve this issue:
-1. Use Python 3.6/3.7 to convert MXNet\* models on Windows
-2. Update MXNet: pip install mxnet=1.7.0.post2
-Note that you might have conflicts between previously installed PyPI dependencies.
+1. Use Python 3.6/3.7 to convert MXNet models on Windows
+2. Update Apache MXNet by using `pip install mxnet==1.7.0.post2`
+Note that it might have conflicts with previously installed PyPI dependencies.
 
-#### 105. What does the message "The IR preparation was executed by the legacy MO path. ..." mean? <a name="question-105"></a>
+#### 101. What does the message "The IR preparation was executed by the legacy MO path. ..." mean? <a name="question-101"></a>
 
-For the models in ONNX* format, there are two available paths of IR conversion.
-The old one is handled by the old Python* implementation, while the new one uses new C++ frontends.
+For the models in ONNX format, there are two available paths of IR conversion.
+The old one is handled by the old Python implementation, while the new one uses new C++ frontends.
 Starting from the 2022.1 version, the default IR conversion path for ONNX models is processed using the new ONNX frontend.
 Certain features, such as `--extensions` and `--transformations_config`, are not yet fully supported on the new frontends.
-For `--extensions`, the new frontends support only paths to shared libraries (.dll and .so). For `--transformations_config`, they support JSON configurations with defined library fields.
-Inputs freezing (enabled by `--freeze_placeholder_with_value` or `--input` arguments) is not supported on the new frontends.
-The IR conversion falls back to the old path if a user does not select any expected path of conversion explicitly (by `--use_new_frontend` or `--use_legacy_frontend` MO arguments) and unsupported pre-defined scenario is detected on the new frontend path.
+The new frontends support only paths to shared libraries (.dll and .so) for `--extensions`. They support JSON configurations with defined library fields for `--transformations_config`.
+Inputs freezing (enabled by `--freeze_placeholder_with_value` or `--input` arguments) is not supported by the new frontends.
+The IR conversion falls back to the old path if a user does not select any expected path of conversion explicitly (with `--use_new_frontend` or `--use_legacy_frontend` MO arguments) and unsupported pre-defined scenario is detected on the new frontend path.

docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md

@@ -1,5 +1,8 @@
 # Supported Framework Layers {#openvino_docs_MO_DG_prepare_model_Supported_Frameworks_Layers}
 
+
+In this article, you can find lists of supported framework layers, divided by frameworks.
+
 ## Caffe Supported Layers
 
 
@@ -16,7 +19,7 @@
 | Crop |  |
 | Deconvolution |  |
 | DetectionOutput |  |
-| Dropout | Not needed for inference |
+| Dropout | Not needed for inference. |
 | Eltwise |  |
 | Flatten |  |
 | GlobalInput |  |
@@ -24,7 +27,7 @@
 | Input |  |
 | LRN |  |
 | Normalize |  |
-| Python | Supported only for the Python Proposal operation |
+| Python | Supported only for the Python Proposal operation. |
 | Permute |  |
 | Pooling |  |
 | Power |  |
@@ -47,10 +50,10 @@
 | Tile |  |
 
 
-## MXNet Supported Symbols
+## Apache MXNet Supported Symbols
 
 
-| Symbol Name in MXNet| Limitations|
+| Symbol Name in Apache MXNet| Limitations|
 | :----------| :----------|
 | _Plus |  |
 | _contrib_arange_like |  |
@@ -58,7 +61,7 @@
 | _contrib_DeformableConvolution |  |
 | _contrib_DeformablePSROIPooling |  |
 | _contrib_div_sqrt_dim |  |
-| _contrib_MultiBoxDetection | "force_suppress" = 1 is not supported, non-default variances are not supported |
+| _contrib_MultiBoxDetection | `force_suppress` = 1 is not supported, non-default variances are not supported. |
 | _contrib_MultiBoxPrior |  |
 | _contrib_Proposal |  |
 | _copy | Not needed for inference |
@@ -70,7 +73,7 @@
 | _random_uniform | Operation provides sequence from uniform distribution, but exact values won't match. |
 | _rnn_param_concat |  |
 | _arange |  |
-| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings |
+| _contrib_AdaptiveAvgPooling2D | Converted to the Average Pooling with fixed paddings. |
 | _maximum |  |
 | _minimum |  |
 | _np_roll |  |
@@ -96,8 +99,8 @@
 | greater_scalar |  |
 | max |  |
 | minus_scalar |  |
-| null | Not needed for inference |
-| LayerNorm | "output_mean_var" = True is not supported |
+| null | Not needed for inference. |
+| LayerNorm | `output_mean_var` = True is not supported. |
 | repeat |  |
 | rnn |  |
 | rnn_param_concat |  |
@@ -114,24 +117,24 @@
 | tile |  |
 | transpose |  |
 | zeros |  |
-| Activation | supported "act_type" = "relu", "sigmoid", "softrelu" or "tanh" |
+| Activation | Supported `act_type` = `relu`, `sigmoid`, `softrelu` or `tanh`. |
 | BatchNorm |  |
 | Concat |  |
 | Convolution |  |
-| Crop | "center_crop" = 1 is not supported |
-| Custom | [Custom Layers in Model Optimizer](customize_model_optimizer/Customize_Model_Optimizer.md) |
+| Crop | `center_crop` = 1 is not supported. |
+| Custom | See [Custom Layers in Model Optimizer].(customize_model_optimizer/Customize_Model_Optimizer.md) |
 | Deconvolution |  |
 | DeformableConvolution |  |
 | DeformablePSROIPooling |  |
-| Dropout | Not needed for inference |
+| Dropout | Not needed for inference. |
 | ElementWiseSum |  |
 | Embedding |  |
 | Flatten |  |
 | FullyConnected |  |
 | InstanceNorm |  |
-| L2Normalization | only 4D input is supported |
+| L2Normalization | Only 4D input is supported. |
 | LRN |  |
-| LeakyReLU | supported "act_type" = "prelu", "elu", "leaky", "gelu" |
+| LeakyReLU | Supported `act_type` = `prelu`, `elu`, `leaky`, `gelu`. |
 | ones_like |  |
 | Pad |  |
 | Pooling |  |
@@ -142,7 +145,7 @@
 | SoftmaxActivation |  |
 | SoftmaxOutput |  |
 | SoftSign |  |
-| Take | The attribute 'mode' is not supported |
+| Take | The attribute `mode` is not supported. |
 | Tile |  |
 | UpSampling |  |
 | Where |  |
@@ -151,7 +154,7 @@
 
 ## TensorFlow Supported Operations
 
-Some TensorFlow operations do not match to any OpenVINO operation, but are still supported by the Model Optimizer and can be used on constant propagation path. These layers are labeled 'Constant propagation' in the table.
+Some of TensorFlow operations do not match any OpenVINO operations. Yet, they are still supported by Model Optimizer and can be used on constant propagation path. These layers are labeled with `Constant propagation` in the table below:
 
 
 | Operation Name in TensorFlow | Limitations|
@@ -165,19 +168,19 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | ArgMax |  |
 | ArgMin |  |
 | Asinh |  |
-| Assert | Not needed for inference |
-| Assign | Not needed for inference |
-| AssignSub | Not needed for inference |
+| Assert | Not needed for inference. |
+| Assign | Not needed for inference. |
+| AssignSub | Not needed for inference. |
 | Atanh |  |
 | AvgPool |  |
-| AvgPoolV2 | Supported only for constant-foldable kernel_size and strides inputs |
+| AvgPoolV2 | Supported only for constant-foldable `kernel_size` and strides inputs. |
 | AvgPool3D |  |
 | BatchMatMul |  |
 | BatchMatMulV2 |  |
 | BatchToSpaceND |  |
 | BiasAdd |  |
 | BlockLSTM |  |
-| Bucketize | CPU only |
+| Bucketize | CPU only. |
 | BroadcastTo |  |
 | Cast |  |
 | Ceil |  |
@@ -191,30 +194,30 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Conv3DBackpropInputV2 |  |
 | Cos |  |
 | Cosh |  |
-| CropAndResize | "method" = "bilinear" only |
-| CTCGreedyDecoder | Supported only with decoded indices output in a dense format |
-| CTCLoss | Supported only with decoded indices input in a dense format |
+| CropAndResize |  `method` = `bilinear` only. |
+| CTCGreedyDecoder | Supported only with decoded indices output in a dense format. |
+| CTCLoss | Supported only with decoded indices input in a dense format. |
 | CumSum |  |
 | DepthToSpace|  |
 | DepthwiseConv2dNative|  |
-| Einsum | Supported only with equation that does not contain repeated labels within a subscript |
+| Einsum | Supported only with equation that does not contain repeated labels within a subscript. |
 | Elu |  |
-| EmptyTensorList | Supported only when it is part of a sub-graph of the special form |
-| Enter | Supported only when it is fused to the TensorIterator layer |
+| EmptyTensorList | Supported only when it is part of a sub-graph of the special form. |
+| Enter | Supported only when it is fused to the TensorIterator layer. |
 | Equal |  |
 | Erf |  |
-| Exit | Supported only when it is fused to the TensorIterator layer |
+| Exit | Supported only when it is fused to the TensorIterator layer. |
 | Exp |  |
 | ExpandDims |  |
-| ExperimentalSparseWeightedSum | CPU only |
+| ExperimentalSparseWeightedSum | CPU only. |
 | ExtractImagePatches |  |
 | EuclideanNorm |  |
 | FakeQuantWithMinMaxVars |  |
 | FakeQuantWithMinMaxVarsPerChannel |  |
-| FFT | Supported only when it is part of a sub-graph of the special form |
-| FFT2D | Supported only when it is part of a sub-graph of the special form |
-| FFT3D | Supported only when it is part of a sub-graph of the special form |
-| FIFOQueueV2 | Supported only when it is part of a sub-graph of the special form |
+| FFT | Supported only when it is part of a sub-graph of the special form. |
+| FFT2D | Supported only when it is part of a sub-graph of the special form. |
+| FFT3D | Supported only when it is part of a sub-graph of the special form. |
+| FIFOQueueV2 | Supported only when it is part of a sub-graph of the special form. |
 | Fill |  |
 | Floor |  |
 | FloorDiv |  |
@@ -228,12 +231,12 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | GatherV2 |  |
 | Greater |  |
 | GreaterEqual |  |
-| Identity | Not needed for shape inference |
+| Identity | Not needed for shape inference. |
 | IdentityN |  |
-| IFFT | Supported only when it is part of a sub-graph of the special form |
-| IFFT2D | Supported only when it is part of a sub-graph of the special form |
-| IFFT3D | Supported only when it is part of a sub-graph of the special form |
-| IteratorGetNext | Supported only when it is part of a sub-graph of the special form |
+| IFFT | Supported only when it is part of a sub-graph of the special form. |
+| IFFT2D | Supported only when it is part of a sub-graph of the special form. |
+| IFFT3D | Supported only when it is part of a sub-graph of the special form. |
+| IteratorGetNext | Supported only when it is part of a sub-graph of the special form. |
 | LRN |  |
 | LeakyRelu |  |
 | Less |  |
@@ -244,23 +247,23 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | LogicalOr |  |
 | LogicalNot |  |
 | LogSoftmax |  |
-| LookupTableInsertV2 | Supported only when it is part of a sub-graph of the special form |
-| LoopCond | Supported only when it is fused to the TensorIterator layer |
+| LookupTableInsertV2 | Supported only when it is part of a sub-graph of the special form. |
+| LoopCond | Supported only when it is fused to the TensorIterator layer. |
 | MatMul |  |
 | Max |  |
 | MaxPool |  |
-| MaxPoolV2 | Supported only for constant-foldable kernel_size and strides inputs |
+| MaxPoolV2 | Supported only for constant-foldable `kernel_size` and strides inputs. |
 | MaxPool3D |  |
 | Maximum |  |
 | Mean |  |
-| Merge | Supported only when it is fused to the TensorIterator layer |
+| Merge | Supported only when it is fused to the TensorIterator layer. |
 | Min |  |
 | Minimum |  |
 | MirrorPad |  |
 | Mod |  |
 | Mul |  |
 | Neg |  |
-| NextIteration | Supported only when it is fused to the TensorIterator layer |
+| NextIteration | Supported only when it is fused to the TensorIterator layer. |
 | NonMaxSuppressionV2 |  |
 | NonMaxSuppressionV3 |  |
 | NonMaxSuppressionV4 |  |
@@ -274,9 +277,9 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Placeholder |  |
 | PlaceholderWithDefault |  |
 | Prod |  |
-| QueueDequeue | Supported only when it is part of a sub-graph of the special form |
-| QueueDequeueUpToV2 | Supported only when it is part of a sub-graph of the special form |
-| QueueDequeueV2 | Supported only when it is part of a sub-graph of the special form |
+| QueueDequeue | Supported only when it is part of a sub-graph of the special form. |
+| QueueDequeueUpToV2 | Supported only when it is part of a sub-graph of the special form. |
+| QueueDequeueV2 | Supported only when it is part of a sub-graph of the special form. |
 | RandomUniform |  |
 | RandomUniformInt |  |
 | Range |  |
@@ -290,7 +293,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | ResizeNearestNeighbor |  |
 | ResourceGather|  |
 | ReverseSequence |  |
-| ReverseV2 | Supported only when it can be converted to the ReverseSequence operation |
+| ReverseV2 | Supported only when it can be converted to the ReverseSequence operation. |
 | Roll |  |
 | Round |  |
 | Pow |  |
@@ -309,10 +312,10 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Softsign |  |
 | SpaceToBatchND |  |
 | SpaceToDepth |  |
-| SparseFillEmptyRows | Supported only when it is part of a sub-graph of the special form |
-| SparseReshape | Supported only when it is part of a sub-graph of the special form |
-| SparseSegmentSum | Supported only when it is part of a sub-graph of the special form |
-| SparseSegmentMean | Supported only when it is part of a sub-graph of the special form |
+| SparseFillEmptyRows | Supported only when it is part of a sub-graph of the special form. |
+| SparseReshape | Supported only when it is part of a sub-graph of the special form. |
+| SparseSegmentSum | Supported only when it is part of a sub-graph of the special form. |
+| SparseSegmentMean | Supported only when it is part of a sub-graph of the special form. |
 | SparseToDense | CPU only |
 | Split |  |
 | SplitV |  |
@@ -320,31 +323,31 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Square |  |
 | SquaredDifference |  |
 | Square|  |
-| Squeeze | The case when squeeze axis is not specified is not supported |
+| Squeeze | Cases in which squeeze axis is not specified are not supported. |
 | StatelessWhile |  |
-| StopGradient | Not needed for shape inference |
-| StridedSlice | Supported only for constant-foldable begin, end, and strides inputs |
+| StopGradient | Not needed for shape inference. |
+| StridedSlice | Supported only for constant-foldable `begin`, `end`, and `strides` inputs. |
 | Sub |  |
 | Sum |  |
 | Swish |  |
 | swish_f32 |  |
-| Switch | Control flow propagation |
+| Switch | Control flow propagation. |
 | Tan |  |
 | Tanh |  |
-| TensorArrayGatherV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorArrayReadV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorArrayScatterV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorArraySizeV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorArrayV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorArrayWriteV3 | Supported only when it is fused to the TensorIterator layer |
-| TensorListPushBack | Supported only when it is part of a sub-graph of the special form |
+| TensorArrayGatherV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorArrayReadV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorArrayScatterV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorArraySizeV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorArrayV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorArrayWriteV3 | Supported only when it is fused to the TensorIterator layer. |
+| TensorListPushBack | Supported only when it is part of a sub-graph of the special form. |
 | Tile |  |
 | TopkV2 |  |
 | Transpose |  |
 | Unpack |  |
 | Variable |  |
 | VariableV2 |  |
-| Where | Supported only when it is part of a sub-graph of the special form |
+| Where | Supported only when it is part of a sub-graph of the special form. |
 | ZerosLike |  |
 
 
@@ -366,7 +369,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Bidirectional |  |
 | Concatenate |  |
 | Conv1D |  |
-| Conv1DTranspose | Not supported if dilation is not equal to 1 |
+| Conv1DTranspose | Not supported if `dilation` is not equal to 1. |
 | Conv2D |  |
 | Conv2DTranspose |  |
 | Conv3D |  |
@@ -375,7 +378,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Cropping2D |  |
 | Cropping3D |  |
 | Dense |  |
-| DenseFeatures | Not supported for categorical and crossed features |
+| DenseFeatures | Not supported for categorical and crossed features. |
 | DepthwiseConv2D |  |
 | Dot |  |
 | Dropout |  |
@@ -407,7 +410,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | Multiply |  |
 | PReLU |  |
 | Permute |  |
-| RNN | Not supported for some custom cells |
+| RNN | Not supported for some custom cells. |
 | ReLU |  |
 | RepeatVector |  |
 | Reshape |  |
@@ -442,7 +445,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | affinetransform |  |
 | backproptruncationcomponent |  |
 | batchnormcomponent |  |
-| clipgradientcomponent | Not needed for inference |
+| clipgradientcomponent | Not needed for inference. |
 | concat |  |
 | convolutional1dcomponent |  |
 | convolutionalcomponent |  |
@@ -452,7 +455,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | fixedaffinecomponent |  |
 | fixedbiascomponent |  |
 | fixedscalecomponent |  |
-| generaldropoutcomponent| Not needed for inference |
+| generaldropoutcomponent| Not needed for inference. |
 | linearcomponent |  |
 | logsoftmaxcomponent |  |
 | lstmnonlinearitycomponent |  |
@@ -461,7 +464,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | maxpoolingcomponent |  |
 | naturalgradientaffinecomponent |  |
 | naturalgradientperelementscalecomponent |  |
-| noopcomponent | Not needed for inference |
+| noopcomponent | Not needed for inference. |
 | normalizecomponent |  |
 | parallelcomponent |  |
 | pnormcomponent |  |
@@ -471,7 +474,7 @@ Some TensorFlow operations do not match to any OpenVINO operation, but are still
 | sigmoidcomponent |  |
 | softmax |  |
 | softmaxComponent |  |
-| specaugmenttimemaskcomponent | Not needed for inference |
+| specaugmenttimemaskcomponent | Not needed for inference. |
 | splicecomponent |  |
 | tanhcomponent |  |
 | tdnncomponent |  |
@@ -667,68 +670,95 @@ paddlepaddle>=2.1
 
 | Operator Name in PaddlePaddle| Limitations|
 | :----------| :----------|
-| adpative_pool2d | 'NHWC' data_layout is not supported |
-| arg_max | 'int32' output data_type is not supported |
+| adpative_pool2d | The `NHWC` data_layout is not supported. |
+| arg_max | The `int32` output data_type is not supported. |
+| assign |  |
 | assign_value |  |
 | batch_norm |  |
-| bilinear_interp | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
-| bilinear_interp_v2 | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
+| bilinear_interp | `NCW`, `NWC`, `NHWC`, `NCDHW`, `NDHWC` data_layout are not supported. |
+| bilinear_interp_v2 | `NCW`, `NWC`, `NHWC`, `NCDHW`, `NDHWC` data_layout are not supported. |
 | bmm |  |
 | cast |  |
 | clip |  |
 | concat |  |
-| conv2d | 'NHWC' data_layout is not supported |
-| depthwise_conv2d | 'NHWC' data_layout is not supported |
+| conv2d | `NHWC` data_layout is not supported. |
 | deformable_conv |  |
+| depthwise_conv2d | `NHWC` data_layout is not supported. |
 | elementwise_add |  |
 | elementwise_div |  |
 | elementwise_max |  |
 | elementwise_min |  |
 | elementwise_mul |  |
+| elementwise_not_equal |  |
 | elementwise_pow |  |
 | elementwise_sub |  |
 | equal |  |
-| expand_v2 |  |
 | exp |  |
+| expand |  |
+| expand_v2 |  |
 | fill_any_like |  |
-| fill_constant_batch_size_like |  |
 | fill_constant |  |
+| fill_constant_batch_size_like |  |
+| fill_zeros_like |  |
 | flatten_contiguous_range |  |
+| floor |  |
+| gather |  |
+| gather_tree |  |
 | gelu |  |
+| generate_proposals_v2 |  |
 | greater_equal |  |
+| greater_than |  |
 | hard_sigmoid |  |
 | hard_swish |  |
 | layer_norm |  |
 | leaky_relu |  |
+| less_than |  |
 | log |  |
+| logical_and |  |
 | logical_not |  |
+| logical_or |  |
+| logical_xor |  |
 | lookup_table_v2 |  |
 | matmul |  |
 | matmul_v2 |  |
-| matrix_nms | Only supports IE CPU plugin with 'number of selected boxes' static shape(e.g.: min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)) |
+| matrix_nms | Only supports IE CPU plugin with *"number of selected boxes"* static shape(e.g.: `min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)`). |
 | max_pool2d_with_index |  |
+| meshgrid |  |
 | mul |  |
-| multiclass_nms3 | Only supports IE CPU plugin with 'number of selected boxes' static shape(e.g.: min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)) |
-| nearest_interp | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
-| nearest_interp_v2 | 'NCW' 'NWC' 'NHWC' 'NCDHW' 'NDHWC' data_layout are not supported |
-| pad3d | 'Circular' mode is not supported |
+| multiclass_nms3 | Only supports IE CPU plugin with *"number of selected boxes"* static shape(e.g.: `min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)`). |
+| nearest_interp | `NCW`, `NWC`, `NHWC`, `NCDHW`, `NDHWC` data_layout are not supported. |
+| nearest_interp_v2 | `NCW`, `NWC`, `NHWC`, `NCDHW`, `NDHWC` data_layout are not supported. |
+| pad3d | `Circular` mode is not supported. |
+| pool2d | `NHWC` data_layout is not supported. |
 | pow |  |
-| pool2d | 'NHWC' data_layout is not supported |
 | prior_box |  |
 | range |  |
+| reduce_max |  |
+| reduce_mean |  |
+| reduce_min |  |
+| reduce_prod |  |
+| reduce_sum |  |
 | relu |  |
 | relu6 |  |
 | reshape2 |  |
-| rnn | 'SimpleRNN' and 'GRU' modes are not supported |
+| rnn | `SimpleRNN` and `GRU` modes are not supported. |
+| roi_align |  |
 | scale |  |
 | shape |  |
+| sigmoid |  |
 | slice |  |
 | softmax |  |
-| sigmoid |  |
+| softplus |  |
 | split |  |
+| sqrt |  |
 | squeeze2 |  |
 | stack |  |
+| strided_slice |  |
+| swish |  |
 | tanh |  |
+| top_k |  |
+| top_k_v2 |  |
 | transpose2 |  |
 | unsqueeze2 |  |
+| where |  |
 | yolo_box |  |

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md

@@ -1,16 +1,15 @@
-# Converting a Caffe* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe}
+# Converting a Caffe Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe}
 
-## Convert a Caffe* Model <a name="Convert_From_Caffe"></a>
-To convert a Caffe\* model, run Model Optimizer with the path to the input model `.caffemodel` file:
+<a name="Convert_From_Caffe"></a>To convert a Caffe model, run Model Optimizer with the path to the input model `.caffemodel` file:
 
 ```sh
  mo --input_model <INPUT_MODEL>.caffemodel
 ```
 
-The following list provides the Caffe\*-specific parameters.
+The following list provides the Caffe-specific parameters.
 
 ```
-Caffe*-specific parameters:
+Caffe-specific parameters:
   --input_proto INPUT_PROTO, -d INPUT_PROTO
                         Deploy-ready prototxt file that contains a topology
                         structure and layer attributes
@@ -45,14 +44,16 @@ Caffe*-specific parameters:
                         attributes without flattening nested parameters.
 ```
 
-### Command-Line Interface (CLI) Examples Using Caffe\*-Specific Parameters
+### CLI Examples Using Caffe-Specific Parameters
 
-* Launching the Model Optimizer for the [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `prototxt` file. This is needed when the name of the Caffe\* model and the `.prototxt` file are different or are placed in different directories. Otherwise, it is enough to provide only the path to the input `model.caffemodel` file.
+* Launching Model Optimizer for [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `prototxt` file.
+   This is needed when the name of the Caffe model and the `.prototxt` file are different or are placed in different directories. Otherwise, it is enough to provide only the path to the input `model.caffemodel` file.
    ```sh
    mo --input_model bvlc_alexnet.caffemodel --input_proto bvlc_alexnet.prototxt
    ```
-* Launching the Model Optimizer for the [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `CustomLayersMapping` file. This is the legacy method of quickly enabling model conversion if your model has custom layers. This requires the Caffe\* system on the computer.
-Optional parameters without default values and not specified by the user in the `.prototxt` file are removed from the Intermediate Representation, and nested parameters are flattened:
+* Launching Model Optimizer for [bvlc_alexnet.caffemodel](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet) with a specified `CustomLayersMapping` file.
+   This is the legacy method of quickly enabling model conversion if your model has custom layers. This requires the Caffe system on the computer.
+The optional parameters without default values and not specified by the user in the `.prototxt` file are removed from the Intermediate Representation, and nested parameters are flattened:
    ```sh
    mo --input_model bvlc_alexnet.caffemodel -k CustomLayersMapping.xml --disable_omitting_optional --enable_flattening_nested_params
    ```
@@ -82,22 +83,22 @@ Optional parameters without default values and not specified by the user in the
    ```
 ## Custom Layer Definition
 
-Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.
+Internally, when you run Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list. If your topology contains such kind of layers, Model Optimizer classifies them as custom.
 
-## Supported Caffe\* Layers
-Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+## Supported Caffe Layers
+For the list of supported standard layers, refer to the [Supported Framework Layers](../Supported_Frameworks_Layers.md) page.
 
 ## Frequently Asked Questions (FAQ)
 
-The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md). The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
+Model Optimizer provides explanatory messages when it is unable to complete conversions due to typographical errors, incorrectly used options, or other issues. A message describes the potential cause of the problem and gives a link to [Model Optimizer FAQ](../Model_Optimizer_FAQ.md) which provides instructions on how to resolve most issues. The FAQ also includes links to relevant sections to help you understand what went wrong.
 
 ## Summary
 
 In this document, you learned:
 
-* Basic information about how the Model Optimizer works with Caffe\* models
-* Which Caffe\* models are supported
-* How to convert a trained Caffe\* model using the Model Optimizer with both framework-agnostic and Caffe-specific command-line options
+* Basic information about how the Model Optimizer works with Caffe models.
+* Which Caffe models are supported.
+* How to convert a trained Caffe model by using Model Optimizer with both framework-agnostic and Caffe-specific command-line options.
 
 ## See Also
 [Model Conversion Tutorials](Convert_Model_Tutorials.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md

@@ -1,17 +1,16 @@
-# Converting a Kaldi* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi}
+# Converting a Kaldi Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi}
 
-> **NOTE**: The Model Optimizer supports the [nnet1](http://kaldi-asr.org/doc/dnn1.html) and [nnet2](http://kaldi-asr.org/doc/dnn2.html) formats of Kaldi models. Support of the [nnet3](http://kaldi-asr.org/doc/dnn3.html) format is limited.
-
-## Convert a Kaldi* Model <a name="Convert_From_Kaldi"></a>
-To convert a Kaldi\* model, run Model Optimizer with the path to the input model `.nnet` or `.mdl` file:
+> **NOTE**: Model Optimizer supports the [nnet1](http://kaldi-asr.org/doc/dnn1.html) and [nnet2](http://kaldi-asr.org/doc/dnn2.html) formats of Kaldi models. The support of the [nnet3](http://kaldi-asr.org/doc/dnn3.html) format is limited.
+ 
+<a name="Convert_From_Kaldi"></a>To convert a Kaldi model, run Model Optimizer with the path to the input model `.nnet` or `.mdl` file:
 
 ```sh
  mo --input_model <INPUT_MODEL>.nnet
 ```
 
-### Using Kaldi\*-Specific Conversion Parameters <a name="kaldi_specific_conversion_params"></a>
+## Using Kaldi-Specific Conversion Parameters <a name="kaldi_specific_conversion_params"></a>
 
-The following list provides the Kaldi\*-specific parameters.
+The following list provides the Kaldi-specific parameters.
 
 ```sh
 Kaldi-specific parameters:
@@ -21,14 +20,14 @@ Kaldi-specific parameters:
   --remove_memory       Remove the Memory layer and add new inputs and outputs instead
 ```
 
-### Examples of CLI Commands
+## Examples of CLI Commands
 
-* To launch the Model Optimizer for the wsj_dnn5b_smbr model with the specified `.nnet` file:
+* To launch Model Optimizer for the `wsj_dnn5b_smbr` model with the specified `.nnet` file:
    ```sh
    mo --input_model wsj_dnn5b_smbr.nnet
    ```
 
-* To launch the Model Optimizer for the wsj_dnn5b_smbr model with existing file that contains counts for the last layer with biases:
+* To launch Model Optimizer for the `wsj_dnn5b_smbr` model with the existing file that contains counts for the last layer with biases:
    ```sh
    mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts
    ```
@@ -44,7 +43,7 @@ Kaldi-specific parameters:
 	\f$|C|\f$ - number of elements in the counts array;
   * The normalized counts are subtracted from biases of the last or next to last layer (if last layer is SoftMax).
 
-  > **NOTE**: Model Optimizer will show warning if model contains counts values inside model and `--counts` option is not used.
+     > **NOTE**: Model Optimizer will show a warning if a model contains values of counts and the `--counts` option is not used.
 
 * If you want to remove the last SoftMax layer in the topology, launch the Model Optimizer with the
 `--remove_output_softmax` flag:
@@ -52,28 +51,30 @@ Kaldi-specific parameters:
    mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax
    ```
 
-The Model Optimizer finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
+   The Model Optimizer finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
 
-  > **NOTE**: Model Optimizer can remove SoftMax layer only if the topology has one output.
+   > **NOTE**: Model Optimizer can remove SoftMax layer only if the topology has one output.
 
-  > **NOTE**: For sample inference of Kaldi models, you can use the OpenVINO Speech Recognition sample application. The sample supports models with one output. If your model has several outputs, specify the desired one with the `--output` option.
+* You can use the *OpenVINO Speech Recognition* sample application for the sample inference of Kaldi models. This sample supports models with only one output. If your model has several outputs, specify the desired one with the `--output` option.
 
- If you want to convert a model for inference on Intel® Movidius™ Myriad™, use the `--remove_memory` option.
-It removes Memory layers from the IR. Instead of it, additional inputs and outputs appear in the IR.
-The Model Optimizer outputs the mapping between inputs and outputs. For example:
+## Converting a Model for Intel® Movidius™ Myriad™ VPU
+
+If you want to convert a model for inference on Intel® Movidius™ Myriad™ VPU, use the `--remove_memory` option.
+It removes the Memory layers from the OpenVINO IR files. Additional inputs and outputs will appear in the IR files instead.
+Model Optimizer will output the mapping between inputs and outputs. For example:
 ```sh
 [ WARNING ]  Add input/output mapped Parameter_0_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out -> Result_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out
 [ WARNING ]  Add input/output mapped Parameter_1_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out -> Result_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out
 [ WARNING ]  Add input/output mapped Parameter_0_for_iteration_Offset_fastlstm3.c_trunc__3390 -> Result_for_iteration_Offset_fastlstm3.c_trunc__3390
 ```
- Based on this mapping, link inputs and outputs in your application manually as follows:
+Based on this mapping, link inputs and outputs in your application manually as follows:
 
 1. Initialize inputs from the mapping as zeros in the first frame of an utterance.
 2. Copy output blobs from the mapping to the corresponding inputs. For example, data from `Result_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out`
 must be copied to `Parameter_0_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r_trunc__2_out`.
 
-## Supported Kaldi\* Layers
-Refer to [Supported Framework Layers ](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+## Supported Kaldi Layers
+For the list of supported standard layers, refer to the [Supported Framework Layers ](../Supported_Frameworks_Layers.md) page.
 
 ## See Also
 [Model Conversion Tutorials](Convert_Model_Tutorials.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md

@@ -1,14 +1,13 @@
-# Converting an MXNet* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet}
+# Converting an MXNet Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet}
 
-## Convert an MXNet* Model <a name="ConvertMxNet"></a>
-To convert an MXNet\* model, run Model Optimizer with a path to the input model `.params` file:
+<a name="ConvertMxNet"></a>To convert an MXNet model, run Model Optimizer with the path to the *`.params`* file of the input model:
 
 ```sh
  mo --input_model model-file-0000.params
 ```
 
-### Using MXNet\*-Specific Conversion Parameters <a name="mxnet_specific_conversion_params"></a>
-The following list provides the MXNet\*-specific parameters.
+## Using MXNet-Specific Conversion Parameters <a name="mxnet_specific_conversion_params"></a>
+The following list provides the MXNet-specific parameters.
 
 ```
 MXNet-specific parameters:
@@ -23,36 +22,36 @@ MXNet-specific parameters:
   --save_params_from_nd
             Enable saving built parameters file from .nd files
   --legacy_mxnet_model
-            Enable MXNet loader to make a model compatible with the latest MXNet version.
-            Use only if your model was trained with MXNet version lower than 1.0.0
+            Enable Apache MXNet loader to make a model compatible with the latest Apache MXNet version.
+            Use only if your model was trained with Apache MXNet version lower than 1.0.0
   --enable_ssd_gluoncv
             Enable transformation for converting the gluoncv ssd topologies.
             Use only if your topology is one of ssd gluoncv topologies
 ```
 
-> **NOTE**: By default, the Model Optimizer does not use the MXNet loader, as it transforms the topology to another format, which is compatible with the latest
-> version of MXNet, but it is required for models trained with lower version of MXNet. If your model was trained with MXNet version lower than 1.0.0, specify the
-> `--legacy_mxnet_model` key to enable the MXNet loader. However, the loader does not support models with custom layers. In this case, you must manually
-> recompile MXNet with custom layers and install it to your environment.
+> **NOTE**: By default, Model Optimizer does not use the Apache MXNet loader. It transforms the topology to another format which is compatible with the latest
+> version of Apache MXNet. However, the Apache MXNet loader is required for models trained with lower version of Apache MXNet. If your model was trained with an Apache MXNet version lower than 1.0.0, specify the
+> `--legacy_mxnet_model` key to enable the Apache MXNet loader. Note that the loader does not support models with custom layers. In this case, you must manually
+> recompile Apache MXNet with custom layers and install it in your environment.
 
 ## Custom Layer Definition
 
-Internally, when you run the Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in this list of known layers, the Model Optimizer classifies them as custom.
+Internally, when you run Model Optimizer, it loads the model, goes through the topology, and tries to find each layer type in a list of known layers. Custom layers are layers that are not included in the list. If your topology contains such kind of layers, Model Optimizer classifies them as custom.
 
-## Supported MXNet\* Layers
-Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+## Supported MXNet Layers
+For the list of supported standard layers, refer to the [Supported Framework Layers](../Supported_Frameworks_Layers.md) page.
 
 ## Frequently Asked Questions (FAQ)
 
-The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md). The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
+Model Optimizer provides explanatory messages when it is unable to complete conversions due to typographical errors, incorrectly used options, or other issues. A message describes the potential cause of the problem and gives a link to [Model Optimizer FAQ](../Model_Optimizer_FAQ.md) which provides instructions on how to resolve most issues. The FAQ also includes links to relevant sections to help you understand what went wrong.
 
 ## Summary
 
 In this document, you learned:
 
-* Basic information about how the Model Optimizer works with MXNet\* models
-* Which MXNet\* models are supported
-* How to convert a trained MXNet\* model using the Model Optimizer with both framework-agnostic and MXNet-specific command-line options
+* Basic information about how Model Optimizer works with MXNet models.
+* Which MXNet models are supported.
+* How to convert a trained MXNet model by using the Model Optimizer with both framework-agnostic and MXNet-specific command-line options.
 
 ## See Also
 [Model Conversion Tutorials](Convert_Model_Tutorials.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md

@@ -1,28 +1,27 @@
 # Converting an ONNX Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX}
 
 ## Introduction to ONNX
-[ONNX*](https://github.com/onnx/onnx) is a representation format for deep learning models. ONNX allows AI developers easily transfer models between different frameworks that helps to choose the best combination for them. Today, PyTorch\*, Caffe2\*, Apache MXNet\*, Microsoft Cognitive Toolkit\* and other tools are developing ONNX support.
+[ONNX](https://github.com/onnx/onnx) is a representation format for deep learning models that allows AI developers to easily transfer models between different frameworks. It is hugely popular among deep learning tools, like PyTorch, Caffe2, Apache MXNet, Microsoft Cognitive Toolkit, and many others.
 
-This page gives instructions on how to convert a model from ONNX format to OpenVINO IR format using Model Optimizer. To use Model Optimizer, install OpenVINO Development Tools by following the [installation instructions here](https://docs.openvino.ai/latest/openvino_docs_install_guides_install_dev_tools.html).
+## Converting an ONNX Model <a name="Convert_From_ONNX"></a>
 
-ONNX models are directly compatible with OpenVINO Runtime and can be loaded in their native `.onnx` format using `net = ie.read_model("model.onnx")`. The benefit of converting ONNX models to the OpenVINO IR format is that it allows them to be easily optimized for target hardware with advanced OpenVINO tools such as [NNCF](../../../optimization_guide/nncf_introduction.md).
+This page provides instructions on how to convert a model from the ONNX format to the OpenVINO IR format using Model Optimizer. To use Model Optimizer, install OpenVINO Development Tools by following the [installation instructions](https://docs.openvino.ai/latest/openvino_docs_install_guides_install_dev_tools.html).
 
-## Convert an ONNX* Model <a name="Convert_From_ONNX"></a>
 The Model Optimizer process assumes you have an ONNX model that was directly downloaded from a public repository or converted from any framework that supports exporting to the ONNX format.
 
-To convert an ONNX\* model, run Model Optimizer with the path to the input model `.onnx` file:
+To convert an ONNX model, run Model Optimizer with the path to the input model `.onnx` file:
 
 ```sh
  mo --input_model <INPUT_MODEL>.onnx
 ```
 
-There are no ONNX\* specific parameters, so only framework-agnostic parameters are available to convert your model. For details, see the General Conversion Parameters section on the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) page.
+There are no ONNX specific parameters, so only framework-agnostic parameters are available to convert your model. For details, see the *General Conversion Parameters* section in the [Converting a Model to Intermediate Representation (IR)](Converting_Model.md) guide.
 
-## Supported ONNX\* Layers
-Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+## Supported ONNX Layers
+For the list of supported standard layers, refer to the [Supported Framework Layers](../Supported_Frameworks_Layers.md) page.
 
-## See Also
-This page provided general instructions for converting ONNX models. See the [Model Conversion Tutorials](Convert_Model_Tutorials.md) page for a set of tutorials that give step-by-step instructions for converting specific ONNX models. Here are some example tutorials:
+## Additional Resources
+See the [Model Conversion Tutorials](Convert_Model_Tutorials.md) page for a set of tutorials providing step-by-step instructions for converting specific ONNX models. Here are some examples:
 * [Convert ONNX* Faster R-CNN Model](onnx_specific/Convert_Faster_RCNN.md)
 * [Convert ONNX* GPT-2 Model](onnx_specific/Convert_GPT2.md)
 * [Convert ONNX* Mask R-CNN Model](onnx_specific/Convert_Mask_RCNN.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md

@@ -1,4 +1,4 @@
-# Converting a PaddlePaddle* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle}
+# Converting a PaddlePaddle Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle}
 
 To convert a PaddlePaddle model, use the `mo` script and specify the path to the input `.pdmodel` model file:
 
@@ -12,7 +12,7 @@ To convert a PaddlePaddle model, use the `mo` script and specify the path to the
 ```
 
 ## Supported PaddlePaddle Layers
-Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+For the list of supported standard layers, refer to the [Supported Framework Layers](../Supported_Frameworks_Layers.md) page.
 
 ## Officially Supported PaddlePaddle Models
 The following PaddlePaddle models have been officially validated and confirmed to work (as of OpenVINO 2022.1):

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md

@@ -1,18 +1,17 @@
-# Converting a PyTorch* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
+# Converting a PyTorch Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
 
-## Typical Steps to Convert PyTorch Model <a name="typical-pytorch"></a>
-PyTorch* framework is supported through export to ONNX\* format. A summary of the steps for optimizing and deploying a model that was trained with the PyTorch\* framework:
+The PyTorch framework is supported through export to the ONNX format. In order to optimize and deploy a model that was trained with it:
 
-1. [Export PyTorch model to ONNX\*](#export-to-onnx).
-2. [Convert an ONNX\* model](Convert_Model_From_ONNX.md) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
+1. [Export a PyTorch model to ONNX](#export-to-onnx).
+2. [Convert the ONNX model](Convert_Model_From_ONNX.md) to produce an optimized [Intermediate Representation](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
 
-## Export PyTorch\* Model to ONNX\* Format <a name="export-to-onnx"></a>
-PyTorch models are defined in a Python\* code, to export such models use `torch.onnx.export()` method. Usually code to
-evaluate or test the model is provided with the model code and can be used to initialize and export model.
-Only the basics will be covered here, the step to export to ONNX\* is crucial but it is covered by PyTorch\* framework.
-For more information, please refer to [Exporting PyTorch models to ONNX format](https://pytorch.org/docs/stable/onnx.html).
+## Exporting a PyTorch Model to ONNX Format <a name="export-to-onnx"></a>
+PyTorch models are defined in Python. To export them, use the `torch.onnx.export()` method. The code to
+evaluate or test the model is usually provided with its code and can be used for its initialization and export.
+The export to ONNX is crucial for this process, but it is covered by PyTorch framework, therefore, It will not be covered here in detail. 
+For more information, refer to the [Exporting PyTorch models to ONNX format](https://pytorch.org/docs/stable/onnx.html) guide.
 
-To export a PyTorch\* model you need to obtain the model as an instance of `torch.nn.Module` class and call the `export` function.
+To export a PyTorch model, you need to obtain the model as an instance of `torch.nn.Module` class and call the `export` function.
 
 ```python
 import torch
@@ -29,9 +28,9 @@ torch.onnx.export(model, (dummy_input, ), 'model.onnx')
 
 ## Known Issues
 
-* Not all PyTorch\* operations can be exported to ONNX\* opset 9 which is used by default, as of version 1.8.1.
+* As of version 1.8.1, not all PyTorch operations can be exported to ONNX opset 9 which is used by default.
 It is recommended to export models to opset 11 or higher when export to default opset 9 is not working. In that case, use `opset_version`
-option of the `torch.onnx.export`. For more information about ONNX* opset, refer to the [Operator Schemas](https://github.com/onnx/onnx/blob/master/docs/Operators.md).
+option of the `torch.onnx.export`. For more information about ONNX opset, refer to the [Operator Schemas](https://github.com/onnx/onnx/blob/master/docs/Operators.md) page.
 
 ## See Also
 [Model Conversion Tutorials](Convert_Model_Tutorials.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -1,33 +1,34 @@
-# Converting a TensorFlow* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
-This page gives instructions on how to convert a model from TensorFlow format to OpenVINO IR format using Model Optimizer. The instructions are different depending on if your model was created with TensorFlow v1.X or TensorFlow v2.X.
+# Converting a TensorFlow Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
 
-To use Model Optimizer, install OpenVINO Development Tools by following the [installation instructions here](https://docs.openvino.ai/latest/openvino_docs_install_guides_install_dev_tools.html).
+This page provides general instructions on how to convert a model from a TensorFlow format to the OpenVINO IR format using Model Optimizer. The instructions are different depending on whether your model was created with TensorFlow v1.X or TensorFlow v2.X.
 
-## Convert TensorFlow 1 Models <a name="Convert_From_TF2X"></a>
+To use Model Optimizer, install OpenVINO Development Tools by following the [installation instructions](../../../install_guides/installing-model-dev-tools.md).
 
-### Convert Frozen Model Format <a name="Convert_From_TF"></a>
-To convert a TensorFlow model, use the `mo` script to simply convert a model with the path to the input model `.pb` file:
+## Converting TensorFlow 1 Models <a name="Convert_From_TF2X"></a>
+
+### Converting Frozen Model Format <a name="Convert_From_TF"></a>
+To convert a TensorFlow model, use the *`mo`* script to simply convert a model with a path to the input model *`.pb`* file:
 
 ```sh
  mo --input_model <INPUT_MODEL>.pb
 ```
 
-### Convert Non-Frozen Model Formats <a name="loading-nonfrozen-models"></a>
+### Converting Non-Frozen Model Formats <a name="loading-nonfrozen-models"></a>
 There are three ways to store non-frozen TensorFlow models and convert them by Model Optimizer:
 
 1. **Checkpoint**. In this case, a model consists of two files: `inference_graph.pb` (or `inference_graph.pbtxt`) and `checkpoint_file.ckpt`.
-If you do not have an inference graph file, refer to [Freezing Custom Models in Python](#freeze-the-tensorflow-model).
-To convert the model with the inference graph in `.pb` format, run the `mo` script with the path to the checkpoint file to convert a model:
+If you do not have an inference graph file, refer to the [Freezing Custom Models in Python](#freeze-the-tensorflow-model) section.
+To convert the model with the inference graph in `.pb` format, run the `mo` script with a path to the checkpoint file:
 ```sh
  mo --input_model <INFERENCE_GRAPH>.pb --input_checkpoint <INPUT_CHECKPOINT>
 ```
-To convert the model with the inference graph in `.pbtxt` format, run the `mo` script with the path to the checkpoint file to convert a model:
+To convert the model with the inference graph in `.pbtxt` format, run the `mo` script with a path to the checkpoint file:
 ```sh
  mo --input_model <INFERENCE_GRAPH>.pbtxt --input_checkpoint <INPUT_CHECKPOINT> --input_model_is_text
 ```
 
 2. **MetaGraph**. In this case, a model consists of three or four files stored in the same directory: `model_name.meta`, `model_name.index`,
-`model_name.data-00000-of-00001` (digit part may vary), and `checkpoint` (optional).
+`model_name.data-00000-of-00001` (the numbers may vary), and `checkpoint` (optional).
 To convert such TensorFlow model, run the `mo` script with a path to the MetaGraph `.meta` file:
 ```sh
  mo --input_meta_graph <INPUT_META_GRAPH>.meta
@@ -45,11 +46,10 @@ If a model contains operations currently unsupported by OpenVINO, prune these op
 To determine custom input nodes, display a graph of the model in TensorBoard. To generate TensorBoard logs of the graph, use the `--tensorboard_logs` option.
 TensorFlow 2.x SavedModel format has a specific graph due to eager execution. In case of pruning, find custom input nodes in the `StatefulPartitionedCall/*` subgraph of TensorFlow 2.x SavedModel format.
 
-### Freezing Custom Models in Python\* <a name="freeze-the-tensorflow-model"></a>
-When a network is defined in Python\* code, you have to create an inference graph file. Usually graphs are built in a form
-that allows model training. That means that all trainable parameters are represented as variables in the graph.
-To be able to use such graph with Model Optimizer such graph should be frozen.
-The graph is frozen and dumped to a file with the following code:
+### Freezing Custom Models in Python <a name="freeze-the-tensorflow-model"></a>
+When a network is defined in Python code, you have to create an inference graph file. Graphs are usually built in a form
+that allows model training. That means all trainable parameters are represented as variables in the graph.
+To be able to use such graph with Model Optimizer, it should be frozen and dumped to a file with the following code:
 
 ```python
 import tensorflow as tf
@@ -60,18 +60,18 @@ graph_io.write_graph(frozen, './', 'inference_graph.pb', as_text=False)
 
 Where:
 
-* `sess` is the instance of the TensorFlow\* Session object where the network topology is defined.
+* `sess` is the instance of the TensorFlow Session object where the network topology is defined.
 * `["name_of_the_output_node"]` is the list of output node names in the graph; `frozen` graph will
     include only those nodes from the original `sess.graph_def` that are directly or indirectly used
-    to compute given output nodes. `'name_of_the_output_node'` here is an example of possible output
+    to compute given output nodes. The `'name_of_the_output_node'` is an example of a possible output
     node name. You should derive the names based on your own graph.
 * `./` is the directory where the inference graph file should be generated.
 * `inference_graph.pb` is the name of the generated inference graph file.
 * `as_text` specifies whether the generated file should be in human readable text format or binary.
 
-## Convert TensorFlow 2 Models <a name="Convert_From_TF2X"></a>
-To convert TensorFlow* 2 models, ensure that `openvino-dev[tensorflow2]` is installed via `pip`.
-TensorFlow* 2.X officially supports two model formats: SavedModel and Keras H5 (or HDF5).
+## Converting TensorFlow 2 Models <a name="Convert_From_TF2X"></a>
+To convert TensorFlow 2 models, ensure that `openvino-dev[tensorflow2]` is installed via `pip`.
+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.
 
 ### SavedModel Format
@@ -82,7 +82,7 @@ To convert such a model, run the `mo` script with a path to the SavedModel direc
  mo --saved_model_dir <SAVED_MODEL_DIRECTORY>
 ```
 
-TensorFlow* 2 SavedModel format strictly requires the 2.x version of TensorFlow installed in the
+TensorFlow 2 SavedModel format strictly requires the 2.x version of TensorFlow installed in the
 environment for conversion to the Intermediate Representation (IR).
 
 If a model contains operations currently unsupported by OpenVINO™,
@@ -92,11 +92,11 @@ options. To determine custom input nodes, visualize a model graph in the TensorB
 To generate TensorBoard logs of the graph, use the Model Optimizer `--tensorboard_logs` command-line
 option.
 
-TensorFlow* 2 SavedModel format has a specific graph structure due to eager execution. In case of
+TensorFlow 2 SavedModel format has a specific graph structure due to eager execution. In case of
 pruning, find custom input nodes in the `StatefulPartitionedCall/*` subgraph.
 
 ### Keras H5
-If you have a model in the HDF5 format, load the model using TensorFlow* 2 and serialize it in the
+If you have a model in the HDF5 format, load the model using TensorFlow 2 and serialize it in the
 SavedModel format. Here is an example of how to do it:
 
 ```python
@@ -117,9 +117,9 @@ 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.
+> **NOTE**: Do not use other hacks to resave TensorFlow 2 models into TensorFlow 1 formats.
 
-## Command-Line Interface (CLI) Examples Using TensorFlow\*-Specific Parameters
+## Command-Line Interface (CLI) Examples Using TensorFlow-Specific Parameters
 * Launching the Model Optimizer for Inception V1 frozen model when model file is a plain text protobuf:
 
 ```sh
@@ -132,29 +132,29 @@ Then follow the above instructions for the SavedModel format.
  mo --input_model inception_v1.pb -b 1 --tensorboard_logdir /tmp/log_dir
 ```
 
-* Launching the Model Optimizer for BERT model in the SavedModel format, with three inputs. Explicitly specify input shapes
+* Launching the Model Optimizer for BERT model in the SavedModel format, with three inputs. Specify explicitly the input shapes
 where the batch size and the sequence length equal 2 and 30 respectively.
 
 ```sh
 mo --saved_model_dir BERT --input mask,word_ids,type_ids --input_shape [2,30],[2,30],[2,30]
 ```
 
-## Supported TensorFlow\* and TensorFlow 2 Keras\* Layers
-Refer to [Supported Framework Layers ](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
+## Supported TensorFlow and TensorFlow 2 Keras Layers
+For the list of supported standard layers, refer to the [Supported Framework Layers ](../Supported_Frameworks_Layers.md) page.
 
 ## Frequently Asked Questions (FAQ)
-The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md). The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
+The Model Optimizer provides explanatory messages if it is unable to run to completion due to typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md). The FAQ provides instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
 
 ## Summary
 In this document, you learned:
 
-* Basic information about how the Model Optimizer works with TensorFlow models
-* Which TensorFlow models formats are supported
-* How to freeze a TensorFlow 1 model
-* How to convert a trained TensorFlow model using the Model Optimizer with both framework-agnostic and TensorFlow-specific command-line options
+* Basic information about how the Model Optimizer works with TensorFlow models.
+* Which TensorFlow models are supported.
+* How to freeze a TensorFlow model.
+* How to convert a trained TensorFlow model using the Model Optimizer with both framework-agnostic and TensorFlow-specific command-line options.
 
-## See Also
-This page provided general instructions for converting TensorFlow models. For step-by-step instructions showing how to convert specific TensorFlow models, see the [Model Conversion Tutorials](Convert_Model_Tutorials.md) page. Here are some example tutorials:
+## Additional Resources
+For step-by-step instructions on how to convert specific TensorFlow models, see the [Model Conversion Tutorials](Convert_Model_Tutorials.md) page. Here are some examples:
 * [Convert TensorFlow EfficientDet Models](tf_specific/Convert_EfficientDet_Models.md)
 * [Convert TensorFlow FaceNet Models](tf_specific/Convert_FaceNet_From_Tensorflow.md)
 * [Convert TensorFlow Object Detection API Models](tf_specific/Convert_Object_Detection_API_Models.md)

docs/MO_DG/prepare_model/convert_model/Convert_Model_Tutorials.md

@@ -37,8 +37,7 @@
 
 @endsphinxdirective
 
-This section provides you with a set of tutorials that demonstrate conversion steps for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models.
-It contains conversion recipes for concrete models, that unnecessarily cover your case.
-Try to convert the model out-of-the-box, meaning only the `--input_model` parameter is specified in the command line, before studying the tutorials.
+This section provides a set of tutorials that demonstrate conversion methods for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models, that unnecessarily cover your case.
+Before studying the tutorials, try to convert the model out-of-the-box by specifying only the `--input_model` parameter in the command line.
 
-You can also find a collection of [Python tutorials](../../../tutorials.md) written for running on Jupyter* notebooks that provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for optimized deep learning inference.
+You will find a collection of [Python tutorials](../../../tutorials.md) written for running on Jupyter notebooks that provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for optimized deep learning inference.

docs/MO_DG/prepare_model/convert_model/Converting_Model.md

@@ -1,51 +1,50 @@
 # Setting Input Shapes {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model}
 
-Model Optimizer provides the option of making models more efficient by providing additional shape definition.
-It is achieved with two parameters: `--input_shape` and `--static_shape`, used under certain conditions.
+With Model Optimizer you can increase your model's efficiency by providing an additional shape definition, with these two parameters: `--input_shape` and `--static_shape`.
 
 @anchor when_to_specify_input_shapes
-## When to Specify --input_shape Command-line Parameter
+## Specifying --input_shape Command-line Parameter
 Model Optimizer supports conversion of models with dynamic input shapes that contain undefined dimensions.
-However, if the shape of data is not going to change from one inference to another,
+However, if the shape of data is not going to change from one inference request to another,
 it is recommended to set up static shapes (when all dimensions are fully defined) for the inputs.
-It can be beneficial from a performance perspective and memory consumption.
+Doing it at this stage, instead of during inference in runtime, can be beneficial in terms of performance and memory consumption.
 To set up static shapes, Model Optimizer provides the `--input_shape` parameter.
-The same functionality is also available in runtime via `reshape` method, please refer to [Changing input shapes](../../../OV_Runtime_UG/ShapeInference.md).
-For more information about dynamic shapes in runtime, refer to [Dynamic Shapes](../../../OV_Runtime_UG/ov_dynamic_shapes.md)
+For more information on input shapes under runtime, refer to the [Changing input shapes](../../../OV_Runtime_UG/ShapeInference.md) guide.
+To learn more about dynamic shapes in runtime, refer to the [Dynamic Shapes](../../../OV_Runtime_UG/ov_dynamic_shapes.md) guide.
 
-OpenVINO Runtime API can have limitations to infer models with undefined dimensions on some hardware (see [Features support matrix](../../../OV_Runtime_UG/supported_plugins/Device_Plugins.md) for reference).
+The OpenVINO Runtime API may present certain limitations in inferring models with undefined dimensions on some hardware. See the [Features support matrix](../../../OV_Runtime_UG/supported_plugins/Device_Plugins.md) for reference.
 In this case, the `--input_shape` parameter and the [reshape method](../../../OV_Runtime_UG/ShapeInference.md) can help to resolve undefined dimensions.
 
-Sometimes Model Optimizer is unable to convert models out-of-the-box (only the `--input_model` parameter is specified).
+Sometimes, Model Optimizer is unable to convert models out-of-the-box (only the `--input_model` parameter is specified).
 Such problem can relate to models with inputs of undefined ranks and a case of cutting off parts of a model.
-In this case, user has to specify input shapes explicitly using `--input_shape` parameter.
+In this case, input shapes must be specified explicitly with the `--input_shape` parameter.
 
-For example, run the Model Optimizer for the TensorFlow* MobileNet model with the single input
-and specify input shape `[2,300,300,3]`.
+For example, run Model Optimizer for the TensorFlow MobileNet model with the single input
+and specify the input shape of `[2,300,300,3]`:
 
 ```sh
 mo --input_model MobileNet.pb --input_shape [2,300,300,3]
 ```
 
 If a model has multiple inputs, `--input_shape` must be used in conjunction with `--input` parameter.
-The parameter `--input` contains a list of input names for which shapes in the same order are defined via `--input_shape`.
-For example, launch the Model Optimizer for the ONNX* OCR model with a pair of inputs `data` and `seq_len` 
-and specify shapes `[3,150,200,1]` and `[3]` for them.
+The `--input` parameter contains a list of input names, for which shapes in the same order are defined via `--input_shape`.
+For example, launch Model Optimizer for the ONNX OCR model with a pair of inputs `data` and `seq_len`
+and specify shapes `[3,150,200,1]` and `[3]` for them:
 
 ```sh
 mo --input_model ocr.onnx --input data,seq_len --input_shape [3,150,200,1],[3]
 ```
 
-The alternative way to specify input shapes is to use the `--input` parameter as follows:
+Alternatively, specify input shapes, using the `--input` parameter as follows:
 
 ```sh
 mo --input_model ocr.onnx --input data[3 150 200 1],seq_len[3]
 ```
 
-The parameter `--input_shape` allows overriding original input shapes to the shapes compatible with a given model.
-Dynamic shapes, i.e. with dynamic dimensions, in the original model can be replaced with static shapes for the converted model, and vice versa.
-The dynamic dimension can be marked in Model Optimizer command-line as `-1` or `?`.
-For example, launch the Model Optimizer for the ONNX* OCR model and specify dynamic batch dimension for inputs.
+The `--input_shape` parameter allows overriding original input shapes to ones compatible with a given model.
+Dynamic shapes, i.e. with dynamic dimensions, can be replaced in the original model with static shapes for the converted model, and vice versa.
+The dynamic dimension can be marked in Model Optimizer command-line as `-1`* or *`?`.
+For example, launch Model Optimizer for the ONNX OCR model and specify dynamic batch dimension for inputs:
 
 ```sh
 mo --input_model ocr.onnx --input data,seq_len --input_shape [-1,150,200,1],[-1]
@@ -53,7 +52,7 @@ mo --input_model ocr.onnx --input data,seq_len --input_shape [-1,150,200,1],[-1]
 
 To optimize memory consumption for models with undefined dimensions in run-time, Model Optimizer provides the capability to define boundaries of dimensions.
 The boundaries of undefined dimension can be specified with ellipsis.
-For example, launch the Model Optimizer for the ONNX* OCR model and specify a boundary for the batch dimension.
+For example, launch Model Optimizer for the ONNX OCR model and specify a boundary for the batch dimension:
 
 ```sh
 mo --input_model ocr.onnx --input data,seq_len --input_shape [1..3,150,200,1],[1..3]
@@ -61,20 +60,20 @@ mo --input_model ocr.onnx --input data,seq_len --input_shape [1..3,150,200,1],[1
 
 Practically, some models are not ready for input shapes change.
 In this case, a new input shape cannot be set via Model Optimizer.
-Learn more about shape [inference troubleshooting](@ref troubleshooting_reshape_errors) and [ways to relax shape inference flow](@ref how-to-fix-non-reshape-able-model). 
+For more information about shape follow the [inference troubleshooting](@ref troubleshooting_reshape_errors) and [ways to relax shape inference flow](@ref how-to-fix-non-reshape-able-model) guides. 
 
-## When to Specify --static_shape Command-line Parameter
+## Specifying --static_shape Command-line Parameter
 Model Optimizer provides the `--static_shape` parameter that allows evaluating shapes of all operations in the model for fixed input shapes
-and to fold shape computing sub-graphs into constants. The resulting IR can be more compact in size and the loading time for such IR can be decreased.
+and folding shape computing sub-graphs into constants. The resulting IR may be more compact in size and the loading time for such IR may decrease.
 However, the resulting IR will not be reshape-able with the help of the [reshape method](../../../OV_Runtime_UG/ShapeInference.md) from OpenVINO Runtime API.
-It is worth noting that the `--input_shape` parameter does not affect reshape-ability of the model.
+It is worth noting that the `--input_shape` parameter does not affect reshapeability of the model.
 
-For example, launch the Model Optimizer for the ONNX* OCR model using `--static_shape`.
+For example, launch Model Optimizer for the ONNX OCR model using `--static_shape`:
 
 ```sh
 mo --input_model ocr.onnx --input data[3 150 200 1],seq_len[3] --static_shape
 ```
 
-## See Also
-* [Introduction](../../Deep_Learning_Model_Optimizer_DevGuide.md)
+## Additional Resources
+* [Introduction to converting models with Model Optimizer](../../Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Cutting Off Parts of a Model](Cutting_Model.md)

docs/MO_DG/prepare_model/convert_model/Cutting_Model.md

@@ -1,16 +1,16 @@
 # Cutting Off Parts of a Model  {#openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model}
 
-Sometimes some parts of a model must be removed while the Model Optimizer is converting models to the Intermediate Representation. This chapter describes methods of doing cutting off parts of a model using Model Optimizer command-line options. Model cutting applies mostly to TensorFlow\* models, but is also useful for other frameworks. In this chapter, TensorFlow examples are used for illustration.
+Sometimes, it is necessary to remove parts of a model when converting it to OpenVINO IR. This chapter describes how to do it, using Model Optimizer command-line options. Model cutting applies mostly to TensorFlow models, which is why TensorFlow will be used in this chapter's examples, but it may be also useful for other frameworks.
 
 ## Purpose of Model Cutting
 
 The following examples are the situations when model cutting is useful or even required:
 
-*   model has pre- or post-processing parts that cannot be translated to existing OpenVINO operations.
-*   model has a training part that is convenient to be kept in the model, but not used during inference.
-*   model is too complex (contains lots of unsupported operations that cannot be easily implemented as custom layers), so the complete model cannot be converted in one shot.
-*   problem with model conversion in the Model Optimizer or inference in the OpenVINO Runtime occurred. To localize the issue, limit the scope for conversion by iteratively searching for problematic places in the model.
-*   single custom layer or a combination of custom layers is isolated for debugging purposes.
+*   A model has pre- or post-processing parts that cannot be translated to existing OpenVINO operations.
+*   A model has a training part that is convenient to be kept in the model but not used during inference.
+*   A model is too complex be converted at once, because it contains a lot of unsupported operations that cannot be easily implemented as custom layers.
+*   A problem occurs with model conversion in Model Optimizer or inference in OpenVINO™ Runtime. To identify the issue, limit the conversion scope by iterative search for problematic areas in the model.
+*   A single custom layer or a combination of custom layers is isolated for debugging purposes.
 
 ## Command-Line Options
 
@@ -19,21 +19,21 @@ Model Optimizer provides command line options `--input` and `--output` to specif
 *   `--input` option accepts a comma-separated list of layer names of the input model that should be treated as new entry points to the model.
 *   `--output` option accepts a comma-separated list of layer names of the input model that should be treated as new exit points from the model.
 
-The `--input` option is required for cases unrelated to model cutting. For example, when the model contains several inputs and `--input_shape` or `--mean_values` options are used, you should use the `--input` option to specify the order of input nodes for correct mapping between multiple items provided in `--input_shape` and `--mean_values` and the inputs in the model. Details on these options are out of scope for this document, which focuses on model cutting.
+The `--input` option is required for cases unrelated to model cutting. For example, when the model contains several inputs and `--input_shape` or `--mean_values` options are used, the `--input` option specifies the order of input nodes for correct mapping between multiple items provided in `--input_shape` and `--mean_values` and the inputs in the model. 
 
-Model cutting is illustrated with Inception V1. This model is in `models/research/slim` repository. [This section](Converting_Model.md) describes pre-work to prepare the model for the Model Optimizer to be ready to proceed with this chapter.
+Model cutting is illustrated with the Inception V1 model, found in the `models/research/slim` repository. To proceed with this chapter, make sure you do the necessary steps to [prepare the model for Model Optimizer](Converting_Model.md).
 
 ## Default Behavior without --input and --output
 
-The input model is converted as a whole if neither `--input` nor `--output` command line options are used. All `Placeholder` operations in a TensorFlow\* graph are automatically identified as entry points. The `Input` layer type is generated for each of them. All nodes that have no consumers are automatically identified as exit points.
+The input model is converted as a whole if neither `--input` nor `--output` command line options are used. All `Placeholder` operations in a TensorFlow graph are automatically identified as entry points. The `Input` layer type is generated for each of them. All nodes that have no consumers are automatically identified as exit points.
 
-For Inception_V1, there is one `Placeholder`: input. If the model is viewed in the TensorBoard\*, the input operation is easy to find:
+For Inception_V1, there is one `Placeholder`: input. If the model is viewed in TensorBoard, the input operation is easy to find:
  
 ![Placeholder in Inception V1](../../img/inception_v1_std_input.png)
 
-There is only one output operation, which enclosed in a nested name scope `InceptionV1/Logits/Predictions`, the `Reshape` operation has a full name `InceptionV1/Logits/Predictions/Reshape_1`.
+`Reshape` is the only output operation, which is enclosed in a nested name scope of `InceptionV1/Logits/Predictions`, under the full name of `InceptionV1/Logits/Predictions/Reshape_1`.
 
-In the TensorBoard, it looks the following way together with some predecessors:
+In TensorBoard, along with some of its predecessors, it looks as follows:
 
 ![TensorBoard with predecessors](../../img/inception_v1_std_output.png)
 
@@ -41,7 +41,7 @@ Convert this model and put the results in a writable output directory:
 ```sh
 mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
 ```
-(The other examples on this page assume that you first cd to the `model_optimizer` directory and add the `--output_dir` argument with a directory where you have write permissions.)
+(The other examples on this page assume that you first go to the `model_optimizer` directory and add the `--output_dir` argument with a directory where you have read/write permissions.)
 
 The output `.xml` file with an Intermediate Representation contains the `Input` layer among other layers in the model:
 ```xml
@@ -78,7 +78,7 @@ The last layer in the model is `InceptionV1/Logits/Predictions/Reshape_1`, which
     </output>
 </layer>
 ```
-Due to automatic identification of inputs and outputs, you do not need to provide the `--input` and `--output` options to convert the whole model. The following commands are equivalent for the Inception V1 model:
+Due to automatic identification of inputs and outputs, providing the `--input` and `--output` options to convert the whole model is not required. The following commands are equivalent for the Inception V1 model:
 ```sh
 mo --input_model inception_v1.pb -b 1 --output_dir <OUTPUT_MODEL_DIR>
 
@@ -88,7 +88,7 @@ The Intermediate Representations are identical for both conversions. The same is
 
 ## Model Cutting
 
-Now consider how to cut some parts of the model off. This chapter uses the first convolution block `InceptionV1/InceptionV1/Conv2d_1a_7x7` of the Inception V1 model to illustrate cutting: 
+Now, consider how to cut some parts of the model off. This chapter describes the first convolution block `InceptionV1/InceptionV1/Conv2d_1a_7x7` of the Inception V1 model to illustrate cutting: 
 
 ![Inception V1 first convolution block](../../img/inception_v1_first_block.png)
 
@@ -138,7 +138,7 @@ If you want to cut your model at the end, you have the following options:
 	</edges>
 </net>
 ```
-   As you can see in the TensorBoard picture, the original model has more nodes than Intermediate Representation. Model Optimizer has fused batch normalization `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm` to the convolution `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution`, and it is not present in the final Intermediate Representation. This is not an effect of the `--output` option, it is usual behavior of the Model Optimizer for batch normalizations and convolutions. The effect of the `--output` is that the `ReLU` layer becomes the last one in the converted model.
+   As shown in the TensorBoard picture, the original model has more nodes than its Intermediate Representation. Model Optimizer has fused batch normalization `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm` with convolution `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution`, which is why it is not present in the final model. This is not an effect of the `--output` option, it is the typical behavior of Model Optimizer for batch normalizations and convolutions. The effect of the `--output` is that the `ReLU` layer becomes the last one in the converted model.
 
 2. The following command cuts the edge that comes from 0 output port of the `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` and the rest of the model, making this node the last one in the model:
    ```sh
@@ -182,7 +182,7 @@ If you want to cut your model at the end, you have the following options:
 	</edges>
 </net>
 ```
-   This type of cutting is useful to cut edges in case of multiple output edges.
+   This type of cutting is useful for cutting multiple output edges.
 
 3. The following command cuts the edge that comes to 0 input port of the `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` and the rest of the model including `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu`, deleting this node and making the previous node `InceptionV1/InceptionV1/Conv2d_1a_7x7/Conv2D` the last in the model:
    ```sh
@@ -222,7 +222,7 @@ If you want to cut your model at the end, you have the following options:
 
 If you want to go further and cut the beginning of the model, leaving only the `ReLU` layer, you have the following options:
 
-1.  You can use the following command line, where `--input` and `--output` specify the same node in the graph:
+1.  Use the following command line, where `--input` and `--output` specify the same node in the graph:
    ```sh
    mo --input_model=inception_v1.pb -b 1 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --input InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
    ```
@@ -250,11 +250,11 @@ If you want to go further and cut the beginning of the model, leaving only the `
 	</edges>
 </net>
 ```
-   `Input` layer is automatically created to feed the layer that is converted from the node specified in `--input`, which is `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` in this case. Model Optimizer does not replace the `ReLU` node by the `Input` layer, it produces such Intermediate Representation to make the node be the first executable node in the final Intermediate Representation. So the Model Optimizer creates enough `Inputs` to feed all input ports of the node that is passed in `--input`.<br>
-Even though `--input_shape` is not specified in the command line, the shapes for layers are inferred from the beginning of the original TensorFlow* model to the point at which the new input is defined. It has the same shape [1,64,112,112] as the model converted as a whole or without cutting off the beginning.
+   `Input` layer is automatically created to feed the layer that is converted from the node specified in `--input`, which is `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` in this case. Model Optimizer does not replace the `ReLU` node by the `Input` layer. It produces such Intermediate Representation to make the node the first executable node in the final Intermediate Representation. Therefore, Model Optimizer creates enough `Inputs` to feed all input ports of the node that is passed in `--input`.<br>
+Even though `--input_shape` is not specified in the command line, the shapes for layers are inferred from the beginning of the original TensorFlow model to the point, at which the new input is defined. It has the same shape [1,64,112,112] as the model converted as a whole or without cutting off the beginning.
 
-2. You can cut edge incoming to layer by port number. To specify incoming port use notation `--input=port:input_node`. 
-So, to cut everything before `ReLU` layer, cut edge incoming in port 0 of `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` node:
+2. Cut the edge incoming to layer by port number. To specify the incoming port, use the following notation `--input=port:input_node`. 
+To cut everything before `ReLU` layer, cut the edge incoming to port 0 of `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` node:
    ```sh
    mo --input_model inception_v1.pb -b 1 --input 0:InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
    ```
@@ -282,11 +282,11 @@ So, to cut everything before `ReLU` layer, cut edge incoming in port 0 of `Incep
 	</edges>
 </net>
 ```
-   `Input` layer is automatically created to feed the layer that is converted from the node specified in `--input`, which is `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` in this case. Model Optimizer does not replace the `ReLU` node by the `Input` layer, it produces such Intermediate Representation to make the node be the first executable node in the final Intermediate Representation. So the Model Optimizer creates enough `Inputs` to feed all input ports of the node that is passed in `--input`.<br>
-Even though `--input_shape` is not specified in the command line, the shapes for layers are inferred from the beginning of the original TensorFlow* model to the point at which the new input is defined. It has the same shape [1,64,112,112] as the model converted as a whole or without cutting off the beginning.
+   `Input` layer is automatically created to feed the layer that is converted from the node specified in `--input`, which is `InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu` in this case. Model Optimizer does not replace the `ReLU` node by the `Input` layer, it produces such Intermediate Representation to make the node be the first executable node in the final Intermediate Representation. Therefore, Model Optimizer creates enough `Inputs` to feed all input ports of the node that is passed in `--input`.<br>
+Even though `--input_shape` is not specified in the command line, the shapes for layers are inferred from the beginning of the original TensorFlow model to the point, at which the new input is defined. It has the same shape [1,64,112,112] as the model converted as a whole or without cutting off the beginning.
 
-3. You can cut edge outcoming from layer by port number. To specify outcoming port use notation `--input=input_node:port`.
-So, to cut everything before `ReLU` layer, cut edge from `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1` node to `ReLU`:
+3. Cut edge outcoming from layer by port number. To specify the outcoming port, use the following notation `--input=input_node:port`.
+To cut everything before `ReLU` layer, cut edge from `InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1` node to `ReLU`:
    ```sh
    mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/BatchNorm/batchnorm/add_1:0 --output InceptionV1/InceptionV1/Conv2d_1a_7x7/Relu --output_dir <OUTPUT_MODEL_DIR>
    ```
@@ -354,18 +354,18 @@ gives the following shapes in the `Input` and `ReLU` layers:
     </output>
 </layer>
 ```
-An input shape [1,20,5,10] in the final Intermediate Representation differs from the shape [1,5,10,20] specified in the command line, because the original TensorFlow\* model uses NHWC layout, but the Intermediate Representation uses NCHW layout. So usual NHWC to NCHW layout conversion occurred.
+An input shape [1,20,5,10] in the final Intermediate Representation differs from the shape [1,5,10,20] specified in the command line, because the original TensorFlow model uses NHWC layout, but the Intermediate Representation uses NCHW layout. Thus, usual NHWC to NCHW layout conversion occurred.
 
-When `--input_shape` is specified, shape inference inside the Model Optimizer is not performed for the nodes in the beginning of the model that are not included in the translated region. It differs from the case when `--input_shape` is not specified as noted in the previous section where the shape inference is still performed for such nodes to deduce shape for the layers that should fall into the final Intermediate Representation. So `--input_shape` should be used for a model with a complex graph with loops, which are not supported by the Model Optimizer, to exclude such parts from the Model Optimizer shape inference process completely.
+When `--input_shape` is specified, shape inference inside Model Optimizer is not performed for the nodes in the beginning of the model that are not included in the translated region. It differs from the case when `--input_shape` is not specified as noted in the previous section, where the shape inference is still performed for such nodes to deduce shape for the layers that should fall into the final Intermediate Representation. Therefore, `--input_shape` should be used for a model with a complex graph with loops, which are not supported by Model Optimizer, to exclude such parts from the Model Optimizer shape inference process completely.
 
 ## Inputs with Multiple Input Ports
 
-There are operations that contain more than one input ports. In the example considered here, the convolution `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution` is such operation. When `--input_shape` is not provided, a new `Input` layer is created for each dynamic input port for the node. If a port is evaluated to a constant blob, this constant remains in the model and a corresponding input layer is not created. TensorFlow convolution used in this model contains two ports:
+There are operations that contain more than one input port. In the example considered here, the convolution `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution` is such operation. When `--input_shape` is not provided, a new `Input` layer is created for each dynamic input port for the node. If a port is evaluated to a constant blob, this constant remains in the model and a corresponding input layer is not created. TensorFlow convolution used in this model contains two ports:
 
 *   port 0: input tensor for convolution (dynamic)
 *   port 1: convolution weights (constant)
 
-Following this behavior, the Model Optimizer creates an `Input` layer for port 0 only, leaving port 1 as a constant. So the result of:
+Following this behavior, Model Optimizer creates an `Input` layer for port 0 only, leaving port 1 as a constant. Thus, the result of:
 
 ```sh
 mo --input_model inception_v1.pb -b 1 --input InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --output_dir <OUTPUT_MODEL_DIR>
@@ -377,13 +377,13 @@ Different behavior occurs when `--input_shape` is also used as an attempt to ove
 ```sh
 mo --input_model inception_v1.pb--input=InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape [1,224,224,3]  --output_dir <OUTPUT_MODEL_DIR>
 ```
-An error occurs (for more information, see <a href="MO_FAQ.html#FAQ30">FAQ #30</a>):
+An error occurs (for more information, see the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md#FAQ30)):
 ```sh
 [ ERROR ]  Node InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution 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.
 For more information, see FAQ #30
 ```
-In this case, when `--input_shape` is specified and the node contains multiple input ports, you need to specify an input port index together with an input node name. The input port index is specified in front of the node name with ':' as a separator (`PORT:NODE`). In the considered case, the port index 0 of the node `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution` should be specified as `0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution`.
+When `--input_shape` is specified and the node contains multiple input ports, you need to provide an input port index together with an input node name. The input port index is specified in front of the node name with ':' as a separator (`PORT:NODE`). In this case, the port index 0 of the node `InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution` should be specified as `0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution`.
 
 The correct command line is:
 ```sh

docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md

@@ -2,32 +2,32 @@
 
 ## Introduction
 
-OpenVINO Runtime CPU and GPU devices can infer models in the low precision. 
-For details, refer to [Model Optimization Guide](@ref openvino_docs_model_optimization_guide).
+OpenVINO Runtime CPU and GPU devices can infer models in low precision. 
+For more details, refer to the [Model Optimization Guide](@ref openvino_docs_model_optimization_guide).
 
-Intermediate Representation (IR) should be specifically formed to be suitable for low precision inference. 
-Such an IR is called a Low Precision IR and you can generate it in two ways:
-- [Quantize regular IR with the Post-Training Optimization tool](@ref pot_introduction)
-- Use the Model Optimizer for a model pretrained for Low Precision inference: TensorFlow\* pre-TFLite models (`.pb` model file with `FakeQuantize*` operations) and ONNX\* quantized models.
-Both TensorFlow and ONNX quantized models could be prepared by [Neural Network Compression Framework](https://github.com/openvinotoolkit/nncf/blob/develop/README.md).
+Intermediate Representation should be specifically formed to be suitable for low precision inference. 
+Such a model is called a Low Precision IR and can be generated in two ways:
+- By [quantizing regular IR with the Post-Training Optimization tool](@ref pot_introduction)
+- Using Model Optimizer for a model pre-trained for Low Precision inference: TensorFlow pre-TFLite models (`.pb` model file with `FakeQuantize*` operations) and ONNX quantized models.
+Both TensorFlow and ONNX quantized models can be prepared by [Neural Network Compression Framework](https://github.com/openvinotoolkit/nncf/blob/develop/README.md).
 
 For an operation to be executed in INT8, it must have `FakeQuantize` operations as inputs.
-See the [specification of `FakeQuantize` operation](../../../ops/quantization/FakeQuantize_1.md) for details. 
+For more details, see the [specification of `FakeQuantize` operation](../../../ops/quantization/FakeQuantize_1.md). 
 
 To execute the `Convolution` operation in INT8 on CPU, both data and weight inputs should have `FakeQuantize` as an input operation:
 ![](../../img/expanded_int8_Convolution_weights.png)
 
-Low precision IR is also suitable for FP32 and FP16 inference if a chosen plugin supports all operations of the IR, because the only difference between a Low Precision IR and FP16 or FP32 IR is the existence of `FakeQuantize` in the Low Precision IR. 
-Plugins with Low Precision Inference support recognize these sub-graphs and quantize them during the inference time. 
-Plugins without Low Precision support execute all operations, including `FakeQuantize`, as is in the FP32 or FP16 precision.   
+Low precision IR is also suitable for FP32 and FP16 inference if a chosen plugin supports all operations of the IR. The only difference between a Low Precision IR and FP16 or FP32 IR is the existence of `FakeQuantize` in the Low Precision IR. 
+Plugins that support Low Precision Inference recognize these sub-graphs and quantize them during inference. 
+The ones that do not, execute all operations, including `FakeQuantize`, as is in the FP32 or FP16 precision.   
 
-Accordingly, the presence of FakeQuantize operations in the IR is a recommendation for a plugin on how to quantize particular operations in the model. 
-If capable, a plugin accepts the recommendation and performs Low Precision Inference, otherwise, the plugin ignores the recommendation and executes a model in the floating-point precision. 
+Consequently, when `FakeQuantize` operations are present in an OpenVINO IR, it suggests to the inference device how to quantize particular operations in the model. 
+If the device is capable, it accepts the suggestion and performs Low Precision Inference. If not, it executes the model in the floating-point precision. 
 
 ## Compressed Low Precision Weights
 
-Weighted operations, like `Convolution`, `MatMul`, and others, store weights as floating-point `Constant` in the graph followed by the `FakeQuantize` operation. 
-`Constant` followed by the `FakeQuantize` operation could be optimized memory-wise due to the `FakeQuantize` operation semantics. 
+Weighted operations, such as `Convolution` and `MatMul`, store weights as the floating-point `Constant` in the graph followed by the `FakeQuantize` operation. 
+The `Constant` followed by the `FakeQuantize` operation could be optimized memory-wise due to the `FakeQuantize` operation semantics. 
 The resulting weights sub-graph stores weights in Low Precision `Constant`, which gets unpacked back to floating point with the `Convert` operation. 
 Weights compression replaces `FakeQuantize` with optional `Subtract` and `Multiply` operation leaving output arithmetically the same and weights storing takes four times less memory.
 

docs/MO_DG/prepare_model/convert_model/kaldi_specific/Aspire_Tdnn_Model.md

@@ -1,45 +1,45 @@
-# Convert Kaldi* ASpIRE Chain Time Delay Neural Network (TDNN) Model {#openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model}
+# Converting a Kaldi ASpIRE Chain Time Delay Neural Network (TDNN) Model {#openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model}
 
-You can [download a pre-trained model](https://kaldi-asr.org/models/1/0001_aspire_chain_model.tar.gz)
-for the ASpIRE Chain Time Delay Neural Network (TDNN) from the Kaldi* project official website.
+At the beginning, you should [download a pre-trained model](https://kaldi-asr.org/models/1/0001_aspire_chain_model.tar.gz)
+for the ASpIRE Chain Time Delay Neural Network (TDNN) from the Kaldi project official website.
 
-## Convert ASpIRE Chain TDNN Model to IR
+## Converting an ASpIRE Chain TDNN Model to IR
 
-To generate the Intermediate Representation (IR) of the model, run the Model Optimizer with the following parameters:
+Generate the Intermediate Representation of the model by running Model Optimizer with the following parameters:
 ```sh
  mo --input_model exp/chain/tdnn_7b/final.mdl --output output
 ```
 
-The IR will have two inputs: `input` for data and `ivector` for ivectors.
+The IR will have two inputs: `input` for data, and `ivector` for ivectors.
 
-## Example: Run ASpIRE Chain TDNN Model with the Speech Recognition Sample
+## Example: Running ASpIRE Chain TDNN Model with the Speech Recognition Sample
 
-These instructions show how to run the converted model with the [Speech Recognition sample](../../../../../samples/cpp/speech_sample/README.md).
-In this example, the input data contains one utterance from one speaker.
+> **NOTE**: Before you continue with this part of the article, get familiar with the [Speech Recognition sample](../../../../../samples/cpp/speech_sample/README.md).
 
-To follow the steps described below, you must first do the following:
+In this example, the input data contains one utterance from one speaker. 
+
+To run the ASpIRE Chain TDNN Model with Speech Recognition sample, You need to prepare environment. Do it by following the steps below :
 1. Download a [Kaldi repository](https://github.com/kaldi-asr/kaldi).
-2. Build it using instructions in `README.md` in the repository.
+2. Build it by following instructions in `README.md` from the repository.
 3. Download the [model archive](https://kaldi-asr.org/models/1/0001_aspire_chain_model.tar.gz) from Kaldi website.
 4. Extract the downloaded model archive to the `egs/aspire/s5` folder of the Kaldi repository.
 
-
-To run the ASpIRE Chain TDNN Model with Speech Recognition sample:
+Once everything has been prepared, you can start a proper run:
 
 1. Prepare the model for decoding. Refer to the `README.txt` file from the downloaded model archive for instructions.
 2. Convert data and ivectors to `.ark` format. Refer to the corresponding sections below for instructions.
 
-### Prepare Data
+### Preparing Data
 
-If you have a `.wav` data file, you can convert it to `.ark` format using the following command:
+If you have a `.wav` data file, convert it to the `.ark` format using the following command:
 ```sh
 <path_to_kaldi_repo>/src/featbin/compute-mfcc-feats --config=<path_to_kaldi_repo>/egs/aspire/s5/conf/mfcc_hires.conf scp:./wav.scp ark,scp:feats.ark,feats.scp
 ```
 Add the `feats.ark` absolute path to `feats.scp` to avoid errors in later commands.
 
-### Prepare Ivectors
+### Preparing Ivectors
 
-To prepare ivectors for the Speech Recognition sample, do the following:
+Prepare ivectors for the Speech Recognition sample:
 
 1. Copy the `feats.scp` file to the `egs/aspire/s5/` directory of the built Kaldi repository and navigate there:
 ```sh
@@ -51,29 +51,27 @@ cd <path_to_kaldi_repo>/egs/aspire/s5/
 ```sh
 ./steps/online/nnet2/extract_ivectors_online.sh --nj 1 --ivector_period <max_frame_count_in_utterance> <data folder> exp/tdnn_7b_chain_online/ivector_extractor <ivector folder>
 ```
-To simplify the preparation of ivectors for the Speech Recognition sample,
-specify the maximum number of frames in utterances as a parameter for `--ivector_period`
+You can simplify the preparation of ivectors for the Speech Recognition sample. To do it, specify the maximum number of frames in utterances as a parameter for `--ivector_period`
 to get only one ivector per utterance.
 
-To get the maximum number of frames in utterances, you can use the following command line:
+To get the maximum number of frames in utterances, use the following command line:
 ```sh
 ../../../src/featbin/feat-to-len scp:feats.scp ark,t: | cut -d' ' -f 2 - | sort -rn | head -1
 ```
-As a result, in `<ivector folder>`, you will find the `ivector_online.1.ark` file.
+As a result, you will find the `ivector_online.1.ark` file in `<ivector folder>`.
 
 3. Go to the `<ivector folder>`:
 ```sh
 cd <ivector folder>
 ```
 
-4. Convert the `ivector_online.1.ark` file to text format using the `copy-feats` tool. Run the following command:
+4. Convert the `ivector_online.1.ark` file to text format, using the `copy-feats` tool. Run the following command:
 ```sh
 <path_to_kaldi_repo>/src/featbin/copy-feats --binary=False ark:ivector_online.1.ark ark,t:ivector_online.1.ark.txt
 ```
 
 5. For the Speech Recognition sample, the `.ark` file must contain an ivector
-for each frame. You must copy the ivector `frame_count` times.
-To do this, you can run the following script in the Python* command prompt:
+for each frame. Copy the ivector `frame_count` times by running the below script in the Python command prompt:
 ```python
 import subprocess
 
@@ -101,12 +99,12 @@ length_file.close()
 <path_to_kaldi_repo>/src/featbin/copy-feats --binary=True ark,t:ivector_online_ie.ark.txt ark:ivector_online_ie.ark
 ```
 
-### Run the Speech Recognition Sample
+### Running the Speech Recognition Sample
 
-Run the Speech Recognition sample with the created ivector `.ark` file as follows:
+Run the Speech Recognition sample with the created ivector `.ark` file:
 ```sh
 speech_sample -i feats.ark,ivector_online_ie.ark -m final.xml -d CPU -o prediction.ark -cw_l 17 -cw_r 12
 ```
 
-Results can be decoded as described in "Use of Sample in Kaldi* Speech Recognition Pipeline" chapter
-in [the Speech Recognition Sample description](../../../../../samples/cpp/speech_sample/README.md).
+Results can be decoded as described in "Use of Sample in Kaldi Speech Recognition Pipeline"
+in the [Speech Recognition Sample description](../../../../../samples/cpp/speech_sample/README.md) article.

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

@@ -1,6 +1,6 @@
-# Convert MXNet GluonCV* Models {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models}
+# Converting MXNet 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.
+This article 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 Model 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
@@ -10,7 +10,7 @@ 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>`:
+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 illustrate 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
  mo --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data --output_dir <OUTPUT_MODEL_DIR>

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

@@ -1,41 +1,41 @@
-# Convert MXNet Style Transfer Model {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet}
+# Converting an MXNet Style Transfer Model {#openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet}
 
-The tutorial explains how to generate a model for style transfer using the public MXNet\* neural style transfer sample.
-To use the style transfer sample from OpenVINO™, follow the steps below as no public pre-trained style transfer model is provided with the OpenVINO toolkit.
+This article provides instructions on how to generate a model for style transfer, using the public MXNet neural style transfer sample.
 
-#### 1. Download or clone the repository with an MXNet neural style transfer sample: [Zhaw's Neural Style Transfer repository](https://github.com/zhaw/neural_style).
+**Step 1**: Download or clone the repository [Zhaw's Neural Style Transfer repository](https://github.com/zhaw/neural_style) with an MXNet neural style transfer sample.
 
-#### 2. Prepare the environment required to work with the cloned repository:
-1. Install packages dependency:<br>
+**Step 2**: Prepare the environment required to work with the cloned repository:
+
+> **NOTE**: Python-tk installation is needed only for Linux. Python for Windows includes it by default.
+
+1. Install packages dependency.<br>
 ```sh
 sudo apt-get install python-tk
 ```
 
-Installing python-tk step is needed only for Linux, as it is included by default in Python\* for Windows\*.
-
-2. Install Python\* requirements:
+2. Install Python requirements:
 ```sh
 pip3 install --user mxnet
 pip3 install --user matplotlib
 pip3 install --user scikit-image
 ```
 
-#### 3. Download the pre-trained [VGG19 model](https://github.com/dmlc/web-data/raw/master/mxnet/neural-style/model/vgg19.params) and save it to the root directory of the cloned repository because the sample expects the model `vgg19.params` file to be in that directory.<br>
+**Step 3**: Download the pretrained [VGG19 model](https://github.com/dmlc/web-data/raw/master/mxnet/neural-style/model/vgg19.params) and save it to the root directory of the cloned repository. The sample expects the model `vgg19.params` file to be in that directory.<br>
 
-#### 4. Modify source code files of style transfer sample from cloned repository.<br>
+**Step 4**: Modify source code files of style transfer sample from the cloned repository:<br>
 
 1. Go to the `fast_mrf_cnn` subdirectory.
 ```sh
 cd ./fast_mrf_cnn
 ```
 
-2. Open the `symbol.py` file and modify the `decoder_symbol()` function. Replace.
+2. Open the `symbol.py` file and modify the `decoder_symbol()` function. You should see the following code there:
 ```py
 def decoder_symbol():
     data = mx.sym.Variable('data')
     data = mx.sym.Convolution(data=data, num_filter=256, kernel=(3,3), pad=(1,1), stride=(1, 1), name='deco_conv1')
 ```
-with the following code:<br>
+Replace the code above with the following:<br>
 ```py
 def decoder_symbol_with_vgg(vgg_symbol):
     data = mx.sym.Convolution(data=vgg_symbol, num_filter=256, kernel=(3,3), pad=(1,1), stride=(1, 1), name='deco_conv1')
@@ -43,64 +43,64 @@ def decoder_symbol_with_vgg(vgg_symbol):
 
 3. Save and close the `symbol.py` file.
 
-4. Open and edit the `make_image.py` file:
-Modify the `__init__()` function in the `Maker` class. Replace:<br>
+4. Open and edit the `make_image.py` file. Go to the `__init__()` function in the `Maker` class:<br>
 ```py
 decoder = symbol.decoder_symbol()
 ```
-with the following code:<br>
+Modfiy it with the following code:<br>
 ```py
 decoder = symbol.decoder_symbol_with_vgg(vgg_symbol)
 ```
 
-5. To join the pre-trained weights with the decoder weights, make the following changes:
-After the code lines for loading the decoder weights:<br>
-```py
-args = mx.nd.load('%s_decoder_args.nd'%model_prefix)
-auxs = mx.nd.load('%s_decoder_auxs.nd'%model_prefix)
-```
-add the following line:<br>
-```py
-arg_dict.update(args)
-```
+5. To join the pretrained weights with the decoder weights, make the following changes:
+   After the code lines for loading the decoder weights:<br>
+   ```py
+   args = mx.nd.load('%s_decoder_args.nd'%model_prefix)
+   auxs = mx.nd.load('%s_decoder_auxs.nd'%model_prefix)
+   ```
 
-6. Use `arg_dict` instead of `args` as a parameter of the `decoder.bind()` function. Replace the line:<br>
+   Add the following line:<br>
+   ```py
+   arg_dict.update(args)
+   ```
+
+6. Use `arg_dict` instead of `args` as a parameter of the `decoder.bind()` function. Find the line below:<br>
 ```py
 self.deco_executor = decoder.bind(ctx=mx.gpu(), args=args, aux_states=auxs)
 ```
-with the following:<br>
+Replace it with the following:<br>
 ```py
 self.deco_executor = decoder.bind(ctx=mx.cpu(), args=arg_dict, aux_states=auxs)
 ```
-7. To save the result model as a `.json` file, add the following code to the end of the `generate()` function in the `Maker` class:<br>
+7. Add the following code to the end of the `generate()` function in the `Maker` class to save the result model as a `.json` file:<br>
 ```py
 self.vgg_executor._symbol.save('{}-symbol.json'.format('vgg19'))
 self.deco_executor._symbol.save('{}-symbol.json'.format('nst_vgg19'))
 ```
 8. Save and close the `make_image.py` file.
 
-#### 5. Run the sample with a decoder model according to the instructions from the `README.md` file in the `fast_mrf_cnn` directory of the cloned repository.
-For example, to run the sample with the pre-trained decoder weights from the `models` folder and output shape, use the following code:<br>
+**Step 5**: Follow the instructions from the `README.md` file in the `fast_mrf_cnn` directory of the cloned repository and run the sample with a decoder model.
+For example, use the following code to run the sample with the pretrained decoder weights from the `models` folder and output shape:<br>
 ```py
 import make_image
 maker = make_image.Maker('models/13', (1024, 768))
 maker.generate('output.jpg', '../images/tubingen.jpg')
 ```
-Where the `models/13` string is composed of the following substrings:
-* `models/`: path to the folder that contains .nd files with pre-trained styles weights
-* `13`: prefix pointing to 13_decoder, which is the default decoder for the repository.
+The `models/13` string in the code above is composed of the following substrings:
+* `models/` -- path to the folder that contains `.nd` files with pretrained styles weights.
+* `13` -- prefix pointing to the default decoder for the repository, `13_decoder`.
 
-> **NOTE**: If you get an error saying "No module named 'cPickle'", try running the script from this step in Python 2. Then return to Python 3 for the remaining steps.
+> **NOTE**: If an error prompts with "No module named `cPickle`", try running the script from Step 5 in Python 2. After that return to Python 3 for the remaining steps.
 
-You can choose any style from [collection of pre-trained weights](https://pan.baidu.com/s/1skMHqYp). (On the Chinese-language page, click the down arrow next to a size in megabytes. Then wait for an overlay box to appear, and click the blue button in it to download.) The `generate()` function generates `nst_vgg19-symbol.json` and `vgg19-symbol.json` files for the specified shape. In the code, it is [1024 x 768] for a 4:3 ratio, and you can specify another, for example, [224,224] for a square ratio.
+Any style can be selected from [collection of pretrained weights](https://pan.baidu.com/s/1skMHqYp). On the Chinese-language page, click the down arrow next to a size in megabytes. Then wait for an overlay box to appear, and click the blue button in it to download. The `generate()` function generates `nst_vgg19-symbol.json` and `vgg19-symbol.json` files for the specified shape. In the code, it is [1024 x 768] for a 4:3 ratio. You can specify another, for example, [224,224] for a square ratio.
 
-#### 6. Run the Model Optimizer to generate an Intermediate Representation (IR):
+**Step 6**: Run the Model Optimizer to generate an Intermediate Representation (IR):
 
 1. Create a new directory. For example:<br>
 ```sh
 mkdir nst_model
 ```
-2. Copy the initial and generated model files to the created directory. For example, to copy the pre-trained decoder weights from the `models` folder to the `nst_model` directory, run the following commands:<br>
+2. Copy the initial and generated model files to the created directory. For example, to copy the pretrained decoder weights from the `models` folder to the `nst_model` directory, run the following commands:<br>
 ```sh
 cp nst_vgg19-symbol.json nst_model
 cp vgg19-symbol.json nst_model
@@ -110,8 +110,8 @@ cp models/13_decoder_auxs.nd nst_model
 ```
 > **NOTE**: Make sure that all the `.params` and `.json` files are in the same directory as the `.nd` files. Otherwise, the conversion process fails.
 
-3. Run the Model Optimizer for MXNet. Use the `--nd_prefix_name` option to specify the decoder prefix and `--input_shape` to specify input shapes in [N,C,W,H] order. For example:<br>
+3. Run the Model Optimizer for Apache MXNet. Use the `--nd_prefix_name` option to specify the decoder prefix and `--input_shape` to specify input shapes in [N,C,W,H] order. For example:<br>
 ```sh
 mo --input_symbol <path/to/nst_model>/nst_vgg19-symbol.json --framework mxnet --output_dir <path/to/output_dir> --input_shape [1,3,224,224] --nd_prefix_name 13_decoder --pretrained_model <path/to/nst_model>/vgg19-0000.params
 ```
-4. The IR is generated (`.bin`, `.xml` and `.mapping` files) in the specified output directory and ready to be consumed by the OpenVINO Runtime.
+4. The IR is generated (`.bin`, `.xml` and `.mapping` files) in the specified output directory, and ready to be consumed by the OpenVINO Runtime.

docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Faster_RCNN.md

@@ -1,10 +1,11 @@
-# Convert ONNX* Faster R-CNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Faster_RCNN}
+# Converting an ONNX Faster R-CNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Faster_RCNN}
 
-These instructions are applicable only to the Faster R-CNN model converted to the ONNX* file format from the [facebookresearch/maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark).
+The instructions below are applicable **only** to the Faster R-CNN model converted to the ONNX file format from the [maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark):
 
-**Step 1**. Download the pre-trained model file from [onnx/models](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn) (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
+1. Download the pretrained model file from [onnx/models](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn):
+   * (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
 
-**Step 2**. To generate the Intermediate Representation (IR) of the model, change your current working directory to the Model Optimizer installation directory and run the Model Optimizer with the following parameters:
+2. Generate the Intermediate Representation of the model, by changing your current working directory to the Model Optimizer installation directory, and running Model Optimizer with the following parameters:
 ```sh
  mo \
 --input_model FasterRCNN-10.onnx \
@@ -14,6 +15,9 @@ These instructions are applicable only to the Faster R-CNN model converted to th
 --transformations_config front/onnx/faster_rcnn.json
 ```
 
-Note that the height and width specified with the `input_shape` command line parameter could be different. Refer to the [documentation](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn) for more information about supported input image dimensions and required pre- and post-processing steps.
+Be aware that the height and width specified with the `input_shape` command line parameter could be different. For more information about supported input image dimensions and required pre- and post-processing steps, refer to the [Faster R-CNN article](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn).
 
-**Step 3**. Interpret the outputs. The generated IR file has several outputs: class indices, probabilities and box coordinates. These are outputs from the "DetectionOutput" layer.
+3. Interpret the outputs of the generated IR: class indices, probabilities and box coordinates. Below are the outputs from the "DetectionOutput" layer:
+   * class indices.
+   * probabilities.
+   * box coordinates.

docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_GPT2.md

@@ -1,17 +1,17 @@
-# Convert ONNX* GPT-2 Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2}
+# Converting an ONNX GPT-2 Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2}
 
-[Public pre-trained GPT-2 model](https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2)  is a large
+[Public pretrained GPT-2 model](https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2) is a large
 transformer-based language model with a simple objective: predict the next word, given all of the previous words within some text.
 
-## Download the Pre-Trained Base GPT-2 Model
+## Downloading the Pre-Trained Base GPT-2 Model
 
-To download the model, click **Download** on [https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.onnx](https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.onnx).
+To download the model, go to [this model](https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.onnx), and press **Download**.
 
-To download the model and sample test data, click **Download** on [https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.tar.gz](https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.tar.gz).
+To download the model and sample test data, go to [this model](https://github.com/onnx/models/blob/master/text/machine_comprehension/gpt-2/model/gpt2-10.tar.gz), and press **Download**.
 
-## Convert ONNX* GPT-2 Model to IR
+## Converting an ONNX GPT-2 Model to IR
 
-To generate the Intermediate Representation (IR) of the model GPT-2, run the Model Optimizer with the following parameters:
+Generate the Intermediate Representation of the model GPT-2 by running Model Optimizer with the following parameters:
 ```sh
 mo --input_model gpt2-10.onnx --input_shape [X,Y,Z] --output_dir <OUTPUT_MODEL_DIR>
 ```

docs/MO_DG/prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md

@@ -1,10 +1,11 @@
-# Convert ONNX* Mask R-CNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN}
+# Converting an ONNX Mask R-CNN Model {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN}
 
-These instructions are applicable only to the Mask R-CNN model converted to the ONNX* file format from the [facebookresearch/maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark).
+The instructions below are applicable **only** to the Mask R-CNN model converted to the ONNX file format from the [maskrcnn-benchmark model](https://github.com/facebookresearch/maskrcnn-benchmark).
 
-**Step 1**. Download the pre-trained model file from [onnx/models](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn) (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
+1. Download the pretrained model file from [onnx/models](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn):
+   * commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117.
 
-**Step 2**. To generate the Intermediate Representation (IR) of the model, change your current working directory to the Model Optimizer installation directory and run the Model Optimizer with the following parameters:
+2. Generate the Intermediate Representation of the model by changing your current working directory to the Model Optimizer installation directory and running Model Optimizer with the following parameters:
 ```sh
  mo \
 --input_model mask_rcnn_R_50_FPN_1x.onnx \
@@ -14,6 +15,12 @@ These instructions are applicable only to the Mask R-CNN model converted to the
 --transformations_config front/onnx/mask_rcnn.json
 ```
 
-Note that the height and width specified with the `input_shape` command line parameter could be different. Refer to the [documentation](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn) for more information about supported input image dimensions and required pre- and post-processing steps.
+Be aware that the height and width specified with the `input_shape` command line parameter could be different. For more information about supported input image dimensions and required pre- and post-processing steps, refer to the [documentation](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn).
 
-**Step 3**. Interpret the outputs. The generated IR file has several outputs: masks, class indices, probabilities and box coordinates. The first one is a layer with the name "6849/sink_port_0". The rest three are outputs from the "DetectionOutput" layer.
+3. Interpret the outputs of the generated IR file: masks, class indices, probabilities and box coordinates.
+   * masks.
+   * class indices.
+   * probabilities.
+   * box coordinates. 
+
+The first one is a layer with the name `6849/sink_port_0`, and rest are outputs from the `DetectionOutput` layer.

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Bert_ner.md

@@ -1,15 +1,18 @@
-# Convert PyTorch* BERT-NER Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Bert_ner}
+# Converting a PyTorch BERT-NER Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Bert_ner}
 
-## Download and Convert the Model to ONNX*
+The goal of this article is to present a step-by-step guide on how to convert PyTorch BERT-NER model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
 
-To download a pre-trained model or train the model yourself, refer
-to the [instruction](https://github.com/kamalkraj/BERT-NER/blob/dev/README.md) in the
-BERT-NER model repository. The model with config files is stored in the `out_base` directory.
 
-To convert the model to ONNX* format, create and run the script with the following content in the root
-directory of the model repository. If you download the pre-trained model, you need
+## Downloading and Converting the Model to ONNX
+
+To download a pretrained model or train the model yourself, refer
+to the [instructions](https://github.com/kamalkraj/BERT-NER/blob/dev/README.md) in the
+BERT-NER model repository. The model with configuration files is stored in the `out_base` directory.
+
+To convert the model to ONNX format, create and run the following script in the root
+directory of the model repository. If you download the pretrained model, you need
 to download [`bert.py`](https://github.com/kamalkraj/BERT-NER/blob/dev/bert.py) to run the script.
-The instruction was tested with the repository hash commit `e5be564156f194f1becb0d82aeaf6e762d9eb9ed`.
+The instructions were tested with the commit-SHA: `e5be564156f194f1becb0d82aeaf6e762d9eb9ed`.
 
 ```python
 import torch
@@ -44,12 +47,12 @@ torch.onnx.export(ner_model,
                   )
 ```
 
-The script generates ONNX* model file `bert-ner.onnx`.
+The script generates ONNX model file `bert-ner.onnx`.
 
-## Convert ONNX* BERT-NER model to IR
+## Converting an ONNX BERT-NER model to IR
 
 ```bash
 mo --input_model bert-ner.onnx --input "input_mask[1 128],segment_ids[1 128],input_ids[1 128]"
 ```
 
-where `1` is `batch_size` and `128` is `sequence_length`.
+where `1` is `batch_size` and `128` is `sequence_length`.

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_Cascade_RCNN_res101.md

@@ -1,6 +1,8 @@
-# Convert PyTorch Cascade RCNN R-101 Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Cascade_RCNN_res101}
+# Converting a PyTorch Cascade RCNN R-101 Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Cascade_RCNN_res101}
 
-## Download and Convert Model to ONNX
+The goal of this article is to present a step-by-step guide on how to convert a PyTorch Cascade RCNN R-101 model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
+
+## Downloading and Converting Model to ONNX
 
 * Clone the [repository](https://github.com/open-mmlab/mmdetection):
 
@@ -9,9 +11,9 @@ git clone https://github.com/open-mmlab/mmdetection
 cd mmdetection
 ```
 
-> **NOTE**: To set up an environment, refer to this [instruction](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md#installation).
+> **NOTE**: To set up an environment, refer to the [instructions](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md#installation).
 
-* Download the pre-trained [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth). You can also find the link to the model [here](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn/README.md).
+* Download the pretrained [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth). The model is also available [here](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn/README.md).
 
 * To convert the model to ONNX format, use this [script](https://github.com/open-mmlab/mmdetection/blob/master/tools/deployment/pytorch2onnx.py).
 
@@ -19,10 +21,10 @@ cd mmdetection
 python3 tools/deployment/pytorch2onnx.py configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth --output-file cascade_rcnn_r101_fpn_1x_coco.onnx
 ```
 
-The script generates ONNX model file `cascade_rcnn_r101_fpn_1x_coco.onnx` in the directory `tools/deployment/`. If required, you can specify the model name or output directory using `--output-file <path-to-dir>/<model-name>.onnx` 
+The script generates ONNX model file `cascade_rcnn_r101_fpn_1x_coco.onnx` in the directory `tools/deployment/`. If required, specify the model name or output directory, using `--output-file <path-to-dir>/<model-name>.onnx`.
 
-## Convert ONNX Cascade RCNN R-101 Model to IR
+## Converting an ONNX Cascade RCNN R-101 Model to OpenVINO IR
 
 ```bash
 mo --input_model cascade_rcnn_r101_fpn_1x_coco.onnx --mean_values [123.675,116.28,103.53] --scale_values [58.395,57.12,57.375]
-```
+```

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_F3Net.md

@@ -1,8 +1,8 @@
-# Convert PyTorch* F3Net Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net}
+# Converting a PyTorch F3Net Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net}
 
 [F3Net](https://github.com/weijun88/F3Net): Fusion, Feedback and Focus for Salient Object Detection
 
-## Clone the F3Net Repository
+## Cloning the F3Net Repository
 
 To clone the repository, run the following command:
 
@@ -10,10 +10,10 @@ To clone the repository, run the following command:
 git clone http://github.com/weijun88/F3Net.git
 ```
 
-## Download and Convert the Model to ONNX*
+## Downloading and Converting the Model to ONNX
 
-To download the pre-trained model or train the model yourself, refer to the
-[instruction](https://github.com/weijun88/F3Net/blob/master/README.md) in the F3Net model repository. First, convert the model to ONNX\* format. Create and run the following Python script in the `src` directory of the model repository:
+To download the pretrained model or train the model yourself, refer to the
+[instructions](https://github.com/weijun88/F3Net/blob/master/README.md) in the F3Net model repository. First, convert the model to ONNX format. Create and run the following Python script in the `src` directory of the model repository:
 ```python
 import torch
 from dataset import Config
@@ -24,9 +24,9 @@ net = F3Net(cfg)
 image = torch.zeros([1, 3, 352, 352])
 torch.onnx.export(net, image, 'f3net.onnx', export_params=True, do_constant_folding=True, opset_version=11)
 ```
-The script generates the ONNX\* model file f3net.onnx. This model conversion was tested with the repository hash commit `eecace3adf1e8946b571a4f4397681252f9dc1b8`.
+The script generates the ONNX model file `f3net.onnx`. The model conversion was tested with the commit-SHA: `eecace3adf1e8946b571a4f4397681252f9dc1b8`.
 
-## Convert ONNX* F3Net Model to IR
+## Converting an ONNX F3Net Model to IR
 
 ```sh
 mo --input_model <MODEL_DIR>/f3net.onnx

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_QuartzNet.md

@@ -1,13 +1,13 @@
-# Convert PyTorch* QuartzNet Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet}
+# Converting a PyTorch QuartzNet Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet}
 
 [NeMo project](https://github.com/NVIDIA/NeMo) provides the QuartzNet model.
 
-## Download the Pre-Trained QuartzNet Model
+## Downloading the Pretrained QuartzNet Model
 
-To download the pre-trained model, refer to the [NeMo Speech Models Catalog](https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels).
-Here are the instructions on how to obtain QuartzNet in ONNX* format.
+To download the pretrained model, refer to the [NeMo Speech Models Catalog](https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels).
+Here are the instructions on how to obtain QuartzNet in ONNX format.
 
-1. Install the NeMo toolkit using the [instructions](https://github.com/NVIDIA/NeMo/tree/main#installation).
+1. Install the NeMo toolkit, using the [instructions](https://github.com/NVIDIA/NeMo/tree/main#installation).
 
 2. Run the following code:
 
@@ -16,16 +16,16 @@ import nemo
 import nemo.collections.asr as nemo_asr
 
 quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
-# Export QuartzNet model to ONNX* format
+# Export QuartzNet model to ONNX format
 quartznet.decoder.export('decoder_qn.onnx')
 quartznet.encoder.export('encoder_qn.onnx')
 quartznet.export('qn.onnx')
 ```
 
-This code produces 3 ONNX* model files: `encoder_qn.onnx`, `decoder_qn.onnx`, `qn.onnx`.
-They are `decoder`, `encoder` and a combined `decoder(encoder(x))` models, respectively.
+This code produces 3 ONNX model files: `encoder_qn.onnx`, `decoder_qn.onnx`, `qn.onnx`.
+They are `decoder`, `encoder`, and a combined `decoder(encoder(x))` models, respectively.
 
-## Convert ONNX* QuartzNet model to IR
+## Converting an ONNX QuartzNet model to IR
 
 If using a combined model:
 ```sh

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RCAN.md

@@ -1,13 +1,12 @@
-# Convert PyTorch* RCAN Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN}
+# Converting a PyTorch RCAN Model {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN}
 
 [RCAN](https://github.com/yulunzhang/RCAN): Image Super-Resolution Using Very Deep Residual Channel Attention Networks
 
-## Download and Convert the Model to ONNX*
+## Downloading and Converting the Model to ONNX
 
-To download the pre-trained model or train the model yourself, refer to the
-[instruction](https://github.com/yulunzhang/RCAN/blob/master/README.md) in the RCAN model repository. Firstly,
-convert the model to ONNX\* format. Create and run the script with the following content in the root
+To download the pretrained model or train the model yourself, refer to the [instruction](https://github.com/yulunzhang/RCAN/blob/master/README.md) in the RCAN model repository. First, convert the model to ONNX format. Create and run the script with the following content in the root
 directory of the model repository:
+
 ```python
 from argparse import Namespace
 
@@ -22,9 +21,9 @@ net.eval()
 dummy_input = torch.randn(1, 3, 360, 640)
 torch.onnx.export(net, dummy_input, 'RCAN.onnx')
 ```
-The script generates the ONNX\* model file RCAN.onnx. You can find more information about model parameters (`n_resblocks`, `n_resgroups`, and others) in the model repository and use different values of them. The model conversion was tested with the repository hash commit `3339ebc59519c3bb2b5719b87dd36515ec7f3ba7`.
+The script generates the ONNX model file `RCAN.onnx`. More information about model parameters (`n_resblocks`, `n_resgroups`, and others) and their different values can be found in the model repository. The model conversion was tested with the commit-SHA: `3339ebc59519c3bb2b5719b87dd36515ec7f3ba7`.
 
-## Convert ONNX* RCAN Model to IR
+## Converting an ONNX RCAN Model to IR
 
 ```sh
 mo --input_model RCAN.onnx