Merge remote-tracking branch 'upstream/master' into feature/snippets/mha

.github/workflows/mo.yml

@@ -2,15 +2,15 @@ name: MO
 on:
   push:
     paths:
-      - 'openvino/tools/mo/**'
-      - '.github/workflows/mo.py'
+      - 'tools/mo/**'
+      - '.github/workflows/mo.yml'
     branches:
       - 'master'
       - 'releases/**'
   pull_request:
     paths:
-      - 'openvino/tools/mo/**'
-      - '.github/workflows/mo.py'
+      - 'tools/mo/**'
+      - '.github/workflows/mo.yml'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -24,48 +24,34 @@ jobs:
         with:
           submodules: recursive
 
-      - name: Setup Python ${{ matrix.python-version }}
+      - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.7
+          python-version: '3.10'
 
       - name: Cache pip
         uses: actions/cache@v1
         with:
           path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('openvino/tools/mo/requirements*.txt') }}
+          key: ${{ runner.os }}-pip-${{ hashFiles('tools/mo/requirements*.txt') }}
           restore-keys: |
             ${{ runner.os }}-pip-
             ${{ runner.os }}-
 
-      # tensorflow 1.15 causes modules import
-      # errors, most likely due to https://github.com/PyCQA/pylint/issues/2603
-      # for tensorflow.core.framework and tensorflow.contrib
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip setuptools
           # For UT
           pip install unittest-xml-reporting==3.0.2
           # MO requirements
-          pip install -r requirements.txt
+          pip install -r requirements_mxnet.txt
+          pip install -r requirements_caffe.txt
+          pip install -r requirements_kaldi.txt
+          pip install -r requirements_onnx.txt
+          pip install -r requirements_tf2.txt
           pip install -r requirements_dev.txt
-          # requrements for CMake
-          sudo apt update
-          sudo apt --assume-yes install libusb-1.0-0-dev
-        working-directory: openvino/tools/mo
+        working-directory: tools/mo
 
       - name: Pylint
-        run: pylint -d C,R,W openvino/tools/mo/ openvino/tools/mo/mo.py
-        working-directory: openvino/tools/mo
-
-      - name: CMake configure
-        run: cmake -B build
-
-      - name: UT
-        run: |
-          export PYTHONPATH=$PYTHONPATH:`pwd`
-          export MO_ROOT=`pwd`
-          env
-          mkdir ../mo-ut-logs
-          python3 -m xmlrunner discover -p *_test.py --output=../mo-ut-logs
-        working-directory: openvino/tools/mo
+        run: pylint -d C,R,W openvino/tools/mo
+        working-directory: tools/mo

docs/nbdoc/consts.py

@@ -8,7 +8,7 @@
 
 repo_name = "openvino_notebooks"
 
-artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20230104220806/dist/rst_files/"
+artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20230109220810/dist/rst_files/"
 
 blacklisted_extensions = ['.xml', '.bin']
 
@@ -18,8 +18,8 @@
 # Templates
 
 binder_template = """
-This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. 
-See the |installation_link| for instructions to run this tutorial locally on Windows, Linux or macOS. 
+This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub.
+See the |installation_link| for instructions to run this tutorial locally on Windows, Linux or macOS.
 To run without installing anything, click the launch binder button.
 
 |binder_link| |github_link|
@@ -28,7 +28,7 @@
 
    <a href="https://github.com/{{ owner }}/{{ repo }}#-installation-guide" target="_blank">installation guide</a>
 
-.. |binder_link| raw:: html 
+.. |binder_link| raw:: html
 
    <a href="https://mybinder.org/v2/gh/{{ owner }}/{{ repo }}/HEAD?filepath={{ folder }}%2F{{ notebook }}%2F{{ notebook }}.ipynb" target="_blank"><img src="https://mybinder.org/badge_logo.svg" alt="Binder"></a>
 
@@ -39,7 +39,7 @@
 \n
 """
 no_binder_template = """
-This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. 
+This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub.
 See the |installation_link| for instructions to run this tutorial locally on Windows, Linux or macOS.
 
 |github_link|

docs/tutorials.md

@@ -5,13 +5,13 @@
 .. _notebook tutorials:
 
 .. meta::
-   :description: A collection of Python tutorials run on Jupyter notebooks. The 
-                 tutorials explain how to use OpenVINO™ toolkit for optimized 
+   :description: A collection of Python tutorials run on Jupyter notebooks. The
+                 tutorials explain how to use OpenVINO™ toolkit for optimized
                  deep learning inference.
-   :keywords: OpenVINO™ toolkit, Jupyter, Jupyter notebooks, tutorials, Python 
-              API, Python, deep learning, inference, model inference, infer a 
-              model, Binder, object detection, quantization, image 
-              classification, speech recognition, OCR, OpenVINO IR, deep 
+   :keywords: OpenVINO™ toolkit, Jupyter, Jupyter notebooks, tutorials, Python
+              API, Python, deep learning, inference, model inference, infer a
+              model, Binder, object detection, quantization, image
+              classification, speech recognition, OCR, OpenVINO IR, deep
               learning model, AI, neural networks
 
 .. toctree::
@@ -22,21 +22,21 @@
    notebooks-installation
    notebooks/notebooks
 
-This collection of Python tutorials are written for running on Jupyter notebooks. 
-The tutorials provide an introduction to the OpenVINO™ toolkit and explain how to 
-use the Python API and tools for optimized deep learning inference. You can run the 
-code one section at a time to see how to integrate your application with OpenVINO 
+This collection of Python tutorials are written for running on Jupyter notebooks.
+The tutorials provide an introduction to the OpenVINO™ toolkit and explain how to
+use the Python API and tools for optimized deep learning inference. You can run the
+code one section at a time to see how to integrate your application with OpenVINO
 libraries.
 
-Notebooks with a |binder logo| button can be run without installing anything. 
-Once you have found the tutorial of your interest, just click the button next to 
+Notebooks with a |binder logo| button can be run without installing anything.
+Once you have found the tutorial of your interest, just click the button next to
 the name of it and `Binder <https://mybinder.org/>`__ will start it in a new tab of a browser.
 Binder is a free online service with limited resources (for more information about it,  
 see the `Additional Resources <#-additional-resources>`__ section).
 
 .. note::
-   For the best performance, more control and resources, you should run the notebooks locally. 
-   Follow the `Installation Guide <notebooks-installation.html>`__ in order to get information 
+   For the best performance, more control and resources, you should run the notebooks locally.
+   Follow the `Installation Guide <notebooks-installation.html>`__ in order to get information
    on how to run and manage the notebooks on your machine.
 
 
@@ -118,7 +118,7 @@ Tutorials that explain how to optimize and quantize models with OpenVINO tools.
    | `105-language-quantize-bert <notebooks/105-language-quantize-bert-with-output.html>`__                                       | Optimize and quantize a pre-trained BERT model                                                                                   |
    +------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `106-auto-device <notebooks/106-auto-device-with-output.html>`__                                                             | Demonstrates how to use AUTO Device                                                                                              |
-   +------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+ 
+   +------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `107-speech-recognition-quantization <notebooks/107-speech-recognition-quantization-with-output.html>`__                     | Optimize and quantize a pre-trained Wav2Vec2 speech model                                                                        |
    +------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
    | `110-ct-segmentation-quantize <notebooks/110-ct-segmentation-quantize-with-output.html>`__                                   | Quantize a kidney segmentation model and show live inference                                                                     |
@@ -210,6 +210,9 @@ Demos that demonstrate inference on a particular model.
    +-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
    | `228-clip-zero-shot-image-classification <notebooks/228-clip-zero-shot-image-classification-with-output.html>`__              | Perform Zero-shot Image Classification with CLIP and OpenVINO                                                                              | |n228-img1|                               |
    +-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
+   | `229-distilbert-sequence-classification <notebooks/229-distilbert-sequence-classification-with-output.html>`__                | Sequence Classification with OpenVINO                                                                                                      | |n229-img1|                               |
+   +-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
+
 
 
 .. raw:: html
@@ -219,7 +222,7 @@ Demos that demonstrate inference on a particular model.
 `Model Training`_
 ------------------
 
-Tutorials that include code to train neural networks. 
+Tutorials that include code to train neural networks.
 
 
 +-------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------+
@@ -409,6 +412,8 @@ Made with `contributors-img <https://contrib.rocks>`__.
    :target: https://user-images.githubusercontent.com/29454499/204548693-1304ef33-c790-490d-8a8b-d5766acb6254.png
 .. |n228-img1| image:: https://user-images.githubusercontent.com/29454499/207795060-437b42f9-e801-4332-a91f-cc26471e5ba2.png
    :target: https://user-images.githubusercontent.com/29454499/207795060-437b42f9-e801-4332-a91f-cc26471e5ba2.png
+.. |n229-img1| image:: https://user-images.githubusercontent.com/95271966/206130638-d9847414-357a-4c79-9ca7-76f4ae5a6d7f.png
+   :target: https://user-images.githubusercontent.com/95271966/206130638-d9847414-357a-4c79-9ca7-76f4ae5a6d7f.png
 .. |n301-img1| image:: https://user-images.githubusercontent.com/15709723/127779607-8fa34947-1c35-4260-8d04-981c41a2a2cc.png
    :target: https://user-images.githubusercontent.com/15709723/127779607-8fa34947-1c35-4260-8d04-981c41a2a2cc.png
 .. |n401-img1| image:: https://user-images.githubusercontent.com/4547501/141471665-82b28c86-cf64-4bfe-98b3-c314658f2d96.gif
@@ -497,4 +502,3 @@ Made with `contributors-img <https://contrib.rocks>`__.
 
 
 @endsphinxdirective
-

samples/cpp/benchmark_app/statistics_report.cpp

@@ -22,7 +22,7 @@ void StatisticsReport::add_parameters(const Category& category, const Parameters
 }
 
 void StatisticsReport::dump() {
-    CsvDumper dumper(true, _config.report_folder + _separator + "benchmark_report.csv");
+    CsvDumper dumper(true, _config.report_folder + _separator + "benchmark_report.csv", 3);
 
     auto dump_parameters = [&dumper](const Parameters& parameters) {
         for (auto& parameter : parameters) {
@@ -88,7 +88,7 @@ void StatisticsReport::dump_performance_counters_request(CsvDumper& dumper, cons
                        ? status_names[(int)layer.status]
                        : "INVALID_STATUS");
         dumper << layer.node_type << layer.exec_type;
-        dumper << std::to_string(layer.real_time.count() / 1000.0) << std::to_string(layer.cpu_time.count() / 1000.0);
+        dumper << layer.real_time.count() / 1000.0 << layer.cpu_time.count() / 1000.0;
         total += layer.real_time;
         total_cpu += layer.cpu_time;
         dumper.endLine();
@@ -135,8 +135,7 @@ void StatisticsReport::dump_sort_performance_counters_request(CsvDumper& dumper,
                            ? status_names[(int)layer.status]
                            : "INVALID_STATUS");
             dumper << layer.node_type << layer.exec_type;
-            dumper << std::to_string(layer.real_time.count() / 1000.0)
-                   << std::to_string(layer.cpu_time.count() / 1000.0);
+            dumper << layer.real_time.count() / 1000.0 << layer.cpu_time.count() / 1000.0;
             dumper << (layer.real_time * 1.0 / total) * 100;
             dumper.endLine();
             layersize += 1;
@@ -191,7 +190,7 @@ void StatisticsReport::dump_performance_counters(const std::vector<PerformanceCo
         slog::info << "Performance counters are empty. No reports are dumped." << slog::endl;
         return;
     }
-    CsvDumper dumper(true, _config.report_folder + _separator + "benchmark_" + _config.report_type + "_report.csv");
+    CsvDumper dumper(true, _config.report_folder + _separator + "benchmark_" + _config.report_type + "_report.csv", 3);
     if (_config.report_type == detailedCntReport) {
         for (auto& pc : perfCounts) {
             dump_performance_counters_request(dumper, pc);