DOCS: ported changes from 2022.1 release branch (#11206)

* Extensibility guide with FE extensions and remove OV_FRAMEWORK_MAP from docs * Rework of Extensibility Intro, adopted examples to missing OPENVINO_FRAMEWORK_MAP * Removed OPENVINO_FRAMEWORK_MAP reference * Frontend extension detailed documentation * Fixed distributed snippets * Fixed snippet inclusion in FE extension document and chapter headers * Fixed wrong name in a snippet reference * Fixed test for template extension due to changed number of loaded extensions * Update docs/Extensibility_UG/frontend_extensions.md Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com> * Minor fixes in extension snippets * Small grammar fix Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com> Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com> * DOCS: transition banner (#10973) * transition banner * minor fix * update transition banner * updates * update custom.js * updates * updates * Documentation fixes (#11044) * Benchmark app usage * Fixed link to the devices * More fixes * Update docs/OV_Runtime_UG/multi_device.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Removed several hardcoded links Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Updated documentation for compile_tool (#11049) * Added deployment guide (#11060) * Added deployment guide * Added local distribution * Updates * Fixed more indentations * Removed obsolete code snippets (#11061) * Removed obsolete code snippets * NCC style * Fixed NCC for BA * Add a troubleshooting issue for PRC installation (#11074) * updates * adding gna to linux * add missing reference * update * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * update * minor updates * add gna item to yum and apt * add gna to get started page * update reference formatting * merge commit * add a troubleshooting issue * update * update * fix CVS-71846 Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> * DOCS: fixed hardcoded links (#11100) * Fixes * Use links * applying reviewers comments to the Opt Guide (#11093) * applying reviewrs comments * fixed refs, more structuring (bold, bullets, etc) * refactoring tput/latency sections * next iteration (mostly latency), also brushed the auto-batching and other sections * updates sync/async images * common opts brushed * WIP tput redesigned * minor brushing of common and auto-batching * Tput fully refactored * fixed doc name in the link * moved int8 perf counters to the right section * fixed links * fixed broken quotes * fixed more links * add ref to the internals to the TOC * Added a note on the batch size Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * [80085] New images for docs (#11114) * change doc structure * fix manager tools * fix manager tools 3 step * fix manager tools 3 step * new img * new img for OV Runtime * fix steps * steps * fix intendents * change list * fix space * fix space * code snippets fix * change display * Benchmarks 2022 1 (#11130) * Minor fixes * Updates for 2022.1 * Edits according to the review * Edits according to review comments * Edits according to review comments * Edits according to review comments * Fixed table * Edits according to review comments * Removed config for Intel® Core™ i7-11850HE * Removed forward-tacotron-duration-prediction-241 graph * Added resnet-18-pytorch * Add info about Docker images in Deployment guide (#11136) * Renamed user guides (#11137) * fix screenshot (#11140) * More conservative recommendations on dynamic shapes usage in docs (#11161) * More conservative recommendations about using dynamic shapes * Duplicated statement from C++ part to Python part of reshape doc (no semantical changes) * Update ShapeInference.md (#11168) * Benchmarks 2022 1 updates (#11180) * Updated graphs * Quick fix for TODO in Dynamic Shapes article * Anchor link fixes * Fixed DM config (#11199) * DOCS: doxy sphinxtabs (#11027) * initial implementation of doxy sphinxtabs * fixes * fixes * fixes * fixes * fixes * WA for ignored visibility attribute * Fixes Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com> Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com> Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com> Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> Co-authored-by: Yuan Xu <yuan1.xu@intel.com> Co-authored-by: Maxim Shevtsov <maxim.y.shevtsov@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> Co-authored-by: Ilya Naumov <ilya.naumov@intel.com> Co-authored-by: Evgenya Stepyreva <evgenya.stepyreva@intel.com>
2022-03-24 22:27:29 +03:00
1 parent 8118147a96
commit a883dc0b85
@@ -10,11 +10,7 @@ Performance can be measured for two inference modes: latency- and throughput-ori

 Upon start-up, the application reads command-line parameters and loads a network and inputs (images/binary files) to the specified device.

-  **NOTE**: By default, OpenVINO™ Toolkit Samples, Tools and Demos expect input with BGR channels order.
-  If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application
-  or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified.
-  For more information about the argument, refer to **When to Reverse Input Channels** section of
-  [Embedding Preprocessing Computation](@ref openvino_docs_MO_DG_Additional_Optimization_Use_Cases).
+> **NOTE**: By default, OpenVINO™ Toolkit Samples, Tools and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of [Embedding Preprocessing Computation](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).

 Device-specific execution parameters (number of streams, threads, and so on) can be either explicitly specified through the command line
 or left default. In the last case, the sample logic will select the values for the optimal throughput.
@@ -150,7 +146,7 @@ If a model has mixed input types, input folder should contain all required files

 To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).

-> **NOTE**: Before running the tool with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+> **NOTE**: Before running the tool with a trained model, make sure the model is converted to the OpenVINO IR (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
 > The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

@@ -171,8 +167,12 @@ This section provides step-by-step instructions on how to run the Benchmark Tool
   ```sh
   omz_downloader --name googlenet-v1 -o <models_dir>
   ```
+<<<<<<< HEAD
 3. Convert the model to the OpenVINO IR format. Run the Model Optimizer using the `mo` command with the path to the model, model format and output directory to generate the IR files:

+=======
+2. Convert the model to the OpenVINO IR format. Run the Model Optimizer using the `mo` command with the path to the model, model format (which must be FP32 for CPU and FPG) and output directory to generate the IR files:
+>>>>>>> cf8ccb590a... Removed obsolete code snippets (#11061)
   ```sh
   mo --input_model <models_dir>/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP32 --output_dir <ir_dir>
   ```
@@ -243,6 +243,6 @@ Below are fragments of sample output static and dynamic networks:
   ```

 ## See Also
-* [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
+* [Using OpenVINO Runtime Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Model Downloader](@ref omz_tools_downloader)
@@ -88,7 +88,7 @@ static void next_step(const std::string additional_info = "") {
    static size_t step_id = 0;
    static const std::map<size_t, std::string> step_names = {
        {1, "Parsing and validating input arguments"},
-        {2, "Loading Inference Engine"},
+        {2, "Loading OpenVINO Runtime"},
        {3, "Setting device configuration"},
        {4, "Reading network files"},
        {5, "Resizing network to match image sizes and given batch"},
@@ -203,7 +203,7 @@ int main(int argc, char* argv[]) {
        /** This vector stores paths to the processed images with input names**/
        auto inputFiles = parse_input_arguments(gflags::GetArgvs());

-        // ----------------- 2. Loading the Inference Engine
+        // ----------------- 2. Loading the OpenVINO Runtime
        // -----------------------------------------------------------
        next_step();

@@ -1098,7 +1098,7 @@ int main(int argc, char* argv[]) {

        if (!FLAGS_dump_config.empty()) {
            dump_config(FLAGS_dump_config, config);
-            slog::info << "Inference Engine configuration settings were dumped to " << FLAGS_dump_config << slog::endl;
+            slog::info << "OpenVINO Runtime configuration settings were dumped to " << FLAGS_dump_config << slog::endl;
        }

        if (!FLAGS_exec_graph_path.empty()) {