Port to master (#6279)

* add single_layer_test for Interpolate-1 (#6133)

* Fixed InferenceEngineConfig.cmake usage in include() (#6136)

* Deprecated API updates (#6252)

* Fixed links to OMZ / DL Streamer (#6257)

* Added doxygen deprecation for LowLatency

Co-authored-by: Elena Gvozdeva <elena.gvozdeva@intel.com>

README.md

@@ -42,7 +42,7 @@ Please report questions, issues and suggestions using:
 ---
 \* Other names and brands may be claimed as the property of others.
 
-[Open Model Zoo]:https://github.com/opencv/open_model_zoo
+[Open Model Zoo]:https://github.com/openvinotoolkit/open_model_zoo
 [Inference Engine]:https://software.intel.com/en-us/articles/OpenVINO-InferEngine
 [Model Optimizer]:https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer
 [nGraph]:https://docs.openvinotoolkit.org/latest/openvino_docs_nGraph_DG_DevGuide.html

docs/IE_DG/API_Changes.md

@@ -10,10 +10,14 @@ The sections below contain detailed list of changes made to the Inference Engine
 
 ### Deprecated API
 
+ **InferenceEngine::Parameter**
+
  * InferenceEngine::Parameter(const std::shared_ptr<ngraph::Variant>&)
  * InferenceEngine::Parameter(std::shared_ptr<ngraph::Variant>& var)
  * std::shared_ptr<ngraph::Variant> InferenceEngine::Parameter::asVariant() const
  * InferenceEngine::Parameter::operator std::shared_ptr<ngraph::Variant>() const
+
+ **GPU plugin configuration keys**
  * KEY_CLDNN_NV12_TWO_INPUTS GPU plugin option. Use KEY_GPU_NV12_TWO_INPUTS instead
  * KEY_CLDNN_PLUGIN_PRIORITY GPU plugin option. Use KEY_GPU_PLUGIN_PRIORITY instead
  * KEY_CLDNN_PLUGIN_THROTTLE GPU plugin option. Use KEY_GPU_PLUGIN_THROTTLE instead
@@ -24,6 +28,38 @@ The sections below contain detailed list of changes made to the Inference Engine
  * KEY_TUNING_MODE GPU plugin option
  * KEY_TUNING_FILE GPU plugin option
 
+ **InferenceEngine::IInferRequest**
+ * IInferRequest interface is deprecated, use InferRequest wrapper:
+  * Constructor for InferRequest from IInferRequest:: Ptr is deprecated
+  * Cast operator for InferRequest to IInferRequest shared pointer is deprecated
+
+ **InferenceEngine::ICNNNetwork**
+ * ICNNNetwork interface is deprecated by means of deprecation of all its methods, use CNNNetwork wrapper
+  * CNNNetwork methods working with ICNNNetwork are deprecated:
+  * Cast to ICNNNetwork shared pointer
+  * Cast to reference to ICNNNetwork interface
+  * Constructor from ICNNNetwork shared pointer
+
+ **InferenceEngine::IExecutableNetwork**
+ * IExecutableNetwork is deprecated, use ExecutableNetwork wrappers:
+  * Constructor of ExecutableNetwork from IExecutableNetwork shared pointer is deprecated
+ * The following ExecutableNetwork methods are deprecated:
+  * ExecutableNetwork::reset
+  * Cast operator to IExecutableNetwork shared pointer
+  * ExecutableNetwork::CreateInferRequestPtr - use ExecutableNetwork::CreateInferRequest instead
+
+ **Extensions API**
+ * InferenceEngine::make_so_pointer which is used to create Extensions library is replaced by std::make_shared<Extension>(..)
+ * InferenceEngine::IExtension::Release is deprecated with no replacement
+ * Use IE_DEFINE_EXTENSION_CREATE_FUNCTION helper macro instead of explicit declaration of CreateExtension function, which create extension.
+
+ **Other changes**
+ * Version::ApiVersion structure is deprecated, Inference Engine does not have API version anymore
+ * LowLatency - use lowLatency2 instead
+ * CONFIG_KEY(DUMP_EXEC_GRAPH_AS_DOT) - use InferenceEngine::ExecutableNetwork::GetExecGraphInfo::serialize() instead
+ * Core::ImportNetwork with no device - pass device name explicitly.
+ * details::InferenceEngineException - use InferenceEngine::Exception and its derivatives instead.
+
 ## 2021.3
 
 ### New API

docs/benchmarks/performance_benchmarks_faq.md

@@ -6,7 +6,7 @@ The following questions and answers are related to [performance benchmarks](./pe
 New performance benchmarks are typically published on every `major.minor` release of the Intel® Distribution of OpenVINO™ toolkit.
 
 #### 2. Where can I find the models used in the performance benchmarks?
-All of the models used are included in the toolkit's [Open Model Zoo](https://github.com/opencv/open_model_zoo) GitHub repository. 
+All of the models used are included in the toolkit's [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) GitHub repository. 
 
 #### 3. Will there be new models added to the list used for benchmarking?
 The models used in the performance benchmarks were chosen based on general adoption and usage in deployment scenarios. We're continuing to add new models that support a diverse set of workloads and usage.
@@ -21,23 +21,23 @@ All of the performance benchmarks were generated using the open-sourced tool wit
 The image size used in the inference depends on the network being benchmarked. The following table shows the list of input sizes for each network model.
 |   **Model**																														 |   **Public Network**                    |     **Task**                | **Input Size** (Height x Width)   |
 |------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------|-----------------------------|-----------------------------------|
-|    [bert-large-uncased-whole-word-masking-squad](https://github.com/opencv/open_model_zoo/tree/develop/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001)   | 	BERT-large	|question / answer	|384|
-|    [deeplabv3-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/deeplabv3)                                    |	  DeepLab v3 Tf	                       |semantic segmentation	     |    513x513                          |
+|    [bert-large-uncased-whole-word-masking-squad](https://github.com/openvinotoolkit/open_model_zoo/tree/develop/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001)   | 	BERT-large	|question / answer	|384|
+|    [deeplabv3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/deeplabv3)                                    |	  DeepLab v3 Tf	                       |semantic segmentation	     |    513x513                          |
 |    [densenet-121-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/densenet-121-tf)                  | 	  Densenet-121 Tf	                   |classification	    |    224x224                 |
-|    [facenet-20180408-102900-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/facenet-20180408-102900)        |    FaceNet TF                           |    face recognition         |    160x160                        |
-|    [faster_rcnn_resnet50_coco-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/faster_rcnn_resnet50_coco)    |    Faster RCNN Tf                       |    object detection           |    600x1024					     |
+|    [facenet-20180408-102900-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/facenet-20180408-102900)        |    FaceNet TF                           |    face recognition         |    160x160                        |
+|    [faster_rcnn_resnet50_coco-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/faster_rcnn_resnet50_coco)    |    Faster RCNN Tf                       |    object detection           |    600x1024					     |
 |    [googlenet-v1-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v1-tf)				     |    GoogLeNet_ILSVRC-2012                |    classification           |    224x224				  |
-|    [inception-v3-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/googlenet-v3)								 |    Inception v3 Tf                      |    classification           |    299x299				  |
-|    [mobilenet-ssd-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-ssd)						     |    SSD (MobileNet)_COCO-2017_Caffe      |    object detection         |    300x300						 |
+|    [inception-v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/googlenet-v3)								 |    Inception v3 Tf                      |    classification           |    299x299				  |
+|    [mobilenet-ssd-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-ssd)						     |    SSD (MobileNet)_COCO-2017_Caffe      |    object detection         |    300x300						 |
 |    [mobilenet-v1-1.0-224-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v1-1.0-224-tf)  |    MobileNet v1 Tf                      |    classification    |    224x224                        |
-|    [mobilenet-v2-1.0-224-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224)			     |    MobileNet v2 Tf                      |    classification           |    224x224						 |
+|    [mobilenet-v2-1.0-224-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224)			     |    MobileNet v2 Tf                      |    classification           |    224x224						 |
 |    [mobilenet-v2-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-pytorch )		 |    Mobilenet V2 PyTorch                 |    classification           |    224x224					     |
-|    [resnet-18-pytorch](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-18-pytorch)		  			     |    ResNet-18 PyTorch                    |    classification           |    224x224						 |
+|    [resnet-18-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-18-pytorch)		  			     |    ResNet-18 PyTorch                    |    classification           |    224x224						 |
 |    [resnet-50-pytorch](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-pytorch)              |    ResNet-50 v1 PyTorch                 |    classification           |    224x224                        |
-|    [resnet-50-TF](https://github.com/opencv/open_model_zoo/tree/master/models/public/resnet-50-tf)								 |    ResNet-50_v1_ILSVRC-2012             |    classification           |    224x224						 |
-|    [se-resnext-50-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/se-resnext-50)						     |    Se-ResNext-50_ILSVRC-2012_Caffe      |    classification           |    224x224						 |
-|    [squeezenet1.1-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/squeezenet1.1)						     |    SqueezeNet_v1.1_ILSVRC-2012_Caffe    |    classification           |    227x227						 |
-|    [ssd300-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/ssd300)										     |    SSD (VGG-16)_VOC-2007_Caffe          |    object detection         |    300x300						 |
+|    [resnet-50-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf)								 |    ResNet-50_v1_ILSVRC-2012             |    classification           |    224x224						 |
+|    [se-resnext-50-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/se-resnext-50)						     |    Se-ResNext-50_ILSVRC-2012_Caffe      |    classification           |    224x224						 |
+|    [squeezenet1.1-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/squeezenet1.1)						     |    SqueezeNet_v1.1_ILSVRC-2012_Caffe    |    classification           |    227x227						 |
+|    [ssd300-CF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd300)										     |    SSD (VGG-16)_VOC-2007_Caffe          |    object detection         |    300x300						 |
 |    [yolo_v3-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tf)                            | 	  TF Keras YOLO v3 Modelset            |	 object detection	      |    300x300                        |
 |    [yolo_v4-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v4-tf)                            | 	  Yolo-V4 TF                           |	 object detection	     |    608x608                        |
 |    [ssd_mobilenet_v1_coco-TF](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_coco)   |    ssd_mobilenet_v1_coco                |    object detection         |    300x300                        |

docs/get_started/get_started_linux.md

@@ -227,7 +227,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
 - Train your own model.
         

docs/get_started/get_started_macos.md

@@ -211,7 +211,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader). 
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader). 
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
 - Train your own model.
         

docs/get_started/get_started_raspbian.md

@@ -13,7 +13,7 @@ On Raspbian* OS, the OpenVINO™ toolkit consists of the following components:
 
 > **NOTE**:
 > * The OpenVINO™ package for Raspberry* does not include the [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To convert models to Intermediate Representation (IR), you need to install it separately to your host machine.
-> * The package does not include the Open Model Zoo demo applications. You can download them separately from the [Open Models Zoo repository](https://github.com/opencv/open_model_zoo).
+> * The package does not include the Open Model Zoo demo applications. You can download them separately from the [Open Models Zoo repository](https://github.com/openvinotoolkit/open_model_zoo).
 
 In addition, [code samples](../IE_DG/Samples_Overview.md) are provided to help you get up and running with the toolkit.
 
@@ -43,7 +43,7 @@ The primary tools for deploying your models and applications are installed to th
 The OpenVINO™ workflow on Raspbian* OS is as follows:
 1. **Get a pre-trained model** for your inference task. If you want to use your model for inference, the model must be converted to the `.bin` and `.xml` Intermediate Representation (IR) files, which are used as input by Inference Engine. On Raspberry PI, OpenVINO™ toolkit includes only the Inference Engine module. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
    
-   * Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
+   * Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
     <br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_group_intel)
    
    * Convert a model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:

docs/get_started/get_started_windows.md

@@ -211,7 +211,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader).
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader).
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
 - Train your own model.
         

docs/index.md

@@ -94,12 +94,12 @@ Intel® Distribution of OpenVINO™ toolkit includes the following components:
 - [Open Model Zoo](@ref omz_models_group_intel)     
     - [Demos](@ref omz_demos): Console applications that provide robust application templates to help you implement specific deep learning scenarios.
     - Additional Tools: A set of tools to work with your models including [Accuracy Checker Utility](@ref omz_tools_accuracy_checker) and [Model Downloader](@ref omz_tools_downloader).
-    - [Documentation for Pretrained Models](@ref omz_models_group_intel): Documentation for pre-trained models that are available in the [Open Model Zoo repository](https://github.com/opencv/open_model_zoo).
-- Deep Learning Streamer (DL Streamer): Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open-source version is available on [GitHub](https://github.com/opencv/gst-video-analytics). For the DL Streamer documentation, see:
+    - [Documentation for Pretrained Models](@ref omz_models_group_intel): Documentation for pre-trained models that are available in the [Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo).
+- Deep Learning Streamer (DL Streamer): Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open-source version is available on [GitHub](https://github.com/openvinotoolkit/dlstreamer_gst). For the DL Streamer documentation, see:
     - [DL Streamer Samples](@ref gst_samples_README)
     - [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/)
-    - [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements)
-    - [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial)
+    - [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements)
+    - [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial)
 - [OpenCV](https://docs.opencv.org/master/) : OpenCV* community version compiled for Intel® hardware
 - [Intel® Media SDK](https://software.intel.com/en-us/media-sdk) (in Intel® Distribution of OpenVINO™ toolkit for Linux only)
 

docs/install_guides/installing-openvino-apt.md

@@ -14,7 +14,7 @@ The following components are installed with the OpenVINO runtime package:
 |-----------|------------|
 | [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)| The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
 | [OpenCV*](https://docs.opencv.org/master/) | OpenCV* community version compiled for Intel® hardware. |
-| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
+| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
 
 ## Included with Developer Package
 
@@ -28,8 +28,8 @@ The following components are installed with the OpenVINO developer package:
 | [Sample Applications](../IE_DG/Samples_Overview.md)           | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
 | [Demo Applications](@ref omz_demos) | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use cases. |
 | Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
-| Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer\*, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo).  |
+| Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer\*, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
 
 ## Set up the Repository
 ### Install the GPG key for the repository

docs/install_guides/installing-openvino-linux.md

@@ -28,8 +28,8 @@ The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
 | [Inference Engine Code Samples](../IE_DG/Samples_Overview.md)           | A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
 | [Demo Applications](@ref omz_demos)           | A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios. |
 | Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
-| Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo).  |
+| Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
 
 **Could Be Optionally Installed**
 

docs/install_guides/installing-openvino-macos.md

@@ -29,7 +29,7 @@ The following components are installed by default:
 | [Sample Applications](../IE_DG/Samples_Overview.md)                                                                                | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
 | [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
 | Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo)  |
 
 **Could Be Optionally Installed**
 

docs/install_guides/installing-openvino-raspbian.md

@@ -28,7 +28,7 @@ The OpenVINO toolkit for Raspbian OS is an archive with pre-installed header fil
 
 > **NOTE**:
 > * The package does not include the [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To convert models to Intermediate Representation (IR), you need to install it separately to your host machine.
-> * The package does not include the Open Model Zoo demo applications. You can download them separately from the [Open Models Zoo repository](https://github.com/opencv/open_model_zoo).
+> * The package does not include the Open Model Zoo demo applications. You can download them separately from the [Open Models Zoo repository](https://github.com/openvinotoolkit/open_model_zoo).
 
 ## Development and Target Platforms
 
@@ -166,7 +166,7 @@ Read the next topic if you want to learn more about OpenVINO workflow for Raspbe
 
 If you want to use your model for inference, the model must be converted to the .bin and .xml Intermediate Representation (IR) files that are used as input by Inference Engine. OpenVINO™ toolkit support on Raspberry Pi only includes the Inference Engine module of the Intel® Distribution of OpenVINO™ toolkit. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
 
-* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
+* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
 
    For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_group_intel)
 

docs/install_guides/installing-openvino-windows.md

@@ -62,7 +62,7 @@ The following components are installed by default:
 |[Inference Engine Samples](../IE_DG/Samples_Overview.md)                             |A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications.  |
 | [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
 | Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo)  |
 
 **Could Be Optionally Installed**
 

docs/install_guides/installing-openvino-yum.md

@@ -16,7 +16,7 @@ The following components are installed with the OpenVINO runtime package:
 |-----------|------------|
 | [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)| The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
 | [OpenCV*](https://docs.opencv.org/master/) | OpenCV* community version compiled for Intel® hardware. |
-| Deep Learning Stream (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
+| Deep Learning Stream (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
 
 ## Set up the Repository
 

docs/optimization_guide/dldt_optimization_guide.md

@@ -142,7 +142,7 @@ CPU plugin completely relies on the Intel® Math Kernel Library for Deep Neur
 
 The only hint you can get from that is how the major primitives are accelerated (and you cannot change this). For example, on the Core machines, you should see variations of the `jit_avx2` when inspecting the <a href="#performance-counters">internal inference performance counters</a> (and additional '_int8' postfix for [int8 inference](../IE_DG/Int8Inference.md)). If you are an advanced user, you can further trace the CPU execution with (see <a href="#vtune-examples">Intel&reg; VTune&trade;</a>).
 
-Internally, the Inference Engine has a threading abstraction level, which allows for compiling the [open source version](https://github.com/opencv/dldt) with either Intel&reg; Threading Building Blocks (Intel&reg; TBB) which is now default, or OpenMP* as an alternative parallelism solution. When using inference on the CPU, this is particularly important to align threading model with the rest of your application (and any third-party libraries that you use) to avoid oversubscription. For more information, see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a> section.
+Internally, the Inference Engine has a threading abstraction level, which allows for compiling the [open source version](https://github.com/openvinotoolkit/openvino) with either Intel&reg; Threading Building Blocks (Intel&reg; TBB) which is now default, or OpenMP* as an alternative parallelism solution. When using inference on the CPU, this is particularly important to align threading model with the rest of your application (and any third-party libraries that you use) to avoid oversubscription. For more information, see <a href="#note-on-app-level-threading">Note on the App-Level Threading</a> section.
 
  Since R1 2019, the OpenVINO&trade; toolkit comes pre-compiled with Intel TBB,
  so any  OpenMP* API or environment settings (like `OMP_NUM_THREADS`) has no effect.
@@ -348,7 +348,7 @@ If you are building an app-level pipeline with third-party components like GStre
 - As explained in the <a href="#cpu-checklist">CPU Checklist</a> section, by default the Inference Engine uses Intel TBB as a parallel engine. Thus, any OpenVINO-internal threading (including CPU inference) uses the same threads pool, provided by the TBB. But there are also other threads in your application, so oversubscription is possible at the application level:
 - The rule of thumb is that you should try to have the overall number of active threads in your application equal to the number of cores in your machine. Keep in mind the spare core(s) that the OpenCL driver under the GPU plugin might also need.
 - One specific workaround to limit the number of threads for the Inference Engine is using the [CPU configuration options](../IE_DG/supported_plugins/CPU.md).
-- To avoid further oversubscription, use the same threading model in all modules/libraries that your application uses. Notice that third party components might bring their own threading. For example, using Inference Engine which is now compiled with the TBB by default might lead to [performance troubles](https://www.threadingbuildingblocks.org/docs/help/reference/appendices/known_issues/interoperability.html) when mixed in the same app with another computationally-intensive library, but compiled with OpenMP. You can try to compile the [open source version](https://github.com/opencv/dldt) of the Inference Engine to use the OpenMP as well. But notice that in general, the TBB offers much better composability, than other threading solutions.
+- To avoid further oversubscription, use the same threading model in all modules/libraries that your application uses. Notice that third party components might bring their own threading. For example, using Inference Engine which is now compiled with the TBB by default might lead to [performance troubles](https://www.threadingbuildingblocks.org/docs/help/reference/appendices/known_issues/interoperability.html) when mixed in the same app with another computationally-intensive library, but compiled with OpenMP. You can try to compile the [open source version](https://github.com/openvinotoolkit/openvino) of the Inference Engine to use the OpenMP as well. But notice that in general, the TBB offers much better composability, than other threading solutions.
 - If your code (or third party libraries) uses GNU OpenMP, the Intel&reg; OpenMP (if you have recompiled Inference Engine with that) must be initialized first. This can be achieved by linking your application with the Intel OpenMP instead of GNU OpenMP, or using `LD_PRELOAD` on Linux* OS.
 
 ### Letting the Inference Engine Accelerate Image Pre-processing/Conversion <a name="image-preprocessing"></a>

inference-engine/cmake/templates/InferenceEngineConfig.cmake.in

@@ -29,6 +29,11 @@
 # Common functions
 #
 
+if(NOT DEFINED CMAKE_FIND_PACKAGE_NAME)
+    set(CMAKE_FIND_PACKAGE_NAME InferenceEngine)
+    set(_need_package_name_reset ON)
+endif()
+
 # we have to use our own version of find_dependency because of support cmake 3.7
 macro(_ie_find_dependency dep)
     set(cmake_fd_quiet_arg)
@@ -138,3 +143,8 @@ unset(IE_PACKAGE_PREFIX_DIR)
 set_and_check(InferenceEngine_INCLUDE_DIRS "@PACKAGE_IE_INCLUDE_DIR@")
 
 check_required_components(${CMAKE_FIND_PACKAGE_NAME})
+
+if(_need_package_name_reset)
+    unset(CMAKE_FIND_PACKAGE_NAME)
+    unset(_need_package_name_reset)
+endif()

inference-engine/include/ie_transformations.hpp

@@ -16,6 +16,7 @@
 namespace InferenceEngine {
 
 /**
+ * @deprecated Use InferenceEngine::lowLatency2 instead
  * @brief The transformation finds all TensorIterator layers in the network, processes all back
  * edges that describe a connection between Result and Parameter of the TensorIterator body,
  * and inserts ReadValue layer between Parameter and the next layers after this Parameter,
@@ -50,7 +51,6 @@ namespace InferenceEngine {
  *    network->infer (...)  // Using stored states, calculating new values for states.
  *
  * @param network A network to apply LowLatency transformation
- * *
  */
 
 INFERENCE_ENGINE_DEPRECATED("This transformation will be removed in 2023.1. "
@@ -84,7 +84,6 @@ INFERENCE_ENGINE_API_CPP(void) LowLatency(InferenceEngine::CNNNetwork& network);
           If "true", then the transformation inserts Constant before ReadValue operation.
           If "false, then the transformation leaves existed initializing subgraph for ReadValue operation.
  * Loop operation by a given number. Does not affect TensorIterators.
- * *
  */
 INFERENCE_ENGINE_API_CPP(void) lowLatency2(InferenceEngine::CNNNetwork& network,
                                            bool use_const_initializer = true);

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/interpolate.hpp

@@ -51,4 +51,27 @@ protected:
     void SetUp() override;
 };
 
+//Interpolate-1 test
+typedef std::tuple<
+        InferenceEngine::Precision,        // Net precision
+        InferenceEngine::Precision,        // Input precision, output is the same
+        InferenceEngine::Layout,           // Input layout, output is the same
+        InferenceEngine::SizeVector,       // Input shapes
+        InferenceEngine::SizeVector,       // Target shapes
+        std::string,                       // InterpolateMode
+        ngraph::AxisSet,                   // Axes
+        bool,                              // AntiAlias
+        std::vector<size_t>,               // Pads
+        LayerTestsUtils::TargetDevice      // Device name
+> Interpolate1LayerTestParams;
+
+class Interpolate1LayerTest : public testing::WithParamInterface<Interpolate1LayerTestParams>,
+                             virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<Interpolate1LayerTestParams> obj);
+
+protected:
+    void SetUp() override;
+};
+
 }  // namespace LayerTestsDefinitions

inference-engine/tests/functional/shared_test_classes/src/single_layer/interpolate.cpp

@@ -94,4 +94,69 @@ void InterpolateLayerTest::SetUp() {
     const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(interpolate)};
     function = std::make_shared<ngraph::Function>(results, params, "interpolate");
 }
+
+//Interpolate-1
+
+std::string Interpolate1LayerTest::getTestCaseName(testing::TestParamInfo<Interpolate1LayerTestParams> obj) {
+    InferenceEngine::Precision netPrecision;
+    InferenceEngine::Precision dataPrecision;
+    InferenceEngine::Layout dataLayout;
+    InferenceEngine::SizeVector inputShapes, targetShapes;
+    std::string mode;
+    ngraph::AxisSet axes;
+    bool antialias;
+    std::vector<size_t> pads;
+    std::string targetDevice;
+    std::tie(netPrecision, dataPrecision, dataLayout, inputShapes, targetShapes,
+             mode, axes, antialias, pads, targetDevice) = obj.param;
+
+    std::ostringstream result;
+    result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
+    result << "TS=" << CommonTestUtils::vec2str(targetShapes) << "_";
+    result << "InterpolateMode=" << mode << "_";
+    result << "Antialias=" << antialias << "_";
+    result << "PB=" << CommonTestUtils::vec2str(pads) << "_";
+    result << "PE=" << CommonTestUtils::vec2str(pads) << "_";
+    result << "Axes=" << CommonTestUtils::vec2str(axes.to_vector()) << "_";
+    result << "netPRC=" << netPrecision.name() << "_";
+    result << "PRC=" << dataPrecision.name() << "_";
+    result << "Layout=" << dataLayout << "_";
+    result << "trgDev=" << targetDevice;
+    return result.str();
+}
+
+void Interpolate1LayerTest::SetUp() {
+    std::vector<size_t> inputShape, targetShape;
+    auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
+    InferenceEngine::Precision dataPrecision;
+    InferenceEngine::Layout dataLayout;
+    std::string mode;
+    ngraph::AxisSet axes;
+    bool antialias;
+    std::vector<size_t> pads;
+    std::tie(netPrecision, dataPrecision, dataLayout, inputShape, targetShape,
+             mode, axes, antialias, pads, targetDevice) = this->GetParam();
+
+    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+    auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+
+    auto sizesConst = ngraph::opset3::Constant(ngraph::element::Type_t::i64, {targetShape.size()}, targetShape);
+    auto sizesInput = std::make_shared<ngraph::opset3::Constant>(sizesConst);
+
+    bool align_corners = true;
+
+    ngraph::op::v0::InterpolateAttrs interpolateAttributes;
+    interpolateAttributes.axes = axes;
+    interpolateAttributes.mode = mode;
+    interpolateAttributes.align_corners = align_corners;
+    interpolateAttributes.antialias = antialias;
+    interpolateAttributes.pads_begin = pads;
+    interpolateAttributes.pads_end = pads;
+
+    auto interpolate = std::make_shared<ngraph::op::v0::Interpolate>(params[0], sizesInput, interpolateAttributes);
+
+    const ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(interpolate)};
+    function = std::make_shared<ngraph::Function>(results, params, "interpolate");
+}
+
 }  // namespace LayerTestsDefinitions

tests/stress_tests/README.md

@@ -87,4 +87,4 @@ grep -rh ./MemCheckTests-logs -e ".*<model " | sed -e "s/.*<model /<model /" | s
 
 [VDP-shared-folders]: https://wiki.ith.intel.com/display/DLSDK/VDP+shared+folders
 [gtest-parallel]: https://github.com/google/gtest-parallel
-[open_model_zoo]: https://github.com/opencv/open_model_zoo
+[open_model_zoo]: https://github.com/openvinotoolkit/open_model_zoo

tests/stress_tests/scripts/get_testdata.py

@@ -124,7 +124,7 @@ def main():
         if os.path.exists(str(omz_path)):
             shutil.rmtree(str(omz_path))
         cmd = 'git clone --single-branch --branch develop' \
-              ' https://github.com/opencv/open_model_zoo {omz_path}'.format(omz_path=omz_path)
+              ' https://github.com/openvinotoolkit/open_model_zoo {omz_path}'.format(omz_path=omz_path)
         run_in_subprocess(cmd)
 
     # prepare virtual environment and install requirements

tools/benchmark/README.md

@@ -189,4 +189,4 @@ Throughput: 73.28 FPS
 ## See Also
 * [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader)
+* [Model Downloader](https://github.com/openvinotoolkit/open_model_zoo/tree/2018/model_downloader)