Merge pull request slyalin#76 from nosovmik/rebase13

Rebase13

.ci/azure/linux.yml

@@ -29,6 +29,7 @@ jobs:
     MODELS_PATH: $(REPO_DIR)/../testdata
     WORK_DIR: $(Pipeline.Workspace)/_w
     BUILD_DIR: $(WORK_DIR)/build
+    BUILD_SAMPLES_DIR: $(WORK_DIR)/build_samples
     BIN_DIR: $(REPO_DIR)/bin/intel64/$(BUILD_TYPE)
     INSTALL_DIR: $(WORK_DIR)/install_pkg
     SETUPVARS: $(INSTALL_DIR)/bin/setupvars.sh
@@ -56,6 +57,7 @@ jobs:
   - script: |
       rm -rf $(WORK_DIR) ; mkdir $(WORK_DIR)
       rm -rf $(BUILD_DIR) ; mkdir $(BUILD_DIR)
+      rm -rf $(BUILD_SAMPLES_DIR) ; mkdir $(BUILD_SAMPLES_DIR)
       echo TargetBranch: $(System.PullRequest.TargetBranch)
       echo SourceBranch: $(Build.SourceBranch)
     displayName: 'Make dir'
@@ -119,12 +121,23 @@ jobs:
     displayName: 'Build Lin'
 
   - script: ls -alR $(REPO_DIR)/bin/
-    displayName: 'List files'
+    displayName: 'List bin files'
 
   - script: cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -P cmake_install.cmake
     workingDirectory: $(BUILD_DIR)
     displayName: 'Install'
 
+  - script: ls -alR $(INSTALL_DIR)
+    displayName: 'List install files'
+
+  - script: $(INSTALL_DIR)/deployment_tools/inference_engine/samples/cpp/build_samples.sh
+    workingDirectory: $(BUILD_SAMPLES_DIR)
+    displayName: 'Build cpp samples'
+
+  - script: $(INSTALL_DIR)/deployment_tools/inference_engine/samples/c/build_samples.sh
+    workingDirectory: $(BUILD_SAMPLES_DIR)
+    displayName: 'Build c samples'
+
   - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml
     displayName: 'nGraph UT'
     continueOnError: false

.ci/azure/windows.yml

@@ -29,6 +29,7 @@ jobs:
     MODELS_PATH: $(REPO_DIR)\..\testdata
     WORK_DIR: $(Pipeline.Workspace)\_w
     BUILD_DIR: D:\build
+    BUILD_SAMPLES_DIR: D:\build_samples
     BIN_DIR: $(REPO_DIR)\bin\intel64
     MSVS_VARS_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat
     MSVC_COMPILER_PATH: C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Tools\MSVC\14.24.28314\bin\Hostx64\x64\cl.exe
@@ -56,6 +57,7 @@ jobs:
   - script: |
       rd /Q /S $(WORK_DIR) & mkdir $(WORK_DIR)
       rd /Q /S $(BUILD_DIR) & mkdir $(BUILD_DIR)
+      rd /Q /S $(BUILD_SAMPLES_DIR) & mkdir $(BUILD_SAMPLES_DIR)
     displayName: 'Make dir'
 
   - script: |
@@ -101,12 +103,23 @@ jobs:
     displayName: 'Build Win'
 
   - script: dir $(REPO_DIR)\bin\ /s
-    displayName: 'List files'
+    displayName: 'List bin files'
 
   - script: cmake -DCMAKE_INSTALL_PREFIX=$(INSTALL_DIR) -P cmake_install.cmake
     workingDirectory: $(BUILD_DIR)
     displayName: 'Install'
 
+  - script: dir $(INSTALL_DIR) /s
+    displayName: 'List install files'
+
+  - script: $(INSTALL_DIR)\deployment_tools\inference_engine\samples\cpp\build_samples_msvc.bat
+    workingDirectory: $(BUILD_SAMPLES_DIR)
+    displayName: 'Build cpp samples'
+
+  - script: $(INSTALL_DIR)\deployment_tools\inference_engine\samples\c\build_samples_msvc.bat
+    workingDirectory: $(BUILD_SAMPLES_DIR)
+    displayName: 'Build c samples'
+
   - script: |
       set PATH=$(TEST_ENV_PATH)
       $(BIN_DIR)\unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-NGraphUT.xml

docs/IE_DG/Samples_Overview.md

@@ -9,7 +9,9 @@ After installation of Intel® Distribution of OpenVINO™ toolkit, С, C++ and P
 
 Inference Engine sample applications include the following:
 
-- **[Automatic Speech Recognition C++ Sample](../../inference-engine/samples/speech_sample/README.md)** – Acoustic model inference based on Kaldi neural networks and speech feature vectors.
+- **Speech Sample** - Acoustic model inference based on Kaldi neural networks and speech feature vectors.
+   - [Automatic Speech Recognition C++ Sample](../../inference-engine/samples/speech_sample/README.md)
+   - [Automatic Speech Recognition Python Sample](../../inference-engine/ie_bridges/python/sample/speech_sample/README.md)
 - **Benchmark Application** – Estimates deep learning inference performance on supported devices for synchronous and asynchronous modes.
    - [Benchmark C++ Tool](../../inference-engine/samples/benchmark_app/README.md)
    - [Benchmark Python Tool](../../inference-engine/tools/benchmark_tool/README.md)

docs/IE_DG/supported_plugins/MULTI.md

@@ -85,7 +85,7 @@ Notice that until R2 you had to calculate number of requests in your application
 @snippet snippets/MULTI5.cpp part5
 
 ## Using the Multi-Device with OpenVINO Samples and Benchmarking the Performance
-Notice that every OpenVINO sample that supports "-d" (which stays for "device") command-line option transparently accepts the multi-device.
+Notice that every OpenVINO sample that supports "-d" (which stands for "device") command-line option transparently accepts the multi-device.
 The [Benchmark Application](../../../inference-engine/samples/benchmark_app/README.md) is the best reference to the optimal usage of the multi-device. As discussed multiple times earlier, you don't need to setup number of requests, CPU streams or threads as the application provides optimal out of the box performance.
 Below is example command-line to evaluate HDDL+GPU performance with that:
 

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

@@ -1,10 +1,13 @@
-# Convert ONNX* DLRM to the Intermediate Representation {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM}
+[DEPRECATED] Convert ONNX* DLRM to the Intermediate Representation {#openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM}
+===============================
 
 > **NOTE**: These instructions are currently deprecated. Since OpenVINO™ 2020.4 version, no specific steps are needed to convert ONNX\* DLRM models. For general instructions on converting ONNX models, please refer to [Converting a ONNX* Model](../Convert_Model_From_ONNX.md) topic.
 
 These instructions are applicable only to the DLRM converted to the ONNX* file format from the [facebookresearch/dlrm model](https://github.com/facebookresearch/dlrm).
 
-**Step 1**. Save trained Pytorch* model to ONNX* format. If you train the model using the [script provided in model repository](https://github.com/facebookresearch/dlrm/blob/master/dlrm_s_pytorch.py), just add the `--save-onnx` flag to the command line parameters and you'll get the `dlrm_s_pytorch.onnx` file containing the model serialized in ONNX* format.
+**Step 1**. Save trained Pytorch* model to ONNX* format or download pretrained ONNX* from 
+[MLCommons/inference/recommendation/dlrm](https://github.com/mlcommons/inference/tree/r1.0/recommendation/dlrm/pytorch#supported-models) repository. 
+If you train the model using the [script provided in model repository](https://github.com/facebookresearch/dlrm/blob/master/dlrm_s_pytorch.py), just add the `--save-onnx` flag to the command line parameters and you'll get the `dlrm_s_pytorch.onnx` file containing the model serialized in ONNX* format.
 
 **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:
 ```sh

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_lm_1b_From_Tensorflow.md

@@ -2,7 +2,7 @@
 
 ## Download the Pre-trained Language Model on One Billion Word Benchmark
 
-TensorFlow* provides [a pre-trained Language Model on One Billion Word Benchmark](https://github.com/tensorflow/models/tree/master/research/lm_1b).
+TensorFlow* provides [a pre-trained Language Model on One Billion Word Benchmark](https://github.com/tensorflow/models/tree/r2.3.0/research/lm_1b).
 
 To download the model for IR conversion, please follow the instruction:
 1. Create new directory to store the model:

docs/doxygen/ie_docs.xml

@@ -53,12 +53,12 @@ limitations under the License.
                             <tab type="user" title="Convert ONNX* Faster R-CNN Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Faster_RCNN"/>
                             <tab type="user" title="Convert ONNX* Mask R-CNN Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN"/>
                             <tab type="user" title="Convert ONNX* GPT-2 Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2"/>
-                            <tab type="user" title="Convert DLRM ONNX* Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM"/>
+                            <tab type="user" title="[DEPRECATED] Convert DLRM ONNX* Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM"/>
                             <tab type="usergroup" title="Converting Your PyTorch* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch">
-                                <tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet"/>
-                                <tab type="user" title="Convert PyTorch* RNN-T Model " url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT"/>
-                                <tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
-                                <tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
+                            <tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet"/>
+                            <tab type="user" title="Convert PyTorch* RNN-T Model " url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT"/>
+                            <tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
+                            <tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
                             </tab>
                         </tab>
                         <tab type="user" title="Model Optimizations Techniques" url="@ref openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques"/>

docs/doxygen/openvino_docs.xml

@@ -180,6 +180,7 @@ limitations under the License.
                 <tab type="user" title="Object Detection SSD Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_object_detection_sample_ssd_README"/>
                 <tab type="user" title="Object Detection SSD C Sample" url="@ref openvino_inference_engine_ie_bridges_c_samples_object_detection_sample_ssd_README"/>
                 <tab type="user" title="Automatic Speech Recognition C++ Sample" url="@ref openvino_inference_engine_samples_speech_sample_README"/>
+                <tab type="user" title="Automatic Speech Recognition Python Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_speech_sample_README"/>
                 <tab type="user" title="Style Transfer C++ Sample" url="@ref openvino_inference_engine_samples_style_transfer_sample_README"/>
                 <tab type="user" title="Style Transfer Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_style_transfer_sample_README"/>
                 <tab type="user" title="Benchmark C++ Tool" url="@ref openvino_inference_engine_samples_benchmark_app_README"/>

docs/ops/image/Interpolate_4.md

@@ -89,18 +89,24 @@
 
 **Inputs**
 
-*   **1**: `data` - Input tensor with data for interpolation. Type of elements is any supported floating point type or `int8` type. Required.
+*   **1**: `data` - tensor of type `T` with data for interpolation. **Required.**
 
-*   **2**: `sizes` - 1D tensor describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. Required.
+*   **2**: `sizes` - 1D tensor of type `T_SIZE` describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.**
 
-*   **3**: `scales` - 1D tensor describing scales for spatial axes. Type of elements is any supported floating point type. Number and order of elements match the number and order of indices in `axes` input. Required.
+*   **3**: `scales` - 1D tensor of type `T_SCALES` describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.**
 
-*   **4**: `axes` - 1D tensor specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively.  Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. Optional with default value `[0,...,rank(data) - 1]`.
+*   **4**: `axes` - 1D tensor of type `T_AXES` specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively.  Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`.
 
 **Outputs**
 
 *   **1**: Resulting interpolated tensor with elements of the same type as input `data` tensor. The shape of the output matches input `data` shape except spatial dimensions mentioned in `axes` attribute. For other dimensions shape matches sizes from `sizes` in order specified in `axes`.
 
+**Types**
+* *T*: any supported numeric type.
+* *T_SIZE*: any supported integer type.
+* *T_SCALES*: any supported floating point type.
+* *T_AXES*: any supported integer type.
+
 
 **Detailed description**
 Calculations are performed according to the following rules.

docs/ops/normalization/LRN_1.md

@@ -6,70 +6,74 @@
 
 **Short description**: Local response normalization.
 
+**Detailed description**:
+Local Response Normalization performs a normalization over local input regions.
+Each input value is divided by
+\f[ (bias + \frac{alpha}{{size}^{len(axes)}} \cdot \sum_{i} data_{i})^{beta} \f]
+The sum is taken over a region of a side length `size` and number of dimensions equal to number of axes.
+The region is centered at the input value that's being normalized (with zero padding added if needed).
+
+Here is an example for 4D `data` input tensor and `axes = [1]`:
+```
+sqr_sum[a, b, c, d] =
+    sum(data[a, max(0, b - size / 2) : min(data.shape[1], b + size / 2 + 1), c, d] ** 2)
+output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
+```
+
+Example for 4D `data` input tensor and `axes = [2, 3]`:
+```
+sqr_sum[a, b, c, d] =
+    sum(data[a, b, max(0, c - size / 2) : min(data.shape[2], c + size / 2 + 1),  max(0, d - size / 2) : min(data.shape[3], d + size / 2 + 1)] ** 2)
+output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
+```
+
 **Attributes**:
 
 * *alpha*
 
-  * **Description**: *alpha* represents the scaling attribute for the normalizing sum. For example, *alpha* equal 0.0001 means that the normalizing sum is multiplied by 0.0001.
+  * **Description**: *alpha* represents the scaling attribute for the normalizing sum. For example, *alpha* equal `0.0001` means that the normalizing sum is multiplied by `0.0001`.
   * **Range of values**: no restrictions
-  * **Type**: float
+  * **Type**: `float`
   * **Default value**: None
   * **Required**: *yes*
 
 * *beta*
 
-  * **Description**: *beta* represents the exponent for the normalizing sum. For example, *beta* equal 0.75 means that the normalizing sum is raised to the power of 0.75.
+  * **Description**: *beta* represents the exponent for the normalizing sum. For example, *beta* equal `0.75` means that the normalizing sum is raised to the power of `0.75`.
   * **Range of values**: positive number
-  * **Type**: float
+  * **Type**: `float`
   * **Default value**: None
   * **Required**: *yes*
 
 * *bias*
 
   * **Description**: *bias* represents the offset. Usually positive number to avoid dividing by zero.
   * **Range of values**: no restrictions
-  * **Type**: float
+  * **Type**: `float`
   * **Default value**: None
   * **Required**: *yes*
 
 * *size*
 
   * **Description**: *size* represents the side length of the region to be used for the normalization sum. The region can have one or more dimensions depending on the second input axes indices.
   * **Range of values**: positive integer
-  * **Type**: int
+  * **Type**: `int`
   * **Default value**: None
   * **Required**: *yes*
 
 **Inputs**
 
-* **1**: `data` - input tensor of any floating point type and arbitrary shape. Required.
+* **1**: `data` - tensor of type `T` and arbitrary shape. **Required.**
 
-* **2**: `axes` - specifies indices of dimensions in `data` that define normalization slices. Required.
+* **2**: `axes` - 1D tensor of type `T_IND` which specifies indices of dimensions in `data` which define normalization slices. **Required.**
 
 **Outputs**
 
-* **1**: Output tensor of the same shape and type as the `data` input tensor.
+* **1**: Output tensor of type `T` and the same shape as the `data` input tensor.
 
-**Detailed description**:
-Local Response Normalization performs a normalization over local input regions.
-Each input value is divided by
-\f[ (bias + \frac{alpha}{{size}^{len(axes)}} \cdot \sum_{i} data_{i})^{beta} \f]
-The sum is taken over a region of a side length `size` and number of dimensions equal to number of axes.
-The region is centered at the input value that's being normalized (with zero padding added if needed).
-
-Here is an example for 4D `data` input tensor and `axes = [1]`:
-```
-sqr_sum[a, b, c, d] =
-    sum(data[a, max(0, b - size / 2) : min(data.shape[1], b + size / 2 + 1), c, d] ** 2)
-output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
-```
-
-Example for 4D `data` input tensor and `axes = [2, 3]`:
-```
-sqr_sum[a, b, c, d] =
-    sum(data[a, b, max(0, c - size / 2) : min(data.shape[2], c + size / 2 + 1),  max(0, d - size / 2) : min(data.shape[3], d + size / 2 + 1)] ** 2)
-output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
-```
+**Types**
+* *T*: any supported floating point type.
+* *T_IND*: any supported integer type.
 
 **Example**