Merge pull request #4 from openvinotoolkit/master

Update forked branch

.ci/azure/mac.yml

@@ -105,11 +105,11 @@ jobs:
     workingDirectory: $(BUILD_DIR)
     displayName: 'Install'
 
-  - script: $(BIN_DIR)/unit-test --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU*:IE_CPU.onnx_model_sigmoid --gtest_output=xml:TEST-NGraphUT.xml
+  - script: $(BIN_DIR)/unit-test --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
     displayName: 'nGraph UT'
     continueOnError: false
 
-  - script: $(BIN_DIR)/InferenceEngineUnitTests --gtest_print_time=1 --gtest_output=xml:TEST-InferenceEngineUnitTests.xml
+  - script: $(BIN_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
 

CMakeLists.txt

@@ -1,4 +1,4 @@
-# Copyright (C) 2018-2020 Intel Corporation
+# Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 #
 
@@ -142,6 +142,7 @@ function(openvino_developer_export_targets)
         "A list of OpenVINO exported components" FORCE)
 endfunction()
 
+add_subdirectory(thirdparty)
 add_subdirectory(openvino)
 build_ngraph()
 add_subdirectory(inference-engine)

cmake/toolchains/ia32.linux.toolchain.cmake

@@ -0,0 +1,27 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(CMAKE_CXX_FLAGS_INIT "-m32")
+set(CMAKE_C_FLAGS_INIT "-m32")
+
+set(CMAKE_SHARED_LINKER_FLAGS_INIT "-m32")
+set(CMAKE_MODULE_LINKER_FLAGS_INIT "-m32")
+set(CMAKE_EXE_LINKER_FLAGS_INIT "-m32")
+
+# Hints for OpenVINO
+
+macro(_set_if_not_defined var val)
+    if(NOT DEFINED ${var})
+        set(${var} ${val} CACHE BOOL "" FORCE)
+    endif()
+endmacro()
+
+# need libusb 32-bits version
+_set_if_not_defined(ENABLE_VPU OFF)
+
+# _mm_loadl_epi64 is not defined
+_set_if_not_defined(ENABLE_SSE42 OFF)
+
+# fix conversion from uint64_t / int64_t to size_t
+_set_if_not_defined(NGRAPH_ONNX_IMPORT_ENABLE OFF)

cmake/toolchains/mt.runtime.win32.toolchain.cmake

@@ -0,0 +1,39 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+#
+# Flags for 3rd party projects
+#
+
+set(use_static_runtime ON)
+
+if(use_static_runtime)
+    foreach(lang C CXX)
+        foreach(build_type "" "_DEBUG" "_MINSIZEREL" "_RELEASE" "_RELWITHDEBINFO")
+            set(flag_var "CMAKE_${lang}_FLAGS${build_type}")
+            string(REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+        endforeach()
+    endforeach()
+endif()
+
+function(onecoreuap_set_runtime var)
+    set(${var} ${use_static_runtime} CACHE BOOL "" FORCE)
+endfunction()
+
+# ONNX
+onecoreuap_set_runtime(ONNX_USE_MSVC_STATIC_RUNTIME)
+# pugixml
+onecoreuap_set_runtime(STATIC_CRT)
+# protobuf
+onecoreuap_set_runtime(protobuf_MSVC_STATIC_RUNTIME)
+# clDNN
+onecoreuap_set_runtime(CLDNN__COMPILE_LINK_USE_STATIC_RUNTIME)
+# google-test
+if(use_static_runtime)
+    set(gtest_force_shared_crt OFF CACHE BOOL "" FORCE)
+else()
+    set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
+endif()
+
+unset(use_static_runtime)

cmake/toolchains/onecoreuap.toolchain.cmake

@@ -1,4 +1,4 @@
-# Copyright (C) 2018-2020 Intel Corporation
+# Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 #
 
@@ -68,31 +68,7 @@ set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${linker_flags}")
 unset(linker_flags)
 
 #
-# Flags for 3rd party projects
+# Static runtime to overcome apiValidator tool restrictions
 #
 
-set(use_static_runtime ON)
-
-if(use_static_runtime)
-    foreach(lang C CXX)
-        foreach(build_type "" "_DEBUG" "_MINSIZEREL" "_RELEASE" "_RELWITHDEBINFO")
-            set(flag_var "CMAKE_${lang}_FLAGS${build_type}")
-            string(REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
-        endforeach()
-    endforeach()
-endif()
-
-function(onecoreuap_set_runtime var)
-    set(${var} ${use_static_runtime} CACHE BOOL "" FORCE)
-endfunction()
-
-# ONNX
-onecoreuap_set_runtime(ONNX_USE_MSVC_STATIC_RUNTIME)
-# pugixml
-onecoreuap_set_runtime(STATIC_CRT)
-# protobuf
-onecoreuap_set_runtime(protobuf_MSVC_STATIC_RUNTIME)
-# clDNN
-onecoreuap_set_runtime(CLDNN__COMPILE_LINK_USE_STATIC_RUNTIME)
-
-unset(use_static_runtime)
+include("${CMAKE_CURRENT_LIST_DIR}/mt.runtime.win32.toolchain.cmake")

docs/HOWTO/Custom_Layers_Guide.md

@@ -369,7 +369,6 @@ python3 mri_reconstruction_demo.py \
 - [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 - [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- [Inference Engine Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 
 ## Converting Models:

docs/IE_DG/API_Changes.md

@@ -156,7 +156,7 @@ The sections below contain detailed list of changes made to the Inference Engine
 
 ### Deprecated API
 
- **Myriad Plugin API:**
+ **MYRIAD Plugin API:**
 
  * VPU_CONFIG_KEY(IGNORE_IR_STATISTIC)
 

docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md

@@ -24,11 +24,11 @@ The `ngraph::onnx_import::Node` class represents a node in ONNX model. It provid
 New operator registration must happen before the ONNX model is read, for example, if an ONNX model uses the 'CustomRelu' operator, `register_operator("CustomRelu", ...)` must be called before InferenceEngine::Core::ReadNetwork.
 Re-registering ONNX operators within the same process is supported. During registration of the existing operator, a warning is printed.
 
-The example below demonstrates an examplary model that requires previously created 'CustomRelu' operator:
+The example below demonstrates an exemplary model that requires previously created 'CustomRelu' operator:
 @snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:model
 
 
-For a reference on how to create a graph with nGraph operations, visit [nGraph tutorial](../nGraphTutorial.md).
+For a reference on how to create a graph with nGraph operations, visit [Custom nGraph Operations](AddingNGraphOps.md).
 For a complete list of predefined nGraph operators, visit [available operations sets](../../ops/opset.md).
 
 If operator is no longer needed, it can be unregistered by calling `unregister_operator`. The function takes three arguments `op_type`, `version`, and `domain`.

docs/IE_DG/InferenceEngine_QueryAPI.md

@@ -32,7 +32,8 @@ MYRIAD.1.4-ma2480
 FPGA.0
 FPGA.1
 CPU
-GPU
+GPU.0
+GPU.1
 ...
 ```
 

docs/IE_DG/Introduction.md

@@ -122,7 +122,4 @@ The open source version is available in the [OpenVINO™ toolkit GitHub reposito
 - [Intel® Deep Learning Deployment Toolkit Web Page](https://software.intel.com/en-us/computer-vision-sdk)
 
 
-[scheme]: img/workflow_steps.png
-
-#### Optimization Notice
-<sup>For complete information about compiler optimizations, see our [Optimization Notice](https://software.intel.com/en-us/articles/optimization-notice#opt-en).</sup>
+[scheme]: img/workflow_steps.png

docs/IE_DG/Samples_Overview.md

@@ -43,7 +43,7 @@ To run the sample applications, you can use images and videos from the media fil
 
 ## Samples that Support Pre-Trained Models
 
-You can download the [pre-trained models](@ref omz_models_intel_index) using the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
 
 ## Build the Sample Applications
 
@@ -127,7 +127,7 @@ You can also build a generated solution manually. For example, if you want to bu
 Microsoft Visual Studio and open the generated solution file from the `C:\Users\<user>\Documents\Intel\OpenVINO\inference_engine_cpp_samples_build\Samples.sln`
 directory.
 
-### <a name="build_samples_linux"></a>Build the Sample Applications on macOS*
+### <a name="build_samples_macos"></a>Build the Sample Applications on macOS*
 
 The officially supported macOS* build environment is the following:
 

docs/IE_DG/ShapeInference.md

@@ -1,6 +1,36 @@
 Using Shape Inference {#openvino_docs_IE_DG_ShapeInference}
 ==========================================
 
+OpenVINO™ provides the following methods for runtime model reshaping:
+
+* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
+   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
+
+> **NOTES**:
+> - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping in most cases.
+> - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
+> - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
+
+* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
+   The meaning of a model batch may vary depending on the model design.
+   This method does not deduce batch placement for inputs from the model architecture.
+   It assumes that the batch is placed at the zero index in the shape for all inputs and uses the `InferenceEngine::CNNNetwork::reshape` method to propagate updated shapes through the model.
+
+   The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
+
+   Use `InferenceEngine::CNNNetwork::reshape` instead of `InferenceEngine::CNNNetwork::setBatchSize` to set new input shapes for the model in case the model has:
+   * Multiple inputs with different zero-index dimension meanings
+   * Input without a batch dimension
+   * 0D, 1D, or 3D shape
+
+   The `InferenceEngine::CNNNetwork::setBatchSize` method is a high-level API method that wraps the `InferenceEngine::CNNNetwork::reshape` method call and works for trivial models from the batch placement standpoint.
+   Use `InferenceEngine::CNNNetwork::reshape` for other models.
+
+   Using the `InferenceEngine::CNNNetwork::setBatchSize` method for models with a non-zero index batch placement or for models with inputs that do not have a batch dimension may lead to undefined behaviour.
+
+You can change input shapes multiple times using the `InferenceEngine::CNNNetwork::reshape` and `InferenceEngine::CNNNetwork::setBatchSize` methods in any order.
+If a model has a hard-coded batch dimension, use `InferenceEngine::CNNNetwork::setBatchSize` first to change the batch, then call `InferenceEngine::CNNNetwork::reshape` to update other dimensions, if needed.
+
 Inference Engine takes three kinds of a model description as an input, which are converted into an `InferenceEngine::CNNNetwork` object:
 1. [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) through `InferenceEngine::Core::ReadNetwork`
 2. [ONNX model](../IE_DG/OnnxImporterTutorial.md) through `InferenceEngine::Core::ReadNetwork`
@@ -23,33 +53,7 @@ for (const auto & parameter : parameters) {
 
 To feed input data of a shape that is different from the model input shape, reshape the model first.
 
-OpenVINO™ provides the following methods for runtime model reshaping:
-
-* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
-   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
-   You can reshape a model multiple times like in this application scheme:
-   ```
-   ReadNetwork -> reshape(input_1_shape) -> LoadNetwork -> infer(input_1)
-              \
-               -> reshape(input_2_shape) -> LoadNetwork -> infer(input_2)
-   ```
-   > **NOTES**:
-   > - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping.
-   > - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
-   > - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
-* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
-   The meaning of a model batch may vary depending on the model design.
-   The `InferenceEngine::CNNNetwork::setBatchSize` method deduces the index of a batch dimension based only on the input rank. 
-   This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
-   The batch-setting algorithm does not involve the shape inference mechanism.
-   Batch of input and output shapes for all layers is set to a new batch value without layer validation.
-   It may cause both positive and negative side effects.
-   Due to the limitations described above, the current method is not recommended to use.
-   If you need to set a new batch size for the model, use the `CNNNetwork::reshape` method instead.
-
-Do not use runtime reshaping methods simultaneously, especially do not call the `CNNNetwork::reshape` method after you use `InferenceEngine::CNNNetwork::setBatchSize`.
-The `InferenceEngine::CNNNetwork::setBatchSize` method causes irreversible conversion of the internal model representation into the legacy model representation.
-The method does not use nGraph for shape inference which leads to reduced reshape opportunities and may affect the performance of the model.
+Once the input shape of `InferenceEngine::CNNNetwork` is set, call the `InferenceEngine::Core::LoadNetwork` method to get an `InferenceEngine::ExecutableNetwork` object for inference with updated shapes.
 
 There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../nGraph_DG/build_function.md).
 
@@ -62,8 +66,8 @@ Shape collision during shape propagation may be a sign that a new shape does not
 Changing the model input shape may result in intermediate operations shape collision.
 
 Examples of such operations:
-- <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#Reshape">`Reshape` operation</a> with a hard-coded output shape value
-- <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#MatMul">`MatMul` operation</a> with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
+- [`Reshape` operation](../ops/shape/Reshape_1.md) with a hard-coded output shape value
+- [`MatMul` operation](../ops/matrix/MatMul_1.md) with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
 
 Model structure and logic should not change significantly after model reshaping.
 - The Global Pooling operation is commonly used to reduce output feature map of classification models output.

docs/IE_DG/protecting_model_guide.md

@@ -59,5 +59,4 @@ should be called with `weights` passed as an empty `Blob`.
 - Inference Engine Developer Guide: [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md)
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](Samples_Overview.md)
 - For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).