* Added info on DockerHub CI Framework
* Feature/azaytsev/change layout (#3295)
* Changes according to feedback comments
* Replaced @ref's with html links
* Fixed links, added a title page for installing from repos and images, fixed formatting issues
* Added links
* minor fix
* Added DL Streamer to the list of components installed by default
* Link fixes
* Link fixes
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* added OpenVINO Model Server
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Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
* Updated openvino_docs.xml
* Updated the link to software license agreements
* Revert "Updated the link to software license agreements"
This reverts commit 706dac500e.
* Docs to Sphinx (#8151)
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* Update AUTO.md
* Update performance_int8_vs_fp32.md
* update
* update md
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* disable ci
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Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
# Conflicts:
# .gitignore
# docs/CMakeLists.txt
# docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
# docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
# docs/IE_DG/Extensibility_DG/VPU_Kernel.md
# docs/IE_DG/InferenceEngine_QueryAPI.md
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# docs/IE_DG/Integrate_with_customer_application_new_API.md
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# inference-engine/include/ie_core.hpp
# inference-engine/include/ie_version.hpp
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# inference-engine/src/plugin_api/exec_graph_info.hpp
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# inference-engine/src/transformations/include/transformations_visibility.hpp
# inference-engine/tools/benchmark_tool/README.md
# ngraph/core/include/ngraph/ngraph.hpp
# ngraph/frontend/onnx_common/include/onnx_common/parser.hpp
# ngraph/python/src/ngraph/utils/node_factory.py
# openvino/itt/include/openvino/itt.hpp
# thirdparty/ade
# tools/benchmark/README.md
* Cherry-picked remove font-family (#8211)
* Cherry-picked: Update get_started_scripts.md (#8338)
* doc updates (#8268)
* Various doc changes
* theme changes
* remove font-family (#8211)
* fix css
* Update uninstalling-openvino.md
* fix css
* fix
* Fixes for Installation Guides
Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
Co-authored-by: kblaszczak-intel <karol.blaszczak@intel.com>
# Conflicts:
# docs/IE_DG/Bfloat16Inference.md
# docs/IE_DG/InferenceEngine_QueryAPI.md
# docs/IE_DG/OnnxImporterTutorial.md
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# thirdparty/ade
* Cherry-picked: doc script changes (#8568)
* fix openvino-sphinx-theme
* add linkcheck target
* fix
* change version
* add doxygen-xfail.txt
* fix
* AA
* fix
* fix
* fix
* fix
* fix
# Conflicts:
# thirdparty/ade
* Cherry-pick: Feature/azaytsev/doc updates gna 2021 4 2 (#8567)
* Various doc changes
* Reformatted C++/Pythob sections. Updated with info from PR8490
* additional fix
* Gemini Lake replaced with Elkhart Lake
* Fixed links in IGs, Added 12th Gen
# Conflicts:
# docs/IE_DG/supported_plugins/GNA.md
# thirdparty/ade
* Cherry-pick: Feature/azaytsev/doc fixes (#8897)
* Various doc changes
* Removed the empty Learning path topic
* Restored the Gemini Lake CPIU list
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* Cherry-pick: doc pytest (#8888)
* docs pytest
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Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
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Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
* Update Custom_Layers_Guide.md
* Changes according to review comments
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Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
* Update Int8Inference.md
* update xfail
* clang format
* updated xfail
Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com>
Co-authored-by: kblaszczak-intel <karol.blaszczak@intel.com>
Co-authored-by: Yury Gorbachev <yury.gorbachev@intel.com>
Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
5.0 KiB
Custom nGraph Operations
Inference Engine Extension API allows you to register operation sets (opsets) with custom nGraph operations to support models with operations which OpenVINO™ does not support out-of-the-box.
Besides creating custom nGraph operations, to support custom operations in your model you must also create a Model Optimizer extension for the custom operations and an Inference Engine device plugin extension for the device you will use for inference.
Operation Class
To add your custom nGraph operation, create a new class that extends ngraph::Op, which is in turn derived from ngraph::Node, the base class for all graph operations in nGraph. Follow the steps below to add a custom nGraph operation:
-
Add the
NGRAPH_RTTI_DECLARATIONandNGRAPH_RTTI_DEFINITIONmacros which define aNodeTypeInfoobject that identifies the type of the operation to the graph users and helps with dynamic type resolution. The type info of an nGraph operation currently consists of a string identifier and a version number, but this may change in the future. -
Implement constructors that optionally take the operation inputs and attributes as parameters.
-
Override the shape inference method
validate_and_infer_types. This method is called multiple times during graph manipulations to determine the shapes and element types of the operations outputs. To access the input shapes and input element types, use theget_input_partial_shape()andget_input_element_type()methods ofngraph::Node. Set the inferred shape and element type of the output usingset_output_type. -
Override the
clone_with_new_inputsmethod, which enables graph manipulation routines to create copies of this operation and connect it to different nodes during optimization. -
Override the
visit_attributesmethod, which enables serialization and deserialization of operation attributes. AnAttributeVisitoris passed to the method, and the implementation is expected to walk over all the attributes in the op using the type-awareon_attributehelper. Helpers are already implemented for standard C++ types likeint64_t,float,bool,vector, and for existing nGraph defined types. -
Override
evaluate, which is an optional method that enables the application of constant folding if there is a custom operation on the constant branch. If your operation containsevaluatemethod you also need to override thehas_evaluatemethod, this method allow to get information about availability ofevaluatemethod for the operation.
Based on that, declaration of an operation class can look as follows:
@snippet template_extension/old/op.hpp op:header
Class Fields
The provided implementation has several fields:
addof typeint64_tis an attribute of a custom operationtype_infoof typengraph::NodeTypeInfodefines type and version of an operation
Operation Constructors
nGraph operation contains two constructors:
- Default constructor, which enables you to create an operation without attributes
- Constructor that creates and validates an operation with specified inputs and attributes
@snippet template_extension/old/op.cpp op:ctor
validate_and_infer_types()
ngraph::Node::validate_and_infer_types method validates operation attributes and calculates output shapes using attributes of the operation.
@snippet template_extension/old/op.cpp op:validate
clone_with_new_inputs()
ngraph::Node::clone_with_new_inputs method creates a copy of the nGraph operation with new inputs.
@snippet template_extension/old/op.cpp op:copy
visit_attributes()
ngraph::Node::visit_attributes method enables you to visit all operation attributes.
@snippet template_extension/old/op.cpp op:visit_attributes
evaluate() and has_evaluate()
ngraph::Node::evaluate method enables you to apply constant folding to an operation.
@snippet template_extension/old/op.cpp op:evaluate
Register Custom Operations in Extension Class
To add custom operations to the Extension class, create an operation set with custom operations and implement the InferenceEngine::IExtension::getOpSets method:
@snippet template_extension/old/extension.cpp extension:getOpSets
This method returns a map of opsets that exist in the extension library. nGraph provides an opset mechanism to group operations into clusters. Different opsets distinguish between different versions of one operation.
When specifying opset names, follow the rules below:
- Use unique opset names.
- Do not use the following built-in opset names:
extension,experimental,opset1,opset2,opset3, ... ,opsetN. - Make sure that the Model Optimizer and your extension use the same opset names.
- IR v10 operations have the mandatory
versionattribute specifying the opset. Operations from the default opset cannot be redefined.
Use a custom opset to create a new operation or extend functionality of an existing operation from another opset.