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Neural networks are graphs consisting of nodes called operators. Each operator corresponds to a mathematical function, usually described in framework's documentation or an AI standard, such as ONNX.
OpenVINO ONNX Frontend is a component responsible for working with ONNX graphs and requires implementation of different ONNX operators in order to use ONNX models.
This task requires extending OpenVINO ONNX Frontend with BitwiseAnd operator, using the existing ov::op::v13::BitwiseAnd.
Necessary help will be provided by ONNX Fronted team.
Prepare an implementation of this operator in form of a function, bitwise_and should be placed in opset 1 namespace.
Register the function in ops_bridge.cpp while keeping alphabetical order
Create test model(s) in ONNX models directory. OpenVINO test infrastructure then converts prototxt files to ONNX models - you will use those models later in tests
Check Python xfailed tests to find a test marked as a xfailed for added functionality. If any exist - remove corresponding lines and try to verify by using cmdline "python -m pytest -k name_of_test".
More details in adding operators to ONNX Frontend guide
Context
Neural networks are graphs consisting of nodes called operators. Each operator corresponds to a mathematical function, usually described in framework's documentation or an AI standard, such as ONNX.
OpenVINO ONNX Frontend is a component responsible for working with ONNX graphs and requires implementation of different ONNX operators in order to use ONNX models.
This task requires extending OpenVINO ONNX Frontend with
BitwiseAnd
operator, using the existing ov::op::v13::BitwiseAnd.Necessary help will be provided by ONNX Fronted team.
What needs to be done?
Operator details can be found in ONNX Operators
More details can be found in ONNX Changelog
.hpp
and.cpp
files herebitwise_and
should be placed in opset 1 namespace.More details in adding operators to ONNX Frontend guide
Example Pull Requests
Resources
Contact points
@mitruska
@gkrivor
@p-wysocki
Ticket
118969
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