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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. Usually the operator is already implemented in OpenVINO and ONNX Frontend serves as an API translation layer.
This task requires extending OpenVINO ONNX Frontend with DeformConv-19 operator.
Necessary help will be provided by ONNX Fronted team.
Prepare an implementation of this operator in form of a function. It should be placed in opset 1 namespace. You can use the implementation of standard ONNX Pad operator as a reference
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
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. Usually the operator is already implemented in OpenVINO and ONNX Frontend serves as an API translation layer.
This task requires extending OpenVINO ONNX Frontend with
DeformConv-19
operator.Necessary help will be provided by ONNX Fronted team.
What needs to be done?
.hpp
and.cpp
files forDeformConv
hereMore details on adding operators to ONNX Frontend guide
Note: Since the only difference between
DeformConv19
andDeformConv22
is new datatype, maybe ONNX can be expanded with both in the same PR?Example Pull Requests
com.microsoft.Pad
#22000Resources
Contact points
@gkrivor
@p-wysocki
@mitruska
Ticket
CVS-119903
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