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  • Adam Osewski's avatar
    Use GroupConvolutionBackpropData v1 in ONNX ConvTranspose. (#4158) · d80c8e42
    Adam Osewski authored 
    * Use ConvolutionBackpropData in ONNX ConvTranspose operator.

* Replace deprecated functions and update documentation.

* Remove GroupConvolutionTranspose fused op.

* Add more UT for GroupConvolutionBackpropData.

- They're transformed from GroupConvolutionTranspose

* Fix validate_and_infed types for ConvolutionBackpropData v1.

* Add new constructor for GroupConvolutionBackpropData

- Additionally do some code formatting.

* Improve validation in GroupConvBackprop v0.

* Update/fix upgrade & downgrade passes for
(Group)ConvolutionBackpropData.

* Add decomposition and validation for GroupConvolutionBackpropData:v1

* Type properties UT for GroupConvolutionBackprop v1

* Remove include of deleted header.

* Add squeeze to opset1::builder.

* Use opset1 squeeze.

* Helper functions for padding and output spatial shape inference.

These helper functions are specialized only for *ConvolutionBackpropData operators.

* Update documentation.

* Add more checks and remove deprecated assert.

* Refactor GroupConvolutionBackpropData validation.

- Use helper functions to infer padding and output shape.
- Change get_output_shape to return only spatial features dimensions

* Remove unnecessary code from donwgrade pass.

The GroupConvolutionBackpropData node has already computed all those required
information.

* Call Node API. Small review suggestions.

* Work only on spatial dimensions inside helper functions.

* Enhance documentation for ConvolutionBackpropData

* Refactoring.

* Reuse values computed in downgraded operator.

* Check for correct filters input shape.

* Add additional check on auto pad value.

* Remove unused variables.

* Rename helper function.

* Add more checks.

* Override is dynamic and add more restrictive check.

* Fix merge artifact and force unsigned type for argument.

* Review comments: renaming.

* Change Convolution + bias pattern.

Change from bias broadcasting to appropriate convolution output shape
into reshape into (1, C, 1, 1) shape, which sould work better.

* Add few more test cases for ONNX ConvTranspose op.

* Skip one UT failing on PlaidML backend due to accuracy.
    d80c8e42
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