Comments (1)
Currently, the shape-inference logic for individual operators enforce any such constraints (by design).
Of course, we can also do it automatically from the schema information, as explained above, and that would be an useful enhancement. I think a good place to do that would be in OpSchema::CheckInputOutputType, which currently only tries to inference, but doesn't validate existing types.
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Related Issues (20)
- Using Pytorch Count of input_names provided during export not matching with session.get_inputs() HOT 1
- ONNX is broken when built with ONNX_USE_LITE_PROTO=OFF (using protobuf-24.4): File already exists in database: onnx/onnx-ml.proto HOT 5
- Schema error trying to create new version of training ops
- ERROR: Could not build wheels for onnx which use PEP 517 and cannot be installed directly HOT 2
- output different between onnx and pytorch HOT 7
- Implement DepthToSpace as a function HOT 2
- Reference implementation of ONNX Reduce sum square is mismatch with ONNX Spec when noop_with_empty_axes == 1 HOT 5
- Clarification of Reshape semantics for attribute 'allowzero' NOT set, zero volume HOT 3
- Change dynamic shape in fixed shape using C++ HOT 1
- The weekly model zoo CI has been failing HOT 12
- Incorrect input names for quantize/dequantize ONNX backend node tests HOT 1
- Fix and test numpy_helper to_array
- How to read onnx and obtain names, imgsz HOT 3
- Model zoo test failures HOT 1
- DequantizeLinear spec clarification: What happens if the subtraction overflows/underflows? HOT 1
- the model for UniqueOp in backend test is not always correct for different input
- A conflict doc abount compatibility between Onnx Version & ML Opset Version HOT 2
- [Shape Inference] Robustness of `ConstantOfShape` operator HOT 2
- Numpy 2.0.0rc2 causes `pytest` on ONNX to fail HOT 2
- How to modify Slice attribute HOT 4
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