1.20.6

• MaxPool, AveragePool
  • Improved conversion stability when H, W and D of MaxPool and AveragePool contain undefined dimensions.
  • The accuracy of the converted model is not always accurate.
  • e.g. YOLOvNn dynamic inputs [N, 3, H, W]
  • YOLOvN has been modified only to avoid a situation where the conversion aborts, although this is undoubtedly not good for the design of the model, as fixed parameters such as the number of classes are embedded in the backward Split operation with fixed values.
  • Concatenating dimensions that have completely different meanings is also a major problem.
  • Unless you replace the PyTorch implementation with Slice, you won't be able to do proper inferencing.
    • ONNX
      
      
    • TFLite
      
      

What's Changed

• Improved conversion stability when H, W and D of MaxPool and AveragePool contain undefined dimensions by @PINTO0309 in #620

Full Changelog: 1.20.5...1.20.6