Import and inference INT8 quantized TFLite model #23409
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| @@ -56,25 +56,32 @@ class TFLiteImporter { | |||
| // Wrap TFLite Tensor to OpenCV Mat without data copying | |||
| Mat parseTensor(const Tensor& tensor); | |||
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| typedef void (TFLiteImporter::*TFLiteImporterNodeParser)(const Operator&, const std::string&, LayerParams&); | |||
| typedef void (TFLiteImporter::*TFLiteImporterNodeParser)(const Operator&, const std::string&, LayerParams&, LayerParams&); | |||
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This should not be a common change.
Many handlers just don't use activParams at all.
Compiler warnings are disabled in dnn module, many warnings are really generated here for that change.
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| // Connect layer to inputs | ||
| int i = 0; | ||
| for (int idx : layerInputs) { | ||
| std::vector<DataLayout> inpLayouts; |
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Problem of handlers API is here.
We should not have any heavy postprocessing after the handler call.
Post-processing should be done in the handler itself (because handler defines post-processing steps).
Provide a method for registration of DNN layers to be called from handlers.
See ONNX importer implementation for reference.
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Thanks! I reworked both fused activations logic and created an addLayer method. Here is a graph for EfficientDet I used to verify that activations were added correctly: efficientdet.dot (efficientdet.pdf). Unfortunately, for this model they work as identity function and even with unconnected activations, test passes.
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@opencv-alalek, thanks for the review! |
Import and inference INT8 quantized TFLite model opencv#23409 ### Pull Request Readiness Checklist * Support quantized TFLite models * Enable fused activations (FP32, INT8) **Merge with extra**: opencv/opencv_extra#1048  on the image, green boxes are from TFLite and red boxes from OpenCV See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
Pull Request Readiness Checklist
Merge with extra: opencv/opencv_extra#1048
on the image, green boxes are from TFLite and red boxes from OpenCV
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
Patch to opencv_extra has the same branch name.