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vpisarevvpisarev
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optimize out scaleLayer & concatLayer whenever possible
fixed problem in concat layer by disabling memory re-use in layers with multiple inputs trying to fix the tests when Halide is used to run deep nets another attempt to fix Halide tests see if the Halide tests will pass with concat layer fusion turned off trying to fix failures in halide tests; another try one more experiment to make halide_concat & halide_enet tests pass continue attempts to fix halide tests moving on uncomment parallel concat layer seemingly fixed failures in Halide tests and re-enabled concat layer fusion; thanks to dkurt for the patch
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5 files changed

+337
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‎modules/dnn/include/opencv2/dnn/dnn.hpp

Lines changed: 26 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -152,6 +152,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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class CV_EXPORTS ActivationLayer;
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class CV_EXPORTS BatchNormLayer;
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class CV_EXPORTS ScaleLayer;
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/** @brief This interface class allows to build new Layers - are building blocks of networks.
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*
@@ -269,6 +270,19 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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*/
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virtual bool setBatchNorm(const Ptr<BatchNormLayer>& layer);
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/**
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* @brief Tries to attach to the layer the subsequent scaling layer, i.e. do the layer fusion in a partial case.
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* @param[in] layer The subsequent scaling layer.
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*
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* Returns true if the scaling layer has been attached successfully.
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*/
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virtual bool setScale(const Ptr<ScaleLayer>& layer);
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/**
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* @brief "Deattaches" all the layers, attached to particular layer.
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*/
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virtual void unsetAttached();
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virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
@@ -495,9 +509,10 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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/** @overload */
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CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes,
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const int layerId,
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std::vector<MatShape>* inLayerShapes,
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std::vector<MatShape>* outLayerShapes) const;
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const int layerId,
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std::vector<MatShape>* inLayerShapes,
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std::vector<MatShape>* outLayerShapes) const;
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/** @brief Computes FLOP for whole loaded model with specified input shapes.
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* @param netInputShapes vector of shapes for all net inputs.
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* @returns computed FLOP.
@@ -507,10 +522,10 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
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/** @overload */
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CV_WRAP int64 getFLOPS(const int layerId,
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const std::vector<MatShape>& netInputShapes) const;
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const std::vector<MatShape>& netInputShapes) const;
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/** @overload */
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CV_WRAP int64 getFLOPS(const int layerId,
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const MatShape& netInputShape) const;
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const MatShape& netInputShape) const;
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/** @brief Returns list of types for layer used in model.
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* @param layersTypes output parameter for returning types.
@@ -557,8 +572,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
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CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
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CV_OUT std::vector<size_t>& blobs) const;
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private:
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/** @brief Enables or disables layer fusion in the network.
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* @param fusion true to enable the fusion, false to disable. The fusion is enabled by default.
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*/
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CV_WRAP void enableFusion(bool fusion);
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private:
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struct Impl;
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Ptr<Impl> impl;
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};

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