dnn: some minor fixes in docs, indentation, unused code

modules/dnn/include/opencv2/dnn.hpp

@@ -44,7 +44,7 @@
 
 // This is an umbrealla header to include into you project.
 // We are free to change headers layout in dnn subfolder, so please include
-// this header for future compartibility
+// this header for future compatibility
 
 
 /** @defgroup dnn Deep Neural Network module

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -152,7 +152,19 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         int outputNameToIndex(String outputName);
     };
 
-    //! Classical recurrent layer
+    /** @brief Classical recurrent layer
+
+    Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
+
+    - input: should contain packed input @f$x_t@f$.
+    - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
+
+    input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
+
+    output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
+
+    If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
+    */
     class CV_EXPORTS RNNLayer : public Layer
     {
     public:
@@ -180,17 +192,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          */
         virtual void setProduceHiddenOutput(bool produce = false) = 0;
 
-        /** Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
-
-        @param input should contain packed input @f$x_t@f$.
-        @param output should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
-
-        @p input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
-
-        @p output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
-
-        If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
-        */
     };
 
     class CV_EXPORTS BaseConvolutionLayer : public Layer

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -371,28 +371,28 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
         /** @brief Runs forward pass to compute output of layer with name @p outputName.
          *  @param outputName name for layer which output is needed to get
          *  @return blob for first output of specified layer.
-          * @details By default runs forward pass for the whole network.
-          */
+         *  @details By default runs forward pass for the whole network.
+         */
         CV_WRAP Mat forward(const String& outputName = String());
 
         /** @brief Runs forward pass to compute output of layer with name @p outputName.
          *  @param outputBlobs contains all output blobs for specified layer.
          *  @param outputName name for layer which output is needed to get
-          * @details If @p outputName is empty, runs forward pass for the whole network.
-          */
+         *  @details If @p outputName is empty, runs forward pass for the whole network.
+         */
         CV_WRAP void forward(std::vector<Mat>& outputBlobs, const String& outputName = String());
 
         /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
          *  @param outputBlobs contains blobs for first outputs of specified layers.
          *  @param outBlobNames names for layers which outputs are needed to get
-          */
+         */
         CV_WRAP void forward(std::vector<Mat>& outputBlobs,
                              const std::vector<String>& outBlobNames);
 
         /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
          *  @param outputBlobs contains all output blobs for each layer specified in @p outBlobNames.
          *  @param outBlobNames names for layers which outputs are needed to get
-          */
+         */
         CV_WRAP void forward(std::vector<std::vector<Mat> >& outputBlobs,
                              const std::vector<String>& outBlobNames);
 
@@ -460,103 +460,103 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
          */
         CV_WRAP std::vector<int> getUnconnectedOutLayers() const;
         /** @brief Returns input and output shapes for all layers in loaded model;
-          *  preliminary inferencing isn't necessary.
-          *  @param netInputShapes shapes for all input blobs in net input layer.
-          *  @param layersIds output parameter for layer IDs.
-          *  @param inLayersShapes output parameter for input layers shapes;
-          * order is the same as in layersIds
-          *  @param outLayersShapes output parameter for output layers shapes;
-          * order is the same as in layersIds
-          */
-         CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
-                                      std::vector<int>* layersIds,
-                                      std::vector<std::vector<MatShape> >* inLayersShapes,
-                                      std::vector<std::vector<MatShape> >* outLayersShapes) const;
-
-         /** @overload */
-         CV_WRAP void getLayersShapes(const MatShape& netInputShape,
-                                      std::vector<int>* layersIds,
-                                      std::vector<std::vector<MatShape> >* inLayersShapes,
-                                      std::vector<std::vector<MatShape> >* outLayersShapes) const;
-
-         /** @brief Returns input and output shapes for layer with specified
-          * id in loaded model; preliminary inferencing isn't necessary.
-          *  @param netInputShape shape input blob in net input layer.
-          *  @param layerId id for layer.
-          *  @param inLayerShapes output parameter for input layers shapes;
-          * order is the same as in layersIds
-          *  @param outLayerShapes output parameter for output layers shapes;
-          * order is the same as in layersIds
-          */
-         CV_WRAP void getLayerShapes(const MatShape& netInputShape,
-                                     const int layerId,
-                                     std::vector<MatShape>* inLayerShapes,
-                                     std::vector<MatShape>* outLayerShapes) const;
+         *  preliminary inferencing isn't necessary.
+         *  @param netInputShapes shapes for all input blobs in net input layer.
+         *  @param layersIds output parameter for layer IDs.
+         *  @param inLayersShapes output parameter for input layers shapes;
+         * order is the same as in layersIds
+         *  @param outLayersShapes output parameter for output layers shapes;
+         * order is the same as in layersIds
+         */
+        CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
+                                     std::vector<int>* layersIds,
+                                     std::vector<std::vector<MatShape> >* inLayersShapes,
+                                     std::vector<std::vector<MatShape> >* outLayersShapes) const;
+
+        /** @overload */
+        CV_WRAP void getLayersShapes(const MatShape& netInputShape,
+                                     std::vector<int>* layersIds,
+                                     std::vector<std::vector<MatShape> >* inLayersShapes,
+                                     std::vector<std::vector<MatShape> >* outLayersShapes) const;
+
+        /** @brief Returns input and output shapes for layer with specified
+         * id in loaded model; preliminary inferencing isn't necessary.
+         *  @param netInputShape shape input blob in net input layer.
+         *  @param layerId id for layer.
+         *  @param inLayerShapes output parameter for input layers shapes;
+         * order is the same as in layersIds
+         *  @param outLayerShapes output parameter for output layers shapes;
+         * order is the same as in layersIds
+         */
+        CV_WRAP void getLayerShapes(const MatShape& netInputShape,
+                                    const int layerId,
+                                    std::vector<MatShape>* inLayerShapes,
+                                    std::vector<MatShape>* outLayerShapes) const;
 
-         /** @overload */
-         CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes,
+        /** @overload */
+        CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes,
                                      const int layerId,
                                      std::vector<MatShape>* inLayerShapes,
                                      std::vector<MatShape>* outLayerShapes) const;
-         /** @brief Computes FLOP for whole loaded model with specified input shapes.
-          * @param netInputShapes vector of shapes for all net inputs.
-          * @returns computed FLOP.
-          */
-         CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const;
-         /** @overload */
-         CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
-         /** @overload */
-         CV_WRAP int64 getFLOPS(const int layerId,
-                               const std::vector<MatShape>& netInputShapes) const;
-         /** @overload */
-         CV_WRAP int64 getFLOPS(const int layerId,
-                               const MatShape& netInputShape) const;
-
-         /** @brief Returns list of types for layer used in model.
-          * @param layersTypes output parameter for returning types.
-          */
-         CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
-
-         /** @brief Returns count of layers of specified type.
-          * @param layerType type.
-          * @returns count of layers
-          */
-         CV_WRAP int getLayersCount(const String& layerType) const;
-
-         /** @brief Computes bytes number which are requered to store
-          * all weights and intermediate blobs for model.
-          * @param netInputShapes vector of shapes for all net inputs.
-          * @param weights output parameter to store resulting bytes for weights.
-          * @param blobs output parameter to store resulting bytes for intermediate blobs.
-          */
-         CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
-                                           CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-         /** @overload */
-         CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
-                                           CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-         /** @overload */
-         CV_WRAP void getMemoryConsumption(const int layerId,
-                                           const std::vector<MatShape>& netInputShapes,
-                                           CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-         /** @overload */
-         CV_WRAP void getMemoryConsumption(const int layerId,
-                                           const MatShape& netInputShape,
-                                           CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-
-         /** @brief Computes bytes number which are requered to store
-          * all weights and intermediate blobs for each layer.
-          * @param netInputShapes vector of shapes for all net inputs.
-          * @param layerIds output vector to save layer IDs.
-          * @param weights output parameter to store resulting bytes for weights.
-          * @param blobs output parameter to store resulting bytes for intermediate blobs.
-          */
-         CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
-                                           CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
-                                           CV_OUT std::vector<size_t>& blobs) const;
-         /** @overload */
-         CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
-                                           CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
-                                           CV_OUT std::vector<size_t>& blobs) const;
+        /** @brief Computes FLOP for whole loaded model with specified input shapes.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @returns computed FLOP.
+         */
+        CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const int layerId,
+                              const std::vector<MatShape>& netInputShapes) const;
+        /** @overload */
+        CV_WRAP int64 getFLOPS(const int layerId,
+                              const MatShape& netInputShape) const;
+
+        /** @brief Returns list of types for layer used in model.
+         * @param layersTypes output parameter for returning types.
+         */
+        CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
+
+        /** @brief Returns count of layers of specified type.
+         * @param layerType type.
+         * @returns count of layers
+         */
+        CV_WRAP int getLayersCount(const String& layerType) const;
+
+        /** @brief Computes bytes number which are requered to store
+         * all weights and intermediate blobs for model.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @param weights output parameter to store resulting bytes for weights.
+         * @param blobs output parameter to store resulting bytes for intermediate blobs.
+         */
+        CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const int layerId,
+                                          const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const int layerId,
+                                          const MatShape& netInputShape,
+                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
+
+        /** @brief Computes bytes number which are requered to store
+         * all weights and intermediate blobs for each layer.
+         * @param netInputShapes vector of shapes for all net inputs.
+         * @param layerIds output vector to save layer IDs.
+         * @param weights output parameter to store resulting bytes for weights.
+         * @param blobs output parameter to store resulting bytes for intermediate blobs.
+         */
+        CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
+                                          CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
+                                          CV_OUT std::vector<size_t>& blobs) const;
+        /** @overload */
+        CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
+                                          CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
+                                          CV_OUT std::vector<size_t>& blobs) const;
     private:
 
         struct Impl;

modules/dnn/src/dnn.cpp

@@ -969,9 +969,6 @@ struct Net::Impl
         }
     }
 
-    #define CV_RETHROW_ERROR(err, newmsg)\
-        cv::error(err.code, newmsg, err.func.c_str(), err.file.c_str(), err.line)
-
     void allocateLayer(int lid, const LayersShapesMap& layersShapes)
     {
         CV_TRACE_FUNCTION();