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Commit 544908d0 authored by Alexander Alekhin's avatar Alexander Alekhin
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dnn: some minor fixes in docs, indentation, unused code

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modules/dnn/include/opencv2/dnn.hpp
View file @ 544908d0
...@@ -44,7 +44,7 @@ ...@@ -44,7 +44,7 @@
// This is an umbrealla header to include into you project. // This is an umbrealla header to include into you project.
// We are free to change headers layout in dnn subfolder, so please include // 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 /** @defgroup dnn Deep Neural Network module
......
modules/dnn/include/opencv2/dnn/all_layers.hpp
View file @ 544908d0
...@@ -152,7 +152,19 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN ...@@ -152,7 +152,19 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
int outputNameToIndex(String outputName); 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 class CV_EXPORTS RNNLayer : public Layer
{ {
public: public:
...@@ -180,17 +192,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN ...@@ -180,17 +192,6 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/ */
virtual void setProduceHiddenOutput(bool produce = false) = 0; 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 class CV_EXPORTS BaseConvolutionLayer : public Layer
......
modules/dnn/include/opencv2/dnn/dnn.hpp
View file @ 544908d0
...@@ -371,28 +371,28 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN ...@@ -371,28 +371,28 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Runs forward pass to compute output of layer with name @p outputName. /** @brief Runs forward pass to compute output of layer with name @p outputName.
* @param outputName name for layer which output is needed to get * @param outputName name for layer which output is needed to get
* @return blob for first output of specified layer. * @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()); CV_WRAP Mat forward(const String& outputName = String());
/** @brief Runs forward pass to compute output of layer with name @p outputName. /** @brief Runs forward pass to compute output of layer with name @p outputName.
* @param outputBlobs contains all output blobs for specified layer. * @param outputBlobs contains all output blobs for specified layer.
* @param outputName name for layer which output is needed to get * @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()); 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. /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
* @param outputBlobs contains blobs for first outputs of specified layers. * @param outputBlobs contains blobs for first outputs of specified layers.
* @param outBlobNames names for layers which outputs are needed to get * @param outBlobNames names for layers which outputs are needed to get
*/ */
CV_WRAP void forward(std::vector<Mat>& outputBlobs, CV_WRAP void forward(std::vector<Mat>& outputBlobs,
const std::vector<String>& outBlobNames); const std::vector<String>& outBlobNames);
/** @brief Runs forward pass to compute outputs of layers listed in @p 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 outputBlobs contains all output blobs for each layer specified in @p outBlobNames.
* @param outBlobNames names for layers which outputs are needed to get * @param outBlobNames names for layers which outputs are needed to get
*/ */
CV_WRAP void forward(std::vector<std::vector<Mat> >& outputBlobs, CV_WRAP void forward(std::vector<std::vector<Mat> >& outputBlobs,
const std::vector<String>& outBlobNames); const std::vector<String>& outBlobNames);
...@@ -460,103 +460,103 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN ...@@ -460,103 +460,103 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/ */
CV_WRAP std::vector<int> getUnconnectedOutLayers() const; CV_WRAP std::vector<int> getUnconnectedOutLayers() const;
/** @brief Returns input and output shapes for all layers in loaded model; /** @brief Returns input and output shapes for all layers in loaded model;
* preliminary inferencing isn't necessary. * preliminary inferencing isn't necessary.
* @param netInputShapes shapes for all input blobs in net input layer. * @param netInputShapes shapes for all input blobs in net input layer.
* @param layersIds output parameter for layer IDs. * @param layersIds output parameter for layer IDs.
* @param inLayersShapes output parameter for input layers shapes; * @param inLayersShapes output parameter for input layers shapes;
* order is the same as in layersIds * order is the same as in layersIds
* @param outLayersShapes output parameter for output layers shapes; * @param outLayersShapes output parameter for output layers shapes;
* order is the same as in layersIds * order is the same as in layersIds
*/ */
CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes, CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
std::vector<int>* layersIds, std::vector<int>* layersIds,
std::vector<std::vector<MatShape> >* inLayersShapes, std::vector<std::vector<MatShape> >* inLayersShapes,
std::vector<std::vector<MatShape> >* outLayersShapes) const; std::vector<std::vector<MatShape> >* outLayersShapes) const;
/** @overload */ /** @overload */
CV_WRAP void getLayersShapes(const MatShape& netInputShape, CV_WRAP void getLayersShapes(const MatShape& netInputShape,
std::vector<int>* layersIds, std::vector<int>* layersIds,
std::vector<std::vector<MatShape> >* inLayersShapes, std::vector<std::vector<MatShape> >* inLayersShapes,
std::vector<std::vector<MatShape> >* outLayersShapes) const; std::vector<std::vector<MatShape> >* outLayersShapes) const;
/** @brief Returns input and output shapes for layer with specified /** @brief Returns input and output shapes for layer with specified
* id in loaded model; preliminary inferencing isn't necessary. * id in loaded model; preliminary inferencing isn't necessary.
* @param netInputShape shape input blob in net input layer. * @param netInputShape shape input blob in net input layer.
* @param layerId id for layer. * @param layerId id for layer.
* @param inLayerShapes output parameter for input layers shapes; * @param inLayerShapes output parameter for input layers shapes;
* order is the same as in layersIds * order is the same as in layersIds
* @param outLayerShapes output parameter for output layers shapes; * @param outLayerShapes output parameter for output layers shapes;
* order is the same as in layersIds * order is the same as in layersIds
*/ */
CV_WRAP void getLayerShapes(const MatShape& netInputShape, CV_WRAP void getLayerShapes(const MatShape& netInputShape,
const int layerId, const int layerId,
std::vector<MatShape>* inLayerShapes, std::vector<MatShape>* inLayerShapes,
std::vector<MatShape>* outLayerShapes) const; std::vector<MatShape>* outLayerShapes) const;
/** @overload */ /** @overload */
CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes, CV_WRAP void getLayerShapes(const std::vector<MatShape>& netInputShapes,
const int layerId, const int layerId,
std::vector<MatShape>* inLayerShapes, std::vector<MatShape>* inLayerShapes,
std::vector<MatShape>* outLayerShapes) const; std::vector<MatShape>* outLayerShapes) const;
/** @brief Computes FLOP for whole loaded model with specified input shapes. /** @brief Computes FLOP for whole loaded model with specified input shapes.
* @param netInputShapes vector of shapes for all net inputs. * @param netInputShapes vector of shapes for all net inputs.
* @returns computed FLOP. * @returns computed FLOP.
*/ */
CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const; CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const;
/** @overload */ /** @overload */
CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const; CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
/** @overload */ /** @overload */
CV_WRAP int64 getFLOPS(const int layerId, CV_WRAP int64 getFLOPS(const int layerId,
const std::vector<MatShape>& netInputShapes) const; const std::vector<MatShape>& netInputShapes) const;
/** @overload */ /** @overload */
CV_WRAP int64 getFLOPS(const int layerId, CV_WRAP int64 getFLOPS(const int layerId,
const MatShape& netInputShape) const; const MatShape& netInputShape) const;
/** @brief Returns list of types for layer used in model. /** @brief Returns list of types for layer used in model.
* @param layersTypes output parameter for returning types. * @param layersTypes output parameter for returning types.
*/ */
CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const; CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
/** @brief Returns count of layers of specified type. /** @brief Returns count of layers of specified type.
* @param layerType type. * @param layerType type.
* @returns count of layers * @returns count of layers
*/ */
CV_WRAP int getLayersCount(const String& layerType) const; CV_WRAP int getLayersCount(const String& layerType) const;
/** @brief Computes bytes number which are requered to store /** @brief Computes bytes number which are requered to store
* all weights and intermediate blobs for model. * all weights and intermediate blobs for model.
* @param netInputShapes vector of shapes for all net inputs. * @param netInputShapes vector of shapes for all net inputs.
* @param weights output parameter to store resulting bytes for weights. * @param weights output parameter to store resulting bytes for weights.
* @param blobs output parameter to store resulting bytes for intermediate blobs. * @param blobs output parameter to store resulting bytes for intermediate blobs.
*/ */
CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes, CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
CV_OUT size_t& weights, CV_OUT size_t& blobs) const; CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */ /** @overload */
CV_WRAP void getMemoryConsumption(const MatShape& netInputShape, CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
CV_OUT size_t& weights, CV_OUT size_t& blobs) const; CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */ /** @overload */
CV_WRAP void getMemoryConsumption(const int layerId, CV_WRAP void getMemoryConsumption(const int layerId,
const std::vector<MatShape>& netInputShapes, const std::vector<MatShape>& netInputShapes,
CV_OUT size_t& weights, CV_OUT size_t& blobs) const; CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @overload */ /** @overload */
CV_WRAP void getMemoryConsumption(const int layerId, CV_WRAP void getMemoryConsumption(const int layerId,
const MatShape& netInputShape, const MatShape& netInputShape,
CV_OUT size_t& weights, CV_OUT size_t& blobs) const; CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @brief Computes bytes number which are requered to store /** @brief Computes bytes number which are requered to store
* all weights and intermediate blobs for each layer. * all weights and intermediate blobs for each layer.
* @param netInputShapes vector of shapes for all net inputs. * @param netInputShapes vector of shapes for all net inputs.
* @param layerIds output vector to save layer IDs. * @param layerIds output vector to save layer IDs.
* @param weights output parameter to store resulting bytes for weights. * @param weights output parameter to store resulting bytes for weights.
* @param blobs output parameter to store resulting bytes for intermediate blobs. * @param blobs output parameter to store resulting bytes for intermediate blobs.
*/ */
CV_WRAP void getMemoryConsumption(const std::vector<MatShape>& netInputShapes, 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<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const; CV_OUT std::vector<size_t>& blobs) const;
/** @overload */ /** @overload */
CV_WRAP void getMemoryConsumption(const MatShape& netInputShape, CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights, CV_OUT std::vector<int>& layerIds, CV_OUT std::vector<size_t>& weights,
CV_OUT std::vector<size_t>& blobs) const; CV_OUT std::vector<size_t>& blobs) const;
private: private:
struct Impl; struct Impl;
......
modules/dnn/src/dnn.cpp
View file @ 544908d0
...@@ -969,9 +969,6 @@ struct Net::Impl ...@@ -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) void allocateLayer(int lid, const LayersShapesMap& layersShapes)
{ {
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
......
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