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

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This commit is contained in:
Alexander Alekhin 2017-06-30 18:46:00 +03:00
parent 520da7aaaf
commit 544908d06c
4 changed files with 108 additions and 110 deletions
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2
modules/dnn/include/opencv2/dnn.hpp
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@ -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

25
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -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
*/ */
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

3
modules/dnn/src/dnn.cpp
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@ -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();