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diff --git a/thirdparty1/linux/include/opencv2/dnn/all_layers.hpp b/thirdparty1/linux/include/opencv2/dnn/all_layers.hpp new file mode 100644 index 0000000..9d26b35 --- /dev/null +++ b/thirdparty1/linux/include/opencv2/dnn/all_layers.hpp @@ -0,0 +1,423 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2013, OpenCV Foundation, all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#ifndef __OPENCV_DNN_DNN_ALL_LAYERS_HPP__ +#define __OPENCV_DNN_DNN_ALL_LAYERS_HPP__ +#include <opencv2/dnn.hpp> + +namespace cv +{ +namespace dnn +{ +//! @addtogroup dnn +//! @{ + +/** @defgroup dnnLayerList Partial List of Implemented Layers + @{ + This subsection of dnn module contains information about bult-in layers and their descriptions. + + Classes listed here, in fact, provides C++ API for creating intances of bult-in layers. + In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones. + You can use both API, but factory API is less convinient for native C++ programming and basically designed for use inside importers (see @ref Importer, @ref createCaffeImporter(), @ref createTorchImporter()). + + Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. + In partuclar, the following layers and Caffe @ref Importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality: + - Convolution + - Deconvolution + - Pooling + - InnerProduct + - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal + - Softmax + - Reshape, Flatten, Slice, Split + - LRN + - MVN + - Dropout (since it does nothing on forward pass -)) +*/ + + //! LSTM recurrent layer + class CV_EXPORTS_W LSTMLayer : public Layer + { + public: + /** Creates instance of LSTM layer */ + static CV_WRAP Ptr<LSTMLayer> create(); + + /** Set trained weights for LSTM layer. + LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights. + + Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state. + Than current output and current cell state is computed as follows: + @f{eqnarray*}{ + h_t &= o_t \odot tanh(c_t), \\ + c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\ + @f} + where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned wights. + + Gates are computed as follows: + @f{eqnarray*}{ + i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\ + f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\ + o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\ + g_t &= tanh &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\ + @f} + where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: + @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$. + + For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$ + (i.e. @f$W_x@f$ is vertical contacentaion of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$. + The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$ + and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$. + + @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_h @f$) + @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_x @f$) + @param b is bias vector (i.e. according to abovemtioned notation is @f$ b @f$) + */ + CV_WRAP virtual void setWeights(const Blob &Wh, const Blob &Wx, const Blob &b) = 0; + + /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. + * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, + * where `Wh` is parameter from setWeights(). + */ + CV_WRAP virtual void setOutShape(const BlobShape &outTailShape = BlobShape::empty()) = 0; + + /** @brief Set @f$ h_{t-1} @f$ value that will be used in next forward() calls. + * @details By-default @f$ h_{t-1} @f$ is inited by zeros and updated after each forward() call. + */ + CV_WRAP virtual void setH(const Blob &H) = 0; + /** @brief Returns current @f$ h_{t-1} @f$ value (deep copy). */ + CV_WRAP virtual Blob getH() const = 0; + + /** @brief Set @f$ c_{t-1} @f$ value that will be used in next forward() calls. + * @details By-default @f$ c_{t-1} @f$ is inited by zeros and updated after each forward() call. + */ + CV_WRAP virtual void setC(const Blob &C) = 0; + /** @brief Returns current @f$ c_{t-1} @f$ value (deep copy). */ + CV_WRAP virtual Blob getC() const = 0; + + /** @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample. + * + * If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams. + * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. + * + * If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`]. + * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. + */ + CV_WRAP virtual void setUseTimstampsDim(bool use = true) = 0; + + /** @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + CV_WRAP virtual void setProduceCellOutput(bool produce = false) = 0; + + /** In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$). + * @param input should contain packed values @f$x_t@f$ + * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true). + * + * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`], + * where `T` specifies number of timpestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]). + * + * If setUseTimstampsDim() is set to fase then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension. + * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]). + */ + void forward(std::vector<Blob*> &input, std::vector<Blob> &output); + + int inputNameToIndex(String inputName); + + int outputNameToIndex(String outputName); + }; + + //! Classical recurrent layer + class CV_EXPORTS_W RNNLayer : public Layer + { + public: + /** Creates instance of RNNLayer */ + static CV_WRAP Ptr<RNNLayer> create(); + + /** Setups learned weights. + + Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows: + @f{eqnarray*}{ + h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \\ + o_t &= tanh&(W_{ho} h_t + b_o), + @f} + + @param Wxh is @f$ W_{xh} @f$ matrix + @param bh is @f$ b_{h} @f$ vector + @param Whh is @f$ W_{hh} @f$ matrix + @param Who is @f$ W_{xo} @f$ matrix + @param bo is @f$ b_{o} @f$ vector + */ + CV_WRAP virtual void setWeights(const Blob &Wxh, const Blob &bh, const Blob &Whh, const Blob &Who, const Blob &bo) = 0; + + /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output. + * @details Shape of the second output is the same as first output. + */ + CV_WRAP 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 Blob with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix. + */ + void forward(std::vector<Blob*> &input, std::vector<Blob> &output); + }; + + class CV_EXPORTS_W BaseConvolutionLayer : public Layer + { + public: + + CV_PROP_RW Size kernel, stride, pad, dilation, adjustPad; + CV_PROP_RW String padMode; + }; + + class CV_EXPORTS_W ConvolutionLayer : public BaseConvolutionLayer + { + public: + + static CV_WRAP Ptr<BaseConvolutionLayer> create(Size kernel = Size(3, 3), Size stride = Size(1, 1), Size pad = Size(0, 0), Size dilation = Size(1, 1)); + }; + + class CV_EXPORTS_W DeconvolutionLayer : public BaseConvolutionLayer + { + public: + + static CV_WRAP Ptr<BaseConvolutionLayer> create(Size kernel = Size(3, 3), Size stride = Size(1, 1), Size pad = Size(0, 0), Size dilation = Size(1, 1), Size adjustPad = Size()); + }; + + class CV_EXPORTS_W LRNLayer : public Layer + { + public: + + enum Type + { + CHANNEL_NRM, + SPATIAL_NRM + }; + CV_PROP_RW int type; + + CV_PROP_RW int size; + CV_PROP_RW double alpha, beta, bias; + CV_PROP_RW bool normBySize; + + static CV_WRAP Ptr<LRNLayer> create(int type = LRNLayer::CHANNEL_NRM, int size = 5, + double alpha = 1, double beta = 0.75, double bias = 1, + bool normBySize = true); + }; + + class CV_EXPORTS_W PoolingLayer : public Layer + { + public: + + enum Type + { + MAX, + AVE, + STOCHASTIC + }; + + CV_PROP_RW int type; + CV_PROP_RW Size kernel, stride, pad; + CV_PROP_RW bool globalPooling; + CV_PROP_RW String padMode; + + static CV_WRAP Ptr<PoolingLayer> create(int type = PoolingLayer::MAX, Size kernel = Size(2, 2), + Size stride = Size(1, 1), Size pad = Size(0, 0), + const cv::String& padMode = ""); + static CV_WRAP Ptr<PoolingLayer> createGlobal(int type = PoolingLayer::MAX); + }; + + class CV_EXPORTS_W SoftmaxLayer : public Layer + { + public: + + static CV_WRAP Ptr<SoftmaxLayer> create(int axis = 1); + }; + + class CV_EXPORTS_W InnerProductLayer : public Layer + { + public: + CV_PROP_RW int axis; + + static CV_WRAP Ptr<InnerProductLayer> create(int axis = 1); + }; + + class CV_EXPORTS_W MVNLayer : public Layer + { + public: + CV_PROP_RW double eps; + CV_PROP_RW bool normVariance, acrossChannels; + + static CV_WRAP Ptr<MVNLayer> create(bool normVariance = true, bool acrossChannels = false, double eps = 1e-9); + }; + + /* Reshaping */ + + class CV_EXPORTS_W ReshapeLayer : public Layer + { + public: + CV_PROP_RW BlobShape newShapeDesc; + CV_PROP_RW Range newShapeRange; + + static CV_WRAP Ptr<ReshapeLayer> create(const BlobShape &newShape, Range applyingRange = Range::all(), + bool enableReordering = false); + }; + + class CV_EXPORTS_W ConcatLayer : public Layer + { + public: + int axis; + + static CV_WRAP Ptr<ConcatLayer> create(int axis = 1); + }; + + class CV_EXPORTS_W SplitLayer : public Layer + { + public: + int outputsCount; //!< Number of copies that will be produced (is ignored when negative). + + static CV_WRAP Ptr<SplitLayer> create(int outputsCount = -1); + }; + + class CV_EXPORTS_W SliceLayer : public Layer + { + public: + CV_PROP_RW int axis; + CV_PROP std::vector<int> sliceIndices; + + static CV_WRAP Ptr<SliceLayer> create(int axis); + static CV_WRAP Ptr<SliceLayer> create(int axis, const std::vector<int> &sliceIndices); + }; + + /* Activations */ + + class CV_EXPORTS_W ReLULayer : public Layer + { + public: + CV_PROP_RW double negativeSlope; + + static CV_WRAP Ptr<ReLULayer> create(double negativeSlope = 0); + }; + + class CV_EXPORTS_W ChannelsPReLULayer : public Layer + { + public: + static CV_WRAP Ptr<ChannelsPReLULayer> create(); + }; + + class CV_EXPORTS_W TanHLayer : public Layer + { + public: + static CV_WRAP Ptr<TanHLayer> create(); + }; + + class CV_EXPORTS_W SigmoidLayer : public Layer + { + public: + static CV_WRAP Ptr<SigmoidLayer> create(); + }; + + class CV_EXPORTS_W BNLLLayer : public Layer + { + public: + static CV_WRAP Ptr<BNLLLayer> create(); + }; + + class CV_EXPORTS_W AbsLayer : public Layer + { + public: + static CV_WRAP Ptr<AbsLayer> create(); + }; + + class CV_EXPORTS_W PowerLayer : public Layer + { + public: + CV_PROP_RW double power, scale, shift; + + static CV_WRAP Ptr<PowerLayer> create(double power = 1, double scale = 1, double shift = 0); + }; + + /* Layers using in semantic segmentation */ + + class CV_EXPORTS_W CropLayer : public Layer + { + public: + CV_PROP int startAxis; + CV_PROP std::vector<int> offset; + + static Ptr<CropLayer> create(int start_axis, const std::vector<int> &offset); + }; + + class CV_EXPORTS_W EltwiseLayer : public Layer + { + public: + enum EltwiseOp + { + PROD = 0, + SUM = 1, + MAX = 2, + }; + + static Ptr<EltwiseLayer> create(EltwiseOp op, const std::vector<int> &coeffs); + }; + + class CV_EXPORTS_W BatchNormLayer : public Layer + { + public: + static CV_WRAP Ptr<BatchNormLayer> create(float eps, bool has_weights, bool has_bias); + }; + + class CV_EXPORTS_W MaxUnpoolLayer : public Layer + { + public: + static CV_WRAP Ptr<MaxUnpoolLayer> create(Size unpoolSize); + }; + +//! @} +//! @} + +} +} +#endif |