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/*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 {
CV__DNN_EXPERIMENTAL_NS_BEGIN
//! @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 -))
*/
class CV_EXPORTS BlankLayer : public Layer
{
public:
static Ptr<BlankLayer> create(const LayerParams ¶ms);
};
//! LSTM recurrent layer
class CV_EXPORTS LSTMLayer : public Layer
{
public:
/** Creates instance of LSTM layer */
static Ptr<LSTMLayer> create(const LayerParams& params);
/** 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$)
*/
virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
/** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
* @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
* where `Wh` is parameter from setWeights().
*/
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
/** @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample.
*
* If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams.
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
*
* If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`].
* In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
*/
virtual void setUseTimstampsDim(bool use = true) = 0;
/** @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
* @details Shape of the second output is the same as first output.
*/
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, ...]).
*/
int inputNameToIndex(String inputName);
int outputNameToIndex(String outputName);
};
/** @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:
/** Creates instance of RNNLayer */
static Ptr<RNNLayer> create(const LayerParams& params);
/** 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
*/
virtual void setWeights(const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &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.
*/
virtual void setProduceHiddenOutput(bool produce = false) = 0;
};
class CV_EXPORTS BaseConvolutionLayer : public Layer
{
public:
Size kernel, stride, pad, dilation, adjustPad;
String padMode;
};
class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer
{
public:
static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
};
class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer
{
public:
static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
};
class CV_EXPORTS LRNLayer : public Layer
{
public:
enum Type
{
CHANNEL_NRM,
SPATIAL_NRM
};
int type;
int size;
float alpha, beta, bias;
bool normBySize;
static Ptr<LRNLayer> create(const LayerParams& params);
};
class CV_EXPORTS PoolingLayer : public Layer
{
public:
enum Type
{
MAX,
AVE,
STOCHASTIC
};
int type;
Size kernel, stride, pad;
bool globalPooling;
bool computeMaxIdx;
String padMode;
static Ptr<PoolingLayer> create(const LayerParams& params);
};
class CV_EXPORTS SoftmaxLayer : public Layer
{
public:
bool logSoftMax;
static Ptr<SoftmaxLayer> create(const LayerParams& params);
};
class CV_EXPORTS InnerProductLayer : public Layer
{
public:
int axis;
static Ptr<InnerProductLayer> create(const LayerParams& params);
};
class CV_EXPORTS MVNLayer : public Layer
{
public:
float eps;
bool normVariance, acrossChannels;
static Ptr<MVNLayer> create(const LayerParams& params);
};
/* Reshaping */
class CV_EXPORTS ReshapeLayer : public Layer
{
public:
MatShape newShapeDesc;
Range newShapeRange;
static Ptr<ReshapeLayer> create(const LayerParams& params);
};
class CV_EXPORTS FlattenLayer : public Layer
{
public:
static Ptr<FlattenLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS ConcatLayer : public Layer
{
public:
int axis;
static Ptr<ConcatLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS SplitLayer : public Layer
{
public:
int outputsCount; //!< Number of copies that will be produced (is ignored when negative).
static Ptr<SplitLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS SliceLayer : public Layer
{
public:
int axis;
std::vector<int> sliceIndices;
static Ptr<SliceLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS PermuteLayer : public Layer
{
public:
static Ptr<PermuteLayer> create(const LayerParams& params);
};
class CV_EXPORTS PaddingLayer : public Layer
{
public:
static Ptr<PaddingLayer> create(const LayerParams& params);
};
/* Activations */
class CV_EXPORTS ActivationLayer : public Layer
{
public:
virtual void forwardSlice(const float* src, float* dst, int len,
size_t outPlaneSize, int cn0, int cn1) const = 0;
};
class CV_EXPORTS ReLULayer : public ActivationLayer
{
public:
float negativeSlope;
static Ptr<ReLULayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
{
public:
static Ptr<ChannelsPReLULayer> create(const LayerParams& params);
};
class CV_EXPORTS ELULayer : public ActivationLayer
{
public:
static Ptr<ELULayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS TanHLayer : public ActivationLayer
{
public:
static Ptr<TanHLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS SigmoidLayer : public ActivationLayer
{
public:
static Ptr<SigmoidLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS BNLLLayer : public ActivationLayer
{
public:
static Ptr<BNLLLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS AbsLayer : public ActivationLayer
{
public:
static Ptr<AbsLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS PowerLayer : public ActivationLayer
{
public:
float power, scale, shift;
static Ptr<PowerLayer> create(const LayerParams ¶ms);
};
/* Layers used in semantic segmentation */
class CV_EXPORTS CropLayer : public Layer
{
public:
int startAxis;
std::vector<int> offset;
static Ptr<CropLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS EltwiseLayer : public Layer
{
public:
enum EltwiseOp
{
PROD = 0,
SUM = 1,
MAX = 2,
};
static Ptr<EltwiseLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS BatchNormLayer : public Layer
{
public:
bool hasWeights, hasBias;
float epsilon;
virtual void getScaleShift(Mat& scale, Mat& shift) const = 0;
static Ptr<BatchNormLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS MaxUnpoolLayer : public Layer
{
public:
Size poolKernel;
Size poolPad;
Size poolStride;
static Ptr<MaxUnpoolLayer> create(const LayerParams ¶ms);
};
class CV_EXPORTS ScaleLayer : public Layer
{
public:
bool hasBias;
static Ptr<ScaleLayer> create(const LayerParams& params);
};
class CV_EXPORTS ShiftLayer : public Layer
{
public:
static Ptr<ShiftLayer> create(const LayerParams& params);
};
class CV_EXPORTS PriorBoxLayer : public Layer
{
public:
static Ptr<PriorBoxLayer> create(const LayerParams& params);
};
class CV_EXPORTS DetectionOutputLayer : public Layer
{
public:
static Ptr<DetectionOutputLayer> create(const LayerParams& params);
};
class NormalizeBBoxLayer : public Layer
{
public:
static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
};
//! @}
//! @}
CV__DNN_EXPERIMENTAL_NS_END
}
}
#endif