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opencv/modules/dnn/include/opencv2/dnn/all_layers.hpp
Alexander Alekhin da0960321b dnn: added "hidden" experimental namespace 
Main purpose of this namespace is to avoid using of incompatible
binaries that will cause applications crashes.

This additional namespace will not impact "Source code API".
This change allows to maintain ABI checks (with easy filtering out).
2017-06-28 20:36:57 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#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 &params);
};
//! 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);
};
//! Classical recurrent layer
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;
/** 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
{
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 &params);
};
class CV_EXPORTS ConcatLayer : public Layer
{
public:
int axis;
static Ptr<ConcatLayer> create(const LayerParams &params);
};
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 &params);
};
class CV_EXPORTS SliceLayer : public Layer
{
public:
int axis;
std::vector<int> sliceIndices;
static Ptr<SliceLayer> create(const LayerParams &params);
};
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 &params);
};
class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
{
public:
static Ptr<ChannelsPReLULayer> create(const LayerParams& params);
};
class CV_EXPORTS TanHLayer : public ActivationLayer
{
public:
static Ptr<TanHLayer> create(const LayerParams &params);
};
class CV_EXPORTS SigmoidLayer : public ActivationLayer
{
public:
static Ptr<SigmoidLayer> create(const LayerParams &params);
};
class CV_EXPORTS BNLLLayer : public ActivationLayer
{
public:
static Ptr<BNLLLayer> create(const LayerParams &params);
};
class CV_EXPORTS AbsLayer : public ActivationLayer
{
public:
static Ptr<AbsLayer> create(const LayerParams &params);
};
class CV_EXPORTS PowerLayer : public ActivationLayer
{
public:
float power, scale, shift;
static Ptr<PowerLayer> create(const LayerParams &params);
};
/* Layers used in semantic segmentation */
class CV_EXPORTS CropLayer : public Layer
{
public:
int startAxis;
std::vector<int> offset;
static Ptr<CropLayer> create(const LayerParams &params);
};
class CV_EXPORTS EltwiseLayer : public Layer
{
public:
enum EltwiseOp
{
PROD = 0,
SUM = 1,
MAX = 2,
};
static Ptr<EltwiseLayer> create(const LayerParams &params);
};
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 &params);
};
class CV_EXPORTS MaxUnpoolLayer : public Layer
{
public:
Size poolKernel;
Size poolPad;
Size poolStride;
static Ptr<MaxUnpoolLayer> create(const LayerParams &params);
};
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
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