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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 readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()). Bult-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. In partuclar, the following layers and Caffe importer were tested to reproduce Caffe 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 create(const LayerParams ¶ms); }; //! LSTM recurrent layer class CV_EXPORTS LSTMLayer : public Layer { public: /** Creates instance of LSTM layer */ static Ptr create(const LayerParams& params); /** @deprecated Use LayerParams::blobs instead. @brief 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$) */ CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0; /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape. * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used, * where `Wh` is parameter from setWeights(). */ virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0; /** @deprecated Use flag `produce_cell_output` in LayerParams. * @brief Specifies either interpet first dimension of input blob as timestamp dimenion either as sample. * * If flag is set to true then shape of input blob will be interpeted as [`T`, `N`, `[data dims]`] where `T` specifies number of timpestamps, `N` is number of independent streams. * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times. * * If flag is set to false then shape of input blob will be interpeted as [`N`, `[data dims]`]. * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`]. */ CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0; /** @deprecated Use flag `use_timestamp_dim` in LayerParams. * @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output. * @details Shape of the second output is the same as first output. */ CV_DEPRECATED 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 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; int numOutput; }; class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS LRNLayer : public Layer { public: int type; int size; float alpha, beta, bias; bool normBySize; static Ptr create(const LayerParams& params); }; class CV_EXPORTS PoolingLayer : public Layer { public: int type; Size kernel, stride, pad; bool globalPooling; bool computeMaxIdx; String padMode; bool ceilMode; // ROIPooling parameters. Size pooledSize; float spatialScale; // PSROIPooling parameters. int psRoiOutChannels; static Ptr create(const LayerParams& params); }; class CV_EXPORTS SoftmaxLayer : public Layer { public: bool logSoftMax; static Ptr create(const LayerParams& params); }; class CV_EXPORTS InnerProductLayer : public Layer { public: int axis; static Ptr create(const LayerParams& params); }; class CV_EXPORTS MVNLayer : public Layer { public: float eps; bool normVariance, acrossChannels; static Ptr create(const LayerParams& params); }; /* Reshaping */ class CV_EXPORTS ReshapeLayer : public Layer { public: MatShape newShapeDesc; Range newShapeRange; static Ptr create(const LayerParams& params); }; class CV_EXPORTS FlattenLayer : public Layer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS ConcatLayer : public Layer { public: int axis; /** * @brief Add zero padding in case of concatenation of blobs with different * spatial sizes. * * Details: https://github.com/torch/nn/blob/master/doc/containers.md#depthconcat */ bool padding; static Ptr 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 create(const LayerParams ¶ms); }; /** * Slice layer has several modes: * 1. Caffe mode * @param[in] axis Axis of split operation * @param[in] slice_point Array of split points * * Number of output blobs equals to number of split points plus one. The * first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis, * the second output blob is a slice of input from @p slice_point[0] to * @p slice_point[1] - 1 by @p axis and the last output blob is a slice of * input from @p slice_point[-1] up to the end of @p axis size. * * 2. TensorFlow mode * @param begin Vector of start indices * @param size Vector of sizes * * More convinient numpy-like slice. One and only output blob * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]` * * 3. Torch mode * @param axis Axis of split operation * * Split input blob on the equal parts by @p axis. */ class CV_EXPORTS SliceLayer : public Layer { public: /** * @brief Vector of slice ranges. * * The first dimension equals number of output blobs. * Inner vector has slice ranges for the first number of input dimensions. */ std::vector > sliceRanges; int axis; static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS PermuteLayer : public Layer { public: static Ptr create(const LayerParams& params); }; /** * @brief Adds extra values for specific axes. * @param paddings Vector of paddings in format * @code * [ pad_before, pad_after, // [0]th dimension * pad_before, pad_after, // [1]st dimension * ... * pad_before, pad_after ] // [n]th dimension * @endcode * that represents number of padded values at every dimension * starting from the first one. The rest of dimensions won't * be padded. * @param value Value to be padded. Defaults to zero. * @param type Padding type: 'constant', 'reflect' * @param input_dims Torch's parameter. If @p input_dims is not equal to the * actual input dimensionality then the `[0]th` dimension * is considered as a batch dimension and @p paddings are shifted * to a one dimension. Defaults to `-1` that means padding * corresponding to @p paddings. */ class CV_EXPORTS PaddingLayer : public Layer { public: static Ptr 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 create(const LayerParams ¶ms); }; class CV_EXPORTS ReLU6Layer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS ELULayer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS TanHLayer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS SigmoidLayer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS BNLLLayer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS AbsLayer : public ActivationLayer { public: static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS PowerLayer : public ActivationLayer { public: float power, scale, shift; static Ptr create(const LayerParams ¶ms); }; /* Layers used in semantic segmentation */ class CV_EXPORTS CropLayer : public Layer { public: int startAxis; std::vector offset; static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS EltwiseLayer : public Layer { public: static Ptr 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 create(const LayerParams ¶ms); }; class CV_EXPORTS MaxUnpoolLayer : public Layer { public: Size poolKernel; Size poolPad; Size poolStride; static Ptr create(const LayerParams ¶ms); }; class CV_EXPORTS ScaleLayer : public Layer { public: bool hasBias; static Ptr create(const LayerParams& params); }; class CV_EXPORTS ShiftLayer : public Layer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS PriorBoxLayer : public Layer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS ReorgLayer : public Layer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS RegionLayer : public Layer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS DetectionOutputLayer : public Layer { public: static Ptr create(const LayerParams& params); }; /** * @brief \f$ L_p \f$ - normalization layer. * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ - * normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one. * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero. * @param across_spatial If true, normalize an input across all non-batch dimensions. * Otherwise normalize an every channel separately. * * Across spatial: * @f[ * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\ * dst(x, y, c) = \frac{ src(x, y, c) }{norm} * @f] * * Channel wise normalization: * @f[ * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\ * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)} * @f] * * Where `x, y` - spatial cooridnates, `c` - channel. * * An every sample in the batch is normalized separately. Optionally, * output is scaled by the trained parameters. */ class NormalizeBBoxLayer : public Layer { public: float pnorm, epsilon; bool acrossSpatial; static Ptr create(const LayerParams& params); }; /** * @brief Resize input 4-dimensional blob by nearest neghbor strategy. * * Layer is used to support TensorFlow's resize_nearest_neighbor op. */ class CV_EXPORTS ResizeNearestNeighborLayer : public Layer { public: static Ptr create(const LayerParams& params); }; class CV_EXPORTS ProposalLayer : public Layer { public: static Ptr create(const LayerParams& params); }; //! @} //! @} CV__DNN_EXPERIMENTAL_NS_END } } #endif