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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_INLINE_NS_BEGIN
//! @addtogroup dnn
//! @{
/** @defgroup dnnLayerList Partial List of Implemented Layers
@{
This subsection of dnn module contains information about built-in layers and their descriptions.
Classes listed here, in fact, provides C++ API for creating instances of built-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 convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
In particular, the following layers and Caffe 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<Layer> create(const LayerParams &params);
};
/**
* Constant layer produces the same data blob at an every forward pass.
*/
class CV_EXPORTS ConstLayer : public Layer
{
public:
static Ptr<Layer> 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);
/** @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 weights.
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 concatenation 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$.
@param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
@param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
@param b is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
*/
CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
/** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
* @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
* where `Wh` is parameter from setWeights().
*/
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
/** @deprecated Use flag `produce_cell_output` in LayerParams.
* @brief Specifies either interpret first dimension of input blob as timestamp dimension either as sample.
*
* If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
*
* If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
* In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
*/
CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
/** @deprecated Use flag `use_timestamp_dim` in LayerParams.
* @brief If this flag is set to true then layer will produce @f$ 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 timestamps, `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 false 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) CV_OVERRIDE;
int outputNameToIndex(const String& outputName) CV_OVERRIDE;
};
/** @brief GRU recurrent one-layer
*
* Accepts input sequence and computes the final hidden state for each element in the batch.
*
* - input[0] containing the features of the input sequence.
* input[0] should have shape [`T`, `N`, `data_dims`] where `T` is sequence length, `N` is batch size, `data_dims` is input size
* - output would have shape [`T`, `N`, `D` * `hidden_size`] where `D = 2` if layer is bidirectional otherwise `D = 1`
*
* Depends on the following attributes:
* - hidden_size - Number of neurons in the hidden layer
* - direction - RNN could be bidirectional or forward
*
* The final hidden state @f$ h_t @f$ computes by the following formulas:
*
@f{eqnarray*}{
r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \\
@f}
* Where @f$x_t@f$ is current input, @f$h_{(t-1)}@f$ is previous or initial hidden state.
*
* @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$b_? \in R^{N_h}@f$.
*
* @f$\odot@f$ is per-element multiply operation.
*/
class CV_EXPORTS GRULayer : public Layer
{
public:
/** Creates instance of GRU layer */
static Ptr<GRULayer> create(const LayerParams& params);
};
/** @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:
CV_DEPRECATED_EXTERNAL Size kernel, stride, pad, dilation, adjustPad;
std::vector<size_t> adjust_pads;
std::vector<size_t> kernel_size, strides, dilations;
std::vector<size_t> pads_begin, pads_end;
String padMode;
int numOutput;
};
class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer
{
public:
static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
};
class CV_EXPORTS ConvolutionLayerInt8 : public BaseConvolutionLayer
{
public:
int input_zp, output_zp;
float input_sc, output_sc;
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:
int type;
int size;
float alpha, beta, bias;
bool normBySize;
static Ptr<LRNLayer> create(const LayerParams& params);
};
/** @brief ArgMax/ArgMin layer
* @note returns indices as floats, which means the supported range is [-2^24; 2^24]
*/
class CV_EXPORTS ArgLayer : public Layer
{
public:
static Ptr<ArgLayer> create(const LayerParams& params);
};
class CV_EXPORTS PoolingLayer : public Layer
{
public:
int type;
std::vector<size_t> kernel_size, strides;
std::vector<size_t> pads_begin, pads_end;
bool globalPooling; //!< Flag is true if at least one of the axes is global pooled.
std::vector<bool> isGlobalPooling;
bool computeMaxIdx;
String padMode;
bool ceilMode;
// If true for average pooling with padding, divide an every output region
// by a whole kernel area. Otherwise exclude zero padded values and divide
// by number of real values.
bool avePoolPaddedArea;
// ROIPooling parameters.
Size pooledSize;
float spatialScale;
// PSROIPooling parameters.
int psRoiOutChannels;
static Ptr<PoolingLayer> create(const LayerParams& params);
};
class CV_EXPORTS PoolingLayerInt8 : public PoolingLayer
{
public:
int input_zp, output_zp;
float input_sc, output_sc;
static Ptr<PoolingLayerInt8> create(const LayerParams& params);
};
class CV_EXPORTS ReduceLayer : public Layer
{
public:
int reduceType;
std::vector<size_t> reduceDims;
static Ptr<ReduceLayer> create(const LayerParams& params);
};
class CV_EXPORTS ReduceLayerInt8 : public ReduceLayer
{
public:
static Ptr<ReduceLayerInt8> create(const LayerParams& params);
};
class CV_EXPORTS SoftmaxLayer : public Layer
{
public:
bool logSoftMax;
static Ptr<SoftmaxLayer> create(const LayerParams& params);
};
class CV_EXPORTS SoftmaxLayerInt8 : public SoftmaxLayer
{
public:
float output_sc;
int output_zp;
static Ptr<SoftmaxLayerInt8> create(const LayerParams& params);
};
class CV_EXPORTS InnerProductLayer : public Layer
{
public:
int axis;
static Ptr<InnerProductLayer> create(const LayerParams& params);
};
class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer
{
public:
int input_zp, output_zp;
float input_sc, output_sc;
static Ptr<InnerProductLayerInt8> 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 QuantizeLayer : public Layer
{
public:
float scale;
int zeropoint;
static Ptr<QuantizeLayer> create(const LayerParams &params);
};
class CV_EXPORTS DequantizeLayer : public Layer
{
public:
float scale;
int zeropoint;
static Ptr<DequantizeLayer> create(const LayerParams &params);
};
class CV_EXPORTS RequantizeLayer : public Layer
{
public:
float scale, shift;
static Ptr<RequantizeLayer> create(const LayerParams &params);
};
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;
int paddingValue;
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);
};
/**
* 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 convenient 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<std::vector<Range> > sliceRanges;
std::vector<std::vector<int> > sliceSteps;
int axis;
int num_split;
static Ptr<SliceLayer> create(const LayerParams &params);
};
class CV_EXPORTS PermuteLayer : public Layer
{
public:
static Ptr<PermuteLayer> create(const LayerParams& params);
};
/**
* Permute channels of 4-dimensional input blob.
* @param group Number of groups to split input channels and pick in turns
* into output blob.
*
* \f[ groupSize = \frac{number\ of\ channels}{group} \f]
* \f[ output(n, c, h, w) = input(n, groupSize \times (c \% group) + \lfloor \frac{c}{group} \rfloor, h, w) \f]
* Read more at https://arxiv.org/pdf/1707.01083.pdf
*/
class CV_EXPORTS ShuffleChannelLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
int group;
};
/**
* @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<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 {};
virtual void forwardSlice(const int* src, const int* lut, int* dst, int len,
size_t outPlaneSize, int cn0, int cn1) const {};
virtual void forwardSlice(const int8_t* src, const int8_t* lut, int8_t* dst, int len,
size_t outPlaneSize, int cn0, int cn1) const {};
};
class CV_EXPORTS ReLULayer : public ActivationLayer
{
public:
float negativeSlope;
static Ptr<ReLULayer> create(const LayerParams &params);
};
class CV_EXPORTS ReLU6Layer : public ActivationLayer
{
public:
float minValue, maxValue;
static Ptr<ReLU6Layer> create(const LayerParams &params);
};
class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS ELULayer : public ActivationLayer
{
public:
float alpha;
static Ptr<ELULayer> create(const LayerParams &params);
};
class CV_EXPORTS TanHLayer : public ActivationLayer
{
public:
static Ptr<TanHLayer> create(const LayerParams &params);
};
class CV_EXPORTS SwishLayer : public ActivationLayer
{
public:
static Ptr<SwishLayer> create(const LayerParams &params);
};
class CV_EXPORTS MishLayer : public ActivationLayer
{
public:
static Ptr<MishLayer> 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);
};
class CV_EXPORTS ExpLayer : public ActivationLayer
{
public:
float base, scale, shift;
static Ptr<ExpLayer> create(const LayerParams &params);
};
class CV_EXPORTS CeilLayer : public ActivationLayer
{
public:
static Ptr<CeilLayer> create(const LayerParams &params);
};
class CV_EXPORTS FloorLayer : public ActivationLayer
{
public:
static Ptr<FloorLayer> create(const LayerParams &params);
};
class CV_EXPORTS LogLayer : public ActivationLayer
{
public:
static Ptr<LogLayer> create(const LayerParams &params);
};
class CV_EXPORTS RoundLayer : public ActivationLayer
{
public:
static Ptr<RoundLayer> create(const LayerParams &params);
};
class CV_EXPORTS SqrtLayer : public ActivationLayer
{
public:
static Ptr<SqrtLayer> create(const LayerParams &params);
};
class CV_EXPORTS NotLayer : public ActivationLayer
{
public:
static Ptr<NotLayer> create(const LayerParams &params);
};
class CV_EXPORTS AcosLayer : public ActivationLayer
{
public:
static Ptr<AcosLayer> create(const LayerParams &params);
};
class CV_EXPORTS AcoshLayer : public ActivationLayer
{
public:
static Ptr<AcoshLayer> create(const LayerParams &params);
};
class CV_EXPORTS AsinLayer : public ActivationLayer
{
public:
static Ptr<AsinLayer> create(const LayerParams &params);
};
class CV_EXPORTS AsinhLayer : public ActivationLayer
{
public:
static Ptr<AsinhLayer> create(const LayerParams &params);
};
class CV_EXPORTS AtanLayer : public ActivationLayer
{
public:
static Ptr<AtanLayer> create(const LayerParams &params);
};
class CV_EXPORTS AtanhLayer : public ActivationLayer
{
public:
static Ptr<AtanhLayer> create(const LayerParams &params);
};
class CV_EXPORTS CosLayer : public ActivationLayer
{
public:
static Ptr<CosLayer> create(const LayerParams &params);
};
class CV_EXPORTS CoshLayer : public ActivationLayer
{
public:
static Ptr<CoshLayer> create(const LayerParams &params);
};
class CV_EXPORTS ErfLayer : public ActivationLayer
{
public:
static Ptr<ErfLayer> create(const LayerParams &params);
};
class CV_EXPORTS HardSwishLayer : public ActivationLayer
{
public:
static Ptr<HardSwishLayer> create(const LayerParams &params);
};
class CV_EXPORTS SinLayer : public ActivationLayer
{
public:
static Ptr<SinLayer> create(const LayerParams &params);
};
class CV_EXPORTS SinhLayer : public ActivationLayer
{
public:
static Ptr<SinhLayer> create(const LayerParams &params);
};
class CV_EXPORTS SoftplusLayer : public ActivationLayer
{
public:
static Ptr<SoftplusLayer> create(const LayerParams &params);
};
class CV_EXPORTS SoftsignLayer : public ActivationLayer
{
public:
static Ptr<SoftsignLayer> create(const LayerParams &params);
};
class CV_EXPORTS TanLayer : public ActivationLayer
{
public:
static Ptr<TanLayer> create(const LayerParams &params);
};
class CV_EXPORTS CeluLayer : public ActivationLayer
{
public:
float alpha;
static Ptr<CeluLayer> create(const LayerParams &params);
};
class CV_EXPORTS HardSigmoidLayer : public ActivationLayer
{
public:
float alpha;
float beta;
static Ptr<HardSigmoidLayer> create(const LayerParams &params);
};
class CV_EXPORTS SeluLayer : public ActivationLayer
{
public:
float alpha;
float gamma;
static Ptr<SeluLayer> create(const LayerParams &params);
};
class CV_EXPORTS ThresholdedReluLayer : public ActivationLayer
{
public:
float alpha;
static Ptr<ThresholdedReluLayer> create(const LayerParams &params);
};
class CV_EXPORTS ActivationLayerInt8 : public ActivationLayer
{
public:
static Ptr<ActivationLayerInt8> create(const LayerParams &params);
};
class CV_EXPORTS SignLayer : public ActivationLayer
{
public:
static Ptr<SignLayer> create(const LayerParams &params);
};
class CV_EXPORTS ShrinkLayer : public ActivationLayer
{
public:
float bias;
float lambd;
static Ptr<ShrinkLayer> create(const LayerParams &params);
};
class CV_EXPORTS ReciprocalLayer : public ActivationLayer
{
public:
static Ptr<ReciprocalLayer> create(const LayerParams &params);
};
/* Layers used in semantic segmentation */
class CV_EXPORTS CropLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams &params);
};
/** @brief Element wise operation on inputs
Extra optional parameters:
- "operation" as string. Values are "sum" (default), "prod", "max", "div", "min"
- "coeff" as float array. Specify weights of inputs for SUM operation
- "output_channels_mode" as string. Values are "same" (default, all input must have the same layout), "input_0", "input_0_truncate", "max_input_channels"
*/
class CV_EXPORTS EltwiseLayer : public Layer
{
public:
static Ptr<EltwiseLayer> create(const LayerParams &params);
};
class CV_EXPORTS EltwiseLayerInt8 : public Layer
{
public:
static Ptr<EltwiseLayerInt8> create(const LayerParams &params);
};
class CV_EXPORTS BatchNormLayer : public ActivationLayer
{
public:
bool hasWeights, hasBias;
float epsilon;
static Ptr<BatchNormLayer> create(const LayerParams &params);
};
class CV_EXPORTS BatchNormLayerInt8 : public BatchNormLayer
{
public:
float input_sc, output_sc;
int input_zp, output_zp;
static Ptr<BatchNormLayerInt8> 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;
int axis;
String mode;
static Ptr<ScaleLayer> create(const LayerParams& params);
};
class CV_EXPORTS ScaleLayerInt8 : public ScaleLayer
{
public:
float output_sc;
int output_zp;
static Ptr<ScaleLayerInt8> create(const LayerParams &params);
};
class CV_EXPORTS ShiftLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS ShiftLayerInt8 : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS CompareLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS DataAugmentationLayer : public Layer
{
public:
static Ptr<DataAugmentationLayer> create(const LayerParams& params);
};
class CV_EXPORTS CorrelationLayer : public Layer
{
public:
static Ptr<CorrelationLayer> create(const LayerParams& params);
};
class CV_EXPORTS AccumLayer : public Layer
{
public:
static Ptr<AccumLayer> create(const LayerParams& params);
};
class CV_EXPORTS FlowWarpLayer : public Layer
{
public:
static Ptr<FlowWarpLayer> create(const LayerParams& params);
};
class CV_EXPORTS PriorBoxLayer : public Layer
{
public:
static Ptr<PriorBoxLayer> create(const LayerParams& params);
};
class CV_EXPORTS ReorgLayer : public Layer
{
public:
static Ptr<ReorgLayer> create(const LayerParams& params);
};
class CV_EXPORTS RegionLayer : public Layer
{
public:
float nmsThreshold;
static Ptr<RegionLayer> create(const LayerParams& params);
};
/**
* @brief Detection output layer.
*
* The layer size is: @f$ (1 \times 1 \times N \times 7) @f$
* where N is [keep_top_k] parameter multiplied by batch size. Each row is:
* [image_id, label, confidence, xmin, ymin, xmax, ymax]
* where image_id is the index of image input in the batch.
*/
class CV_EXPORTS DetectionOutputLayer : public Layer
{
public:
static Ptr<DetectionOutputLayer> 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 coordinates, `c` - channel.
*
* An every sample in the batch is normalized separately. Optionally,
* output is scaled by the trained parameters.
*/
class CV_EXPORTS NormalizeBBoxLayer : public Layer
{
public:
float pnorm, epsilon;
CV_DEPRECATED_EXTERNAL bool acrossSpatial;
static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
};
/**
* @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
*
* Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops.
*/
class CV_EXPORTS ResizeLayer : public Layer
{
public:
static Ptr<ResizeLayer> create(const LayerParams& params);
};
/**
* @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2
*
* It differs from @ref ResizeLayer in output shape and resize scales computations.
*/
class CV_EXPORTS InterpLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS ProposalLayer : public Layer
{
public:
static Ptr<ProposalLayer> create(const LayerParams& params);
};
class CV_EXPORTS CropAndResizeLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS CumSumLayer : public Layer
{
public:
int exclusive;
int reverse;
static Ptr<CumSumLayer> create(const LayerParams& params);
};
//! @}
//! @}
CV__DNN_INLINE_NS_END
}
}
#endif
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