590 lines
21 KiB
C++
590 lines
21 KiB
C++
/*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 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 partuclar, 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 ¶ms);
|
|
};
|
|
|
|
//! 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 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$)
|
|
*/
|
|
CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
|
|
|
|
/** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
|
|
* @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
|
|
* where `Wh` is parameter from setWeights().
|
|
*/
|
|
virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
|
|
|
|
/** @deprecated Use flag `produce_cell_output` in LayerParams.
|
|
* @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
|
|
*
|
|
* If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
|
|
* In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
|
|
*
|
|
* If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
|
|
* In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
|
|
*/
|
|
CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
|
|
|
|
/** @deprecated Use flag `use_timestamp_dim` in LayerParams.
|
|
* @brief If this flag is set to true then layer will produce @f$ 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 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;
|
|
int numOutput;
|
|
};
|
|
|
|
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:
|
|
int type;
|
|
|
|
int size;
|
|
float alpha, beta, bias;
|
|
bool normBySize;
|
|
|
|
static Ptr<LRNLayer> 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;
|
|
// 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 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;
|
|
/**
|
|
* @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<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);
|
|
};
|
|
|
|
/**
|
|
* 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;
|
|
int axis;
|
|
|
|
static Ptr<SliceLayer> create(const LayerParams ¶ms);
|
|
};
|
|
|
|
class CV_EXPORTS PermuteLayer : public Layer
|
|
{
|
|
public:
|
|
static Ptr<PermuteLayer> 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<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 ReLU6Layer : public ActivationLayer
|
|
{
|
|
public:
|
|
float minValue, maxValue;
|
|
|
|
static Ptr<ReLU6Layer> create(const LayerParams ¶ms);
|
|
};
|
|
|
|
class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
|
|
{
|
|
public:
|
|
static Ptr<Layer> 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:
|
|
static Ptr<EltwiseLayer> create(const LayerParams ¶ms);
|
|
};
|
|
|
|
class CV_EXPORTS BatchNormLayer : public Layer
|
|
{
|
|
public:
|
|
bool hasWeights, hasBias;
|
|
float epsilon;
|
|
|
|
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;
|
|
int axis;
|
|
|
|
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 ReorgLayer : public Layer
|
|
{
|
|
public:
|
|
static Ptr<ReorgLayer> create(const LayerParams& params);
|
|
};
|
|
|
|
class CV_EXPORTS RegionLayer : public Layer
|
|
{
|
|
public:
|
|
static Ptr<RegionLayer> create(const LayerParams& params);
|
|
};
|
|
|
|
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 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<NormalizeBBoxLayer> 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<ResizeNearestNeighborLayer> create(const LayerParams& params);
|
|
};
|
|
|
|
class CV_EXPORTS ProposalLayer : public Layer
|
|
{
|
|
public:
|
|
static Ptr<ProposalLayer> create(const LayerParams& params);
|
|
};
|
|
|
|
//! @}
|
|
//! @}
|
|
CV__DNN_EXPERIMENTAL_NS_END
|
|
}
|
|
}
|
|
#endif
|