mlpack 3.4.2
adaptive_mean_pooling.hpp
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1
13#ifndef MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP
14#define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP
15
16#include <mlpack/prereqs.hpp>
17#include "layer_types.hpp"
18
19namespace mlpack {
20namespace ann {
21
30template <
31 typename InputDataType = arma::mat,
32 typename OutputDataType = arma::mat
33>
35{
36 public:
39
46 AdaptiveMeanPooling(const size_t outputWidth,
47 const size_t outputHeight);
48
54 AdaptiveMeanPooling(const std::tuple<size_t, size_t>& outputShape);
55
63 template<typename eT>
64 void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);
65
75 template<typename eT>
76 void Backward(const arma::Mat<eT>& input,
77 const arma::Mat<eT>& gy,
78 arma::Mat<eT>& g);
79
81 const OutputDataType& OutputParameter() const
82 { return poolingLayer.OutputParameter(); }
83
85 OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); }
86
88 const OutputDataType& Delta() const { return poolingLayer.Delta(); }
90 OutputDataType& Delta() { return poolingLayer.Delta(); }
91
93 size_t InputWidth() const { return poolingLayer.InputWidth(); }
95 size_t& InputWidth() { return poolingLayer.InputWidth(); }
96
98 size_t InputHeight() const { return poolingLayer.InputHeight(); }
100 size_t& InputHeight() { return poolingLayer.InputHeight(); }
101
103 size_t OutputWidth() const { return outputWidth; }
105 size_t& OutputWidth() { return outputWidth; }
106
108 size_t OutputHeight() const { return outputHeight; }
110 size_t& OutputHeight() { return outputHeight; }
111
113 size_t InputSize() const { return poolingLayer.InputSize(); }
114
116 size_t OutputSize() const { return poolingLayer.OutputSize(); }
117
121 template<typename Archive>
122 void serialize(Archive& ar, const unsigned int version);
123
124 private:
128 void IntializeAdaptivePadding()
129 {
130 poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() /
131 outputWidth);
132 poolingLayer.StrideHeight() = std::floor(poolingLayer.InputHeight() /
133 outputHeight);
134
135 poolingLayer.KernelWidth() = poolingLayer.InputWidth() -
136 (outputWidth - 1) * poolingLayer.StrideWidth();
137 poolingLayer.KernelHeight() = poolingLayer.InputHeight() -
138 (outputHeight - 1) * poolingLayer.StrideHeight();
139
140 if (poolingLayer.KernelHeight() <= 0 || poolingLayer.KernelWidth() <= 0 ||
141 poolingLayer.StrideWidth() <= 0 || poolingLayer.StrideHeight() <= 0)
142 {
143 Log::Fatal << "Given output shape (" << outputWidth << ", "
144 << outputHeight << ") is not possible for given input shape ("
145 << poolingLayer.InputWidth() << ", " << poolingLayer.InputHeight()
146 << ")." << std::endl;
147 }
148 }
149
151 MeanPooling<InputDataType, OutputDataType> poolingLayer;
152
154 size_t outputWidth;
155
157 size_t outputHeight;
158
160 bool reset;
161}; // class AdaptiveMeanPooling
162
163} // namespace ann
164} // namespace mlpack
165
166// Include implementation.
167#include "adaptive_mean_pooling_impl.hpp"
168
169#endif
static MLPACK_EXPORT util::PrefixedOutStream Fatal
Prints fatal messages prefixed with [FATAL], then terminates the program.
Definition: log.hpp:90
Implementation of the AdaptiveMeanPooling.
AdaptiveMeanPooling()
Create the AdaptiveMeanPooling object.
size_t & InputHeight()
Modify the input height.
AdaptiveMeanPooling(const std::tuple< size_t, size_t > &outputShape)
Create the AdaptiveMeanPooling object.
size_t InputWidth() const
Get the input width.
void Forward(const arma::Mat< eT > &input, arma::Mat< eT > &output)
Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activ...
size_t InputSize() const
Get the input size.
size_t & InputWidth()
Modify the input width.
void serialize(Archive &ar, const unsigned int version)
Serialize the layer.
void Backward(const arma::Mat< eT > &input, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
Ordinary feed backward pass of a neural network, using 3rd-order tensors as input,...
size_t & OutputHeight()
Modify the output height.
size_t OutputHeight() const
Get the output height.
size_t OutputSize() const
Get the output size.
size_t OutputWidth() const
Get the output width.
AdaptiveMeanPooling(const size_t outputWidth, const size_t outputHeight)
Create the AdaptiveMeanPooling object.
size_t InputHeight() const
Get the input height.
const OutputDataType & OutputParameter() const
Get the output parameter.
OutputDataType & OutputParameter()
Modify the output parameter.
size_t & OutputWidth()
Modify the output width.
OutputDataType & Delta()
Modify the delta.
const OutputDataType & Delta() const
Get the delta.
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1
The core includes that mlpack expects; standard C++ includes and Armadillo.