mlpack 3.4.2
atrous_convolution.hpp
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1
13#ifndef MLPACK_METHODS_ANN_LAYER_ATROUS_CONVOLUTION_HPP
14#define MLPACK_METHODS_ANN_LAYER_ATROUS_CONVOLUTION_HPP
15
16#include <mlpack/prereqs.hpp>
17
23
24#include "layer_types.hpp"
25#include "padding.hpp"
26
27namespace mlpack{
28namespace ann {
29
45template <
46 typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>,
47 typename BackwardConvolutionRule = NaiveConvolution<FullConvolution>,
48 typename GradientConvolutionRule = NaiveConvolution<ValidConvolution>,
49 typename InputDataType = arma::mat,
50 typename OutputDataType = arma::mat
51>
53{
54 public:
57
77 AtrousConvolution(const size_t inSize,
78 const size_t outSize,
79 const size_t kernelWidth,
80 const size_t kernelHeight,
81 const size_t strideWidth = 1,
82 const size_t strideHeight = 1,
83 const size_t padW = 0,
84 const size_t padH = 0,
85 const size_t inputWidth = 0,
86 const size_t inputHeight = 0,
87 const size_t dilationWidth = 1,
88 const size_t dilationHeight = 1,
89 const std::string& paddingType = "None");
90
114 AtrousConvolution(const size_t inSize,
115 const size_t outSize,
116 const size_t kernelWidth,
117 const size_t kernelHeight,
118 const size_t strideWidth,
119 const size_t strideHeight,
120 const std::tuple<size_t, size_t>& padW,
121 const std::tuple<size_t, size_t>& padH,
122 const size_t inputWidth = 0,
123 const size_t inputHeight = 0,
124 const size_t dilationWidth = 1,
125 const size_t dilationHeight = 1,
126 const std::string& paddingType = "None");
127
128 /*
129 * Set the weight and bias term.
130 */
131 void Reset();
132
140 template<typename eT>
141 void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);
142
152 template<typename eT>
153 void Backward(const arma::Mat<eT>& /* input */,
154 const arma::Mat<eT>& gy,
155 arma::Mat<eT>& g);
156
157 /*
158 * Calculate the gradient using the output delta and the input activation.
159 *
160 * @param input The input parameter used for calculating the gradient.
161 * @param error The calculated error.
162 * @param gradient The calculated gradient.
163 */
164 template<typename eT>
165 void Gradient(const arma::Mat<eT>& /* input */,
166 const arma::Mat<eT>& error,
167 arma::Mat<eT>& gradient);
168
170 OutputDataType const& Parameters() const { return weights; }
172 OutputDataType& Parameters() { return weights; }
173
175 arma::cube const& Weight() const { return weight; }
177 arma::cube& Weight() { return weight; }
178
180 arma::mat const& Bias() const { return bias; }
182 arma::mat& Bias() { return bias; }
183
185 OutputDataType const& OutputParameter() const { return outputParameter; }
187 OutputDataType& OutputParameter() { return outputParameter; }
188
190 OutputDataType const& Delta() const { return delta; }
192 OutputDataType& Delta() { return delta; }
193
195 OutputDataType const& Gradient() const { return gradient; }
197 OutputDataType& Gradient() { return gradient; }
198
200 size_t InputWidth() const { return inputWidth; }
202 size_t& InputWidth() { return inputWidth; }
203
205 size_t InputHeight() const { return inputHeight; }
207 size_t& InputHeight() { return inputHeight; }
208
210 size_t OutputWidth() const { return outputWidth; }
212 size_t& OutputWidth() { return outputWidth; }
213
215 size_t OutputHeight() const { return outputHeight; }
217 size_t& OutputHeight() { return outputHeight; }
218
220 size_t InputSize() const { return inSize; }
221
223 size_t OutputSize() const { return outSize; }
224
226 size_t KernelWidth() const { return kernelWidth; }
228 size_t& KernelWidth() { return kernelWidth; }
229
231 size_t KernelHeight() const { return kernelHeight; }
233 size_t& KernelHeight() { return kernelHeight; }
234
236 size_t StrideWidth() const { return strideWidth; }
238 size_t& StrideWidth() { return strideWidth; }
239
241 size_t StrideHeight() const { return strideHeight; }
243 size_t& StrideHeight() { return strideHeight; }
244
246 size_t DilationWidth() const { return dilationWidth; }
248 size_t& DilationWidth() { return dilationWidth; }
249
251 size_t DilationHeight() const { return dilationHeight; }
253 size_t& DilationHeight() { return dilationHeight; }
254
256 ann::Padding<> const& Padding() const { return padding; }
258 ann::Padding<>& Padding() { return padding; }
259
261 size_t WeightSize() const
262 {
263 return (outSize * inSize * kernelWidth * kernelHeight) + outSize;
264 }
265
269 template<typename Archive>
270 void serialize(Archive& ar, const unsigned int /* version */);
271
272 private:
273 /*
274 * Return the convolution output size.
275 *
276 * @param size The size of the input (row or column).
277 * @param k The size of the filter (width or height).
278 * @param s The stride size (x or y direction).
279 * @param pSideOne The size of the padding (width or height) on one side.
280 * @param pSideTwo The size of the padding (width or height) on another side.
281 * @param d The dilation size.
282 * @return The convolution output size.
283 */
284 size_t ConvOutSize(const size_t size,
285 const size_t k,
286 const size_t s,
287 const size_t pSideOne,
288 const size_t pSideTwo,
289 const size_t d)
290 {
291 return std::floor(size + pSideOne + pSideTwo - d * (k - 1) - 1) / s + 1;
292 }
293
294 /*
295 * Function to assign padding such that output size is same as input size.
296 */
297 void InitializeSamePadding(size_t& padWLeft,
298 size_t& padWRight,
299 size_t& padHBottom,
300 size_t& padHTop) const;
301
302 /*
303 * Rotates a 3rd-order tensor counterclockwise by 180 degrees.
304 *
305 * @param input The input data to be rotated.
306 * @param output The rotated output.
307 */
308 template<typename eT>
309 void Rotate180(const arma::Cube<eT>& input, arma::Cube<eT>& output)
310 {
311 output = arma::Cube<eT>(input.n_rows, input.n_cols, input.n_slices);
312
313 // * left-right flip, up-down flip */
314 for (size_t s = 0; s < output.n_slices; s++)
315 output.slice(s) = arma::fliplr(arma::flipud(input.slice(s)));
316 }
317
318 /*
319 * Rotates a dense matrix counterclockwise by 180 degrees.
320 *
321 * @param input The input data to be rotated.
322 * @param output The rotated output.
323 */
324 template<typename eT>
325 void Rotate180(const arma::Mat<eT>& input, arma::Mat<eT>& output)
326 {
327 // * left-right flip, up-down flip */
328 output = arma::fliplr(arma::flipud(input));
329 }
330
332 size_t inSize;
333
335 size_t outSize;
336
338 size_t batchSize;
339
341 size_t kernelWidth;
342
344 size_t kernelHeight;
345
347 size_t strideWidth;
348
350 size_t strideHeight;
351
353 OutputDataType weights;
354
356 arma::cube weight;
357
359 arma::mat bias;
360
362 size_t inputWidth;
363
365 size_t inputHeight;
366
368 size_t outputWidth;
369
371 size_t outputHeight;
372
374 size_t dilationWidth;
375
377 size_t dilationHeight;
378
380 arma::cube outputTemp;
381
383 arma::cube inputPaddedTemp;
384
386 arma::cube gTemp;
387
389 arma::cube gradientTemp;
390
392 ann::Padding<> padding;
393
395 OutputDataType delta;
396
398 OutputDataType gradient;
399
401 OutputDataType outputParameter;
402}; // class AtrousConvolution
403
404} // namespace ann
405} // namespace mlpack
406
408namespace boost {
409namespace serialization {
410
411template<
412 typename ForwardConvolutionRule,
413 typename BackwardConvolutionRule,
414 typename GradientConvolutionRule,
415 typename InputDataType,
416 typename OutputDataType
417>
418struct version<
419 mlpack::ann::AtrousConvolution<ForwardConvolutionRule,
420 BackwardConvolutionRule, GradientConvolutionRule, InputDataType,
421 OutputDataType> >
422{
423 BOOST_STATIC_CONSTANT(int, value = 2);
424};
425
426} // namespace serialization
427} // namespace boost
428
429// Include implementation.
430#include "atrous_convolution_impl.hpp"
431
432#endif
Implementation of the Atrous Convolution class.
arma::cube const & Weight() const
Get the weight of the layer.
OutputDataType const & Delta() const
Get the delta.
size_t StrideHeight() const
Get the stride height.
size_t & DilationWidth()
Modify the dilation rate on the X axis.
OutputDataType const & Parameters() const
Get the parameters.
AtrousConvolution(const size_t inSize, const size_t outSize, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const std::tuple< size_t, size_t > &padW, const std::tuple< size_t, size_t > &padH, const size_t inputWidth=0, const size_t inputHeight=0, const size_t dilationWidth=1, const size_t dilationHeight=1, const std::string &paddingType="None")
Create the AtrousConvolution object using the specified number of input maps, output maps,...
ann::Padding const & Padding() const
Get the internal Padding layer.
size_t & InputHeight()
Modify the input height.
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...
arma::cube & Weight()
Modify the weight of the layer.
OutputDataType const & OutputParameter() const
Get the output parameter.
size_t InputSize() const
Get the input size.
size_t & InputWidth()
Modify input the width.
size_t KernelWidth() const
Get the kernel width.
size_t KernelHeight() const
Get the kernel height.
size_t & StrideWidth()
Modify the stride width.
size_t WeightSize() const
Get size of the weight matrix.
arma::mat const & Bias() const
Get the bias of the layer.
size_t & KernelWidth()
Modify the kernel width.
size_t & OutputHeight()
Modify the output height.
AtrousConvolution()
Create the AtrousConvolution object.
size_t OutputHeight() const
Get the output height.
size_t OutputSize() const
Get the output size.
void Gradient(const arma::Mat< eT > &, const arma::Mat< eT > &error, arma::Mat< eT > &gradient)
size_t OutputWidth() const
Get the output width.
size_t & DilationHeight()
Modify the dilation rate on the Y axis.
OutputDataType const & Gradient() const
Get the gradient.
size_t InputHeight() const
Get the input height.
OutputDataType & Gradient()
Modify the gradient.
size_t DilationHeight() const
Get the dilation rate on the Y axis.
size_t & KernelHeight()
Modify the kernel height.
AtrousConvolution(const size_t inSize, const size_t outSize, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth=1, const size_t strideHeight=1, const size_t padW=0, const size_t padH=0, const size_t inputWidth=0, const size_t inputHeight=0, const size_t dilationWidth=1, const size_t dilationHeight=1, const std::string &paddingType="None")
Create the AtrousConvolution object using the specified number of input maps, output maps,...
size_t StrideWidth() const
Get the stride width.
size_t & StrideHeight()
Modify the stride height.
void Backward(const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backw...
OutputDataType & OutputParameter()
Modify the output parameter.
size_t & OutputWidth()
Modify the output width.
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
OutputDataType & Parameters()
Modify the parameters.
arma::mat & Bias()
Modify the bias of the layer.
ann::Padding & Padding()
Modify the internal Padding layer.
size_t DilationWidth() const
Get the dilation rate on the X axis.
OutputDataType & Delta()
Modify the delta.
Implementation of the Padding module class.
Definition: padding.hpp:35
Set the serialization version of the adaboost class.
Definition: adaboost.hpp:198
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.