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Registered MLPPConvolutions into the ClassDB, and fixed all warnings in it.

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Relintai 2023-02-12 18:20:53 +01:00
parent bee57218a2
commit be9947a8b9
3 changed files with 65 additions and 54 deletions
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103
mlpp/convolutions/convolutions.cpp
View File

@ -17,18 +17,18 @@ MLPPConvolutions::MLPPConvolutions() :
std::vector<std::vector<real_t>> MLPPConvolutions::convolve(std::vector<std::vector<real_t>> input, std::vector<std::vector<real_t>> filter, int S, int P) {
MLPPLinAlg alg;
std::vector<std::vector<real_t>> featureMap;
int N = input.size();
int F = filter.size();
int mapSize = (N - F + 2 * P) / S + 1; // This is computed as ⌊mapSize⌋ by def- thanks C++!
uint32_t N = input.size();
uint32_t F = filter.size();
uint32_t mapSize = (N - F + 2 * P) / S + 1; // This is computed as ⌊mapSize⌋ by def- thanks C++!
if (P != 0) {
std::vector<std::vector<real_t>> paddedInput;
paddedInput.resize(N + 2 * P);
for (int i = 0; i < paddedInput.size(); i++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
paddedInput[i].resize(N + 2 * P);
}
for (int i = 0; i < paddedInput.size(); i++) {
for (int j = 0; j < paddedInput[i].size(); j++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
for (uint32_t j = 0; j < paddedInput[i].size(); j++) {
if (i - P < 0 || j - P < 0 || i - P > input.size() - 1 || j - P > input[0].size() - 1) {
paddedInput[i][j] = 0;
} else {
@ -37,22 +37,22 @@ std::vector<std::vector<real_t>> MLPPConvolutions::convolve(std::vector<std::vec
}
}
input.resize(paddedInput.size());
for (int i = 0; i < paddedInput.size(); i++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
input[i].resize(paddedInput[i].size());
}
input = paddedInput;
}
featureMap.resize(mapSize);
for (int i = 0; i < mapSize; i++) {
for (uint32_t i = 0; i < mapSize; i++) {
featureMap[i].resize(mapSize);
}
for (int i = 0; i < mapSize; i++) {
for (int j = 0; j < mapSize; j++) {
for (uint32_t i = 0; i < mapSize; i++) {
for (uint32_t j = 0; j < mapSize; j++) {
std::vector<real_t> convolvingInput;
for (int k = 0; k < F; k++) {
for (int p = 0; p < F; p++) {
for (uint32_t k = 0; k < F; k++) {
for (uint32_t p = 0; p < F; p++) {
if (i == 0 && j == 0) {
convolvingInput.push_back(input[i + k][j + p]);
} else if (i == 0) {
@ -73,20 +73,20 @@ std::vector<std::vector<real_t>> MLPPConvolutions::convolve(std::vector<std::vec
std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::convolve(std::vector<std::vector<std::vector<real_t>>> input, std::vector<std::vector<std::vector<real_t>>> filter, int S, int P) {
MLPPLinAlg alg;
std::vector<std::vector<std::vector<real_t>>> featureMap;
int N = input[0].size();
int F = filter[0].size();
int C = filter.size() / input.size();
int mapSize = (N - F + 2 * P) / S + 1; // This is computed as ⌊mapSize⌋ by def.
uint32_t N = input[0].size();
uint32_t F = filter[0].size();
uint32_t C = filter.size() / input.size();
uint32_t mapSize = (N - F + 2 * P) / S + 1; // This is computed as ⌊mapSize⌋ by def.
if (P != 0) {
for (int c = 0; c < input.size(); c++) {
for (uint32_t c = 0; c < input.size(); c++) {
std::vector<std::vector<real_t>> paddedInput;
paddedInput.resize(N + 2 * P);
for (int i = 0; i < paddedInput.size(); i++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
paddedInput[i].resize(N + 2 * P);
}
for (int i = 0; i < paddedInput.size(); i++) {
for (int j = 0; j < paddedInput[i].size(); j++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
for (uint32_t j = 0; j < paddedInput[i].size(); j++) {
if (i - P < 0 || j - P < 0 || i - P > input[c].size() - 1 || j - P > input[c][0].size() - 1) {
paddedInput[i][j] = 0;
} else {
@ -95,7 +95,7 @@ std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::convolve(std::ve
}
}
input[c].resize(paddedInput.size());
for (int i = 0; i < paddedInput.size(); i++) {
for (uint32_t i = 0; i < paddedInput.size(); i++) {
input[c][i].resize(paddedInput[i].size());
}
input[c] = paddedInput;
@ -103,20 +103,20 @@ std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::convolve(std::ve
}
featureMap.resize(C);
for (int i = 0; i < featureMap.size(); i++) {
for (uint32_t i = 0; i < featureMap.size(); i++) {
featureMap[i].resize(mapSize);
for (int j = 0; j < featureMap[i].size(); j++) {
for (uint32_t j = 0; j < featureMap[i].size(); j++) {
featureMap[i][j].resize(mapSize);
}
}
for (int c = 0; c < C; c++) {
for (int i = 0; i < mapSize; i++) {
for (int j = 0; j < mapSize; j++) {
for (uint32_t c = 0; c < C; c++) {
for (uint32_t i = 0; i < mapSize; i++) {
for (uint32_t j = 0; j < mapSize; j++) {
std::vector<real_t> convolvingInput;
for (int t = 0; t < input.size(); t++) {
for (int k = 0; k < F; k++) {
for (int p = 0; p < F; p++) {
for (uint32_t t = 0; t < input.size(); t++) {
for (uint32_t k = 0; k < F; k++) {
for (uint32_t p = 0; p < F; p++) {
if (i == 0 && j == 0) {
convolvingInput.push_back(input[t][i + k][j + p]);
} else if (i == 0) {
@ -139,16 +139,16 @@ std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::convolve(std::ve
std::vector<std::vector<real_t>> MLPPConvolutions::pool(std::vector<std::vector<real_t>> input, int F, int S, std::string type) {
MLPPLinAlg alg;
std::vector<std::vector<real_t>> pooledMap;
int N = input.size();
int mapSize = floor((N - F) / S + 1);
uint32_t N = input.size();
uint32_t mapSize = floor((N - F) / S + 1);
pooledMap.resize(mapSize);
for (int i = 0; i < mapSize; i++) {
for (uint32_t i = 0; i < mapSize; i++) {
pooledMap[i].resize(mapSize);
}
for (int i = 0; i < mapSize; i++) {
for (int j = 0; j < mapSize; j++) {
for (uint32_t i = 0; i < mapSize; i++) {
for (uint32_t j = 0; j < mapSize; j++) {
std::vector<real_t> poolingInput;
for (int k = 0; k < F; k++) {
for (int p = 0; p < F; p++) {
@ -164,7 +164,7 @@ std::vector<std::vector<real_t>> MLPPConvolutions::pool(std::vector<std::vector<
}
}
if (type == "Average") {
MLPPStat stat;
MLPPStat stat;
pooledMap[i][j] = stat.mean(poolingInput);
} else if (type == "Min") {
pooledMap[i][j] = alg.min(poolingInput);
@ -178,7 +178,7 @@ std::vector<std::vector<real_t>> MLPPConvolutions::pool(std::vector<std::vector<
std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::pool(std::vector<std::vector<std::vector<real_t>>> input, int F, int S, std::string type) {
std::vector<std::vector<std::vector<real_t>>> pooledMap;
for (int i = 0; i < input.size(); i++) {
for (uint32_t i = 0; i < input.size(); i++) {
pooledMap.push_back(pool(input[i], F, S, type));
}
return pooledMap;
@ -187,7 +187,7 @@ std::vector<std::vector<std::vector<real_t>>> MLPPConvolutions::pool(std::vector
real_t MLPPConvolutions::globalPool(std::vector<std::vector<real_t>> input, std::string type) {
MLPPLinAlg alg;
if (type == "Average") {
MLPPStat stat;
MLPPStat stat;
return stat.mean(alg.flatten(input));
} else if (type == "Min") {
return alg.min(alg.flatten(input));
@ -198,7 +198,7 @@ real_t MLPPConvolutions::globalPool(std::vector<std::vector<real_t>> input, std:
std::vector<real_t> MLPPConvolutions::globalPool(std::vector<std::vector<std::vector<real_t>>> input, std::string type) {
std::vector<real_t> pooledMap;
for (int i = 0; i < input.size(); i++) {
for (uint32_t i = 0; i < input.size(); i++) {
pooledMap.push_back(globalPool(input[i], type));
}
return pooledMap;
@ -212,7 +212,7 @@ real_t MLPPConvolutions::gaussian2D(real_t x, real_t y, real_t std) {
std::vector<std::vector<real_t>> MLPPConvolutions::gaussianFilter2D(int size, real_t std) {
std::vector<std::vector<real_t>> filter;
filter.resize(size);
for (int i = 0; i < filter.size(); i++) {
for (uint32_t i = 0; i < filter.size(); i++) {
filter[i].resize(size);
}
for (int i = 0; i < size; i++) {
@ -232,12 +232,12 @@ heights and widths.
std::vector<std::vector<real_t>> MLPPConvolutions::dx(std::vector<std::vector<real_t>> input) {
std::vector<std::vector<real_t>> deriv; // We assume a gray scale image.
deriv.resize(input.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
deriv[i].resize(input[i].size());
}
for (int i = 0; i < input.size(); i++) {
for (int j = 0; j < input[i].size(); j++) {
for (uint32_t i = 0; i < input.size(); i++) {
for (uint32_t j = 0; j < input[i].size(); j++) {
if (j != 0 && j != input.size() - 1) {
deriv[i][j] = input[i][j + 1] - input[i][j - 1];
} else if (j == 0) {
@ -253,12 +253,12 @@ std::vector<std::vector<real_t>> MLPPConvolutions::dx(std::vector<std::vector<re
std::vector<std::vector<real_t>> MLPPConvolutions::dy(std::vector<std::vector<real_t>> input) {
std::vector<std::vector<real_t>> deriv;
deriv.resize(input.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
deriv[i].resize(input[i].size());
}
for (int i = 0; i < input.size(); i++) {
for (int j = 0; j < input[i].size(); j++) {
for (uint32_t i = 0; i < input.size(); i++) {
for (uint32_t j = 0; j < input[i].size(); j++) {
if (i != 0 && i != input.size() - 1) {
deriv[i][j] = input[i - 1][j] - input[i + 1][j];
} else if (i == 0) {
@ -281,14 +281,14 @@ std::vector<std::vector<real_t>> MLPPConvolutions::gradMagnitude(std::vector<std
std::vector<std::vector<real_t>> MLPPConvolutions::gradOrientation(std::vector<std::vector<real_t>> input) {
std::vector<std::vector<real_t>> deriv;
deriv.resize(input.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
deriv[i].resize(input[i].size());
}
std::vector<std::vector<real_t>> xDeriv = dx(input);
std::vector<std::vector<real_t>> yDeriv = dy(input);
for (int i = 0; i < deriv.size(); i++) {
for (int j = 0; j < deriv[i].size(); j++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
for (uint32_t j = 0; j < deriv[i].size(); j++) {
deriv[i][j] = std::atan2(yDeriv[i][j], xDeriv[i][j]);
}
}
@ -325,9 +325,9 @@ std::vector<std::vector<std::string>> MLPPConvolutions::harrisCornerDetection(st
std::vector<std::vector<std::string>> imageTypes;
imageTypes.resize(r.size());
alg.printMatrix(r);
for (int i = 0; i < r.size(); i++) {
for (uint32_t i = 0; i < r.size(); i++) {
imageTypes[i].resize(r[i].size());
for (int j = 0; j < r[i].size(); j++) {
for (uint32_t j = 0; j < r[i].size(); j++) {
if (r[i][j] > 0) {
imageTypes[i][j] = "C";
} else if (r[i][j] < 0) {
@ -371,3 +371,6 @@ std::vector<std::vector<real_t>> MLPPConvolutions::getRobertsHorizontal() {
std::vector<std::vector<real_t>> MLPPConvolutions::getRobertsVertical() {
return robertsVertical;
}
void MLPPConvolutions::_bind_methods() {
}

12
mlpp/convolutions/convolutions.h
View File

@ -2,12 +2,16 @@
#ifndef MLPP_CONVOLUTIONS_H
#define MLPP_CONVOLUTIONS_H
#include <vector>
#include <string>
#include <vector>
#include "core/math/math_defs.h"
class MLPPConvolutions {
#include "core/object/reference.h"
class MLPPConvolutions : public Reference {
GDCLASS(MLPPConvolutions, Reference);
public:
MLPPConvolutions();
std::vector<std::vector<real_t>> convolve(std::vector<std::vector<real_t>> input, std::vector<std::vector<real_t>> filter, int S, int P = 0);
@ -38,7 +42,9 @@ public:
std::vector<std::vector<real_t>> getRobertsHorizontal();
std::vector<std::vector<real_t>> getRobertsVertical();
private:
protected:
static void _bind_methods();
std::vector<std::vector<real_t>> prewittHorizontal;
std::vector<std::vector<real_t>> prewittVertical;
std::vector<std::vector<real_t>> sobelHorizontal;

4
register_types.cpp
View File

@ -28,6 +28,7 @@ SOFTWARE.
#include "mlpp/lin_alg/mlpp_vector.h"
#include "mlpp/activation/activation.h"
#include "mlpp/convolutions/convolutions.h"
#include "mlpp/cost/cost.h"
#include "mlpp/gauss_markov_checker/gauss_markov_checker.h"
#include "mlpp/hypothesis_testing/hypothesis_testing.h"
@ -42,6 +43,7 @@ SOFTWARE.
#include "mlpp/output_layer/output_layer.h"
#include "mlpp/auto_encoder/auto_encoder.h"
#include "mlpp/dual_svc/dual_svc.h"
#include "mlpp/exp_reg/exp_reg.h"
#include "mlpp/gan/gan.h"
#include "mlpp/gaussian_nb/gaussian_nb.h"
@ -61,7 +63,6 @@ SOFTWARE.
#include "mlpp/tanh_reg/tanh_reg.h"
#include "mlpp/uni_lin_reg/uni_lin_reg.h"
#include "mlpp/wgan/wgan.h"
#include "mlpp/dual_svc/dual_svc.h"
#include "test/mlpp_tests.h"
@ -79,6 +80,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
ClassDB::register_class<MLPPNumericalAnalysis>();
ClassDB::register_class<MLPPHypothesisTesting>();
ClassDB::register_class<MLPPGaussMarkovChecker>();
ClassDB::register_class<MLPPConvolutions>();
ClassDB::register_class<MLPPHiddenLayer>();
ClassDB::register_class<MLPPOutputLayer>();