double a_plus() {
// Allocate patches
float *patches = (float*)malloc(N*K*sizeof(float));
// Allocate patches_sr
float *patches_sr = (float*)malloc(N*F*sizeof(float));
// Allocate LR dictionary
float *dict_lr = (float*)malloc(K*C*sizeof(float));
// Allocate Projections
float* projs = (float*)malloc(C*K*F*sizeof(float));
// Allocate nearest indices
int *nn_inds = (int*)malloc(N*sizeof(int));
// Allocate similarities
float *similarities = (float*)malloc(N*C*sizeof(float));
// Calculate nearest dictionary atoms
double start_time = omp_get_wtime();
dot(patches, dict_lr, similarities, N, K, C);
arr_abs(similarities, N, C);
nearest_atoms(similarities, nn_inds, N, C);
// Project back to HR space
for (int n=0; n<N; n++) {
int ind = nn_inds[n];
for (int f=0; f<F; f++) {
float acc = 0;
for (int k=0; k<K; k++)
acc += patches[n*K+k] * projs[ind*K*F+k*F+f];
patches_sr[n*F+f] = acc;
}
}
double duration = omp_get_wtime();
// Deallocate
free(patches);
free(patches_sr);
free(dict_lr);
free(projs);
free(nn_inds);
free(similarities);
return duration;
}
/** Train the unsupervised SOM network.
*
* The network is initialized with random (non-graduate) values. It is then
* trained with the image pixels (RGB). Once the network is done training the
* centroids of the resulting clusters is returned.
*
* @note The length of the clusters depends on the parameter 'n'. The greatest
* n, the more colors in the clusters, the less posterized the image.
*
* @param[out] res The resulting R, G and B clusters centroids.
* @param[in] imgPixels The original image pixels.
* @param[in] nbPixels The number of pixels of th image (height * width).
* @param[in] nbNeurons The posterization leveldefined by its number of
* neurons.
* @param[in] noEpoch Number of training passes (a too small number of epochs
* won't be enough for the network to correctly know the image but a too high
* value will force the network to modify the wieghts so much that the image
* can actually looks "ugly").
* @param[in] thresh The threshold value. The SOM delta parameter and the
* training rate are dicreasing from one epoch to the next. The treshold value
* set a stop condifition in case the value has fallen under this minimum
* value.
*
* @return SOM_OK if everything goes right or SOM_NO_MEMORY if a memory
* allocation (malloc) fail.
*/
int som_train(float **res, float **imgPixels, unsigned int nbPixels,
int nbNeurons, int noEpoch, float thresh){
int mapWidth = // Map width. This can be calculated from its
(int)sqrt(nbNeurons); // number of neurons as the map is square.
int mapHeight = // Map height. This can be calculated from its
(int)sqrt(nbNeurons); // number of neurons as the map is square.
float delta = INT_MAX; // Start with an almost impossible value
int it = 0; // Count the network iterations
unsigned int pick; // The choosen input pixel
float rad; // Neighbooring radius (keep dicreasing)
float eta; // Learning rate (keep dicreasing)
float pickRGB[3] = {0}; // Contains the choosen input RGB vector
size_t choosen; // Best Matching Unit (BMU)
int choosen_x; // BMU abscissa
int choosen_y; // BMU ordinate
float *neigh = // Neighbooring mask
malloc(sizeof(float) * nbNeurons);
if(neigh == NULL){
return SOM_NO_MEMORY;
}
memset(neigh, 0, nbNeurons);
float *WR = // RED part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(WR == NULL){
return SOM_NO_MEMORY;
}
memset(WR, 0, nbNeurons);
float *WG = // GREEN part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(WG == NULL){
return SOM_NO_MEMORY;
}
memset(WG, 0, nbNeurons);
float *WB = // BLUE part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(WB == NULL){
return SOM_NO_MEMORY;
}
memset(WB, 0, nbNeurons);
float *deltaR = // New RED part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(deltaR == NULL){
return SOM_NO_MEMORY;
}
memset(deltaR, 0, nbNeurons);
float *deltaG = // New GREEN part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(deltaG == NULL){
return SOM_NO_MEMORY;
}
memset(deltaG, 0, nbNeurons);
float *deltaB = // New BLUE part of the network weight vectors
malloc(sizeof(float) * nbNeurons);
if(deltaB == NULL){
return SOM_NO_MEMORY;
}
memset(deltaB, 0, nbNeurons);
float *absDeltaR = // Absolute value of deltaR
malloc(sizeof(float) * nbNeurons);
if(absDeltaR == NULL){
return SOM_NO_MEMORY;
}
memset(absDeltaR, 0, nbNeurons);
float *absDeltaG = // Absolute value of deltaG
malloc(sizeof(float) * nbNeurons);
if(absDeltaG == NULL){
return SOM_NO_MEMORY;
}
memset(absDeltaG, 0, nbNeurons);
float *absDeltaB = // Absolute value of deltaB
malloc(sizeof(float) * nbNeurons);
if(absDeltaB == NULL){
return SOM_NO_MEMORY;
}
memset(absDeltaB, 0, nbNeurons);
float *dists = // Vectors euclidian distances from input form
malloc(sizeof(float) * nbNeurons);
if(dists == NULL){
return SOM_NO_MEMORY;
}
memset(dists, 0, nbNeurons);
/* Randomly initialize weight vectors */
srand(time(NULL));
random_sample(WR, nbNeurons);
random_sample(WG, nbNeurons);
random_sample(WB, nbNeurons);
while(it < noEpoch && delta >= thresh){
/* Randomly choose an input form */
pick = random_uint(nbPixels);
pickRGB[0] = imgPixels[pick][0];
pickRGB[1] = imgPixels[pick][1];
pickRGB[2] = imgPixels[pick][2];
/* Compute every vectors euclidian distance from the input form */
euclidian(dists, nbNeurons, WR, WG, WB, pickRGB);
/* Determine the BMU */
choosen = arr_min_idx(dists, nbNeurons);
choosen_x = (int)choosen % mapWidth;
choosen_y = choosen / mapHeight;
/* Compute the new neighbooring radius */
rad = som_radius(it, noEpoch, mapWidth, mapHeight);
/* Find the BMU neighboors */
som_neighbourhood(neigh, choosen_x, choosen_y, rad, mapWidth,
mapHeight);
/* Compute the new learning rate */
eta = som_learning_rate(it, noEpoch);
/* Compute new value of the network weight vectors */
compute_delta(deltaR, eta, neigh, nbNeurons, pickRGB[0], WR);
compute_delta(deltaG, eta, neigh, nbNeurons, pickRGB[1], WG);
compute_delta(deltaB, eta, neigh, nbNeurons, pickRGB[2], WB);
/* Update the network weight vectors values */
arr_add(WR, deltaR, nbNeurons);
arr_add(WG, deltaG, nbNeurons);
arr_add(WB, deltaB, nbNeurons);
arr_abs(absDeltaR, deltaR, nbNeurons);
arr_abs(absDeltaG, deltaG, nbNeurons);
arr_abs(absDeltaB, deltaB, nbNeurons);
delta = arr_sum(absDeltaR, nbNeurons) +
arr_sum(absDeltaG, nbNeurons) +
arr_sum(absDeltaB, nbNeurons);
it++;
}
res[0] = WR;
res[1] = WG;
res[2] = WB;
free(dists);
free(deltaR);
free(deltaG);
free(deltaB);
free(absDeltaR);
free(absDeltaG);
free(absDeltaB);
free(neigh);
return SOM_OK;
}