void SGDSolver<Dtype>::ApplyUpdate() {
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
}
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id) {
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
}
this->net_->Update();
}
void SGDSolver<Dtype>::ApplyUpdate() {
Dtype rate = GetLearningRate();
if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << this->iter_
<< ", lr = " << rate;
}
ClipGradients();
for (int param_id = 0; param_id < this->net_->learnable_params().size();
++param_id) {
Normalize(param_id);
Regularize(param_id);
ComputeUpdateValue(param_id, rate);
}
this->net_->Update();
// Increment the internal iter_ counter -- its value should always indicate
// the number of times the weights have been updated.
++this->iter_;
}
void anlm(float* ima, int v, int f, int rician, const int* dims)
{
float *means, *variances, *Estimate, *average, *bias;
unsigned char *Label;
int ndim = 3;
double SNR,h,mean,var,estimate,d;
int vol,slice,label,Ndims,i,j,k,ii,jj,kk,ni,nj,nk,indice,Nthreads,ini,fin,r;
myargument *ThreadArgs;
Ndims = (int)floor(pow((2.0*f+1.0),ndim));
slice = dims[0]*dims[1];
vol = dims[0]*dims[1]*dims[2];
/*Allocate memory */
average = (float*)malloc(Ndims*sizeof(float));
means = (float*)malloc(vol*sizeof(float));
variances = (float*)malloc(vol*sizeof(float));
Estimate = (float*)malloc(vol*sizeof(float));
Label = (unsigned char*)malloc(vol*sizeof(unsigned char));
if (rician) bias = (float*)malloc(vol*sizeof(float));
for (i = 0; i < vol; i++)
{
Estimate[i] = 0.0;
Label[i] = 0;
if (rician) bias[i] = 0.0;
}
max = 0.0;
for (k = 0;k<dims[2];k++)
{
for (j = 0;j<dims[1];j++)
{
for (i = 0;i<dims[0];i++)
{
if (ima[k*(slice)+(j*dims[0])+i]>max) max = (double)ima[k*(slice)+(j*dims[0])+i];
mean = 0.0;
indice = 0;
for (ii = -1;ii<= 1;ii++)
{
for (jj = -1;jj<= 1;jj++)
{
for (kk = -1;kk<= 1;kk++)
{
ni = i+ii;
nj = j+jj;
nk = k+kk;
if (ni<0) ni = -ni;
if (nj<0) nj = -nj;
if (nk<0) nk = -nk;
if (ni>= dims[0]) ni = 2*dims[0]-ni-1;
if (nj>= dims[1]) nj = 2*dims[1]-nj-1;
if (nk>= dims[2]) nk = 2*dims[2]-nk-1;
mean += (double)ima[nk*(slice)+(nj*dims[0])+ni];
indice++;
}
}
}
mean /= (double)indice;
means[k*(slice)+(j*dims[0])+i] = (float)mean;
}
}
}
for (k = 0;k<dims[2];k++)
{
for (j = 0;j<dims[1];j++)
{
for (i = 0;i<dims[0];i++)
{
var = 0;
indice = 0;
for (ii = -1;ii<= 1;ii++)
{
for (jj = -1;jj<= 1;jj++)
{
for (kk = -1;kk<= 1;kk++)
{
ni = i+ii;
nj = j+jj;
nk = k+kk;
if (ni>= 0 && nj>= 0 && nk>0 && ni<dims[0] && nj<dims[1] && nk<dims[2])
{
d = (double)ima[nk*(slice)+(nj*dims[0])+ni]-(double)means[k*(slice)+(j*dims[0])+i];
var += d*d;
indice++;
}
}
}
}
var /= (indice-1);
variances[k*(slice)+(j*dims[0])+i] = (float)var;
}
}
}
Nthreads = 1;
#ifdef _OPENMP
Nthreads = omp_get_num_procs();
printf("Using %d processors\n",Nthreads);fflush(stdout);
#endif
/* Reserve room for handles of threads in ThreadList */
ThreadArgs = (myargument*) malloc( Nthreads*sizeof(myargument));
#ifdef _OPENMP
# pragma omp parallel for default(shared) private(i, ini, fin)
#endif
for (i = 0; i<Nthreads; i++)
{
/* Make Thread Structure */
ini = (i*dims[2])/Nthreads;
fin = ((i+1)*dims[2])/Nthreads;
ThreadArgs[i].cols = dims[0];
ThreadArgs[i].rows = dims[1];
ThreadArgs[i].slices = dims[2];
ThreadArgs[i].in_image = ima;
ThreadArgs[i].var_image = variances;
ThreadArgs[i].means_image = means;
ThreadArgs[i].estimate = Estimate;
ThreadArgs[i].bias = bias;
ThreadArgs[i].label = Label;
ThreadArgs[i].ini = ini;
ThreadArgs[i].fin = fin;
ThreadArgs[i].radioB = v;
ThreadArgs[i].radioS = f;
(void)ThreadFunc(ThreadArgs[i]);
}
if (rician)
{
r = 5;
Regularize(bias,variances,r,dims[0],dims[1],dims[2]);
for (i = 0;i<vol;i++)
{
if (variances[i]>0.0)
{
SNR = (double)means[i]/sqrt((double)variances[i]);
bias[i] = 2*(variances[i]/(float)Epsi(SNR));
#if defined(_WIN32)
if (_isnan(bias[i])) bias[i] = 0.0;
#else
if (isnan(bias[i])) bias[i] = 0.0;
#endif
}
}
}
/* Aggregation of the estimators (i.e. means computation) */
label = 0;
estimate = 0.0;
for (i = 0;i<vol;i++)
{
label = Label[i];
if (label > 0)
{
estimate = (double)Estimate[i];
estimate /= (double)label;
if (rician)
{
estimate = (estimate-(double)bias[i])<0?0:(estimate-(double)bias[i]);
ima[i] = (float)sqrt(estimate);
}
else ima[i] = (float)estimate;
}
}
free(ThreadArgs);
free(average);
free(means);
free(variances);
free(Estimate);
free(Label);
if (rician) free(bias);
return;
}
