Turbulence::Turbulence(int gens, int seed)
{
_gens = gens;
_sz = 1 << (_gens - 1);
_seed = seed;
_mag = 1;
_x0 = _x1 = _y0 = _y1 = _z0 = _z1 = 0;
_timeOff = 0;
_off[0] = _off[1] = _off[2] = 0;
float* xbuf = new float[_sz*_sz];
float* ybuf = new float[_sz*_sz];
float* zbuf = new float[_sz*_sz];
mkimg(xbuf, _sz);
_seed++;
mkimg(ybuf, _sz);
_seed++;
mkimg(zbuf, _sz);
// "Normalize" them to proper spherical magnitudes, and calculate
// range information for the packing.
for(int i=0; i<_sz*_sz; i++) {
float n = cubenorm(xbuf[i], ybuf[i], zbuf[i]);
xbuf[i] *= n;
ybuf[i] *= n;
zbuf[i] *= n;
_x0 = xbuf[i] < _x0 ? xbuf[i] : _x0;
_x1 = xbuf[i] > _x1 ? xbuf[i] : _x1;
_y0 = ybuf[i] < _y0 ? ybuf[i] : _y0;
_y1 = ybuf[i] > _y1 ? ybuf[i] : _y1;
_z0 = zbuf[i] < _z0 ? zbuf[i] : _z0;
_z1 = zbuf[i] > _z1 ? zbuf[i] : _z1;
}
// Pack into 3 byte tuples for storage.
_data = new unsigned char[3*_sz*_sz];
for(int i=0; i<_sz*_sz; i++) {
float x = xbuf[i], y = ybuf[i], z = zbuf[i];
unsigned char* tuple = _data + 3*i;
tuple[0] = f2cu((x - _x0) / (_x1 - _x0));
tuple[1] = f2cu((y - _y0) / (_y1 - _y0));
tuple[2] = f2cu((z - _z0) / (_z1 - _z0));
}
delete[] xbuf;
delete[] ybuf;
delete[] zbuf;
}
void mkqcow2(const char *file, unsigned size_mb)
{
return mkimg(file, "qcow2", size_mb);
}
int main(int argc, char **argv)
{
//test_resize("data/bad.jpg");
//test_box();
//test_convolutional_layer();
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
gpu_index = find_int_arg(argc, argv, "-i", 0);
if(find_arg(argc, argv, "-nogpu")) {
gpu_index = -1;
}
#ifndef GPU
gpu_index = -1;
#else
if(gpu_index >= 0){
cuda_set_device(gpu_index);
}
#endif
if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "yolo")){
run_yolo(argc, argv);
} else if (0 == strcmp(argv[1], "super")){
run_super(argc, argv);
} else if (0 == strcmp(argv[1], "lsd")){
run_lsd(argc, argv);
} else if (0 == strcmp(argv[1], "detector")){
run_detector(argc, argv);
} else if (0 == strcmp(argv[1], "detect")){
float thresh = find_float_arg(argc, argv, "-thresh", .5);
char *filename = (argc > 4) ? argv[4]: 0;
char *outfile = find_char_arg(argc, argv, "-out", 0);
int fullscreen = find_arg(argc, argv, "-fullscreen");
test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
} else if (0 == strcmp(argv[1], "cifar")){
run_cifar(argc, argv);
} else if (0 == strcmp(argv[1], "go")){
run_go(argc, argv);
} else if (0 == strcmp(argv[1], "rnn")){
run_char_rnn(argc, argv);
} else if (0 == strcmp(argv[1], "coco")){
run_coco(argc, argv);
} else if (0 == strcmp(argv[1], "classify")){
predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
} else if (0 == strcmp(argv[1], "classifier")){
run_classifier(argc, argv);
} else if (0 == strcmp(argv[1], "regressor")){
run_regressor(argc, argv);
} else if (0 == strcmp(argv[1], "isegmenter")){
run_isegmenter(argc, argv);
} else if (0 == strcmp(argv[1], "segmenter")){
run_segmenter(argc, argv);
} else if (0 == strcmp(argv[1], "art")){
run_art(argc, argv);
} else if (0 == strcmp(argv[1], "tag")){
run_tag(argc, argv);
} else if (0 == strcmp(argv[1], "3d")){
composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
} else if (0 == strcmp(argv[1], "test")){
test_resize(argv[2]);
} else if (0 == strcmp(argv[1], "nightmare")){
run_nightmare(argc, argv);
} else if (0 == strcmp(argv[1], "rgbgr")){
rgbgr_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "reset")){
reset_normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "denormalize")){
denormalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "statistics")){
statistics_net(argv[2], argv[3]);
} else if (0 == strcmp(argv[1], "normalize")){
normalize_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "rescale")){
rescale_net(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "ops")){
operations(argv[2]);
} else if (0 == strcmp(argv[1], "speed")){
speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
} else if (0 == strcmp(argv[1], "oneoff")){
oneoff(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "oneoff2")){
oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "print")){
print_weights(argv[2], argv[3], atoi(argv[4]));
} else if (0 == strcmp(argv[1], "partial")){
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "average")){
average(argc, argv);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "mkimg")){
mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
} else if (0 == strcmp(argv[1], "imtest")){
test_resize(argv[2]);
} else {
fprintf(stderr, "Not an option: %s\n", argv[1]);
}
return 0;
}
void mrqcof(float **y, float **sig, struct image *psf, float a[],
int ia[], int ma, double **alpha, double beta[], double *chisq,
void (*funcs)(int x, int y, float *, float [], int [], int,
struct fitpars *), struct fitpars *fpar, struct convpars *cpar)
{
extern struct image *mask;
extern struct sampars sample;
extern struct image model;
extern unsigned long *pos;
extern struct derivs df;
struct fitpars *fptr;
struct derivs *dptr;
char name[80];
int ix, iy, j,k,l,m,mfit=0;
float ymod,wt,sig2i,dy,*dyda;
dyda=vector(1,ma);
for (j=1;j<=ma;j++)
if (ia[j]==1) mfit++;
for (j=1;j<=mfit;j++) {
for (k=1;k<=j;k++) alpha[j][k]=0.0;
beta[j]=0.0;
}
mkmodel (&model, &df, fpar, cpar, 0);
#if CKIMG
sprintf (model.name, "gal+psf-model.fits"); /* Output image */
writefits ("test.fits", &model, "gal + PSF", 0);
fptr = fpar;
dptr = &df;
while (fptr != NULL) {
for (j=0; j<= NPARS; j++){
if (fptr->ia[j] == 1 || j==0) {
sprintf (name, "deriv%d.fits", j);
mkimg (dptr->dpm[j], dptr->naxes, name, "small.fits");
};
};
fptr = fptr->next;
dptr = dptr->next;
};
#endif
sample.nmask = 0;
*chisq=0.0;
for (iy=1; iy<= model.naxes[2]; iy++) {
for (ix=1;ix<=model.naxes[1];ix++) {
if (mask->z[iy][ix] < 1.) { /* Calculate chi^2 only */
(*funcs)(ix,iy,&ymod,dyda,ia,ma,fpar); /* for unflagged */
sig2i=1.0/(sig[iy][ix]*sig[iy][ix]); /* pixels */
dy=y[iy][ix]-ymod;
for (j=0,l=1;l<=ma;l++) {
if (ia[l]==1) {
wt=dyda[l]*sig2i;
for (j++,k=0,m=1;m<=l;m++)
if (ia[m]==1) alpha[j][++k] += wt*dyda[m];
beta[j] += dy*wt;
};
};
*chisq += dy*dy*sig2i;
} else
sample.nmask++; /* count up the number of flagged pixels */
};
};
for (j=2;j<=mfit;j++)
for (k=1;k<j;k++) alpha[k][j]=alpha[j][k];
free_vector(dyda,1,ma);
}
