int main(int argc, char** argv)
{
// Bonus: get the desired size of array from the command line
int n = 2;
if (argc > 1)
{
n = atoi(argv[1]);
}
int arr[n];
// Method 1: write code to populate array within main(). This method works
for (int i=0; i<n; ++i)
arr[i] = i;
// Method 2: try to create "range array" using a separate function. THIS WILL
//NOT WORK! Try commenting out "Method 1" above and uncommenting the line
//below and see what happens. You should get a "segmentation fault".
//
//arr = create_range_array(n);
// print the array
print_array(arr, n);
// scale and print
scale_array(2.0, arr, n);
print_array(arr, n);
return 0;
}
static void tv_iter(int nx, const PetscScalar *x, PetscScalar *y){
// deg: degree of polynomials
// nx: some context variable from Petsc interface
// x: vector with which matrix A is multiplied
// y = A*x
const int MAT_ENTRIES = pars -> get_int("MAT_ENTRIES");
//hard coded atm, move to parameters later
const int DEG = pars -> get_int("DEG");
//const int DEG = 8;
std::vector<PetscScalar> Told(MAT_ENTRIES);
std::vector<PetscScalar> Tcur(MAT_ENTRIES);
std::vector<PetscScalar> Tnew(MAT_ENTRIES);
std::vector<PetscScalar> tmp(MAT_ENTRIES);
// initialize chebyshev polynomials
tv2( nx, &x[0], &Tcur[0]);
equal_arrays(&x[0], &Told[0]);
// if deg is 1 or less return initialised values
if (DEG >=1){
// else iteratively calculate chebyshev polynomials up to deg
// T_n(B) = 2*B(T_{n-1}(B))-T_{n-2}(B)
for (int n = 2; n <= DEG; n++){
// store B(Tcur(Bx)) in tmp1
tv2(nx, &Tcur[0], &tmp[0]);
// scale tmp1
scale_array(2., &tmp[0], &tmp[0]);
//calculate new polynomial
subtract_arrays(&Told[0], &tmp[0], &y[0]);
// overwrite new variables
equal_arrays(&Tcur[0], &Told[0]);
equal_arrays(&y[0], &Tcur[0]);
}
}
}
static void compute_residual(struct element **sel, double ****x, double ****f,
double ****r, int var, void (*opeval) ())
{
const int n = (*sel)->basis->ntot;
global_opeval(sel, x, r, var, opeval);
scale_array(***r, n * (*sel)->nel, -1.0);
add_array(***f, ***r, n * (*sel)->nel, 1.0);
}
image random_unit_vector_image(int w, int h, int c)
{
image im = make_image(w, h, c);
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
im.data[i] = rand_normal();
}
float mag = mag_array(im.data, im.w*im.h*im.c);
scale_array(im.data, im.w*im.h*im.c, 1./mag);
return im;
}
void slerp(float *start, float *end, float s, int n, float *out)
{
float omega = acos(dot_cpu(n, start, 1, end, 1));
float so = sin(omega);
fill_cpu(n, 0, out, 1);
axpy_cpu(n, sin((1-s)*omega)/so, start, 1, out, 1);
axpy_cpu(n, sin(s*omega)/so, end, 1, out, 1);
float mag = mag_array(out, n);
scale_array(out, n, 1./mag);
}
int sample_array(float *a, int n)
{
float sum = sum_array(a, n);
scale_array(a, n, 1./sum);
float r = rand_uniform(0, 1);
int i;
for(i = 0; i < n; ++i){
r = r - a[i];
if (r <= 0) return i;
}
return n-1;
}
void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch)
{
#ifdef GPU
char *backup_directory = "/home/kunle12/backup/";
srand(time(0));
char *base = basecfg(cfg);
char *abase = basecfg(acfg);
printf("%s\n", base);
network *gnet = load_network(cfg, weight, clear);
network *anet = load_network(acfg, aweight, clear);
//float orig_rate = anet->learning_rate;
int i, j, k;
layer imlayer = {0};
for (i = 0; i < gnet->n; ++i) {
if (gnet->layers[i].out_c == 3) {
imlayer = gnet->layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
int imgs = gnet->batch*gnet->subdivisions;
i = *gnet->seen/imgs;
data train, buffer;
list *plist = get_paths(train_images);
//int N = plist->size;
char **paths = (char **)list_to_array(plist);
load_args args= get_base_args(anet);
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
args.threads=16;
args.classes = 1;
char *ls[2] = {"imagenet", "zzzzzzzz"};
args.labels = ls;
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
gnet->train = 1;
anet->train = 1;
int x_size = gnet->inputs*gnet->batch;
int y_size = gnet->truths*gnet->batch;
float *imerror = cuda_make_array(0, y_size);
//int ay_size = anet->truths*anet->batch;
float aloss_avg = -1;
//data generated = copy_data(train);
if (maxbatch == 0) maxbatch = gnet->max_batches;
while (get_current_batch(gnet) < maxbatch) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
//translate_data_rows(train, -.5);
//scale_data_rows(train, 2);
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
data gen = copy_data(train);
for (j = 0; j < imgs; ++j) {
train.y.vals[j][0] = 1;
gen.y.vals[j][0] = 0;
}
time=clock();
for(j = 0; j < gnet->subdivisions; ++j){
get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0);
int z;
for(z = 0; z < x_size; ++z){
gnet->input[z] = rand_normal();
}
for(z = 0; z < gnet->batch; ++z){
float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
}
/*
for(z = 0; z < 100; ++z){
printf("%f, ", gnet->input[z]);
}
printf("\n");
printf("input: %f %f\n", mean_array(gnet->input, x_size), variance_array(gnet->input, x_size));
*/
//cuda_push_array(gnet->input_gpu, gnet->input, x_size);
//cuda_push_array(gnet->truth_gpu, gnet->truth, y_size);
*gnet->seen += gnet->batch;
forward_network(gnet);
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
anet->delta_gpu = imerror;
forward_network(anet);
backward_network(anet);
//float genaloss = *anet->cost / anet->batch;
//printf("%f\n", genaloss);
scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
//printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
//printf("features %f\n", cuda_mag_array(gnet->layers[gnet->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
backward_network(gnet);
/*
for(k = 0; k < gnet->n; ++k){
layer l = gnet->layers[k];
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
printf("%d: %f %f\n", k, mean_array(l.output, l.outputs*l.batch), variance_array(l.output, l.outputs*l.batch));
}
*/
for(k = 0; k < gnet->batch; ++k){
int index = j*gnet->batch + k;
copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1);
}
}
harmless_update_network_gpu(anet);
data merge = concat_data(train, gen);
//randomize_data(merge);
float aloss = train_network(anet, merge);
//translate_image(im, 1);
//scale_image(im, .5);
//translate_image(im2, 1);
//scale_image(im2, .5);
#ifdef OPENCV
if(display){
image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
show_image(im, "gen", 1);
show_image(im2, "train", 1);
save_image(im, "gen");
save_image(im2, "train");
}
#endif
/*
if(aloss < .1){
anet->learning_rate = 0;
} else if (aloss > .3){
anet->learning_rate = orig_rate;
}
*/
update_network_gpu(gnet);
free_data(merge);
free_data(train);
free_data(gen);
if (aloss_avg < 0) aloss_avg = aloss;
aloss_avg = aloss_avg*.9 + aloss*.1;
printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
if(i%10000==0){
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(gnet, buff);
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
save_weights(anet, buff);
}
if(i%1000==0){
char buff[256];
sprintf(buff, "%s/%s.backup", backup_directory, base);
save_weights(gnet, buff);
sprintf(buff, "%s/%s.backup", backup_directory, abase);
save_weights(anet, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
save_weights(gnet, buff);
#endif
free_network(gnet);
free_network(anet);
}