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NO FUCKING SPOILERS DOUG

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This commit is contained in:
Joseph Redmon
2018-01-16 14:30:00 -08:00
parent 6e79145309
commit e3931c75cd
23 changed files with 450 additions and 90 deletions
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2
examples/classifier.c
View File

@ -578,6 +578,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
}
image im = load_image_color(input, 0, 0);
image r = letterbox_image(im, net->w, net->h);
//image r = resize_min(im, 320);
//printf("%d %d\n", r.w, r.h);
//resize_network(net, r.w, r.h);
//printf("%d %d\n", r.w, r.h);

120
examples/lsd.c
View File

@ -383,6 +383,92 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
}
*/
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);
}
image random_unit_vector_image(w, h, 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 inter_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
char buff[256];
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
}
}
image start = random_unit_vector_image(net->w, net->h, net->c);
image end = random_unit_vector_image(net->w, net->h, net->c);
image im = make_image(net->w, net->h, net->c);
image orig = copy_image(start);
int c = 0;
int count = 0;
int max_count = 15;
while(1){
++c;
if(count == max_count){
count = 0;
free_image(start);
start = end;
end = random_unit_vector_image(net->w, net->h, net->c);
if(c > 300){
end = orig;
}
if(c>300 + max_count) return;
}
++count;
slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);
float *X = im.data;
time=clock();
network_predict(net, X);
image out = get_network_image_layer(net, imlayer);
//yuv_to_rgb(out);
normalize_image(out);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
//char buff[256];
sprintf(buff, "out%05d", c);
show_image(out, "out");
save_image(out, "out");
save_image(out, buff);
#ifdef OPENCV
//cvWaitKey(0);
#endif
}
}
void test_dcgan(char *cfgfile, char *weightfile)
{
network *net = load_network(cfgfile, weightfile, 0);
@ -409,7 +495,7 @@ void test_dcgan(char *cfgfile, char *weightfile)
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);
scale_array(im.data, im.w*im.h*im.c, 1./mag);
float *X = im.data;
time=clock();
@ -429,7 +515,7 @@ void test_dcgan(char *cfgfile, char *weightfile)
}
void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images)
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/pjreddie/backup/";
@ -441,7 +527,6 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
network *anet = load_network(acfg, aweight, clear);
//float orig_rate = anet->learning_rate;
int start = 0;
int i, j, k;
layer imlayer = {0};
for (i = 0; i < gnet->n; ++i) {
@ -488,8 +573,8 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
//data generated = copy_data(train);
while (get_current_batch(gnet) < gnet->max_batches) {
start += 1;
if (maxbatch == 0) maxbatch = gnet->max_batches;
while (get_current_batch(gnet) < maxbatch) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -519,6 +604,13 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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);
@ -533,18 +625,26 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
backward_network(anet);
float genaloss = *anet->cost / anet->batch;
printf("%f\n", genaloss);
//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));
//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);
@ -1104,6 +1204,7 @@ void run_lsd(int argc, char **argv)
int clear = find_arg(argc, argv, "-clear");
int display = find_arg(argc, argv, "-display");
int batches = find_int_arg(argc, argv, "-b", 0);
char *file = find_char_arg(argc, argv, "-file", "/home/pjreddie/data/imagenet/imagenet1k.train.list");
char *cfg = argv[3];
@ -1115,9 +1216,10 @@ void run_lsd(int argc, char **argv)
//else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear);
//else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
//else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file);
if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file, batches);
else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display);
else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "inter")) inter_dcgan(cfg, weights);
else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0);
else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1);
/*