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it's raining really hard outside :-( :rain: :storm: ☁️

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This commit is contained in:
Joseph Redmon
2017-10-17 11:41:34 -07:00
parent 532c6e1481
commit cd5d393b46
27 changed files with 1340 additions and 1669 deletions
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368
examples/lsd.c
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@ -16,9 +16,9 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
char *gbase = basecfg(gcfg);
char *abase = basecfg(acfg);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet.learning_rate, gnet.momentum, gnet.decay);
int imgs = gnet.batch*gnet.subdivisions;
int i = *gnet.seen/imgs;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
int imgs = gnet->batch*gnet->subdivisions;
int i = *gnet->seen/imgs;
data train, tbuffer;
data style, sbuffer;
@ -55,27 +55,27 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
float aloss_avg = -1;
float floss_avg = -1;
fnet.train=1;
int x_size = fnet.inputs*fnet.batch;
int y_size = fnet.truths*fnet.batch;
fnet->train=1;
int x_size = fnet->inputs*fnet->batch;
int y_size = fnet->truths*fnet->batch;
float *X = calloc(x_size, sizeof(float));
float *y = calloc(y_size, sizeof(float));
int ax_size = anet.inputs*anet.batch;
int ay_size = anet.truths*anet.batch;
fill_gpu(ay_size, .9, anet.truth_gpu, 1);
anet.delta_gpu = cuda_make_array(0, ax_size);
anet.train = 1;
int ax_size = anet->inputs*anet->batch;
int ay_size = anet->truths*anet->batch;
fill_gpu(ay_size, .9, anet->truth_gpu, 1);
anet->delta_gpu = cuda_make_array(0, ax_size);
anet->train = 1;
int gx_size = gnet.inputs*gnet.batch;
int gy_size = gnet.truths*gnet.batch;
int gx_size = gnet->inputs*gnet->batch;
int gy_size = gnet->truths*gnet->batch;
gstate.input = cuda_make_array(0, gx_size);
gstate.truth = 0;
gstate.delta = 0;
gstate.train = 1;
while (get_current_batch(gnet) < gnet.max_batches) {
while (get_current_batch(gnet) < gnet->max_batches) {
i += 1;
time=clock();
pthread_join(tload_thread, 0);
@ -92,20 +92,20 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
int j, k;
float floss = 0;
for(j = 0; j < fnet.subdivisions; ++j){
layer imlayer = gnet.layers[gnet.n - 1];
get_next_batch(train, fnet.batch, j*fnet.batch, X, y);
for(j = 0; j < fnet->subdivisions; ++j){
layer imlayer = gnet->layers[gnet->n - 1];
get_next_batch(train, fnet->batch, j*fnet->batch, X, y);
cuda_push_array(fstate.input, X, x_size);
cuda_push_array(gstate.input, X, gx_size);
*gnet.seen += gnet.batch;
*gnet->seen += gnet->batch;
forward_network_gpu(fnet, fstate);
float *feats = fnet.layers[fnet.n - 2].output_gpu;
float *feats = fnet->layers[fnet->n - 2].output_gpu;
copy_gpu(y_size, feats, 1, fstate.truth, 1);
forward_network_gpu(gnet, gstate);
float *gen = gnet.layers[gnet.n-1].output_gpu;
float *gen = gnet->layers[gnet->n-1].output_gpu;
copy_gpu(x_size, gen, 1, fstate.input, 1);
fill_gpu(x_size, 0, fstate.delta, 1);
@ -135,11 +135,11 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
backward_network_gpu(gnet, gstate);
floss += get_network_cost(fnet) /(fnet.subdivisions*fnet.batch);
floss += get_network_cost(fnet) /(fnet->subdivisions*fnet->batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
for(k = 0; k < gnet.batch; ++k){
int index = j*gnet.batch + k;
for(k = 0; k < gnet->batch; ++k){
int index = j*gnet->batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
generated.y.vals[index][0] = .1;
style.y.vals[index][0] = .9;
@ -148,7 +148,7 @@ void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg
*/
/*
image sim = float_to_image(anet.w, anet.h, anet.c, style.X.vals[j]);
image sim = float_to_image(anet->w, anet->h, anet->c, style.X.vals[j]);
show_image(sim, "style");
cvWaitKey(0);
*/
@ -208,16 +208,16 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
int i, j, k;
layer imlayer = {0};
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = net.layers[i];
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = net->layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
i = *net.seen/imgs;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
int imgs = net->batch*net->subdivisions;
i = *net->seen/imgs;
data train, buffer;
@ -226,21 +226,21 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.min = net.min_crop;
args.max = net.max_crop;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.min = net->min_crop;
args.max = net->max_crop;
args.angle = net->angle;
args.aspect = net->aspect;
args.exposure = net->exposure;
args.saturation = net->saturation;
args.hue = net->hue;
args.size = net->w;
args.type = CLASSIFICATION_DATA;
args.classes = 1;
char *ls[1] = {"coco"};
@ -252,7 +252,7 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
network_state gstate = {0};
gstate.index = 0;
gstate.net = net;
int x_size = get_network_input_size(net)*net.batch;
int x_size = get_network_input_size(net)*net->batch;
int y_size = x_size;
gstate.input = cuda_make_array(0, x_size);
gstate.truth = cuda_make_array(0, y_size);
@ -265,7 +265,7 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
network_state astate = {0};
astate.index = 0;
astate.net = anet;
int ay_size = get_network_output_size(anet)*anet.batch;
int ay_size = get_network_output_size(anet)*anet->batch;
astate.input = 0;
astate.truth = 0;
astate.delta = 0;
@ -280,7 +280,7 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
//data generated = copy_data(train);
while (get_current_batch(net) < net.max_batches) {
while (get_current_batch(net) < net->max_batches) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -291,31 +291,31 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
data gray = copy_data(train);
for(j = 0; j < imgs; ++j){
image gim = float_to_image(net.w, net.h, net.c, gray.X.vals[j]);
image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
grayscale_image_3c(gim);
train.y.vals[j][0] = .9;
image yim = float_to_image(net.w, net.h, net.c, train.X.vals[j]);
image yim = float_to_image(net->w, net->h, net->c, train.X.vals[j]);
//rgb_to_yuv(yim);
}
time=clock();
float gloss = 0;
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, pixs, y);
get_next_batch(gray, net.batch, j*net.batch, graypixs, y);
for(j = 0; j < net->subdivisions; ++j){
get_next_batch(train, net->batch, j*net->batch, pixs, y);
get_next_batch(gray, net->batch, j*net->batch, graypixs, y);
cuda_push_array(gstate.input, graypixs, x_size);
cuda_push_array(gstate.truth, pixs, y_size);
*/
/*
image origi = float_to_image(net.w, net.h, 3, pixs);
image grayi = float_to_image(net.w, net.h, 3, graypixs);
image origi = float_to_image(net->w, net->h, 3, pixs);
image grayi = float_to_image(net->w, net->h, 3, graypixs);
show_image(grayi, "gray");
show_image(origi, "orig");
cvWaitKey(0);
*/
/*
*net.seen += net.batch;
*net->seen += net->batch;
forward_network_gpu(net, gstate);
fill_gpu(imlayer.outputs, 0, imerror, 1);
@ -325,22 +325,22 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
forward_network_gpu(anet, astate);
backward_network_gpu(anet, astate);
scal_gpu(imlayer.outputs, .1, net.layers[net.n-1].delta_gpu, 1);
scal_gpu(imlayer.outputs, .1, net->layers[net->n-1].delta_gpu, 1);
backward_network_gpu(net, gstate);
scal_gpu(imlayer.outputs, 1000, imerror, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs));
printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs));
axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
gloss += get_network_cost(net) /(net.subdivisions*net.batch);
gloss += get_network_cost(net) /(net->subdivisions*net->batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
for(k = 0; k < net->batch; ++k){
int index = j*net->batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
gray.y.vals[index][0] = .1;
}
@ -385,11 +385,8 @@ void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear
void test_dcgan(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
@ -397,8 +394,8 @@ void test_dcgan(char *cfgfile, char *weightfile)
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
@ -406,7 +403,7 @@ void test_dcgan(char *cfgfile, char *weightfile)
}
while(1){
image im = make_image(net.w, net.h, net.c);
image im = make_image(net->w, net->h, net->c);
int i;
for(i = 0; i < im.w*im.h*im.c; ++i){
im.data[i] = rand_normal();
@ -449,23 +446,23 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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;
network *gnet = load_network(cfg, weight, 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) {
if (gnet.layers[i].out_c == 3) {
imlayer = gnet.layers[i];
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;
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;
@ -487,20 +484,20 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
gnet.train = 1;
anet.train = 1;
gnet->train = 1;
anet->train = 1;
int x_size = gnet.inputs*gnet.batch;
int y_size = gnet.truths*gnet.batch;
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;
//int ay_size = anet->truths*anet->batch;
float aloss_avg = -1;
//data generated = copy_data(train);
while (get_current_batch(gnet) < gnet.max_batches) {
while (get_current_batch(gnet) < gnet->max_batches) {
start += 1;
i += 1;
time=clock();
@ -521,41 +518,41 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
}
time=clock();
for(j = 0; j < gnet.subdivisions; ++j){
get_next_batch(train, gnet.batch, j*gnet.batch, gnet.truth, 0);
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();
gnet->input[z] = rand_normal();
}
cuda_push_array(gnet.input_gpu, gnet.input, x_size);
cuda_push_array(gnet.truth_gpu, gnet.truth, y_size);
*gnet.seen += gnet.batch;
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_gpu(gnet);
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
fill_gpu(anet.truths*anet.batch, .95, anet.truth_gpu, 1);
copy_gpu(anet.inputs*anet.batch, imlayer.output_gpu, 1, anet.input_gpu, 1);
anet.delta_gpu = imerror;
fill_gpu(anet->truths*anet->batch, .95, anet->truth_gpu, 1);
copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1);
anet->delta_gpu = imerror;
forward_network_gpu(anet);
backward_network_gpu(anet);
float genaloss = *anet.cost / anet.batch;
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, .00, gnet.layers[gnet.n-1].delta_gpu, 1);
scal_gpu(imlayer.outputs*imlayer.batch, .00, 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("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);
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
backward_network_gpu(gnet);
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);
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);
@ -570,8 +567,8 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
//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]);
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");
show_image(im2, "train");
cvWaitKey(50);
@ -580,9 +577,9 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
/*
if(aloss < .1){
anet.learning_rate = 0;
anet->learning_rate = 0;
} else if (aloss > .3){
anet.learning_rate = orig_rate;
anet->learning_rate = orig_rate;
}
*/
@ -627,21 +624,21 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
char *base = basecfg(cfg);
char *abase = basecfg(acfg);
printf("%s\n", base);
network net = load_network(cfg, weight, clear);
network anet = load_network(acfg, aweight, clear);
network *net = load_network(cfg, weight, clear);
network *anet = load_network(acfg, aweight, clear);
int i, j, k;
layer imlayer = {0};
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = net.layers[i];
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = net->layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
i = *net.seen/imgs;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
int imgs = net->batch*net->subdivisions;
i = *net->seen/imgs;
data train, buffer;
@ -663,17 +660,17 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
int x_size = net.inputs*net.batch;
int x_size = net->inputs*net->batch;
//int y_size = x_size;
net.delta = 0;
net.train = 1;
net->delta = 0;
net->train = 1;
float *pixs = calloc(x_size, sizeof(float));
float *graypixs = calloc(x_size, sizeof(float));
//float *y = calloc(y_size, sizeof(float));
//int ay_size = anet.outputs*anet.batch;
anet.delta = 0;
anet.train = 1;
//int ay_size = anet->outputs*anet->batch;
anet->delta = 0;
anet->train = 1;
float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch);
@ -682,7 +679,7 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
//data generated = copy_data(train);
while (get_current_batch(net) < net.max_batches) {
while (get_current_batch(net) < net->max_batches) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -693,7 +690,7 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
data gray = copy_data(train);
for(j = 0; j < imgs; ++j){
image gim = float_to_image(net.w, net.h, net.c, gray.X.vals[j]);
image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
grayscale_image_3c(gim);
train.y.vals[j][0] = .95;
gray.y.vals[j][0] = .05;
@ -701,44 +698,44 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
time=clock();
float gloss = 0;
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, pixs, 0);
get_next_batch(gray, net.batch, j*net.batch, graypixs, 0);
cuda_push_array(net.input_gpu, graypixs, net.inputs*net.batch);
cuda_push_array(net.truth_gpu, pixs, net.truths*net.batch);
for(j = 0; j < net->subdivisions; ++j){
get_next_batch(train, net->batch, j*net->batch, pixs, 0);
get_next_batch(gray, net->batch, j*net->batch, graypixs, 0);
cuda_push_array(net->input_gpu, graypixs, net->inputs*net->batch);
cuda_push_array(net->truth_gpu, pixs, net->truths*net->batch);
/*
image origi = float_to_image(net.w, net.h, 3, pixs);
image grayi = float_to_image(net.w, net.h, 3, graypixs);
image origi = float_to_image(net->w, net->h, 3, pixs);
image grayi = float_to_image(net->w, net->h, 3, graypixs);
show_image(grayi, "gray");
show_image(origi, "orig");
cvWaitKey(0);
*/
*net.seen += net.batch;
*net->seen += net->batch;
forward_network_gpu(net);
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
copy_gpu(anet.inputs*anet.batch, imlayer.output_gpu, 1, anet.input_gpu, 1);
fill_gpu(anet.inputs*anet.batch, .95, anet.truth_gpu, 1);
anet.delta_gpu = imerror;
copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1);
fill_gpu(anet->inputs*anet->batch, .95, anet->truth_gpu, 1);
anet->delta_gpu = imerror;
forward_network_gpu(anet);
backward_network_gpu(anet);
scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net.layers[net.n-1].delta_gpu, 1);
scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net->layers[net->n-1].delta_gpu, 1);
scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
printf("features %f\n", cuda_mag_array(net.layers[net.n-1].delta_gpu, imlayer.outputs*imlayer.batch));
printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net.layers[net.n-1].delta_gpu, 1);
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net->layers[net->n-1].delta_gpu, 1);
backward_network_gpu(net);
gloss += *net.cost /(net.subdivisions*net.batch);
gloss += *net->cost /(net->subdivisions*net->batch);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
for(k = 0; k < net->batch; ++k){
int index = j*net->batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
}
}
@ -752,8 +749,8 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
#ifdef OPENCV
if(display){
image im = float_to_image(anet.w, anet.h, anet.c, gray.X.vals[0]);
image im2 = float_to_image(anet.w, anet.h, anet.c, train.X.vals[0]);
image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
show_image(im, "gen");
show_image(im2, "train");
cvWaitKey(50);
@ -801,27 +798,27 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
if(weightfile){
load_weights(&net, weightfile);
}
if(clear) *net.seen = 0;
if(clear) *net->seen = 0;
char *abase = basecfg(acfgfile);
network anet = parse_network_cfg(acfgfile);
if(aweightfile){
load_weights(&anet, aweightfile);
}
if(clear) *anet.seen = 0;
if(clear) *anet->seen = 0;
int i, j, k;
layer imlayer = {0};
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
imlayer = net.layers[i];
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = net->layers[i];
break;
}
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
i = *net.seen/imgs;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
int imgs = net->batch*net->subdivisions;
i = *net->seen/imgs;
data train, buffer;
@ -830,21 +827,21 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.min = net.min_crop;
args.max = net.max_crop;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.min = net->min_crop;
args.max = net->max_crop;
args.angle = net->angle;
args.aspect = net->aspect;
args.exposure = net->exposure;
args.saturation = net->saturation;
args.hue = net->hue;
args.size = net->w;
args.type = CLASSIFICATION_DATA;
args.classes = 1;
char *ls[1] = {"coco"};
@ -856,8 +853,8 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
network_state gstate = {0};
gstate.index = 0;
gstate.net = net;
int x_size = get_network_input_size(net)*net.batch;
int y_size = 1*net.batch;
int x_size = get_network_input_size(net)*net->batch;
int y_size = 1*net->batch;
gstate.input = cuda_make_array(0, x_size);
gstate.truth = 0;
gstate.delta = 0;
@ -868,7 +865,7 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
network_state astate = {0};
astate.index = 0;
astate.net = anet;
int ay_size = get_network_output_size(anet)*anet.batch;
int ay_size = get_network_output_size(anet)*anet->batch;
astate.input = 0;
astate.truth = 0;
astate.delta = 0;
@ -883,7 +880,7 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
//data generated = copy_data(train);
while (get_current_batch(net) < net.max_batches) {
while (get_current_batch(net) < net->max_batches) {
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -896,10 +893,10 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
time=clock();
float gloss = 0;
for(j = 0; j < net.subdivisions; ++j){
get_next_batch(train, net.batch, j*net.batch, X, y);
for(j = 0; j < net->subdivisions; ++j){
get_next_batch(train, net->batch, j*net->batch, X, y);
cuda_push_array(gstate.input, X, x_size);
*net.seen += net.batch;
*net->seen += net->batch;
forward_network_gpu(net, gstate);
fill_gpu(imlayer.outputs, 0, imerror, 1);
@ -917,11 +914,11 @@ void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfi
printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
gloss += get_network_cost(net) /(net.subdivisions*net.batch);
gloss += get_network_cost(net) /(net->subdivisions*net->batch);
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
for(k = 0; k < net.batch; ++k){
int index = j*net.batch + k;
for(k = 0; k < net->batch; ++k){
int index = j*net->batch + k;
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
generated.y.vals[index][0] = 0;
}
@ -977,10 +974,10 @@ void train_lsd(char *cfgfile, char *weightfile, int clear)
if(weightfile){
load_weights(&net, weightfile);
}
if(clear) *net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = net.batch*net.subdivisions;
int i = *net.seen/imgs;
if(clear) *net->seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
int imgs = net->batch*net->subdivisions;
int i = *net->seen/imgs;
data train, buffer;
@ -989,21 +986,21 @@ void train_lsd(char *cfgfile, char *weightfile, int clear)
char **paths = (char **)list_to_array(plist);
load_args args = {0};
args.w = net.w;
args.h = net.h;
args.w = net->w;
args.h = net->h;
args.paths = paths;
args.n = imgs;
args.m = plist->size;
args.d = &buffer;
args.min = net.min_crop;
args.max = net.max_crop;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.min = net->min_crop;
args.max = net->max_crop;
args.angle = net->angle;
args.aspect = net->aspect;
args.exposure = net->exposure;
args.saturation = net->saturation;
args.hue = net->hue;
args.size = net->w;
args.type = CLASSIFICATION_DATA;
args.classes = 1;
char *ls[1] = {"coco"};
@ -1012,7 +1009,7 @@ void train_lsd(char *cfgfile, char *weightfile, int clear)
pthread_t load_thread = load_data_in_thread(args);
clock_t time;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
while(get_current_batch(net) < net->max_batches){
i += 1;
time=clock();
pthread_join(load_thread, 0);
@ -1045,13 +1042,10 @@ void train_lsd(char *cfgfile, char *weightfile, int clear)
}
*/
void test_lsd(char *cfgfile, char *weightfile, char *filename, int gray)
void test_lsd(char *cfg, char *weights, char *filename, int gray)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
network *net = load_network(cfg, weights, 0);
set_batch_network(net, 1);
srand(2222222);
clock_t time;
@ -1059,8 +1053,8 @@ void test_lsd(char *cfgfile, char *weightfile, char *filename, int gray)
char *input = buff;
int i, imlayer = 0;
for (i = 0; i < net.n; ++i) {
if (net.layers[i].out_c == 3) {
for (i = 0; i < net->n; ++i) {
if (net->layers[i].out_c == 3) {
imlayer = i;
printf("%d\n", i);
break;
@ -1078,8 +1072,8 @@ void test_lsd(char *cfgfile, char *weightfile, char *filename, int gray)
strtok(input, "\n");
}
image im = load_image_color(input, 0, 0);
image resized = resize_min(im, net.w);
image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
image resized = resize_min(im, net->w);
image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
if(gray) grayscale_image_3c(crop);
float *X = crop.data;