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OK SHOULD I START WORKING ON CVPR OR WHAT?

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This commit is contained in:
Joseph Redmon
2017-11-07 16:10:33 -08:00
parent c725270342
commit 3fb3eec650
12 changed files with 1003 additions and 208 deletions
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195
examples/attention.c
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@ -3,16 +3,64 @@
#include <sys/time.h>
#include <assert.h>
void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfile2, char *weightfile2, int *gpus, int ngpus, int clear)
void extend_data_truth(data *d, int n, float val)
{
int i;
int i, j;
for(i = 0; i < d->y.rows; ++i){
d->y.vals[i] = realloc(d->y.vals[i], (d->y.cols+n)*sizeof(float));
for(j = 0; j < n; ++j){
d->y.vals[i][d->y.cols + j] = val;
}
}
d->y.cols += n;
}
float avg_loss = -1;
matrix network_loss_data(network *net, data test)
{
int i,b;
int k = 1;
matrix pred = make_matrix(test.X.rows, k);
float *X = calloc(net->batch*test.X.cols, sizeof(float));
float *y = calloc(net->batch*test.y.cols, sizeof(float));
for(i = 0; i < test.X.rows; i += net->batch){
for(b = 0; b < net->batch; ++b){
if(i+b == test.X.rows) break;
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
memcpy(y+b*test.y.cols, test.y.vals[i+b], test.y.cols*sizeof(float));
}
network orig = *net;
net->input = X;
net->truth = y;
net->train = 0;
net->delta = 0;
forward_network(net);
*net = orig;
float *delta = net->layers[net->n-1].output;
for(b = 0; b < net->batch; ++b){
if(i+b == test.X.rows) break;
int t = max_index(y + b*test.y.cols, 1000);
float err = sum_array(delta + b*net->outputs, net->outputs);
pred.vals[i+b][0] = -err;
//pred.vals[i+b][0] = 1-delta[b*net->outputs + t];
}
}
free(X);
free(y);
return pred;
}
void train_attention(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
int i, j;
float avg_cls_loss = -1;
float avg_att_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
printf("%d\n", ngpus);
network **attnets = calloc(ngpus, sizeof(network*));
network **clsnets = calloc(ngpus, sizeof(network*));
network **nets = calloc(ngpus, sizeof(network*));
srand(time(0));
int seed = rand();
@ -21,14 +69,11 @@ void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfi
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
attnets[i] = load_network(cfgfile, weightfile, clear);
attnets[i]->learning_rate *= ngpus;
clsnets[i] = load_network(cfgfile2, weightfile2, clear);
clsnets[i]->learning_rate *= ngpus;
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i]->learning_rate *= ngpus;
}
srand(time(0));
network *net = attnets[0];
//network *clsnet = clsnets[0];
network *net = nets[0];
int imgs = net->batch * net->subdivisions * ngpus;
@ -47,15 +92,18 @@ void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfi
int N = plist->size;
double time;
int divs=3;
int size=2;
load_args args = {0};
args.w = 4*net->w;
args.h = 4*net->h;
args.size = 4*net->w;
args.w = divs*net->w/size;
args.h = divs*net->h/size;
args.size = divs*net->w/size;
args.threads = 32;
args.hierarchy = net->hierarchy;
args.min = net->min_ratio*net->w;
args.max = net->max_ratio*net->w;
args.min = net->min_ratio*args.w;
args.max = net->max_ratio*args.w;
args.angle = net->angle;
args.aspect = net->aspect;
args.exposure = net->exposure;
@ -83,25 +131,81 @@ void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfi
train = buffer;
load_thread = load_data(args);
data resized = resize_data(train, net->w, net->h);
extend_data_truth(&resized, divs*divs, 0);
data *tiles = tile_data(train, divs, size);
printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
time = what_time_is_it_now();
float loss = 0;
#ifdef GPU
if(ngpus == 1){
loss = train_network(net, train);
} else {
loss = train_networks(attnets, ngpus, train, 4);
float aloss = 0;
float closs = 0;
int z;
for (i = 0; i < divs*divs/ngpus; ++i) {
#pragma omp parallel for
for(j = 0; j < ngpus; ++j){
int index = i*ngpus + j;
extend_data_truth(tiles+index, divs*divs, SECRET_NUM);
matrix deltas = network_loss_data(nets[j], tiles[index]);
for(z = 0; z < resized.y.rows; ++z){
resized.y.vals[z][train.y.cols + index] = deltas.vals[z][0];
}
free_matrix(deltas);
}
}
int *inds = calloc(resized.y.rows, sizeof(int));
for(z = 0; z < resized.y.rows; ++z){
int index = max_index(resized.y.vals[z] + train.y.cols, divs*divs);
inds[z] = index;
for(i = 0; i < divs*divs; ++i){
resized.y.vals[z][train.y.cols + i] = (i == index)? 1 : 0;
}
}
data best = select_data(tiles, inds);
free(inds);
#ifdef GPU
if (ngpus == 1) {
closs = train_network(net, best);
} else {
closs = train_networks(nets, ngpus, best, 4);
}
#endif
for (i = 0; i < divs*divs; ++i) {
printf("%.2f ", resized.y.vals[0][train.y.cols + i]);
if((i+1)%divs == 0) printf("\n");
free_data(tiles[i]);
}
free_data(best);
printf("\n");
image im = float_to_image(64,64,3,resized.X.vals[0]);
//show_image(im, "orig");
//cvWaitKey(100);
/*
image im1 = float_to_image(64,64,3,tiles[i].X.vals[0]);
image im2 = float_to_image(64,64,3,resized.X.vals[0]);
show_image(im1, "tile");
show_image(im2, "res");
*/
#ifdef GPU
if (ngpus == 1) {
aloss = train_network(net, resized);
} else {
aloss = train_networks(nets, ngpus, resized, 4);
}
#else
loss = train_network(net, train);
#endif
for(i = 0; i < divs*divs; ++i){
printf("%f ", nets[0]->output[1000 + i]);
if ((i+1) % divs == 0) printf("\n");
}
printf("\n");
free_data(resized);
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
free_data(train);
if(avg_cls_loss == -1) avg_cls_loss = closs;
if(avg_att_loss == -1) avg_att_loss = aloss;
avg_cls_loss = avg_cls_loss*.9 + closs*.1;
avg_att_loss = avg_att_loss*.9 + aloss*.1;
printf("%ld, %.3f: Att: %f, %f avg, Class: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, aloss, avg_att_loss, closs, avg_cls_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
if(*net->seen/N > epoch){
epoch = *net->seen/N;
char buff[256];
@ -152,6 +256,11 @@ void validate_attention_single(char *datacfg, char *filename, char *weightfile)
float avg_acc = 0;
float avg_topk = 0;
int *indexes = calloc(topk, sizeof(int));
int divs = 4;
int size = 2;
int extra = 0;
float *avgs = calloc(classes, sizeof(float));
int *inds = calloc(divs*divs, sizeof(int));
for(i = 0; i < m; ++i){
int class = -1;
@ -163,14 +272,38 @@ void validate_attention_single(char *datacfg, char *filename, char *weightfile)
}
}
image im = load_image_color(paths[i], 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*divs/size);
image crop = crop_image(resized, (resized.w - net->w*divs/size)/2, (resized.h - net->h*divs/size)/2, net->w*divs/size, net->h*divs/size);
image rcrop = resize_image(crop, net->w, net->h);
//show_image(im, "orig");
//show_image(crop, "cropped");
//cvWaitKey(0);
float *pred = network_predict(net, crop.data);
float *pred = network_predict(net, rcrop.data);
//pred[classes + 56] = 0;
for(j = 0; j < divs*divs; ++j){
printf("%.2f ", pred[classes + j]);
if((j+1)%divs == 0) printf("\n");
}
printf("\n");
copy_cpu(classes, pred, 1, avgs, 1);
top_k(pred + classes, divs*divs, divs*divs, inds);
show_image(crop, "crop");
for(j = 0; j < extra; ++j){
int index = inds[j];
int row = index / divs;
int col = index % divs;
int y = row * crop.h / divs - (net->h - crop.h/divs)/2;
int x = col * crop.w / divs - (net->w - crop.w/divs)/2;
printf("%d %d %d %d\n", row, col, y, x);
image tile = crop_image(crop, x, y, net->w, net->h);
float *pred = network_predict(net, tile.data);
axpy_cpu(classes, 1., pred, 1, avgs, 1);
show_image(tile, "tile");
cvWaitKey(10);
}
if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
if(rcrop.data != resized.data) free_image(rcrop);
if(resized.data != im.data) free_image(resized);
free_image(im);
free_image(crop);
@ -318,7 +451,7 @@ void run_attention(int argc, char **argv)
char *filename = (argc > 6) ? argv[6]: 0;
char *layer_s = (argc > 7) ? argv[7]: 0;
if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top);
else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, filename, layer_s, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights);
else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights);
}