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NIPS

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This commit is contained in:
Joseph Redmon
2015-06-10 00:11:41 -07:00
parent 7fe80a2bb5
commit cbc9984a17
21 changed files with 7440 additions and 770 deletions
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69
src/detection_layer.c
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@ -10,15 +10,15 @@
int get_detection_layer_locations(detection_layer l)
{
return l.inputs / (l.classes+l.coords+l.rescore+l.background);
return l.inputs / (l.classes+l.coords+l.joint+(l.background || l.objectness));
}
int get_detection_layer_output_size(detection_layer l)
{
return get_detection_layer_locations(l)*(l.background + l.classes + l.coords);
return get_detection_layer_locations(l)*((l.background || l.objectness) + l.classes + l.coords);
}
detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int joint, int rescore, int background, int objectness)
{
detection_layer l = {0};
l.type = DETECTION;
@ -28,7 +28,8 @@ detection_layer make_detection_layer(int batch, int inputs, int classes, int coo
l.classes = classes;
l.coords = coords;
l.rescore = rescore;
l.nuisance = nuisance;
l.objectness = objectness;
l.joint = joint;
l.cost = calloc(1, sizeof(float));
l.does_cost=1;
l.background = background;
@ -47,28 +48,6 @@ detection_layer make_detection_layer(int batch, int inputs, int classes, int coo
return l;
}
void dark_zone(detection_layer l, int class, int start, network_state state)
{
int index = start+l.background+class;
int size = l.classes+l.coords+l.background;
int location = (index%(7*7*size)) / size ;
int r = location / 7;
int c = location % 7;
int dr, dc;
for(dr = -1; dr <= 1; ++dr){
for(dc = -1; dc <= 1; ++dc){
if(!(dr || dc)) continue;
if((r + dr) > 6 || (r + dr) < 0) continue;
if((c + dc) > 6 || (c + dc) < 0) continue;
int di = (dr*7 + dc) * size;
if(state.truth[index+di]) continue;
l.output[index + di] = 0;
//if(!state.truth[start+di]) continue;
//l.output[start + di] = 1;
}
}
}
typedef struct{
float dx, dy, dw, dh;
} dbox;
@ -258,24 +237,6 @@ void test_box()
wiou = ((1-wiou)*(1-wiou) - iou)/(.00001);
hiou = ((1-hiou)*(1-hiou) - iou)/(.00001);
printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou);
/*
while(count++ < 300){
dbox d = diou(a, b);
printf("%f %f %f %f\n", a.x, a.y, a.w, a.h);
a.x += .1*d.dx;
a.w += .1*d.dw;
a.y += .1*d.dy;
a.h += .1*d.dh;
printf("inter: %f\n", box_intersection(a, b));
printf("union: %f\n", box_union(a, b));
printf("IOU: %f\n", box_iou(a, b));
if(d.dx==0 && d.dw==0 && d.dy==0 && d.dh==0) {
printf("break!!!\n");
break;
}
}
*/
}
dbox diou(box a, box b)
@ -308,10 +269,10 @@ void forward_detection_layer(const detection_layer l, network_state state)
int locations = get_detection_layer_locations(l);
int i,j;
for(i = 0; i < l.batch*locations; ++i){
int mask = (!state.truth || state.truth[out_i + l.background + l.classes + 2]);
int mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]);
float scale = 1;
if(l.rescore) scale = state.input[in_i++];
else if(l.nuisance){
if(l.joint) scale = state.input[in_i++];
else if(l.objectness){
l.output[out_i++] = 1-state.input[in_i++];
scale = mask;
}
@ -320,7 +281,7 @@ void forward_detection_layer(const detection_layer l, network_state state)
for(j = 0; j < l.classes; ++j){
l.output[out_i++] = scale*state.input[in_i++];
}
if(l.nuisance){
if(l.objectness){
}else if(l.background){
softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
@ -337,7 +298,7 @@ void forward_detection_layer(const detection_layer l, network_state state)
int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) {
int classes = l.nuisance+l.classes;
int classes = l.objectness+l.classes;
int offset = i*(classes+l.coords);
for (j = offset; j < offset+classes; ++j) {
*(l.cost) += pow(state.truth[j] - l.output[j], 2);
@ -372,7 +333,7 @@ void forward_detection_layer(const detection_layer l, network_state state)
l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
if(0){
if(l.rescore){
for (j = offset; j < offset+classes; ++j) {
if(state.truth[j]) state.truth[j] = iou;
l.delta[j] = state.truth[j] - l.output[j];
@ -392,21 +353,21 @@ void backward_detection_layer(const detection_layer l, network_state state)
for(i = 0; i < l.batch*locations; ++i){
float scale = 1;
float latent_delta = 0;
if(l.rescore) scale = state.input[in_i++];
else if (l.nuisance) state.delta[in_i++] = -l.delta[out_i++];
if(l.joint) scale = state.input[in_i++];
else if (l.objectness) state.delta[in_i++] = -l.delta[out_i++];
else if (l.background) state.delta[in_i++] = scale*l.delta[out_i++];
for(j = 0; j < l.classes; ++j){
latent_delta += state.input[in_i]*l.delta[out_i];
state.delta[in_i++] = scale*l.delta[out_i++];
}
if (l.nuisance) {
if (l.objectness) {
}else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i);
for(j = 0; j < l.coords; ++j){
state.delta[in_i++] = l.delta[out_i++];
}
if(l.rescore) state.delta[in_i-l.coords-l.classes-l.rescore-l.background] = latent_delta;
if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] = latent_delta;
}
}