From c7a700dc2249e8bd3a2c9120dfd09240e413c8bd Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 05 Nov 2016 21:09:21 +0000
Subject: [PATCH] new font strategy
---
src/region_layer.c | 137 +++++++++++++++++++++++++--------------------
1 files changed, 77 insertions(+), 60 deletions(-)
diff --git a/src/region_layer.c b/src/region_layer.c
index 5f8b3cc..c3935ca 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -48,11 +48,17 @@
return l;
}
+#define LOG 1
+
box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
{
box b;
b.x = (i + .5)/w + x[index + 0] * biases[2*n];
b.y = (j + .5)/h + x[index + 1] * biases[2*n + 1];
+ if(LOG){
+ b.x = (i + logistic_activate(x[index + 0])) / w;
+ b.y = (j + logistic_activate(x[index + 1])) / h;
+ }
b.w = exp(x[index + 2]) * biases[2*n];
b.h = exp(x[index + 3]) * biases[2*n+1];
return b;
@@ -65,11 +71,19 @@
float tx = (truth.x - (i + .5)/w) / biases[2*n];
float ty = (truth.y - (j + .5)/h) / biases[2*n + 1];
+ if(LOG){
+ tx = (truth.x*w - i);
+ ty = (truth.y*h - j);
+ }
float tw = log(truth.w / biases[2*n]);
float th = log(truth.h / biases[2*n + 1]);
delta[index + 0] = scale * (tx - x[index + 0]);
delta[index + 1] = scale * (ty - x[index + 1]);
+ if(LOG){
+ delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
+ delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
+ }
delta[index + 2] = scale * (tw - x[index + 2]);
delta[index + 3] = scale * (th - x[index + 3]);
return iou;
@@ -85,8 +99,7 @@
return (x != x);
}
-#define LOG 0
-
+void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
void forward_region_layer(const region_layer l, network_state state)
{
int i,j,b,t,n;
@@ -97,7 +110,9 @@
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
l.output[index + 4] = logistic_activate(l.output[index + 4]);
- if(l.softmax){
+ if(l.softmax_tree){
+ softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
+ } else if(l.softmax){
softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
}
}
@@ -128,12 +143,12 @@
l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
if(best_iou > .5) l.delta[index + 4] = 0;
- if(*(state.net.seen) < 6400){
+ if(*(state.net.seen) < 12800){
box truth = {0};
truth.x = (i + .5)/l.w;
truth.y = (j + .5)/l.h;
- truth.w = .5;
- truth.h = .5;
+ truth.w = l.biases[2*n];
+ truth.h = l.biases[2*n+1];
delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
//l.delta[index + 0] = .1 * (0 - l.output[index + 0]);
//l.delta[index + 1] = .1 * (0 - l.output[index + 1]);
@@ -145,7 +160,7 @@
}
for(t = 0; t < 30; ++t){
box truth = float_to_box(state.truth + t*5 + b*l.truths);
- int class = state.truth[t*5 + b*l.truths + 4];
+
if(!truth.x) break;
float best_iou = 0;
int best_index = 0;
@@ -160,7 +175,11 @@
for(n = 0; n < l.n; ++n){
int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
- printf("pred: (%f, %f) %f x %f\n", pred.x*l.w - i - .5, pred.y * l.h - j - .5, pred.w, pred.h);
+ if(l.bias_match){
+ pred.w = l.biases[2*n];
+ pred.h = l.biases[2*n+1];
+ }
+ printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
pred.x = 0;
pred.y = 0;
float iou = box_iou(pred, truth_shift);
@@ -170,7 +189,7 @@
best_n = n;
}
}
- printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x * l.w - i - .5, truth.y*l.h - j - .5, truth.w, truth.h);
+ printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
if(iou > .5) recall += 1;
@@ -182,41 +201,32 @@
if (l.rescore) {
l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
}
- //printf("%f\n", l.delta[best_index+1]);
- /*
- if(isnan(l.delta[best_index+1])){
- printf("%f\n", true_scale);
- printf("%f\n", l.output[best_index + 1]);
- printf("%f\n", truth.w);
- printf("%f\n", truth.h);
- error("bad");
- }
- */
- for(n = 0; n < l.classes; ++n){
- l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
- if(n == class) avg_cat += l.output[best_index + 5 + n];
- }
- /*
- if(0){
- printf("truth: %f %f %f %f\n", truth.x, truth.y, truth.w, truth.h);
- printf("pred: %f %f %f %f\n\n", pred.x, pred.y, pred.w, pred.h);
- float aspect = exp(true_aspect);
- float scale = logistic_activate(true_scale);
- float move_x = true_dx;
- float move_y = true_dy;
- box b;
- b.w = sqrt(scale * aspect);
- b.h = b.w * 1./aspect;
- b.x = move_x * b.w + (i + .5)/l.w;
- b.y = move_y * b.h + (j + .5)/l.h;
- printf("%f %f\n", b.x, truth.x);
- printf("%f %f\n", b.y, truth.y);
- printf("%f %f\n", b.w, truth.w);
- printf("%f %f\n", b.h, truth.h);
- //printf("%f\n", box_iou(b, truth));
+
+ int class = state.truth[t*5 + b*l.truths + 4];
+ if (l.map) class = l.map[class];
+ if(l.softmax_tree){
+ float pred = 1;
+ while(class >= 0){
+ pred *= l.output[best_index + 5 + class];
+ int g = l.softmax_tree->group[class];
+ int i;
+ int offset = l.softmax_tree->group_offset[g];
+ for(i = 0; i < l.softmax_tree->group_size[g]; ++i){
+ int index = best_index + 5 + offset + i;
+ l.delta[index] = l.class_scale * (0 - l.output[index]);
+ }
+ l.delta[best_index + 5 + class] = l.class_scale * (1 - l.output[best_index + 5 + class]);
+
+ class = l.softmax_tree->parent[class];
+ }
+ avg_cat += pred;
+ } else {
+ for(n = 0; n < l.classes; ++n){
+ l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
+ if(n == class) avg_cat += l.output[best_index + 5 + n];
+ }
}
- */
++count;
}
}
@@ -244,24 +254,31 @@
int p_index = index * (l.classes + 5) + 4;
float scale = predictions[p_index];
int box_index = index * (l.classes + 5);
- boxes[index].x = (predictions[box_index + 0] + col + .5) / l.w * w;
- boxes[index].y = (predictions[box_index + 1] + row + .5) / l.h * h;
- if(0){
- boxes[index].x = (logistic_activate(predictions[box_index + 0]) + col) / l.w * w;
- boxes[index].y = (logistic_activate(predictions[box_index + 1]) + row) / l.h * h;
- }
- boxes[index].w = pow(logistic_activate(predictions[box_index + 2]), (l.sqrt?2:1)) * w;
- boxes[index].h = pow(logistic_activate(predictions[box_index + 3]), (l.sqrt?2:1)) * h;
- if(1){
- boxes[index].x = ((col + .5)/l.w + predictions[box_index + 0] * .5) * w;
- boxes[index].y = ((row + .5)/l.h + predictions[box_index + 1] * .5) * h;
- boxes[index].w = (exp(predictions[box_index + 2]) * .5) * w;
- boxes[index].h = (exp(predictions[box_index + 3]) * .5) * h;
- }
- for(j = 0; j < l.classes; ++j){
- int class_index = index * (l.classes + 5) + 5;
- float prob = scale*predictions[class_index+j];
- probs[index][j] = (prob > thresh) ? prob : 0;
+ boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
+ boxes[index].x *= w;
+ boxes[index].y *= h;
+ boxes[index].w *= w;
+ boxes[index].h *= h;
+
+ int class_index = index * (l.classes + 5) + 5;
+ if(l.softmax_tree){
+
+ hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
+ int found = 0;
+ for(j = l.classes - 1; j >= 0; --j){
+ if(!found && predictions[class_index + j] > .5){
+ found = 1;
+ } else {
+ predictions[class_index + j] = 0;
+ }
+ float prob = predictions[class_index+j];
+ probs[index][j] = (scale > thresh) ? prob : 0;
+ }
+ }else{
+ for(j = 0; j < l.classes; ++j){
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
}
if(only_objectness){
probs[index][0] = scale;
--
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