From 37259078f02b797ece6bf67aa6179cb05ba92d47 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Sat, 05 Aug 2017 13:23:53 +0000
Subject: [PATCH] Add tracking for image in example of usage SO/DLL
---
src/detection_layer.c | 51 +++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 41 insertions(+), 10 deletions(-)
diff --git a/src/detection_layer.c b/src/detection_layer.c
index 1b0f126..cd98b4b 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -22,13 +22,20 @@
l.coords = coords;
l.rescore = rescore;
l.side = side;
+ l.w = side;
+ l.h = side;
assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
l.cost = calloc(1, sizeof(float));
l.outputs = l.inputs;
l.truths = l.side*l.side*(1+l.coords+l.classes);
l.output = calloc(batch*l.outputs, sizeof(float));
l.delta = calloc(batch*l.outputs, sizeof(float));
+
+ l.forward = forward_detection_layer;
+ l.backward = backward_detection_layer;
#ifdef GPU
+ l.forward_gpu = forward_detection_layer_gpu;
+ l.backward_gpu = backward_detection_layer_gpu;
l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif
@@ -44,17 +51,16 @@
int locations = l.side*l.side;
int i,j;
memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
+ //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
int b;
if (l.softmax){
for(b = 0; b < l.batch; ++b){
int index = b*l.inputs;
for (i = 0; i < locations; ++i) {
int offset = i*l.classes;
- softmax_array(l.output + index + offset, l.classes, 1,
+ softmax(l.output + index + offset, l.classes, 1,
l.output + index + offset);
}
- int offset = locations*l.classes;
- activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC);
}
}
if(state.train){
@@ -133,11 +139,9 @@
best_index = 0;
}
}
- /*
- if(1 && *(state.net.seen) < 100000){
+ if(l.random && *(state.net.seen) < 64000){
best_index = rand()%l.n;
}
- */
int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
int tbox_index = truth_index + 1 + l.classes;
@@ -175,10 +179,6 @@
avg_iou += iou;
++count;
}
- if(l.softmax){
- gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords),
- LOGISTIC, l.delta + index + locations*l.classes);
- }
}
if(0){
@@ -208,9 +208,11 @@
}
+ *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
+ //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
}
}
@@ -219,6 +221,35 @@
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}
+void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
+{
+ int i,j,n;
+ float *predictions = l.output;
+ //int per_cell = 5*num+classes;
+ for (i = 0; i < l.side*l.side; ++i){
+ int row = i / l.side;
+ int col = i % l.side;
+ for(n = 0; n < l.n; ++n){
+ int index = i*l.n + n;
+ int p_index = l.side*l.side*l.classes + i*l.n + n;
+ float scale = predictions[p_index];
+ int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
+ boxes[index].x = (predictions[box_index + 0] + col) / l.side * w;
+ boxes[index].y = (predictions[box_index + 1] + row) / l.side * h;
+ boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
+ boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
+ for(j = 0; j < l.classes; ++j){
+ int class_index = i*l.classes;
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
+}
+
#ifdef GPU
void forward_detection_layer_gpu(const detection_layer l, network_state state)
--
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