From 3459c47bfa02d23d300df9a44ba3ec9c958850fe Mon Sep 17 00:00:00 2001
From: Philip Kahn <tigerhawkvok@gmail.com>
Date: Fri, 04 May 2018 21:00:11 +0000
Subject: [PATCH] Also replace root version to Python3

---
 src/detection_layer.c |   70 ++++++++++++++++++++++++++++++++++-
 1 files changed, 68 insertions(+), 2 deletions(-)

diff --git a/src/detection_layer.c b/src/detection_layer.c
index 8b4045a..0a1c107 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
@@ -44,14 +51,15 @@
     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,
-                        l.output + index + offset);
+                softmax(l.output + index + offset, l.classes, 1,
+                        l.output + index + offset, 1);
             }
         }
     }
@@ -204,6 +212,7 @@
 
 
         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);
     }
 }
 
@@ -212,6 +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)
@@ -247,3 +285,31 @@
 }
 #endif
 
+void get_detection_detections(layer l, int w, int h, float thresh, detection *dets)
+{
+	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;
+			box b;
+			b.x = (predictions[box_index + 0] + col) / l.side * w;
+			b.y = (predictions[box_index + 1] + row) / l.side * h;
+			b.w = pow(predictions[box_index + 2], (l.sqrt ? 2 : 1)) * w;
+			b.h = pow(predictions[box_index + 3], (l.sqrt ? 2 : 1)) * h;
+			dets[index].bbox = b;
+			dets[index].objectness = scale;
+			for (j = 0; j < l.classes; ++j) {
+				int class_index = i*l.classes;
+				float prob = scale*predictions[class_index + j];
+				dets[index].prob[j] = (prob > thresh) ? prob : 0;
+			}
+		}
+	}
+}
\ No newline at end of file

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