Fix mAP

1 files changed, 17 insertions, 4 deletions

diff --git a/evaluate.py b/evaluate.py
index ca6c8db..9218649 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -134,20 +134,33 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
         
         if args.mAP:
             precision = np.zeros((probe_label.shape[0], args.neighbors))
+            recall = np.zeros((probe_label.shape[0], args.neighbors))
             mAP = np.zeros(probe_label.shape[0])
-            truths = precision
+            max_level_precision = np.zeros((probe_label.shape[0],11))
+            
             for i in range(probe_label.shape[0]):
                 truth_count=0
                 false_count=0
                 for j in range(args.neighbors):
                     if probe_label[i] == nneighbors[i][j]:
-                        truths[i][j] = 1
                         truth_count+=1
                         precision[i][j] = truth_count/(j+1)
-                        mAP[i] += precision[i][j]
+                    else:
+                        false_count+=1
+                        precision[i][j]= 1 - false_count/(j+1)
                 if truth_count!=0:
-                    mAP[i] = mAP[i]/truth_count
+                    recall_step = 1/truth_count
+                    for j in range(args.neighbors):
+                        if probe_label[i] == nneighbors[i][j]:
+                            recall[i][j:] += recall_step
+                else:
+                    recall[i][:] = 1
+            for i in range(probe_label.shape[0]):
+                for j in range(11):
+                    max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
                     #print(mAP[i])
+            for i in range(probe_label.shape[0]):
+                mAP[i] = sum(max_level_precision[i])/11
             print('mAP:',np.mean(mAP))
                     
     return target_pred