Add test for lambda

1 files changed, 41 insertions, 18 deletions

diff --git a/opt.py b/opt.py
index 0f94de4..3d68adc 100644..100755
--- a/opt.py
+++ b/opt.py
@@ -162,7 +162,8 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                 #mAP[i] = sum(max_level_precision[i])/11
                 mAP[i] = sum(precision[i])/args.neighbors
             print('mAP:',np.mean(mAP))
-                    
+            return np.mean(mAP)
+        
     return target_pred
 
 def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
@@ -235,6 +236,9 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
         accuracy[0] = draw_results(test_label, target_pred)
     else:
         for q in range(args.comparison):
+            if args.mAP:
+                return test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
+            
             target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
             for i in range(args.multrank):
                 return draw_results(test_label, target_pred[i])
@@ -251,17 +255,7 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
         plt.grid(True)
         plt.show()
 
-def main():
-    mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
-    camId = mat['camId']
-    filelist = mat['filelist']
-    labels = mat['labels']
-    gallery_idx = mat['gallery_idx'] - 1
-    query_idx = mat['query_idx'] - 1
-    train_idx = mat['train_idx'] - 1
-    with open(os.path.join(args.data,'feature_data.json'), 'r') as read_file:
-        feature_vectors = np.array(json.load(read_file))
- 
+def kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
     axis = 0
     search = 0
     steps = 0
@@ -269,12 +263,16 @@ def main():
     neg = False
     outofaxis = False
     start = np.array([1,1])
-    args.PCA = 10
+    if args.mAP:
+        args.neighbors = 10
+    args.PCA = 50
     args.train = True
     args.rerank = True
     args.reranka = 1
     args.rerankb = 1
     opt = np.array([1,1])
+    checktab = np.zeros((100,100))
+    checktab[1][1]=1
     max_acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
     print('origin')
     print('vertical')
@@ -297,8 +295,10 @@ def main():
                         p = search*2 + start[0]
                     args.reranka = p
                     if not outofaxis:
-                        print('p:',p,' q:',q)
-                        acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+                        if checktab[p][q] == 0:
+                            checktab[p][q] = 1
+                            print('p:',p,' q:',q)
+                            acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
                     if acc > max_acc:
                         print('new p:',p, ' for accuracy:', acc)
                         max_acc=acc
@@ -318,8 +318,10 @@ def main():
                         q = search*2 + start[1]
                     args.rerankb = q
                     if not outofaxis:
-                        print('p:',p,' q:',q)
-                        acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+                        if checktab[p][q] == 0:
+                            checktab[p][q]=1
+                            print('p:',p,' q:',q)
+                            acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
                     if acc > max_acc:
                         print('new q:',q, ' for accuracy:', acc)
                         max_acc=acc
@@ -351,8 +353,29 @@ def main():
             opt[1] = start[1]
             steps=0
             vertical=True
-    print('Maximum Accuracy:',max_acc,' found at p:',opt[0],'|q:',opt[1])  
+    print('Maximum Accuracy:',max_acc,' found at p:',opt[0],'|q:',opt[1])
+    return max_acc, opt
 
+def main():
+    mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
+    camId = mat['camId']
+    filelist = mat['filelist']
+    labels = mat['labels']
+    gallery_idx = mat['gallery_idx'] - 1
+    query_idx = mat['query_idx'] - 1
+    train_idx = mat['train_idx'] - 1
+    with open(os.path.join(args.data,'feature_data.json'), 'r') as read_file:
+        feature_vectors = np.array(json.load(read_file))
+    l=0
+    max_acc = np.zeros(11)
+    opt = np.zeros((11,2))
+    while l < 11:
+        args.rerankl = l/10
+        print('testing for lambda:',args.rerankl)
+        max_acc[l], opt[l] = kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
+        l +=1
+    print('Max accuracy:',np.max(max_acc),' at p:',opt[np.argmax(max_acc)][0], '| q:',opt[np.argmax(max_acc)][1],'| lambda:',np.argmax(max_acc)/10)
+    
 if __name__ == "__main__":
     main()