Rewrite train mode

1 files changed, 38 insertions, 18 deletions

diff --git a/evaluate.py b/evaluate.py
index c5528af..6561b81 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -52,6 +52,8 @@ parser.add_argument("-1", "--normalise", help="Normalized features", action='sto
 parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to M", type=int, default=1)
 parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and Improved metric", type=int, default=1)
 parser.add_argument("--data", help="Data folder with features data", default='data')
+parser.add_argument("-V", "--validation", help="Validation Mode", action='store_true')
+
 
 
 args = parser.parse_args()
@@ -147,22 +149,43 @@ def main():
     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))
+    
     if args.train:
-        query_idx = train_idx.reshape(train_idx.shape[0])
-        gallery_idx = train_idx.reshape(train_idx.shape[0])
+        cam = camId[train_idx]
+        cam = cam.reshape((cam.shape[0],1))
+        labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
+        tt = np.hstack((train_idx, cam)) 
+        s_train, s_test, s_train_label, s_test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
+        train, test, train_label, test_label = train_test_split(s_train, s_train_label, test_size=0.3, random_state=0)
+        #to make it smaller we do a double split
+        del labs
+        del cam
+        train_data = feature_vectors[train[:,0]]
+        test_data = feature_vectors[test[:,0]]
+        train_cam = train[:,1]
+        test_cam = test[:,1]
+        showfiles_train = filelist[train[:,0]]
+        showfiles_test = filelist[train[:,0]]
+        del train
+        del test 
+        del tt
     else:
-        query_idx = query_idx.reshape(query_idx.shape[0])
-        gallery_idx = gallery_idx.reshape(gallery_idx.shape[0])
-    camId = camId.reshape(camId.shape[0])
-
-    showfiles_train = filelist[gallery_idx]
-    showfiles_test = filelist[query_idx]
-    train_data = feature_vectors[gallery_idx]
-    test_data = feature_vectors[query_idx]
-    train_label = labels[gallery_idx]
-    test_label = labels[query_idx]
-    train_cam = camId[gallery_idx]
-    test_cam = camId[query_idx]
+        if args.validation:
+            query_idx = train_idx.reshape(train_idx.shape[0])
+            gallery_idx = train_idx.reshape(train_idx.shape[0])
+        else:
+            query_idx = query_idx.reshape(query_idx.shape[0])
+            gallery_idx = gallery_idx.reshape(gallery_idx.shape[0])
+        camId = camId.reshape(camId.shape[0])
+
+        showfiles_train = filelist[gallery_idx]
+        showfiles_test = filelist[query_idx]
+        train_data = feature_vectors[gallery_idx]
+        test_data = feature_vectors[query_idx]
+        train_label = labels[gallery_idx]
+        test_label = labels[query_idx]
+        train_cam = camId[gallery_idx]
+        test_cam = camId[query_idx]
 
     accuracy = np.zeros((2, args.multrank))
     test_table = np.arange(1, args.multrank+1)
@@ -173,10 +196,7 @@ def main():
         test_data = np.divide(test_data, LA.norm(test_data, axis=0))
     if(args.kmean):
         debug("Using Kmeans")
-        train_data, train_label, train_cam = create_kmean_clusters(feature_vectors,
-                                                                   labels,
-                                                                   gallery_idx,
-                                                                   camId)
+        train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId)
     for q in range(args.comparison):
         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):