certified

sets/CelebA.py

@@ -9,10 +9,10 @@ class CelebA(Data):
 
     def get_set(self):
         set = datasets.CelebA(
-            root = "./data", 
+            root = "../data", 
             split='all', 
             target_type='identity', 
-            download=True, 
+            download=False, 
             transform=None
         )
         # set the target first

sets/Extractor.py

@@ -75,7 +75,7 @@ def extract_selected_binary(rec_path, idx_path, output_dir, top_labels):
             current_count = save_counters[label]
             img_filename = f"{current_count}.jpg"
             img_path = os.path.join(target_folder, img_filename)
-            if(current_count > 200):
+            if(current_count > 405):
                 continue
             
             with open(img_path, 'wb') as img_f:
@@ -119,9 +119,9 @@ if __name__ == "__main__":
 '''
 if __name__ == "__main__":
     base_dir = os.path.dirname(os.path.abspath(__file__))
-    REC = os.path.join(base_dir, 'casia', 'train.rec')
-    IDX = os.path.join(base_dir, 'casia', 'train.idx')
-    OUT = os.path.join(base_dir, 'casia-set')
+    REC = os.path.join(base_dir, '../data/casia-set', 'train.rec')
+    IDX = os.path.join(base_dir, '../data/casia-set', 'train.idx')
+    OUT = os.path.join(base_dir, '../data/casia-set')
 
     # Step 1: Trust the binary, not the text file
     top_verified_labels = get_top_identities_binary(REC, IDX, top_n=50)