unregister if already registered

object_detection/detectron2_training-kfold.ipynb

@@ -49,8 +49,7 @@
     "import glob\n",
     "from sklearn.model_selection import KFold\n",
     "import json\n",
-    "from collections import defaultdict\n",
-    "from detectron2.data.datasets import register_coco_instances"
+    "from collections import defaultdict"
    ]
   },
   {
@@ -150,15 +149,24 @@
     "    annotations_count = len(data_dict['annotations'])\n",
     "    print(f\"Number of images: {images_count}, Number of annotations: {annotations_count}\")\n",
     "\n",
+    "    \n",
+    "def unregister_coco_instances(name):\n",
+    "    if name in DatasetCatalog.list():\n",
+    "        DatasetCatalog.remove(name)\n",
+    "        MetadataCatalog.remove(name)\n",
+    "\n",
     "# Generate K-Fold cross-validation\n",
     "kf = KFold(n_splits=NUM_FOLDS)\n",
     "pairs = []\n",
     "for fold, (train_indices, test_indices) in enumerate(kf.split(image_ids)):\n",
     "    train_data, test_data = split_data(train_indices, test_indices)\n",
-    "    # Register COCO instances for training and validation. \n",
-    "    # Note: The 'train2017' folder is retained as the base path for images.\n",
     "    train_file = f\"train_coco_{fold}_fold.json\"\n",
     "    test_file = f\"test_coco_{fold}_fold.json\"\n",
+    "    # Unregister instances with the same names only if they exist\n",
+    "    unregister_coco_instances(train_file)\n",
+    "    unregister_coco_instances(test_file)\n",
+    "    # Register COCO instances for training and validation. \n",
+    "    # Note: The 'train2017' folder is retained as the base path for images.\n",
     "    register_coco_instances(train_file, {}, train_file, \"train2017\")\n",
     "    register_coco_instances(test_file, {}, test_file, \"train2017\")\n",
     "    pairs.append([train_file,test_file])\n",
@@ -215,8 +223,6 @@
     "  \n",
     "    def data_loader_mapper(self, batch):\n",
     "        return batch\n",
-    "    \n",
-    "\n",
     "\n",
     "    def run_hooks(self):\n",
     "        val_loss = self.validation()\n",