Update EMT.py

EMT.py

@@ -69,36 +69,71 @@ class EMT(datasets.GeneratorBasedBuilder):
             "train": _TRAIN_IMAGE_ARCHIVE_URL,
             "test": _TEST_IMAGE_ARCHIVE_URL,
         }
-    
+        
         annotation_urls = {
             "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
             "test": _TEST_ANNOTATION_ARCHIVE_URL,
         }
+        
+        # Based on the requested split, we only download the relevant data
+        split = self.config.name  # Determine the requested split (train or test)
     
-        # Ensure paths are correctly resolved
-        extracted_paths = dl_manager.download_and_extract(annotation_urls)
-        image_archives = dl_manager.download_and_extract(image_urls)
-    
-        # ✅ Ensure annotation paths point to the correct subdirectory
-        train_annotation_path = os.path.join(extracted_paths["train"], "annotations", "train")
-        test_annotation_path = os.path.join(extracted_paths["test"], "annotations", "test")
+        # Ensure paths are correctly resolved for the requested split
+        extracted_paths = dl_manager.download_and_extract({split: annotation_urls[split]})
+        image_archives = dl_manager.download_and_extract({split: image_urls[split]})
     
+        # Ensure annotation paths point to the correct subdirectory
+        annotation_path = os.path.join(extracted_paths[split], "annotations", split)
+        image_path = image_archives[split]
+        
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+                name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(image_archives["train"]),
-                    "annotation_path": train_annotation_path,  # ✅ Corrected path
+                    "images": dl_manager.iter_archive(image_path),
+                    "annotation_path": annotation_path,
                 },
             ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "images": dl_manager.iter_archive(image_archives["test"]),
-                    "annotation_path": test_annotation_path,  # ✅ Corrected path
-                },
-            ),
-    ]
+        ]
+
+
+    # def _split_generators(self, dl_manager):
+    #     """Download (if not cached) and prepare dataset splits."""
+        
+    #     image_urls = {
+    #         "train": _TRAIN_IMAGE_ARCHIVE_URL,
+    #         "test": _TEST_IMAGE_ARCHIVE_URL,
+    #     }
+    
+    #     annotation_urls = {
+    #         "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
+    #         "test": _TEST_ANNOTATION_ARCHIVE_URL,
+    #     }
+    
+    #     # Ensure paths are correctly resolved
+    #     extracted_paths = dl_manager.download_and_extract(annotation_urls)
+    #     image_archives = dl_manager.download_and_extract(image_urls)
+    
+    #     # ✅ Ensure annotation paths point to the correct subdirectory
+    #     train_annotation_path = os.path.join(extracted_paths["train"], "annotations", "train")
+    #     test_annotation_path = os.path.join(extracted_paths["test"], "annotations", "test")
+    
+    #     return [
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.TRAIN,
+    #             gen_kwargs={
+    #                 "images": dl_manager.iter_archive(image_archives["train"]),
+    #                 "annotation_path": train_annotation_path,  # ✅ Corrected path
+    #             },
+    #         ),
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.TEST,
+    #             gen_kwargs={
+    #                 "images": dl_manager.iter_archive(image_archives["test"]),
+    #                 "annotation_path": test_annotation_path,  # ✅ Corrected path
+    #             },
+    #         ),
+    # ]
 
     def _generate_examples(self, images, annotation_path):
         """Generate dataset examples by matching images to their corresponding annotations."""