Update EMT.py

EMT.py

@@ -67,62 +67,72 @@ class EMT(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Download train/test images and annotations."""
-
-        # Download and extract images
-        image_paths = {
-            "train": dl_manager.download_and_extract(_TRAIN_IMAGE_ARCHIVE_URL),
-            "test": dl_manager.download_and_extract(_TEST_IMAGE_ARCHIVE_URL),
+        image_urls = {
+            "train": _TRAIN_IMAGE_ARCHIVE_URL,
+            "test": _TEST_IMAGE_ARCHIVE_URL,
         }
-        
-        # Download annotations (extracted automatically)
-        annotation_paths = {
-            "train": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/train"),
-            "test": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/test"),
+    
+        # Download the individual annotation files for train and test
+        annotation_urls = {
+            "train": _ANNOTATION_REPO + "/train/",
+            "test": _ANNOTATION_REPO + "/test/",
         }
-
+    
+        # Download image files
+        images = {
+            "train": dl_manager.iter_archive(image_urls["train"]),
+            "test": dl_manager.iter_archive(image_urls["test"]),
+        }
+    
+        # Download annotation files
+        annotations = {
+            "train": dl_manager.download_and_extract(annotation_urls["train"]),
+            "test": dl_manager.download_and_extract(annotation_urls["test"]),
+        }
+    
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(image_paths["train"]),  
-                    "annotation_path": annotation_paths["train"],
+                    "images": images["train"],
+                    "annotation_path": annotations["train"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(image_paths["test"]), 
-                    "annotation_path": annotation_paths["test"],
+                    "images": images["test"],
+                    "annotation_path": annotations["test"],
                 },
             ),
         ]
-
+    
     def _generate_examples(self, images, annotation_path):
         """Generate dataset examples by matching images to their corresponding annotations."""
         
-        # Load ALL annotations into memory before iterating over images
         annotations = {}
-
-        for ann_file in os.listdir(annotation_path):  # Iterate over all annotation files
+        
+        # Load all annotations into memory
+        for ann_file in os.listdir(annotation_path):
+            video_name = os.path.splitext(ann_file)[0]  # Get video folder name
             ann_path = os.path.join(annotation_path, ann_file)
-            video_name = os.path.splitext(ann_file)[0]  # Extract video folder name
-
+            
             with open(ann_path, "r", encoding="utf-8") as f:
                 for line in f:
                     parts = line.strip().split()
                     if len(parts) < 8:
                         continue
-
+    
                     frame_id, track_id, class_name = parts[:3]
                     bbox = list(map(float, parts[4:8]))
                     class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
                     img_name = f"{frame_id}.jpg"
-
-                    # Store annotation in a simple dictionary
+    
+                    # Store annotation in a dictionary
                     key = f"{video_name}/{img_name}"
                     if key not in annotations:
                         annotations[key] = []
-                    
+    
                     annotations[key].append(
                         {
                             "bbox": bbox,
@@ -131,14 +141,14 @@ class EMT(datasets.GeneratorBasedBuilder):
                             "class_name": class_name,
                         }
                     )
-
+        
         # Yield dataset entries
         idx = 0
         for file_path, file_obj in images:
             img_name = os.path.basename(file_path)
-            video_name = os.path.basename(os.path.dirname(file_path))
-            key = f"{video_name}/{img_name}"  # Match image to preloaded annotations
-
+            video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
+            key = f"{video_name}/{img_name}"
+    
             if key in annotations:
                 yield idx, {
                     "image": {"path": file_path, "bytes": file_obj.read()},