🐛 fix inconsistent image channel

robomind2lerobot/robomind_h5.py

@@ -144,9 +144,7 @@ class RoboMINDDataset(LeRobotDataset):
         # Add frame features to episode_buffer
         for key, value in frame.items():
             if key not in self.features:
-                raise ValueError(
+                raise ValueError(f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'.")
-                    f"An element of the frame is not in the features. '{key}' not in '{self.features.keys()}'."
-                )
 
             if self.features[key]["dtype"] in ["video"]:
                 img_path = self._get_image_file_path(
@@ -161,9 +159,7 @@ class RoboMINDDataset(LeRobotDataset):
 
         self.episode_buffer["size"] += 1
 
-    def save_episode(
+    def save_episode(self, split, action_config: dict, episode_data: dict | None = None, keep_images: bool = False) -> None:
-        self, split, action_config: dict, episode_data: dict | None = None, keep_images: bool = False
-    ) -> None:
         """
         This will save to disk the current episode in self.episode_buffer.
 
@@ -254,35 +250,13 @@ def save_as_lerobot_dataset(task: tuple[dict, Path, str], src_path, benchmark, e
     task_type, splits, local_dir, task_instruction = task
 
     config = ROBOMIND_CONFIG[embodiment]
-    # HACK:
+    features = generate_features_from_config(config)
-    # 1. not consistent image shape...
+
-    # 2. franka and ur image is bgr...
+    # [HACK]: franka and ur image is bgr...
     bgr2rgb = False
     if embodiment in ["franka_1rgb", "franka_3rgb", "franka_fr3_dual", "ur_1rgb"]:
         bgr2rgb = True
 
-    if "1_0" in benchmark:
-        match embodiment:
-            case "tienkung_gello_1rgb":
-                if task_type in (
-                    "clean_table_2_241211",
-                    "clean_table_3_241210",
-                    "clean_table_3_241211",
-                    "place_paper_cup_dustbin_241212",
-                    "place_plate_table_241211",
-                    "place_plate_table_241211_12",
-                    "place_plate_table_241212",
-                ):
-                    for value in config["images"].values():
-                        value["shape"] = (720, 1280) + (value["shape"][2],)
-
-            case "tienkung_xsens_1rgb":
-                if task_type == "switch_manipulation":
-                    for value in config["images"].values():
-                        value["shape"] = (720, 1280) + (value["shape"][2],)
-
-    features = generate_features_from_config(config)
-
     if local_dir.exists():
         shutil.rmtree(local_dir)
 
@@ -312,14 +286,27 @@ def save_as_lerobot_dataset(task: tuple[dict, Path, str], src_path, benchmark, e
         for episode_path in path.glob("**/trajectory.hdf5"):
             status, raw_dataset, err = load_local_dataset(episode_path, config, save_depth, bgr2rgb)
             if status and len(raw_dataset) >= 50:
-                for frame_data in raw_dataset:
+                try:
-                    dataset.add_frame(frame_data, task_instruction)
+                    for frame_data in raw_dataset:
-                dataset.save_episode(split, action_config.get(episode_path.parent.parent.name, {}))
+                        dataset.add_frame(frame_data, task_instruction)
-                logging.info(f"process done for {path}, len {len(raw_dataset)}")
+                    dataset.save_episode(split, action_config.get(episode_path.parent.parent.name, {}))
+                    logging.info(f"process done for {path}, len {len(raw_dataset)}")
+                except Exception:
+                    # [HACK]: not consistent image shape...
+                    if config["images"]["camera_top"]["shape"] == (720, 1280, 3):
+                        config["images"]["camera_top"]["shape"] = (480, 640, 3)
+                        config["images"]["camera_top_depth"]["shape"] = (480, 640, 1)
+                    else:
+                        config["images"]["camera_top"]["shape"] = (720, 1280, 3)
+                        config["images"]["camera_top_depth"]["shape"] = (720, 1280, 1)
+                    save_as_lerobot_dataset(task, src_path, benchmark, embodiment, save_depth)
+                    return
             else:
                 logging.warning(f"Skipped {episode_path}: len of dataset:{len(raw_dataset)} or {str(err)}")
             gc.collect()
 
+    if dataset.meta.total_episodes == 0:
+        shutil.rmtree(local_dir)
     del dataset