Revert "refactor: parallelizate fl annotation processing (#182)"

This reverts commit 3872c99.
tier4 · Jan 1, 2025 · 37366d3 · 37366d3
1 parent 1335cb4
commit 37366d3
Showing 1 changed file with 55 additions and 85 deletions.
diff --git a/perception_dataset/fastlabel_to_t4/fastlabel_2d_to_t4_converter.py b/perception_dataset/fastlabel_to_t4/fastlabel_2d_to_t4_converter.py
@@ -1,6 +1,5 @@
 import base64
 from collections import defaultdict
-from concurrent.futures import ProcessPoolExecutor, as_completed
 import json
 import os.path as osp
 from pathlib import Path
@@ -191,96 +190,67 @@ def _format_fastlabel_annotation(self, annotations: Dict[str, List[Dict[str, Any
         fl_annotations: Dict[str, Dict[int, List[Dict[str, Any]]]] = {}
 
         for filename, ann_list in sorted(annotations.items()):
-            dataset_name = Path(filename).stem
-            # Process annotations for each file in parallel
-            file_annotations = self._process_annotations_for_file(ann_list)
-
-            # Combine results
-            if dataset_name not in fl_annotations:
-                fl_annotations[dataset_name] = defaultdict(list)
-
-            for file_id, ann_list in file_annotations.items():
-                fl_annotations[dataset_name][file_id].extend(ann_list)
-
-        return fl_annotations
-
-    def process_annotation(self, a, frame_no, camera, width, height):
-        occlusion_state = "occlusion_state.none"
-        visibility = "Not available"
-        instance_id = ""
-
-        # process attributes
-        for att in a["attributes"]:
-            if att["key"] == "id":
-                instance_id = att["value"]
-            if "occlusion_state" in att["key"]:
-                for v in att["value"]:
-                    if frame_no in range(v[0], v[1]):
-                        occlusion_state = "occlusion_state." + att["key"].split("_")[-1]
-                        visibility = self._convert_occlusion_to_visibility(
-                            att["key"].split("_")[-1]
-                        )
-                        break
-
-        # category and label generation
-        category_label = self._label_converter.convert_label(a["title"])
-        label_t4_dict = {
-            "category_name": category_label,
-            "instance_id": instance_id,
-            "attribute_names": [occlusion_state],
-            "visibility_name": visibility,
-        }
-
-        # bbox or segmentation type processing
-        if a["type"] == "bbox":
-            label_t4_dict.update(
-                {
-                    "two_d_box": a["points"],
-                    "sensor_id": self._camera2idx[camera],
-                }
-            )
-        elif a["type"] == "segmentation":
-            label_t4_dict.update(
-                {
-                    "two_d_segmentation": _rle_from_points(a["points"], width, height),
-                    "sensor_id": self._camera2idx[camera],
-                }
-            )
-            if (
-                self._label_converter.is_object_label(category_label)
-                and category_label not in self._surface_categories
-            ):
-                label_t4_dict["two_d_box"] = _convert_polygon_to_bbox(a["points"][0][0])
-                print(f"Converted polygon to bbox for {category_label}")
-
-        return label_t4_dict
+            dataset_name: str = Path(filename).stem
+            for ann in ann_list:
+                filename: str = ann["name"].split("/")[-1]
+                file_id: int = int(filename.split(".")[0])
+                frame_no: int = file_id + 1
+                camera: str = ann["name"].split("/")[-2]
 
-    def _process_annotations_for_file(self, ann_list):
-        file_annotations = defaultdict(list)
+                if dataset_name not in fl_annotations:
+                    fl_annotations[dataset_name] = defaultdict(list)
 
-        # parallelize for each annotation in the file
-        with ProcessPoolExecutor() as executor:
-            futures = []
-            for ann in ann_list:
-                filename = ann["name"].split("/")[-1]
-                file_id = int(filename.split(".")[0])
-                frame_no = file_id + 1
-                camera = ann["name"].split("/")[-2]
-                width = ann["width"]
-                height = ann["height"]
+                width: int = ann["width"]
+                height: int = ann["height"]
 
                 for a in ann["annotations"]:
-                    futures.append(
-                        executor.submit(
-                            self.process_annotation, a, frame_no, camera, width, height
+                    occlusion_state: str = "occlusion_state.none"
+                    visibility: str = "Not available"
+                    instance_id = ""
+                    for att in a["attributes"]:
+                        if att["key"] == "id":
+                            instance_id = att["value"]
+                        if "occlusion_state" in att["key"]:
+                            for v in att["value"]:
+                                if frame_no in range(v[0], v[1]):
+                                    occlusion_state = (
+                                        "occlusion_state." + att["key"].split("_")[-1]
+                                    )
+                                    visibility = self._convert_occlusion_to_visibility(
+                                        att["key"].split("_")[-1]
+                                    )
+                                    break
+                    category_label = self._label_converter.convert_label(a["title"])
+                    label_t4_dict: Dict[str, Any] = {
+                        "category_name": category_label,
+                        "instance_id": instance_id,
+                        "attribute_names": [occlusion_state],
+                        "visibility_name": visibility,
+                    }
+                    if a["type"] == "bbox":
+                        label_t4_dict.update(
+                            {
+                                "two_d_box": a["points"],
+                                "sensor_id": self._camera2idx[camera],
+                            }
                         )
-                    )
-
-            for future in as_completed(futures):
-                label_t4_dict = future.result()
-                file_annotations[file_id].append(label_t4_dict)
+                    elif a["type"] == "segmentation":
+                        label_t4_dict.update(
+                            {
+                                "two_d_segmentation": _rle_from_points(a["points"], width, height),
+                                "sensor_id": self._camera2idx[camera],
+                            }
+                        )
+                        if (
+                            self._label_converter.is_object_label(category_label)
+                            and category_label not in self._surface_categories
+                        ):
+                            label_t4_dict["two_d_box"] = _convert_polygon_to_bbox(
+                                a["points"][0][0]
+                            )
+                    fl_annotations[dataset_name][file_id].append(label_t4_dict)
 
-        return file_annotations
+        return fl_annotations
 
 
 def _rle_from_points(points: Points2DLike, width: int, height: int) -> Dict[str, Any]: