option to not use process pool

Author: Matt Sokoloff

labelbox/data/serialization/coco/converter.py

@@ -51,7 +51,7 @@ def serialize_instances(labels: LabelCollection,
             image_root: Where to save images to
             ignore_existing_data: Whether or not to raise an exception if images already exist.
                 This exists only to support detectons panoptic fpn model which requires two mscoco payloads for the same images.
-            max_workers : Number of workers to process dataset with
+            max_workers : Number of workers to process dataset with. A value of 0 will process all data in the main process
         Returns:
             A dictionary containing labels in the coco object format.
         """
@@ -66,7 +66,7 @@ def serialize_panoptic(labels: LabelCollection,
                            mask_root: Union[Path, str],
                            all_stuff: bool = False,
                            ignore_existing_data=False,
-                           max_workers=8) -> Dict[str, Any]:
+                           max_workers: int = 8) -> Dict[str, Any]:
         """
         Convert a Labelbox LabelCollection into an mscoco dataset.
         This function will only convert masks, polygons, and rectangles.
@@ -81,7 +81,7 @@ def serialize_panoptic(labels: LabelCollection,
                 To convert them to stuff class set `all_stuff=True`.
             ignore_existing_data: Whether or not to raise an exception if images already exist.
                 This exists only to support detectons panoptic fpn model which requires two mscoco payloads for the same images.
-            max_workers : Number of workers to process dataset with
+            max_workers : Number of workers to process dataset with. A value of 0 will process all data in the main process.
         Returns:
             A dictionary containing labels in the coco panoptic format.
         """

labelbox/data/serialization/coco/instance_dataset.py

@@ -135,16 +135,25 @@ def from_common(cls,
         futures = []
         coco_categories = {}
 
-        with ProcessPoolExecutor(max_workers=max_workers) as exc:
-            futures = [
-                exc.submit(process_label, label, idx, image_root)
+        if max_workers:
+            with ProcessPoolExecutor(max_workers=max_workers) as exc:
+                futures = [
+                    exc.submit(process_label, label, idx, image_root)
+                    for idx, label in enumerate(labels)
+                ]
+                results = [
+                    future.result() for future in tqdm(as_completed(futures))
+                ]
+        else:
+            results = [
+                process_label(label, idx, image_root)
                 for idx, label in enumerate(labels)
             ]
-            for future in tqdm(as_completed(futures)):
-                image, annotations, categories = future.result()
-                images.append(image)
-                all_coco_annotations.extend(annotations)
-                coco_categories.update(categories)
+
+        for result in results:
+            images.append(result[0])
+            all_coco_annotations.extend(result[1])
+            coco_categories.update(result[2])
 
         category_mapping = {
             category_id: idx + 1

labelbox/data/serialization/coco/panoptic_dataset.py

@@ -113,17 +113,27 @@ def from_common(cls,
         coco_categories = {}
         coco_things = {}
         images = []
-        with ProcessPoolExecutor(max_workers=max_workers) as exc:
-            futures = [
-                exc.submit(process_label, label, idx, image_root, mask_root,
-                           all_stuff) for idx, label in enumerate(labels)
+
+        if max_workers:
+            with ProcessPoolExecutor(max_workers=max_workers) as exc:
+                futures = [
+                    exc.submit(process_label, label, idx, image_root, mask_root,
+                               all_stuff) for idx, label in enumerate(labels)
+                ]
+                results = [
+                    future.result() for future in tqdm(as_completed(futures))
+                ]
+        else:
+            results = [
+                process_label(label, idx, image_root, mask_root, all_stuff)
+                for idx, label in enumerate(labels)
             ]
-            for future in tqdm(as_completed(futures)):
-                image, annotation, categories, things = future.result()
-                images.append(image)
-                all_coco_annotations.append(annotation)
-                coco_categories.update(categories)
-                coco_things.update(things)
+
+        for result in results:
+            images.append(result[0])
+            all_coco_annotations.extend(result[1])
+            coco_categories.update(result[2])
+            coco_things.update(result[3])
 
         category_mapping = {
             category_id: idx + 1