Huge optimization to annotation import library (#1727)

Author: Gabefire

libs/labelbox/tests/data/annotation_import/conftest.py

@@ -787,7 +787,6 @@ def rectangle_inference_with_confidence(prediction_id_mapping):
         if "rectangle_nested" not in feature:
             continue
         rectangle = feature["rectangle_nested"].copy()
-        print(rectangle)
         rectangle.update({
             "bbox": {
                 "top": 48,

libs/labelbox/tests/data/annotation_import/test_annotation_import_limit.py

@@ -0,0 +1,33 @@
+import itertools
+import uuid
+from labelbox.schema.annotation_import import AnnotationImport, MALPredictionImport
+from labelbox.schema.media_type import MediaType
+import pytest
+from unittest.mock import patch
+
+
+@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 1)
+def test_above_annotation_limit_on_single_import_on_single_data_row(annotations_by_media_type):
+
+    annotations_ndjson =  list(itertools.chain.from_iterable(annotations_by_media_type[MediaType.Image]))
+    data_row_id = annotations_ndjson[0]["dataRow"]["id"]
+
+    data_row_annotations = [annotation for annotation in annotations_ndjson if annotation["dataRow"]["id"] == data_row_id and "bbox" in annotation]
+    
+    with pytest.raises(ValueError):
+        AnnotationImport._validate_data_rows([data_row_annotations[0]]*2)
+
+
+@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 1)
+def test_above_annotation_limit_divided_among_different_rows(annotations_by_media_type):
+
+    annotations_ndjson =  list(itertools.chain.from_iterable(annotations_by_media_type[MediaType.Image]))
+    data_row_id = annotations_ndjson[0]["dataRow"]["id"]
+    
+    first_data_row_annotation = [annotation for annotation in annotations_ndjson if annotation["dataRow"]["id"] == data_row_id and "bbox" in annotation][0]
+    
+    second_data_row_annotation = first_data_row_annotation.copy()
+    second_data_row_annotation["dataRow"]["id"] == "data_row_id_2"
+    
+    with pytest.raises(ValueError):
+        AnnotationImport._validate_data_rows([first_data_row_annotation, second_data_row_annotation]*2)

libs/labelbox/tests/data/annotation_import/test_bulk_import_request.py

@@ -219,110 +219,3 @@ def test_delete(module_project, predictions):
     bulk_import_request.delete()
     all_import_requests = module_project.bulk_import_requests()
     assert len(list(all_import_requests)) == 0
-
-
-def _create_label(row_index, data_row_uids, label_name_ids=['bbox']):
-    label_name = label_name_ids[row_index % len(label_name_ids)]
-    data_row_uid = data_row_uids[row_index % len(data_row_uids)]
-    return Label(data=GenericDataRowData(uid=data_row_uid),
-                 annotations=[
-                     VideoObjectAnnotation(name=label_name,
-                                           keyframe=True,
-                                           frame=4,
-                                           segment_index=0,
-                                           value=Rectangle(
-                                               start=Point(x=100, y=100),
-                                               end=Point(x=105, y=105),
-                                           ))
-                 ])
-
-
-@pytest.mark.parametrize("configured_project", [MediaType.Video], indirect = True)
-@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 20)
-def test_below_annotation_limit_on_single_data_row(
-        client, configured_project, video_data, rand_gen):
-    _, data_row_uids = video_data
-    configured_project.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [_create_label(index, data_row_uids) for index in range(19)]
-    import_annotations = MALPredictionImport.create_from_objects(
-        client=client,
-        project_id=configured_project.uid,
-        name=f"import {str(uuid.uuid4())}",
-        predictions=labels)
-    import_annotations.wait_until_done()
-
-    assert import_annotations.errors == []
-
-
-@pytest.mark.parametrize("configured_project", [MediaType.Video], indirect = True)
-@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 20)
-def test_above_annotation_limit_on_single_label_on_single_data_row(
-        client, configured_project, video_data, rand_gen):
-    _, data_row_uids = video_data
-
-    configured_project.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [_create_label(index, data_row_uids) for index in range(21)]
-    with pytest.raises(ValueError):
-        import_annotations = MALPredictionImport.create_from_objects(
-            client=client,
-            project_id=configured_project.uid,
-            name=f"import {str(uuid.uuid4())}",
-            predictions=labels)
-        import_annotations.wait_until_done()
-
-@pytest.mark.parametrize("configured_project", [MediaType.Video], indirect = True)
-@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 20)
-def test_above_annotation_limit_divided_among_different_rows(
-        client, configured_project, video_data_100_rows,
-        rand_gen):
-    _, data_row_uids = video_data_100_rows
-
-    configured_project.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [_create_label(index, data_row_uids) for index in range(21)]
-
-    import_annotations = MALPredictionImport.create_from_objects(
-        client=client,
-        project_id=configured_project.uid,
-        name=f"import {str(uuid.uuid4())}",
-        predictions=labels)
-
-    assert import_annotations.errors == []
-
-
-@pytest.mark.parametrize("configured_project", [MediaType.Video], indirect = True)
-@patch('labelbox.schema.annotation_import.ANNOTATION_PER_LABEL_LIMIT', 20)
-def test_above_annotation_limit_divided_among_labels_on_one_row(
-        client, configured_project, video_data, rand_gen):
-    _, data_row_uids = video_data
-
-    configured_project.create_batch(
-        rand_gen(str),
-        data_row_uids,  # sample of data row objects
-        5  # priority between 1(Highest) - 5(lowest)
-    )
-    labels = [
-        _create_label(index,
-                      data_row_uids,
-                      label_name_ids=['bbox', 'bbox_tool_with_nested_text'])
-        for index in range(21)
-    ]
-
-    import_annotations = MALPredictionImport.create_from_objects(
-        client=client,
-        project_id=configured_project.uid,
-        name=f"import {str(uuid.uuid4())}",
-        predictions=labels)
-
-    assert import_annotations.errors == []