Merge pull request #671 from Labelbox/develop

3.26.0

Author: msokoloff1

CHANGELOG.md

@@ -1,4 +1,13 @@
 # Changelog
+
+# Version 3.26.0 (2022-08-12)
+## Added
+* `Batch.delete()` which will delete an existing `Batch`
+* `Batch.delete_labels()`  which will delete all `Label`’s created after a `Project`’s mode has been set to batch.
+    * Note: Does not include labels that were imported via model-assisted labeling or label imports
+* Support for creating model config when creating a model run
+* `RAW_TEXT` and `TEXT_FILE` attachment types to replace the `TEXT` type.
+
 # Version 3.25.3 (2022-08-10)
 ## Fixed
 * Label export will continue polling if the downloadUrl is None
@@ -29,7 +38,7 @@
     * `Batch.export_data_rows(include_metadata=True)`
     * `Dataset.export_data_rows(include_metadata=True)`
     * `Project.export_queued_data_rows(include_metadata=True)`
-* `VideoObjectAnnotation` has `segment_index` to group video annotations into video segments    
+* `VideoObjectAnnotation` has `segment_index` to group video annotations into video segments
 
 ## Removed
 * `Project.video_label_generator`. Use `Project.label_generator` instead.
@@ -40,7 +49,7 @@
     * 150,000 rows per upload without metadata
     * 30,000 rows per upload with metadata
 
-    
+
 # Version 3.24.1 (2022-07-07)
 ## Updated
 * Added `refresh_ontology()` as part of create/update/delete metadata schema functions
@@ -657,4 +666,4 @@ Changelog not maintained before version 2.2.
 
 ### Changed
 * `Model.create_model_run()`
-    * Add training metadata config as a model run creation param
+    * Add training metadata config as a model run creation param

labelbox/__init__.py

@@ -1,5 +1,5 @@
 name = "labelbox"
-__version__ = "3.25.3"
+__version__ = "3.26.0"
 
 from labelbox.client import Client
 from labelbox.schema.project import Project

labelbox/data/serialization/coco/instance_dataset.py

@@ -28,15 +28,14 @@ def mask_to_coco_object_annotation(
     xmin, ymin, xmax, ymax = shapely.bounds
     # Iterate over polygon once or multiple polygon for each item
     area = shapely.area
-    if shapely.type == 'Polygon':
-        shapely = [shapely]
 
     return COCOObjectAnnotation(
         id=annot_idx,
         image_id=image_id,
         category_id=category_id,
         segmentation=[
-            np.array(s.exterior.coords).ravel().tolist() for s in shapely
+            np.array(s.exterior.coords).ravel().tolist()
+            for s in ([shapely] if shapely.type == "Polygon" else shapely.geoms)
         ],
         area=area,
         bbox=[xmin, ymin, xmax - xmin, ymax - ymin],

labelbox/schema/asset_attachment.py

@@ -9,7 +9,7 @@ class AssetAttachment(DbObject):
     """ Asset attachment provides extra context about an asset while labeling.
 
     Attributes:
-        attachment_type (str): IMAGE, VIDEO, TEXT, IMAGE_OVERLAY, or HTML
+        attachment_type (str): IMAGE, VIDEO, IMAGE_OVERLAY, HTML, RAW_TEXT, or TEXT_URL. TEXT attachment type is deprecated.
         attachment_value (str): URL to an external file or a string of text
     """
 
@@ -19,6 +19,8 @@ class AttachmentType(Enum):
         TEXT = "TEXT"
         IMAGE_OVERLAY = "IMAGE_OVERLAY"
         HTML = "HTML"
+        RAW_TEXT = "RAW_TEXT"
+        TEXT_URL = "TEXT_URL"
 
     for topic in AttachmentType:
         vars()[topic.name] = topic.value

labelbox/schema/batch.py

@@ -68,14 +68,15 @@ def remove_queued_data_rows(self) -> None:
 
         project_id_param = "projectId"
         batch_id_param = "batchId"
-        self.client.execute("""mutation ArchiveBatchPyApi($%s: ID!, $%s: ID!) {
-            project(where: {id: $%s}) { archiveBatch(batchId: $%s) { id archivedAt } }
+        self.client.execute(
+            """mutation RemoveQueuedDataRowsFromBatchPyApi($%s: ID!, $%s: ID!) {
+            project(where: {id: $%s}) { removeQueuedDataRowsFromBatch(batchId: $%s) { id } }
         }""" % (project_id_param, batch_id_param, project_id_param,
                 batch_id_param), {
                     project_id_param: self.project_id,
                     batch_id_param: self.uid
                 },
-                            experimental=True)
+            experimental=True)
 
     def export_data_rows(self,
                          timeout_seconds=120,
@@ -125,3 +126,50 @@ def export_data_rows(self,
             logger.debug("Batch '%s' data row export, waiting for server...",
                          self.uid)
             time.sleep(sleep_time)
+
+    def delete(self) -> None:
+        """ Deletes the given batch.
+
+        Note: Batch deletion for batches that has labels is forbidden. 
+
+        Args:
+            batch (Batch): Batch to remove queued data rows from
+        """
+
+        project_id_param = "projectId"
+        batch_id_param = "batchId"
+        self.client.execute("""mutation DeleteBatchPyApi($%s: ID!, $%s: ID!) {
+            project(where: {id: $%s}) { deleteBatch(batchId: $%s) { deletedBatchId } }
+        }""" % (project_id_param, batch_id_param, project_id_param,
+                batch_id_param), {
+                    project_id_param: self.project_id,
+                    batch_id_param: self.uid
+                },
+                            experimental=True)
+
+    def delete_labels(self, set_labels_as_template=False) -> None:
+        """ Deletes labels that were created for data rows in the batch.
+
+        Args:
+            batch (Batch): Batch to remove queued data rows from
+            set_labels_as_template (bool): When set to true, the deleted labels will be kept as templates.
+        """
+
+        project_id_param = "projectId"
+        batch_id_param = "batchId"
+        type_param = "type"
+        res = self.client.execute(
+            """mutation DeleteBatchLabelsPyApi($%s: ID!, $%s: ID!, $%s: DeleteBatchLabelsType!) {
+            project(where: {id: $%s}) { deleteBatchLabels(batchId: $%s, data:{ type: $%s }) { deletedLabelIds } }
+        }""" % (project_id_param, batch_id_param, type_param, project_id_param,
+                batch_id_param, type_param), {
+                    project_id_param:
+                        self.project_id,
+                    batch_id_param:
+                        self.uid,
+                    type_param:
+                        "RequeueDataWithLabelAsTemplate"
+                        if set_labels_as_template else "RequeueData"
+                },
+            experimental=True)
+        return res

tests/integration/test_batch.py

@@ -50,6 +50,15 @@ def test_archive_batch(configured_project: Project, small_dataset: Dataset):
     assert len(exported_data_rows) == 0
 
 
+def test_delete(configured_project: Project, small_dataset: Dataset):
+    data_rows = [dr.uid for dr in list(small_dataset.export_data_rows())]
+    configured_project.update(queue_mode=Project.QueueMode.Batch)
+    batch = configured_project.create_batch("batch to delete", data_rows)
+    batch.delete()
+
+    assert len(list(configured_project.batches())) == 0
+
+
 def test_batch_project(configured_project: Project, small_dataset: Dataset):
     data_rows = [dr.uid for dr in list(small_dataset.export_data_rows())]
     configured_project.update(queue_mode=Project.QueueMode.Batch)
@@ -80,3 +89,29 @@ def test_export_data_rows(configured_project: Project, dataset: Dataset):
 
     assert len(result) == n_data_rows
     assert set(data_rows) == set(exported_data_rows)
+
+
+@pytest.mark.skip(
+    reason="Test cannot be used effectively with MAL/LabelImport. \
+Fix/Unskip after resolving deletion with MAL/LabelImport")
+def test_delete_labels(configured_project_with_label):
+    project, dataset, _, _ = configured_project_with_label
+
+    data_rows = [dr.uid for dr in list(dataset.export_data_rows())]
+    project.update(queue_mode=Project.QueueMode.Batch)
+    batch = project.create_batch("batch to delete labels", data_rows)
+
+
+@pytest.mark.skip(
+    reason="Test cannot be used effectively with MAL/LabelImport. \
+Fix/Unskip after resolving deletion with MAL/LabelImport")
+def test_delete_labels_with_templates(configured_project: Project,
+                                      small_dataset: Dataset):
+    data_rows = [dr.uid for dr in list(small_dataset.export_data_rows())]
+    configured_project.update(queue_mode=Project.QueueMode.Batch)
+    batch = configured_project.create_batch(
+        "batch to delete labels w templates", data_rows)
+    exported_data_rows = list(batch.export_data_rows())
+    res = batch.delete_labels(labels_as_template=True)
+    exported_data_rows = list(batch.export_data_rows())
+    assert len(exported_data_rows) == 5