Add 'metadata' field representing data row metadata as DataRowMetadataFields

Author: Kevin Kim

labelbox/orm/db_object.py

@@ -70,6 +70,15 @@ def _set_field_values(self, field_values):
                         "field %s", value, field)
             elif isinstance(field.field_type, Field.EnumType):
                 value = field.field_type.enum_cls(value)
+            elif isinstance(field.field_type,
+                            Field.ListType) and field.name == "metadata":
+                mdo = self.client.get_data_row_metadata_ontology()
+                try:
+                    value = mdo.parse_metadata_fields(value)
+                except ValueError:
+                    logger.warning(
+                        "Failed to convert value '%s' to metadata for field %s",
+                        value, field)
             setattr(self, field.name, value)
 
     def __repr__(self):

labelbox/schema/data_row.py

@@ -36,10 +36,15 @@ class DataRow(DbObject, Updateable, BulkDeletable):
     created_at = Field.DateTime("created_at")
     media_attributes = Field.Json("media_attributes")
     metadata_fields = Field.List(
-        DataRowMetadataField,
+        dict,
         graphql_type="DataRowCustomMetadataUpsertInput!",
         name="metadata_fields",
         result_subquery="metadataFields { schemaId name value kind }")
+    metadata = Field.List(DataRowMetadataField,
+                          graphql_type="DataRowCustomMetadataUpsertInput!",
+                          name="metadata",
+                          graphql_name="customMetadata",
+                          result_subquery="customMetadata { schemaId value }")
 
     # Relationships
     dataset = Relationship.ToOne("Dataset")

labelbox/schema/data_row_metadata.py

@@ -1,6 +1,5 @@
 # type: ignore
 from datetime import datetime
-import warnings
 from copy import deepcopy
 from enum import Enum
 from itertools import chain
@@ -224,34 +223,43 @@ def parse_metadata(
 
         for dr in unparsed:
             fields = []
-            for f in dr["fields"]:
-                if f["schemaId"] not in self.fields_by_id:
-                    # Update metadata ontology if field can't be found
-                    self.refresh_ontology()
-                    if f["schemaId"] not in self.fields_by_id:
-                        raise ValueError(
-                            f"Schema Id `{f['schemaId']}` not found in ontology"
-                        )
-
-                schema = self.fields_by_id[f["schemaId"]]
-                if schema.kind == DataRowMetadataKind.enum:
-                    continue
-                elif schema.kind == DataRowMetadataKind.option:
-                    field = DataRowMetadataField(schema_id=schema.parent,
-                                                 value=schema.uid)
-                elif schema.kind == DataRowMetadataKind.datetime:
-                    field = DataRowMetadataField(
-                        schema_id=schema.uid,
-                        value=datetime.fromisoformat(f["value"][:-1] +
-                                                     "+00:00"))
-                else:
-                    field = DataRowMetadataField(schema_id=schema.uid,
-                                                 value=f["value"])
-                fields.append(field)
+            if "fields" in dr:
+                fields = self.parse_metadata_fields(dr["fields"])
             parsed.append(
                 DataRowMetadata(data_row_id=dr["dataRowId"], fields=fields))
         return parsed
 
+    def parse_metadata_fields(
+            self, unparsed: List[Dict[str,
+                                      Dict]]) -> List[DataRowMetadataField]:
+        parsed = []
+        if isinstance(unparsed, dict):
+            raise ValueError("Pass a list of dictionaries")
+
+        for f in unparsed:
+            if f["schemaId"] not in self.fields_by_id:
+                # Update metadata ontology if field can't be found
+                self.refresh_ontology()
+                if f["schemaId"] not in self.fields_by_id:
+                    raise ValueError(
+                        f"Schema Id `{f['schemaId']}` not found in ontology")
+
+            schema = self.fields_by_id[f["schemaId"]]
+            if schema.kind == DataRowMetadataKind.enum:
+                continue
+            elif schema.kind == DataRowMetadataKind.option:
+                field = DataRowMetadataField(schema_id=schema.parent,
+                                             value=schema.uid)
+            elif schema.kind == DataRowMetadataKind.datetime:
+                field = DataRowMetadataField(
+                    schema_id=schema.uid,
+                    value=datetime.fromisoformat(f["value"][:-1] + "+00:00"))
+            else:
+                field = DataRowMetadataField(schema_id=schema.uid,
+                                             value=f["value"])
+            parsed.append(field)
+        return parsed
+
     def bulk_upsert(
             self, metadata: List[DataRowMetadata]
     ) -> List[DataRowMetadataBatchResponse]:

tests/integration/test_data_row_metadata.py

@@ -1,4 +1,3 @@
-import time
 from datetime import datetime
 
 import pytest
@@ -282,3 +281,34 @@ def test_parse_raw_metadata(mdo):
     for row in parsed:
         for field in row.fields:
             assert mdo._parse_upsert(field)
+
+
+def test_parse_raw_metadata_fields(mdo):
+    example = [
+        {
+            'schemaId': 'cko8s9r5v0001h2dk9elqdidh',
+            'value': 'my-new-message'
+        },
+        {
+            'schemaId': 'cko8sbczn0002h2dkdaxb5kal',
+            'value': {}
+        },
+        {
+            'schemaId': 'cko8sbscr0003h2dk04w86hof',
+            'value': {}
+        },
+        {
+            'schemaId': 'cko8sdzv70006h2dk8jg64zvb',
+            'value': '2021-07-20T21:41:14.606710Z'
+        },
+        {
+            'schemaId': FAKE_SCHEMA_ID,
+            'value': 0.5
+        },
+    ]
+
+    parsed = mdo.parse_metadata_fields(example)
+    assert len(parsed) == 4
+
+    for field in parsed:
+        assert mdo._parse_upsert(field)

tests/integration/test_data_rows.py

@@ -1,7 +1,5 @@
-import imghdr
 from tempfile import NamedTemporaryFile
 import uuid
-import time
 from datetime import datetime
 
 import pytest
@@ -22,6 +20,14 @@
 ].sort()
 
 
+@pytest.fixture
+def mdo(client):
+    mdo = client.get_data_row_metadata_ontology()
+    mdo._raw_ontology = mdo._get_ontology()
+    mdo._build_ontology()
+    yield mdo
+
+
 def make_metadata_fields():
     embeddings = [0.0] * 128
     msg = "A message"
@@ -57,12 +63,6 @@ def make_metadata_fields_dict():
     return fields
 
 
-def filter_precomputed_embeddings(metadata_fields):
-    return list(
-        filter(lambda md: md["name"] != "precomputedImageEmbedding",
-               metadata_fields))
-
-
 def test_get_data_row(datarow, client):
     assert client.get_data_row(datarow.uid)
 
@@ -235,7 +235,7 @@ def test_create_data_row_with_invalid_input(dataset, image_url):
         dataset.create_data_row(dr, row_data=image_url)
 
 
-def test_create_data_row_with_metadata(dataset, image_url):
+def test_create_data_row_with_metadata(mdo, dataset, image_url):
     client = dataset.client
     assert len(list(dataset.data_rows())) == 0
 
@@ -249,13 +249,17 @@ def test_create_data_row_with_metadata(dataset, image_url):
     assert requests.get(image_url).content == \
         requests.get(data_row.row_data).content
     assert data_row.media_attributes is not None
-    filtered_md_fields = filter_precomputed_embeddings(data_row.metadata_fields)
-    assert len(filtered_md_fields) == 4
-    assert [m["schemaId"] for m in filtered_md_fields
+    metadata_fields = data_row.metadata_fields
+    metadata = data_row.metadata
+    assert len(metadata_fields) == 4
+    assert len(metadata) == 4
+    assert [m["schemaId"] for m in metadata_fields
            ].sort() == EXPECTED_METADATA_SCHEMA_IDS
+    for m in metadata:
+        assert mdo._parse_upsert(m)
 
 
-def test_create_data_row_with_metadata_dict(dataset, image_url):
+def test_create_data_row_with_metadata_dict(mdo, dataset, image_url):
     client = dataset.client
     assert len(list(dataset.data_rows())) == 0
 
@@ -269,10 +273,14 @@ def test_create_data_row_with_metadata_dict(dataset, image_url):
     assert requests.get(image_url).content == \
         requests.get(data_row.row_data).content
     assert data_row.media_attributes is not None
-    filtered_md_fields = filter_precomputed_embeddings(data_row.metadata_fields)
-    assert len(filtered_md_fields) == 4
-    assert [m["schemaId"] for m in filtered_md_fields
+    metadata_fields = data_row.metadata_fields
+    metadata = data_row.metadata
+    assert len(metadata_fields) == 4
+    assert len(metadata) == 4
+    assert [m["schemaId"] for m in metadata_fields
            ].sort() == EXPECTED_METADATA_SCHEMA_IDS
+    for m in metadata:
+        assert mdo._parse_upsert(m)
 
 
 def test_create_data_row_with_invalid_metadata(dataset, image_url):
@@ -284,7 +292,7 @@ def test_create_data_row_with_invalid_metadata(dataset, image_url):
         dataset.create_data_row(row_data=image_url, metadata_fields=fields)
 
 
-def test_create_data_rows_with_metadata(dataset, image_url):
+def test_create_data_rows_with_metadata(mdo, dataset, image_url):
     client = dataset.client
     assert len(list(dataset.data_rows())) == 0
 
@@ -322,11 +330,14 @@ def test_create_data_rows_with_metadata(dataset, image_url):
             requests.get(row.row_data).content
         assert row.media_attributes is not None
 
-        # Remove 'precomputedImageEmbedding' metadata if automatically added
-        filtered_md_fields = filter_precomputed_embeddings(row.metadata_fields)
-        assert len(filtered_md_fields) == 4
-        assert [m["schemaId"] for m in filtered_md_fields
+        metadata_fields = row.metadata_fields
+        metadata = row.metadata
+        assert len(metadata_fields) == 4
+        assert len(metadata) == 4
+        assert [m["schemaId"] for m in metadata_fields
                ].sort() == EXPECTED_METADATA_SCHEMA_IDS
+        for m in metadata:
+            assert mdo._parse_upsert(m)
 
 
 def test_create_data_rows_with_invalid_metadata(dataset, image_url):