Fix sample validation for complex types

Author: JortBergfeld

src/datumaro/experimental/dataset.py

@@ -10,6 +10,7 @@
 from functools import cache
 from typing import TYPE_CHECKING, Annotated, Any, Generic, TypeGuard, Union, cast, get_args, get_origin, get_type_hints
 
+import numpy as np
 import polars as pl
 from typing_extensions import TypeVar, dataclass_transform
 
@@ -86,7 +87,7 @@ def _validate_attribute_type(self, expected_type: Any, value: Any) -> bool:
             collections.abc.Callable,
         }:
             return callable(value)
-        return isinstance(value, expected_type)
+        return isinstance(value, origin or expected_type)
 
     @classmethod
     @cache

tests/unit/experimental/test_sample.py

@@ -5,6 +5,7 @@
 from typing import Any
 
 import numpy as np
+import numpy.typing as npt
 import polars as pl
 import pytest
 
@@ -17,6 +18,7 @@
     bbox_field,
     image_field,
     image_info_field,
+    score_field,
 )
 from datumaro.experimental.fields.images import image_path_field
 from datumaro.experimental.schema import Schema, Semantic
@@ -185,3 +187,11 @@ class ExtendedSample(BaseSample):
     assert len(extended_schema.attributes) == 3
     assert "image_info" in extended_schema.attributes
     assert "image_info" not in base_schema.attributes
+
+
+def test_sample_with_is_list():
+    class MySample(Sample):
+        confidence: npt.NDArray[np.float32] | None = score_field(dtype=pl.Float32(), is_list=True)
+
+    # Assert that sample can be created without validation errors
+    MySample(confidence=np.array([0.8]))