Make Grounding DINO DetectionResult a Pydantic model.

invokeai/backend/grounded_sam/detection_result.py

@@ -1,11 +1,10 @@
-from dataclasses import dataclass
 from typing import Any, Optional
 
 import numpy.typing as npt
+from pydantic import BaseModel, ConfigDict
 
 
-@dataclass
-class BoundingBox:
+class BoundingBox(BaseModel):
     """Bounding box helper class."""
 
     xmin: int
@@ -18,24 +17,14 @@ class BoundingBox:
         return [self.xmin, self.ymin, self.xmax, self.ymax]
 
 
-@dataclass
-class DetectionResult:
+class DetectionResult(BaseModel):
     """Detection result from Grounding DINO or Grounded SAM."""
 
     score: float
     label: str
     box: BoundingBox
     mask: Optional[npt.NDArray[Any]] = None
-
-    @classmethod
-    def from_dict(cls, detection_dict: dict[str, Any]):
-        return cls(
-            score=detection_dict["score"],
-            label=detection_dict["label"],
-            box=BoundingBox(
-                xmin=detection_dict["box"]["xmin"],
-                ymin=detection_dict["box"]["ymin"],
-                xmax=detection_dict["box"]["xmax"],
-                ymax=detection_dict["box"]["ymax"],
-            ),
-        )
+    model_config = ConfigDict(
+        # Allow arbitrary types for mask, since it will be a numpy array.
+        arbitrary_types_allowed=True
+    )

invokeai/backend/grounded_sam/grounding_dino_pipeline.py

@@ -17,7 +17,7 @@ class GroundingDinoPipeline:
 
     def detect(self, image: Image.Image, candidate_labels: list[str], threshold: float = 0.1) -> list[DetectionResult]:
         results = self._pipeline(image=image, candidate_labels=candidate_labels, threshold=threshold)
-        results = [DetectionResult.from_dict(result) for result in results]
+        results = [DetectionResult.model_validate(result) for result in results]
         return results
 
     def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> "GroundingDinoPipeline":