apply Black 25.11.0 style in fbcode/deeplearning/projects (21/92)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476315

fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f

sam3/agent/helpers/boxes.py

@@ -84,9 +84,9 @@ class BoxMode(IntEnum):
         ], "Relative mode not yet supported!"
 
         if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:
-            assert (
-                arr.shape[-1] == 5
-            ), "The last dimension of input shape must be 5 for XYWHA format"
+            assert arr.shape[-1] == 5, (
+                "The last dimension of input shape must be 5 for XYWHA format"
+            )
             original_dtype = arr.dtype
             arr = arr.double()
 
@@ -244,9 +244,9 @@ class Boxes:
         if isinstance(item, int):
             return Boxes(self.tensor[item].view(1, -1))
         b = self.tensor[item]
-        assert (
-            b.dim() == 2
-        ), "Indexing on Boxes with {} failed to return a matrix!".format(item)
+        assert b.dim() == 2, (
+            "Indexing on Boxes with {} failed to return a matrix!".format(item)
+        )
         return Boxes(b)
 
     def __len__(self) -> int:
@@ -425,7 +425,7 @@ def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
         Tensor: iou, sized [N].
     """
     assert len(boxes1) == len(boxes2), (
-        "boxlists should have the same" "number of entries, got {}, {}".format(
+        "boxlists should have the samenumber of entries, got {}, {}".format(
             len(boxes1), len(boxes2)
         )
     )

sam3/agent/helpers/masks.py

@@ -13,7 +13,6 @@ from torch import device
 
 from .boxes import Boxes
 from .memory import retry_if_cuda_oom
-
 from .roi_align import ROIAlign
 
 
@@ -142,10 +141,10 @@ class BitMasks:
         if isinstance(item, int):
             return BitMasks(self.tensor[item].unsqueeze(0))
         m = self.tensor[item]
-        assert (
-            m.dim() == 3
-        ), "Indexing on BitMasks with {} returns a tensor with shape {}!".format(
-            item, m.shape
+        assert m.dim() == 3, (
+            "Indexing on BitMasks with {} returns a tensor with shape {}!".format(
+                item, m.shape
+            )
         )
         return BitMasks(m)
 

sam3/agent/helpers/rotated_boxes.py

@@ -363,9 +363,9 @@ class RotatedBoxes(Boxes):
         if isinstance(item, int):
             return RotatedBoxes(self.tensor[item].view(1, -1))
         b = self.tensor[item]
-        assert (
-            b.dim() == 2
-        ), "Indexing on RotatedBoxes with {} failed to return a matrix!".format(item)
+        assert b.dim() == 2, (
+            "Indexing on RotatedBoxes with {} failed to return a matrix!".format(item)
+        )
         return RotatedBoxes(b)
 
     def __len__(self) -> int:

sam3/agent/helpers/visualizer.py

@@ -20,7 +20,6 @@ from matplotlib.backends.backend_agg import FigureCanvasAgg
 from PIL import Image
 
 from .boxes import Boxes, BoxMode
-
 from .color_map import random_color
 from .keypoints import Keypoints
 from .masks import BitMasks, PolygonMasks
@@ -222,9 +221,9 @@ class _PanopticPrediction:
                 empty_ids.append(id)
         if len(empty_ids) == 0:
             return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
+        assert len(empty_ids) == 1, (
+            ">1 ids corresponds to no labels. This is currently not supported"
+        )
         return (self._seg != empty_ids[0]).numpy().astype(np.bool)
 
     def semantic_masks(self):