Apply pre-commit formatting to existing files

benchmark/benchmark_vision_models.py

@@ -36,7 +36,9 @@
     tuple[CoreMode, ...], core_modes_for_target_device("aries-rb")
 )
 # Utility-only embedding tasks have no benchmark engine/CLI route yet.
-TASK_CHOICES = tuple(task for task in VISION_TASKS if task not in {"person_reid", "face_recognition"})
+TASK_CHOICES = tuple(
+    task for task in VISION_TASKS if task not in {"person_reid", "face_recognition"}
+)
 SUPPORTED_TARGET_DEVICES = frozenset({"aries-rb", "regulus-ra", "regulus-rb"})
 
 

mblt_vision/_hub_validation.py

@@ -59,7 +59,9 @@ def check_hub_artifacts(
             "Hub verification requires nonempty repo_id and revision strings."
         )
     board = normalize_target_device(
-        files.get("target_device", "aries-rb") if target_device is None else target_device
+        files.get("target_device", "aries-rb")
+        if target_device is None
+        else target_device
     )
     paths = []
     for framework in dict.fromkeys(frameworks):

mblt_vision/image_classification/__init__.py

@@ -197,21 +197,35 @@
 LeViT_192 = create_model_class("LeViT_192", __name__)
 LeViT_256 = create_model_class("LeViT_256", __name__)
 LeViT_384 = create_model_class("LeViT_384", __name__)
-ViT_RelPos_Medium_Patch16_RPN_224 = create_model_class("ViT_RelPos_Medium_Patch16_RPN_224", __name__)
-ViT_RelPos_Base_Patch32_Plus_RPN_256 = create_model_class("ViT_RelPos_Base_Patch32_Plus_RPN_256", __name__)
+ViT_RelPos_Medium_Patch16_RPN_224 = create_model_class(
+    "ViT_RelPos_Medium_Patch16_RPN_224", __name__
+)
+ViT_RelPos_Base_Patch32_Plus_RPN_256 = create_model_class(
+    "ViT_RelPos_Base_Patch32_Plus_RPN_256", __name__
+)
 ViT_Base_Patch16_RPN_224 = create_model_class("ViT_Base_Patch16_RPN_224", __name__)
 ViT_Base_Patch16_CLIP_224 = create_model_class("ViT_Base_Patch16_CLIP_224", __name__)
 ViT_Base_Patch32_CLIP_448 = create_model_class("ViT_Base_Patch32_CLIP_448", __name__)
 ViT_Medium_Patch16_GAP_256 = create_model_class("ViT_Medium_Patch16_GAP_256", __name__)
 ViT_Large_Patch14_CLIP_224 = create_model_class("ViT_Large_Patch14_CLIP_224", __name__)
 ViT_Base_Patch32_CLIP_224 = create_model_class("ViT_Base_Patch32_CLIP_224", __name__)
-ViT_RelPos_Small_Patch16_224 = create_model_class("ViT_RelPos_Small_Patch16_224", __name__)
+ViT_RelPos_Small_Patch16_224 = create_model_class(
+    "ViT_RelPos_Small_Patch16_224", __name__
+)
 ViT_Base_Patch32_CLIP_384 = create_model_class("ViT_Base_Patch32_CLIP_384", __name__)
-ViT_SRelPos_Medium_Patch16_224 = create_model_class("ViT_SRelPos_Medium_Patch16_224", __name__)
+ViT_SRelPos_Medium_Patch16_224 = create_model_class(
+    "ViT_SRelPos_Medium_Patch16_224", __name__
+)
 ViT_Huge_Patch14_CLIP_224 = create_model_class("ViT_Huge_Patch14_CLIP_224", __name__)
-ViT_RelPos_Base_Patch16_224 = create_model_class("ViT_RelPos_Base_Patch16_224", __name__)
-ViT_RelPos_Medium_Patch16_224 = create_model_class("ViT_RelPos_Medium_Patch16_224", __name__)
-ViT_SRelPos_Small_Patch16_224 = create_model_class("ViT_SRelPos_Small_Patch16_224", __name__)
+ViT_RelPos_Base_Patch16_224 = create_model_class(
+    "ViT_RelPos_Base_Patch16_224", __name__
+)
+ViT_RelPos_Medium_Patch16_224 = create_model_class(
+    "ViT_RelPos_Medium_Patch16_224", __name__
+)
+ViT_SRelPos_Small_Patch16_224 = create_model_class(
+    "ViT_SRelPos_Small_Patch16_224", __name__
+)
 AlexNet = create_model_class("AlexNet", __name__)
 CAFormer_B36 = create_model_class("CAFormer_B36", __name__)
 CAFormer_M36 = create_model_class("CAFormer_M36", __name__)

mblt_vision/utils/datasets/readiness.py

@@ -1072,7 +1072,11 @@ def _reid_ready(root: Path, dataset: str) -> bool:
 
 def _face_ready(root: Path, dataset: str) -> bool:
     """Validate prepared aligned pairs and image decoding, never realign crops."""
-    loader = {"lfw": LFWDataset, "agedb-30": AgeDB30Dataset, "cfp-fp": CFPFPDataset}.get(dataset)
+    loader = {
+        "lfw": LFWDataset,
+        "agedb-30": AgeDB30Dataset,
+        "cfp-fp": CFPFPDataset,
+    }.get(dataset)
     if (
         loader is None
         or _path_has_symlink_component(root)

mblt_vision/utils/datasets/reid.py

@@ -56,7 +56,9 @@ def __init__(
                     )
                 pid, camera = map(int, match.groups())
                 if pid < -1:
-                    raise ValueError(f"Unsupported negative person identity {pid}: {path}")
+                    raise ValueError(
+                        f"Unsupported negative person identity {pid}: {path}"
+                    )
                 if pid == -1:
                     if self.config["skip_junk"]:
                         continue

mblt_vision/utils/postprocess/embedding_post.py

@@ -92,5 +92,7 @@ def __init__(self, pre_cfg: dict, post_cfg: dict) -> None:
         super().__init__(pre_cfg, post_cfg)
         dataset = post_cfg.get("dataset")
         if not isinstance(dataset, str) or not dataset:
-            raise ValueError("Face recognition postprocessing requires post_cfg.dataset")
+            raise ValueError(
+                "Face recognition postprocessing requires post_cfg.dataset"
+            )
         self.dataset = get_dataset_config_for_task("face_recognition", dataset)["name"]

tests/reference/ops_reid_metrics.py

@@ -2,9 +2,12 @@
 ec537764e36ca10da963fcb7afab157ea60ca022 (mblt-model-ops).
 Snapshot permits ordinary offline differential tests without sibling repos.
 """
+
 import numpy as np
+
 MAX_RANK = 50
 
+
 def euclidean_distance(query: np.ndarray, gallery: np.ndarray) -> np.ndarray:
     """Squared euclidean distance, the reference ``euclidean_distance`` without the sqrt.
 
@@ -16,6 +19,7 @@ def euclidean_distance(query: np.ndarray, gallery: np.ndarray) -> np.ndarray:
     g2 = np.sum(gallery**2, axis=1, keepdims=True).T
     return q2 + g2 - 2.0 * (query @ gallery.T)
 
+
 def evaluate(
     distmat: np.ndarray,
     q_pids: np.ndarray,

tests/test_reid.py

@@ -112,7 +112,9 @@ def test_loader_metadata_and_source(tmp_path, dataset, loader):
     assert cfg["eval_protocol"]["id"] == cfg["evaluation"]["protocol"]
 
 
-@pytest.mark.parametrize("filename", ["bad.jpg", "1_c9_bad.jpg", "-1_c1_junk.jpg", "-2_c1_bad.jpg"])
+@pytest.mark.parametrize(
+    "filename", ["bad.jpg", "1_c9_bad.jpg", "-1_c1_junk.jpg", "-2_c1_bad.jpg"]
+)
 def test_duke_invalid_names(tmp_path, filename):
     (tmp_path / "query").mkdir()
     (tmp_path / "bounding_box_test").mkdir()

tests/test_results.py

@@ -63,7 +63,9 @@ def test_instance_segmentation_plot_supports_nonzero_coco_labels(
     cv2.imwrite(str(source_path), image)
 
     monkeypatch.setattr(results_module, "scale_boxes", lambda *args, **kwargs: args[1])
-    monkeypatch.setattr(results_module, "scale_masks", lambda mask, img0_shape, **kwargs: mask)
+    monkeypatch.setattr(
+        results_module, "scale_masks", lambda mask, img0_shape, **kwargs: mask
+    )
     monkeypatch.setattr(results_module, "crop_mask", lambda mask, boxes: mask)
 
     box_cls = torch.tensor([[4.0, 6.0, 20.0, 24.0, 0.9, 45.0]], dtype=torch.float32)