Omit ultralytics/utils/callbacks from coverage (#4345)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/engine/results.py

@@ -318,6 +318,7 @@ class Results(SimpleClass):
                 texts.append(('%g ' * len(line)).rstrip() % line)
 
         if texts:
+            Path(txt_file).parent.mkdir(parents=True, exist_ok=True)  # make directory
             with open(txt_file, 'a') as f:
                 f.writelines(text + '\n' for text in texts)
 

ultralytics/engine/validator.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 """
-Check a model's accuracy on a test or val split of a dataset
+Check a model's accuracy on a test or val split of a dataset.
 
 Usage:
     $ yolo mode=val model=yolov8n.pt data=coco128.yaml imgsz=640

ultralytics/models/fastsam/model.py

@@ -12,16 +12,18 @@ class FastSAM(Model):
     """
     FastSAM model interface.
 
-    Usage - Predict:
+    Example:
+        ```python
         from ultralytics import FastSAM
 
         model = FastSAM('last.pt')
         results = model.predict('ultralytics/assets/bus.jpg')
+        ```
     """
 
     def __init__(self, model='FastSAM-x.pt'):
         """Call the __init__ method of the parent class (YOLO) with the updated default model"""
-        if model == 'FastSAM.pt':
+        if str(model) == 'FastSAM.pt':
             model = 'FastSAM-x.pt'
         assert Path(model).suffix not in ('.yaml', '.yml'), 'FastSAM models only support pre-trained models.'
         super().__init__(model=model, task='segment')

ultralytics/models/nas/model.py

@@ -2,11 +2,13 @@
 """
 YOLO-NAS model interface.
 
-Usage - Predict:
+Example:
+    ```python
     from ultralytics import NAS
 
     model = NAS('yolo_nas_s')
     results = model.predict('ultralytics/assets/bus.jpg')
+    ```
 """
 
 from pathlib import Path

ultralytics/models/sam/model.py

@@ -3,6 +3,8 @@
 SAM model interface
 """
 
+from pathlib import Path
+
 from ultralytics.engine.model import Model
 from ultralytics.utils.torch_utils import model_info
 
@@ -16,9 +18,8 @@ class SAM(Model):
     """
 
     def __init__(self, model='sam_b.pt') -> None:
-        if model and not model.endswith('.pt') and not model.endswith('.pth'):
-            # Should raise AssertionError instead?
-            raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint')
+        if model and Path(model).suffix not in ('.pt', '.pth'):
+            raise NotImplementedError('SAM prediction requires pre-trained *.pt or *.pth model.')
         super().__init__(model=model, task='segment')
 
     def _load(self, weights: str, task=None):

ultralytics/trackers/utils/matching.py

@@ -36,7 +36,7 @@ def merge_matches(m1, m2, shape):
 
 
 def _indices_to_matches(cost_matrix, indices, thresh):
-    """_indices_to_matches: Return matched and unmatched indices given a cost matrix, indices, and a threshold."""
+    """Return matched and unmatched indices given a cost matrix, indices, and a threshold."""
     matched_cost = cost_matrix[tuple(zip(*indices))]
     matched_mask = (matched_cost <= thresh)
 
@@ -81,8 +81,12 @@ def ious(atlbrs, btlbrs):
     ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
     if ious.size == 0:
         return ious
-
     ious = bbox_ious(np.ascontiguousarray(atlbrs, dtype=np.float32), np.ascontiguousarray(btlbrs, dtype=np.float32))
+
+    # TODO: replace bbox_ious() with numpy-capable update of utils.metrics.box_iou
+    # from ...utils.metrics import box_iou
+    # ious = box_iou()
+
     return ious
 
 
@@ -102,8 +106,7 @@ def iou_distance(atracks, btracks):
     else:
         atlbrs = [track.tlbr for track in atracks]
         btlbrs = [track.tlbr for track in btracks]
-    _ious = ious(atlbrs, btlbrs)
-    return 1 - _ious  # cost matrix
+    return 1 - ious(atlbrs, btlbrs)  # cost matrix
 
 
 def v_iou_distance(atracks, btracks):
@@ -122,8 +125,7 @@ def v_iou_distance(atracks, btracks):
     else:
         atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
         btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
-    _ious = ious(atlbrs, btlbrs)
-    return 1 - _ious  # cost matrix
+    return 1 - ious(atlbrs, btlbrs)  # cost matrix
 
 
 def embedding_distance(tracks, detections, metric='cosine'):