[run-slow] grounding_dino

tests/models/grounding_dino/test_modeling_grounding_dino.py

@@ -57,34 +57,34 @@
 
 
 def generate_fake_bounding_boxes(n_boxes):
-    """Generate bounding boxes in the format (cx, cy, w, h)"""
+    """Generate bounding boxes in the format (center_x, center_y, width, height)"""
     # Validate the input
     if not isinstance(n_boxes, int):
         raise ValueError("n_boxes must be an integer")
     if n_boxes <= 0:
         raise ValueError("n_boxes must be a positive integer")
 
-    # Generate random bounding boxes in the format (cx, cy, w, h)
+    # Generate random bounding boxes in the format (center_x, center_y, width, height)
     bounding_boxes = torch.rand((n_boxes, 4))
 
     # Extract the components
-    cx = bounding_boxes[:, 0]
-    cy = bounding_boxes[:, 1]
-    w = bounding_boxes[:, 2]
-    h = bounding_boxes[:, 3]
+    center_x = bounding_boxes[:, 0]
+    center_y = bounding_boxes[:, 1]
+    width = bounding_boxes[:, 2]
+    height = bounding_boxes[:, 3]
 
     # Ensure width and height do not exceed bounds
-    w = torch.min(w, torch.tensor(1.0))
-    h = torch.min(h, torch.tensor(1.0))
+    width = torch.min(width, torch.tensor(1.0))
+    height = torch.min(height, torch.tensor(1.0))
 
     # Ensure the bounding box stays within the normalized space
-    cx = torch.where(cx - w / 2 < 0, w / 2, cx)
-    cx = torch.where(cx + w / 2 > 1, 1 - w / 2, cx)
-    cy = torch.where(cy - h / 2 < 0, h / 2, cy)
-    cy = torch.where(cy + h / 2 > 1, 1 - h / 2, cy)
+    center_x = torch.where(center_x - width / 2 < 0, width / 2, center_x)
+    center_x = torch.where(center_x + width / 2 > 1, 1 - width / 2, center_x)
+    center_y = torch.where(center_y - height / 2 < 0, height / 2, center_y)
+    center_y = torch.where(center_y + height / 2 > 1, 1 - height / 2, center_y)
 
     # Combine back into bounding boxes
-    bounding_boxes = torch.stack([cx, cy, w, h], dim=1)
+    bounding_boxes = torch.stack([center_x, center_y, width, height], dim=1)
 
     return bounding_boxes