ultralytics 8.2.50 new Streamlit live inference Solution (#14210)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/models/fastsam/prompt.py

@@ -286,7 +286,7 @@ class FastSAMPrompt:
     def box_prompt(self, bbox):
         """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
         if self.results[0].masks is not None:
-            assert bbox[2] != 0 and bbox[3] != 0
+            assert bbox[2] != 0 and bbox[3] != 0, "Bounding box width and height should not be zero"
             masks = self.results[0].masks.data
             target_height, target_width = self.results[0].orig_shape
             h = masks.shape[1]

ultralytics/models/sam/amg.py

@@ -133,7 +133,7 @@ def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tup
     """Remove small disconnected regions or holes in a mask, returning the mask and a modification indicator."""
     import cv2  # type: ignore
 
-    assert mode in {"holes", "islands"}
+    assert mode in {"holes", "islands"}, f"Provided mode {mode} is invalid"
     correct_holes = mode == "holes"
     working_mask = (correct_holes ^ mask).astype(np.uint8)
     n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)

ultralytics/models/sam/modules/tiny_encoder.py

@@ -261,7 +261,7 @@ class Attention(torch.nn.Module):
         """
         super().__init__()
 
-        assert isinstance(resolution, tuple) and len(resolution) == 2
+        assert isinstance(resolution, tuple) and len(resolution) == 2, "'resolution' argument not tuple of length 2"
         self.num_heads = num_heads
         self.scale = key_dim**-0.5
         self.key_dim = key_dim

ultralytics/models/yolo/world/train_world.py

@@ -72,8 +72,8 @@ class WorldTrainerFromScratch(WorldTrainer):
         """
         final_data = {}
         data_yaml = self.args.data
-        assert data_yaml.get("train", False)  # object365.yaml
-        assert data_yaml.get("val", False)  # lvis.yaml
+        assert data_yaml.get("train", False), "train dataset not found"  # object365.yaml
+        assert data_yaml.get("val", False), "validation dataset not found"  # lvis.yaml
         data = {k: [check_det_dataset(d) for d in v.get("yolo_data", [])] for k, v in data_yaml.items()}
         assert len(data["val"]) == 1, f"Only support validating on 1 dataset for now, but got {len(data['val'])}."
         val_split = "minival" if "lvis" in data["val"][0]["val"] else "val"