Merge branch 'main' into new-cache

.github/workflows/ci.yaml

@@ -123,7 +123,7 @@ jobs:
         run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov8s-worldv2.pt' imgsz=160 verbose=0.318
       - name: Benchmark SegmentationModel
         shell: bash
-        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.281
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.280
       - name: Benchmark ClassificationModel
         shell: bash
         run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.166

.github/workflows/cla.yml

@@ -1,4 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA
+# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged
 
 name: CLA Assistant
 on:
@@ -21,7 +23,7 @@ jobs:
         uses: contributor-assistant/github-action@v2.3.2
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # must be repository secret token
+          # must be repository secret PAT
           PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
         with:
           path-to-signatures: "signatures/version1/cla.json"

ultralytics/data/augment.py

@@ -975,17 +975,22 @@ class Format:
                     1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio, img.shape[1] // self.mask_ratio
                 )
             labels["masks"] = masks
-        if self.normalize:
-            instances.normalize(w, h)
         labels["img"] = self._format_img(img)
         labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl)
         labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
         if self.return_keypoint:
             labels["keypoints"] = torch.from_numpy(instances.keypoints)
+            if self.normalize:
+                labels["keypoints"][..., 0] /= w
+                labels["keypoints"][..., 1] /= h
         if self.return_obb:
             labels["bboxes"] = (
                 xyxyxyxy2xywhr(torch.from_numpy(instances.segments)) if len(instances.segments) else torch.zeros((0, 5))
             )
+        # NOTE: need to normalize obb in xywhr format for width-height consistency
+        if self.normalize:
+            labels["bboxes"][:, [0, 2]] /= w
+            labels["bboxes"][:, [1, 3]] /= h
         # Then we can use collate_fn
         if self.batch_idx:
             labels["batch_idx"] = torch.zeros(nl)

ultralytics/engine/exporter.py

@@ -529,12 +529,16 @@ class Exporter:
                 f"or in {ROOT}. See PNNX repo for full installation instructions."
             )
             system = "macos" if MACOS else "windows" if WINDOWS else "linux-aarch64" if ARM64 else "linux"
-            _, assets = get_github_assets(repo="pnnx/pnnx", retry=True)
+
-            if assets:
+            # PNNX link fixed at 20240226 due to bug in 20240410
-                url = [x for x in assets if f"{system}.zip" in x][0]
+            # try:
-            else:
+            #     _, assets = get_github_assets(repo="pnnx/pnnx", retry=True)
-                url = f"https://github.com/pnnx/pnnx/releases/download/20240226/pnnx-20240226-{system}.zip"
+            #     url = [x for x in assets if f"{system}.zip" in x][0]
-                LOGGER.warning(f"{prefix} WARNING ⚠️ PNNX GitHub assets not found, using default {url}")
+            # except Exception as e:
+            #     url = f"https://github.com/pnnx/pnnx/releases/download/20240226/pnnx-20240226-{system}.zip"
+            #     LOGGER.warning(f"{prefix} WARNING ⚠️ PNNX GitHub assets not found: {e}, using default {url}")
+
+            url = f"https://github.com/pnnx/pnnx/releases/download/20240226/pnnx-20240226-{system}.zip"
             asset = attempt_download_asset(url, repo="pnnx/pnnx", release="latest")
             if check_is_path_safe(Path.cwd(), asset):  # avoid path traversal security vulnerability
                 unzip_dir = Path(asset).with_suffix("")

ultralytics/utils/__init__.py

@@ -406,6 +406,20 @@ DEFAULT_CFG_KEYS = DEFAULT_CFG_DICT.keys()
 DEFAULT_CFG = IterableSimpleNamespace(**DEFAULT_CFG_DICT)
 
 
+def read_device_model() -> str:
+    """
+    Reads the device model information from the system and caches it for quick access. Used by is_jetson() and
+    is_raspberrypi().
+
+    Returns:
+        (str): Model file contents if read successfully or empty string otherwise.
+    """
+    with contextlib.suppress(Exception):
+        with open("/proc/device-tree/model") as f:
+            return f.read()
+    return ""
+
+
 def is_ubuntu() -> bool:
     """
     Check if the OS is Ubuntu.
@@ -473,10 +487,18 @@ def is_raspberrypi() -> bool:
     Returns:
         (bool): True if running on a Raspberry Pi, False otherwise.
     """
-    with contextlib.suppress(Exception):
+    return "Raspberry Pi" in PROC_DEVICE_MODEL
-        with open("/proc/device-tree/model") as f:
+
-            return "Raspberry Pi" in f.read()
+
-    return False
+def is_jetson() -> bool:
+    """
+    Determines if the Python environment is running on a Jetson Nano or Jetson Orin device by checking the device model
+    information.
+
+    Returns:
+        (bool): True if running on a Jetson Nano or Jetson Orin, False otherwise.
+    """
+    return "NVIDIA" in PROC_DEVICE_MODEL  # i.e. "NVIDIA Jetson Nano" or "NVIDIA Orin NX"
 
 
 def is_online() -> bool:
@@ -658,9 +680,11 @@ def get_user_config_dir(sub_dir="Ultralytics"):
 
 
 # Define constants (required below)
+PROC_DEVICE_MODEL = read_device_model()  # is_jetson() and is_raspberrypi() depend on this constant
 ONLINE = is_online()
 IS_COLAB = is_colab()
 IS_DOCKER = is_docker()
+IS_JETSON = is_jetson()
 IS_JUPYTER = is_jupyter()
 IS_KAGGLE = is_kaggle()
 IS_PIP_PACKAGE = is_pip_package()
@@ -696,8 +720,8 @@ def colorstr(*input):
         (str): The input string wrapped with ANSI escape codes for the specified color and style.
 
     Examples:
-        >>> colorstr('blue', 'bold', 'hello world')
+        >>> colorstr("blue", "bold", "hello world")
-        >>> '\033[34m\033[1mhello world\033[0m'
+        >>> "\033[34m\033[1mhello world\033[0m"
     """
     *args, string = input if len(input) > 1 else ("blue", "bold", input[0])  # color arguments, string
     colors = {

ultralytics/utils/plotting.py

@@ -838,16 +838,16 @@ def plot_images(
             if len(bboxes):
                 boxes = bboxes[idx]
                 conf = confs[idx] if confs is not None else None  # check for confidence presence (label vs pred)
-                is_obb = boxes.shape[-1] == 5  # xywhr
-                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                 if len(boxes):
                     if boxes[:, :4].max() <= 1.1:  # if normalized with tolerance 0.1
-                        boxes[..., 0::2] *= w  # scale to pixels
+                        boxes[..., [0, 2]] *= w  # scale to pixels
-                        boxes[..., 1::2] *= h
+                        boxes[..., [1, 3]] *= h
                     elif scale < 1:  # absolute coords need scale if image scales
                         boxes[..., :4] *= scale
-                boxes[..., 0::2] += x
+                boxes[..., 0] += x
-                boxes[..., 1::2] += y
+                boxes[..., 1] += y
+                is_obb = boxes.shape[-1] == 5  # xywhr
+                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                 for j, box in enumerate(boxes.astype(np.int64).tolist()):
                     c = classes[j]
                     color = colors(c)