ultralytics 8.0.188 fix .grad attribute leaf Tensor Warning (#5094)

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

docs/index.md

@@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea
 
 <div align="center">
   <p>
-    <a href="https://github.com/ultralytics/ultralytics" target="_blank">
+    <a href="https://yolovision.ultralytics.com" target="_blank">
     <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
   </p>
   <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>

examples/tutorial.ipynb

@@ -563,6 +563,18 @@
         "Additional content below."
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pip install from source\n",
+        "!pip install git+https://github.com/ultralytics/ultralytics@main"
+      ],
+      "metadata": {
+        "id": "pIdE6i8C3LYp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [

tests/test_cuda.py

@@ -16,6 +16,7 @@ DATASETS_DIR = Path(SETTINGS['datasets_dir'])
 WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
 MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt'  # test spaces in path
 DATA = 'coco8.yaml'
+BUS = ASSETS / 'bus.jpg'
 
 
 def test_checks():
@@ -29,6 +30,30 @@ def test_train():
     YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=device)  # requires imgsz>=64
 
 
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
+def test_predict_multiple_devices():
+    model = YOLO('yolov8n.pt')
+    model = model.cpu()
+    assert str(model.device) == 'cpu'
+    _ = model(BUS)  # CPU inference
+    assert str(model.device) == 'cpu'
+
+    model = model.to('cuda:0')
+    assert str(model.device) == 'cuda:0'
+    _ = model(BUS)  # CUDA inference
+    assert str(model.device) == 'cuda:0'
+
+    model = model.cpu()
+    assert str(model.device) == 'cpu'
+    _ = model(BUS)  # CPU inference
+    assert str(model.device) == 'cpu'
+
+    model = model.cuda()
+    assert str(model.device) == 'cuda:0'
+    _ = model(BUS)  # CUDA inference
+    assert str(model.device) == 'cuda:0'
+
+
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
 def test_autobatch():
     from ultralytics.utils.autobatch import check_train_batch_size
@@ -57,10 +82,10 @@ def test_predict_sam():
     model.info()
 
     # Run inference
-    model(ASSETS / 'bus.jpg', device=0)
+    model(BUS, device=0)
 
     # Run inference with bboxes prompt
-    model(ASSETS / 'zidane.jpg', bboxes=[439, 437, 524, 709], device=0)
+    model(BUS, bboxes=[439, 437, 524, 709], device=0)
 
     # Run inference with points prompt
     model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.187'
+__version__ = '8.0.188'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/model.py

@@ -8,8 +8,7 @@ from typing import Union
 from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir
 from ultralytics.hub.utils import HUB_WEB_ROOT
 from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
-from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, emojis, yaml_load
+from ultralytics.utils import ASSETS, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, checks, emojis, yaml_load
-from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
 from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
 
 
@@ -139,7 +138,7 @@ class Model(nn.Module):
             self.overrides = self.model.args = self._reset_ckpt_args(self.model.args)
             self.ckpt_path = self.model.pt_path
         else:
-            weights = check_file(weights)
+            weights = checks.check_file(weights)
             self.model, self.ckpt = weights, None
             self.task = task or guess_model_task(weights)
             self.ckpt_path = weights
@@ -251,8 +250,7 @@ class Model(nn.Module):
         if not hasattr(self.predictor, 'trackers'):
             from ultralytics.trackers import register_tracker
             register_tracker(self, persist)
-        # ByteTrack-based method needs low confidence predictions as input
+        kwargs['conf'] = kwargs.get('conf') or 0.1  # ByteTrack-based method needs low confidence predictions as input
-        kwargs['conf'] = kwargs.get('conf') or 0.1
         kwargs['mode'] = 'track'
         return self.predict(source=source, stream=stream, **kwargs)
 
@@ -266,7 +264,6 @@ class Model(nn.Module):
         """
         custom = {'rect': True}  # method defaults
         args = {**self.overrides, **custom, **kwargs, 'mode': 'val'}  # highest priority args on the right
-        args['imgsz'] = check_imgsz(args['imgsz'], max_dim=1)
 
         validator = (validator or self._smart_load('validator'))(args=args, _callbacks=self.callbacks)
         validator(model=self.model)
@@ -321,9 +318,9 @@ class Model(nn.Module):
             if any(kwargs):
                 LOGGER.warning('WARNING ⚠️ using HUB training arguments, ignoring local training arguments.')
             kwargs = self.session.train_args
-        check_pip_update_available()
+        checks.check_pip_update_available()
 
-        overrides = yaml_load(check_yaml(kwargs['cfg'])) if kwargs.get('cfg') else self.overrides
+        overrides = yaml_load(checks.check_yaml(kwargs['cfg'])) if kwargs.get('cfg') else self.overrides
         custom = {'data': TASK2DATA[self.task]}  # method defaults
         args = {**overrides, **custom, **kwargs, 'mode': 'train'}  # highest priority args on the right
         if args.get('resume'):
@@ -366,7 +363,7 @@ class Model(nn.Module):
         self._check_is_pytorch_model()
         self = super()._apply(fn)  # noqa
         self.predictor = None  # reset predictor as device may have changed
-        self.overrides['device'] = str(self.device)  # i.e. device(type='cuda', index=0) -> 'cuda:0'
+        self.overrides['device'] = self.device  # was str(self.device) i.e. device(type='cuda', index=0) -> 'cuda:0'
         return self
 
     @property

ultralytics/engine/validator.py

@@ -95,6 +95,7 @@ class BaseValidator:
         (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
         if self.args.conf is None:
             self.args.conf = 0.001  # default conf=0.001
+        self.args.imgsz = check_imgsz(self.args.imgsz, max_dim=1)
 
         self.plots = {}
         self.callbacks = _callbacks or callbacks.get_default_callbacks()

ultralytics/models/fastsam/prompt.py

@@ -87,7 +87,7 @@ class FastSAMPrompt:
         pbar = TQDM(annotations, total=len(annotations))
         for ann in pbar:
             result_name = os.path.basename(ann.path)
-            image = ann.orig_img
+            image = ann.orig_img[..., ::-1]  # BGR to RGB
             original_h, original_w = ann.orig_shape
             # for macOS only
             # plt.switch_backend('TkAgg')
@@ -108,8 +108,7 @@ class FastSAMPrompt:
                         mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
                         masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
 
-                self.fast_show_mask(
+                self.fast_show_mask(masks,
-                    masks,
                                     plt.gca(),
                                     random_color=mask_random_color,
                                     bbox=bbox,
@@ -117,8 +116,7 @@ class FastSAMPrompt:
                                     pointlabel=point_label,
                                     retinamask=retina,
                                     target_height=original_h,
-                    target_width=original_w,
+                                    target_width=original_w)
-                )
 
                 if with_contours:
                     contour_all = []
@@ -134,17 +132,11 @@ class FastSAMPrompt:
                     contour_mask = temp / 255 * color.reshape(1, 1, -1)
                     plt.imshow(contour_mask)
 
-            plt.axis('off')
+            # Save the figure
-            fig = plt.gcf()
-
-            # Check if the canvas has been drawn
-            if fig.canvas.get_renderer() is None:  # macOS requires this or tests fail
-                fig.canvas.draw()
-
             save_path = Path(output) / result_name
             save_path.parent.mkdir(exist_ok=True, parents=True)
-            image = Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
+            plt.axis('off')
-            image.save(save_path)
+            plt.savefig(save_path, bbox_inches='tight', pad_inches=0, transparent=True)
             plt.close()
             pbar.set_description(f'Saving {result_name} to {save_path}')
 
@@ -263,8 +255,8 @@ class FastSAMPrompt:
             orig_masks_area = torch.sum(masks, dim=(1, 2))
 
             union = bbox_area + orig_masks_area - masks_area
-            IoUs = masks_area / union
+            iou = masks_area / union
-            max_iou_index = torch.argmax(IoUs)
+            max_iou_index = torch.argmax(iou)
 
             self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
         return self.results

ultralytics/nn/autobackend.py

@@ -39,6 +39,7 @@ def check_class_names(names):
 
 class AutoBackend(nn.Module):
 
+    @torch.no_grad()
     def __init__(self,
                  weights='yolov8n.pt',
                  device=torch.device('cpu'),
@@ -309,6 +310,11 @@ class AutoBackend(nn.Module):
             names = self._apply_default_class_names(data)
         names = check_class_names(names)
 
+        # Disable gradients
+        if pt:
+            for p in model.parameters():
+                p.requires_grad = False
+
         self.__dict__.update(locals())  # assign all variables to self
 
     def forward(self, im, augment=False, visualize=False):

ultralytics/utils/__init__.py

@@ -327,8 +327,9 @@ def yaml_save(file='data.yaml', data=None, header=''):
         file.parent.mkdir(parents=True, exist_ok=True)
 
     # Convert Path objects to strings
+    valid_types = int, float, str, bool, list, tuple, dict, type(None)
     for k, v in data.items():
-        if isinstance(v, Path):
+        if not isinstance(v, valid_types):
             data[k] = str(v)
 
     # Dump data to file in YAML format

ultralytics/utils/checks.py

@@ -55,7 +55,7 @@ def parse_requirements(file_path=ROOT.parent / 'requirements.txt', package=''):
         line = line.strip()
         if line and not line.startswith('#'):
             line = line.split('#')[0].strip()  # ignore inline comments
-            match = re.match(r'([a-zA-Z0-9-_]+)([<>!=~]+.*)?', line)
+            match = re.match(r'([a-zA-Z0-9-_]+)\s*([<>!=~]+.*)?', line)
             if match:
                 requirements.append(SimpleNamespace(name=match[1], specifier=match[2].strip() if match[2] else ''))
 

ultralytics/utils/torch_utils.py

@@ -44,6 +44,9 @@ def smart_inference_mode():
 
     def decorate(fn):
         """Applies appropriate torch decorator for inference mode based on torch version."""
+        if TORCH_1_9 and torch.is_inference_mode_enabled():
+            return fn  # already in inference_mode, act as a pass-through
+        else:
             return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
 
     return decorate