`ultralytics 8.0.158` add benchmarks to coverage (#4432)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>

.github/workflows/ci.yaml

@@ -106,11 +106,7 @@ jobs:
         shell: bash  # for Windows compatibility
         run: |
           python -m pip install --upgrade pip wheel
-          if [ "${{ matrix.os }}" == "macos-latest" ]; then
-              pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
-            else
-              pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
-          fi
+          pip install -e ".[export]" coverage --extra-index-url https://download.pytorch.org/whl/cpu
           yolo export format=tflite imgsz=32 || true
       - name: Check environment
         run: |
@@ -125,16 +121,25 @@ jobs:
           pip list
       - name: Benchmark DetectionModel
         shell: bash
-        run: yolo benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
       - name: Benchmark SegmentationModel
         shell: bash
-        run: yolo benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
       - name: Benchmark ClassificationModel
         shell: bash
-        run: yolo benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.36
       - name: Benchmark PoseModel
         shell: bash
-        run: yolo benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
+        run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
+      - name: Merge Coverage Reports
+        run: |
+          coverage xml -o coverage-benchmarks.xml
+      - name: Upload Coverage Reports to CodeCov
+        uses: codecov/codecov-action@v3
+        with:
+          flags: Benchmarks
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
       - name: Benchmark Summary
         run: |
           cat benchmarks.log
@@ -183,9 +188,11 @@ jobs:
       - name: Pytest tests
         shell: bash  # for Windows compatibility
         run: pytest --cov=ultralytics/ --cov-report xml tests/
-      - name: Upload coverage reports to Codecov
+      - name: Upload Coverage Reports to CodeCov
         if: github.repository == 'ultralytics/ultralytics' && matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
         uses: codecov/codecov-action@v3
+        with:
+          flags: Tests
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 

.gitignore

@@ -118,6 +118,9 @@ venv.bak/
 .spyderproject
 .spyproject
 
+# VSCode project settings
+.vscode/
+
 # Rope project settings
 .ropeproject
 

docs/reference/engine/exporter.md

@@ -1,6 +1,6 @@
 ---
-description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, iOSDetectModel, and try exporting with examples.
-keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
+description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, IOSDetectModel, and try exporting with examples.
+keywords: Ultralytics, Exporter, IOSDetectModel, Export Formats, Try export
 ---
 
 # Reference for `ultralytics/engine/exporter.py`
@@ -14,7 +14,7 @@ keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
 <br><br>
 
 ---
-## ::: ultralytics.engine.exporter.iOSDetectModel
+## ::: ultralytics.engine.exporter.IOSDetectModel
 <br><br>
 
 ---
@@ -28,7 +28,3 @@ keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
 ---
 ## ::: ultralytics.engine.exporter.try_export
 <br><br>
-
----
-## ::: ultralytics.engine.exporter.export
-<br><br>

docs/reference/models/rtdetr/train.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorc
 ---
 ## ::: ultralytics.models.rtdetr.train.RTDETRTrainer
 <br><br>
-
----
-## ::: ultralytics.models.rtdetr.train.train
-<br><br>

docs/reference/models/yolo/classify/predict.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, classification predictor, predict, YOLO, AI models, model
 ---
 ## ::: ultralytics.models.yolo.classify.predict.ClassificationPredictor
 <br><br>
-
----
-## ::: ultralytics.models.yolo.classify.predict.predict
-<br><br>

docs/reference/models/yolo/classify/train.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, Classification Trainer, deep learning, training pro
 ---
 ## ::: ultralytics.models.yolo.classify.train.ClassificationTrainer
 <br><br>
-
----
-## ::: ultralytics.models.yolo.classify.train.train
-<br><br>

docs/reference/models/yolo/classify/val.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, ClassificationValidator, model validation, model fi
 ---
 ## ::: ultralytics.models.yolo.classify.val.ClassificationValidator
 <br><br>
-
----
-## ::: ultralytics.models.yolo.classify.val.val
-<br><br>

docs/reference/models/yolo/detect/predict.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, DetectionPredictor, detect, predict, object detecti
 ---
 ## ::: ultralytics.models.yolo.detect.predict.DetectionPredictor
 <br><br>
-
----
-## ::: ultralytics.models.yolo.detect.predict.predict
-<br><br>

docs/reference/models/yolo/detect/train.md

@@ -12,7 +12,3 @@ keywords: Ultralytics YOLO, YOLO, Detection Trainer, Model Training, Machine Lea
 ---
 ## ::: ultralytics.models.yolo.detect.train.DetectionTrainer
 <br><br>
-
----
-## ::: ultralytics.models.yolo.detect.train.train
-<br><br>

docs/reference/models/yolo/detect/val.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, Detection Validator, model valuation, precision, re
 ---
 ## ::: ultralytics.models.yolo.detect.val.DetectionValidator
 <br><br>
-
----
-## ::: ultralytics.models.yolo.detect.val.val
-<br><br>

docs/reference/models/yolo/pose/predict.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, PosePredictor, machine learning, AI, predictive mod
 ---
 ## ::: ultralytics.models.yolo.pose.predict.PosePredictor
 <br><br>
-
----
-## ::: ultralytics.models.yolo.pose.predict.predict
-<br><br>

docs/reference/models/yolo/pose/train.md

@@ -12,7 +12,3 @@ keywords: Ultralytics, YOLO, PoseTrainer, pose training, AI modeling, custom dat
 ---
 ## ::: ultralytics.models.yolo.pose.train.PoseTrainer
 <br><br>
-
----
-## ::: ultralytics.models.yolo.pose.train.train
-<br><br>

docs/reference/models/yolo/pose/val.md

@@ -12,7 +12,3 @@ keywords: PoseValidator, Ultralytics, YOLO, Object detection, Pose validation
 ---
 ## ::: ultralytics.models.yolo.pose.val.PoseValidator
 <br><br>
-
----
-## ::: ultralytics.models.yolo.pose.val.val
-<br><br>

docs/reference/models/yolo/segment/predict.md

@@ -12,7 +12,3 @@ keywords: YOLO, Ultralytics, object detection, segmentation predictor
 ---
 ## ::: ultralytics.models.yolo.segment.predict.SegmentationPredictor
 <br><br>
-
----
-## ::: ultralytics.models.yolo.segment.predict.predict
-<br><br>

docs/usage/engine.md

@@ -14,7 +14,7 @@ the required functions or operations as long the as correct formats are followed
 custom model and dataloader by just overriding these functions:
 
 * `get_model(cfg, weights)` - The function that builds the model to be trained
-* `get_dataloder()` - The function that builds the dataloader
+* `get_dataloader()` - The function that builds the dataloader
   More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
 
 ## DetectionTrainer

mkdocs.yml

@@ -401,6 +401,7 @@ plugins:
       handlers:
         python:
           options:
+            docstring_style: google
             show_root_heading: true
             show_source: true
   - ultralytics:

requirements.txt

@@ -1,5 +1,5 @@
 # Ultralytics requirements
-# Usage: pip install -r requirements.txt
+# Example: pip install -r requirements.txt
 
 # Base ----------------------------------------
 matplotlib>=3.2.2

tests/test_cli.py

@@ -40,6 +40,14 @@ def test_train(task, model, data):
 
 @pytest.mark.parametrize('task,model,data', TASK_ARGS)
 def test_val(task, model, data):
+    # Download annotations to run pycocotools eval
+    # from ultralytics.utils import SETTINGS, Path
+    # from ultralytics.utils.downloads import download
+    # url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+    # download(f'{url}instances_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8/annotations')
+    # download(f'{url}person_keypoints_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8-pose/annotations')
+
+    # Validate
     run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json')
 
 

tests/test_python.py

@@ -132,13 +132,13 @@ def test_val():
 
 def test_train_scratch():
     model = YOLO(CFG)
-    model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='disk', batch=-1)  # test disk caching with AutoBatch
+    model.train(data='coco8.yaml', epochs=2, imgsz=32, cache='disk', batch=-1, close_mosaic=1)
     model(SOURCE)
 
 
 def test_train_pretrained():
     model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
-    model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5)  # test RAM caching
+    model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5)
     model(SOURCE)
 
 
@@ -283,6 +283,12 @@ def test_data_converter():
     coco80_to_coco91_class()
 
 
+def test_data_annotator():
+    from ultralytics.data.annotator import auto_annotate
+
+    auto_annotate(ASSETS, det_model='yolov8n.pt', sam_model='mobile_sam.pt', output_dir=TMP / 'auto_annotate_labels')
+
+
 def test_events():
     # Test event sending
     from ultralytics.hub.utils import Events
@@ -304,12 +310,15 @@ def test_utils_init():
 
 
 def test_utils_checks():
-    from ultralytics.utils.checks import check_requirements, check_yolov5u_filename, git_describe
+    from ultralytics.utils.checks import (check_imgsz, check_requirements, check_yolov5u_filename, git_describe,
+                                          print_args)
 
     check_yolov5u_filename('yolov5n.pt')
     # check_imshow(warn=True)
     git_describe(ROOT)
     check_requirements()  # check requirements.txt
+    check_imgsz([600, 600], max_dim=1)
+    print_args()
 
 
 def test_utils_benchmarks():

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.157'
+__version__ = '8.0.158'
 
 from ultralytics.hub import start
 from ultralytics.models import RTDETR, SAM, YOLO

ultralytics/data/annotator.py

@@ -8,6 +8,7 @@ from ultralytics import SAM, YOLO
 def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None):
     """
     Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
+
     Args:
         data (str): Path to a folder containing images to be annotated.
         det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.
@@ -15,12 +16,20 @@ def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='',
         device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
         output_dir (str | None | optional): Directory to save the annotated results.
             Defaults to a 'labels' folder in the same directory as 'data'.
+
+    Example:
+        ```python
+        from ultralytics.data.annotator import auto_annotate
+
+        auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
+        ```
     """
     det_model = YOLO(det_model)
     sam_model = SAM(sam_model)
 
+    data = Path(data)
     if not output_dir:
-        output_dir = Path(str(data)).parent / 'labels'
+        output_dir = data.parent / f'{data.stem}_auto_annotate_labels'
     Path(output_dir).mkdir(exist_ok=True, parents=True)
 
     det_results = det_model(data, stream=True, device=device)

ultralytics/data/augment.py

@@ -402,7 +402,7 @@ class RandomPerspective:
             keypoints (ndarray): keypoints, [N, 17, 3].
             M (ndarray): affine matrix.
 
-        Return:
+        Returns:
             new_keypoints (ndarray): keypoints after affine, [N, 17, 3].
         """
         n, nkpt = keypoints.shape[:2]

ultralytics/engine/exporter.py

@@ -484,7 +484,7 @@ class Exporter:
             classifier_config = ct.ClassifierConfig(list(self.model.names.values())) if self.args.nms else None
             model = self.model
         elif self.model.task == 'detect':
-            model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
+            model = IOSDetectModel(self.model, self.im) if self.args.nms else self.model
         else:
             if self.args.nms:
                 LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is only available for Detect models like 'yolov8n.pt'.")
@@ -846,12 +846,11 @@ class Exporter:
         out0, out1 = iter(spec.description.output)
         if MACOS:
             from PIL import Image
-            img = Image.new('RGB', (w, h))  # img(192 width, 320 height)
-            # img = torch.zeros((*opt.img_size, 3)).numpy()  # img size(320,192,3) iDetection
+            img = Image.new('RGB', (w, h))  # w=192, h=320
             out = model.predict({'image': img})
-            out0_shape = out[out0.name].shape
-            out1_shape = out[out1.name].shape
-        else:  # linux and windows can not run model.predict(), get sizes from pytorch output y
+            out0_shape = out[out0.name].shape  # (3780, 80)
+            out1_shape = out[out1.name].shape  # (3780, 4)
+        else:  # linux and windows can not run model.predict(), get sizes from PyTorch model output y
             out0_shape = self.output_shape[2], self.output_shape[1] - 4  # (3780, 80)
             out1_shape = self.output_shape[2], 4  # (3780, 4)
 
@@ -963,11 +962,11 @@ class Exporter:
             callback(self)
 
 
-class iOSDetectModel(torch.nn.Module):
-    """Wrap an Ultralytics YOLO model for iOS export."""
+class IOSDetectModel(torch.nn.Module):
+    """Wrap an Ultralytics YOLO model for Apple iOS CoreML export."""
 
     def __init__(self, model, im):
-        """Initialize the iOSDetectModel class with a YOLO model and example image."""
+        """Initialize the IOSDetectModel class with a YOLO model and example image."""
         super().__init__()
         b, c, h, w = im.shape  # batch, channel, height, width
         self.model = model
@@ -981,21 +980,3 @@ class iOSDetectModel(torch.nn.Module):
         """Normalize predictions of object detection model with input size-dependent factors."""
         xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
         return cls, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)
-
-
-def export(cfg=DEFAULT_CFG):
-    """Export a YOLOv model to a specific format."""
-    cfg.model = cfg.model or 'yolov8n.yaml'
-    cfg.format = cfg.format or 'torchscript'
-
-    from ultralytics import YOLO
-    model = YOLO(cfg.model)
-    model.export(**vars(cfg))
-
-
-if __name__ == '__main__':
-    """
-    CLI:
-    yolo mode=export model=yolov8n.yaml format=onnx
-    """
-    export()

ultralytics/engine/predictor.py

@@ -138,12 +138,14 @@ class BasePredictor:
         return self.model(im, augment=self.args.augment, visualize=visualize)
 
     def pre_transform(self, im):
-        """Pre-transform input image before inference.
+        """
+        Pre-transform input image before inference.
 
         Args:
             im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
 
-        Return: A list of transformed imgs.
+        Returns:
+            (list): A list of transformed images.
         """
         same_shapes = all(x.shape == im[0].shape for x in im)
         auto = same_shapes and self.model.pt

ultralytics/models/fastsam/prompt.py

@@ -26,7 +26,7 @@ class FastSAMPrompt:
             import clip  # for linear_assignment
         except ImportError:
             from ultralytics.utils.checks import check_requirements
-            check_requirements('git+https://github.com/openai/CLIP.git')  # required before installing lap from source
+            check_requirements('git+https://github.com/openai/CLIP.git')
             import clip
         self.clip = clip
 
@@ -91,8 +91,6 @@ class FastSAMPrompt:
                 y1 = min(y1, y_t)
                 x2 = max(x2, x_t + w_t)
                 y2 = max(y2, y_t + h_t)
-            h = y2 - y1
-            w = x2 - x1
         return [x1, y1, x2, y2]
 
     def plot(self,
@@ -104,9 +102,11 @@ class FastSAMPrompt:
              mask_random_color=True,
              better_quality=True,
              retina=False,
-             withContours=True):
+             with_countouers=True):
         if isinstance(annotations[0], dict):
             annotations = [annotation['segmentation'] for annotation in annotations]
+        if isinstance(annotations, torch.Tensor):
+            annotations = annotations.cpu().numpy()
         result_name = os.path.basename(self.img_path)
         image = self.ori_img
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
@@ -123,41 +123,22 @@ class FastSAMPrompt:
 
         plt.imshow(image)
         if better_quality:
-            if isinstance(annotations[0], torch.Tensor):
-                annotations = np.array(annotations.cpu())
             for i, mask in enumerate(annotations):
                 mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
                 annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
-        if self.device == 'cpu':
-            annotations = np.array(annotations)
-            self.fast_show_mask(
-                annotations,
-                plt.gca(),
-                random_color=mask_random_color,
-                bbox=bbox,
-                points=points,
-                pointlabel=point_label,
-                retinamask=retina,
-                target_height=original_h,
-                target_width=original_w,
-            )
-        else:
-            if isinstance(annotations[0], np.ndarray):
-                annotations = torch.from_numpy(annotations)
-            self.fast_show_mask_gpu(
-                annotations,
-                plt.gca(),
-                random_color=mask_random_color,
-                bbox=bbox,
-                points=points,
-                pointlabel=point_label,
-                retinamask=retina,
-                target_height=original_h,
-                target_width=original_w,
-            )
-        if isinstance(annotations, torch.Tensor):
-            annotations = annotations.cpu().numpy()
-        if withContours:
+        self.fast_show_mask(
+            annotations,
+            plt.gca(),
+            random_color=mask_random_color,
+            bbox=bbox,
+            points=points,
+            pointlabel=point_label,
+            retinamask=retina,
+            target_height=original_h,
+            target_width=original_w,
+        )
+
+        if with_countouers:
             contour_all = []
             temp = np.zeros((original_h, original_w, 1))
             for i, mask in enumerate(annotations):
@@ -184,8 +165,8 @@ class FastSAMPrompt:
         LOGGER.info(f'Saved to {save_path.absolute()}')
 
     #   CPU post process
+    @staticmethod
     def fast_show_mask(
-        self,
         annotation,
         ax,
         random_color=False,
@@ -196,32 +177,29 @@ class FastSAMPrompt:
         target_height=960,
         target_width=960,
     ):
-        msak_sum = annotation.shape[0]
-        height = annotation.shape[1]
-        weight = annotation.shape[2]
-        # 将annotation 按照面积 排序
+        n, h, w = annotation.shape  # batch, height, width
+
         areas = np.sum(annotation, axis=(1, 2))
-        sorted_indices = np.argsort(areas)
-        annotation = annotation[sorted_indices]
+        annotation = annotation[np.argsort(areas)]
 
         index = (annotation != 0).argmax(axis=0)
         if random_color:
-            color = np.random.random((msak_sum, 1, 1, 3))
+            color = np.random.random((n, 1, 1, 3))
         else:
-            color = np.ones((msak_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
-        transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6
+            color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
+        transparency = np.ones((n, 1, 1, 1)) * 0.6
         visual = np.concatenate([color, transparency], axis=-1)
         mask_image = np.expand_dims(annotation, -1) * visual
 
-        show = np.zeros((height, weight, 4))
-        h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
+        show = np.zeros((h, w, 4))
+        h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
         indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-        # 使用向量化索引更新show的值
+
         show[h_indices, w_indices, :] = mask_image[indices]
         if bbox is not None:
             x1, y1, x2, y2 = bbox
             ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
-        # draw point
+        # Draw point
         if points is not None:
             plt.scatter(
                 [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
@@ -240,63 +218,6 @@ class FastSAMPrompt:
             show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
         ax.imshow(show)
 
-    def fast_show_mask_gpu(
-        self,
-        annotation,
-        ax,
-        random_color=False,
-        bbox=None,
-        points=None,
-        pointlabel=None,
-        retinamask=True,
-        target_height=960,
-        target_width=960,
-    ):
-        msak_sum = annotation.shape[0]
-        height = annotation.shape[1]
-        weight = annotation.shape[2]
-        areas = torch.sum(annotation, dim=(1, 2))
-        sorted_indices = torch.argsort(areas, descending=False)
-        annotation = annotation[sorted_indices]
-        # 找每个位置第一个非零值下标
-        index = (annotation != 0).to(torch.long).argmax(dim=0)
-        if random_color:
-            color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device)
-        else:
-            color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor([30 / 255, 144 / 255, 1.0]).to(
-                annotation.device)
-        transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6
-        visual = torch.cat([color, transparency], dim=-1)
-        mask_image = torch.unsqueeze(annotation, -1) * visual
-        # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
-        show = torch.zeros((height, weight, 4)).to(annotation.device)
-        h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight), indexing='ij')
-        indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-        # 使用向量化索引更新show的值
-        show[h_indices, w_indices, :] = mask_image[indices]
-        show_cpu = show.cpu().numpy()
-        if bbox is not None:
-            x1, y1, x2, y2 = bbox
-            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
-        # draw point
-        if points is not None:
-            plt.scatter(
-                [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
-                [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],
-                s=20,
-                c='y',
-            )
-            plt.scatter(
-                [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],
-                [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],
-                s=20,
-                c='m',
-            )
-        if not retinamask:
-            show_cpu = cv2.resize(show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
-        ax.imshow(show_cpu)
-
-    # clip
     @torch.no_grad()
     def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
         preprocessed_images = [preprocess(image).to(device) for image in elements]

ultralytics/models/nas/predict.py

@@ -5,7 +5,6 @@ import torch
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
 from ultralytics.utils import ops
-from ultralytics.utils.ops import xyxy2xywh
 
 
 class NASPredictor(BasePredictor):
@@ -14,7 +13,7 @@ class NASPredictor(BasePredictor):
         """Postprocess predictions and returns a list of Results objects."""
 
         # Cat boxes and class scores
-        boxes = xyxy2xywh(preds_in[0][0])
+        boxes = ops.xyxy2xywh(preds_in[0][0])
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
 
         preds = ops.non_max_suppression(preds,

ultralytics/models/nas/val.py

@@ -4,7 +4,6 @@ import torch
 
 from ultralytics.models.yolo.detect import DetectionValidator
 from ultralytics.utils import ops
-from ultralytics.utils.ops import xyxy2xywh
 
 __all__ = ['NASValidator']
 
@@ -13,7 +12,7 @@ class NASValidator(DetectionValidator):
 
     def postprocess(self, preds_in):
         """Apply Non-maximum suppression to prediction outputs."""
-        boxes = xyxy2xywh(preds_in[0][0])
+        boxes = ops.xyxy2xywh(preds_in[0][0])
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
         return ops.non_max_suppression(preds,
                                        self.args.conf,

ultralytics/models/rtdetr/predict.py

@@ -9,6 +9,19 @@ from ultralytics.utils import ops
 
 
 class RTDETRPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on an RT-DETR detection model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.rtdetr import RTDETRPredictor
+
+        args = dict(model='rtdetr-l.pt', source=ASSETS)
+        predictor = RTDETRPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def postprocess(self, preds, img, orig_imgs):
         """Postprocess predictions and returns a list of Results objects."""
@@ -38,7 +51,9 @@ class RTDETRPredictor(BasePredictor):
         Args:
             im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
 
-        Return: A list of transformed imgs.
+        Notes: The size must be square(640) and scaleFilled.
+
+        Returns:
+            (list): A list of transformed imgs.
         """
-        # The size must be square(640) and scaleFilled.
         return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]

ultralytics/models/rtdetr/train.py

@@ -6,12 +6,28 @@ import torch
 
 from ultralytics.models.yolo.detect import DetectionTrainer
 from ultralytics.nn.tasks import RTDETRDetectionModel
-from ultralytics.utils import DEFAULT_CFG, RANK, colorstr
+from ultralytics.utils import RANK, colorstr
 
 from .val import RTDETRDataset, RTDETRValidator
 
 
 class RTDETRTrainer(DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on an RT-DETR detection model.
+
+    Notes:
+        - F.grid_sample used in rt-detr does not support the `deterministic=True` argument.
+        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
+
+    Example:
+        ```python
+        from ultralytics.models.rtdetr.train import RTDETRTrainer
+
+        args = dict(model='rtdetr-l.yaml', data='coco8.yaml', imgsz=640, epochs=3)
+        trainer = RTDETRTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def get_model(self, cfg=None, weights=None, verbose=True):
         """Return a YOLO detection model."""
@@ -54,27 +70,3 @@ class RTDETRTrainer(DetectionTrainer):
             gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
             gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
         return batch
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train and optimize RTDETR model given training data and device."""
-    model = 'rtdetr-l.yaml'
-    data = cfg.data or 'coco8.yaml'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    # NOTE: F.grid_sample which is in rt-detr does not support deterministic=True
-    # NOTE: amp training causes nan outputs and end with error while doing bipartite graph matching
-    args = dict(model=model,
-                data=data,
-                device=device,
-                imgsz=640,
-                exist_ok=True,
-                batch=4,
-                deterministic=False,
-                amp=False)
-    trainer = RTDETRTrainer(overrides=args)
-    trainer.train()
-
-
-if __name__ == '__main__':
-    train()

ultralytics/models/rtdetr/val.py

@@ -67,6 +67,18 @@ class RTDETRDataset(YOLODataset):
 
 
 class RTDETRValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on an RT-DETR detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.rtdetr import RTDETRValidator
+
+        args = dict(model='rtdetr-l.pt', data='coco8.yaml')
+        validator = RTDETRValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def build_dataset(self, img_path, mode='val', batch=None):
         """Build YOLO Dataset

ultralytics/models/sam/predict.py

@@ -55,12 +55,14 @@ class Predictor(BasePredictor):
         return img
 
     def pre_transform(self, im):
-        """Pre-transform input image before inference.
+        """
+        Pre-transform input image before inference.
 
         Args:
             im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
 
-        Return: A list of transformed imgs.
+        Returns:
+            (list): A list of transformed images.
         """
         assert len(im) == 1, 'SAM model has not supported batch inference yet!'
         return [LetterBox(self.args.imgsz, auto=False, center=False)(image=x) for x in im]

ultralytics/models/yolo/classify/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from ultralytics.models.yolo.classify.predict import ClassificationPredictor, predict
-from ultralytics.models.yolo.classify.train import ClassificationTrainer, train
-from ultralytics.models.yolo.classify.val import ClassificationValidator, val
+from ultralytics.models.yolo.classify.predict import ClassificationPredictor
+from ultralytics.models.yolo.classify.train import ClassificationTrainer
+from ultralytics.models.yolo.classify.val import ClassificationValidator
 
-__all__ = 'ClassificationPredictor', 'predict', 'ClassificationTrainer', 'train', 'ClassificationValidator', 'val'
+__all__ = 'ClassificationPredictor', 'ClassificationTrainer', 'ClassificationValidator'

ultralytics/models/yolo/classify/predict.py

@@ -4,10 +4,26 @@ import torch
 
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
-from ultralytics.utils import ASSETS, DEFAULT_CFG
+from ultralytics.utils import DEFAULT_CFG
 
 
 class ClassificationPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a classification model.
+
+    Notes:
+        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.classify import ClassificationPredictor
+
+        args = dict(model='yolov8n-cls.pt', source=ASSETS)
+        predictor = ClassificationPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         super().__init__(cfg, overrides, _callbacks)
@@ -30,21 +46,3 @@ class ClassificationPredictor(BasePredictor):
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
 
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Run YOLO model predictions on input images/videos."""
-    model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = ClassificationPredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/classify/train.py

@@ -13,6 +13,21 @@ from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_di
 
 
 class ClassificationTrainer(BaseTrainer):
+    """
+    A class extending the BaseTrainer class for training based on a classification model.
+
+    Notes:
+        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.classify import ClassificationTrainer
+
+        args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3)
+        trainer = ClassificationTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         """Initialize a ClassificationTrainer object with optional configuration overrides and callbacks."""
@@ -137,22 +152,3 @@ class ClassificationTrainer(BaseTrainer):
             cls=batch['cls'].view(-1),  # warning: use .view(), not .squeeze() for Classify models
             fname=self.save_dir / f'train_batch{ni}.jpg',
             on_plot=self.on_plot)
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train a YOLO classification model."""
-    model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
-    data = cfg.data or 'mnist160'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    args = dict(model=model, data=data, device=device)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).train(**args)
-    else:
-        trainer = ClassificationTrainer(overrides=args)
-        trainer.train()
-
-
-if __name__ == '__main__':
-    train()

ultralytics/models/yolo/classify/val.py

@@ -4,12 +4,27 @@ import torch
 
 from ultralytics.data import ClassificationDataset, build_dataloader
 from ultralytics.engine.validator import BaseValidator
-from ultralytics.utils import DEFAULT_CFG, LOGGER
+from ultralytics.utils import LOGGER
 from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
 from ultralytics.utils.plotting import plot_images
 
 
 class ClassificationValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a classification model.
+
+    Notes:
+        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.classify import ClassificationValidator
+
+        args = dict(model='yolov8n-cls.pt', data='imagenet10')
+        validator = ClassificationValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
@@ -92,21 +107,3 @@ class ClassificationValidator(BaseValidator):
                     fname=self.save_dir / f'val_batch{ni}_pred.jpg',
                     names=self.names,
                     on_plot=self.on_plot)  # pred
-
-
-def val(cfg=DEFAULT_CFG, use_python=False):
-    """Validate YOLO model using custom data."""
-    model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
-    data = cfg.data or 'mnist160'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = ClassificationValidator(args=args)
-        validator(model=args['model'])
-
-
-if __name__ == '__main__':
-    val()

ultralytics/models/yolo/detect/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from .predict import DetectionPredictor, predict
-from .train import DetectionTrainer, train
-from .val import DetectionValidator, val
+from .predict import DetectionPredictor
+from .train import DetectionTrainer
+from .val import DetectionValidator
 
-__all__ = 'DetectionPredictor', 'predict', 'DetectionTrainer', 'train', 'DetectionValidator', 'val'
+__all__ = 'DetectionPredictor', 'DetectionTrainer', 'DetectionValidator'

ultralytics/models/yolo/detect/predict.py

@@ -4,10 +4,23 @@ import torch
 
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
-from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
+from ultralytics.utils import ops
 
 
 class DetectionPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.detect import DetectionPredictor
+
+        args = dict(model='yolov8n.pt', source=ASSETS)
+        predictor = DetectionPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def postprocess(self, preds, img, orig_imgs):
         """Post-processes predictions and returns a list of Results objects."""
@@ -27,21 +40,3 @@ class DetectionPredictor(BasePredictor):
             img_path = path[i] if isinstance(path, list) else path
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Runs YOLO model inference on input image(s)."""
-    model = cfg.model or 'yolov8n.pt'
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = DetectionPredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/detect/train.py

@@ -8,12 +8,24 @@ from ultralytics.data import build_dataloader, build_yolo_dataset
 from ultralytics.engine.trainer import BaseTrainer
 from ultralytics.models import yolo
 from ultralytics.nn.tasks import DetectionModel
-from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK
+from ultralytics.utils import LOGGER, RANK
 from ultralytics.utils.plotting import plot_images, plot_labels, plot_results
 from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
 
 
 class DetectionTrainer(BaseTrainer):
+    """
+    A class extending the BaseTrainer class for training based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.detect import DetectionTrainer
+
+        args = dict(model='yolov8n.pt', data='coco8.yaml', epochs=3)
+        trainer = DetectionTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def build_dataset(self, img_path, mode='train', batch=None):
         """
@@ -102,22 +114,3 @@ class DetectionTrainer(BaseTrainer):
         boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
         cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
         plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train and optimize YOLO model given training data and device."""
-    model = cfg.model or 'yolov8n.pt'
-    data = cfg.data or 'coco8.yaml'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    args = dict(model=model, data=data, device=device)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).train(**args)
-    else:
-        trainer = DetectionTrainer(overrides=args)
-        trainer.train()
-
-
-if __name__ == '__main__':
-    train()

ultralytics/models/yolo/detect/val.py

@@ -8,7 +8,7 @@ import torch
 
 from ultralytics.data import build_dataloader, build_yolo_dataset, converter
 from ultralytics.engine.validator import BaseValidator
-from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
+from ultralytics.utils import LOGGER, ops
 from ultralytics.utils.checks import check_requirements
 from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
 from ultralytics.utils.plotting import output_to_target, plot_images
@@ -16,6 +16,18 @@ from ultralytics.utils.torch_utils import de_parallel
 
 
 class DetectionValidator(BaseValidator):
+    """
+    A class extending the BaseValidator class for validation based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.detect import DetectionValidator
+
+        args = dict(model='yolov8n.pt', data='coco8.yaml')
+        validator = DetectionValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize detection model with necessary variables and settings."""
@@ -254,21 +266,3 @@ class DetectionValidator(BaseValidator):
             except Exception as e:
                 LOGGER.warning(f'pycocotools unable to run: {e}')
         return stats
-
-
-def val(cfg=DEFAULT_CFG, use_python=False):
-    """Validate trained YOLO model on validation dataset."""
-    model = cfg.model or 'yolov8n.pt'
-    data = cfg.data or 'coco8.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = DetectionValidator(args=args)
-        validator(model=args['model'])
-
-
-if __name__ == '__main__':
-    val()

ultralytics/models/yolo/pose/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from .predict import PosePredictor, predict
-from .train import PoseTrainer, train
-from .val import PoseValidator, val
+from .predict import PosePredictor
+from .train import PoseTrainer
+from .val import PoseValidator
 
-__all__ = 'PoseTrainer', 'train', 'PoseValidator', 'val', 'PosePredictor', 'predict'
+__all__ = 'PoseTrainer', 'PoseValidator', 'PosePredictor'

ultralytics/models/yolo/pose/predict.py

@@ -2,10 +2,23 @@
 
 from ultralytics.engine.results import Results
 from ultralytics.models.yolo.detect.predict import DetectionPredictor
-from ultralytics.utils import ASSETS, DEFAULT_CFG, LOGGER, ops
+from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
 
 
 class PosePredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a pose model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.pose import PosePredictor
+
+        args = dict(model='yolov8n-pose.pt', source=ASSETS)
+        predictor = PosePredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         super().__init__(cfg, overrides, _callbacks)
@@ -40,21 +53,3 @@ class PosePredictor(DetectionPredictor):
                         boxes=pred[:, :6],
                         keypoints=pred_kpts))
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Runs YOLO to predict objects in an image or video."""
-    model = cfg.model or 'yolov8n-pose.pt'
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = PosePredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/pose/train.py

@@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
 
 
 class PoseTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on a pose model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.pose import PoseTrainer
+
+        args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
+        trainer = PoseTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         """Initialize a PoseTrainer object with specified configurations and overrides."""
@@ -59,22 +71,3 @@ class PoseTrainer(yolo.detect.DetectionTrainer):
     def plot_metrics(self):
         """Plots training/val metrics."""
         plot_results(file=self.csv, pose=True, on_plot=self.on_plot)  # save results.png
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train the YOLO model on the given data and device."""
-    model = cfg.model or 'yolov8n-pose.yaml'
-    data = cfg.data or 'coco8-pose.yaml'
-    device = cfg.device if cfg.device is not None else ''
-
-    args = dict(model=model, data=data, device=device)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).train(**args)
-    else:
-        trainer = PoseTrainer(overrides=args)
-        trainer.train()
-
-
-if __name__ == '__main__':
-    train()

ultralytics/models/yolo/pose/val.py

@@ -6,13 +6,25 @@ import numpy as np
 import torch
 
 from ultralytics.models.yolo.detect import DetectionValidator
-from ultralytics.utils import DEFAULT_CFG, LOGGER, ops
+from ultralytics.utils import LOGGER, ops
 from ultralytics.utils.checks import check_requirements
 from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou
 from ultralytics.utils.plotting import output_to_target, plot_images
 
 
 class PoseValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on a pose model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.pose import PoseValidator
+
+        args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
+        validator = PoseValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
@@ -201,21 +213,3 @@ class PoseValidator(DetectionValidator):
             except Exception as e:
                 LOGGER.warning(f'pycocotools unable to run: {e}')
         return stats
-
-
-def val(cfg=DEFAULT_CFG, use_python=False):
-    """Performs validation on YOLO model using given data."""
-    model = cfg.model or 'yolov8n-pose.pt'
-    data = cfg.data or 'coco8-pose.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = PoseValidator(args=args)
-        validator(model=args['model'])
-
-
-if __name__ == '__main__':
-    val()

ultralytics/models/yolo/segment/__init__.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from .predict import SegmentationPredictor, predict
-from .train import SegmentationTrainer, train
-from .val import SegmentationValidator, val
+from .predict import SegmentationPredictor
+from .train import SegmentationTrainer
+from .val import SegmentationValidator
 
-__all__ = 'SegmentationPredictor', 'predict', 'SegmentationTrainer', 'train', 'SegmentationValidator', 'val'
+__all__ = 'SegmentationPredictor', 'SegmentationTrainer', 'SegmentationValidator'

ultralytics/models/yolo/segment/predict.py

@@ -4,10 +4,23 @@ import torch
 
 from ultralytics.engine.results import Results
 from ultralytics.models.yolo.detect.predict import DetectionPredictor
-from ultralytics.utils import ASSETS, DEFAULT_CFG, ops
+from ultralytics.utils import DEFAULT_CFG, ops
 
 
 class SegmentationPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.segment import SegmentationPredictor
+
+        args = dict(model='yolov8n-seg.pt', source=ASSETS)
+        predictor = SegmentationPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         super().__init__(cfg, overrides, _callbacks)
@@ -42,21 +55,3 @@ class SegmentationPredictor(DetectionPredictor):
             results.append(
                 Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
         return results
-
-
-def predict(cfg=DEFAULT_CFG, use_python=False):
-    """Runs YOLO object detection on an image or video source."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    source = cfg.source or ASSETS
-
-    args = dict(model=model, source=source)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model)(**args)
-    else:
-        predictor = SegmentationPredictor(overrides=args)
-        predictor.predict_cli()
-
-
-if __name__ == '__main__':
-    predict()

ultralytics/models/yolo/segment/train.py

@@ -9,6 +9,18 @@ from ultralytics.utils.plotting import plot_images, plot_results
 
 
 class SegmentationTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.segment import SegmentationTrainer
+
+        args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
+        trainer = SegmentationTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
         """Initialize a SegmentationTrainer object with given arguments."""
@@ -46,19 +58,11 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
         plot_results(file=self.csv, segment=True, on_plot=self.on_plot)  # save results.png
 
 
-def train(cfg=DEFAULT_CFG, use_python=False):
+def train(cfg=DEFAULT_CFG):
     """Train a YOLO segmentation model based on passed arguments."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    data = cfg.data or 'coco8-seg.yaml'
-    device = cfg.device if cfg.device is not None else ''
-
-    args = dict(model=model, data=data, device=device)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).train(**args)
-    else:
-        trainer = SegmentationTrainer(overrides=args)
-        trainer.train()
+    args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
+    trainer = SegmentationTrainer(overrides=args)
+    trainer.train()
 
 
 if __name__ == '__main__':

ultralytics/models/yolo/segment/val.py

@@ -15,6 +15,18 @@ from ultralytics.utils.plotting import output_to_target, plot_images
 
 
 class SegmentationValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.segment import SegmentationValidator
+
+        args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml')
+        validator = SegmentationValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
@@ -233,18 +245,11 @@ class SegmentationValidator(DetectionValidator):
         return stats
 
 
-def val(cfg=DEFAULT_CFG, use_python=False):
+def val(cfg=DEFAULT_CFG):
     """Validate trained YOLO model on validation data."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    data = cfg.data or 'coco8-seg.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = SegmentationValidator(args=args)
-        validator(model=args['model'])
+    args = dict(model=cfg.model or 'yolov8n-seg.pt', data=cfg.data or 'coco8-seg.yaml')
+    validator = SegmentationValidator(args=args)
+    validator(model=args['model'])
 
 
 if __name__ == '__main__':

ultralytics/nn/autobackend.py

@@ -414,13 +414,10 @@ class AutoBackend(nn.Module):
                         scale, zero_point = output['quantization']
                         x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                     if x.ndim > 2:  # if task is not classification
-                        # Denormalize xywh with input image size
+                        # Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
                         # xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
-                        # See this PR for details: https://github.com/ultralytics/ultralytics/pull/1695
-                        x[:, 0] *= w
-                        x[:, 1] *= h
-                        x[:, 2] *= w
-                        x[:, 3] *= h
+                        x[:, [0, 2]] *= w
+                        x[:, [1, 3]] *= h
                     y.append(x)
             # TF segment fixes: export is reversed vs ONNX export and protos are transposed
             if len(y) == 2:  # segment with (det, proto) output order reversed

ultralytics/utils/__init__.py

@@ -169,7 +169,7 @@ def plt_settings(rcparams=None, backend='Agg'):
     """
     Decorator to temporarily set rc parameters and the backend for a plotting function.
 
-    Usage:
+    Example:
         decorator: @plt_settings({"font.size": 12})
         context manager: with plt_settings({"font.size": 12}):
 

ultralytics/utils/ops.py

@@ -18,8 +18,7 @@ from .metrics import box_iou
 
 class Profile(contextlib.ContextDecorator):
     """
-    YOLOv8 Profile class.
-    Usage: as a decorator with @Profile() or as a context manager with 'with Profile():'
+    YOLOv8 Profile class. Use as a decorator with @Profile() or as a context manager with 'with Profile():'.
     """
 
     def __init__(self, t=0.0):

ultralytics/utils/tal.py

@@ -10,12 +10,14 @@ TORCH_1_10 = check_version(torch.__version__, '1.10.0')
 
 
 def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
-    """select the positive anchor center in gt
+    """
+    Select the positive anchor center in gt.
 
     Args:
         xy_centers (Tensor): shape(h*w, 4)
         gt_bboxes (Tensor): shape(b, n_boxes, 4)
-    Return:
+
+    Returns:
         (Tensor): shape(b, n_boxes, h*w)
     """
     n_anchors = xy_centers.shape[0]
@@ -27,13 +29,14 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
 
 
 def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
-    """if an anchor box is assigned to multiple gts,
-        the one with the highest iou will be selected.
+    """
+    If an anchor box is assigned to multiple gts, the one with the highest IoI will be selected.
 
     Args:
         mask_pos (Tensor): shape(b, n_max_boxes, h*w)
         overlaps (Tensor): shape(b, n_max_boxes, h*w)
-    Return:
+
+    Returns:
         target_gt_idx (Tensor): shape(b, h*w)
         fg_mask (Tensor): shape(b, h*w)
         mask_pos (Tensor): shape(b, n_max_boxes, h*w)