add yolo v10 and modify pipeline

ultralytics/models/nas/__init__.py

@@ -4,4 +4,4 @@ from .model import NAS
 from .predict import NASPredictor
 from .val import NASValidator
 
-__all__ = 'NASPredictor', 'NASValidator', 'NAS'
+__all__ = "NASPredictor", "NASValidator", "NAS"

ultralytics/models/nas/model.py

@@ -17,26 +17,47 @@ import torch
 
 from ultralytics.engine.model import Model
 from ultralytics.utils.torch_utils import model_info, smart_inference_mode
-
 from .predict import NASPredictor
 from .val import NASValidator
 
 
 class NAS(Model):
+    """
+    YOLO NAS model for object detection.
 
-    def __init__(self, model='yolo_nas_s.pt') -> None:
-        assert Path(model).suffix not in ('.yaml', '.yml'), 'YOLO-NAS models only support pre-trained models.'
-        super().__init__(model, task='detect')
+    This class provides an interface for the YOLO-NAS models and extends the `Model` class from Ultralytics engine.
+    It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models.
+
+    Example:
+        ```python
+        from ultralytics import NAS
+
+        model = NAS('yolo_nas_s')
+        results = model.predict('ultralytics/assets/bus.jpg')
+        ```
+
+    Attributes:
+        model (str): Path to the pre-trained model or model name. Defaults to 'yolo_nas_s.pt'.
+
+    Note:
+        YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files.
+    """
+
+    def __init__(self, model="yolo_nas_s.pt") -> None:
+        """Initializes the NAS model with the provided or default 'yolo_nas_s.pt' model."""
+        assert Path(model).suffix not in (".yaml", ".yml"), "YOLO-NAS models only support pre-trained models."
+        super().__init__(model, task="detect")
 
     @smart_inference_mode()
     def _load(self, weights: str, task: str):
-        # Load or create new NAS model
+        """Loads an existing NAS model weights or creates a new NAS model with pretrained weights if not provided."""
         import super_gradients
+
         suffix = Path(weights).suffix
-        if suffix == '.pt':
+        if suffix == ".pt":
             self.model = torch.load(weights)
-        elif suffix == '':
-            self.model = super_gradients.training.models.get(weights, pretrained_weights='coco')
+        elif suffix == "":
+            self.model = super_gradients.training.models.get(weights, pretrained_weights="coco")
         # Standardize model
         self.model.fuse = lambda verbose=True: self.model
         self.model.stride = torch.tensor([32])
@@ -44,7 +65,7 @@ class NAS(Model):
         self.model.is_fused = lambda: False  # for info()
         self.model.yaml = {}  # for info()
         self.model.pt_path = weights  # for export()
-        self.model.task = 'detect'  # for export()
+        self.model.task = "detect"  # for export()
 
     def info(self, detailed=False, verbose=True):
         """
@@ -58,4 +79,5 @@ class NAS(Model):
 
     @property
     def task_map(self):
-        return {'detect': {'predictor': NASPredictor, 'validator': NASValidator}}
+        """Returns a dictionary mapping tasks to respective predictor and validator classes."""
+        return {"detect": {"predictor": NASPredictor, "validator": NASValidator}}

ultralytics/models/nas/predict.py

@@ -8,6 +8,29 @@ from ultralytics.utils import ops
 
 
 class NASPredictor(BasePredictor):
+    """
+    Ultralytics YOLO NAS Predictor for object detection.
+
+    This class extends the `BasePredictor` from Ultralytics engine and is responsible for post-processing the
+    raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
+    scaling the bounding boxes to fit the original image dimensions.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing.
+
+    Example:
+        ```python
+        from ultralytics import NAS
+
+        model = NAS('yolo_nas_s')
+        predictor = model.predictor
+        # Assumes that raw_preds, img, orig_imgs are available
+        results = predictor.postprocess(raw_preds, img, orig_imgs)
+        ```
+
+    Note:
+        Typically, this class is not instantiated directly. It is used internally within the `NAS` class.
+    """
 
     def postprocess(self, preds_in, img, orig_imgs):
         """Postprocess predictions and returns a list of Results objects."""
@@ -16,12 +39,14 @@ class NASPredictor(BasePredictor):
         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,
-                                        self.args.conf,
-                                        self.args.iou,
-                                        agnostic=self.args.agnostic_nms,
-                                        max_det=self.args.max_det,
-                                        classes=self.args.classes)
+        preds = ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            agnostic=self.args.agnostic_nms,
+            max_det=self.args.max_det,
+            classes=self.args.classes,
+        )
 
         if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

ultralytics/models/nas/val.py

@@ -5,20 +5,46 @@ import torch
 from ultralytics.models.yolo.detect import DetectionValidator
 from ultralytics.utils import ops
 
-__all__ = ['NASValidator']
+__all__ = ["NASValidator"]
 
 
 class NASValidator(DetectionValidator):
+    """
+    Ultralytics YOLO NAS Validator for object detection.
+
+    Extends `DetectionValidator` from the Ultralytics models package and is designed to post-process the raw predictions
+    generated by YOLO NAS models. It performs non-maximum suppression to remove overlapping and low-confidence boxes,
+    ultimately producing the final detections.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU thresholds.
+        lb (torch.Tensor): Optional tensor for multilabel NMS.
+
+    Example:
+        ```python
+        from ultralytics import NAS
+
+        model = NAS('yolo_nas_s')
+        validator = model.validator
+        # Assumes that raw_preds are available
+        final_preds = validator.postprocess(raw_preds)
+        ```
+
+    Note:
+        This class is generally not instantiated directly but is used internally within the `NAS` class.
+    """
 
     def postprocess(self, preds_in):
         """Apply Non-maximum suppression to prediction outputs."""
         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,
-                                       self.args.iou,
-                                       labels=self.lb,
-                                       multi_label=False,
-                                       agnostic=self.args.single_cls,
-                                       max_det=self.args.max_det,
-                                       max_time_img=0.5)
+        return ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            labels=self.lb,
+            multi_label=False,
+            agnostic=self.args.single_cls,
+            max_det=self.args.max_det,
+            max_time_img=0.5,
+        )