ultralytics 8.0.136 refactor and simplify package (#3748)

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

ultralytics/models/nas/__init__.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from .model import NAS
+from .predict import NASPredictor
+from .val import NASValidator
+
+__all__ = 'NASPredictor', 'NASValidator', 'NAS'

ultralytics/models/nas/model.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+"""
+YOLO-NAS model interface.
+
+Usage - Predict:
+    from ultralytics import NAS
+
+    model = NAS('yolo_nas_s')
+    results = model.predict('ultralytics/assets/bus.jpg')
+"""
+
+from pathlib import Path
+
+import torch
+
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, ROOT, is_git_dir
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.torch_utils import model_info, smart_inference_mode
+
+from .predict import NASPredictor
+from .val import NASValidator
+
+
+class NAS:
+
+    def __init__(self, model='yolo_nas_s.pt') -> None:
+        # Load or create new NAS model
+        import super_gradients
+
+        self.predictor = None
+        suffix = Path(model).suffix
+        if suffix == '.pt':
+            self._load(model)
+        elif suffix == '':
+            self.model = super_gradients.training.models.get(model, pretrained_weights='coco')
+        self.task = 'detect'
+        self.model.args = DEFAULT_CFG_DICT  # attach args to model
+
+        # Standardize model
+        self.model.fuse = lambda verbose=True: self.model
+        self.model.stride = torch.tensor([32])
+        self.model.names = dict(enumerate(self.model._class_names))
+        self.model.is_fused = lambda: False  # for info()
+        self.model.yaml = {}  # for info()
+        self.model.pt_path = model  # for export()
+        self.model.task = 'detect'  # for export()
+        self.info()
+
+    @smart_inference_mode()
+    def _load(self, weights: str):
+        self.model = torch.load(weights)
+
+    @smart_inference_mode()
+    def predict(self, source=None, stream=False, **kwargs):
+        """
+        Perform prediction using the YOLO model.
+
+        Args:
+            source (str | int | PIL | np.ndarray): The source of the image to make predictions on.
+                          Accepts all source types accepted by the YOLO model.
+            stream (bool): Whether to stream the predictions or not. Defaults to False.
+            **kwargs : Additional keyword arguments passed to the predictor.
+                       Check the 'configuration' section in the documentation for all available options.
+
+        Returns:
+            (List[ultralytics.engine.results.Results]): The prediction results.
+        """
+        if source is None:
+            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
+            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
+        overrides = dict(conf=0.25, task='detect', mode='predict')
+        overrides.update(kwargs)  # prefer kwargs
+        if not self.predictor:
+            self.predictor = NASPredictor(overrides=overrides)
+            self.predictor.setup_model(model=self.model)
+        else:  # only update args if predictor is already setup
+            self.predictor.args = get_cfg(self.predictor.args, overrides)
+        return self.predictor(source, stream=stream)
+
+    def train(self, **kwargs):
+        """Function trains models but raises an error as NAS models do not support training."""
+        raise NotImplementedError("NAS models don't support training")
+
+    def val(self, **kwargs):
+        """Run validation given dataset."""
+        overrides = dict(task='detect', mode='val')
+        overrides.update(kwargs)  # prefer kwargs
+        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
+        args.imgsz = check_imgsz(args.imgsz, max_dim=1)
+        validator = NASValidator(args=args)
+        validator(model=self.model)
+        self.metrics = validator.metrics
+        return validator.metrics
+
+    @smart_inference_mode()
+    def export(self, **kwargs):
+        """
+        Export model.
+
+        Args:
+            **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
+        """
+        overrides = dict(task='detect')
+        overrides.update(kwargs)
+        overrides['mode'] = 'export'
+        args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
+        args.task = self.task
+        if args.imgsz == DEFAULT_CFG.imgsz:
+            args.imgsz = self.model.args['imgsz']  # use trained imgsz unless custom value is passed
+        if args.batch == DEFAULT_CFG.batch:
+            args.batch = 1  # default to 1 if not modified
+        return Exporter(overrides=args)(model=self.model)
+
+    def info(self, detailed=False, verbose=True):
+        """
+        Logs model info.
+
+        Args:
+            detailed (bool): Show detailed information about model.
+            verbose (bool): Controls verbosity.
+        """
+        return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
+
+    def __call__(self, source=None, stream=False, **kwargs):
+        """Calls the 'predict' function with given arguments to perform object detection."""
+        return self.predict(source, stream, **kwargs)
+
+    def __getattr__(self, attr):
+        """Raises error if object has no requested attribute."""
+        name = self.__class__.__name__
+        raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")

ultralytics/models/nas/predict.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+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):
+
+    def postprocess(self, preds_in, img, orig_imgs):
+        """Postprocesses predictions and returns a list of Results objects."""
+
+        # Cat boxes and class scores
+        boxes = 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)
+
+        results = []
+        for i, pred in enumerate(preds):
+            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            if not isinstance(orig_imgs, torch.Tensor):
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+            path = self.batch[0]
+            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

ultralytics/models/nas/val.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import ops
+from ultralytics.utils.ops import xyxy2xywh
+
+__all__ = ['NASValidator']
+
+
+class NASValidator(DetectionValidator):
+
+    def postprocess(self, preds_in):
+        """Apply Non-maximum suppression to prediction outputs."""
+        boxes = 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)