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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
- 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."""
- # Cat boxes and class scores
- 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,
- )
- if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
- orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
- results = []
- for i, pred in enumerate(preds):
- orig_img = orig_imgs[i]
- pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
- img_path = self.batch[0][i]
- results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
- return results
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