| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950 |
- # Ultralytics YOLO 🚀, AGPL-3.0 license
- import torch
- from ultralytics.models.yolo.detect import DetectionValidator
- from ultralytics.utils import ops
- __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.
- 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,
- )
|
