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import torch
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import ops
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__all__ = ["NASValidator"]
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class NASValidator(DetectionValidator):
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"""
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Ultralytics YOLO NAS Validator for object detection.
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Extends `DetectionValidator` from the Ultralytics models package and is designed to post-process the raw predictions
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generated by YOLO NAS models. It performs non-maximum suppression to remove overlapping and low-confidence boxes,
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ultimately producing the final detections.
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Attributes:
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args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU.
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lb (torch.Tensor): Optional tensor for multilabel NMS.
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Example:
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```python
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from ultralytics import NAS
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model = NAS("yolo_nas_s")
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validator = model.validator
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# Assumes that raw_preds are available
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final_preds = validator.postprocess(raw_preds)
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```
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Note:
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This class is generally not instantiated directly but is used internally within the `NAS` class.
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"""
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def postprocess(self, preds_in):
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"""Apply Non-maximum suppression to prediction outputs."""
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boxes = ops.xyxy2xywh(preds_in[0][0])
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preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
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return ops.non_max_suppression(
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preds,
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self.args.conf,
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self.args.iou,
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labels=self.lb,
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multi_label=False,
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agnostic=self.args.single_cls or self.args.agnostic_nms,
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max_det=self.args.max_det,
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max_time_img=0.5,
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)
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