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50 lines
1.6 KiB
50 lines
1.6 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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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 thresholds. |
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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, |
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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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