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# 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 thresholds.
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,
)
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