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# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import ops
from ultralytics.utils.ops import xyxy2xywh
__all__ = ['NASValidator']
class NASValidator(DetectionValidator):
def postprocess(self, preds_in):
"""Apply Non-maximum suppression to prediction outputs."""
boxes = 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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