9181ff62f5
Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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.
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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 super().postprocess(
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preds,
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max_time_img=0.5,
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)
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