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import torch
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from ultralytics.engine.predictor import BasePredictor
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from ultralytics.engine.results import Results
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from ultralytics.utils import ops
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class NASPredictor(BasePredictor):
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"""
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Ultralytics YOLO NAS Predictor for object detection.
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This class extends the `BasePredictor` from Ultralytics engine and is responsible for post-processing the
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raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
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scaling the bounding boxes to fit the original image dimensions.
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Attributes:
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args (Namespace): Namespace containing various configurations for post-processing.
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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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predictor = model.predictor
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# Assumes that raw_preds, img, orig_imgs are available
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results = predictor.postprocess(raw_preds, img, orig_imgs)
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```
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Note:
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Typically, this class is not instantiated directly. It is used internally within the `NAS` class.
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"""
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def postprocess(self, preds_in, img, orig_imgs):
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"""Postprocess predictions and returns a list of Results objects."""
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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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preds = 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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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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classes=self.args.classes,
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)
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if not isinstance(orig_imgs, list):
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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results = []
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for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
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return results
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