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60 lines
2.1 KiB
60 lines
2.1 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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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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# Cat boxes and class scores |
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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): # input images are a torch.Tensor, not a list |
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) |
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results = [] |
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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) |
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img_path = self.batch[0][i] |
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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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