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61 lines
2.2 KiB
61 lines
2.2 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import torch |
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from ultralytics.data.augment import LetterBox |
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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 RTDETRPredictor(BasePredictor): |
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""" |
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A class extending the BasePredictor class for prediction based on an RT-DETR detection model. |
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Example: |
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```python |
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from ultralytics.utils import ASSETS |
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from ultralytics.models.rtdetr import RTDETRPredictor |
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args = dict(model='rtdetr-l.pt', source=ASSETS) |
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predictor = RTDETRPredictor(overrides=args) |
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predictor.predict_cli() |
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``` |
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""" |
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def postprocess(self, preds, img, orig_imgs): |
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"""Postprocess predictions and returns a list of Results objects.""" |
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nd = preds[0].shape[-1] |
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bboxes, scores = preds[0].split((4, nd - 4), dim=-1) |
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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, bbox in enumerate(bboxes): # (300, 4) |
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bbox = ops.xywh2xyxy(bbox) |
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score, cls = scores[i].max(-1, keepdim=True) # (300, 1) |
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idx = score.squeeze(-1) > self.args.conf # (300, ) |
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if self.args.classes is not None: |
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idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx |
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pred = torch.cat([bbox, score, cls], dim=-1)[idx] # filter |
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orig_img = orig_imgs[i] |
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oh, ow = orig_img.shape[:2] |
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pred[..., [0, 2]] *= ow |
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pred[..., [1, 3]] *= oh |
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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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def pre_transform(self, im): |
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"""Pre-transform input image before inference. |
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Args: |
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im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list. |
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Notes: The size must be square(640) and scaleFilled. |
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Returns: |
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(list): A list of transformed imgs. |
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""" |
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return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]
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