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