85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
# 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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RT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions using
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Baidu's RT-DETR model.
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This class leverages the power of Vision Transformers to provide real-time object detection while maintaining
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high accuracy. It supports key features like efficient hybrid encoding and IoU-aware query selection.
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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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Attributes:
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imgsz (int): Image size for inference (must be square and scale-filled).
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args (dict): Argument overrides for the predictor.
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"""
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def postprocess(self, preds, img, orig_imgs):
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"""
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Postprocess the raw predictions from the model to generate bounding boxes and confidence scores.
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The method filters detections based on confidence and class if specified in `self.args`.
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Args:
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preds (list): List of [predictions, extra] from the model.
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img (torch.Tensor): Processed input images.
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orig_imgs (list or torch.Tensor): Original, unprocessed images.
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Returns:
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(list[Results]): A list of Results objects containing the post-processed bounding boxes, confidence scores,
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and class labels.
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"""
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if not isinstance(preds, (list, tuple)): # list for PyTorch inference but list[0] Tensor for export inference
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preds = [preds, None]
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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 bbox, score, orig_img, img_path in zip(bboxes, scores, orig_imgs, self.batch[0]): # (300, 4)
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bbox = ops.xywh2xyxy(bbox)
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max_score, cls = score.max(-1, keepdim=True) # (300, 1)
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idx = max_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, max_score, cls], dim=-1)[idx] # filter
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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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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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"""
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Pre-transforms the input images before feeding them into the model for inference. The input images are
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letterboxed to ensure a square aspect ratio and scale-filled. The size must be square(640) and scaleFilled.
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Args:
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im (list[np.ndarray] |torch.Tensor): Input images of shape (N,3,h,w) for tensor, [(h,w,3) x N] for list.
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Returns:
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(list): List of pre-transformed images ready for model inference.
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"""
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letterbox = LetterBox(self.imgsz, auto=False, scaleFill=True)
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return [letterbox(image=x) for x in im]
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