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83 lines
3.3 KiB
83 lines
3.3 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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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 (torch.Tensor): Raw predictions 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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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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""" |
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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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