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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): | |
| """ | |
| RT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions using | |
| Baidu's RT-DETR model. | |
| This class leverages the power of Vision Transformers to provide real-time object detection while maintaining | |
| high accuracy. It supports key features like efficient hybrid encoding and IoU-aware query selection. | |
| 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() | |
| ``` | |
| Attributes: | |
| imgsz (int): Image size for inference (must be square and scale-filled). | |
| args (dict): Argument overrides for the predictor. | |
| """ | |
| def postprocess(self, preds, img, orig_imgs): | |
| """ | |
| Postprocess the raw predictions from the model to generate bounding boxes and confidence scores. | |
| The method filters detections based on confidence and class if specified in `self.args`. | |
| Args: | |
| preds (list): List of [predictions, extra] from the model. | |
| img (torch.Tensor): Processed input images. | |
| orig_imgs (list or torch.Tensor): Original, unprocessed images. | |
| Returns: | |
| (list[Results]): A list of Results objects containing the post-processed bounding boxes, confidence scores, | |
| and class labels. | |
| """ | |
| if not isinstance(preds, (list, tuple)): # list for PyTorch inference but list[0] Tensor for export inference | |
| preds = [preds, None] | |
| nd = preds[0].shape[-1] | |
| bboxes, scores = preds[0].split((4, nd - 4), dim=-1) | |
| if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list | |
| orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) | |
| results = [] | |
| 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] | |
| oh, ow = orig_img.shape[:2] | |
| 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-transforms the input images before feeding them into the model for inference. The input images are | |
| letterboxed to ensure a square aspect ratio and scale-filled. The size must be square(640) and scaleFilled. | |
| Args: | |
| im (list[np.ndarray] |torch.Tensor): Input images of shape (N,3,h,w) for tensor, [(h,w,3) x N] for list. | |
| Returns: | |
| (list): List of pre-transformed images ready for model inference. | |
| """ | |
| letterbox = LetterBox(self.imgsz, auto=False, scaleFill=True) | |
| return [letterbox(image=x) for x in im] | |