92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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.
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This class leverages Vision Transformers to provide real-time object detection while maintaining high accuracy.
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It supports key features like efficient hybrid encoding and IoU-aware query selection.
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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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model (torch.nn.Module): The loaded RT-DETR model.
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batch (list): Current batch of processed inputs.
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Methods:
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postprocess: Postprocess raw model predictions to generate bounding boxes and confidence scores.
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pre_transform: Pre-transform input images before feeding them into the model for inference.
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Examples:
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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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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`. It converts
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model predictions to Results objects containing properly scaled bounding boxes.
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Args:
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preds (list | tuple): List of [predictions, extra] from the model, where predictions contain
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bounding boxes and scores.
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img (torch.Tensor): Processed input images with shape (N, 3, H, W).
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orig_imgs (list | torch.Tensor): Original, unprocessed images.
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Returns:
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results (List[Results]): A list of Results objects containing the post-processed bounding boxes,
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confidence scores, 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 # scale x coordinates to original width
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pred[..., [1, 3]] *= oh # scale y coordinates to original height
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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-transform input images before feeding them into the model for inference.
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The input images are letterboxed to ensure a square aspect ratio and scale-filled. The size must be square
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(640) and scale_filled.
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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,
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[(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, scale_fill=True)
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return [letterbox(image=x) for x in im]
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