Reference for ultralytics/models/rtdetr/train.py

class RTDETRTrainer(DetectionTrainer):
    """
    A class extending the DetectionTrainer class for training based on an RT-DETR detection model.

    Notes:
        - F.grid_sample used in rt-detr does not support the `deterministic=True` argument.
        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.

    Example:
        ```python
        from ultralytics.models.rtdetr.train import RTDETRTrainer

        args = dict(model='rtdetr-l.yaml', data='coco8.yaml', imgsz=640, epochs=3)
        trainer = RTDETRTrainer(overrides=args)
        trainer.train()
        ```
    """

    def get_model(self, cfg=None, weights=None, verbose=True):
        """Return a YOLO detection model."""
        model = RTDETRDetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
        if weights:
            model.load(weights)
        return model

    def build_dataset(self, img_path, mode='val', batch=None):
        """Build RTDETR Dataset

        Args:
            img_path (str): Path to the folder containing images.
            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
        """
        return RTDETRDataset(
            img_path=img_path,
            imgsz=self.args.imgsz,
            batch_size=batch,
            augment=mode == 'train',  # no augmentation
            hyp=self.args,
            rect=False,  # no rect
            cache=self.args.cache or None,
            prefix=colorstr(f'{mode}: '),
            data=self.data)

    def get_validator(self):
        """Returns a DetectionValidator for RTDETR model validation."""
        self.loss_names = 'giou_loss', 'cls_loss', 'l1_loss'
        return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))

    def preprocess_batch(self, batch):
        """Preprocesses a batch of images by scaling and converting to float."""
        batch = super().preprocess_batch(batch)
        bs = len(batch['img'])
        batch_idx = batch['batch_idx']
        gt_bbox, gt_class = [], []
        for i in range(bs):
            gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
            gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
        return batch