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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| from copy import copy | |
| import torch | |
| from ultralytics.models.yolo.detect import DetectionTrainer | |
| from ultralytics.nn.tasks import RTDETRDetectionModel | |
| from ultralytics.utils import RANK, colorstr | |
| from .val import RTDETRDataset, RTDETRValidator | |
| 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 | |