更新 detacttracking

detecttracking/ultralytics/models/rtdetr/train.py

@@ -0,0 +1,72 @@
+# 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