107 lines
4.0 KiB
Python
107 lines
4.0 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from copy import copy
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import torch
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from ultralytics.models.yolo.detect import DetectionTrainer
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from ultralytics.nn.tasks import RTDETRDetectionModel
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from ultralytics.utils import RANK, colorstr
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from .val import RTDETRDataset, RTDETRValidator
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class RTDETRTrainer(DetectionTrainer):
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"""
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Trainer class for the RT-DETR model developed by Baidu for real-time object detection.
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This class extends the DetectionTrainer class for YOLO to adapt to the specific features and architecture of RT-DETR.
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The model leverages Vision Transformers and has capabilities like IoU-aware query selection and adaptable inference
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speed.
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Attributes:
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loss_names (Tuple[str]): Names of the loss components used for training.
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data (Dict): Dataset configuration containing class count and other parameters.
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args (Dict): Training arguments and hyperparameters.
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save_dir (Path): Directory to save training results.
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test_loader (DataLoader): DataLoader for validation/testing data.
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Notes:
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- F.grid_sample used in RT-DETR does not support the `deterministic=True` argument.
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- AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
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Examples:
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>>> from ultralytics.models.rtdetr.train import RTDETRTrainer
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>>> args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3)
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>>> trainer = RTDETRTrainer(overrides=args)
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>>> trainer.train()
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"""
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def get_model(self, cfg=None, weights=None, verbose=True):
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"""
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Initialize and return an RT-DETR model for object detection tasks.
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Args:
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cfg (Dict, optional): Model configuration.
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weights (str, optional): Path to pre-trained model weights.
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verbose (bool): Verbose logging if True.
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Returns:
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(RTDETRDetectionModel): Initialized model.
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"""
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model = RTDETRDetectionModel(cfg, nc=self.data["nc"], verbose=verbose and RANK == -1)
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if weights:
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model.load(weights)
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return model
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def build_dataset(self, img_path, mode="val", batch=None):
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"""
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Build and return an RT-DETR dataset for training or validation.
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Args:
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img_path (str): Path to the folder containing images.
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mode (str): Dataset mode, either 'train' or 'val'.
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batch (int, optional): Batch size for rectangle training.
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Returns:
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(RTDETRDataset): Dataset object for the specific mode.
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"""
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return RTDETRDataset(
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img_path=img_path,
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imgsz=self.args.imgsz,
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batch_size=batch,
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augment=mode == "train",
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hyp=self.args,
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rect=False,
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cache=self.args.cache or None,
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single_cls=self.args.single_cls or False,
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prefix=colorstr(f"{mode}: "),
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classes=self.args.classes,
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data=self.data,
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fraction=self.args.fraction if mode == "train" else 1.0,
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)
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def get_validator(self):
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"""Returns a DetectionValidator suitable for RT-DETR model validation."""
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self.loss_names = "giou_loss", "cls_loss", "l1_loss"
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return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
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def preprocess_batch(self, batch):
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"""
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Preprocess a batch of images by scaling and converting to float format.
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Args:
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batch (Dict): Dictionary containing a batch of images, bboxes, and labels.
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Returns:
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(Dict): Preprocessed batch with ground truth bounding boxes and classes separated by batch index.
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"""
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batch = super().preprocess_batch(batch)
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bs = len(batch["img"])
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batch_idx = batch["batch_idx"]
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gt_bbox, gt_class = [], []
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for i in range(bs):
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gt_bbox.append(batch["bboxes"][batch_idx == i].to(batch_idx.device))
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gt_class.append(batch["cls"][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
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return batch
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