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Reference for ultralytics/models/rtdetr/val.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/rtdetr/val.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.rtdetr.val.RTDETRDataset

RTDETRDataset(*args, data=None, **kwargs)

Bases: YOLODataset

Real-Time DEtection and TRacking (RT-DETR) dataset class extending the base YOLODataset class.

This specialized dataset class is designed for use with the RT-DETR object detection model and is optimized for real-time detection and tracking tasks.

Source code in ultralytics/models/rtdetr/val.py 
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def __init__(self, *args, data=None, **kwargs):
    """Initialize the RTDETRDataset class by inheriting from the YOLODataset class."""
    super().__init__(*args, data=data, **kwargs)

build_transforms

build_transforms(hyp=None)

Temporary, only for evaluation.

Source code in ultralytics/models/rtdetr/val.py 
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def build_transforms(self, hyp=None):
    """Temporary, only for evaluation."""
    if self.augment:
        hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
        hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
        transforms = v8_transforms(self, self.imgsz, hyp, stretch=True)
    else:
        # transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scaleFill=True)])
        transforms = Compose([])
    transforms.append(
        Format(
            bbox_format="xywh",
            normalize=True,
            return_mask=self.use_segments,
            return_keypoint=self.use_keypoints,
            batch_idx=True,
            mask_ratio=hyp.mask_ratio,
            mask_overlap=hyp.overlap_mask,
        )
    )
    return transforms

load_image

load_image(i, rect_mode=False)

Loads 1 image from dataset index 'i', returns (im, resized hw).

Source code in ultralytics/models/rtdetr/val.py 
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def load_image(self, i, rect_mode=False):
    """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
    return super().load_image(i=i, rect_mode=rect_mode)




ultralytics.models.rtdetr.val.RTDETRValidator

RTDETRValidator(
    dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None
)

Bases: DetectionValidator

RTDETRValidator extends the DetectionValidator class to provide validation capabilities specifically tailored for the RT-DETR (Real-Time DETR) object detection model.

The class allows building of an RTDETR-specific dataset for validation, applies Non-maximum suppression for post-processing, and updates evaluation metrics accordingly.

Example 
from ultralytics.models.rtdetr import RTDETRValidator

args = dict(model="rtdetr-l.pt", data="coco8.yaml")
validator = RTDETRValidator(args=args)
validator()
Note

For further details on the attributes and methods, refer to the parent DetectionValidator class.

Source code in ultralytics/models/yolo/detect/val.py 
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
    """Initialize detection model with necessary variables and settings."""
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
    self.nt_per_class = None
    self.nt_per_image = None
    self.is_coco = False
    self.is_lvis = False
    self.class_map = None
    self.args.task = "detect"
    self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
    self.iouv = torch.linspace(0.5, 0.95, 10)  # IoU vector for mAP@0.5:0.95
    self.niou = self.iouv.numel()
    self.lb = []  # for autolabelling
    if self.args.save_hybrid:
        LOGGER.warning(
            "WARNING ⚠️ 'save_hybrid=True' will append ground truth to predictions for autolabelling.\n"
            "WARNING ⚠️ 'save_hybrid=True' will cause incorrect mAP.\n"
        )

build_dataset

build_dataset(img_path, mode='val', batch=None)

Build an RTDETR Dataset.

Parameters:

NameTypeDescriptionDefault
img_pathstr

Path to the folder containing images.

required
modestr

train mode or val mode, users are able to customize different augmentations for each mode.

'val'
batchint

Size of batches, this is for rect. Defaults to None.

None
Source code in ultralytics/models/rtdetr/val.py 
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def build_dataset(self, img_path, mode="val", batch=None):
    """
    Build an 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=False,  # no augmentation
        hyp=self.args,
        rect=False,  # no rect
        cache=self.args.cache or None,
        prefix=colorstr(f"{mode}: "),
        data=self.data,
    )

postprocess

postprocess(preds)

Apply Non-maximum suppression to prediction outputs.

Source code in ultralytics/models/rtdetr/val.py 
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def postprocess(self, preds):
    """Apply Non-maximum suppression to prediction outputs."""
    if not isinstance(preds, (list, tuple)):  # list for PyTorch inference but list[0] Tensor for export inference
        preds = [preds, None]

    bs, _, nd = preds[0].shape
    bboxes, scores = preds[0].split((4, nd - 4), dim=-1)
    bboxes *= self.args.imgsz
    outputs = [torch.zeros((0, 6), device=bboxes.device)] * bs
    for i, bbox in enumerate(bboxes):  # (300, 4)
        bbox = ops.xywh2xyxy(bbox)
        score, cls = scores[i].max(-1)  # (300, )
        # Do not need threshold for evaluation as only got 300 boxes here
        # idx = score > self.args.conf
        pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1)  # filter
        # Sort by confidence to correctly get internal metrics
        pred = pred[score.argsort(descending=True)]
        outputs[i] = pred  # [idx]

    return outputs



📅 Created 11 months ago ✏️ Updated 1 month ago
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