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Reference for ultralytics/models/rtdetr/predict.py

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


ultralytics.models.rtdetr.predict.RTDETRPredictor 

RTDETRPredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: BasePredictor

RT-DETR (Real-Time Detection Transformer) Predictor extending the BasePredictor class for making predictions using Baidu's RT-DETR model.

This class leverages the power of Vision Transformers to provide real-time object detection while maintaining high accuracy. It supports key features like efficient hybrid encoding and IoU-aware query selection.

Example 
from ultralytics.utils import ASSETS
from ultralytics.models.rtdetr import RTDETRPredictor

args = dict(model="rtdetr-l.pt", source=ASSETS)
predictor = RTDETRPredictor(overrides=args)
predictor.predict_cli()

Attributes:

Name Type Description
imgsz int

Image size for inference (must be square and scale-filled).
args dict

Argument overrides for the predictor.

Parameters:

Name Type Description Default
cfg str

Path to a configuration file. Defaults to DEFAULT_CFG.

 DEFAULT_CFG
overrides dict

Configuration overrides. Defaults to None.

 None
Source code in ultralytics/engine/predictor.py 
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """
    Initializes the BasePredictor class.

    Args:
        cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
        overrides (dict, optional): Configuration overrides. Defaults to None.
    """
    self.args = get_cfg(cfg, overrides)
    self.save_dir = get_save_dir(self.args)
    if self.args.conf is None:
        self.args.conf = 0.25  # default conf=0.25
    self.done_warmup = False
    if self.args.show:
        self.args.show = check_imshow(warn=True)

    # Usable if setup is done
    self.model = None
    self.data = self.args.data  # data_dict
    self.imgsz = None
    self.device = None
    self.dataset = None
    self.vid_writer = {}  # dict of {save_path: video_writer, ...}
    self.plotted_img = None
    self.source_type = None
    self.seen = 0
    self.windows = []
    self.batch = None
    self.results = None
    self.transforms = None
    self.callbacks = _callbacks or callbacks.get_default_callbacks()
    self.txt_path = None
    self._lock = threading.Lock()  # for automatic thread-safe inference
    callbacks.add_integration_callbacks(self)

postprocess 

postprocess(preds, img, orig_imgs)

Postprocess the raw predictions from the model to generate bounding boxes and confidence scores.

The method filters detections based on confidence and class if specified in self.args.

Parameters:

Name Type Description Default
preds list

List of [predictions, extra] from the model.

 required
img Tensor

Processed input images.

 required
orig_imgs list or Tensor

Original, unprocessed images.

 required

Returns:

Type Description
list[Results]

A list of Results objects containing the post-processed bounding boxes, confidence scores, and class labels.
Source code in ultralytics/models/rtdetr/predict.py 
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def postprocess(self, preds, img, orig_imgs):
    """
    Postprocess the raw predictions from the model to generate bounding boxes and confidence scores.

    The method filters detections based on confidence and class if specified in `self.args`.

    Args:
        preds (list): List of [predictions, extra] from the model.
        img (torch.Tensor): Processed input images.
        orig_imgs (list or torch.Tensor): Original, unprocessed images.

    Returns:
        (list[Results]): A list of Results objects containing the post-processed bounding boxes, confidence scores,
            and class labels.
    """
    if not isinstance(preds, (list, tuple)):  # list for PyTorch inference but list[0] Tensor for export inference
        preds = [preds, None]

    nd = preds[0].shape[-1]
    bboxes, scores = preds[0].split((4, nd - 4), dim=-1)

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for bbox, score, orig_img, img_path in zip(bboxes, scores, orig_imgs, self.batch[0]):  # (300, 4)
        bbox = ops.xywh2xyxy(bbox)
        max_score, cls = score.max(-1, keepdim=True)  # (300, 1)
        idx = max_score.squeeze(-1) > self.args.conf  # (300, )
        if self.args.classes is not None:
            idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx
        pred = torch.cat([bbox, max_score, cls], dim=-1)[idx]  # filter
        oh, ow = orig_img.shape[:2]
        pred[..., [0, 2]] *= ow
        pred[..., [1, 3]] *= oh
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
    return results

pre_transform 

pre_transform(im)

Pre-transforms the input images before feeding them into the model for inference. The input images are letterboxed to ensure a square aspect ratio and scale-filled. The size must be square(640) and scaleFilled.

Parameters:

Name Type Description Default
im list[ndarray] | Tensor

Input images of shape (N,3,h,w) for tensor, [(h,w,3) x N] for list.

 required

Returns:

Type Description
list

List of pre-transformed images ready for model inference.
Source code in ultralytics/models/rtdetr/predict.py 
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def pre_transform(self, im):
    """
    Pre-transforms the input images before feeding them into the model for inference. The input images are
    letterboxed to ensure a square aspect ratio and scale-filled. The size must be square(640) and scaleFilled.

    Args:
        im (list[np.ndarray] |torch.Tensor): Input images of shape (N,3,h,w) for tensor, [(h,w,3) x N] for list.

    Returns:
        (list): List of pre-transformed images ready for model inference.
    """
    letterbox = LetterBox(self.imgsz, auto=False, scaleFill=True)
    return [letterbox(image=x) for x in im]



📅 Created 1 year ago ✏️ Updated 3 months ago
jk4e glenn-jocher Burhan-Q