Reference for ultralytics/models/rtdetr/predict.py

class RTDETRPredictor(BasePredictor):

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`. It converts model
    predictions to Results objects containing properly scaled bounding boxes.

    Args:
        preds (list | tuple): List of [predictions, extra] from the model, where predictions contain bounding boxes
            and scores.
        img (torch.Tensor): Processed input images with shape (N, 3, H, W).
        orig_imgs (list | torch.Tensor): Original, unprocessed images.

    Returns:
        results (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)[..., ::-1]

    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
        pred = pred[pred[:, 4].argsort(descending=True)][: self.args.max_det]
        oh, ow = orig_img.shape[:2]
        pred[..., [0, 2]] *= ow  # scale x coordinates to original width
        pred[..., [1, 3]] *= oh  # scale y coordinates to original height
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
    return results

def pre_transform(self, im):
    """Pre-transform 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 scale_filled.

    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, scale_fill=True)
    return [letterbox(image=x) for x in im]