Reference for ultralytics/nn/tasks.py#

class BaseModel(torch.nn.Module):
    """Base class for all YOLO models in the Ultralytics family.

    This class provides common functionality for YOLO models including forward pass handling, model fusion, information
    display, and weight loading capabilities.

    Attributes:
        model (torch.nn.Sequential): The neural network model.
        save (list): List of layer indices to save outputs from.
        stride (torch.Tensor): Model stride values.

    Methods:
        forward: Perform forward pass for training or inference.
        predict: Perform inference on input tensor.
        fuse: Fuse Conv/BatchNorm layers and reparameterize for optimization.
        info: Print model information.
        load: Load weights into the model.
        loss: Compute loss for training.

    Examples:
        Use BaseModel functionality through a subclass
        >>> model = DetectionModel("yolo26n.yaml")
        >>> model.info()  # Display model information
    """

def _apply(self, fn):
    """Apply a function to all tensors in the model, including Detect head attributes like stride and anchors.

    Args:
        fn (function): The function to apply to the model.

    Returns:
        (BaseModel): An updated BaseModel object.
    """
    super()._apply(fn)
    m = self.model[-1]  # Detect()
    if isinstance(
        m, Detect
    ):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect, YOLOESegment
        m.stride = fn(m.stride)
        m.anchors = fn(m.anchors)
        m.strides = fn(m.strides)
    elif isinstance(m, RTDETRDecoder):
        m.anchors = fn(m.anchors)
        m.valid_mask = fn(m.valid_mask)
    return self

def _predict_augment(self, x):
    """Perform augmentations on input image x and return augmented inference."""
    LOGGER.warning(
        f"{self.__class__.__name__} does not support 'augment=True' prediction. "
        f"Reverting to single-scale prediction."
    )
    return self._predict_once(x)

def _predict_once(self, x, profile=False, embed=None):
    """Perform a forward pass through the network.

    Args:
        x (torch.Tensor): The input tensor to the model.
        profile (bool): Print the computation time of each layer if True.
        embed (list, optional): A list of layer indices to return embeddings from.

    Returns:
        (torch.Tensor | tuple[torch.Tensor, ...]): The last output of the model, or per-image embedding vectors if
            `embed` is given.
    """
    y, dt, embeddings = [], [], []  # outputs
    embed = frozenset(embed) if embed else {-1}
    max_idx = max(embed)
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        x = m(x)  # run
        y.append(x if m.i in self.save else None)  # save output
        if m.i in embed:
            embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max_idx:
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    return x

def _profile_one_layer(self, m, x, dt):
    """Profile the computation time and FLOPs of a single layer of the model on a given input.

    Args:
        m (torch.nn.Module): The layer to be profiled.
        x (torch.Tensor): The input data to the layer.
        dt (list): A list to store the computation time of the layer.
    """
    try:
        import thop
    except ImportError:
        thop = None  # conda support without 'ultralytics-thop' installed

    c = m == self.model[-1] and isinstance(x, list)  # is final layer list, copy input as inplace fix
    flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0
    device = next(self.parameters()).device
    t = time_sync(device)
    for _ in range(10):
        m(x.copy() if c else x)
    dt.append((time_sync(device) - t) * 100)
    if m == self.model[0]:
        LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
    LOGGER.info(f"{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f}  {m.type}")
    if c:
        LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

def _remap_cls_by_names(self, csd: dict[str, torch.Tensor], src_model: torch.nn.Module, verbose: bool = True):
    """Remap pretrained classification head rows to current class order by name.

    Copies rows from pretrained cls layers into the current model's state_dict where the destination class name
    matches a source class name (case-insensitive, whitespace-stripped). Useful when fine-tuning across datasets
    with overlapping classes, whether the class counts differ (e.g. Objects365 -> COCO) or match but the class
    order differs. Mutates the destination tensors in-place via state_dict references; matched cls tensors are
    removed from `csd` so the subsequent intersect_dicts skips them.

    Args:
        csd (dict): Pretrained checkpoint state_dict (will be mutated).
        src_model (torch.nn.Module): Pretrained module, used to read `.names` and `.nc`.
        verbose (bool): Log mapping summary.

    Returns:
        (int): Number of cls tensors remapped (counted toward "Transferred" log line).
    """
    src_names = getattr(src_model, "names", None)
    tgt_names = getattr(self, "names", None)
    if not (isinstance(src_names, dict) and isinstance(tgt_names, dict)):
        return 0
    src_nc, tgt_nc = len(src_names), len(tgt_names)

    def _norm(s):
        return str(s).strip().lower()

    # Skip default placeholder names {0:"0", 1:"1", ...} (also catches empty dicts) — nothing to match on
    if any(all(str(k) == str(v) for k, v in n.items()) for n in (src_names, tgt_names)):
        return 0

    src_lookup = {_norm(v): k for k, v in src_names.items()}
    idx = torch.tensor([src_lookup.get(_norm(tgt_names.get(k)), -1) for k in range(tgt_nc)], dtype=torch.long)
    n_match = int((idx >= 0).sum())
    # Skip if nothing matches, or class names already share order and count (intersect_dicts copies directly)
    if n_match == 0 or (src_nc == tgt_nc and torch.equal(idx, torch.arange(tgt_nc))):
        return 0

    valid = idx >= 0
    state_dict = self.state_dict()
    # Exact class-logit conv weight/bias keys from the detection head(s) — restricting to these avoids
    # class-ordering tensors that merely share the nc dimension (backbone blocks, box/mask/pose branches).
    cls_keys = {
        f"{name}.{attr}.{i}.{len(seq) - 1}.{p}"
        for name, m in self.named_modules()
        if isinstance(m, Detect)
        for attr in ("cv3", "one2one_cv3")
        for i, seq in enumerate(getattr(m, attr, ()))
        if getattr(seq[-1], "out_channels", None) == tgt_nc
        for p in ("weight", "bias")
    }
    remapped = 0
    for k in cls_keys & csd.keys():
        v_src, v_tgt = csd[k], state_dict[k]
        if v_src.shape[1:] != v_tgt.shape[1:]:  # cls-conv weight input width (c3) differs across nc; copy bias only
            continue
        v_tgt[valid] = v_src[idx[valid]].to(v_tgt.dtype)
        csd.pop(k)  # prevent intersect_dicts from copying these rows in the wrong (source) order
        remapped += 1
    if verbose and remapped:
        LOGGER.info(f"Remapped {n_match}/{tgt_nc} cls head rows from pretrained weights by class name")
    return remapped

def forward(self, x, *args, **kwargs):
    """Perform forward pass of the model for either training or inference.

    If x is a dict, calculates and returns the loss for training. Otherwise, returns predictions for inference.

    Args:
        x (torch.Tensor | dict): Input tensor for inference, or dict with image tensor and labels for training.
        *args (Any): Variable length argument list.
        **kwargs (Any): Arbitrary keyword arguments.

    Returns:
        (Any): Loss tuple from `loss()` if x is a dict (training), or network predictions from `predict()`
            (inference).
    """
    if isinstance(x, dict):  # for cases of training and validating while training.
        return self.loss(x, *args, **kwargs)
    return self.predict(x, *args, **kwargs)

def fuse(self, verbose=True, imgsz=640):
    """Fuse Conv/ConvTranspose and BatchNorm layers, and reparameterize RepConv/RepVGGDW for improved efficiency.

    Args:
        verbose (bool): Whether to print model information after fusion.
        imgsz (int | list): Input image size used for FLOPs calculation.

    Returns:
        (BaseModel): The fused model.
    """
    # BN folds into a QAT conv exactly, its per-channel weight range scales along; merged branches and transposed
    # convs have no such rescale for their ranges, so they stay as trained
    skip = (Conv2, ConvTranspose, RepConv, RepVGGDW) if is_qat(self) else ()
    if not self.is_fused():
        for m in self.model.modules():
            if isinstance(m, skip):
                continue
            if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
                if isinstance(m, Conv2):
                    m.fuse_convs()
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
            if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
                m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
            if isinstance(m, RepConv):
                m.fuse_convs()
                m.forward = m.forward_fuse  # update forward
            if isinstance(m, RepVGGDW):
                m.fuse()
                m.forward = m.forward_fuse
            if isinstance(m, Detect):
                m.fuse()  # remove the unused detection branch
        self.info(verbose=verbose, imgsz=imgsz)

    return self

def info(self, detailed=False, verbose=True, imgsz=640):
    """Print model information.

    Args:
        detailed (bool): If True, prints out detailed information about the model.
        verbose (bool): If True, prints out the model information.
        imgsz (int | list): The size of the image used for computing model information.

    Returns:
        (tuple | None): Number of layers, parameters, gradients, and GFLOPs, or None if `verbose` is False.
    """
    return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)

def init_criterion(self):
    """Initialize the loss criterion for the BaseModel."""
    raise NotImplementedError("compute_loss() needs to be implemented by task heads")

def is_fused(self):
    """Return True once fuse() has nothing left to do."""
    return not any(
        (isinstance(m, (Conv, ConvTranspose)) and hasattr(m, "bn"))
        or (isinstance(m, (RepConv, RepVGGDW)) and hasattr(m, "conv1"))
        or (isinstance(m, Detect) and m.cv2 is not None and getattr(m, "one2one_cv2", None) is not None)
        for m in self.modules()
    )

def load(self, weights, verbose=True):
    """Load weights into the model.

    Args:
        weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
        verbose (bool, optional): Whether to log the transfer progress.
    """
    model = (weights.get("ema") or weights["model"]) if isinstance(weights, dict) else weights  # ema first
    csd = model.float().state_dict()  # checkpoint state_dict as FP32

    # Remap classification head rows by class-name when nc differs (e.g. Obj365 -> COCO fine-tune)
    cls_remapped = self._remap_cls_by_names(csd, model, verbose=verbose)

    updated_csd = intersect_dicts(csd, self.state_dict())  # intersect
    self.load_state_dict(updated_csd, strict=False)  # load
    len_updated_csd = len(updated_csd) + cls_remapped
    first_conv = "model.0.conv.weight"  # hard-coded to yolo models for now
    # mostly used to boost multi-channel training
    state_dict = self.state_dict()
    if first_conv not in updated_csd and first_conv in state_dict:
        c1, c2, h, w = state_dict[first_conv].shape
        cc1, cc2, ch, cw = csd[first_conv].shape
        if ch == h and cw == w:
            c1, c2 = min(c1, cc1), min(c2, cc2)
            state_dict[first_conv][:c1, :c2] = csd[first_conv][:c1, :c2]
            len_updated_csd += 1
    self.pt_path = getattr(model, "pt_path", None)  # provenance follows the weights selected above
    if verbose:
        LOGGER.info(f"Transferred {len_updated_csd}/{len(self.model.state_dict())} items from pretrained weights")
        if getattr(model, "is_fused", lambda: False)() and not self.is_fused():
            LOGGER.warning("Pretrained weights are fused for inference; train from the unfused checkpoint instead.")

def loss(self, batch, preds=None):
    """Compute loss.

    Args:
        batch (dict): Batch to compute loss on.
        preds (torch.Tensor | list[torch.Tensor], optional): Predictions.

    Returns:
        loss (torch.Tensor): Loss tensor for backpropagation.
        loss_items (dict[str, torch.Tensor]): Detached loss components, as returned by the criterion.
    """
    if getattr(self, "criterion", None) is None:
        self.criterion = self.init_criterion()

    if preds is None:
        preds = self.forward(batch["img"])
    return self.criterion(preds, batch)

def predict(self, x, profile=False, augment=False, embed=None):
    """Perform a forward pass through the network.

    Args:
        x (torch.Tensor): The input tensor to the model.
        profile (bool): Print the computation time of each layer if True.
        augment (bool): Augment image during prediction.
        embed (list, optional): A list of layer indices to return embeddings from.

    Returns:
        (torch.Tensor | tuple[torch.Tensor, ...]): The last output of the model, or per-image embedding vectors if
            `embed` is given.
    """
    if augment:
        return self._predict_augment(x)
    return self._predict_once(x, profile, embed)

class DetectionModel(BaseModel):
    """YOLO detection model.

    This class implements the YOLO detection architecture, handling model initialization, forward pass, augmented
    inference, and loss computation for object detection tasks.

    Attributes:
        yaml (dict): Model configuration dictionary.
        model (torch.nn.Sequential): The neural network model.
        save (list): List of layer indices to save outputs from.
        names (dict): Class names dictionary.
        inplace (bool): Whether to use inplace operations.
        end2end (bool): Whether the model uses end-to-end detection.
        stride (torch.Tensor): Model stride values.

    Methods:
        __init__: Initialize the YOLO detection model.
        _predict_augment: Perform augmented inference.
        _descale_pred: De-scale predictions following augmented inference.
        _clip_augmented: Clip YOLO augmented inference tails.
        init_criterion: Initialize the loss criterion.

    Examples:
        Initialize a detection model
        >>> model = DetectionModel("yolo26n.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n.yaml", ch=3, nc=None, verbose=True):
        """Initialize the YOLO detection model with the given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        _initialize_yolo_model(self, cfg, ch, nc, verbose)

        # Build strides
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, YOLOEDetect, YOLOESegment
            s = 256  # 2x min stride
            m.inplace = self.inplace

            def _forward(x):
                """Perform a forward pass through the model, handling different Detect subclass types accordingly."""
                output = self.forward(x)
                if "one2many" in output:
                    output = output["one2many"]
                return output["feats"]

            self.model.eval()  # Avoid changing batch statistics until training begins
            m.training = True  # Setting it to True to properly return strides
            m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))])  # forward
            self.stride = m.stride
            self.model.train()  # Set model back to training(default) mode
            m.bias_init()  # only run once
        else:
            self.stride = torch.Tensor([32])  # default stride, e.g., RTDETR

        # Init weights, biases
        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")

@property
def end2end(self):
    """Return whether the model uses end-to-end NMS-free detection."""
    return getattr(self.model[-1], "end2end", False)

def _clip_augmented(self, y):
    """Clip YOLO augmented inference tails.

    Args:
        y (list[torch.Tensor]): List of detection tensors.

    Returns:
        (list[torch.Tensor]): Clipped detection tensors.
    """
    nl = self.model[-1].nl  # number of detection layers (P3-P5)
    g = sum(4**x for x in range(nl))  # grid points
    e = 1  # exclude layer count
    i = (y[0].shape[-1] // g) * sum(4**x for x in range(e))  # indices
    y[0] = y[0][..., :-i]  # large
    i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
    y[-1] = y[-1][..., i:]  # small
    return y

@staticmethod
def _descale_pred(p, flips, scale, img_size, dim=1):
    """De-scale predictions following augmented inference (inverse operation).

    Args:
        p (torch.Tensor): Predictions tensor.
        flips (int | None): Flip type (None=none, 2=ud, 3=lr).
        scale (float): Scale factor.
        img_size (tuple): Original image size (height, width).
        dim (int): Dimension to split at.

    Returns:
        (torch.Tensor): De-scaled predictions.
    """
    p[:, :4] /= scale  # de-scale
    x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
    if flips == 2:
        y = img_size[0] - y  # de-flip ud
    elif flips == 3:
        x = img_size[1] - x  # de-flip lr
    return torch.cat((x, y, wh, cls), dim)

def _predict_augment(self, x):
    """Perform augmentations on input image x and return augmented inference and train outputs.

    Args:
        x (torch.Tensor): Input image tensor.

    Returns:
        (tuple[torch.Tensor, None]): Augmented inference output and None for train output.
    """
    if getattr(self, "end2end", False) or type(self.model[-1]) is not Detect:
        LOGGER.warning("Model does not support 'augment=True', reverting to single-scale prediction.")
        return self._predict_once(x)
    img_size = x.shape[-2:]  # height, width
    s = [1, 0.83, 0.67]  # scales
    f = [None, 3, None]  # flips (2-ud, 3-lr)
    y = []  # outputs
    for si, fi in zip(s, f):
        xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
        yi = super().predict(xi)[0]  # forward
        yi = self._descale_pred(yi, fi, si, img_size)
        y.append(yi)
    y = self._clip_augmented(y)  # clip augmented tails
    return torch.cat(y, -1), None  # augmented inference, train

@end2end.setter
def end2end(self, value):
    """Select the inference head while retaining both branches for training."""
    if isinstance(self.model[-1], Detect):
        self.model[-1].end2end = value

def init_criterion(self):
    """Initialize the loss criterion for the DetectionModel."""
    return E2ELoss(self) if getattr(self.model[-1], "one2one_cv2", None) is not None else v8DetectionLoss(self)

def set_head_attr(self, **kwargs):
    """Set attributes of the model head (last layer).

    Args:
        **kwargs (Any): Arbitrary keyword arguments representing attributes to set.
    """
    head = self.model[-1]
    for k, v in kwargs.items():
        if not hasattr(head, k):
            LOGGER.warning(f"Head has no attribute '{k}'.")
            continue
        setattr(head, k, v)

class OBBModel(DetectionModel):
    """YOLO Oriented Bounding Box (OBB) model.

    This class extends DetectionModel to handle oriented bounding box detection tasks, providing specialized loss
    computation for rotated object detection.

    Methods:
        __init__: Initialize YOLO OBB model.
        init_criterion: Initialize the loss criterion for OBB detection.

    Examples:
        Initialize an OBB model
        >>> model = OBBModel("yolo26n-obb.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-obb.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLO OBB model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def init_criterion(self):
    """Initialize the loss criterion for the model."""
    return E2ELoss(self, v8OBBLoss) if getattr(self.model[-1], "one2one_cv2", None) is not None else v8OBBLoss(self)

class SegmentationModel(DetectionModel):
    """YOLO segmentation model.

    This class extends DetectionModel to handle instance segmentation tasks, providing specialized loss computation for
    pixel-level object detection and segmentation.

    Methods:
        __init__: Initialize YOLO segmentation model.
        init_criterion: Initialize the loss criterion for segmentation.

    Examples:
        Initialize a segmentation model
        >>> model = SegmentationModel("yolo26n-seg.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-seg.yaml", ch=3, nc=None, verbose=True):
        """Initialize Ultralytics YOLO segmentation model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def init_criterion(self):
    """Initialize the loss criterion for the SegmentationModel."""
    return (
        E2ELoss(self, v8SegmentationLoss)
        if getattr(self.model[-1], "one2one_cv2", None) is not None
        else v8SegmentationLoss(self)
    )

class SemanticSegmentationModel(BaseModel):
    """YOLO semantic segmentation model.

    This class implements a semantic segmentation model that produces per-pixel class predictions. Unlike
    SegmentationModel (instance segmentation), this does not produce bounding boxes.

    Methods:
        __init__: Initialize the semantic segmentation model.
        init_criterion: Initialize the loss criterion for semantic segmentation.

    Examples:
        Initialize a semantic segmentation model
        >>> model = SemanticSegmentationModel("yolo26n-sem.yaml", ch=3, nc=19)
    """

    def __init__(self, cfg="yolo26n-sem.yaml", ch=3, nc=None, verbose=True):
        """Initialize the YOLO semantic segmentation model.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        _initialize_yolo_model(self, cfg, ch, nc, verbose)

        # Build strides: track smallest spatial size across all layers to find the deepest
        # backbone stride (e.g. P5/32). Head input alone is insufficient: the FPN upsamples
        # P5 away before the head, but the encoder still requires inputs aligned to that
        # deepest stride or FPN concats fail on rounding mismatches.
        m = self.model[-1]
        if isinstance(m, SemanticSegment):
            s = 256
            self.model.eval()
            m.training = True  # get training output (stride-4)
            min_h = [s]

            def _record(_m, _inp, out, _h=min_h):
                if isinstance(out, torch.Tensor) and out.ndim == 4:
                    _h[0] = min(_h[0], out.shape[-2])

            hooks = [layer.register_forward_hook(_record) for layer in self.model]
            try:
                self.forward(torch.zeros(1, ch, s, s))
            finally:
                for h in hooks:
                    h.remove()
            m.stride = torch.tensor([s / min_h[0]], dtype=torch.float32)  # e.g., 256/8 = 32
            self.stride = m.stride
            self.model.train()
        else:
            self.stride = torch.Tensor([32])

        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")

def _apply(self, fn):
    """Apply a function to all tensors in the model."""
    super()._apply(fn)
    m = self.model[-1]
    if isinstance(m, SemanticSegment):
        m.stride = fn(m.stride)
    return self

def init_criterion(self):
    """Initialize the loss criterion for semantic segmentation."""
    return SemanticSegmentationLoss(self)

class PoseModel(DetectionModel):
    """YOLO pose model.

    This class extends DetectionModel to handle human pose estimation tasks, providing specialized loss computation for
    keypoint detection and pose estimation.

    Attributes:
        kpt_shape (tuple): Shape of keypoints data (num_keypoints, num_dimensions).

    Methods:
        __init__: Initialize YOLO pose model.
        init_criterion: Initialize the loss criterion for pose estimation.

    Examples:
        Initialize a pose model
        >>> model = PoseModel("yolo26n-pose.yaml", ch=3, nc=1, data_kpt_shape=(17, 3))
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
        """Initialize Ultralytics YOLO Pose model.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            data_kpt_shape (tuple): Keypoint shape (num_keypoints, num_dims) that overrides the YAML `kpt_shape` if set.
            verbose (bool): Whether to display model information.
        """
        if not isinstance(cfg, dict):
            cfg = yaml_model_load(cfg)  # load model YAML
        if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
            LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
            cfg["kpt_shape"] = data_kpt_shape
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def init_criterion(self):
    """Initialize the loss criterion for the PoseModel."""
    loss = PoseLoss26 if isinstance(self.model[-1], Pose26) else v8PoseLoss
    return E2ELoss(self, loss) if getattr(self.model[-1], "one2one_cv2", None) is not None else loss(self)

class DepthModel(DetectionModel):
    """YOLO depth estimation model.

    This class extends DetectionModel for monocular depth estimation, using YOLO backbone + FPN with a DPT-style dense
    depth decoder head. Follows the Depth Anything approach adapted to YOLO architecture.

    Examples:
        >>> model = DepthModel("yolo26n-depth.yaml", ch=3)
        >>> results = model(image_tensor)
    """

    def __init__(self, cfg="yolo26n-depth.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLO Depth model.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def init_criterion(self):
    """Initialize the depth loss criterion."""
    return DepthLoss26(self)

class ClassificationModel(BaseModel):
    """YOLO classification model.

    This class implements the YOLO classification architecture for image classification tasks, providing model
    initialization, configuration, and output reshaping capabilities.

    Attributes:
        yaml (dict): Model configuration dictionary.
        model (torch.nn.Sequential): The neural network model.
        stride (torch.Tensor): Model stride values.
        names (dict): Class names dictionary.

    Methods:
        __init__: Initialize ClassificationModel.
        _from_yaml: Set model configurations and define architecture.
        reshape_outputs: Update model to specified class count.
        init_criterion: Initialize the loss criterion.

    Examples:
        Initialize a classification model
        >>> model = ClassificationModel("yolo26n-cls.yaml", ch=3, nc=1000)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolo26n-cls.yaml", ch=3, nc=None, verbose=True):
        """Initialize ClassificationModel with YAML, channels, number of classes, verbose flag.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__()
        self._from_yaml(cfg, ch, nc, verbose)

def _from_yaml(self, cfg, ch, nc, verbose):
    """Set Ultralytics YOLO model configurations and define the model architecture.

    Args:
        cfg (str | dict): Model configuration file path or dictionary.
        ch (int): Number of input channels.
        nc (int, optional): Number of classes.
        verbose (bool): Whether to display model information.
    """
    self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict

    # Define model
    ch = self.yaml["channels"] = self.yaml.get("channels", ch)  # input channels
    if nc and nc != self.yaml["nc"]:
        LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
        self.yaml["nc"] = nc  # override YAML value
    elif not nc and not self.yaml.get("nc", None):
        raise ValueError("nc not specified. Must specify nc in model.yaml or function arguments.")
    self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
    self.stride = torch.Tensor([1])  # no stride constraints
    self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
    self.info()

def init_criterion(self):
    """Initialize the loss criterion for the ClassificationModel."""
    return v8ClassificationLoss()

@staticmethod
def reshape_outputs(model, nc):
    """Update a YOLO or TorchVision classification model to class count 'nc' if required.

    Args:
        model (torch.nn.Module): Model to update.
        nc (int): New number of classes.
    """
    name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1]  # last module
    if isinstance(m, Classify):  # YOLO Classify() head
        if m.linear.out_features != nc:
            m.linear = torch.nn.Linear(m.linear.in_features, nc)
    elif isinstance(m, torch.nn.Linear):  # ResNet, EfficientNet
        if m.out_features != nc:
            setattr(model, name, torch.nn.Linear(m.in_features, nc))
    elif isinstance(m, torch.nn.Sequential):
        types = [type(x) for x in m]
        if torch.nn.Linear in types:
            i = len(types) - 1 - types[::-1].index(torch.nn.Linear)  # last torch.nn.Linear index
            if m[i].out_features != nc:
                m[i] = torch.nn.Linear(m[i].in_features, nc)
        elif torch.nn.Conv2d in types:
            i = len(types) - 1 - types[::-1].index(torch.nn.Conv2d)  # last torch.nn.Conv2d index
            if m[i].out_channels != nc:
                m[i] = torch.nn.Conv2d(
                    m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None
                )

class RTDETRDetectionModel(DetectionModel):
    """RTDETR (Real-Time DEtection TRansformer) Detection Model class.

    This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both
    the training and inference processes. RTDETR is a transformer-based object detection model that extends from the
    DetectionModel base class.

    Attributes:
        nc (int): Number of classes for detection.
        criterion (RTDETRDetectionLoss): Loss function for training.

    Methods:
        __init__: Initialize the RTDETRDetectionModel.
        init_criterion: Initialize the loss criterion.
        loss: Compute loss for training.
        predict: Perform forward pass through the model.

    Examples:
        Initialize an RTDETR model
        >>> model = RTDETRDetectionModel("rtdetr-l.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
        """Initialize the RTDETRDetectionModel.

        Args:
            cfg (str | dict): Configuration file name or path.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Print additional information during initialization.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def _remap_cls_by_names(self, csd: dict[str, torch.Tensor], src_model: torch.nn.Module, verbose: bool = True):
    """Remap RT-DETR decoder cls-head rows by class name.

    Overrides BaseModel's YOLO-specific implementation: RT-DETR's classification tensors live under
    `score_head` and `class_embed` inside `RTDETRDecoder` rather than `Detect.cv3`. All of them are
    row-per-class, including the training-only `denoising_class_embed` embedding, so matched class rows
    transfer even when source and target `nc` differ; any residual shape mismatch is dropped by
    `intersect_dicts`.

    Args:
        csd (dict): Pretrained checkpoint state_dict (will be mutated).
        src_model (torch.nn.Module): Pretrained module, used to read `.names`.
        verbose (bool): Log mapping summary.

    Returns:
        (int): Number of cls tensors remapped (counted toward "Transferred" log line).
    """
    src_names = getattr(src_model, "names", None)
    tgt_names = getattr(self, "names", None)
    if not (isinstance(src_names, dict) and isinstance(tgt_names, dict)):
        return 0
    # Skip default placeholder names {0:"0", 1:"1", ...} (also catches empty dicts)
    if any(all(str(k) == str(v) for k, v in n.items()) for n in (src_names, tgt_names)):
        return 0

    src_lookup = {str(v).strip().lower(): k for k, v in src_names.items()}
    tgt_nc = len(tgt_names)
    idx = torch.tensor(
        [src_lookup.get(str(tgt_names[k]).strip().lower(), -1) for k in range(tgt_nc)], dtype=torch.long
    )
    n_match = int((idx >= 0).sum())
    # Skip if nothing matches, or class names already share order and count (intersect_dicts handles it directly)
    if n_match == 0 or (len(src_names) == tgt_nc and torch.equal(idx, torch.arange(tgt_nc))):
        return 0

    valid = idx >= 0
    state_dict = self.state_dict()
    cls_keys = {k for k in csd if ("score_head" in k or "class_embed" in k) and k in state_dict}
    remapped = 0
    for k in cls_keys:
        v_src, v_tgt = csd[k], state_dict[k]
        if v_src.ndim != v_tgt.ndim or v_src.shape[1:] != v_tgt.shape[1:]:
            continue
        v_tgt[valid] = v_src[idx[valid]].to(v_tgt.dtype)
        csd.pop(k)  # prevent intersect_dicts from copying these rows in the wrong (source) order
        remapped += 1
    if verbose and remapped:
        LOGGER.info(f"Remapped {n_match}/{tgt_nc} decoder cls head rows from pretrained weights by class name")
    return remapped

def init_criterion(self):
    """Initialize the loss criterion for the RTDETRDetectionModel."""
    from ultralytics.models.utils.loss import RTDETRDetectionLoss

    return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)

def loss(self, batch, preds=None):
    """Compute the loss for the given batch of data.

    Args:
        batch (dict): Dictionary containing image and label data.
        preds (tuple, optional): Precomputed model predictions.

    Returns:
        loss (torch.Tensor): Total loss value.
        loss_items (dict): Main three detached losses in a dict.
    """
    if getattr(self, "criterion", None) is None:
        self.criterion = self.init_criterion()

    img = batch["img"]
    # NOTE: preprocess gt_bbox and gt_labels to list.
    bs = img.shape[0]
    batch_idx = batch["batch_idx"]
    gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
    targets = {
        "cls": batch["cls"].to(img.device, dtype=torch.long).view(-1),
        "bboxes": batch["bboxes"].to(device=img.device),
        "batch_idx": batch_idx.to(img.device, dtype=torch.long).view(-1),
        "gt_groups": gt_groups,
    }

    if preds is None:
        preds = self.predict(img, batch=targets)
    dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds if self.training else preds[1]
    if dn_meta is None:
        dn_bboxes, dn_scores = None, None
    else:
        dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta["dn_num_split"], dim=2)
        dn_scores, dec_scores = torch.split(dec_scores, dn_meta["dn_num_split"], dim=2)

    dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes])  # (7, bs, 300, 4)
    dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])

    loss = self.criterion(
        (dec_bboxes, dec_scores), targets, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta
    )
    # NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
    return sum(loss.values()), {
        "giou_loss": loss["loss_giou"].detach(),
        "cls_loss": loss["loss_class"].detach(),
        "l1_loss": loss["loss_bbox"].detach(),
    }

def predict(self, x, profile=False, batch=None, augment=False, embed=None):
    """Perform a forward pass through the model.

    Args:
        x (torch.Tensor): The input tensor.
        profile (bool): If True, profile the computation time for each layer.
        batch (dict, optional): Ground truth targets passed to the decoder head for denoising during training.
        augment (bool): Unused, accepted for API compatibility.
        embed (list, optional): A list of layer indices to return embeddings from.

    Returns:
        (torch.Tensor | tuple): Decoder head output, or per-image embedding vectors if `embed` is given.
    """
    y, dt, embeddings = [], [], []  # outputs
    embed = frozenset(embed) if embed else {-1}
    max_idx = max(embed)
    for m in self.model[:-1]:  # except the head part
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        x = m(x)  # run
        y.append(x if m.i in self.save else None)  # save output
        if m.i in embed:
            embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max_idx:
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    head = self.model[-1]
    x = head([y[j] for j in head.f], batch)  # head inference
    return x

class WorldModel(DetectionModel):
    """YOLOv8 World Model.

    This class implements the YOLOv8 World model for open-vocabulary object detection, supporting text-based class
    specification and CLIP model integration for zero-shot detection capabilities.

    Attributes:
        txt_feats (torch.Tensor): Text feature embeddings for classes.
        clip_model (torch.nn.Module): CLIP model for text encoding.

    Methods:
        __init__: Initialize YOLOv8 world model.
        set_classes: Set classes for offline inference.
        get_text_pe: Get text prompt embeddings.
        predict: Perform forward pass with text features.
        loss: Compute loss with text features.

    Examples:
        Initialize a world model
        >>> model = WorldModel("yolov8s-world.yaml", ch=3, nc=80)
        >>> model.set_classes(["person", "car", "bicycle"])
        >>> results = model.predict(image_tensor)
    """

    def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOv8 world model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        self.txt_feats = torch.randn(1, nc or 80, 512)  # features placeholder
        self.clip_model = None  # CLIP model placeholder
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def get_text_pe(self, text, batch=80, cache_clip_model=True):
    """Get text prompt embeddings using the CLIP model.

    Args:
        text (list[str]): List of class names.
        batch (int): Batch size for processing text tokens.
        cache_clip_model (bool): Whether to cache the CLIP model.

    Returns:
        (torch.Tensor): Text prompt embeddings.
    """
    from ultralytics.nn.text_model import build_text_model

    device = next(self.model.parameters()).device
    if not getattr(self, "clip_model", None) and cache_clip_model:
        # For backwards compatibility of models lacking clip_model attribute
        self.clip_model = build_text_model("clip:ViT-B/32", device=device)
    model = self.clip_model if cache_clip_model else build_text_model("clip:ViT-B/32", device=device)
    text_token = model.tokenize(text)
    txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
    txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
    return txt_feats.reshape(-1, len(text), txt_feats.shape[-1])

def loss(self, batch, preds=None):
    """Compute loss.

    Args:
        batch (dict): Batch to compute loss on.
        preds (torch.Tensor | list[torch.Tensor], optional): Predictions.

    Returns:
        loss (torch.Tensor): Loss tensor for backpropagation.
        loss_items (dict[str, torch.Tensor]): Detached loss components, as returned by the criterion.
    """
    if getattr(self, "criterion", None) is None:
        self.criterion = self.init_criterion()

    if preds is None:
        preds = self.forward(batch["img"], txt_feats=batch["txt_feats"])
    return self.criterion(preds, batch)

def predict(self, x, profile=False, txt_feats=None, augment=False, embed=None):
    """Perform a forward pass through the model.

    Args:
        x (torch.Tensor): The input tensor.
        profile (bool): If True, profile the computation time for each layer.
        txt_feats (torch.Tensor, optional): Text features to use instead of the cached `self.txt_feats`.
        augment (bool): Unused, accepted for API compatibility.
        embed (list, optional): A list of layer indices to return embeddings from.

    Returns:
        (torch.Tensor | tuple): Model output, or per-image embedding vectors if `embed` is given.
    """
    txt_feats = (self.txt_feats if txt_feats is None else txt_feats).type_as(x)
    if txt_feats.shape[0] != x.shape[0] or self.model[-1].export:
        txt_feats = txt_feats.expand(x.shape[0], -1, -1)
    ori_txt_feats = txt_feats.clone()
    y, dt, embeddings = [], [], []  # outputs
    embed = frozenset(embed) if embed else {-1}
    max_idx = max(embed)
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        if isinstance(m, C2fAttn):
            x = m(x, txt_feats)
        elif isinstance(m, WorldDetect):
            x = m(x, ori_txt_feats)
        elif isinstance(m, ImagePoolingAttn):
            txt_feats = m(x, txt_feats)
        else:
            x = m(x)  # run

        y.append(x if m.i in self.save else None)  # save output
        if m.i in embed:
            embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max_idx:
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    return x

def set_classes(self, text, batch=80, cache_clip_model=True):
    """Set classes in advance so that model could do offline-inference without clip model.

    Args:
        text (list[str]): List of class names.
        batch (int): Batch size for processing text tokens.
        cache_clip_model (bool): Whether to cache the CLIP model.
    """
    self.txt_feats = self.get_text_pe(text, batch=batch, cache_clip_model=cache_clip_model)
    self.model[-1].nc = len(text)

class YOLOEModel(DetectionModel):
    """YOLOE detection model.

    This class implements the YOLOE architecture for efficient object detection with text and visual prompts, supporting
    both prompt-based and prompt-free inference modes.

    Attributes:
        pe (torch.Tensor): Prompt embeddings for classes.
        clip_model (torch.nn.Module): CLIP model for text encoding.

    Methods:
        __init__: Initialize YOLOE model.
        get_text_pe: Get text prompt embeddings.
        get_visual_pe: Get visual embeddings.
        set_vocab: Set vocabulary for prompt-free model.
        get_vocab: Get fused vocabulary layer.
        set_classes: Set classes for offline inference.
        get_cls_pe: Get class prompt embeddings.
        predict: Perform forward pass with prompts.
        loss: Compute loss with prompts.

    Examples:
        Initialize a YOLOE model
        >>> model = YOLOEModel("yoloe-v8s.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor, tpe=text_embeddings)
    """

    def __init__(self, cfg="yoloe-v8s.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOE model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
        self.text_model = self.yaml.get("text_model", "mobileclip:blt")

def get_cls_pe(self, tpe, vpe):
    """Get class prompt embeddings.

    Args:
        tpe (torch.Tensor | None): Text prompt embeddings.
        vpe (torch.Tensor | None): Visual prompt embeddings.

    Returns:
        (torch.Tensor): Class prompt embeddings.
    """
    all_pe = []
    if tpe is not None:
        assert tpe.ndim == 3
        all_pe.append(tpe)
    if vpe is not None:
        assert vpe.ndim == 3
        all_pe.append(vpe)
    if not all_pe:
        all_pe.append(getattr(self, "pe", torch.zeros(1, 80, 512)))
    return torch.cat(all_pe, dim=1)

@smart_inference_mode()
def get_text_pe(self, text, batch=80, cache_clip_model=False, without_reprta=False):
    """Get text prompt embeddings using the CLIP model.

    Args:
        text (list[str]): List of class names.
        batch (int): Batch size for processing text tokens.
        cache_clip_model (bool): Whether to cache the CLIP model.
        without_reprta (bool): Whether to return text embeddings without reprta module processing.

    Returns:
        (torch.Tensor): Text prompt embeddings in the model's parameter dtype.
    """
    from ultralytics.nn.text_model import build_text_model

    assert len(text), f"Expected at least one class name, but got {text}"
    param = next(self.model.parameters())
    device = param.device
    if not getattr(self, "clip_model", None) and cache_clip_model:
        # For backwards compatibility of models lacking clip_model attribute
        self.clip_model = build_text_model(getattr(self, "text_model", "mobileclip:blt"), device=device)

    model = (
        self.clip_model
        if cache_clip_model
        else build_text_model(getattr(self, "text_model", "mobileclip:blt"), device=device)
    )
    text_token = model.tokenize(text)
    txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
    txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
    txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1]).to(param.dtype)  # CLIP always emits float32
    if without_reprta:
        return txt_feats

    head = self.model[-1]
    assert isinstance(head, YOLOEDetect)
    return head.get_tpe(txt_feats)  # run auxiliary text head

@smart_inference_mode()
def get_visual_pe(self, img, visual):
    """Get visual prompt embeddings.

    Args:
        img (torch.Tensor): Input image tensor.
        visual (torch.Tensor): Visual prompts, either (B, N, H, W) prompt masks or (B, N, D) embeddings.

    Returns:
        (torch.Tensor): Visual prompt embeddings.
    """
    return self(img, vpe=visual, return_vpe=True)

def get_vocab(self, names):
    """Get fused vocabulary layer from the model.

    Args:
        names (list[str]): List of class names.

    Returns:
        (nn.ModuleList): List of vocabulary modules.
    """
    assert not self.training
    head = self.model[-1]
    assert isinstance(head, YOLOEDetect)
    assert not head.is_fused
    names = list(check_class_names(names).values())  # validate before fusing the head, which cannot be undone

    tpe = self.get_text_pe(names)
    self.set_classes(names, tpe)
    device = next(self.model.parameters()).device
    head.fuse(self.pe.to(device))  # fuse prompt embeddings to classify head

    cv3 = head.one2one_cv3 if head.end2end else head.cv3
    vocab = nn.ModuleList()
    for cls_head in cv3:
        assert isinstance(cls_head, nn.Sequential)
        vocab.append(cls_head[-1])
    return vocab

def loss(self, batch, preds=None):
    """Compute loss.

    Args:
        batch (dict): Batch to compute loss on.
        preds (torch.Tensor | list[torch.Tensor], optional): Predictions.

    Returns:
        loss (torch.Tensor): Loss tensor for backpropagation.
        loss_items (dict[str, torch.Tensor]): Detached loss components, as returned by the criterion.
    """
    if getattr(self, "criterion", None) is None:
        from ultralytics.utils.loss import TVPDetectLoss

        visual_prompt = batch.get("visuals", None) is not None  # TODO
        self.criterion = (
            (
                E2ELoss(self, TVPDetectLoss)
                if getattr(self.model[-1], "one2one_cv2", None) is not None
                else TVPDetectLoss(self)
            )
            if visual_prompt
            else self.init_criterion()
        )
    if preds is None:
        preds = self.forward(
            batch["img"],
            tpe=None if "visuals" in batch else batch.get("txt_feats", None),
            vpe=batch.get("visuals", None),
        )
    return self.criterion(preds, batch)

def predict(self, x, profile=False, tpe=None, augment=False, embed=None, vpe=None, return_vpe=False):
    """Perform a forward pass through the model.

    Args:
        x (torch.Tensor): The input tensor.
        profile (bool): If True, profile the computation time for each layer.
        tpe (torch.Tensor, optional): Text prompt embeddings.
        augment (bool): Unused, accepted for API compatibility.
        embed (list, optional): A list of layer indices to return embeddings from.
        vpe (torch.Tensor, optional): Visual prompt embeddings.
        return_vpe (bool): If True, return visual prompt embeddings.

    Returns:
        (torch.Tensor | tuple): Model output, visual prompt embeddings if `return_vpe`, or per-image embedding
            vectors if `embed` is given.
    """
    y, dt, embeddings = [], [], []  # outputs
    b = x.shape[0]
    embed = frozenset(embed) if embed else {-1}
    max_idx = max(embed)
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        if isinstance(m, YOLOEDetect):
            vpe = m.get_vpe(x, vpe) if vpe is not None else None
            if return_vpe:
                assert vpe is not None
                assert not self.training
                return vpe
            cls_pe = self.get_cls_pe(m.get_tpe(tpe), vpe).type_as(x[0])
            if cls_pe.shape[0] != b or m.export:
                cls_pe = cls_pe.expand(b, -1, -1)
            x.append(cls_pe)  # adding cls embedding
        x = m(x)  # run

        y.append(x if m.i in self.save else None)  # save output
        if m.i in embed:
            embeddings.append(torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max_idx:
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    return x

def set_classes(self, names, embeddings):
    """Set classes in advance so that model could do offline-inference without clip model.

    Args:
        names (list[str]): List of class names.
        embeddings (torch.Tensor): Embeddings tensor.
    """
    assert not hasattr(self.model[-1], "lrpc"), (
        "Prompt-free model does not support setting classes. Please try with Text/Visual prompt models."
    )
    assert embeddings.ndim == 3
    self.names = check_class_names(names)  # validate before any state is written
    self.pe = embeddings
    self.model[-1].nc = len(names)

def set_vocab(self, vocab, names, one2one_vocab=None):
    """Set vocabulary for the prompt-free model.

    Args:
        vocab (nn.ModuleList): One-to-many vocabulary items returned by ``get_vocab`` for ``names``.
        names (list[str]): List of class names.
        one2one_vocab (nn.ModuleList | None): One-to-one vocabulary items. When provided, both the one-to-many and
            one-to-one prompt-free heads are built, so the ``nms`` argument keeps selecting between them at
            inference. When omitted, only the branch selected by ``end2end`` is built, as before.
    """
    assert not self.training
    head = self.model[-1]
    assert isinstance(head, YOLOEDetect)
    names = check_class_names(names)  # validate before the re-parameterization below, which cannot be undone
    # Cache anchors for head
    with torch.no_grad():  # a tracked warmup would build a graph through the backbone
        self(next(self.parameters()).new_empty(1, 3, self.args["imgsz"], self.args["imgsz"]))  # warmup

    # re-parameterization for prompt-free model, one LRPC head per (vocabulary, loc branch, cls branch)
    if one2one_vocab is None:  # single vocabulary for the branch end2end selects
        e2e = "one2one_" if head.end2end else ""
        branches = {"lrpc": (vocab, getattr(head, f"{e2e}cv2"), getattr(head, f"{e2e}cv3"))}
    else:
        branches = {
            "lrpc": (vocab, head.cv2, head.cv3),
            "one2one_lrpc": (one2one_vocab, head.one2one_cv2, head.one2one_cv3),
        }
    assert all(len(v) == head.nl for v, _, _ in branches.values()), f"Each vocabulary needs {head.nl} items."
    for name, (v, cv2, cv3) in branches.items():
        lrpc = (LRPCHead(cls, pf[-1], loc[-1], enabled=i != 2) for i, (cls, pf, loc) in enumerate(zip(v, cv3, cv2)))
        setattr(head, name, nn.ModuleList(lrpc))
        for loc_head, cls_head in zip(cv2, cv3):
            assert isinstance(loc_head, nn.Sequential)
            assert isinstance(cls_head, nn.Sequential)
            del loc_head[-1]
            del cls_head[-1]
    if one2one_vocab is None:
        head.fuse()  # LRPC is built for one branch; discard the other before inference can select it.
    self.model[-1].nc = len(names)
    self.names = names

class YOLOESegModel(YOLOEModel, SegmentationModel):
    """YOLOE segmentation model.

    This class extends YOLOEModel to handle instance segmentation tasks with text and visual prompts, providing
    specialized loss computation for pixel-level object detection and segmentation.

    Methods:
        __init__: Initialize YOLOE segmentation model.
        loss: Compute loss with prompts for segmentation.

    Examples:
        Initialize a YOLOE segmentation model
        >>> model = YOLOESegModel("yoloe-v8s-seg.yaml", ch=3, nc=80)
        >>> results = model.predict(image_tensor, tpe=text_embeddings)
    """

    def __init__(self, cfg="yoloe-v8s-seg.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOE segmentation model with given config and parameters.

        Args:
            cfg (str | dict): Model configuration file path or dictionary.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes.
            verbose (bool): Whether to display model information.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

def loss(self, batch, preds=None):
    """Compute loss.

    Args:
        batch (dict): Batch to compute loss on.
        preds (torch.Tensor | list[torch.Tensor], optional): Predictions.

    Returns:
        loss (torch.Tensor): Loss tensor for backpropagation.
        loss_items (dict[str, torch.Tensor]): Detached loss components, as returned by the criterion.
    """
    if getattr(self, "criterion", None) is None:
        from ultralytics.utils.loss import TVPSegmentLoss

        visual_prompt = batch.get("visuals", None) is not None  # TODO
        self.criterion = (
            (
                E2ELoss(self, TVPSegmentLoss)
                if getattr(self.model[-1], "one2one_cv2", None) is not None
                else TVPSegmentLoss(self)
            )
            if visual_prompt
            else self.init_criterion()
        )

    return super().loss(batch, preds)

class Ensemble(torch.nn.ModuleList):
    """Ensemble of models.

    This class allows combining multiple YOLO models into an ensemble for improved performance through model averaging
    or other ensemble techniques.

    Methods:
        __init__: Initialize an ensemble of models.
        forward: Generate predictions from all models in the ensemble.

    Examples:
        Create an ensemble of models
        >>> ensemble = Ensemble()
        >>> ensemble.append(model1)
        >>> ensemble.append(model2)
        >>> results = ensemble(image_tensor)
    """

    def __init__(self):
        """Initialize an ensemble of models."""
        super().__init__()

def forward(self, x, augment=False, profile=False):
    """Run ensemble forward pass and concatenate predictions from all models.

    Args:
        x (torch.Tensor): Input tensor.
        augment (bool): Whether to augment the input.
        profile (bool): Whether to profile the model.

    Returns:
        y (torch.Tensor): Predictions from all models concatenated along the anchor dimension.
        train_out (None): Always None for ensemble inference.
    """
    y = [module(x, augment=augment, profile=profile)[0] for module in self]
    # y = torch.stack(y).max(0)[0]  # max ensemble
    # y = torch.stack(y).mean(0)  # mean ensemble
    y = torch.cat(y, 2)  # nms ensemble, y shape(B, C, HW*num_models)
    return y, None  # inference, train output

class _SafeLoad:
    """Opt-in restricted checkpoint loading that reconstructs only known model classes and builds models without eval.

    Loading uses `weights_only=True` plus an allow-list of known classes. Enabled per-process by the
    `ULTRALYTICS_SAFE_LOAD` env flag, or per-call by `torch_safe_load(..., safe_only=True)`. Default loading (flag off)
    is unchanged. The globals a restricted load registers stay registered for the process, so they also apply to any
    other `torch.load(weights_only=True)` call made afterwards.
    """

    # Restricted loading needs torch 2.6+: the checkpoint global scan and `(obj, "module.Name")` allow-list aliases.
    # On older torch restricted loading degrades to a standard load.
    SUPPORTED = hasattr(torch.serialization, "get_unsafe_globals_in_checkpoint")
    _registry = None  # {"module.Name": allow-list entry}, built once per process
    _lock = (
        threading.Lock()
    )  # add_safe_globals rebinds a process-global set; held across _build(), so no load may run at import
    _local = threading.local()  # per-thread flag set while a weights_only load is in progress

@classmethod
def _build(cls):
    """Build the known data globals and legacy aliases; model classes are resolved only when referenced."""
    import enum
    import pathlib

    import ultralytics.utils.loss as ul_loss

    allow = []

    # Non-nn.Module data globals in official checkpoints, incl. the pre-8.0.44 `ultralytics.yolo.utils` path.
    scalar = np.float64(0).__reduce__()[0]
    allow += [np.dtype, (scalar, "numpy.core.multiarray.scalar"), (scalar, "numpy._core.multiarray.scalar")]
    allow.append(IterableSimpleNamespace)
    allow.append((IterableSimpleNamespace, "ultralytics.yolo.utils.IterableSimpleNamespace"))

    # Legacy/cross-platform aliases (pickled paths with no current class namespace), mirroring temporary_modules().
    def _getattr(obj, name):  # ckpts pickle `Detect.forward` and `InterpolationMode.BILINEAR` via getattr
        if isinstance(obj, type) and not name.startswith("__") and issubclass(obj, (nn.Module, enum.Enum)):
            return getattr(obj, name)
        if isinstance(obj, nn.Module) and name in {"forward", "forward_fuse"}:
            return getattr(type(obj), name).__get__(obj)
        raise pickle.UnpicklingError(
            f"unsafe getattr({type(obj).__name__}, {name!r}) blocked during restricted model load"
        )

    allow += [
        (nn.Identity, "ultralytics.nn.modules.block.Silence"),  # YOLOv9e
        (DetectionModel, "ultralytics.nn.tasks.YOLOv10DetectionModel"),  # YOLOv10
        (ul_loss.E2EDetectLoss, "ultralytics.utils.loss.v10DetectLoss"),  # YOLOv10
        (_getattr, "builtins.getattr"),  # non-det YOLOv8, YOLO11 ckpts (restrict to nn.Module attrs)
    ]
    # Map either OS's pickled path to the local class; Python 3.13 pickles name them under `pathlib._local`.
    path = pathlib.WindowsPath if WINDOWS else pathlib.PosixPath
    allow += [(path, f"{m}.{c}") for m in ("pathlib", "pathlib._local") for c in ("PosixPath", "WindowsPath")]
    return {
        (e[1] if isinstance(e, tuple) else f"{e.__module__}.{e.__qualname__}"): (
            e[0] if isinstance(e, tuple) else e
        )
        for e in allow
    }

@staticmethod
def activation(act):
    """Resolve a model-yaml `activation` spec to a `torch.nn` module instance without `eval()`.

    Accepts only the documented `[torch.]nn.<Class>(literal args)` shape (e.g. `nn.SiLU()`,
    `torch.nn.LeakyReLU(0.1)`) with literal arguments, and rejects anything else.

    Args:
        act (str): Activation spec from the model YAML.

    Returns:
        (torch.nn.Module): The instantiated activation module.

    Raises:
        TypeError: If the spec is not a literal-argument `torch.nn` module call.
    """
    import ast

    try:
        call = ast.parse(act.strip(), mode="eval").body
        assert isinstance(call, ast.Call)
        attrs = []
        node = call.func
        while isinstance(node, ast.Attribute):  # unwind e.g. torch.nn.SiLU -> ["SiLU","nn","torch"]
            attrs.append(node.attr)
            node = node.value
        assert isinstance(node, ast.Name)
        attrs.append(node.id)  # e.g. ["SiLU", "nn"] or ["SiLU", "nn", "torch"]
        assert attrs[1:] in (["nn"], ["nn", "torch"]), "activation must be a torch.nn class"
        klass = getattr(nn, attrs[0])
        assert isinstance(klass, type) and issubclass(klass, nn.Module)
        args = [ast.literal_eval(a) for a in call.args]
        kwargs = {kw.arg: ast.literal_eval(kw.value) for kw in call.keywords}
        return klass(*args, **kwargs)
    except Exception as e:
        raise TypeError(
            emojis(f"ERROR ❌️ unsupported activation '{act}' blocked during restricted model load.")
        ) from e

@classmethod
@contextlib.contextmanager
def loading(cls, weight):
    """Prepare a `weights_only=True` load by registering the globals a checkpoint needs and marking the thread.

    Marking the thread restricted means a checkpoint that reaches model construction (parse_model) also uses the
    no-eval, known-layer path.

    Globals are registered with `add_safe_globals` for the life of the process, never scoped per load: the
    `safe_globals()` context manager removes its entries from a process-global set on exit, so with concurrent
    loads one thread's exit strips the allow-list out of another thread's in-flight unpickle. Registering only
    the globals a checkpoint references also keeps the restricted unpickler fast — torch rebuilds its lookup from
    the whole registered set on every GLOBAL/NEWOBJ/REDUCE/BUILD opcode, so a 660-entry allow-list nearly doubled
    the load time of a checkpoint that references 20 of them.

    Args:
        weight (str | Path): Path to the checkpoint about to be loaded.
    """
    try:
        needed = torch.serialization.get_unsafe_globals_in_checkpoint(weight)
    except ValueError:  # Not a torch.save() zip archive; torch.load reports the format error, nothing to register
        needed = []
    with cls._lock:
        if cls._registry is None:
            cls._registry = cls._build()
        for name in needed:
            module, _, attr = name.rpartition(".")
            if name not in cls._registry and (
                module in {"torch.nn.modules", "ultralytics.nn.modules", "ultralytics.nn.tasks"}
                or module.rpartition(".")[0] in {"torch.nn.modules", "ultralytics.nn.modules"}
                or module in {"ultralytics.utils.loss", "ultralytics.utils.tal"}
            ):
                obj = getattr(importlib.import_module(module), attr, None)
                if isinstance(obj, type) and (
                    obj.__module__ == module
                    if module in {"ultralytics.utils.loss", "ultralytics.utils.tal"}
                    else issubclass(obj, nn.Module)
                ):
                    cls._registry[name] = obj
        if any(name.startswith("torchvision.transforms.") for name in needed):
            # Classification preprocessing transforms; imported only for checkpoints that serialize them.
            import torchvision.transforms.transforms as tvt
            from torchvision.transforms.functional import InterpolationMode

            for obj in (tvt.Compose, tvt.Normalize, tvt.Resize, tvt.CenterCrop, tvt.ToTensor, InterpolationMode):
                cls._registry[f"{obj.__module__}.{obj.__qualname__}"] = obj
        if "ultralytics.nn.text_model.CLIP" in needed:
            import clip

            from ultralytics.nn.text_model import CLIP

            for obj in (
                CLIP,
                clip.model.CLIP,
                clip.model.LayerNorm,
                clip.model.QuickGELU,
                clip.model.ResidualAttentionBlock,
                clip.model.Transformer,
                clip.model.VisionTransformer,
                clip.clip._convert_image_to_rgb,
            ):
                cls._registry[f"{obj.__module__}.{obj.__qualname__}"] = obj
        entries = [(cls._registry[name], name) for name in needed if name in cls._registry]
        if "numpy.dtype" in needed:
            entries.append(type(np.dtype(np.float64)))  # Built dynamically, absent from the checkpoint global scan.
        if entries:
            torch.serialization.add_safe_globals(entries)
    cls._local.active = True
    try:
        yield
    finally:
        cls._local.active = False

@classmethod
def restricted(cls):
    """Return whether model construction should use the no-eval, known-layer path (env flag or in-progress load)."""
    return cls.SUPPORTED and (SAFE_LOAD or getattr(cls._local, "active", False))

def _initialize_yolo_model(model, cfg, ch, nc, verbose):
    """Initialize common YOLO model attributes from a YAML config."""
    model.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
    if model.yaml["backbone"][0][2] == "Silence":
        LOGGER.warning(
            "YOLOv9 `Silence` module is deprecated in favor of torch.nn.Identity. "
            "Please delete local *.pt file and re-download the latest model checkpoint."
        )
        model.yaml["backbone"][0][2] = "nn.Identity"

    model.yaml["channels"] = ch  # save channels
    if nc and nc != model.yaml["nc"]:
        LOGGER.info(f"Overriding model.yaml nc={model.yaml['nc']} with nc={nc}")
        model.yaml["nc"] = nc  # override YAML value
    model.model, model.save = parse_model(deepcopy(model.yaml), ch=ch, verbose=verbose)  # model, savelist
    model.names = {i: f"{i}" for i in range(model.yaml["nc"])}  # default names dict
    model.inplace = model.yaml.get("inplace", True)

@contextlib.contextmanager
def temporary_modules(modules=None, attributes=None):
    """Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`).

    This function can be used to change the module paths during runtime. It's useful when refactoring code, where you've
    moved a module from one location to another, but you still want to support the old import paths for backwards
    compatibility.

    Args:
        modules (dict, optional): A dictionary mapping old module paths to new module paths.
        attributes (dict, optional): A dictionary mapping old module attributes to new module attributes.

    Examples:
        >>> with temporary_modules({"old.module": "new.module"}, {"old.module.attribute": "new.module.attribute"}):
        ...     import old.module  # this will now import new.module
        ...     from old.module import attribute  # this will now import new.module.attribute

    Notes:
        The changes are only in effect inside the context manager and are undone once the context manager exits.
        Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger
        applications or libraries. Use this function with caution.
    """
    if modules is None:
        modules = {}
    if attributes is None:
        attributes = {}
    import sys
    from importlib import import_module

    missing = object()
    previous = []  # (module, attribute, prior value) so exiting restores e.g. pathlib.WindowsPath
    with _temporary_modules_lock:
        try:
            # Set attributes in sys.modules under their old name
            for old, new in attributes.items():
                old_module, old_attr = old.rsplit(".", 1)
                new_module, new_attr = new.rsplit(".", 1)
                module = import_module(old_module)
                previous.append((module, old_attr, module.__dict__.get(old_attr, missing)))
                setattr(module, old_attr, getattr(import_module(new_module), new_attr))

            # Set modules in sys.modules under their old name
            for old, new in modules.items():
                sys.modules[old] = import_module(new)

            yield
        finally:
            # Remove the temporary module paths and attributes
            for old in modules:
                if old in sys.modules:
                    del sys.modules[old]
            for module, attr, value in previous:
                if value is missing:
                    delattr(module, attr)
                else:
                    setattr(module, attr, value)

def torch_safe_load(weight, safe_only=None):
    """Load a PyTorch checkpoint with torch.load(), handling legacy module paths and common load failures.

    If a ModuleNotFoundError is raised for a third-party module (and `safe_only` is off), a warning is logged, the
    missing module is installed via check_requirements(), and the load is retried. A corrupt cached official asset
    requested by bare name is re-downloaded once; other unreadable files raise a TypeError.

    Args:
        weight (str | Path): The file path of the PyTorch model.
        safe_only (bool, optional): Load with `torch.load(weights_only=True)`, reconstructing only the known
            Ultralytics/torch model classes on the allow-list. Defaults to the `ULTRALYTICS_SAFE_LOAD` environment
            variable (off), so standard usage is unchanged; set the env to opt in.

    Returns:
        ckpt (dict): The loaded model checkpoint.
        file (str): The loaded filename.

    Examples:
        >>> from ultralytics.nn.tasks import torch_safe_load
        >>> ckpt, file = torch_safe_load("path/to/best.pt", safe_only=True)
    """
    from ultralytics.utils.downloads import GITHUB_ASSETS_NAMES, attempt_download_asset

    if safe_only is None:
        safe_only = SAFE_LOAD
    if safe_only and not _SafeLoad.SUPPORTED:
        safe_only = False
    check_suffix(file=weight, suffix=".pt")
    file = attempt_download_asset(weight)  # search online if missing locally

    def _load():
        with temporary_modules(
            modules={
                "ultralytics.yolo.utils": "ultralytics.utils",
                "ultralytics.yolo.v8": "ultralytics.models.yolo",
                "ultralytics.yolo.data": "ultralytics.data",
            },
            attributes={
                "ultralytics.nn.modules.block.Silence": "torch.nn.Identity",  # YOLOv9e
                "ultralytics.nn.tasks.YOLOv10DetectionModel": "ultralytics.nn.tasks.DetectionModel",  # YOLOv10
                "ultralytics.utils.loss.v10DetectLoss": "ultralytics.utils.loss.E2EDetectLoss",  # YOLOv10
                # resolve cross-platform pathlib pickle incompatibility
                **(
                    {"pathlib.PosixPath": "pathlib.WindowsPath"}
                    if WINDOWS
                    else {"pathlib.WindowsPath": "pathlib.PosixPath"}
                ),
            },
        ):
            if safe_only:
                with _SafeLoad.loading(file):  # weights_only load against the known-class allow-list
                    return torch_load(file, map_location="cpu", weights_only=True)
            return torch_load(file, map_location="cpu")

    # weights_only=True raises on a TorchScript archive; the default path returns a ScriptModule instead.
    torchscript_error = emojis(
        f"ERROR ❌️ {weight} is a TorchScript archive, not an Ultralytics PyTorch checkpoint.\n"
        f"Load the original .pt weights, or export again with format='torchscript' and load that file directly."
    )

    try:
        ckpt = _load()

    except (RuntimeError, EOFError, pickle.UnpicklingError) as e:
        # An unreadable file reaches the loader as one of three internal errors depending on how it is damaged:
        # RuntimeError for a truncated zip, EOFError for an empty one, UnpicklingError for bytes that are not a
        # pickle at all (an image or archive renamed .pt). They are one user-facing condition, so they share one
        # handler and one message.
        if isinstance(e, RuntimeError) and "TorchScript archive" in str(e):
            raise TypeError(torchscript_error) from e
        if isinstance(e, RuntimeError) and "PytorchStreamReader" not in str(e):
            raise  # an unrelated RuntimeError is a real failure, not a damaged file
        if safe_only and isinstance(e, pickle.UnpicklingError):
            # weights_only=True refused a global outside the allow-list: a format problem, not a damaged file
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} references types outside the supported Ultralytics checkpoint format. "
                    f"Use an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        # Recover only a corrupt cached official asset requested by bare name; never touch user-supplied paths.
        name = Path(str(weight)).name
        if str(weight) != name or name not in GITHUB_ASSETS_NAMES:
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} is not a loadable checkpoint — the file is empty, truncated or corrupted "
                    f"({type(e).__name__}: {e}).\nRecommended fixes are to re-download or re-export the file, or to "
                    f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        LOGGER.warning(f"Corrupt cache {file}, re-downloading {weight}...")
        Path(file).unlink(missing_ok=True)
        file = attempt_download_asset(weight)
        ckpt = _load()

    except ModuleNotFoundError as e:  # e.name is missing module name
        if e.name in {"models", "models.yolo", "models.common", "models.experimental"}:
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained "
                    f"with https://github.com/ultralytics/yolov5. This model is NOT forwards compatible with "
                    f"YOLOv8 at https://github.com/ultralytics/ultralytics."
                    f"\nRecommended fixes are to train a new model using the latest 'ultralytics' package or to "
                    f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        elif e.name == "numpy._core":
            raise ModuleNotFoundError(
                emojis(
                    f"ERROR ❌️ {weight} requires numpy>=1.26.1, however numpy=={__import__('numpy').__version__} is installed."
                )
            ) from e
        elif e.name and e.name.startswith("ultralytics."):
            raise ModuleNotFoundError(
                emojis(
                    f"ERROR ❌️ {weight} requires missing Ultralytics module '{e.name}'. "
                    "Train a new model using the latest 'ultralytics' package or run a command with an official "
                    "Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
                )
            ) from e
        if safe_only:
            # Under weights_only loading, do not auto-install a module named by the checkpoint or fall back to a
            # weights_only=False reload.
            raise
        LOGGER.warning(
            f"{weight} appears to require '{e.name}', which is not in Ultralytics requirements."
            f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
            f"\nRecommended fixes are to train a new model using the latest 'ultralytics' package or to "
            f"run a command with an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
        )
        check_requirements(e.name)  # install missing module
        ckpt = torch_load(file, map_location="cpu")

    if isinstance(ckpt, torch.jit.ScriptModule):
        raise TypeError(torchscript_error)  # default path: torch.load dispatched to torch.jit.load and succeeded

    if not isinstance(ckpt, dict):
        # File is likely a YOLO instance saved with i.e. torch.save(model, "saved_model.pt")
        LOGGER.warning(
            f"The file '{weight}' appears to be improperly saved or formatted. "
            f"For optimal results, use model.save('filename.pt') to correctly save YOLO models."
        )
        ckpt = {"model": ckpt.model}

    return ckpt, file

def load_checkpoint(weight, device=None, inplace=True, fuse=False):
    """Load single model weights.

    Args:
        weight (str | Path): Model weight path.
        device (torch.device, optional): Device to load model to.
        inplace (bool): Whether to do inplace operations.
        fuse (bool): Whether to fuse model.

    Returns:
        model (torch.nn.Module): Loaded FP32 model in eval mode.
        ckpt (dict): Model checkpoint dictionary.
    """
    if str(weight).lower().startswith(REMOTE_FILE_PREFIXES):
        weight = check_file(weight, download_dir=SETTINGS["weights_dir"])
    ckpt, weight = torch_safe_load(weight)  # load ckpt
    args = {**DEFAULT_CFG_DICT, **(ckpt.get("train_args", {}))}  # combine model and default args, preferring model args
    candidate = ckpt.get("ema") or ckpt.get("model")
    if not isinstance(candidate, torch.nn.Module):
        raise TypeError(
            emojis(
                f"ERROR ❌️ {weight} references types outside the supported Ultralytics checkpoint format. "
                f"Use an official Ultralytics model, i.e. 'yolo predict model=yolo26n.pt'"
            )
        )
    model = candidate.float()  # FP32 model
    if ckpt.get("modelopt"):  # QAT checkpoint: re-apply the fake-quantization it learned
        restore_qat(model, ckpt["modelopt"])

    # Model compatibility updates
    model.args = args  # attach args to model
    model.pt_path = str(weight)  # attach *.pt file path to model as string (avoids WindowsPath pickle issues)
    model.task = getattr(model, "task", guess_model_task(model))
    if not hasattr(model, "stride"):
        model.stride = torch.tensor([32.0])

    model = (model.fuse() if fuse and hasattr(model, "fuse") else model).eval().to(device)  # model in eval mode

    # Module updates
    for m in model.modules():
        if hasattr(m, "inplace"):
            m.inplace = inplace
        elif isinstance(m, torch.nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model and ckpt
    return model, ckpt

def parse_model(d, ch, verbose=True):
    """Parse a YOLO model.yaml dictionary into a PyTorch model.

    Args:
        d (dict): Model dictionary.
        ch (int): Input channels.
        verbose (bool): Whether to print model details.

    Returns:
        model (torch.nn.Sequential): PyTorch model.
        save (list): Sorted list of layer indices whose outputs need to be saved.
    """
    import ast

    # Args
    legacy = True  # backward compatibility for v3/v5/v8/v9 models
    max_channels = float("inf")
    nc, act, scales, end2end = (d.get(x) for x in ("nc", "activation", "scales", "end2end"))
    reg_max = d.get("reg_max", 16)
    depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
    scale = d.get("scale")
    if scales:
        if not scale:
            scale = next(iter(scales.keys()))
            LOGGER.warning(f"no model scale passed. Assuming scale='{scale}'.")
        depth, width, max_channels = scales[scale]

    restricted = _SafeLoad.restricted()
    default_act = Conv.default_act  # restore before returning: Conv.default_act is process-wide state
    if act:
        # redefine default activation, i.e. Conv.default_act = torch.nn.SiLU(). Under restricted loading, resolve the
        # spec without eval() (see _SafeLoad.activation).
        Conv.default_act = _SafeLoad.activation(act) if restricted else eval(act)
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print

    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    base_modules = frozenset(
        {
            Classify,
            Conv,
            ConvTranspose,
            GhostConv,
            Bottleneck,
            GhostBottleneck,
            SPP,
            SPPF,
            C2fPSA,
            C2PSA,
            DWConv,
            Focus,
            BottleneckCSP,
            C1,
            C2,
            C2f,
            C3k2,
            RepNCSPELAN4,
            ELAN1,
            ADown,
            AConv,
            SPPELAN,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            torch.nn.ConvTranspose2d,
            DWConvTranspose2d,
            C3x,
            RepC3,
            PSA,
            SCDown,
            C2fCIB,
            A2C2f,
        }
    )
    repeat_modules = frozenset(  # modules with 'repeat' arguments
        {
            BottleneckCSP,
            C1,
            C2,
            C2f,
            C3k2,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            C3x,
            RepC3,
            C2fPSA,
            C2fCIB,
            C2PSA,
            A2C2f,
        }
    )
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        m = (
            getattr(torch.nn, m[3:])
            if m.startswith("nn.")
            else getattr(__import__("torchvision").ops, m[16:])
            if m.startswith("torchvision.ops.")
            else globals()[m]
        )  # get module
        if restricted and not (isinstance(m, type) and issubclass(m, torch.nn.Module)):
            # Under restricted loading, only known model layers may be named here.
            raise TypeError(emojis(f"ERROR ❌️ module '{m}' is not a permitted model layer under restricted loading."))
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
        if m in base_modules:
            c1, c2 = ch[f], args[0]
            if m is not Classify:  # Classify() output must stay at nc; every other layer scales by width
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            if m is C2fAttn:  # set 1) embed channels and 2) num heads
                args[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])
                hidden_channels = int(c2 * (args[6] if len(args) > 6 else 0.5))
                if hidden_channels % args[2]:
                    raise ValueError(
                        f"C2fAttn hidden channels {hidden_channels} (from c2={c2}) must be divisible by nh={args[2]}; "
                        "adjust width_multiple, nh, or C2fAttn expansion"
                    )
                args[1] = hidden_channels

            args = [c1, c2, *args[1:]]
            if m in repeat_modules:
                args.insert(2, n)  # number of repeats
                n = 1
            if m is C3k2:  # for M/L/X sizes
                legacy = False
                if scale in {"m", "l", "x"}:
                    args[3:4] = [True]  # slice assignment also supplies c3k when the YAML omits it
            if m is A2C2f:
                legacy = False
                if scale in {"l", "x"}:  # for L/X sizes
                    args.extend((True, 1.2))
            if m is C2fCIB:
                legacy = False
        elif m is AIFI:
            args = [ch[f], *args]
        elif m in frozenset({HGStem, HGBlock}):
            c1, cm, c2 = ch[f], args[0], args[1]
            args = [c1, cm, c2, *args[2:]]
            if m is HGBlock:
                args.insert(4, n)  # number of repeats
                n = 1
        elif m is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is torch.nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in frozenset(
            {
                Detect,
                WorldDetect,
                YOLOEDetect,
                Segment,
                Segment26,
                YOLOESegment,
                YOLOESegment26,
                Pose,
                Pose26,
                OBB,
                OBB26,
            }
        ):
            args.extend([reg_max, end2end, [ch[x] for x in f]])
            if m is Segment or m is YOLOESegment or m is Segment26 or m is YOLOESegment26:
                args[2] = make_divisible(min(args[2], max_channels) * width, 8)
            if m in {Detect, YOLOEDetect, Segment, Segment26, YOLOESegment, YOLOESegment26, Pose, Pose26, OBB, OBB26}:
                m.legacy = legacy
        elif m is Depth:
            args = [*args[:1], [ch[x] for x in f]]  # c_mid, ch tuple; drops the legacy mode arg old checkpoints store
        elif m is SemanticSegment:
            args.append([ch[x] for x in f])  # nc, ch tuple
        elif m is v10Detect:
            args.append([ch[x] for x in f])
        elif m is ImagePoolingAttn:
            args.insert(1, [ch[x] for x in f])  # channels as second arg
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        elif m is CBLinear:
            c2 = args[0]
            c1 = ch[f]
            args = [c1, c2, *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        elif m in frozenset({TorchVision, Index}):
            c2 = args[0]
            c1 = ch[f]
            args = [*args[1:]]
        else:
            c2 = ch[f]

        m_ = torch.nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        if m is SPPF and len(args) <= 3:  # Legacy YAML rows predate the unactivated YOLO26 SPPF.
            for block in m_ if n > 1 else [m_]:
                block.cv1.act = Conv.default_act
        t = str(m)[8:-2].replace("__main__.", "")  # module type
        m_.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i, f, t  # attach index, 'from' index, type
        if verbose:
            LOGGER.info(f"{i:>3}{f!s:>20}{n_:>3}{m_.np:10.0f}  {t:<45}{args!s:<30}")  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    Conv.default_act = default_act
    return torch.nn.Sequential(*layers), sorted(save)

def yaml_model_load(path):
    """Load a YOLO model from a YAML file.

    Args:
        path (str | Path): Path to the YAML file.

    Returns:
        (dict): Model dictionary.
    """
    path = Path(path)
    if path.stem in (f"yolov{d}{x}6" for x in "nsmlx" for d in (5, 8)):
        new_stem = re.sub(r"(\d+)([nslmx])6(.+)?$", r"\1\2-p6\3", path.stem)
        LOGGER.warning(f"Ultralytics YOLO P6 models now use -p6 suffix. Renaming {path.stem} to {new_stem}.")
        path = path.with_name(new_stem + path.suffix)

    unified_path = re.sub(r"(\d+)([nslmx])(.+)?$", r"\1\3", str(path))  # i.e. yolov8x.yaml -> yolov8.yaml
    yaml_file = check_yaml(path, hard=False) or check_yaml(unified_path)
    d = YAML.load(yaml_file)  # model dict
    d["scale"] = guess_model_scale(path)
    d["yaml_file"] = str(path)
    return d

def guess_model_scale(model_path):
    """Extract the size character n, s, m, l, or x of the model's scale from the model path.

    Args:
        model_path (str | Path): The path to the YOLO model's YAML file.

    Returns:
        (str): The size character of the model's scale (n, s, m, l, or x), or empty string if not found.
    """
    try:
        return re.search(r"yolo(e-)?[v]?\d+([nslmx])", Path(model_path).stem).group(2)
    except AttributeError:
        return ""

def guess_model_task(model):
    """Guess the task of a PyTorch model from its architecture or configuration.

    Args:
        model (torch.nn.Module | dict | str | Path): PyTorch model, model configuration dict, or model file path.

    Returns:
        (str): Task of the model ('detect', 'segment', 'semantic', 'depth', 'classify', 'pose', 'obb').
    """

    def cfg2task(cfg):
        """Guess from YAML dictionary."""
        m = cfg["head"][-1][-2].lower()  # output module name
        if m in {"classify", "classifier", "cls", "fc"}:
            return "classify"
        if "detect" in m:
            return "detect"
        if "semanticsegment" in m:
            return "semantic"
        if "segment" in m:
            return "segment"
        if "pose" in m:
            return "pose"
        if "obb" in m:
            return "obb"
        if "depth" in m:
            return "depth"

    # Guess from model cfg
    if isinstance(model, dict):
        with contextlib.suppress(Exception):
            return cfg2task(model)
    # Guess from PyTorch model
    if isinstance(model, torch.nn.Module):  # PyTorch model
        for x in "model.args", "model.model.args", "model.model.model.args":
            with contextlib.suppress(Exception):
                return eval(x)["task"]  # nosec B307: safe eval of known attribute paths
        for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
            with contextlib.suppress(Exception):
                return cfg2task(eval(x))  # nosec B307: safe eval of known attribute paths
        for m in model.modules():
            if isinstance(m, SemanticSegment):
                return "semantic"
            elif isinstance(m, (Segment, YOLOESegment)):
                return "segment"
            elif isinstance(m, Classify):
                return "classify"
            elif isinstance(m, Pose):
                return "pose"
            elif isinstance(m, OBB):
                return "obb"
            elif isinstance(m, Depth):
                return "depth"
            elif isinstance(m, (Detect, WorldDetect, YOLOEDetect, v10Detect)):
                return "detect"

    if isinstance(model, (str, Path)):
        from ultralytics.nn.backends.base import BaseBackend

        if task := BaseBackend.read_metadata(model).get("task"):  # exports embed their task, i.e. a renamed best.onnx
            return task

        # Guess from model filename
        model = Path(model)
        if "-sem" in model.stem or "semantic" in model.parts:
            return "semantic"
        elif "-seg" in model.stem or "segment" in model.parts:
            return "segment"
        elif "-cls" in model.stem or "classify" in model.parts:
            return "classify"
        elif "-pose" in model.stem or "pose" in model.parts:
            return "pose"
        elif "-obb" in model.stem or "obb" in model.parts:
            return "obb"
        elif "-depth" in model.stem or "depth" in model.parts:
            return "depth"
        elif "detect" in model.parts:
            return "detect"

    # Unable to determine task from model
    LOGGER.warning(
        "Unable to automatically guess model task, assuming 'task=detect'. "
        "Explicitly define task for your model, i.e. 'task=detect', 'segment', 'semantic', 'depth', 'classify', 'pose' "
        "or 'obb'."
    )
    return "detect"  # assume detect