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Reference for ultralytics/utils/loss.py

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


ultralytics.utils.loss.VarifocalLoss 

VarifocalLoss(gamma: float = 2.0, alpha: float = 0.75)

Bases: Module

Varifocal loss by Zhang et al.

Implements the Varifocal Loss function for addressing class imbalance in object detection by focusing on hard-to-classify examples and balancing positive/negative samples.

Attributes:

Name Type Description
gamma float

The focusing parameter that controls how much the loss focuses on hard-to-classify examples.
alpha float

The balancing factor used to address class imbalance.
References

https://arxiv.org/abs/2008.13367

Source code in ultralytics/utils/loss.py 
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def __init__(self, gamma: float = 2.0, alpha: float = 0.75):
    """Initialize the VarifocalLoss class with focusing and balancing parameters."""
    super().__init__()
    self.gamma = gamma
    self.alpha = alpha

forward 

forward(pred_score: Tensor, gt_score: Tensor, label: Tensor) -> torch.Tensor

Compute varifocal loss between predictions and ground truth.

Source code in ultralytics/utils/loss.py 
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def forward(self, pred_score: torch.Tensor, gt_score: torch.Tensor, label: torch.Tensor) -> torch.Tensor:
    """Compute varifocal loss between predictions and ground truth."""
    weight = self.alpha * pred_score.sigmoid().pow(self.gamma) * (1 - label) + gt_score * label
    with autocast(enabled=False):
        loss = (
            (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction="none") * weight)
            .mean(1)
            .sum()
        )
    return loss




ultralytics.utils.loss.FocalLoss 

FocalLoss(gamma: float = 1.5, alpha: float = 0.25)

Bases: Module

Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5).

Implements the Focal Loss function for addressing class imbalance by down-weighting easy examples and focusing on hard negatives during training.

Attributes:

Name Type Description
gamma float

The focusing parameter that controls how much the loss focuses on hard-to-classify examples.
alpha Tensor

The balancing factor used to address class imbalance.
Source code in ultralytics/utils/loss.py 
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def __init__(self, gamma: float = 1.5, alpha: float = 0.25):
    """Initialize FocalLoss class with focusing and balancing parameters."""
    super().__init__()
    self.gamma = gamma
    self.alpha = torch.tensor(alpha)

forward 

forward(pred: Tensor, label: Tensor) -> torch.Tensor

Calculate focal loss with modulating factors for class imbalance.

Source code in ultralytics/utils/loss.py 
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def forward(self, pred: torch.Tensor, label: torch.Tensor) -> torch.Tensor:
    """Calculate focal loss with modulating factors for class imbalance."""
    loss = F.binary_cross_entropy_with_logits(pred, label, reduction="none")
    # p_t = torch.exp(-loss)
    # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability

    # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
    pred_prob = pred.sigmoid()  # prob from logits
    p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
    modulating_factor = (1.0 - p_t) ** self.gamma
    loss *= modulating_factor
    if (self.alpha > 0).any():
        self.alpha = self.alpha.to(device=pred.device, dtype=pred.dtype)
        alpha_factor = label * self.alpha + (1 - label) * (1 - self.alpha)
        loss *= alpha_factor
    return loss.mean(1).sum()




ultralytics.utils.loss.DFLoss 

DFLoss(reg_max: int = 16)

Bases: Module

Criterion class for computing Distribution Focal Loss (DFL).

Source code in ultralytics/utils/loss.py 
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def __init__(self, reg_max: int = 16) -> None:
    """Initialize the DFL module with regularization maximum."""
    super().__init__()
    self.reg_max = reg_max

__call__ 

__call__(pred_dist: Tensor, target: Tensor) -> torch.Tensor

Return sum of left and right DFL losses from https://ieeexplore.ieee.org/document/9792391.

Source code in ultralytics/utils/loss.py 
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def __call__(self, pred_dist: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
    """Return sum of left and right DFL losses from https://ieeexplore.ieee.org/document/9792391."""
    target = target.clamp_(0, self.reg_max - 1 - 0.01)
    tl = target.long()  # target left
    tr = tl + 1  # target right
    wl = tr - target  # weight left
    wr = 1 - wl  # weight right
    return (
        F.cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape) * wl
        + F.cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape) * wr
    ).mean(-1, keepdim=True)




ultralytics.utils.loss.BboxLoss 

BboxLoss(reg_max: int = 16)

Bases: Module

Criterion class for computing training losses for bounding boxes.

Source code in ultralytics/utils/loss.py 
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def __init__(self, reg_max: int = 16):
    """Initialize the BboxLoss module with regularization maximum and DFL settings."""
    super().__init__()
    self.dfl_loss = DFLoss(reg_max) if reg_max > 1 else None

forward 

forward(
    pred_dist: Tensor,
    pred_bboxes: Tensor,
    anchor_points: Tensor,
    target_bboxes: Tensor,
    target_scores: Tensor,
    target_scores_sum: Tensor,
    fg_mask: Tensor,
) -> tuple[torch.Tensor, torch.Tensor]

Compute IoU and DFL losses for bounding boxes.

Source code in ultralytics/utils/loss.py 
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def forward(
    self,
    pred_dist: torch.Tensor,
    pred_bboxes: torch.Tensor,
    anchor_points: torch.Tensor,
    target_bboxes: torch.Tensor,
    target_scores: torch.Tensor,
    target_scores_sum: torch.Tensor,
    fg_mask: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Compute IoU and DFL losses for bounding boxes."""
    weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
    iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
    loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum

    # DFL loss
    if self.dfl_loss:
        target_ltrb = bbox2dist(anchor_points, target_bboxes, self.dfl_loss.reg_max - 1)
        loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight
        loss_dfl = loss_dfl.sum() / target_scores_sum
    else:
        loss_dfl = torch.tensor(0.0).to(pred_dist.device)

    return loss_iou, loss_dfl




ultralytics.utils.loss.RotatedBboxLoss 

RotatedBboxLoss(reg_max: int)

Bases: BboxLoss

Criterion class for computing training losses for rotated bounding boxes.

Source code in ultralytics/utils/loss.py 
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def __init__(self, reg_max: int):
    """Initialize the RotatedBboxLoss module with regularization maximum and DFL settings."""
    super().__init__(reg_max)

forward 

forward(
    pred_dist: Tensor,
    pred_bboxes: Tensor,
    anchor_points: Tensor,
    target_bboxes: Tensor,
    target_scores: Tensor,
    target_scores_sum: Tensor,
    fg_mask: Tensor,
) -> tuple[torch.Tensor, torch.Tensor]

Compute IoU and DFL losses for rotated bounding boxes.

Source code in ultralytics/utils/loss.py 
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def forward(
    self,
    pred_dist: torch.Tensor,
    pred_bboxes: torch.Tensor,
    anchor_points: torch.Tensor,
    target_bboxes: torch.Tensor,
    target_scores: torch.Tensor,
    target_scores_sum: torch.Tensor,
    fg_mask: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Compute IoU and DFL losses for rotated bounding boxes."""
    weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
    iou = probiou(pred_bboxes[fg_mask], target_bboxes[fg_mask])
    loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum

    # DFL loss
    if self.dfl_loss:
        target_ltrb = bbox2dist(anchor_points, xywh2xyxy(target_bboxes[..., :4]), self.dfl_loss.reg_max - 1)
        loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), target_ltrb[fg_mask]) * weight
        loss_dfl = loss_dfl.sum() / target_scores_sum
    else:
        loss_dfl = torch.tensor(0.0).to(pred_dist.device)

    return loss_iou, loss_dfl




ultralytics.utils.loss.KeypointLoss 

KeypointLoss(sigmas: Tensor)

Bases: Module

Criterion class for computing keypoint losses.

Source code in ultralytics/utils/loss.py 
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def __init__(self, sigmas: torch.Tensor) -> None:
    """Initialize the KeypointLoss class with keypoint sigmas."""
    super().__init__()
    self.sigmas = sigmas

forward 

forward(
    pred_kpts: Tensor, gt_kpts: Tensor, kpt_mask: Tensor, area: Tensor
) -> torch.Tensor

Calculate keypoint loss factor and Euclidean distance loss for keypoints.

Source code in ultralytics/utils/loss.py 
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def forward(
    self, pred_kpts: torch.Tensor, gt_kpts: torch.Tensor, kpt_mask: torch.Tensor, area: torch.Tensor
) -> torch.Tensor:
    """Calculate keypoint loss factor and Euclidean distance loss for keypoints."""
    d = (pred_kpts[..., 0] - gt_kpts[..., 0]).pow(2) + (pred_kpts[..., 1] - gt_kpts[..., 1]).pow(2)
    kpt_loss_factor = kpt_mask.shape[1] / (torch.sum(kpt_mask != 0, dim=1) + 1e-9)
    # e = d / (2 * (area * self.sigmas) ** 2 + 1e-9)  # from formula
    e = d / ((2 * self.sigmas).pow(2) * (area + 1e-9) * 2)  # from cocoeval
    return (kpt_loss_factor.view(-1, 1) * ((1 - torch.exp(-e)) * kpt_mask)).mean()




ultralytics.utils.loss.v8DetectionLoss 

v8DetectionLoss(model, tal_topk: int = 10)

Criterion class for computing training losses for YOLOv8 object detection.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model, tal_topk: int = 10):  # model must be de-paralleled
    """Initialize v8DetectionLoss with model parameters and task-aligned assignment settings."""
    device = next(model.parameters()).device  # get model device
    h = model.args  # hyperparameters

    m = model.model[-1]  # Detect() module
    self.bce = nn.BCEWithLogitsLoss(reduction="none")
    self.hyp = h
    self.stride = m.stride  # model strides
    self.nc = m.nc  # number of classes
    self.no = m.nc + m.reg_max * 4
    self.reg_max = m.reg_max
    self.device = device

    self.use_dfl = m.reg_max > 1

    self.assigner = TaskAlignedAssigner(topk=tal_topk, num_classes=self.nc, alpha=0.5, beta=6.0)
    self.bbox_loss = BboxLoss(m.reg_max).to(device)
    self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the sum of the loss for box, cls and dfl multiplied by batch size.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
    loss = torch.zeros(3, device=self.device)  # box, cls, dfl
    feats = preds[1] if isinstance(preds, tuple) else preds
    pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
        (self.reg_max * 4, self.nc), 1
    )

    pred_scores = pred_scores.permute(0, 2, 1).contiguous()
    pred_distri = pred_distri.permute(0, 2, 1).contiguous()

    dtype = pred_scores.dtype
    batch_size = pred_scores.shape[0]
    imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
    anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)

    # Targets
    targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
    targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
    gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
    mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)

    # Pboxes
    pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
    # dfl_conf = pred_distri.view(batch_size, -1, 4, self.reg_max).detach().softmax(-1)
    # dfl_conf = (dfl_conf.amax(-1).mean(-1) + dfl_conf.amax(-1).amin(-1)) / 2

    _, target_bboxes, target_scores, fg_mask, _ = self.assigner(
        # pred_scores.detach().sigmoid() * 0.8 + dfl_conf.unsqueeze(-1) * 0.2,
        pred_scores.detach().sigmoid(),
        (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
        anchor_points * stride_tensor,
        gt_labels,
        gt_bboxes,
        mask_gt,
    )

    target_scores_sum = max(target_scores.sum(), 1)

    # Cls loss
    # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
    loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE

    # Bbox loss
    if fg_mask.sum():
        target_bboxes /= stride_tensor
        loss[0], loss[2] = self.bbox_loss(
            pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
        )

    loss[0] *= self.hyp.box  # box gain
    loss[1] *= self.hyp.cls  # cls gain
    loss[2] *= self.hyp.dfl  # dfl gain

    return loss * batch_size, loss.detach()  # loss(box, cls, dfl)

bbox_decode 

bbox_decode(anchor_points: Tensor, pred_dist: Tensor) -> torch.Tensor

Decode predicted object bounding box coordinates from anchor points and distribution.

Source code in ultralytics/utils/loss.py 
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def bbox_decode(self, anchor_points: torch.Tensor, pred_dist: torch.Tensor) -> torch.Tensor:
    """Decode predicted object bounding box coordinates from anchor points and distribution."""
    if self.use_dfl:
        b, a, c = pred_dist.shape  # batch, anchors, channels
        pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
        # pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
        # pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
    return dist2bbox(pred_dist, anchor_points, xywh=False)

preprocess 

preprocess(
    targets: Tensor, batch_size: int, scale_tensor: Tensor
) -> torch.Tensor

Preprocess targets by converting to tensor format and scaling coordinates.

Source code in ultralytics/utils/loss.py 
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def preprocess(self, targets: torch.Tensor, batch_size: int, scale_tensor: torch.Tensor) -> torch.Tensor:
    """Preprocess targets by converting to tensor format and scaling coordinates."""
    nl, ne = targets.shape
    if nl == 0:
        out = torch.zeros(batch_size, 0, ne - 1, device=self.device)
    else:
        i = targets[:, 0]  # image index
        _, counts = i.unique(return_counts=True)
        counts = counts.to(dtype=torch.int32)
        out = torch.zeros(batch_size, counts.max(), ne - 1, device=self.device)
        for j in range(batch_size):
            matches = i == j
            if n := matches.sum():
                out[j, :n] = targets[matches, 1:]
        out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
    return out




ultralytics.utils.loss.v8SegmentationLoss 

v8SegmentationLoss(model)

Bases: v8DetectionLoss

Criterion class for computing training losses for YOLOv8 segmentation.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):  # model must be de-paralleled
    """Initialize the v8SegmentationLoss class with model parameters and mask overlap setting."""
    super().__init__(model)
    self.overlap = model.args.overlap_mask

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate and return the combined loss for detection and segmentation.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate and return the combined loss for detection and segmentation."""
    loss = torch.zeros(4, device=self.device)  # box, seg, cls, dfl
    feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
    batch_size, _, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width
    pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
        (self.reg_max * 4, self.nc), 1
    )

    # B, grids, ..
    pred_scores = pred_scores.permute(0, 2, 1).contiguous()
    pred_distri = pred_distri.permute(0, 2, 1).contiguous()
    pred_masks = pred_masks.permute(0, 2, 1).contiguous()

    dtype = pred_scores.dtype
    imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
    anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)

    # Targets
    try:
        batch_idx = batch["batch_idx"].view(-1, 1)
        targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
        gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
    except RuntimeError as e:
        raise TypeError(
            "ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n"
            "This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
            "i.e. 'yolo train model=yolo11n-seg.pt data=coco8.yaml'.\nVerify your dataset is a "
            "correctly formatted 'segment' dataset using 'data=coco8-seg.yaml' "
            "as an example.\nSee https://docs.ultralytics.com/datasets/segment/ for help."
        ) from e

    # Pboxes
    pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)

    _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
        pred_scores.detach().sigmoid(),
        (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
        anchor_points * stride_tensor,
        gt_labels,
        gt_bboxes,
        mask_gt,
    )

    target_scores_sum = max(target_scores.sum(), 1)

    # Cls loss
    # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
    loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE

    if fg_mask.sum():
        # Bbox loss
        loss[0], loss[3] = self.bbox_loss(
            pred_distri,
            pred_bboxes,
            anchor_points,
            target_bboxes / stride_tensor,
            target_scores,
            target_scores_sum,
            fg_mask,
        )
        # Masks loss
        masks = batch["masks"].to(self.device).float()
        if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
            masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]

        loss[1] = self.calculate_segmentation_loss(
            fg_mask, masks, target_gt_idx, target_bboxes, batch_idx, proto, pred_masks, imgsz, self.overlap
        )

    # WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
    else:
        loss[1] += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss

    loss[0] *= self.hyp.box  # box gain
    loss[1] *= self.hyp.box  # seg gain
    loss[2] *= self.hyp.cls  # cls gain
    loss[3] *= self.hyp.dfl  # dfl gain

    return loss * batch_size, loss.detach()  # loss(box, cls, dfl)

calculate_segmentation_loss 

calculate_segmentation_loss(
    fg_mask: Tensor,
    masks: Tensor,
    target_gt_idx: Tensor,
    target_bboxes: Tensor,
    batch_idx: Tensor,
    proto: Tensor,
    pred_masks: Tensor,
    imgsz: Tensor,
    overlap: bool,
) -> torch.Tensor

Calculate the loss for instance segmentation.

Parameters:

Name Type Description Default
fg_mask Tensor

A binary tensor of shape (BS, N_anchors) indicating which anchors are positive.

 required
masks Tensor

Ground truth masks of shape (BS, H, W) if overlap is False, otherwise (BS, ?, H, W).

 required
target_gt_idx Tensor

Indexes of ground truth objects for each anchor of shape (BS, N_anchors).

 required
target_bboxes Tensor

Ground truth bounding boxes for each anchor of shape (BS, N_anchors, 4).

 required
batch_idx Tensor

Batch indices of shape (N_labels_in_batch, 1).

 required
proto Tensor

Prototype masks of shape (BS, 32, H, W).

 required
pred_masks Tensor

Predicted masks for each anchor of shape (BS, N_anchors, 32).

 required
imgsz Tensor

Size of the input image as a tensor of shape (2), i.e., (H, W).

 required
overlap bool

Whether the masks in masks tensor overlap.

 required

Returns:

Type Description
Tensor

The calculated loss for instance segmentation.
Notes

The batch loss can be computed for improved speed at higher memory usage. For example, pred_mask can be computed as follows: pred_mask = torch.einsum('in,nhw->ihw', pred, proto) # (i, 32) @ (32, 160, 160) -> (i, 160, 160)

Source code in ultralytics/utils/loss.py 
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def calculate_segmentation_loss(
    self,
    fg_mask: torch.Tensor,
    masks: torch.Tensor,
    target_gt_idx: torch.Tensor,
    target_bboxes: torch.Tensor,
    batch_idx: torch.Tensor,
    proto: torch.Tensor,
    pred_masks: torch.Tensor,
    imgsz: torch.Tensor,
    overlap: bool,
) -> torch.Tensor:
    """
    Calculate the loss for instance segmentation.

    Args:
        fg_mask (torch.Tensor): A binary tensor of shape (BS, N_anchors) indicating which anchors are positive.
        masks (torch.Tensor): Ground truth masks of shape (BS, H, W) if `overlap` is False, otherwise (BS, ?, H, W).
        target_gt_idx (torch.Tensor): Indexes of ground truth objects for each anchor of shape (BS, N_anchors).
        target_bboxes (torch.Tensor): Ground truth bounding boxes for each anchor of shape (BS, N_anchors, 4).
        batch_idx (torch.Tensor): Batch indices of shape (N_labels_in_batch, 1).
        proto (torch.Tensor): Prototype masks of shape (BS, 32, H, W).
        pred_masks (torch.Tensor): Predicted masks for each anchor of shape (BS, N_anchors, 32).
        imgsz (torch.Tensor): Size of the input image as a tensor of shape (2), i.e., (H, W).
        overlap (bool): Whether the masks in `masks` tensor overlap.

    Returns:
        (torch.Tensor): The calculated loss for instance segmentation.

    Notes:
        The batch loss can be computed for improved speed at higher memory usage.
        For example, pred_mask can be computed as follows:
            pred_mask = torch.einsum('in,nhw->ihw', pred, proto)  # (i, 32) @ (32, 160, 160) -> (i, 160, 160)
    """
    _, _, mask_h, mask_w = proto.shape
    loss = 0

    # Normalize to 0-1
    target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]]

    # Areas of target bboxes
    marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2)

    # Normalize to mask size
    mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device)

    for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)):
        fg_mask_i, target_gt_idx_i, pred_masks_i, proto_i, mxyxy_i, marea_i, masks_i = single_i
        if fg_mask_i.any():
            mask_idx = target_gt_idx_i[fg_mask_i]
            if overlap:
                gt_mask = masks_i == (mask_idx + 1).view(-1, 1, 1)
                gt_mask = gt_mask.float()
            else:
                gt_mask = masks[batch_idx.view(-1) == i][mask_idx]

            loss += self.single_mask_loss(
                gt_mask, pred_masks_i[fg_mask_i], proto_i, mxyxy_i[fg_mask_i], marea_i[fg_mask_i]
            )

        # WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
        else:
            loss += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss

    return loss / fg_mask.sum()

single_mask_loss staticmethod 

single_mask_loss(
    gt_mask: Tensor, pred: Tensor, proto: Tensor, xyxy: Tensor, area: Tensor
) -> torch.Tensor

Compute the instance segmentation loss for a single image.

Parameters:

Name Type Description Default
gt_mask Tensor

Ground truth mask of shape (N, H, W), where N is the number of objects.

 required
pred Tensor

Predicted mask coefficients of shape (N, 32).

 required
proto Tensor

Prototype masks of shape (32, H, W).

 required
xyxy Tensor

Ground truth bounding boxes in xyxy format, normalized to [0, 1], of shape (N, 4).

 required
area Tensor

Area of each ground truth bounding box of shape (N,).

 required

Returns:

Type Description
Tensor

The calculated mask loss for a single image.
Notes

The function uses the equation pred_mask = torch.einsum('in,nhw->ihw', pred, proto) to produce the predicted masks from the prototype masks and predicted mask coefficients.

Source code in ultralytics/utils/loss.py 
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@staticmethod
def single_mask_loss(
    gt_mask: torch.Tensor, pred: torch.Tensor, proto: torch.Tensor, xyxy: torch.Tensor, area: torch.Tensor
) -> torch.Tensor:
    """
    Compute the instance segmentation loss for a single image.

    Args:
        gt_mask (torch.Tensor): Ground truth mask of shape (N, H, W), where N is the number of objects.
        pred (torch.Tensor): Predicted mask coefficients of shape (N, 32).
        proto (torch.Tensor): Prototype masks of shape (32, H, W).
        xyxy (torch.Tensor): Ground truth bounding boxes in xyxy format, normalized to [0, 1], of shape (N, 4).
        area (torch.Tensor): Area of each ground truth bounding box of shape (N,).

    Returns:
        (torch.Tensor): The calculated mask loss for a single image.

    Notes:
        The function uses the equation pred_mask = torch.einsum('in,nhw->ihw', pred, proto) to produce the
        predicted masks from the prototype masks and predicted mask coefficients.
    """
    pred_mask = torch.einsum("in,nhw->ihw", pred, proto)  # (n, 32) @ (32, 80, 80) -> (n, 80, 80)
    loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
    return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).sum()




ultralytics.utils.loss.v8PoseLoss 

v8PoseLoss(model)

Bases: v8DetectionLoss

Criterion class for computing training losses for YOLOv8 pose estimation.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):  # model must be de-paralleled
    """Initialize v8PoseLoss with model parameters and keypoint-specific loss functions."""
    super().__init__(model)
    self.kpt_shape = model.model[-1].kpt_shape
    self.bce_pose = nn.BCEWithLogitsLoss()
    is_pose = self.kpt_shape == [17, 3]
    nkpt = self.kpt_shape[0]  # number of keypoints
    sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt
    self.keypoint_loss = KeypointLoss(sigmas=sigmas)

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the total loss and detach it for pose estimation.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate the total loss and detach it for pose estimation."""
    loss = torch.zeros(5, device=self.device)  # box, cls, dfl, kpt_location, kpt_visibility
    feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
    pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
        (self.reg_max * 4, self.nc), 1
    )

    # B, grids, ..
    pred_scores = pred_scores.permute(0, 2, 1).contiguous()
    pred_distri = pred_distri.permute(0, 2, 1).contiguous()
    pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()

    dtype = pred_scores.dtype
    imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
    anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)

    # Targets
    batch_size = pred_scores.shape[0]
    batch_idx = batch["batch_idx"].view(-1, 1)
    targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
    targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
    gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
    mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)

    # Pboxes
    pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
    pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape))  # (b, h*w, 17, 3)

    _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
        pred_scores.detach().sigmoid(),
        (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
        anchor_points * stride_tensor,
        gt_labels,
        gt_bboxes,
        mask_gt,
    )

    target_scores_sum = max(target_scores.sum(), 1)

    # Cls loss
    # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
    loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE

    # Bbox loss
    if fg_mask.sum():
        target_bboxes /= stride_tensor
        loss[0], loss[4] = self.bbox_loss(
            pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
        )
        keypoints = batch["keypoints"].to(self.device).float().clone()
        keypoints[..., 0] *= imgsz[1]
        keypoints[..., 1] *= imgsz[0]

        loss[1], loss[2] = self.calculate_keypoints_loss(
            fg_mask, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts
        )

    loss[0] *= self.hyp.box  # box gain
    loss[1] *= self.hyp.pose  # pose gain
    loss[2] *= self.hyp.kobj  # kobj gain
    loss[3] *= self.hyp.cls  # cls gain
    loss[4] *= self.hyp.dfl  # dfl gain

    return loss * batch_size, loss.detach()  # loss(box, cls, dfl)

calculate_keypoints_loss 

calculate_keypoints_loss(
    masks: Tensor,
    target_gt_idx: Tensor,
    keypoints: Tensor,
    batch_idx: Tensor,
    stride_tensor: Tensor,
    target_bboxes: Tensor,
    pred_kpts: Tensor,
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the keypoints loss for the model.

This function calculates the keypoints loss and keypoints object loss for a given batch. The keypoints loss is based on the difference between the predicted keypoints and ground truth keypoints. The keypoints object loss is a binary classification loss that classifies whether a keypoint is present or not.

Parameters:

Name Type Description Default
masks Tensor

Binary mask tensor indicating object presence, shape (BS, N_anchors).

 required
target_gt_idx Tensor

Index tensor mapping anchors to ground truth objects, shape (BS, N_anchors).

 required
keypoints Tensor

Ground truth keypoints, shape (N_kpts_in_batch, N_kpts_per_object, kpts_dim).

 required
batch_idx Tensor

Batch index tensor for keypoints, shape (N_kpts_in_batch, 1).

 required
stride_tensor Tensor

Stride tensor for anchors, shape (N_anchors, 1).

 required
target_bboxes Tensor

Ground truth boxes in (x1, y1, x2, y2) format, shape (BS, N_anchors, 4).

 required
pred_kpts Tensor

Predicted keypoints, shape (BS, N_anchors, N_kpts_per_object, kpts_dim).

 required

Returns:

Name Type Description
kpts_loss Tensor

The keypoints loss.
kpts_obj_loss Tensor

The keypoints object loss.
Source code in ultralytics/utils/loss.py 
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def calculate_keypoints_loss(
    self,
    masks: torch.Tensor,
    target_gt_idx: torch.Tensor,
    keypoints: torch.Tensor,
    batch_idx: torch.Tensor,
    stride_tensor: torch.Tensor,
    target_bboxes: torch.Tensor,
    pred_kpts: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Calculate the keypoints loss for the model.

    This function calculates the keypoints loss and keypoints object loss for a given batch. The keypoints loss is
    based on the difference between the predicted keypoints and ground truth keypoints. The keypoints object loss is
    a binary classification loss that classifies whether a keypoint is present or not.

    Args:
        masks (torch.Tensor): Binary mask tensor indicating object presence, shape (BS, N_anchors).
        target_gt_idx (torch.Tensor): Index tensor mapping anchors to ground truth objects, shape (BS, N_anchors).
        keypoints (torch.Tensor): Ground truth keypoints, shape (N_kpts_in_batch, N_kpts_per_object, kpts_dim).
        batch_idx (torch.Tensor): Batch index tensor for keypoints, shape (N_kpts_in_batch, 1).
        stride_tensor (torch.Tensor): Stride tensor for anchors, shape (N_anchors, 1).
        target_bboxes (torch.Tensor): Ground truth boxes in (x1, y1, x2, y2) format, shape (BS, N_anchors, 4).
        pred_kpts (torch.Tensor): Predicted keypoints, shape (BS, N_anchors, N_kpts_per_object, kpts_dim).

    Returns:
        kpts_loss (torch.Tensor): The keypoints loss.
        kpts_obj_loss (torch.Tensor): The keypoints object loss.
    """
    batch_idx = batch_idx.flatten()
    batch_size = len(masks)

    # Find the maximum number of keypoints in a single image
    max_kpts = torch.unique(batch_idx, return_counts=True)[1].max()

    # Create a tensor to hold batched keypoints
    batched_keypoints = torch.zeros(
        (batch_size, max_kpts, keypoints.shape[1], keypoints.shape[2]), device=keypoints.device
    )

    # TODO: any idea how to vectorize this?
    # Fill batched_keypoints with keypoints based on batch_idx
    for i in range(batch_size):
        keypoints_i = keypoints[batch_idx == i]
        batched_keypoints[i, : keypoints_i.shape[0]] = keypoints_i

    # Expand dimensions of target_gt_idx to match the shape of batched_keypoints
    target_gt_idx_expanded = target_gt_idx.unsqueeze(-1).unsqueeze(-1)

    # Use target_gt_idx_expanded to select keypoints from batched_keypoints
    selected_keypoints = batched_keypoints.gather(
        1, target_gt_idx_expanded.expand(-1, -1, keypoints.shape[1], keypoints.shape[2])
    )

    # Divide coordinates by stride
    selected_keypoints[..., :2] /= stride_tensor.view(1, -1, 1, 1)

    kpts_loss = 0
    kpts_obj_loss = 0

    if masks.any():
        gt_kpt = selected_keypoints[masks]
        area = xyxy2xywh(target_bboxes[masks])[:, 2:].prod(1, keepdim=True)
        pred_kpt = pred_kpts[masks]
        kpt_mask = gt_kpt[..., 2] != 0 if gt_kpt.shape[-1] == 3 else torch.full_like(gt_kpt[..., 0], True)
        kpts_loss = self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area)  # pose loss

        if pred_kpt.shape[-1] == 3:
            kpts_obj_loss = self.bce_pose(pred_kpt[..., 2], kpt_mask.float())  # keypoint obj loss

    return kpts_loss, kpts_obj_loss

kpts_decode staticmethod 

kpts_decode(anchor_points: Tensor, pred_kpts: Tensor) -> torch.Tensor

Decode predicted keypoints to image coordinates.

Source code in ultralytics/utils/loss.py 
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@staticmethod
def kpts_decode(anchor_points: torch.Tensor, pred_kpts: torch.Tensor) -> torch.Tensor:
    """Decode predicted keypoints to image coordinates."""
    y = pred_kpts.clone()
    y[..., :2] *= 2.0
    y[..., 0] += anchor_points[:, [0]] - 0.5
    y[..., 1] += anchor_points[:, [1]] - 0.5
    return y




ultralytics.utils.loss.v8ClassificationLoss

Criterion class for computing training losses for classification.

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Compute the classification loss between predictions and true labels.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Compute the classification loss between predictions and true labels."""
    preds = preds[1] if isinstance(preds, (list, tuple)) else preds
    loss = F.cross_entropy(preds, batch["cls"], reduction="mean")
    return loss, loss.detach()




ultralytics.utils.loss.v8OBBLoss 

v8OBBLoss(model)

Bases: v8DetectionLoss

Calculates losses for object detection, classification, and box distribution in rotated YOLO models.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):
    """Initialize v8OBBLoss with model, assigner, and rotated bbox loss; model must be de-paralleled."""
    super().__init__(model)
    self.assigner = RotatedTaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
    self.bbox_loss = RotatedBboxLoss(self.reg_max).to(self.device)

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate and return the loss for oriented bounding box detection.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate and return the loss for oriented bounding box detection."""
    loss = torch.zeros(3, device=self.device)  # box, cls, dfl
    feats, pred_angle = preds if isinstance(preds[0], list) else preds[1]
    batch_size = pred_angle.shape[0]  # batch size, number of masks, mask height, mask width
    pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
        (self.reg_max * 4, self.nc), 1
    )

    # b, grids, ..
    pred_scores = pred_scores.permute(0, 2, 1).contiguous()
    pred_distri = pred_distri.permute(0, 2, 1).contiguous()
    pred_angle = pred_angle.permute(0, 2, 1).contiguous()

    dtype = pred_scores.dtype
    imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
    anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)

    # targets
    try:
        batch_idx = batch["batch_idx"].view(-1, 1)
        targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"].view(-1, 5)), 1)
        rw, rh = targets[:, 4] * imgsz[0].item(), targets[:, 5] * imgsz[1].item()
        targets = targets[(rw >= 2) & (rh >= 2)]  # filter rboxes of tiny size to stabilize training
        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
        gt_labels, gt_bboxes = targets.split((1, 5), 2)  # cls, xywhr
        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
    except RuntimeError as e:
        raise TypeError(
            "ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n"
            "This error can occur when incorrectly training a 'OBB' model on a 'detect' dataset, "
            "i.e. 'yolo train model=yolo11n-obb.pt data=coco8.yaml'.\nVerify your dataset is a "
            "correctly formatted 'OBB' dataset using 'data=dota8.yaml' "
            "as an example.\nSee https://docs.ultralytics.com/datasets/obb/ for help."
        ) from e

    # Pboxes
    pred_bboxes = self.bbox_decode(anchor_points, pred_distri, pred_angle)  # xyxy, (b, h*w, 4)

    bboxes_for_assigner = pred_bboxes.clone().detach()
    # Only the first four elements need to be scaled
    bboxes_for_assigner[..., :4] *= stride_tensor
    _, target_bboxes, target_scores, fg_mask, _ = self.assigner(
        pred_scores.detach().sigmoid(),
        bboxes_for_assigner.type(gt_bboxes.dtype),
        anchor_points * stride_tensor,
        gt_labels,
        gt_bboxes,
        mask_gt,
    )

    target_scores_sum = max(target_scores.sum(), 1)

    # Cls loss
    # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
    loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE

    # Bbox loss
    if fg_mask.sum():
        target_bboxes[..., :4] /= stride_tensor
        loss[0], loss[2] = self.bbox_loss(
            pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
        )
    else:
        loss[0] += (pred_angle * 0).sum()

    loss[0] *= self.hyp.box  # box gain
    loss[1] *= self.hyp.cls  # cls gain
    loss[2] *= self.hyp.dfl  # dfl gain

    return loss * batch_size, loss.detach()  # loss(box, cls, dfl)

bbox_decode 

bbox_decode(
    anchor_points: Tensor, pred_dist: Tensor, pred_angle: Tensor
) -> torch.Tensor

Decode predicted object bounding box coordinates from anchor points and distribution.

Parameters:

Name Type Description Default
anchor_points Tensor

Anchor points, (h*w, 2).

 required
pred_dist Tensor

Predicted rotated distance, (bs, h*w, 4).

 required
pred_angle Tensor

Predicted angle, (bs, h*w, 1).

 required

Returns:

Type Description
Tensor

Predicted rotated bounding boxes with angles, (bs, h*w, 5).
Source code in ultralytics/utils/loss.py 
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def bbox_decode(
    self, anchor_points: torch.Tensor, pred_dist: torch.Tensor, pred_angle: torch.Tensor
) -> torch.Tensor:
    """
    Decode predicted object bounding box coordinates from anchor points and distribution.

    Args:
        anchor_points (torch.Tensor): Anchor points, (h*w, 2).
        pred_dist (torch.Tensor): Predicted rotated distance, (bs, h*w, 4).
        pred_angle (torch.Tensor): Predicted angle, (bs, h*w, 1).

    Returns:
        (torch.Tensor): Predicted rotated bounding boxes with angles, (bs, h*w, 5).
    """
    if self.use_dfl:
        b, a, c = pred_dist.shape  # batch, anchors, channels
        pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
    return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)

preprocess 

preprocess(
    targets: Tensor, batch_size: int, scale_tensor: Tensor
) -> torch.Tensor

Preprocess targets for oriented bounding box detection.

Source code in ultralytics/utils/loss.py 
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def preprocess(self, targets: torch.Tensor, batch_size: int, scale_tensor: torch.Tensor) -> torch.Tensor:
    """Preprocess targets for oriented bounding box detection."""
    if targets.shape[0] == 0:
        out = torch.zeros(batch_size, 0, 6, device=self.device)
    else:
        i = targets[:, 0]  # image index
        _, counts = i.unique(return_counts=True)
        counts = counts.to(dtype=torch.int32)
        out = torch.zeros(batch_size, counts.max(), 6, device=self.device)
        for j in range(batch_size):
            matches = i == j
            if n := matches.sum():
                bboxes = targets[matches, 2:]
                bboxes[..., :4].mul_(scale_tensor)
                out[j, :n] = torch.cat([targets[matches, 1:2], bboxes], dim=-1)
    return out




ultralytics.utils.loss.E2EDetectLoss 

E2EDetectLoss(model)

Criterion class for computing training losses for end-to-end detection.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):
    """Initialize E2EDetectLoss with one-to-many and one-to-one detection losses using the provided model."""
    self.one2many = v8DetectionLoss(model, tal_topk=10)
    self.one2one = v8DetectionLoss(model, tal_topk=1)

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the sum of the loss for box, cls and dfl multiplied by batch size.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
    preds = preds[1] if isinstance(preds, tuple) else preds
    one2many = preds["one2many"]
    loss_one2many = self.one2many(one2many, batch)
    one2one = preds["one2one"]
    loss_one2one = self.one2one(one2one, batch)
    return loss_one2many[0] + loss_one2one[0], loss_one2many[1] + loss_one2one[1]




ultralytics.utils.loss.TVPDetectLoss 

TVPDetectLoss(model)

Criterion class for computing training losses for text-visual prompt detection.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):
    """Initialize TVPDetectLoss with task-prompt and visual-prompt criteria using the provided model."""
    self.vp_criterion = v8DetectionLoss(model)
    # NOTE: store following info as it's changeable in __call__
    self.ori_nc = self.vp_criterion.nc
    self.ori_no = self.vp_criterion.no
    self.ori_reg_max = self.vp_criterion.reg_max

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the loss for text-visual prompt detection.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate the loss for text-visual prompt detection."""
    feats = preds[1] if isinstance(preds, tuple) else preds
    assert self.ori_reg_max == self.vp_criterion.reg_max  # TODO: remove it

    if self.ori_reg_max * 4 + self.ori_nc == feats[0].shape[1]:
        loss = torch.zeros(3, device=self.vp_criterion.device, requires_grad=True)
        return loss, loss.detach()

    vp_feats = self._get_vp_features(feats)
    vp_loss = self.vp_criterion(vp_feats, batch)
    box_loss = vp_loss[0][1]
    return box_loss, vp_loss[1]




ultralytics.utils.loss.TVPSegmentLoss 

TVPSegmentLoss(model)

Bases: TVPDetectLoss

Criterion class for computing training losses for text-visual prompt segmentation.

Source code in ultralytics/utils/loss.py 
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def __init__(self, model):
    """Initialize TVPSegmentLoss with task-prompt and visual-prompt criteria using the provided model."""
    super().__init__(model)
    self.vp_criterion = v8SegmentationLoss(model)

__call__ 

__call__(
    preds: Any, batch: dict[str, Tensor]
) -> tuple[torch.Tensor, torch.Tensor]

Calculate the loss for text-visual prompt segmentation.

Source code in ultralytics/utils/loss.py 
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def __call__(self, preds: Any, batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
    """Calculate the loss for text-visual prompt segmentation."""
    feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
    assert self.ori_reg_max == self.vp_criterion.reg_max  # TODO: remove it

    if self.ori_reg_max * 4 + self.ori_nc == feats[0].shape[1]:
        loss = torch.zeros(4, device=self.vp_criterion.device, requires_grad=True)
        return loss, loss.detach()

    vp_feats = self._get_vp_features(feats)
    vp_loss = self.vp_criterion((vp_feats, pred_masks, proto), batch)
    cls_loss = vp_loss[0][2]
    return cls_loss, vp_loss[1]





📅 Created 1 year ago ✏️ Updated 5 months ago
glenn-jocher Burhan-Q RizwanMunawar jk4e Laughing-q