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ultralytics/models/utils/__init__.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

ultralytics/models/utils/loss.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.utils.loss import FocalLoss, VarifocalLoss
+from ultralytics.utils.metrics import bbox_iou
+
+from .ops import HungarianMatcher
+
+
+class DETRLoss(nn.Module):
+    """
+    DETR (DEtection TRansformer) Loss class for calculating various loss components.
+
+    This class computes classification loss, bounding box loss, GIoU loss, and optionally auxiliary losses for the
+    DETR object detection model.
+
+    Attributes:
+        nc (int): Number of classes.
+        loss_gain (dict[str, float]): Coefficients for different loss components.
+        aux_loss (bool): Whether to compute auxiliary losses.
+        use_fl (bool): Whether to use FocalLoss.
+        use_vfl (bool): Whether to use VarifocalLoss.
+        use_uni_match (bool): Whether to use a fixed layer for auxiliary branch label assignment.
+        uni_match_ind (int): Index of fixed layer to use if use_uni_match is True.
+        matcher (HungarianMatcher): Object to compute matching cost and indices.
+        fl (FocalLoss | None): Focal Loss object if use_fl is True, otherwise None.
+        vfl (VarifocalLoss | None): Varifocal Loss object if use_vfl is True, otherwise None.
+        device (torch.device): Device on which tensors are stored.
+    """
+
+    def __init__(
+        self,
+        nc: int = 80,
+        loss_gain: dict[str, float] | None = None,
+        aux_loss: bool = True,
+        use_fl: bool = True,
+        use_vfl: bool = False,
+        use_uni_match: bool = False,
+        uni_match_ind: int = 0,
+        gamma: float = 1.5,
+        alpha: float = 0.25,
+    ):
+        """
+        Initialize DETR loss function with customizable components and gains.
+
+        Uses default loss_gain if not provided. Initializes HungarianMatcher with preset cost gains. Supports auxiliary
+        losses and various loss types.
+
+        Args:
+            nc (int): Number of classes.
+            loss_gain (dict[str, float], optional): Coefficients for different loss components.
+            aux_loss (bool): Whether to use auxiliary losses from each decoder layer.
+            use_fl (bool): Whether to use FocalLoss.
+            use_vfl (bool): Whether to use VarifocalLoss.
+            use_uni_match (bool): Whether to use fixed layer for auxiliary branch label assignment.
+            uni_match_ind (int): Index of fixed layer for uni_match.
+            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.
+        """
+        super().__init__()
+
+        if loss_gain is None:
+            loss_gain = {"class": 1, "bbox": 5, "giou": 2, "no_object": 0.1, "mask": 1, "dice": 1}
+        self.nc = nc
+        self.matcher = HungarianMatcher(cost_gain={"class": 2, "bbox": 5, "giou": 2})
+        self.loss_gain = loss_gain
+        self.aux_loss = aux_loss
+        self.fl = FocalLoss(gamma, alpha) if use_fl else None
+        self.vfl = VarifocalLoss(gamma, alpha) if use_vfl else None
+
+        self.use_uni_match = use_uni_match
+        self.uni_match_ind = uni_match_ind
+        self.device = None
+
+    def _get_loss_class(
+        self, pred_scores: torch.Tensor, targets: torch.Tensor, gt_scores: torch.Tensor, num_gts: int, postfix: str = ""
+    ) -> dict[str, torch.Tensor]:
+        """
+        Compute classification loss based on predictions, target values, and ground truth scores.
+
+        Args:
+            pred_scores (torch.Tensor): Predicted class scores with shape (B, N, C).
+            targets (torch.Tensor): Target class indices with shape (B, N).
+            gt_scores (torch.Tensor): Ground truth confidence scores with shape (B, N).
+            num_gts (int): Number of ground truth objects.
+            postfix (str, optional): String to append to the loss name for identification in multi-loss scenarios.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing classification loss value.
+
+        Notes:
+            The function supports different classification loss types:
+            - Varifocal Loss (if self.vfl is True and num_gts > 0)
+            - Focal Loss (if self.fl is True)
+            - BCE Loss (default fallback)
+        """
+        # Logits: [b, query, num_classes], gt_class: list[[n, 1]]
+        name_class = f"loss_class{postfix}"
+        bs, nq = pred_scores.shape[:2]
+        # one_hot = F.one_hot(targets, self.nc + 1)[..., :-1]  # (bs, num_queries, num_classes)
+        one_hot = torch.zeros((bs, nq, self.nc + 1), dtype=torch.int64, device=targets.device)
+        one_hot.scatter_(2, targets.unsqueeze(-1), 1)
+        one_hot = one_hot[..., :-1]
+        gt_scores = gt_scores.view(bs, nq, 1) * one_hot
+
+        if self.fl:
+            if num_gts and self.vfl:
+                loss_cls = self.vfl(pred_scores, gt_scores, one_hot)
+            else:
+                loss_cls = self.fl(pred_scores, one_hot.float())
+            loss_cls /= max(num_gts, 1) / nq
+        else:
+            loss_cls = nn.BCEWithLogitsLoss(reduction="none")(pred_scores, gt_scores).mean(1).sum()  # YOLO CLS loss
+
+        return {name_class: loss_cls.squeeze() * self.loss_gain["class"]}
+
+    def _get_loss_bbox(
+        self, pred_bboxes: torch.Tensor, gt_bboxes: torch.Tensor, postfix: str = ""
+    ) -> dict[str, torch.Tensor]:
+        """
+        Compute bounding box and GIoU losses for predicted and ground truth bounding boxes.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes with shape (N, 4).
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape (N, 4).
+            postfix (str, optional): String to append to the loss names for identification in multi-loss scenarios.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing:
+                - loss_bbox{postfix}: L1 loss between predicted and ground truth boxes, scaled by the bbox loss gain.
+                - loss_giou{postfix}: GIoU loss between predicted and ground truth boxes, scaled by the giou loss gain.
+
+        Notes:
+            If no ground truth boxes are provided (empty list), zero-valued tensors are returned for both losses.
+        """
+        # Boxes: [b, query, 4], gt_bbox: list[[n, 4]]
+        name_bbox = f"loss_bbox{postfix}"
+        name_giou = f"loss_giou{postfix}"
+
+        loss = {}
+        if len(gt_bboxes) == 0:
+            loss[name_bbox] = torch.tensor(0.0, device=self.device)
+            loss[name_giou] = torch.tensor(0.0, device=self.device)
+            return loss
+
+        loss[name_bbox] = self.loss_gain["bbox"] * F.l1_loss(pred_bboxes, gt_bboxes, reduction="sum") / len(gt_bboxes)
+        loss[name_giou] = 1.0 - bbox_iou(pred_bboxes, gt_bboxes, xywh=True, GIoU=True)
+        loss[name_giou] = loss[name_giou].sum() / len(gt_bboxes)
+        loss[name_giou] = self.loss_gain["giou"] * loss[name_giou]
+        return {k: v.squeeze() for k, v in loss.items()}
+
+    # This function is for future RT-DETR Segment models
+    # def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
+    #     # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
+    #     name_mask = f'loss_mask{postfix}'
+    #     name_dice = f'loss_dice{postfix}'
+    #
+    #     loss = {}
+    #     if sum(len(a) for a in gt_mask) == 0:
+    #         loss[name_mask] = torch.tensor(0., device=self.device)
+    #         loss[name_dice] = torch.tensor(0., device=self.device)
+    #         return loss
+    #
+    #     num_gts = len(gt_mask)
+    #     src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
+    #     src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
+    #     # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
+    #     loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
+    #                                                                     torch.tensor([num_gts], dtype=torch.float32))
+    #     loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
+    #     return loss
+
+    # This function is for future RT-DETR Segment models
+    # @staticmethod
+    # def _dice_loss(inputs, targets, num_gts):
+    #     inputs = F.sigmoid(inputs).flatten(1)
+    #     targets = targets.flatten(1)
+    #     numerator = 2 * (inputs * targets).sum(1)
+    #     denominator = inputs.sum(-1) + targets.sum(-1)
+    #     loss = 1 - (numerator + 1) / (denominator + 1)
+    #     return loss.sum() / num_gts
+
+    def _get_loss_aux(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        match_indices: list[tuple] | None = None,
+        postfix: str = "",
+        masks: torch.Tensor | None = None,
+        gt_mask: torch.Tensor | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Get auxiliary losses for intermediate decoder layers.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes from auxiliary layers.
+            pred_scores (torch.Tensor): Predicted scores from auxiliary layers.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            gt_cls (torch.Tensor): Ground truth classes.
+            gt_groups (list[int]): Number of ground truths per image.
+            match_indices (list[tuple], optional): Pre-computed matching indices.
+            postfix (str, optional): String to append to loss names.
+            masks (torch.Tensor, optional): Predicted masks if using segmentation.
+            gt_mask (torch.Tensor, optional): Ground truth masks if using segmentation.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary of auxiliary losses.
+        """
+        # NOTE: loss class, bbox, giou, mask, dice
+        loss = torch.zeros(5 if masks is not None else 3, device=pred_bboxes.device)
+        if match_indices is None and self.use_uni_match:
+            match_indices = self.matcher(
+                pred_bboxes[self.uni_match_ind],
+                pred_scores[self.uni_match_ind],
+                gt_bboxes,
+                gt_cls,
+                gt_groups,
+                masks=masks[self.uni_match_ind] if masks is not None else None,
+                gt_mask=gt_mask,
+            )
+        for i, (aux_bboxes, aux_scores) in enumerate(zip(pred_bboxes, pred_scores)):
+            aux_masks = masks[i] if masks is not None else None
+            loss_ = self._get_loss(
+                aux_bboxes,
+                aux_scores,
+                gt_bboxes,
+                gt_cls,
+                gt_groups,
+                masks=aux_masks,
+                gt_mask=gt_mask,
+                postfix=postfix,
+                match_indices=match_indices,
+            )
+            loss[0] += loss_[f"loss_class{postfix}"]
+            loss[1] += loss_[f"loss_bbox{postfix}"]
+            loss[2] += loss_[f"loss_giou{postfix}"]
+            # if masks is not None and gt_mask is not None:
+            #     loss_ = self._get_loss_mask(aux_masks, gt_mask, match_indices, postfix)
+            #     loss[3] += loss_[f'loss_mask{postfix}']
+            #     loss[4] += loss_[f'loss_dice{postfix}']
+
+        loss = {
+            f"loss_class_aux{postfix}": loss[0],
+            f"loss_bbox_aux{postfix}": loss[1],
+            f"loss_giou_aux{postfix}": loss[2],
+        }
+        # if masks is not None and gt_mask is not None:
+        #     loss[f'loss_mask_aux{postfix}'] = loss[3]
+        #     loss[f'loss_dice_aux{postfix}'] = loss[4]
+        return loss
+
+    @staticmethod
+    def _get_index(match_indices: list[tuple]) -> tuple[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        """
+        Extract batch indices, source indices, and destination indices from match indices.
+
+        Args:
+            match_indices (list[tuple]): List of tuples containing matched indices.
+
+        Returns:
+            batch_idx (tuple[torch.Tensor, torch.Tensor]): Tuple containing (batch_idx, src_idx).
+            dst_idx (torch.Tensor): Destination indices.
+        """
+        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(match_indices)])
+        src_idx = torch.cat([src for (src, _) in match_indices])
+        dst_idx = torch.cat([dst for (_, dst) in match_indices])
+        return (batch_idx, src_idx), dst_idx
+
+    def _get_assigned_bboxes(
+        self, pred_bboxes: torch.Tensor, gt_bboxes: torch.Tensor, match_indices: list[tuple]
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Assign predicted bounding boxes to ground truth bounding boxes based on match indices.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            match_indices (list[tuple]): List of tuples containing matched indices.
+
+        Returns:
+            pred_assigned (torch.Tensor): Assigned predicted bounding boxes.
+            gt_assigned (torch.Tensor): Assigned ground truth bounding boxes.
+        """
+        pred_assigned = torch.cat(
+            [
+                t[i] if len(i) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+                for t, (i, _) in zip(pred_bboxes, match_indices)
+            ]
+        )
+        gt_assigned = torch.cat(
+            [
+                t[j] if len(j) > 0 else torch.zeros(0, t.shape[-1], device=self.device)
+                for t, (_, j) in zip(gt_bboxes, match_indices)
+            ]
+        )
+        return pred_assigned, gt_assigned
+
+    def _get_loss(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        masks: torch.Tensor | None = None,
+        gt_mask: torch.Tensor | None = None,
+        postfix: str = "",
+        match_indices: list[tuple] | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Calculate losses for a single prediction layer.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes.
+            pred_scores (torch.Tensor): Predicted class scores.
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes.
+            gt_cls (torch.Tensor): Ground truth classes.
+            gt_groups (list[int]): Number of ground truths per image.
+            masks (torch.Tensor, optional): Predicted masks if using segmentation.
+            gt_mask (torch.Tensor, optional): Ground truth masks if using segmentation.
+            postfix (str, optional): String to append to loss names.
+            match_indices (list[tuple], optional): Pre-computed matching indices.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary of losses.
+        """
+        if match_indices is None:
+            match_indices = self.matcher(
+                pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=masks, gt_mask=gt_mask
+            )
+
+        idx, gt_idx = self._get_index(match_indices)
+        pred_bboxes, gt_bboxes = pred_bboxes[idx], gt_bboxes[gt_idx]
+
+        bs, nq = pred_scores.shape[:2]
+        targets = torch.full((bs, nq), self.nc, device=pred_scores.device, dtype=gt_cls.dtype)
+        targets[idx] = gt_cls[gt_idx]
+
+        gt_scores = torch.zeros([bs, nq], device=pred_scores.device)
+        if len(gt_bboxes):
+            gt_scores[idx] = bbox_iou(pred_bboxes.detach(), gt_bboxes, xywh=True).squeeze(-1)
+
+        return {
+            **self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix),
+            **self._get_loss_bbox(pred_bboxes, gt_bboxes, postfix),
+            # **(self._get_loss_mask(masks, gt_mask, match_indices, postfix) if masks is not None and gt_mask is not None else {})
+        }
+
+    def forward(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        batch: dict[str, Any],
+        postfix: str = "",
+        **kwargs: Any,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Calculate loss for predicted bounding boxes and scores.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes, shape (L, B, N, 4).
+            pred_scores (torch.Tensor): Predicted class scores, shape (L, B, N, C).
+            batch (dict[str, Any]): Batch information containing cls, bboxes, and gt_groups.
+            postfix (str, optional): Postfix for loss names.
+            **kwargs (Any): Additional arguments, may include 'match_indices'.
+
+        Returns:
+            (dict[str, torch.Tensor]): Computed losses, including main and auxiliary (if enabled).
+
+        Notes:
+            Uses last elements of pred_bboxes and pred_scores for main loss, and the rest for auxiliary losses if
+            self.aux_loss is True.
+        """
+        self.device = pred_bboxes.device
+        match_indices = kwargs.get("match_indices", None)
+        gt_cls, gt_bboxes, gt_groups = batch["cls"], batch["bboxes"], batch["gt_groups"]
+
+        total_loss = self._get_loss(
+            pred_bboxes[-1], pred_scores[-1], gt_bboxes, gt_cls, gt_groups, postfix=postfix, match_indices=match_indices
+        )
+
+        if self.aux_loss:
+            total_loss.update(
+                self._get_loss_aux(
+                    pred_bboxes[:-1], pred_scores[:-1], gt_bboxes, gt_cls, gt_groups, match_indices, postfix
+                )
+            )
+
+        return total_loss
+
+
+class RTDETRDetectionLoss(DETRLoss):
+    """
+    Real-Time DeepTracker (RT-DETR) Detection Loss class that extends the DETRLoss.
+
+    This class computes the detection loss for the RT-DETR model, which includes the standard detection loss as well as
+    an additional denoising training loss when provided with denoising metadata.
+    """
+
+    def forward(
+        self,
+        preds: tuple[torch.Tensor, torch.Tensor],
+        batch: dict[str, Any],
+        dn_bboxes: torch.Tensor | None = None,
+        dn_scores: torch.Tensor | None = None,
+        dn_meta: dict[str, Any] | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Forward pass to compute detection loss with optional denoising loss.
+
+        Args:
+            preds (tuple[torch.Tensor, torch.Tensor]): Tuple containing predicted bounding boxes and scores.
+            batch (dict[str, Any]): Batch data containing ground truth information.
+            dn_bboxes (torch.Tensor, optional): Denoising bounding boxes.
+            dn_scores (torch.Tensor, optional): Denoising scores.
+            dn_meta (dict[str, Any], optional): Metadata for denoising.
+
+        Returns:
+            (dict[str, torch.Tensor]): Dictionary containing total loss and denoising loss if applicable.
+        """
+        pred_bboxes, pred_scores = preds
+        total_loss = super().forward(pred_bboxes, pred_scores, batch)
+
+        # Check for denoising metadata to compute denoising training loss
+        if dn_meta is not None:
+            dn_pos_idx, dn_num_group = dn_meta["dn_pos_idx"], dn_meta["dn_num_group"]
+            assert len(batch["gt_groups"]) == len(dn_pos_idx)
+
+            # Get the match indices for denoising
+            match_indices = self.get_dn_match_indices(dn_pos_idx, dn_num_group, batch["gt_groups"])
+
+            # Compute the denoising training loss
+            dn_loss = super().forward(dn_bboxes, dn_scores, batch, postfix="_dn", match_indices=match_indices)
+            total_loss.update(dn_loss)
+        else:
+            # If no denoising metadata is provided, set denoising loss to zero
+            total_loss.update({f"{k}_dn": torch.tensor(0.0, device=self.device) for k in total_loss.keys()})
+
+        return total_loss
+
+    @staticmethod
+    def get_dn_match_indices(
+        dn_pos_idx: list[torch.Tensor], dn_num_group: int, gt_groups: list[int]
+    ) -> list[tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Get match indices for denoising.
+
+        Args:
+            dn_pos_idx (list[torch.Tensor]): List of tensors containing positive indices for denoising.
+            dn_num_group (int): Number of denoising groups.
+            gt_groups (list[int]): List of integers representing number of ground truths per image.
+
+        Returns:
+            (list[tuple[torch.Tensor, torch.Tensor]]): List of tuples containing matched indices for denoising.
+        """
+        dn_match_indices = []
+        idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
+        for i, num_gt in enumerate(gt_groups):
+            if num_gt > 0:
+                gt_idx = torch.arange(end=num_gt, dtype=torch.long) + idx_groups[i]
+                gt_idx = gt_idx.repeat(dn_num_group)
+                assert len(dn_pos_idx[i]) == len(gt_idx), (
+                    f"Expected the same length, but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively."
+                )
+                dn_match_indices.append((dn_pos_idx[i], gt_idx))
+            else:
+                dn_match_indices.append((torch.zeros([0], dtype=torch.long), torch.zeros([0], dtype=torch.long)))
+        return dn_match_indices

ultralytics/models/utils/ops.py

@@ -0,0 +1,319 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+
+from ultralytics.utils.metrics import bbox_iou
+from ultralytics.utils.ops import xywh2xyxy, xyxy2xywh
+
+
+class HungarianMatcher(nn.Module):
+    """
+    A module implementing the HungarianMatcher for optimal assignment between predictions and ground truth.
+
+    HungarianMatcher performs optimal bipartite assignment over predicted and ground truth bounding boxes using a cost
+    function that considers classification scores, bounding box coordinates, and optionally mask predictions. This is
+    used in end-to-end object detection models like DETR.
+
+    Attributes:
+        cost_gain (dict[str, float]): Dictionary of cost coefficients for 'class', 'bbox', 'giou', 'mask', and 'dice'
+            components.
+        use_fl (bool): Whether to use Focal Loss for classification cost calculation.
+        with_mask (bool): Whether the model makes mask predictions.
+        num_sample_points (int): Number of sample points used in mask cost calculation.
+        alpha (float): Alpha factor in Focal Loss calculation.
+        gamma (float): Gamma factor in Focal Loss calculation.
+
+    Methods:
+        forward: Compute optimal assignment between predictions and ground truths for a batch.
+        _cost_mask: Compute mask cost and dice cost if masks are predicted.
+
+    Examples:
+        Initialize a HungarianMatcher with custom cost gains
+        >>> matcher = HungarianMatcher(cost_gain={"class": 2, "bbox": 5, "giou": 2})
+
+        Perform matching between predictions and ground truth
+        >>> pred_boxes = torch.rand(2, 100, 4)  # batch_size=2, num_queries=100
+        >>> pred_scores = torch.rand(2, 100, 80)  # 80 classes
+        >>> gt_boxes = torch.rand(10, 4)  # 10 ground truth boxes
+        >>> gt_classes = torch.randint(0, 80, (10,))
+        >>> gt_groups = [5, 5]  # 5 GT boxes per image
+        >>> indices = matcher(pred_boxes, pred_scores, gt_boxes, gt_classes, gt_groups)
+    """
+
+    def __init__(
+        self,
+        cost_gain: dict[str, float] | None = None,
+        use_fl: bool = True,
+        with_mask: bool = False,
+        num_sample_points: int = 12544,
+        alpha: float = 0.25,
+        gamma: float = 2.0,
+    ):
+        """
+        Initialize HungarianMatcher for optimal assignment of predicted and ground truth bounding boxes.
+
+        Args:
+            cost_gain (dict[str, float], optional): Dictionary of cost coefficients for different matching cost
+                components. Should contain keys 'class', 'bbox', 'giou', 'mask', and 'dice'.
+            use_fl (bool): Whether to use Focal Loss for classification cost calculation.
+            with_mask (bool): Whether the model makes mask predictions.
+            num_sample_points (int): Number of sample points used in mask cost calculation.
+            alpha (float): Alpha factor in Focal Loss calculation.
+            gamma (float): Gamma factor in Focal Loss calculation.
+        """
+        super().__init__()
+        if cost_gain is None:
+            cost_gain = {"class": 1, "bbox": 5, "giou": 2, "mask": 1, "dice": 1}
+        self.cost_gain = cost_gain
+        self.use_fl = use_fl
+        self.with_mask = with_mask
+        self.num_sample_points = num_sample_points
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(
+        self,
+        pred_bboxes: torch.Tensor,
+        pred_scores: torch.Tensor,
+        gt_bboxes: torch.Tensor,
+        gt_cls: torch.Tensor,
+        gt_groups: list[int],
+        masks: torch.Tensor | None = None,
+        gt_mask: list[torch.Tensor] | None = None,
+    ) -> list[tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Compute optimal assignment between predictions and ground truth using Hungarian algorithm.
+
+        This method calculates matching costs based on classification scores, bounding box coordinates, and optionally
+        mask predictions, then finds the optimal bipartite assignment between predictions and ground truth.
+
+        Args:
+            pred_bboxes (torch.Tensor): Predicted bounding boxes with shape (batch_size, num_queries, 4).
+            pred_scores (torch.Tensor): Predicted classification scores with shape (batch_size, num_queries,
+                num_classes).
+            gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape (num_gts, 4).
+            gt_cls (torch.Tensor): Ground truth class labels with shape (num_gts,).
+            gt_groups (list[int]): Number of ground truth boxes for each image in the batch.
+            masks (torch.Tensor, optional): Predicted masks with shape (batch_size, num_queries, height, width).
+            gt_mask (list[torch.Tensor], optional): Ground truth masks, each with shape (num_masks, Height, Width).
+
+        Returns:
+            (list[tuple[torch.Tensor, torch.Tensor]]): A list of size batch_size, each element is a tuple
+                (index_i, index_j), where index_i is the tensor of indices of the selected predictions (in order)
+                and index_j is the tensor of indices of the corresponding selected ground truth targets (in order).
+                For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
+        """
+        bs, nq, nc = pred_scores.shape
+
+        if sum(gt_groups) == 0:
+            return [(torch.tensor([], dtype=torch.long), torch.tensor([], dtype=torch.long)) for _ in range(bs)]
+
+        # Flatten to compute cost matrices in batch format
+        pred_scores = pred_scores.detach().view(-1, nc)
+        pred_scores = F.sigmoid(pred_scores) if self.use_fl else F.softmax(pred_scores, dim=-1)
+        pred_bboxes = pred_bboxes.detach().view(-1, 4)
+
+        # Compute classification cost
+        pred_scores = pred_scores[:, gt_cls]
+        if self.use_fl:
+            neg_cost_class = (1 - self.alpha) * (pred_scores**self.gamma) * (-(1 - pred_scores + 1e-8).log())
+            pos_cost_class = self.alpha * ((1 - pred_scores) ** self.gamma) * (-(pred_scores + 1e-8).log())
+            cost_class = pos_cost_class - neg_cost_class
+        else:
+            cost_class = -pred_scores
+
+        # Compute L1 cost between boxes
+        cost_bbox = (pred_bboxes.unsqueeze(1) - gt_bboxes.unsqueeze(0)).abs().sum(-1)  # (bs*num_queries, num_gt)
+
+        # Compute GIoU cost between boxes, (bs*num_queries, num_gt)
+        cost_giou = 1.0 - bbox_iou(pred_bboxes.unsqueeze(1), gt_bboxes.unsqueeze(0), xywh=True, GIoU=True).squeeze(-1)
+
+        # Combine costs into final cost matrix
+        C = (
+            self.cost_gain["class"] * cost_class
+            + self.cost_gain["bbox"] * cost_bbox
+            + self.cost_gain["giou"] * cost_giou
+        )
+
+        # Add mask costs if available
+        if self.with_mask:
+            C += self._cost_mask(bs, gt_groups, masks, gt_mask)
+
+        # Set invalid values (NaNs and infinities) to 0
+        C[C.isnan() | C.isinf()] = 0.0
+
+        C = C.view(bs, nq, -1).cpu()
+        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
+        gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)  # (idx for queries, idx for gt)
+        return [
+            (torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
+            for k, (i, j) in enumerate(indices)
+        ]
+
+    # This function is for future RT-DETR Segment models
+    # def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
+    #     assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
+    #     # all masks share the same set of points for efficient matching
+    #     sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
+    #     sample_points = 2.0 * sample_points - 1.0
+    #
+    #     out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
+    #     out_mask = out_mask.flatten(0, 1)
+    #
+    #     tgt_mask = torch.cat(gt_mask).unsqueeze(1)
+    #     sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
+    #     tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
+    #
+    #     with torch.amp.autocast("cuda", enabled=False):
+    #         # binary cross entropy cost
+    #         pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
+    #         neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
+    #         cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
+    #         cost_mask /= self.num_sample_points
+    #
+    #         # dice cost
+    #         out_mask = F.sigmoid(out_mask)
+    #         numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
+    #         denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
+    #         cost_dice = 1 - (numerator + 1) / (denominator + 1)
+    #
+    #         C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
+    #     return C
+
+
+def get_cdn_group(
+    batch: dict[str, Any],
+    num_classes: int,
+    num_queries: int,
+    class_embed: torch.Tensor,
+    num_dn: int = 100,
+    cls_noise_ratio: float = 0.5,
+    box_noise_scale: float = 1.0,
+    training: bool = False,
+) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None, dict[str, Any] | None]:
+    """
+    Generate contrastive denoising training group with positive and negative samples from ground truths.
+
+    This function creates denoising queries for contrastive denoising training by adding noise to ground truth
+    bounding boxes and class labels. It generates both positive and negative samples to improve model robustness.
+
+    Args:
+        batch (dict[str, Any]): Batch dictionary containing 'gt_cls' (torch.Tensor with shape (num_gts,)),
+            'gt_bboxes' (torch.Tensor with shape (num_gts, 4)), and 'gt_groups' (list[int]) indicating number of
+            ground truths per image.
+        num_classes (int): Total number of object classes.
+        num_queries (int): Number of object queries.
+        class_embed (torch.Tensor): Class embedding weights to map labels to embedding space.
+        num_dn (int): Number of denoising queries to generate.
+        cls_noise_ratio (float): Noise ratio for class labels.
+        box_noise_scale (float): Noise scale for bounding box coordinates.
+        training (bool): Whether model is in training mode.
+
+    Returns:
+        padding_cls (torch.Tensor | None): Modified class embeddings for denoising with shape (bs, num_dn, embed_dim).
+        padding_bbox (torch.Tensor | None): Modified bounding boxes for denoising with shape (bs, num_dn, 4).
+        attn_mask (torch.Tensor | None): Attention mask for denoising with shape (tgt_size, tgt_size).
+        dn_meta (dict[str, Any] | None): Meta information dictionary containing denoising parameters.
+
+    Examples:
+        Generate denoising group for training
+        >>> batch = {
+        ...     "cls": torch.tensor([0, 1, 2]),
+        ...     "bboxes": torch.rand(3, 4),
+        ...     "batch_idx": torch.tensor([0, 0, 1]),
+        ...     "gt_groups": [2, 1],
+        ... }
+        >>> class_embed = torch.rand(80, 256)  # 80 classes, 256 embedding dim
+        >>> cdn_outputs = get_cdn_group(batch, 80, 100, class_embed, training=True)
+    """
+    if (not training) or num_dn <= 0 or batch is None:
+        return None, None, None, None
+    gt_groups = batch["gt_groups"]
+    total_num = sum(gt_groups)
+    max_nums = max(gt_groups)
+    if max_nums == 0:
+        return None, None, None, None
+
+    num_group = num_dn // max_nums
+    num_group = 1 if num_group == 0 else num_group
+    # Pad gt to max_num of a batch
+    bs = len(gt_groups)
+    gt_cls = batch["cls"]  # (bs*num, )
+    gt_bbox = batch["bboxes"]  # bs*num, 4
+    b_idx = batch["batch_idx"]
+
+    # Each group has positive and negative queries
+    dn_cls = gt_cls.repeat(2 * num_group)  # (2*num_group*bs*num, )
+    dn_bbox = gt_bbox.repeat(2 * num_group, 1)  # 2*num_group*bs*num, 4
+    dn_b_idx = b_idx.repeat(2 * num_group).view(-1)  # (2*num_group*bs*num, )
+
+    # Positive and negative mask
+    # (bs*num*num_group, ), the second total_num*num_group part as negative samples
+    neg_idx = torch.arange(total_num * num_group, dtype=torch.long, device=gt_bbox.device) + num_group * total_num
+
+    if cls_noise_ratio > 0:
+        # Apply class label noise to half of the samples
+        mask = torch.rand(dn_cls.shape) < (cls_noise_ratio * 0.5)
+        idx = torch.nonzero(mask).squeeze(-1)
+        # Randomly assign new class labels
+        new_label = torch.randint_like(idx, 0, num_classes, dtype=dn_cls.dtype, device=dn_cls.device)
+        dn_cls[idx] = new_label
+
+    if box_noise_scale > 0:
+        known_bbox = xywh2xyxy(dn_bbox)
+
+        diff = (dn_bbox[..., 2:] * 0.5).repeat(1, 2) * box_noise_scale  # 2*num_group*bs*num, 4
+
+        rand_sign = torch.randint_like(dn_bbox, 0, 2) * 2.0 - 1.0
+        rand_part = torch.rand_like(dn_bbox)
+        rand_part[neg_idx] += 1.0
+        rand_part *= rand_sign
+        known_bbox += rand_part * diff
+        known_bbox.clip_(min=0.0, max=1.0)
+        dn_bbox = xyxy2xywh(known_bbox)
+        dn_bbox = torch.logit(dn_bbox, eps=1e-6)  # inverse sigmoid
+
+    num_dn = int(max_nums * 2 * num_group)  # total denoising queries
+    dn_cls_embed = class_embed[dn_cls]  # bs*num * 2 * num_group, 256
+    padding_cls = torch.zeros(bs, num_dn, dn_cls_embed.shape[-1], device=gt_cls.device)
+    padding_bbox = torch.zeros(bs, num_dn, 4, device=gt_bbox.device)
+
+    map_indices = torch.cat([torch.tensor(range(num), dtype=torch.long) for num in gt_groups])
+    pos_idx = torch.stack([map_indices + max_nums * i for i in range(num_group)], dim=0)
+
+    map_indices = torch.cat([map_indices + max_nums * i for i in range(2 * num_group)])
+    padding_cls[(dn_b_idx, map_indices)] = dn_cls_embed
+    padding_bbox[(dn_b_idx, map_indices)] = dn_bbox
+
+    tgt_size = num_dn + num_queries
+    attn_mask = torch.zeros([tgt_size, tgt_size], dtype=torch.bool)
+    # Match query cannot see the reconstruct
+    attn_mask[num_dn:, :num_dn] = True
+    # Reconstruct cannot see each other
+    for i in range(num_group):
+        if i == 0:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
+        if i == num_group - 1:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * i * 2] = True
+        else:
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
+            attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * 2 * i] = True
+    dn_meta = {
+        "dn_pos_idx": [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
+        "dn_num_group": num_group,
+        "dn_num_split": [num_dn, num_queries],
+    }
+
+    return (
+        padding_cls.to(class_embed.device),
+        padding_bbox.to(class_embed.device),
+        attn_mask.to(class_embed.device),
+        dn_meta,
+    )