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Reference for ultralytics/models/utils/ops.py

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ultralytics.models.utils.ops.HungarianMatcher

Bases: Module

A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in an end-to-end fashion.

HungarianMatcher performs optimal assignment over the predicted and ground truth bounding boxes using a cost function that considers classification scores, bounding box coordinates, and optionally, mask predictions.

Attributes:

Name Type Description
cost_gain dict

Dictionary of cost coefficients: 'class', 'bbox', 'giou', 'mask', and 'dice'.

use_fl bool

Indicates whether to use Focal Loss for the classification cost calculation.

with_mask bool

Indicates whether the model makes mask predictions.

num_sample_points int

The number of sample points used in mask cost calculation.

alpha float

The alpha factor in Focal Loss calculation.

gamma float

The gamma factor in Focal Loss calculation.

Methods:

Name Description
forward

Computes the assignment between predictions and ground truths for a batch.

_cost_mask

Computes the mask cost and dice cost if masks are predicted.

Source code in ultralytics/models/utils/ops.py
class HungarianMatcher(nn.Module):
    """
    A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in an
    end-to-end fashion.

    HungarianMatcher performs optimal assignment over the predicted and ground truth bounding boxes using a cost
    function that considers classification scores, bounding box coordinates, and optionally, mask predictions.

    Attributes:
        cost_gain (dict): Dictionary of cost coefficients: 'class', 'bbox', 'giou', 'mask', and 'dice'.
        use_fl (bool): Indicates whether to use Focal Loss for the classification cost calculation.
        with_mask (bool): Indicates whether the model makes mask predictions.
        num_sample_points (int): The number of sample points used in mask cost calculation.
        alpha (float): The alpha factor in Focal Loss calculation.
        gamma (float): The gamma factor in Focal Loss calculation.

    Methods:
        forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None): Computes the
            assignment between predictions and ground truths for a batch.
        _cost_mask(bs, num_gts, masks=None, gt_mask=None): Computes the mask cost and dice cost if masks are predicted.
    """

    def __init__(self, cost_gain=None, use_fl=True, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0):
        """Initializes HungarianMatcher with cost coefficients, Focal Loss, mask prediction, sample points, and alpha
        gamma factors.
        """
        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, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None):
        """
        Forward pass for HungarianMatcher. This function computes costs based on prediction and ground truth
        (classification cost, L1 cost between boxes and GIoU cost between boxes) and finds the optimal matching between
        predictions and ground truth based on these costs.

        Args:
            pred_bboxes (Tensor): Predicted bounding boxes with shape [batch_size, num_queries, 4].
            pred_scores (Tensor): Predicted scores with shape [batch_size, num_queries, num_classes].
            gt_cls (torch.Tensor): Ground truth classes with shape [num_gts, ].
            gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape [num_gts, 4].
            gt_groups (List[int]): List of length equal to batch size, containing the number of ground truths for
                each image.
            masks (Tensor, optional): Predicted masks with shape [batch_size, num_queries, height, width].
                Defaults to None.
            gt_mask (List[Tensor], optional): List of ground truth masks, each with shape [num_masks, Height, Width].
                Defaults to None.

        Returns:
            (List[Tuple[Tensor, 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)
                - 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)]

        # We flatten to compute the cost matrices in a batch
        # [batch_size * num_queries, num_classes]
        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)
        # [batch_size * num_queries, 4]
        pred_bboxes = pred_bboxes.detach().view(-1, 4)

        # Compute the 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 the L1 cost between boxes
        cost_bbox = (pred_bboxes.unsqueeze(1) - gt_bboxes.unsqueeze(0)).abs().sum(-1)  # (bs*num_queries, num_gt)

        # Compute the 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)

        # Final cost matrix
        C = (
            self.cost_gain["class"] * cost_class
            + self.cost_gain["bbox"] * cost_bbox
            + self.cost_gain["giou"] * cost_giou
        )
        # Compute the mask cost and dice cost
        if self.with_mask:
            C += self._cost_mask(bs, gt_groups, masks, gt_mask)

        # Set invalid values (NaNs and infinities) to 0 (fixes ValueError: matrix contains invalid numeric entries)
        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)
        ]

__init__(cost_gain=None, use_fl=True, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0)

Initializes HungarianMatcher with cost coefficients, Focal Loss, mask prediction, sample points, and alpha gamma factors.

Source code in ultralytics/models/utils/ops.py
def __init__(self, cost_gain=None, use_fl=True, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0):
    """Initializes HungarianMatcher with cost coefficients, Focal Loss, mask prediction, sample points, and alpha
    gamma factors.
    """
    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

forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None)

Forward pass for HungarianMatcher. This function computes costs based on prediction and ground truth (classification cost, L1 cost between boxes and GIoU cost between boxes) and finds the optimal matching between predictions and ground truth based on these costs.

Parameters:

Name Type Description Default
pred_bboxes Tensor

Predicted bounding boxes with shape [batch_size, num_queries, 4].

required
pred_scores Tensor

Predicted scores with shape [batch_size, num_queries, num_classes].

required
gt_cls Tensor

Ground truth classes with shape [num_gts, ].

required
gt_bboxes Tensor

Ground truth bounding boxes with shape [num_gts, 4].

required
gt_groups List[int]

List of length equal to batch size, containing the number of ground truths for each image.

required
masks Tensor

Predicted masks with shape [batch_size, num_queries, height, width]. Defaults to None.

None
gt_mask List[Tensor]

List of ground truth masks, each with shape [num_masks, Height, Width]. Defaults to None.

None

Returns:

Type Description
List[Tuple[Tensor, 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) - 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)

Source code in ultralytics/models/utils/ops.py
def forward(self, pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None):
    """
    Forward pass for HungarianMatcher. This function computes costs based on prediction and ground truth
    (classification cost, L1 cost between boxes and GIoU cost between boxes) and finds the optimal matching between
    predictions and ground truth based on these costs.

    Args:
        pred_bboxes (Tensor): Predicted bounding boxes with shape [batch_size, num_queries, 4].
        pred_scores (Tensor): Predicted scores with shape [batch_size, num_queries, num_classes].
        gt_cls (torch.Tensor): Ground truth classes with shape [num_gts, ].
        gt_bboxes (torch.Tensor): Ground truth bounding boxes with shape [num_gts, 4].
        gt_groups (List[int]): List of length equal to batch size, containing the number of ground truths for
            each image.
        masks (Tensor, optional): Predicted masks with shape [batch_size, num_queries, height, width].
            Defaults to None.
        gt_mask (List[Tensor], optional): List of ground truth masks, each with shape [num_masks, Height, Width].
            Defaults to None.

    Returns:
        (List[Tuple[Tensor, 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)
            - 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)]

    # We flatten to compute the cost matrices in a batch
    # [batch_size * num_queries, num_classes]
    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)
    # [batch_size * num_queries, 4]
    pred_bboxes = pred_bboxes.detach().view(-1, 4)

    # Compute the 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 the L1 cost between boxes
    cost_bbox = (pred_bboxes.unsqueeze(1) - gt_bboxes.unsqueeze(0)).abs().sum(-1)  # (bs*num_queries, num_gt)

    # Compute the 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)

    # Final cost matrix
    C = (
        self.cost_gain["class"] * cost_class
        + self.cost_gain["bbox"] * cost_bbox
        + self.cost_gain["giou"] * cost_giou
    )
    # Compute the mask cost and dice cost
    if self.with_mask:
        C += self._cost_mask(bs, gt_groups, masks, gt_mask)

    # Set invalid values (NaNs and infinities) to 0 (fixes ValueError: matrix contains invalid numeric entries)
    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)
    ]



ultralytics.models.utils.ops.get_cdn_group(batch, num_classes, num_queries, class_embed, num_dn=100, cls_noise_ratio=0.5, box_noise_scale=1.0, training=False)

Get contrastive denoising training group. This function creates a contrastive denoising training group with positive and negative samples from the ground truths (gt). It applies noise to the class labels and bounding box coordinates, and returns the modified labels, bounding boxes, attention mask and meta information.

Parameters:

Name Type Description Default
batch dict

A dict that includes 'gt_cls' (torch.Tensor with shape [num_gts, ]), 'gt_bboxes' (torch.Tensor with shape [num_gts, 4]), 'gt_groups' (List(int)) which is a list of batch size length indicating the number of gts of each image.

required
num_classes int

Number of classes.

required
num_queries int

Number of queries.

required
class_embed Tensor

Embedding weights to map class labels to embedding space.

required
num_dn int

Number of denoising. Defaults to 100.

100
cls_noise_ratio float

Noise ratio for class labels. Defaults to 0.5.

0.5
box_noise_scale float

Noise scale for bounding box coordinates. Defaults to 1.0.

1.0
training bool

If it's in training mode. Defaults to False.

False

Returns:

Type Description
Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Dict]]

The modified class embeddings, bounding boxes, attention mask and meta information for denoising. If not in training mode or 'num_dn' is less than or equal to 0, the function returns None for all elements in the tuple.

Source code in ultralytics/models/utils/ops.py
def get_cdn_group(
    batch, num_classes, num_queries, class_embed, num_dn=100, cls_noise_ratio=0.5, box_noise_scale=1.0, training=False
):
    """
    Get contrastive denoising training group. This function creates a contrastive denoising training group with positive
    and negative samples from the ground truths (gt). It applies noise to the class labels and bounding box coordinates,
    and returns the modified labels, bounding boxes, attention mask and meta information.

    Args:
        batch (dict): A dict that includes 'gt_cls' (torch.Tensor with shape [num_gts, ]), 'gt_bboxes'
            (torch.Tensor with shape [num_gts, 4]), 'gt_groups' (List(int)) which is a list of batch size length
            indicating the number of gts of each image.
        num_classes (int): Number of classes.
        num_queries (int): Number of queries.
        class_embed (torch.Tensor): Embedding weights to map class labels to embedding space.
        num_dn (int, optional): Number of denoising. Defaults to 100.
        cls_noise_ratio (float, optional): Noise ratio for class labels. Defaults to 0.5.
        box_noise_scale (float, optional): Noise scale for bounding box coordinates. Defaults to 1.0.
        training (bool, optional): If it's in training mode. Defaults to False.

    Returns:
        (Tuple[Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Dict]]): The modified class embeddings,
            bounding boxes, attention mask and meta information for denoising. If not in training mode or 'num_dn'
            is less than or equal to 0, the function returns None for all elements in the tuple.
    """

    if (not training) or num_dn <= 0:
        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:
        # Half of bbox prob
        mask = torch.rand(dn_cls.shape) < (cls_noise_ratio * 0.5)
        idx = torch.nonzero(mask).squeeze(-1)
        # Randomly put a new one here
        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
    # class_embed = torch.cat([class_embed, torch.zeros([1, class_embed.shape[-1]], device=class_embed.device)])
    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,
    )





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1), Laughing-q (1)