tal.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

import torch
import torch.nn as nn
import numpy as np

TORCH_1_10 = True

def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
    """
    Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4).

    Args:
        box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4).
        box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 4).
        xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in
                               (x1, y1, x2, y2) format. Defaults to True.
        GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False.
        DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False.
        CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False.
        eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:
        (torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags.
    """

    # Get the coordinates of bounding boxes
    if xywh:  # transform from xywh to xyxy
        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
    else:  # x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps

    # Intersection area
    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * \
            (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp_(0)

    # Union Area
    union = w1 * h1 + w2 * h2 - inter + eps

    # IoU
    iou = inter / union
    if CIoU or DIoU or GIoU:
        cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1)  # convex (smallest enclosing box) width
        ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1)  # convex height
        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center dist ** 2
            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
                v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
                with torch.no_grad():
                    alpha = v / (v - iou + (1 + eps))
                return iou - (rho2 / c2 + v * alpha)  # CIoU
            return iou - rho2 / c2  # DIoU
        c_area = cw * ch + eps  # convex area
        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
    return iou  # IoU


def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
    """select the positive anchor center in gt

    Args:
        xy_centers (Tensor): shape(h*w, 4)
        gt_bboxes (Tensor): shape(b, n_boxes, 4)
    Return:
        (Tensor): shape(b, n_boxes, h*w)
    """
    n_anchors = xy_centers.shape[0]
    bs, n_boxes, _ = gt_bboxes.shape
    lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2)  # left-top, right-bottom
    bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
    # return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
    return bbox_deltas.amin(3).gt_(eps)


def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
    """if an anchor box is assigned to multiple gts,
        the one with the highest iou will be selected.

    Args:
        mask_pos (Tensor): shape(b, n_max_boxes, h*w)
        overlaps (Tensor): shape(b, n_max_boxes, h*w)
    Return:
        target_gt_idx (Tensor): shape(b, h*w)
        fg_mask (Tensor): shape(b, h*w)
        mask_pos (Tensor): shape(b, n_max_boxes, h*w)
    """
    # (b, n_max_boxes, h*w) -> (b, h*w)
    fg_mask = mask_pos.sum(-2)
    if fg_mask.max() > 1:  # one anchor is assigned to multiple gt_bboxes
        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1)  # (b, n_max_boxes, h*w)
        max_overlaps_idx = overlaps.argmax(1)  # (b, h*w)

        is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
        is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)

        mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float()  # (b, n_max_boxes, h*w)
        fg_mask = mask_pos.sum(-2)
    # Find each grid serve which gt(index)
    target_gt_idx = mask_pos.argmax(-2)  # (b, h*w)
    return target_gt_idx, fg_mask, mask_pos


class TaskAlignedAssigner(nn.Module):
    """
    A task-aligned assigner for object detection.

    This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric,
    which combines both classification and localization information.

    Attributes:
        topk (int): The number of top candidates to consider.
        num_classes (int): The number of object classes.
        alpha (float): The alpha parameter for the classification component of the task-aligned metric.
        beta (float): The beta parameter for the localization component of the task-aligned metric.
        eps (float): A small value to prevent division by zero.
    """

    def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
        """Initialize a TaskAlignedAssigner object with customizable hyperparameters."""
        super().__init__()
        self.topk = topk
        self.num_classes = num_classes
        self.bg_idx = num_classes
        self.alpha = alpha
        self.beta = beta
        self.eps = eps

    @torch.no_grad()
    def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
        """
        Compute the task-aligned assignment.
        Reference https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py

        Args:
            pd_scores (Tensor): shape(bs, num_total_anchors, num_classes)
            pd_bboxes (Tensor): shape(bs, num_total_anchors, 4)
            anc_points (Tensor): shape(num_total_anchors, 2)
            gt_labels (Tensor): shape(bs, n_max_boxes, 1)
            gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
            mask_gt (Tensor): shape(bs, n_max_boxes, 1)

        Returns:
            target_labels (Tensor): shape(bs, num_total_anchors)
            target_bboxes (Tensor): shape(bs, num_total_anchors, 4)
            target_scores (Tensor): shape(bs, num_total_anchors, num_classes)
            fg_mask (Tensor): shape(bs, num_total_anchors)
            target_gt_idx (Tensor): shape(bs, num_total_anchors)
        """
        self.bs = pd_scores.size(0)
        self.n_max_boxes = gt_bboxes.size(1)

        if self.n_max_boxes == 0:
            device = gt_bboxes.device
            return (torch.full_like(pd_scores[..., 0], self.bg_idx).to(device), torch.zeros_like(pd_bboxes).to(device),
                    torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device),
                    torch.zeros_like(pd_scores[..., 0]).to(device))

        mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points,
                                                             mask_gt)

        target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)

        # Assigned target
        target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)

        # Normalize
        align_metric *= mask_pos
        pos_align_metrics = align_metric.amax(axis=-1, keepdim=True)  # b, max_num_obj
        pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True)  # b, max_num_obj
        norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
        target_scores = target_scores * norm_align_metric

        return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx

    def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
        """Get in_gts mask, (b, max_num_obj, h*w)."""
        mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
        # Get anchor_align metric, (b, max_num_obj, h*w)
        align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
        # Get topk_metric mask, (b, max_num_obj, h*w)
        mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
        # Merge all mask to a final mask, (b, max_num_obj, h*w)
        mask_pos = mask_topk * mask_in_gts * mask_gt

        return mask_pos, align_metric, overlaps

    def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt):
        """Compute alignment metric given predicted and ground truth bounding boxes."""
        na = pd_bboxes.shape[-2]
        mask_gt = mask_gt.bool()  # b, max_num_obj, h*w
        overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
        bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)

        ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
        ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes)  # b, max_num_obj
        ind[1] = gt_labels.squeeze(-1)  # b, max_num_obj
        # Get the scores of each grid for each gt cls
        bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt]  # b, max_num_obj, h*w

        # (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
        pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
        gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
        overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)

        align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
        return align_metric, overlaps

    def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
        """
        Select the top-k candidates based on the given metrics.

        Args:
            metrics (Tensor): A tensor of shape (b, max_num_obj, h*w), where b is the batch size,
                              max_num_obj is the maximum number of objects, and h*w represents the
                              total number of anchor points.
            largest (bool): If True, select the largest values; otherwise, select the smallest values.
            topk_mask (Tensor): An optional boolean tensor of shape (b, max_num_obj, topk), where
                                topk is the number of top candidates to consider. If not provided,
                                the top-k values are automatically computed based on the given metrics.

        Returns:
            (Tensor): A tensor of shape (b, max_num_obj, h*w) containing the selected top-k candidates.
        """

        # (b, max_num_obj, topk)
        topk_metrics, topk_idxs = torch.topk(metrics, self.topk, dim=-1, largest=largest)
        if topk_mask is None:
            topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
        # (b, max_num_obj, topk)
        topk_idxs.masked_fill_(~topk_mask, 0)

        # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
        count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
        ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
        for k in range(self.topk):
            # Expand topk_idxs for each value of k and add 1 at the specified positions
            count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
        # count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
        # filter invalid bboxes
        count_tensor.masked_fill_(count_tensor > 1, 0)

        return count_tensor.to(metrics.dtype)

    def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
        """
        Compute target labels, target bounding boxes, and target scores for the positive anchor points.

        Args:
            gt_labels (Tensor): Ground truth labels of shape (b, max_num_obj, 1), where b is the
                                batch size and max_num_obj is the maximum number of objects.
            gt_bboxes (Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4).
            target_gt_idx (Tensor): Indices of the assigned ground truth objects for positive
                                    anchor points, with shape (b, h*w), where h*w is the total
                                    number of anchor points.
            fg_mask (Tensor): A boolean tensor of shape (b, h*w) indicating the positive
                              (foreground) anchor points.

        Returns:
            (Tuple[Tensor, Tensor, Tensor]): A tuple containing the following tensors:
                - target_labels (Tensor): Shape (b, h*w), containing the target labels for
                                          positive anchor points.
                - target_bboxes (Tensor): Shape (b, h*w, 4), containing the target bounding boxes
                                          for positive anchor points.
                - target_scores (Tensor): Shape (b, h*w, num_classes), containing the target scores
                                          for positive anchor points, where num_classes is the number
                                          of object classes.
        """

        # Assigned target labels, (b, 1)
        batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
        target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes  # (b, h*w)
        target_labels = gt_labels.long().flatten()[target_gt_idx]  # (b, h*w)

        # Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w, 4)
        target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]

        # Assigned target scores
        target_labels.clamp_(0)

        # 10x faster than F.one_hot()
        target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
                                    dtype=torch.int64,
                                    device=target_labels.device)  # (b, h*w, 80)
        target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)

        fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)  # (b, h*w, 80)
        target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)

        return target_labels, target_bboxes, target_scores


def make_anchors(feats, strides, grid_cell_offset=0.5):
    """Generate anchors from features."""
    anchor_points, stride_tensor = [], []
    assert feats is not None
    dtype, device = feats[0].dtype, feats[0].device
    for i, stride in enumerate(strides):
        _, _, h, w = feats[i].shape
        sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset  # shift x
        sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset  # shift y
        sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
        anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
        stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
    return torch.cat(anchor_points), torch.cat(stride_tensor)


def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
    """Transform distance(ltrb) to box(xywh or xyxy)."""
    lt, rb = distance.chunk(2, dim)
    x1y1 = anchor_points - lt
    x2y2 = anchor_points + rb
    if xywh:
        c_xy = (x1y1 + x2y2) / 2
        wh = x2y2 - x1y1
        return torch.cat((c_xy, wh), dim)  # xywh bbox
    return torch.cat((x1y1, x2y2), dim)  # xyxy bbox


def bbox2dist(anchor_points, bbox, reg_max):
    """Transform bbox(xyxy) to dist(ltrb)."""
    x1y1, x2y2 = bbox.chunk(2, -1)
    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp_(0, reg_max - 0.01)  # dist (lt, rb)


def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
    """Transform distance(ltrb) to box(xywh or xyxy)."""
    lt, rb = distance.chunk(2, dim)
    x1y1 = anchor_points - lt
    x2y2 = anchor_points + rb
    if xywh:
        c_xy = (x1y1 + x2y2) / 2
        wh = x2y2 - x1y1
        return torch.cat((c_xy, wh), dim)  # xywh bbox
    return torch.cat((x1y1, x2y2), dim)  # xyxy bbox


def xywh2xyxy(x):
    """
    Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the
    top-left corner and (x2, y2) is the bottom-right corner.

    Args:
        x (np.ndarray) or (torch.Tensor): The input bounding box coordinates in (x, y, width, height) format.
    Returns:
        y (np.ndarray) or (torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
    """
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
    return y