# Ultralytics YOLO 🚀, AGPL-3.0 license import torch import torch.nn as nn from .checks import check_version from .metrics import bbox_iou TORCH_1_10 = check_version(torch.__version__, '1.10.0') 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) Returns: (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 IoI will be selected. Args: mask_pos (Tensor): shape(b, n_max_boxes, h*w) overlaps (Tensor): shape(b, n_max_boxes, h*w) Returns: 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, gt_regression): """ 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), None) 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, regression_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask, gt_regression) # Normalize align_metric *= mask_pos pos_align_metrics = align_metric.amax(dim=-1, keepdim=True) # b, max_num_obj pos_overlaps = (overlaps * mask_pos).amax(dim=-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, regression_scores 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, gt_regression=None): """ 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) 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) if gt_regression is not None: target_regression = gt_regression.view(-1, 6)[target_gt_idx] # Convert fg_mask to boolean type fg_mask_bool = fg_mask.bool() # Now create fg_regression_mask fg_regression_mask = fg_mask_bool.unsqueeze(-1).repeat(1, 1, 6) # Expanding to shape (b, h*w, 6) # Now apply masking to target_regression target_regression = torch.where(fg_regression_mask, target_regression, torch.zeros_like(target_regression)) return target_labels, target_bboxes, target_scores, target_regression else: target_regression = None return target_labels, target_bboxes, target_scores, target_regression 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)