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@@ -1,8 +1,201 @@
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import torch
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+import torch.nn as nn
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import torch.nn.functional as F
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-from utils.box_ops import box_iou
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+from utils.box_ops import box_iou, bbox_iou
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+# -------------------------- Basic Functions --------------------------
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+def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
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+ """select the positive anchors's center in gt
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+ Args:
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+ xy_centers (Tensor): shape(bs*n_max_boxes, num_total_anchors, 4)
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+ gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
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+ Return:
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+ (Tensor): shape(bs, n_max_boxes, num_total_anchors)
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+ """
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+ n_anchors = xy_centers.size(0)
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+ bs, n_max_boxes, _ = gt_bboxes.size()
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+ _gt_bboxes = gt_bboxes.reshape([-1, 4])
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+ xy_centers = xy_centers.unsqueeze(0).repeat(bs * n_max_boxes, 1, 1)
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+ gt_bboxes_lt = _gt_bboxes[:, 0:2].unsqueeze(1).repeat(1, n_anchors, 1)
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+ gt_bboxes_rb = _gt_bboxes[:, 2:4].unsqueeze(1).repeat(1, n_anchors, 1)
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+ b_lt = xy_centers - gt_bboxes_lt
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+ b_rb = gt_bboxes_rb - xy_centers
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+ bbox_deltas = torch.cat([b_lt, b_rb], dim=-1)
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+ bbox_deltas = bbox_deltas.reshape([bs, n_max_boxes, n_anchors, -1])
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+ return (bbox_deltas.min(axis=-1)[0] > eps).to(gt_bboxes.dtype)
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+
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+def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
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+ """if an anchor box is assigned to multiple gts,
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+ the one with the highest iou will be selected.
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+ Args:
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+ mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
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+ overlaps (Tensor): shape(bs, n_max_boxes, num_total_anchors)
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+ Return:
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+ target_gt_idx (Tensor): shape(bs, num_total_anchors)
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+ fg_mask (Tensor): shape(bs, num_total_anchors)
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+ mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
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+ """
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+ fg_mask = mask_pos.sum(axis=-2)
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+ if fg_mask.max() > 1:
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+ mask_multi_gts = (fg_mask.unsqueeze(1) > 1).repeat([1, n_max_boxes, 1])
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+ max_overlaps_idx = overlaps.argmax(axis=1)
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+ is_max_overlaps = F.one_hot(max_overlaps_idx, n_max_boxes)
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+ is_max_overlaps = is_max_overlaps.permute(0, 2, 1).to(overlaps.dtype)
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+ mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos)
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+ fg_mask = mask_pos.sum(axis=-2)
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+ target_gt_idx = mask_pos.argmax(axis=-2)
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+ return target_gt_idx, fg_mask , mask_pos
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+
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+def iou_calculator(box1, box2, eps=1e-9):
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+ """Calculate iou for batch
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+ Args:
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+ box1 (Tensor): shape(bs, n_max_boxes, 1, 4)
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+ box2 (Tensor): shape(bs, 1, num_total_anchors, 4)
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+ Return:
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+ (Tensor): shape(bs, n_max_boxes, num_total_anchors)
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+ """
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+ box1 = box1.unsqueeze(2) # [N, M1, 4] -> [N, M1, 1, 4]
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+ box2 = box2.unsqueeze(1) # [N, M2, 4] -> [N, 1, M2, 4]
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+ px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
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+ gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
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+ x1y1 = torch.maximum(px1y1, gx1y1)
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+ x2y2 = torch.minimum(px2y2, gx2y2)
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+ overlap = (x2y2 - x1y1).clip(0).prod(-1)
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+ area1 = (px2y2 - px1y1).clip(0).prod(-1)
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+ area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
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+ union = area1 + area2 - overlap + eps
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+
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+ return overlap / union
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+
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+
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+# -------------------------- Task Aligned Assigner --------------------------
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+class TaskAlignedAssigner(nn.Module):
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+ def __init__(self, topk=10, alpha=0.5, beta=6.0, eps=1e-9, num_classes=80):
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+ super(TaskAlignedAssigner, self).__init__()
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+ self.topk = topk
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+ self.num_classes = num_classes
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+ self.bg_idx = num_classes
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+ self.alpha = alpha
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+ self.beta = beta
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+ self.eps = eps
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+
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+ @torch.no_grad()
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+ def forward(self,
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+ pd_scores,
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+ pd_bboxes,
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+ anc_points,
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+ gt_labels,
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+ gt_bboxes):
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+ """This code referenced to
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+ https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py
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+ Args:
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+ pd_scores (Tensor): shape(bs, num_total_anchors, num_classes)
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+ pd_bboxes (Tensor): shape(bs, num_total_anchors, 4)
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+ anc_points (Tensor): shape(num_total_anchors, 2)
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+ gt_labels (Tensor): shape(bs, n_max_boxes, 1)
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+ gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
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+ Returns:
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+ target_labels (Tensor): shape(bs, num_total_anchors)
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+ target_bboxes (Tensor): shape(bs, num_total_anchors, 4)
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+ target_scores (Tensor): shape(bs, num_total_anchors, num_classes)
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+ fg_mask (Tensor): shape(bs, num_total_anchors)
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+ """
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+ self.bs = pd_scores.size(0)
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+ self.n_max_boxes = gt_bboxes.size(1)
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+
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+ mask_pos, align_metric, overlaps = self.get_pos_mask(
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+ pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points)
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+
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+ target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(
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+ mask_pos, overlaps, self.n_max_boxes)
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+
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+ # assigned target
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+ target_labels, target_bboxes, target_scores = self.get_targets(
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+ gt_labels, gt_bboxes, target_gt_idx, fg_mask)
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+
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+ # normalize
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+ align_metric *= mask_pos
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+ pos_align_metrics = align_metric.amax(axis=-1, keepdim=True) # b, max_num_obj
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+ pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True) # b, max_num_obj
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+ norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
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+ target_scores = target_scores * norm_align_metric
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+
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+ return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
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+
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+
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+ def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points):
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+ # get anchor_align metric, (b, max_num_obj, h*w)
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+ align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes)
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+ # get in_gts mask, (b, max_num_obj, h*w)
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+ mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
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+ # get topk_metric mask, (b, max_num_obj, h*w)
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+ mask_topk = self.select_topk_candidates(align_metric * mask_in_gts)
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+ # merge all mask to a final mask, (b, max_num_obj, h*w)
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+ mask_pos = mask_topk * mask_in_gts
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+
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+ return mask_pos, align_metric, overlaps
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+
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+
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+ def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes):
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+ ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # 2, b, max_num_obj
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+ ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes) # b, max_num_obj
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+ ind[1] = gt_labels.long().squeeze(-1) # b, max_num_obj
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+ # get the scores of each grid for each gt cls
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+ bbox_scores = pd_scores[ind[0], :, ind[1]] # b, max_num_obj, h*w
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+
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+ overlaps = bbox_iou(gt_bboxes.unsqueeze(2), pd_bboxes.unsqueeze(1), xywh=False).squeeze(3).clamp(0)
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+ align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
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+
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+ return align_metric, overlaps
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+
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+
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+ def select_topk_candidates(self, metrics, largest=True):
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+ """
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+ Args:
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+ metrics: (b, max_num_obj, h*w).
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+ topk_mask: (b, max_num_obj, topk) or None
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+ """
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+
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+ num_anchors = metrics.shape[-1] # h*w
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+ # (b, max_num_obj, topk)
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+ topk_metrics, topk_idxs = torch.topk(metrics, self.topk, dim=-1, largest=largest)
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+ topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).tile([1, 1, self.topk])
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+ # (b, max_num_obj, topk)
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+ topk_idxs[~topk_mask] = 0
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+ # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
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+ is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2)
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+ # filter invalid bboxes
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+ is_in_topk = torch.where(is_in_topk > 1, 0, is_in_topk)
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+ return is_in_topk.to(metrics.dtype)
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+
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+
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+ def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
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+ """
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+ Args:
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+ gt_labels: (b, max_num_obj, 1)
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+ gt_bboxes: (b, max_num_obj, 4)
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+ target_gt_idx: (b, h*w)
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+ fg_mask: (b, h*w)
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+ """
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+
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+ # assigned target labels, (b, 1)
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+ batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
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+ target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes # (b, h*w)
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+ target_labels = gt_labels.long().flatten()[target_gt_idx] # (b, h*w)
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+
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+ # assigned target boxes, (b, max_num_obj, 4) -> (b, h*w)
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+ target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
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+
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+ # assigned target scores
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+ target_labels.clamp(0)
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+ target_scores = F.one_hot(target_labels, self.num_classes) # (b, h*w, 80)
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+ fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes) # (b, h*w, 80)
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+ target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
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+
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+ return target_labels, target_bboxes, target_scores
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+
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+
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# -------------------------- Aligned SimOTA Assigner --------------------------
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class AlignedSimOTA(object):
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"""
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yjh0410