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276 lines
13 KiB
276 lines
13 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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import torch.nn as nn
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from .checks import check_version
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from .metrics import bbox_iou
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TORCH_1_10 = check_version(torch.__version__, '1.10.0')
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def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
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"""select the positive anchor center in gt
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Args:
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xy_centers (Tensor): shape(h*w, 4)
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gt_bboxes (Tensor): shape(b, n_boxes, 4)
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Return:
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(Tensor): shape(b, n_boxes, h*w)
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"""
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n_anchors = xy_centers.shape[0]
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bs, n_boxes, _ = gt_bboxes.shape
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lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
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bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
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# return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
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return bbox_deltas.amin(3).gt_(eps)
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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(b, n_max_boxes, h*w)
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overlaps (Tensor): shape(b, n_max_boxes, h*w)
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Return:
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target_gt_idx (Tensor): shape(b, h*w)
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fg_mask (Tensor): shape(b, h*w)
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mask_pos (Tensor): shape(b, n_max_boxes, h*w)
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"""
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# (b, n_max_boxes, h*w) -> (b, h*w)
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fg_mask = mask_pos.sum(-2)
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if fg_mask.max() > 1: # one anchor is assigned to multiple gt_bboxes
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mask_multi_gts = (fg_mask.unsqueeze(1) > 1).repeat([1, n_max_boxes, 1]) # (b, n_max_boxes, h*w)
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max_overlaps_idx = overlaps.argmax(1) # (b, h*w)
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is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
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is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
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mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float() # (b, n_max_boxes, h*w)
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fg_mask = mask_pos.sum(-2)
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# Find each grid serve which gt(index)
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target_gt_idx = mask_pos.argmax(-2) # (b, h*w)
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return target_gt_idx, fg_mask, mask_pos
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class TaskAlignedAssigner(nn.Module):
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"""
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A task-aligned assigner for object detection.
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This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric,
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which combines both classification and localization information.
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Attributes:
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topk (int): The number of top candidates to consider.
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num_classes (int): The number of object classes.
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alpha (float): The alpha parameter for the classification component of the task-aligned metric.
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beta (float): The beta parameter for the localization component of the task-aligned metric.
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eps (float): A small value to prevent division by zero.
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"""
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def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
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"""Initialize a TaskAlignedAssigner object with customizable hyperparameters."""
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super().__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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@torch.no_grad()
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def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
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"""
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Compute the task-aligned assignment.
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Reference 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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mask_gt (Tensor): shape(bs, n_max_boxes, 1)
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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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target_gt_idx (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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if self.n_max_boxes == 0:
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device = gt_bboxes.device
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return (torch.full_like(pd_scores[..., 0], self.bg_idx).to(device), torch.zeros_like(pd_bboxes).to(device),
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torch.zeros_like(pd_scores).to(device), torch.zeros_like(pd_scores[..., 0]).to(device),
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torch.zeros_like(pd_scores[..., 0]).to(device))
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mask_pos, align_metric, overlaps = self.get_pos_mask(pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points,
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mask_gt)
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target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
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# Assigned target
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target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)
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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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return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
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def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
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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 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, mask_in_gts * mask_gt)
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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, topk_mask=mask_gt.repeat([1, 1, self.topk]).bool())
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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 * mask_gt
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return mask_pos, align_metric, overlaps
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def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt):
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"""Compute alignment metric given predicted and ground truth bounding boxes."""
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na = pd_bboxes.shape[-2]
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mask_gt = mask_gt.bool() # b, max_num_obj, h*w
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overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device)
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bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
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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[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt] # b, max_num_obj, h*w
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# (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
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pd_boxes = pd_bboxes.unsqueeze(1).repeat(1, self.n_max_boxes, 1, 1)[mask_gt]
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gt_boxes = gt_bboxes.unsqueeze(2).repeat(1, 1, na, 1)[mask_gt]
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overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp(0)
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align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
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return align_metric, overlaps
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def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
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"""
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Select the top-k candidates based on the given metrics.
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Args:
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metrics (Tensor): A tensor of shape (b, max_num_obj, h*w), where b is the batch size,
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max_num_obj is the maximum number of objects, and h*w represents the
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total number of anchor points.
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largest (bool): If True, select the largest values; otherwise, select the smallest values.
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topk_mask (Tensor): An optional boolean tensor of shape (b, max_num_obj, topk), where
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topk is the number of top candidates to consider. If not provided,
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the top-k values are automatically computed based on the given metrics.
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Returns:
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(Tensor): A tensor of shape (b, max_num_obj, h*w) containing the selected top-k candidates.
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"""
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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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if topk_mask is None:
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topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
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# (b, max_num_obj, topk)
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topk_idxs.masked_fill_(~topk_mask, 0)
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# (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
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count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
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ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
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for k in range(self.topk):
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# Expand topk_idxs for each value of k and add 1 at the specified positions
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count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
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# filter invalid bboxes
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count_tensor.masked_fill_(count_tensor > 1, 0)
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return count_tensor.to(metrics.dtype)
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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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Compute target labels, target bounding boxes, and target scores for the positive anchor points.
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Args:
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gt_labels (Tensor): Ground truth labels of shape (b, max_num_obj, 1), where b is the
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batch size and max_num_obj is the maximum number of objects.
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gt_bboxes (Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4).
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target_gt_idx (Tensor): Indices of the assigned ground truth objects for positive
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anchor points, with shape (b, h*w), where h*w is the total
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number of anchor points.
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fg_mask (Tensor): A boolean tensor of shape (b, h*w) indicating the positive
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(foreground) anchor points.
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Returns:
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(Tuple[Tensor, Tensor, Tensor]): A tuple containing the following tensors:
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- target_labels (Tensor): Shape (b, h*w), containing the target labels for
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positive anchor points.
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- target_bboxes (Tensor): Shape (b, h*w, 4), containing the target bounding boxes
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for positive anchor points.
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- target_scores (Tensor): Shape (b, h*w, num_classes), containing the target scores
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for positive anchor points, where num_classes is the number
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of object classes.
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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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# 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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# Assigned target scores
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target_labels.clamp_(0)
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# 10x faster than F.one_hot()
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target_scores = torch.zeros((target_labels.shape[0], target_labels.shape[1], self.num_classes),
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dtype=torch.int64,
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device=target_labels.device) # (b, h*w, 80)
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target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
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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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return target_labels, target_bboxes, target_scores
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def make_anchors(feats, strides, grid_cell_offset=0.5):
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"""Generate anchors from features."""
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anchor_points, stride_tensor = [], []
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assert feats is not None
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dtype, device = feats[0].dtype, feats[0].device
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for i, stride in enumerate(strides):
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_, _, h, w = feats[i].shape
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sx = torch.arange(end=w, device=device, dtype=dtype) + grid_cell_offset # shift x
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sy = torch.arange(end=h, device=device, dtype=dtype) + grid_cell_offset # shift y
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sy, sx = torch.meshgrid(sy, sx, indexing='ij') if TORCH_1_10 else torch.meshgrid(sy, sx)
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anchor_points.append(torch.stack((sx, sy), -1).view(-1, 2))
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stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
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return torch.cat(anchor_points), torch.cat(stride_tensor)
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def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
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"""Transform distance(ltrb) to box(xywh or xyxy)."""
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lt, rb = distance.chunk(2, dim)
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x1y1 = anchor_points - lt
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x2y2 = anchor_points + rb
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if xywh:
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c_xy = (x1y1 + x2y2) / 2
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wh = x2y2 - x1y1
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return torch.cat((c_xy, wh), dim) # xywh bbox
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return torch.cat((x1y1, x2y2), dim) # xyxy bbox
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def bbox2dist(anchor_points, bbox, reg_max):
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"""Transform bbox(xyxy) to dist(ltrb)."""
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x1y1, x2y2 = bbox.chunk(2, -1)
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return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01) # dist (lt, rb)
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