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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- from mmcv.runner import force_fp32
-
- from mmdet.core import bbox_overlaps, multi_apply, reduce_mean
- from ..builder import HEADS, build_loss
- from .gfl_head import GFLHead
-
-
- @HEADS.register_module()
- class LDHead(GFLHead):
- """Localization distillation Head. (Short description)
-
- It utilizes the learned bbox distributions to transfer the localization
- dark knowledge from teacher to student. Original paper: `Localization
- Distillation for Object Detection. <https://arxiv.org/abs/2102.12252>`_
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- loss_ld (dict): Config of Localization Distillation Loss (LD),
- T is the temperature for distillation.
- """
-
- def __init__(self,
- num_classes,
- in_channels,
- loss_ld=dict(
- type='LocalizationDistillationLoss',
- loss_weight=0.25,
- T=10),
- **kwargs):
-
- super(LDHead, self).__init__(num_classes, in_channels, **kwargs)
- self.loss_ld = build_loss(loss_ld)
-
- def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
- bbox_targets, stride, soft_targets, num_total_samples):
- """Compute loss of a single scale level.
-
- Args:
- anchors (Tensor): Box reference for each scale level with shape
- (N, num_total_anchors, 4).
- cls_score (Tensor): Cls and quality joint scores for each scale
- level has shape (N, num_classes, H, W).
- bbox_pred (Tensor): Box distribution logits for each scale
- level with shape (N, 4*(n+1), H, W), n is max value of integral
- set.
- labels (Tensor): Labels of each anchors with shape
- (N, num_total_anchors).
- label_weights (Tensor): Label weights of each anchor with shape
- (N, num_total_anchors)
- bbox_targets (Tensor): BBox regression targets of each anchor
- weight shape (N, num_total_anchors, 4).
- stride (tuple): Stride in this scale level.
- num_total_samples (int): Number of positive samples that is
- reduced over all GPUs.
-
- Returns:
- dict[tuple, Tensor]: Loss components and weight targets.
- """
- assert stride[0] == stride[1], 'h stride is not equal to w stride!'
- anchors = anchors.reshape(-1, 4)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- bbox_pred = bbox_pred.permute(0, 2, 3,
- 1).reshape(-1, 4 * (self.reg_max + 1))
- soft_targets = soft_targets.permute(0, 2, 3,
- 1).reshape(-1,
- 4 * (self.reg_max + 1))
-
- bbox_targets = bbox_targets.reshape(-1, 4)
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
-
- # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = ((labels >= 0)
- & (labels < bg_class_ind)).nonzero().squeeze(1)
- score = label_weights.new_zeros(labels.shape)
-
- if len(pos_inds) > 0:
- pos_bbox_targets = bbox_targets[pos_inds]
- pos_bbox_pred = bbox_pred[pos_inds]
- pos_anchors = anchors[pos_inds]
- pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
-
- weight_targets = cls_score.detach().sigmoid()
- weight_targets = weight_targets.max(dim=1)[0][pos_inds]
- pos_bbox_pred_corners = self.integral(pos_bbox_pred)
- pos_decode_bbox_pred = self.bbox_coder.decode(
- pos_anchor_centers, pos_bbox_pred_corners)
- pos_decode_bbox_targets = pos_bbox_targets / stride[0]
- score[pos_inds] = bbox_overlaps(
- pos_decode_bbox_pred.detach(),
- pos_decode_bbox_targets,
- is_aligned=True)
- pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
- pos_soft_targets = soft_targets[pos_inds]
- soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1)
-
- target_corners = self.bbox_coder.encode(pos_anchor_centers,
- pos_decode_bbox_targets,
- self.reg_max).reshape(-1)
-
- # regression loss
- loss_bbox = self.loss_bbox(
- pos_decode_bbox_pred,
- pos_decode_bbox_targets,
- weight=weight_targets,
- avg_factor=1.0)
-
- # dfl loss
- loss_dfl = self.loss_dfl(
- pred_corners,
- target_corners,
- weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
- avg_factor=4.0)
-
- # ld loss
- loss_ld = self.loss_ld(
- pred_corners,
- soft_corners,
- weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
- avg_factor=4.0)
-
- else:
- loss_ld = bbox_pred.sum() * 0
- loss_bbox = bbox_pred.sum() * 0
- loss_dfl = bbox_pred.sum() * 0
- weight_targets = bbox_pred.new_tensor(0)
-
- # cls (qfl) loss
- loss_cls = self.loss_cls(
- cls_score, (labels, score),
- weight=label_weights,
- avg_factor=num_total_samples)
-
- return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum()
-
- def forward_train(self,
- x,
- out_teacher,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None,
- **kwargs):
- """
- Args:
- x (list[Tensor]): Features from FPN.
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes (Tensor): Ground truth bboxes of the image,
- shape (num_gts, 4).
- gt_labels (Tensor): Ground truth labels of each box,
- shape (num_gts,).
- gt_bboxes_ignore (Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
- proposal_cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used
-
- Returns:
- tuple[dict, list]: The loss components and proposals of each image.
-
- - losses (dict[str, Tensor]): A dictionary of loss components.
- - proposal_list (list[Tensor]): Proposals of each image.
- """
- outs = self(x)
- soft_target = out_teacher[1]
- if gt_labels is None:
- loss_inputs = outs + (gt_bboxes, soft_target, img_metas)
- else:
- loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas)
- losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
- if proposal_cfg is None:
- return losses
- else:
- proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
- return losses, proposal_list
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def loss(self,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- gt_labels,
- soft_target,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- cls_scores (list[Tensor]): Cls and quality scores for each scale
- level has shape (N, num_classes, H, W).
- bbox_preds (list[Tensor]): Box distribution logits for each scale
- level with shape (N, 4*(n+1), H, W), n is max value of integral
- set.
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): class indices corresponding to each box
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (list[Tensor] | None): specify which bounding
- boxes can be ignored when computing the loss.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
-
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.prior_generator.num_levels
-
- device = cls_scores[0].device
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels)
- if cls_reg_targets is None:
- return None
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
- num_total_samples = reduce_mean(
- torch.tensor(num_total_pos, dtype=torch.float,
- device=device)).item()
- num_total_samples = max(num_total_samples, 1.0)
-
- losses_cls, losses_bbox, losses_dfl, losses_ld, \
- avg_factor = multi_apply(
- self.loss_single,
- anchor_list,
- cls_scores,
- bbox_preds,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- self.prior_generator.strides,
- soft_target,
- num_total_samples=num_total_samples)
-
- avg_factor = sum(avg_factor) + 1e-6
- avg_factor = reduce_mean(avg_factor).item()
- losses_bbox = [x / avg_factor for x in losses_bbox]
- losses_dfl = [x / avg_factor for x in losses_dfl]
- return dict(
- loss_cls=losses_cls,
- loss_bbox=losses_bbox,
- loss_dfl=losses_dfl,
- loss_ld=losses_ld)
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