Source code for mmdet.models.dense_heads.ld_head

import torch
from mmcv.runner import force_fp32

from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox,
                        multi_apply, reduce_mean)
from ..builder import HEADS, build_loss
from .gfl_head import GFLHead


[docs]@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)
[docs] 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 wight 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 = distance2bbox(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 = bbox2distance(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()
[docs] 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
[docs] @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.anchor_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.anchor_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)