# Copyright (c) OpenMMLab. All rights reserved. from typing import List, Tuple import torch from torch import Tensor from mmdet.registry import MODELS from mmdet.structures import SampleList from mmdet.structures.bbox import bbox_overlaps from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean from ..utils import multi_apply, unpack_gt_instances from .gfl_head import GFLHead @MODELS.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. `_ Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. loss_ld (:obj:`ConfigDict` or dict): Config of Localization Distillation Loss (LD), T is the temperature for distillation. """ def __init__(self, num_classes: int, in_channels: int, loss_ld: ConfigType = dict( type='LocalizationDistillationLoss', loss_weight=0.25, T=10), **kwargs) -> dict: super().__init__( num_classes=num_classes, in_channels=in_channels, **kwargs) self.loss_ld = MODELS.build(loss_ld) def loss_by_feat_single(self, anchors: Tensor, cls_score: Tensor, bbox_pred: Tensor, labels: Tensor, label_weights: Tensor, bbox_targets: Tensor, stride: Tuple[int], soft_targets: Tensor, avg_factor: int): """Calculate the loss of a single scale level based on the features extracted by the detection head. 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 with shape (N, num_total_anchors, 4). stride (tuple): Stride in this scale level. soft_targets (Tensor): Soft BBox regression targets. avg_factor (int): Average factor that is used to average the loss. When using sampling method, avg_factor is usually the sum of positive and negative priors. When using `PseudoSampler`, `avg_factor` is usually equal to the number of positive priors. 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=avg_factor) return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum() def loss(self, x: List[Tensor], out_teacher: Tuple[Tensor], batch_data_samples: SampleList) -> dict: """ Args: x (list[Tensor]): Features from FPN. out_teacher (tuple[Tensor]): The output of teacher. batch_data_samples (list[:obj:`DetDataSample`]): The batch data samples. It usually includes information such as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`. 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. """ outputs = unpack_gt_instances(batch_data_samples) batch_gt_instances, batch_gt_instances_ignore, batch_img_metas \ = outputs outs = self(x) soft_targets = out_teacher[1] loss_inputs = outs + (batch_gt_instances, batch_img_metas, soft_targets) losses = self.loss_by_feat( *loss_inputs, batch_gt_instances_ignore=batch_gt_instances_ignore) return losses def loss_by_feat( self, cls_scores: List[Tensor], bbox_preds: List[Tensor], batch_gt_instances: InstanceList, batch_img_metas: List[dict], soft_targets: List[Tensor], batch_gt_instances_ignore: OptInstanceList = None) -> dict: """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. batch_gt_instances (list[:obj:`InstanceData`]): Batch of gt_instance. It usually includes ``bboxes`` and ``labels`` attributes. soft_targets (list[Tensor]): Soft BBox regression targets. batch_img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional): Batch of gt_instances_ignore. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. 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, batch_img_metas, device=device) cls_reg_targets = self.get_targets( anchor_list, valid_flag_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore=batch_gt_instances_ignore) (anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) = cls_reg_targets avg_factor = reduce_mean( torch.tensor(avg_factor, dtype=torch.float, device=device)).item() losses_cls, losses_bbox, losses_dfl, losses_ld, \ avg_factor = multi_apply( self.loss_by_feat_single, anchor_list, cls_scores, bbox_preds, labels_list, label_weights_list, bbox_targets_list, self.prior_generator.strides, soft_targets, avg_factor=avg_factor) 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)