Source code for mmdet.models.dense_heads.lad_head

import torch
from mmcv.runner import force_fp32

from mmdet.core import bbox_overlaps, multi_apply
from ..builder import HEADS
from .paa_head import PAAHead, levels_to_images


@HEADS.register_module()
class LADHead(PAAHead):
    """Label Assignment Head from the paper: `Improving Object Detection by
    Label Assignment Distillation <https://arxiv.org/pdf/2108.10520.pdf>`_"""

    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
    def get_label_assignment(self,
                             cls_scores,
                             bbox_preds,
                             iou_preds,
                             gt_bboxes,
                             gt_labels,
                             img_metas,
                             gt_bboxes_ignore=None):
        """Get label assignment (from teacher).

        Args:
            cls_scores (list[Tensor]): Box scores for each scale level.
                Has shape (N, num_anchors * num_classes, H, W)
            bbox_preds (list[Tensor]): Box energies / deltas for each scale
                level with shape (N, num_anchors * 4, H, W)
            iou_preds (list[Tensor]): iou_preds for each scale
                level with shape (N, num_anchors * 1, H, W)
            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 are computing the loss.

        Returns:
            tuple: Returns a tuple containing label assignment variables.

                - labels (Tensor): Labels of all anchors, each with
                    shape (num_anchors,).
                - labels_weight (Tensor): Label weights of all anchor.
                    each with shape (num_anchors,).
                - bboxes_target (Tensor): BBox targets of all anchors.
                    each with shape (num_anchors, 4).
                - bboxes_weight (Tensor): BBox weights of all anchors.
                    each with shape (num_anchors, 4).
                - pos_inds_flatten (Tensor): Contains all index of positive
                    sample in all anchor.
                - pos_anchors (Tensor): Positive anchors.
                - num_pos (int): Number of positive anchors.
        """

        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,
        )
        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
         pos_gt_index) = cls_reg_targets
        cls_scores = levels_to_images(cls_scores)
        cls_scores = [
            item.reshape(-1, self.cls_out_channels) for item in cls_scores
        ]
        bbox_preds = levels_to_images(bbox_preds)
        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
                                       cls_scores, bbox_preds, labels,
                                       labels_weight, bboxes_target,
                                       bboxes_weight, pos_inds)

        with torch.no_grad():
            reassign_labels, reassign_label_weight, \
                reassign_bbox_weights, num_pos = multi_apply(
                    self.paa_reassign,
                    pos_losses_list,
                    labels,
                    labels_weight,
                    bboxes_weight,
                    pos_inds,
                    pos_gt_index,
                    anchor_list)
            num_pos = sum(num_pos)
        # convert all tensor list to a flatten tensor
        labels = torch.cat(reassign_labels, 0).view(-1)
        flatten_anchors = torch.cat(
            [torch.cat(item, 0) for item in anchor_list])
        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
        bboxes_target = torch.cat(bboxes_target,
                                  0).view(-1, bboxes_target[0].size(-1))

        pos_inds_flatten = ((labels >= 0)
                            &
                            (labels < self.num_classes)).nonzero().reshape(-1)

        if num_pos:
            pos_anchors = flatten_anchors[pos_inds_flatten]
        else:
            pos_anchors = None

        label_assignment_results = (labels, labels_weight, bboxes_target,
                                    bboxes_weight, pos_inds_flatten,
                                    pos_anchors, num_pos)
        return label_assignment_results

    def forward_train(self,
                      x,
                      label_assignment_results,
                      img_metas,
                      gt_bboxes,
                      gt_labels=None,
                      gt_bboxes_ignore=None,
                      **kwargs):
        """Forward train with the available label assignment (student receives
        from teacher).

        Args:
            x (list[Tensor]): Features from FPN.
            label_assignment_results (tuple): As the outputs defined in the
                function `self.get_label_assignment`.
            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).

        Returns:
            losses: (dict[str, Tensor]): A dictionary of loss components.
        """
        outs = self(x)
        if gt_labels is None:
            loss_inputs = outs + (gt_bboxes, img_metas)
        else:
            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
        losses = self.loss(
            *loss_inputs,
            gt_bboxes_ignore=gt_bboxes_ignore,
            label_assignment_results=label_assignment_results)
        return losses

    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
    def loss(self,
             cls_scores,
             bbox_preds,
             iou_preds,
             gt_bboxes,
             gt_labels,
             img_metas,
             gt_bboxes_ignore=None,
             label_assignment_results=None):
        """Compute losses of the head.

        Args:
            cls_scores (list[Tensor]): Box scores for each scale level
                Has shape (N, num_anchors * num_classes, H, W)
            bbox_preds (list[Tensor]): Box energies / deltas for each scale
                level with shape (N, num_anchors * 4, H, W)
            iou_preds (list[Tensor]): iou_preds for each scale
                level with shape (N, num_anchors * 1, H, W)
            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 are computing the loss.
            label_assignment_results (tuple): As the outputs defined in the
                function `self.get_label_assignment`.

        Returns:
            dict[str, Tensor]: A dictionary of loss gmm_assignment.
        """

        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds_flatten,
         pos_anchors, num_pos) = label_assignment_results

        cls_scores = levels_to_images(cls_scores)
        cls_scores = [
            item.reshape(-1, self.cls_out_channels) for item in cls_scores
        ]
        bbox_preds = levels_to_images(bbox_preds)
        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
        iou_preds = levels_to_images(iou_preds)
        iou_preds = [item.reshape(-1, 1) for item in iou_preds]

        # convert all tensor list to a flatten tensor
        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))

        losses_cls = self.loss_cls(
            cls_scores,
            labels,
            labels_weight,
            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
        if num_pos:
            pos_bbox_pred = self.bbox_coder.decode(
                pos_anchors, bbox_preds[pos_inds_flatten])
            pos_bbox_target = bboxes_target[pos_inds_flatten]
            iou_target = bbox_overlaps(
                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
            losses_iou = self.loss_centerness(
                iou_preds[pos_inds_flatten],
                iou_target.unsqueeze(-1),
                avg_factor=num_pos)
            losses_bbox = self.loss_bbox(
                pos_bbox_pred, pos_bbox_target, avg_factor=num_pos)

        else:
            losses_iou = iou_preds.sum() * 0
            losses_bbox = bbox_preds.sum() * 0

        return dict(
            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)