Source code for mmrotate.models.dense_heads.rotated_atss_head

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import images_to_levels, multi_apply, unmap

from mmrotate.core import obb2hbb, rotated_anchor_inside_flags
from ..builder import ROTATED_HEADS
from .rotated_retina_head import RotatedRetinaHead
from .utils import get_num_level_anchors_inside


@ROTATED_HEADS.register_module()
class RotatedATSSHead(RotatedRetinaHead):
    r"""An anchor-based head used in `ATSS
    <https://arxiv.org/abs/1912.02424>`_.

    The head contains two subnetworks. The first classifies anchor boxes and
    the second regresses deltas for the anchors.

    """  # noqa: W605

    def _get_targets_single(self,
                            flat_anchors,
                            valid_flags,
                            num_level_anchors,
                            gt_bboxes,
                            gt_bboxes_ignore,
                            gt_labels,
                            img_meta,
                            label_channels=1,
                            unmap_outputs=True):
        """Compute regression and classification targets for anchors in a
        single image.

        Args:
            flat_anchors (torch.Tensor): Multi-level anchors of the image,
                which are concatenated into a single tensor of shape \
                (num_anchors, 5)
            valid_flags (torch.Tensor): Multi level valid flags of the image,
                which are concatenated into a single tensor of \
                shape (num_anchors,).
            num_level_anchors (torch.Tensor): Number of anchors of each \
                scale level
            gt_bboxes (torch.Tensor): Ground truth bboxes of the image,
                shape (num_gts, 5).
            img_meta (dict): Meta info of the image.
            gt_bboxes_ignore (torch.Tensor): Ground truth bboxes to be \
                ignored, shape (num_ignored_gts, 5).
            img_meta (dict): Meta info of the image.
            gt_labels (torch.Tensor): Ground truth labels of each box,
                shape (num_gts,).
            label_channels (int): Channel of label.
            unmap_outputs (bool): Whether to map outputs back to the original \
                set of anchors.

        Returns:
            tuple:
                labels_list (list[Tensor]): Labels of all anchor
                label_weights_list (list[Tensor]): Label weights of all anchor
                bbox_targets_list (list[Tensor]): BBox targets of all anchor
                bbox_weights_list (list[Tensor]): BBox weights of all anchor
                pos_inds (int): Indices of positive anchor
                neg_inds (int): Indices of negative anchor
                sampling_result: object `SamplingResult`, sampling result.
        """
        inside_flags = rotated_anchor_inside_flags(
            flat_anchors, valid_flags, img_meta['img_shape'][:2],
            self.train_cfg.allowed_border)
        if not inside_flags.any():
            return (None, ) * 7
        # assign gt and sample anchors
        anchors = flat_anchors[inside_flags, :]

        num_level_anchors_inside = get_num_level_anchors_inside(
            num_level_anchors, inside_flags)
        if self.assign_by_circumhbbox is not None:
            gt_bboxes_assign = obb2hbb(gt_bboxes, self.assign_by_circumhbbox)
            assign_result = self.assigner.assign(
                anchors, num_level_anchors_inside, gt_bboxes_assign,
                gt_bboxes_ignore, None if self.sampling else gt_labels)
        else:
            assign_result = self.assigner.assign(
                anchors, num_level_anchors_inside, gt_bboxes, gt_bboxes_ignore,
                None if self.sampling else gt_labels)

        sampling_result = self.sampler.sample(assign_result, anchors,
                                              gt_bboxes)

        num_valid_anchors = anchors.shape[0]
        bbox_targets = torch.zeros_like(anchors)
        bbox_weights = torch.zeros_like(anchors)
        labels = anchors.new_full((num_valid_anchors, ),
                                  self.num_classes,
                                  dtype=torch.long)
        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)

        pos_inds = sampling_result.pos_inds
        neg_inds = sampling_result.neg_inds
        if len(pos_inds) > 0:
            if not self.reg_decoded_bbox:
                pos_bbox_targets = self.bbox_coder.encode(
                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
            else:
                pos_bbox_targets = sampling_result.pos_gt_bboxes
            bbox_targets[pos_inds, :] = pos_bbox_targets
            bbox_weights[pos_inds, :] = 1.0
            if gt_labels is None:
                # Only rpn gives gt_labels as None
                # Foreground is the first class since v2.5.0
                labels[pos_inds] = 0
            else:
                labels[pos_inds] = gt_labels[
                    sampling_result.pos_assigned_gt_inds]
            if self.train_cfg.pos_weight <= 0:
                label_weights[pos_inds] = 1.0
            else:
                label_weights[pos_inds] = self.train_cfg.pos_weight
        if len(neg_inds) > 0:
            label_weights[neg_inds] = 1.0

        # map up to original set of anchors
        if unmap_outputs:
            num_total_anchors = flat_anchors.size(0)
            labels = unmap(
                labels, num_total_anchors, inside_flags,
                fill=self.num_classes)  # fill bg label
            label_weights = unmap(label_weights, num_total_anchors,
                                  inside_flags)
            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)

        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
                neg_inds, sampling_result)

    def get_targets(self,
                    anchor_list,
                    valid_flag_list,
                    gt_bboxes_list,
                    img_metas,
                    gt_bboxes_ignore_list=None,
                    gt_labels_list=None,
                    label_channels=1,
                    unmap_outputs=True,
                    return_sampling_results=False):
        """Compute regression and classification targets for anchors in
        multiple images.

        Args:
            anchor_list (list[list[Tensor]]): Multi level anchors of each \
                image. The outer list indicates images, and the inner list \
                corresponds to feature levels of the image. Each element of \
                the inner list is a tensor of shape (num_anchors, 5).
            valid_flag_list (list[list[Tensor]]): Multi level valid flags of \
                each image. The outer list indicates images, and the inner \
                list corresponds to feature levels of the image. Each element \
                of the inner list is a tensor of shape (num_anchors, )
            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
            img_metas (list[dict]): Meta info of each image.
            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be \
                ignored.
            gt_labels_list (list[Tensor]): Ground truth labels of each box.
            label_channels (int): Channel of label.
            unmap_outputs (bool): Whether to map outputs back to the original \
                set of anchors.

        Returns:
            tuple: Usually returns a tuple containing learning targets.

                - labels_list (list[Tensor]): Labels of each level.
                - label_weights_list (list[Tensor]): Label weights of \
                    each level
                - bbox_targets_list (list[Tensor]): BBox targets of each level
                - bbox_weights_list (list[Tensor]): BBox weights of each level
                - num_total_pos (int): Number of positive samples in all images
                - num_total_neg (int): Number of negative samples in all images
            additional_returns: This function enables user-defined returns \
                from self._get_targets_single`. These returns are currently \
                refined to properties at each feature map (HxW dimension).
                The results will be concatenated after the end
        """
        num_imgs = len(img_metas)
        assert len(anchor_list) == len(valid_flag_list) == num_imgs

        # anchor number of multi levels
        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
        num_level_anchors_list = [num_level_anchors] * num_imgs
        # concat all level anchors to a single tensor
        concat_anchor_list = []
        concat_valid_flag_list = []
        for i in range(num_imgs):
            assert len(anchor_list[i]) == len(valid_flag_list[i])
            concat_anchor_list.append(torch.cat(anchor_list[i]))
            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))

        # compute targets for each image
        if gt_bboxes_ignore_list is None:
            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
        if gt_labels_list is None:
            gt_labels_list = [None for _ in range(num_imgs)]
        results = multi_apply(
            self._get_targets_single,
            concat_anchor_list,
            concat_valid_flag_list,
            num_level_anchors_list,
            gt_bboxes_list,
            gt_bboxes_ignore_list,
            gt_labels_list,
            img_metas,
            label_channels=label_channels,
            unmap_outputs=unmap_outputs)
        (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
         pos_inds_list, neg_inds_list, sampling_results_list) = results[:7]
        rest_results = list(results[7:])  # user-added return values
        # no valid anchors
        if any([labels is None for labels in all_labels]):
            return None
        # sampled anchors of all images
        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
        # split targets to a list w.r.t. multiple levels
        labels_list = images_to_levels(all_labels, num_level_anchors)
        label_weights_list = images_to_levels(all_label_weights,
                                              num_level_anchors)
        bbox_targets_list = images_to_levels(all_bbox_targets,
                                             num_level_anchors)
        bbox_weights_list = images_to_levels(all_bbox_weights,
                                             num_level_anchors)
        res = (labels_list, label_weights_list, bbox_targets_list,
               bbox_weights_list, num_total_pos, num_total_neg)
        if return_sampling_results:
            res = res + (sampling_results_list, )
        for i, r in enumerate(rest_results):  # user-added return values
            rest_results[i] = images_to_levels(r, num_level_anchors)

        return res + tuple(rest_results)