# 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)
