import torch

from mmdet.core import bbox2roi, build_assigner, build_sampler
from .. import builder
from ..registry import DETECTORS
from .two_stage import TwoStageDetector


@DETECTORS.register_module
class MaskScoringRCNN(TwoStageDetector):
    """Mask Scoring RCNN.

    https://arxiv.org/abs/1903.00241
    """

    def __init__(self,
                 backbone,
                 rpn_head,
                 bbox_roi_extractor,
                 bbox_head,
                 mask_roi_extractor,
                 mask_head,
                 train_cfg,
                 test_cfg,
                 neck=None,
                 shared_head=None,
                 mask_iou_head=None,
                 pretrained=None):
        super(MaskScoringRCNN, self).__init__(
            backbone=backbone,
            neck=neck,
            shared_head=shared_head,
            rpn_head=rpn_head,
            bbox_roi_extractor=bbox_roi_extractor,
            bbox_head=bbox_head,
            mask_roi_extractor=mask_roi_extractor,
            mask_head=mask_head,
            train_cfg=train_cfg,
            test_cfg=test_cfg,
            pretrained=pretrained)

        self.mask_iou_head = builder.build_head(mask_iou_head)
        self.mask_iou_head.init_weights()

    # TODO: refactor forward_train in two stage to reduce code redundancy
    def forward_train(self,
                      img,
                      img_meta,
                      gt_bboxes,
                      gt_labels,
                      gt_bboxes_ignore=None,
                      gt_masks=None,
                      proposals=None):
        x = self.extract_feat(img)

        losses = dict()

        # RPN forward and loss
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta,
                                          self.train_cfg.rpn)
            rpn_losses = self.rpn_head.loss(
                *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
            losses.update(rpn_losses)

            proposal_cfg = self.train_cfg.get('rpn_proposal',
                                              self.test_cfg.rpn)
            proposal_inputs = rpn_outs + (img_meta, proposal_cfg)
            proposal_list = self.rpn_head.get_bboxes(*proposal_inputs)
        else:
            proposal_list = proposals

        # assign gts and sample proposals
        if self.with_bbox or self.with_mask:
            bbox_assigner = build_assigner(self.train_cfg.rcnn.assigner)
            bbox_sampler = build_sampler(
                self.train_cfg.rcnn.sampler, context=self)
            num_imgs = img.size(0)
            if gt_bboxes_ignore is None:
                gt_bboxes_ignore = [None for _ in range(num_imgs)]
            sampling_results = []
            for i in range(num_imgs):
                assign_result = bbox_assigner.assign(proposal_list[i],
                                                     gt_bboxes[i],
                                                     gt_bboxes_ignore[i],
                                                     gt_labels[i])
                sampling_result = bbox_sampler.sample(
                    assign_result,
                    proposal_list[i],
                    gt_bboxes[i],
                    gt_labels[i],
                    feats=[lvl_feat[i][None] for lvl_feat in x])
                sampling_results.append(sampling_result)

        # bbox head forward and loss
        if self.with_bbox:
            rois = bbox2roi([res.bboxes for res in sampling_results])
            # TODO: a more flexible way to decide which feature maps to use
            bbox_feats = self.bbox_roi_extractor(
                x[:self.bbox_roi_extractor.num_inputs], rois)
            if self.with_shared_head:
                bbox_feats = self.shared_head(bbox_feats)
            cls_score, bbox_pred = self.bbox_head(bbox_feats)

            bbox_targets = self.bbox_head.get_target(sampling_results,
                                                     gt_bboxes, gt_labels,
                                                     self.train_cfg.rcnn)
            loss_bbox = self.bbox_head.loss(cls_score, bbox_pred,
                                            *bbox_targets)
            losses.update(loss_bbox)

        # mask head forward and loss
        if self.with_mask:
            if not self.share_roi_extractor:
                pos_rois = bbox2roi(
                    [res.pos_bboxes for res in sampling_results])
                mask_feats = self.mask_roi_extractor(
                    x[:self.mask_roi_extractor.num_inputs], pos_rois)
                if self.with_shared_head:
                    mask_feats = self.shared_head(mask_feats)
            else:
                pos_inds = []
                device = bbox_feats.device
                for res in sampling_results:
                    pos_inds.append(
                        torch.ones(
                            res.pos_bboxes.shape[0],
                            device=device,
                            dtype=torch.uint8))
                    pos_inds.append(
                        torch.zeros(
                            res.neg_bboxes.shape[0],
                            device=device,
                            dtype=torch.uint8))
                pos_inds = torch.cat(pos_inds)
                mask_feats = bbox_feats[pos_inds]
            mask_pred = self.mask_head(mask_feats)

            mask_targets = self.mask_head.get_target(sampling_results,
                                                     gt_masks,
                                                     self.train_cfg.rcnn)
            pos_labels = torch.cat(
                [res.pos_gt_labels for res in sampling_results])
            loss_mask = self.mask_head.loss(mask_pred, mask_targets,
                                            pos_labels)
            losses.update(loss_mask)

            # mask iou head forward and loss
            pos_mask_pred = mask_pred[range(mask_pred.size(0)), pos_labels]
            mask_iou_pred = self.mask_iou_head(mask_feats, pos_mask_pred)
            pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)
                                                    ), pos_labels]
            mask_iou_targets = self.mask_iou_head.get_target(
                sampling_results, gt_masks, pos_mask_pred, mask_targets,
                self.train_cfg.rcnn)
            loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred,
                                                    mask_iou_targets)
            losses.update(loss_mask_iou)
        return losses

    def simple_test_mask(self,
                         x,
                         img_meta,
                         det_bboxes,
                         det_labels,
                         rescale=False):
        # image shape of the first image in the batch (only one)
        ori_shape = img_meta[0]['ori_shape']
        scale_factor = img_meta[0]['scale_factor']

        if det_bboxes.shape[0] == 0:
            segm_result = [[] for _ in range(self.mask_head.num_classes - 1)]
            mask_scores = [[] for _ in range(self.mask_head.num_classes - 1)]
        else:
            # if det_bboxes is rescaled to the original image size, we need to
            # rescale it back to the testing scale to obtain RoIs.
            _bboxes = (
                det_bboxes[:, :4] * scale_factor if rescale else det_bboxes)
            mask_rois = bbox2roi([_bboxes])
            mask_feats = self.mask_roi_extractor(
                x[:len(self.mask_roi_extractor.featmap_strides)], mask_rois)
            if self.with_shared_head:
                mask_feats = self.shared_head(mask_feats)
            mask_pred = self.mask_head(mask_feats)
            segm_result = self.mask_head.get_seg_masks(mask_pred, _bboxes,
                                                       det_labels,
                                                       self.test_cfg.rcnn,
                                                       ori_shape, scale_factor,
                                                       rescale)
            # get mask scores with mask iou head
            mask_iou_pred = self.mask_iou_head(
                mask_feats,
                mask_pred[range(det_labels.size(0)), det_labels + 1])
            mask_scores = self.mask_iou_head.get_mask_scores(
                mask_iou_pred, det_bboxes, det_labels)
        return segm_result, mask_scores
