import copy

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops.nms import batched_nms
from mmcv.utils import force_fp32

from ..builder import HEADS
from .anchor_head import AnchorHead


@HEADS.register_module()
class RPNHead(AnchorHead):
    """RPN head.

    Args:
        in_channels (int): Number of channels in the input feature map.
        init_cfg (dict or list[dict], optional): Initialization config dict.
    """  # noqa: W605

    def __init__(self,
                 in_channels,
                 init_cfg=dict(type='Normal', layer='Conv2d', std=0.01),
                 **kwargs):
        super(RPNHead, self).__init__(
            1, in_channels, init_cfg=init_cfg, **kwargs)

    def _init_layers(self):
        """Initialize layers of the head."""
        self.rpn_conv = nn.Conv2d(
            self.in_channels, self.feat_channels, 3, padding=1)
        self.rpn_cls = nn.Conv2d(self.feat_channels,
                                 self.num_anchors * self.cls_out_channels, 1)
        self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)

    def forward_single(self, x):
        """Forward feature map of a single scale level."""
        x = self.rpn_conv(x)
        x = F.relu(x, inplace=True)
        rpn_cls_score = self.rpn_cls(x)
        rpn_bbox_pred = self.rpn_reg(x)
        return rpn_cls_score, rpn_bbox_pred

    def loss(self,
             cls_scores,
             bbox_preds,
             gt_bboxes,
             img_metas,
             gt_bboxes_ignore=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)
            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.
            img_metas (list[dict]): Meta information of each image, e.g.,
                image size, scaling factor, etc.
            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
                boxes can be ignored when computing the loss.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        losses = super(RPNHead, self).loss(
            cls_scores,
            bbox_preds,
            gt_bboxes,
            None,
            img_metas,
            gt_bboxes_ignore=gt_bboxes_ignore)
        return dict(
            loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox'])

    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
    def get_bboxes(self,
                   cls_scores,
                   bbox_preds,
                   img_metas,
                   cfg=None,
                   rescale=False,
                   with_nms=True):
        """Transform network output for a batch into bbox predictions.

        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)
            img_metas (list[dict]): Meta information of each image, e.g.,
                image size, scaling factor, etc.
            cfg (mmcv.Config | None): Test / postprocessing configuration,
                if None, test_cfg would be used
            rescale (bool): If True, return boxes in original image space.
                Default: False.
            with_nms (bool): If True, do nms before return boxes.
                Default: True.

        Returns:
            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
                The first item is an (n, 5) tensor, where the first 4 columns
                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
                5-th column is a score between 0 and 1. The second item is a
                (n,) tensor where each item is the predicted class label of the
                corresponding box.
        """
        assert with_nms, '``with_nms`` in RPNHead should always True'
        assert len(cls_scores) == len(bbox_preds)
        num_levels = len(cls_scores)
        device = cls_scores[0].device
        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
        mlvl_anchors = self.anchor_generator.grid_anchors(
            featmap_sizes, device=device)

        result_list = []
        for img_id in range(len(img_metas)):
            cls_score_list = [
                cls_scores[i][img_id].detach() for i in range(num_levels)
            ]
            bbox_pred_list = [
                bbox_preds[i][img_id].detach() for i in range(num_levels)
            ]
            img_shape = img_metas[img_id]['img_shape']
            scale_factor = img_metas[img_id]['scale_factor']
            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
                                                mlvl_anchors, img_shape,
                                                scale_factor, cfg, rescale)
            result_list.append(proposals)
        return result_list

    def _get_bboxes_single(self,
                           cls_scores,
                           bbox_preds,
                           mlvl_anchors,
                           img_shape,
                           scale_factor,
                           cfg,
                           rescale=False):
        """Transform outputs for a single batch item into bbox predictions.

          Args:
            cls_scores (list[Tensor]): Box scores of all scale level
                each item has shape (num_anchors * num_classes, H, W).
            bbox_preds (list[Tensor]): Box energies / deltas of all
                scale level, each item has shape (num_anchors * 4, H, W).
            mlvl_anchors (list[Tensor]): Anchors of all scale level
                each item has shape (num_total_anchors, 4).
            img_shape (tuple[int]): Shape of the input image,
                (height, width, 3).
            scale_factor (ndarray): Scale factor of the image arrange as
                (w_scale, h_scale, w_scale, h_scale).
            cfg (mmcv.Config): Test / postprocessing configuration,
                if None, test_cfg would be used.
            rescale (bool): If True, return boxes in original image space.
                Default: False.

        Returns:
            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
                5-th column is a score between 0 and 1.
        """
        cfg = self.test_cfg if cfg is None else cfg
        cfg = copy.deepcopy(cfg)
        # bboxes from different level should be independent during NMS,
        # level_ids are used as labels for batched NMS to separate them
        level_ids = []
        mlvl_scores = []
        mlvl_bbox_preds = []
        mlvl_valid_anchors = []
        for idx in range(len(cls_scores)):
            rpn_cls_score = cls_scores[idx]
            rpn_bbox_pred = bbox_preds[idx]
            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
            if self.use_sigmoid_cls:
                rpn_cls_score = rpn_cls_score.reshape(-1)
                scores = rpn_cls_score.sigmoid()
            else:
                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
                # We set FG labels to [0, num_class-1] and BG label to
                # num_class in RPN head since mmdet v2.5, which is unified to
                # be consistent with other head since mmdet v2.0. In mmdet v2.0
                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
                scores = rpn_cls_score.softmax(dim=1)[:, 0]
            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
            anchors = mlvl_anchors[idx]
            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
                # sort is faster than topk
                # _, topk_inds = scores.topk(cfg.nms_pre)
                ranked_scores, rank_inds = scores.sort(descending=True)
                topk_inds = rank_inds[:cfg.nms_pre]
                scores = ranked_scores[:cfg.nms_pre]
                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
                anchors = anchors[topk_inds, :]
            mlvl_scores.append(scores)
            mlvl_bbox_preds.append(rpn_bbox_pred)
            mlvl_valid_anchors.append(anchors)
            level_ids.append(
                scores.new_full((scores.size(0), ), idx, dtype=torch.long))

        scores = torch.cat(mlvl_scores)
        anchors = torch.cat(mlvl_valid_anchors)
        rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
        proposals = self.bbox_coder.decode(
            anchors, rpn_bbox_pred, max_shape=img_shape)
        ids = torch.cat(level_ids)

        if cfg.min_bbox_size > 0:
            w = proposals[:, 2] - proposals[:, 0]
            h = proposals[:, 3] - proposals[:, 1]
            valid_mask = (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size)
            if not valid_mask.all():
                proposals = proposals[valid_mask]
                scores = scores[valid_mask]
                ids = ids[valid_mask]
        if proposals.numel() > 0:
            dets, keep = batched_nms(proposals, scores, ids, cfg.nms)
        else:
            return proposals.new_zeros(0, 5)

        return dets[:cfg.max_per_img]

    def onnx_export(self, x, img_metas):
        """Test without augmentation.

        Args:
            x (tuple[Tensor]): Features from the upstream network, each is
                a 4D-tensor.
            img_metas (list[dict]): Meta info of each image.

        Returns:
            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
                and class labels of shape [N, num_det].
        """
        cls_scores, bbox_preds = self(x)

        assert len(cls_scores) == len(bbox_preds)
        num_levels = len(cls_scores)

        device = cls_scores[0].device
        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
        mlvl_anchors = self.anchor_generator.grid_anchors(
            featmap_sizes, device=device)

        cls_scores = [cls_scores[i].detach() for i in range(num_levels)]
        bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)]

        assert len(
            img_metas
        ) == 1, 'Only support one input image while in exporting to ONNX'
        img_shapes = img_metas[0]['img_shape_for_onnx']

        cfg = copy.deepcopy(self.test_cfg)

        mlvl_scores = []
        mlvl_bbox_preds = []
        mlvl_valid_anchors = []
        batch_size = cls_scores[0].shape[0]
        nms_pre_tensor = torch.tensor(
            cfg.nms_pre, device=cls_scores[0].device, dtype=torch.long)
        for idx in range(len(cls_scores)):
            rpn_cls_score = cls_scores[idx]
            rpn_bbox_pred = bbox_preds[idx]
            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
            rpn_cls_score = rpn_cls_score.permute(0, 2, 3, 1)
            if self.use_sigmoid_cls:
                rpn_cls_score = rpn_cls_score.reshape(batch_size, -1)
                scores = rpn_cls_score.sigmoid()
            else:
                rpn_cls_score = rpn_cls_score.reshape(batch_size, -1, 2)
                # We set FG labels to [0, num_class-1] and BG label to
                # num_class in RPN head since mmdet v2.5, which is unified to
                # be consistent with other head since mmdet v2.0. In mmdet v2.0
                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
                scores = rpn_cls_score.softmax(-1)[..., 0]
            rpn_bbox_pred = rpn_bbox_pred.permute(0, 2, 3, 1).reshape(
                batch_size, -1, 4)
            anchors = mlvl_anchors[idx]
            anchors = anchors.expand_as(rpn_bbox_pred)
            # Get top-k prediction
            from mmcv.core.export import get_k_for_topk
            nms_pre = get_k_for_topk(nms_pre_tensor, rpn_bbox_pred.shape[1])
            if nms_pre > 0:
                _, topk_inds = scores.topk(nms_pre)
                batch_inds = torch.arange(batch_size).view(
                    -1, 1).expand_as(topk_inds)
                # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
                # Mind k<=3480 in TensorRT for TopK
                transformed_inds = scores.shape[1] * batch_inds + topk_inds
                scores = scores.reshape(-1, 1)[transformed_inds].reshape(
                    batch_size, -1)
                rpn_bbox_pred = rpn_bbox_pred.reshape(
                    -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4)
                anchors = anchors.reshape(-1, 4)[transformed_inds, :].reshape(
                    batch_size, -1, 4)
            mlvl_scores.append(scores)
            mlvl_bbox_preds.append(rpn_bbox_pred)
            mlvl_valid_anchors.append(anchors)

        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
        batch_mlvl_anchors = torch.cat(mlvl_valid_anchors, dim=1)
        batch_mlvl_rpn_bbox_pred = torch.cat(mlvl_bbox_preds, dim=1)
        batch_mlvl_proposals = self.bbox_coder.decode(
            batch_mlvl_anchors, batch_mlvl_rpn_bbox_pred, max_shape=img_shapes)

        # Use ONNX::NonMaxSuppression in deployment
        from mmcv.core.export import add_dummy_nms_for_onnx
        batch_mlvl_scores = batch_mlvl_scores.unsqueeze(2)
        score_threshold = cfg.nms.get('score_thr', 0.0)
        nms_pre = cfg.get('deploy_nms_pre', -1)
        dets, _ = add_dummy_nms_for_onnx(batch_mlvl_proposals,
                                         batch_mlvl_scores, cfg.max_per_img,
                                         cfg.nms.iou_threshold,
                                         score_threshold, nms_pre,
                                         cfg.max_per_img)
        return dets
