# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcv.cnn import ConvModule, normal_init
from mmcv.cnn.bricks import build_conv_layer
from mmcv.runner import BaseModule
from torch import nn as nn

from mmdet3d.models.layers import nms_bev, nms_normal_bev
from mmdet3d.models.layers.pointnet_modules import build_sa_module
from mmdet3d.registry import MODELS, TASK_UTILS
from mmdet3d.structures.bbox_3d import (LiDARInstance3DBoxes,
                                        rotation_3d_in_axis, xywhr2xyxyr)
from mmdet.models.utils import multi_apply


@MODELS.register_module()
class PointRCNNBboxHead(BaseModule):
    """PointRCNN RoI Bbox head.

    Args:
        num_classes (int): The number of classes to prediction.
        in_channels (int)： Input channels of point features.
        mlp_channels (list[int]): the number of mlp channels
        pred_layer_cfg (dict, optional): Config of classfication and
            regression prediction layers. Defaults to None.
        num_points (tuple, optional): The number of points which each SA
            module samples. Defaults to (128, 32, -1).
        radius (tuple, optional): Sampling radius of each SA module.
            Defaults to (0.2, 0.4, 100).
        num_samples (tuple, optional): The number of samples for ball query
            in each SA module. Defaults to (64, 64, 64).
        sa_channels (tuple, optional): Out channels of each mlp in SA module.
            Defaults to ((128, 128, 128), (128, 128, 256), (256, 256, 512)).
        bbox_coder (dict, optional): Config dict of box coders.
            Defaults to dict(type='DeltaXYZWLHRBBoxCoder').
        sa_cfg (dict, optional): Config of set abstraction module, which may
            contain the following keys and values:

            - pool_mod (str): Pool method ('max' or 'avg') for SA modules.
            - use_xyz (bool): Whether to use xyz as a part of features.
            - normalize_xyz (bool): Whether to normalize xyz with radii in
              each SA module.
            Defaults to dict(type='PointSAModule', pool_mod='max',
                use_xyz=True).
        conv_cfg (dict, optional): Config dict of convolutional layers.
             Defaults to dict(type='Conv1d').
        norm_cfg (dict, optional): Config dict of normalization layers.
             Defaults to dict(type='BN1d').
        act_cfg (dict, optional): Config dict of activation layers.
            Defaults to dict(type='ReLU').
        bias (str, optional): Type of bias. Defaults to 'auto'.
        loss_bbox (dict, optional): Config of regression loss function.
            Defaults to dict(type='SmoothL1Loss', beta=1.0 / 9.0,
                reduction='sum', loss_weight=1.0).
        loss_cls (dict, optional): Config of classification loss function.
             Defaults to dict(type='CrossEntropyLoss', use_sigmoid=True,
                reduction='sum', loss_weight=1.0).
        with_corner_loss (bool, optional): Whether using corner loss.
            Defaults to True.
        init_cfg (dict, optional): Config of initialization. Defaults to None.
    """

    def __init__(
            self,
            num_classes,
            in_channels,
            mlp_channels,
            pred_layer_cfg=None,
            num_points=(128, 32, -1),
            radius=(0.2, 0.4, 100),
            num_samples=(64, 64, 64),
            sa_channels=((128, 128, 128), (128, 128, 256), (256, 256, 512)),
            bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
            sa_cfg=dict(type='PointSAModule', pool_mod='max', use_xyz=True),
            conv_cfg=dict(type='Conv1d'),
            norm_cfg=dict(type='BN1d'),
            act_cfg=dict(type='ReLU'),
            bias='auto',
            loss_bbox=dict(
                type='SmoothL1Loss',
                beta=1.0 / 9.0,
                reduction='sum',
                loss_weight=1.0),
            loss_cls=dict(
                type='CrossEntropyLoss',
                use_sigmoid=True,
                reduction='sum',
                loss_weight=1.0),
            with_corner_loss=True,
            init_cfg=None):
        super(PointRCNNBboxHead, self).__init__(init_cfg=init_cfg)
        self.num_classes = num_classes
        self.num_sa = len(sa_channels)
        self.with_corner_loss = with_corner_loss
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        self.bias = bias

        self.loss_bbox = MODELS.build(loss_bbox)
        self.loss_cls = MODELS.build(loss_cls)
        self.bbox_coder = TASK_UTILS.build(bbox_coder)
        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)

        self.in_channels = in_channels
        mlp_channels = [self.in_channels] + mlp_channels
        shared_mlps = nn.Sequential()
        for i in range(len(mlp_channels) - 1):
            shared_mlps.add_module(
                f'layer{i}',
                ConvModule(
                    mlp_channels[i],
                    mlp_channels[i + 1],
                    kernel_size=(1, 1),
                    stride=(1, 1),
                    inplace=False,
                    conv_cfg=dict(type='Conv2d')))
        self.xyz_up_layer = nn.Sequential(*shared_mlps)

        c_out = mlp_channels[-1]
        self.merge_down_layer = ConvModule(
            c_out * 2,
            c_out,
            kernel_size=(1, 1),
            stride=(1, 1),
            inplace=False,
            conv_cfg=dict(type='Conv2d'))

        pre_channels = c_out

        self.SA_modules = nn.ModuleList()
        sa_in_channel = pre_channels

        for sa_index in range(self.num_sa):
            cur_sa_mlps = list(sa_channels[sa_index])
            cur_sa_mlps = [sa_in_channel] + cur_sa_mlps
            sa_out_channel = cur_sa_mlps[-1]

            cur_num_points = num_points[sa_index]
            if cur_num_points <= 0:
                cur_num_points = None
            self.SA_modules.append(
                build_sa_module(
                    num_point=cur_num_points,
                    radius=radius[sa_index],
                    num_sample=num_samples[sa_index],
                    mlp_channels=cur_sa_mlps,
                    cfg=sa_cfg))
            sa_in_channel = sa_out_channel
        self.cls_convs = self._add_conv_branch(
            pred_layer_cfg.in_channels, pred_layer_cfg.cls_conv_channels)
        self.reg_convs = self._add_conv_branch(
            pred_layer_cfg.in_channels, pred_layer_cfg.reg_conv_channels)

        prev_channel = pred_layer_cfg.cls_conv_channels[-1]
        self.conv_cls = build_conv_layer(
            self.conv_cfg,
            in_channels=prev_channel,
            out_channels=self.num_classes,
            kernel_size=1)
        prev_channel = pred_layer_cfg.reg_conv_channels[-1]
        self.conv_reg = build_conv_layer(
            self.conv_cfg,
            in_channels=prev_channel,
            out_channels=self.bbox_coder.code_size * self.num_classes,
            kernel_size=1)

        if init_cfg is None:
            self.init_cfg = dict(type='Xavier', layer=['Conv2d', 'Conv1d'])

    def _add_conv_branch(self, in_channels, conv_channels):
        """Add shared or separable branch.

        Args:
            in_channels (int): Input feature channel.
            conv_channels (tuple): Middle feature channels.
        """
        conv_spec = [in_channels] + list(conv_channels)
        # add branch specific conv layers
        conv_layers = nn.Sequential()
        for i in range(len(conv_spec) - 1):
            conv_layers.add_module(
                f'layer{i}',
                ConvModule(
                    conv_spec[i],
                    conv_spec[i + 1],
                    kernel_size=1,
                    padding=0,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    act_cfg=self.act_cfg,
                    bias=self.bias,
                    inplace=True))
        return conv_layers

    def init_weights(self):
        """Initialize weights of the head."""
        super().init_weights()
        for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
        normal_init(self.conv_reg.weight, mean=0, std=0.001)

    def forward(self, feats):
        """Forward pass.

        Args:
            feats (torch.Torch): Features from RCNN modules.

        Returns:
            tuple[torch.Tensor]: Score of class and bbox predictions.
        """
        input_data = feats.clone().detach()
        xyz_input = input_data[..., 0:self.in_channels].transpose(
            1, 2).unsqueeze(dim=3).contiguous().clone().detach()
        xyz_features = self.xyz_up_layer(xyz_input)
        rpn_features = input_data[..., self.in_channels:].transpose(
            1, 2).unsqueeze(dim=3)
        merged_features = torch.cat((xyz_features, rpn_features), dim=1)
        merged_features = self.merge_down_layer(merged_features)
        l_xyz, l_features = [input_data[..., 0:3].contiguous()], \
                            [merged_features.squeeze(dim=3)]
        for i in range(len(self.SA_modules)):
            li_xyz, li_features, cur_indices = \
                self.SA_modules[i](l_xyz[i], l_features[i])
            l_xyz.append(li_xyz)
            l_features.append(li_features)

        shared_features = l_features[-1]
        x_cls = shared_features
        x_reg = shared_features
        x_cls = self.cls_convs(x_cls)
        rcnn_cls = self.conv_cls(x_cls)
        x_reg = self.reg_convs(x_reg)
        rcnn_reg = self.conv_reg(x_reg)
        rcnn_cls = rcnn_cls.transpose(1, 2).contiguous().squeeze(dim=1)
        rcnn_reg = rcnn_reg.transpose(1, 2).contiguous().squeeze(dim=1)
        return rcnn_cls, rcnn_reg

    def loss(self, cls_score, bbox_pred, rois, labels, bbox_targets,
             pos_gt_bboxes, reg_mask, label_weights, bbox_weights):
        """Computing losses.

        Args:
            cls_score (torch.Tensor): Scores of each RoI.
            bbox_pred (torch.Tensor): Predictions of bboxes.
            rois (torch.Tensor): RoI bboxes.
            labels (torch.Tensor): Labels of class.
            bbox_targets (torch.Tensor): Target of positive bboxes.
            pos_gt_bboxes (torch.Tensor): Ground truths of positive bboxes.
            reg_mask (torch.Tensor): Mask for positive bboxes.
            label_weights (torch.Tensor): Weights of class loss.
            bbox_weights (torch.Tensor): Weights of bbox loss.

        Returns:
            dict: Computed losses.

                - loss_cls (torch.Tensor): Loss of classes.
                - loss_bbox (torch.Tensor): Loss of bboxes.
                - loss_corner (torch.Tensor): Loss of corners.
        """
        losses = dict()
        rcnn_batch_size = cls_score.shape[0]
        # calculate class loss
        cls_flat = cls_score.view(-1)
        loss_cls = self.loss_cls(cls_flat, labels, label_weights)
        losses['loss_cls'] = loss_cls

        # calculate regression loss
        code_size = self.bbox_coder.code_size
        pos_inds = (reg_mask > 0)

        pos_bbox_pred = bbox_pred.view(rcnn_batch_size, -1)[pos_inds].clone()
        bbox_weights_flat = bbox_weights[pos_inds].view(-1, 1).repeat(
            1, pos_bbox_pred.shape[-1])
        loss_bbox = self.loss_bbox(
            pos_bbox_pred.unsqueeze(dim=0),
            bbox_targets.unsqueeze(dim=0).detach(),
            bbox_weights_flat.unsqueeze(dim=0))
        losses['loss_bbox'] = loss_bbox

        if pos_inds.any() != 0 and self.with_corner_loss:
            rois = rois.detach()
            pos_roi_boxes3d = rois[..., 1:].view(-1, code_size)[pos_inds]
            pos_roi_boxes3d = pos_roi_boxes3d.view(-1, code_size)
            batch_anchors = pos_roi_boxes3d.clone().detach()
            pos_rois_rotation = pos_roi_boxes3d[..., 6].view(-1)
            roi_xyz = pos_roi_boxes3d[..., 0:3].view(-1, 3)
            batch_anchors[..., 0:3] = 0
            # decode boxes
            pred_boxes3d = self.bbox_coder.decode(
                batch_anchors,
                pos_bbox_pred.view(-1, code_size)).view(-1, code_size)

            pred_boxes3d[..., 0:3] = rotation_3d_in_axis(
                pred_boxes3d[..., 0:3].unsqueeze(1), (pos_rois_rotation),
                axis=2).squeeze(1)

            pred_boxes3d[:, 0:3] += roi_xyz

            # calculate corner loss
            loss_corner = self.get_corner_loss_lidar(pred_boxes3d,
                                                     pos_gt_bboxes)

            losses['loss_corner'] = loss_corner
        else:
            losses['loss_corner'] = loss_cls.new_tensor(0)

        return losses

    def get_corner_loss_lidar(self, pred_bbox3d, gt_bbox3d, delta=1.0):
        """Calculate corner loss of given boxes.

        Args:
            pred_bbox3d (torch.FloatTensor): Predicted boxes in shape (N, 7).
            gt_bbox3d (torch.FloatTensor): Ground truth boxes in shape (N, 7).
            delta (float, optional): huber loss threshold. Defaults to 1.0

        Returns:
            torch.FloatTensor: Calculated corner loss in shape (N).
        """
        assert pred_bbox3d.shape[0] == gt_bbox3d.shape[0]

        # This is a little bit hack here because we assume the box for
        # PointRCNN is in LiDAR coordinates

        gt_boxes_structure = LiDARInstance3DBoxes(gt_bbox3d)
        pred_box_corners = LiDARInstance3DBoxes(pred_bbox3d).corners
        gt_box_corners = gt_boxes_structure.corners

        # This flip only changes the heading direction of GT boxes
        gt_bbox3d_flip = gt_boxes_structure.clone()
        gt_bbox3d_flip.tensor[:, 6] += np.pi
        gt_box_corners_flip = gt_bbox3d_flip.corners

        corner_dist = torch.min(
            torch.norm(pred_box_corners - gt_box_corners, dim=2),
            torch.norm(pred_box_corners - gt_box_corners_flip, dim=2))
        # huber loss
        abs_error = corner_dist.abs()
        quadratic = abs_error.clamp(max=delta)
        linear = (abs_error - quadratic)
        corner_loss = 0.5 * quadratic**2 + delta * linear
        return corner_loss.mean(dim=1)

    def get_targets(self, sampling_results, rcnn_train_cfg, concat=True):
        """Generate targets.

        Args:
            sampling_results (list[:obj:`SamplingResult`]):
                Sampled results from rois.
            rcnn_train_cfg (:obj:`ConfigDict`): Training config of rcnn.
            concat (bool, optional): Whether to concatenate targets between
                batches. Defaults to True.

        Returns:
            tuple[torch.Tensor]: Targets of boxes and class prediction.
        """
        pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
        pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
        iou_list = [res.iou for res in sampling_results]
        targets = multi_apply(
            self._get_target_single,
            pos_bboxes_list,
            pos_gt_bboxes_list,
            iou_list,
            cfg=rcnn_train_cfg)
        (label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights,
         bbox_weights) = targets

        if concat:
            label = torch.cat(label, 0)
            bbox_targets = torch.cat(bbox_targets, 0)
            pos_gt_bboxes = torch.cat(pos_gt_bboxes, 0)
            reg_mask = torch.cat(reg_mask, 0)

            label_weights = torch.cat(label_weights, 0)
            label_weights /= torch.clamp(label_weights.sum(), min=1.0)

            bbox_weights = torch.cat(bbox_weights, 0)
            bbox_weights /= torch.clamp(bbox_weights.sum(), min=1.0)

        return (label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights,
                bbox_weights)

    def _get_target_single(self, pos_bboxes, pos_gt_bboxes, ious, cfg):
        """Generate training targets for a single sample.

        Args:
            pos_bboxes (torch.Tensor): Positive boxes with shape
                (N, 7).
            pos_gt_bboxes (torch.Tensor): Ground truth boxes with shape
                (M, 7).
            ious (torch.Tensor): IoU between `pos_bboxes` and `pos_gt_bboxes`
                in shape (N, M).
            cfg (dict): Training configs.

        Returns:
            tuple[torch.Tensor]: Target for positive boxes.
                (label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights,
                bbox_weights)
        """
        cls_pos_mask = ious > cfg.cls_pos_thr
        cls_neg_mask = ious < cfg.cls_neg_thr
        interval_mask = (cls_pos_mask == 0) & (cls_neg_mask == 0)
        # iou regression target
        label = (cls_pos_mask > 0).float()
        label[interval_mask] = (ious[interval_mask] - cfg.cls_neg_thr) / \
            (cfg.cls_pos_thr - cfg.cls_neg_thr)
        # label weights
        label_weights = (label >= 0).float()
        # box regression target
        reg_mask = pos_bboxes.new_zeros(ious.size(0)).long()
        reg_mask[0:pos_gt_bboxes.size(0)] = 1
        bbox_weights = (reg_mask > 0).float()
        if reg_mask.bool().any():
            pos_gt_bboxes_ct = pos_gt_bboxes.clone().detach()
            roi_center = pos_bboxes[..., 0:3]
            roi_ry = pos_bboxes[..., 6] % (2 * np.pi)

            # canonical transformation
            pos_gt_bboxes_ct[..., 0:3] -= roi_center
            pos_gt_bboxes_ct[..., 6] -= roi_ry
            pos_gt_bboxes_ct[..., 0:3] = rotation_3d_in_axis(
                pos_gt_bboxes_ct[..., 0:3].unsqueeze(1), -(roi_ry),
                axis=2).squeeze(1)

            # flip orientation if gt have opposite orientation
            ry_label = pos_gt_bboxes_ct[..., 6] % (2 * np.pi)  # 0 ~ 2pi
            is_opposite = (ry_label > np.pi * 0.5) & (ry_label < np.pi * 1.5)
            ry_label[is_opposite] = (ry_label[is_opposite] + np.pi) % (
                2 * np.pi)  # (0 ~ pi/2, 3pi/2 ~ 2pi)
            flag = ry_label > np.pi
            ry_label[flag] = ry_label[flag] - np.pi * 2  # (-pi/2, pi/2)
            ry_label = torch.clamp(ry_label, min=-np.pi / 2, max=np.pi / 2)
            pos_gt_bboxes_ct[..., 6] = ry_label

            rois_anchor = pos_bboxes.clone().detach()
            rois_anchor[:, 0:3] = 0
            rois_anchor[:, 6] = 0
            bbox_targets = self.bbox_coder.encode(rois_anchor,
                                                  pos_gt_bboxes_ct)
        else:
            # no fg bbox
            bbox_targets = pos_gt_bboxes.new_empty((0, 7))

        return (label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights,
                bbox_weights)

    def get_bboxes(self,
                   rois,
                   cls_score,
                   bbox_pred,
                   class_labels,
                   img_metas,
                   cfg=None):
        """Generate bboxes from bbox head predictions.

        Args:
            rois (torch.Tensor): RoI bounding boxes.
            cls_score (torch.Tensor): Scores of bounding boxes.
            bbox_pred (torch.Tensor): Bounding boxes predictions
            class_labels (torch.Tensor): Label of classes
            img_metas (list[dict]): Point cloud and image's meta info.
            cfg (:obj:`ConfigDict`, optional): Testing config.
                Defaults to None.

        Returns:
            list[tuple]: Decoded bbox, scores and labels after nms.
        """
        roi_batch_id = rois[..., 0]
        roi_boxes = rois[..., 1:]  # boxes without batch id
        batch_size = int(roi_batch_id.max().item() + 1)

        # decode boxes
        roi_ry = roi_boxes[..., 6].view(-1)
        roi_xyz = roi_boxes[..., 0:3].view(-1, 3)
        local_roi_boxes = roi_boxes.clone().detach()
        local_roi_boxes[..., 0:3] = 0
        rcnn_boxes3d = self.bbox_coder.decode(local_roi_boxes, bbox_pred)
        rcnn_boxes3d[..., 0:3] = rotation_3d_in_axis(
            rcnn_boxes3d[..., 0:3].unsqueeze(1), roi_ry, axis=2).squeeze(1)
        rcnn_boxes3d[:, 0:3] += roi_xyz

        # post processing
        result_list = []
        for batch_id in range(batch_size):
            cur_class_labels = class_labels[batch_id]
            cur_cls_score = cls_score[roi_batch_id == batch_id].view(-1)

            cur_box_prob = cur_cls_score.unsqueeze(1)
            cur_rcnn_boxes3d = rcnn_boxes3d[roi_batch_id == batch_id]
            keep = self.multi_class_nms(cur_box_prob, cur_rcnn_boxes3d,
                                        cfg.score_thr, cfg.nms_thr,
                                        img_metas[batch_id],
                                        cfg.use_rotate_nms)
            selected_bboxes = cur_rcnn_boxes3d[keep]
            selected_label_preds = cur_class_labels[keep]
            selected_scores = cur_cls_score[keep]

            result_list.append(
                (img_metas[batch_id]['box_type_3d'](selected_bboxes,
                                                    self.bbox_coder.code_size),
                 selected_scores, selected_label_preds))
        return result_list

    def multi_class_nms(self,
                        box_probs,
                        box_preds,
                        score_thr,
                        nms_thr,
                        input_meta,
                        use_rotate_nms=True):
        """Multi-class NMS for box head.

        Note:
            This function has large overlap with the `box3d_multiclass_nms`
            implemented in `mmdet3d.core.post_processing`. We are considering
            merging these two functions in the future.

        Args:
            box_probs (torch.Tensor): Predicted boxes probabilities in
                shape (N,).
            box_preds (torch.Tensor): Predicted boxes in shape (N, 7+C).
            score_thr (float): Threshold of scores.
            nms_thr (float): Threshold for NMS.
            input_meta (dict): Meta information of the current sample.
            use_rotate_nms (bool, optional): Whether to use rotated nms.
                Defaults to True.

        Returns:
            torch.Tensor: Selected indices.
        """
        if use_rotate_nms:
            nms_func = nms_bev
        else:
            nms_func = nms_normal_bev

        assert box_probs.shape[
            1] == self.num_classes, f'box_probs shape: {str(box_probs.shape)}'
        selected_list = []
        selected_labels = []
        boxes_for_nms = xywhr2xyxyr(input_meta['box_type_3d'](
            box_preds, self.bbox_coder.code_size).bev)

        score_thresh = score_thr if isinstance(
            score_thr, list) else [score_thr for x in range(self.num_classes)]
        nms_thresh = nms_thr if isinstance(
            nms_thr, list) else [nms_thr for x in range(self.num_classes)]
        for k in range(0, self.num_classes):
            class_scores_keep = box_probs[:, k] >= score_thresh[k]

            if class_scores_keep.int().sum() > 0:
                original_idxs = class_scores_keep.nonzero(
                    as_tuple=False).view(-1)
                cur_boxes_for_nms = boxes_for_nms[class_scores_keep]
                cur_rank_scores = box_probs[class_scores_keep, k]

                cur_selected = nms_func(cur_boxes_for_nms, cur_rank_scores,
                                        nms_thresh[k])

                if cur_selected.shape[0] == 0:
                    continue
                selected_list.append(original_idxs[cur_selected])
                selected_labels.append(
                    torch.full([cur_selected.shape[0]],
                               k + 1,
                               dtype=torch.int64,
                               device=box_preds.device))

        keep = torch.cat(
            selected_list, dim=0) if len(selected_list) > 0 else []
        return keep
