# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Union

from mmdet3d.registry import MODELS
from mmdet3d.utils import ConfigType, OptConfigType, OptMultiConfig
from ...structures.det3d_data_sample import SampleList
from .base import Base3DDetector


@MODELS.register_module()
class TwoStage3DDetector(Base3DDetector):
    """Base class of two-stage 3D detector.

    It inherits original ``:class:Base3DDetector``. This class could serve as a
    base class for all two-stage 3D detectors.
    """

    def __init__(
        self,
        backbone: ConfigType,
        neck: OptConfigType = None,
        rpn_head: OptConfigType = None,
        roi_head: OptConfigType = None,
        train_cfg: OptConfigType = None,
        test_cfg: OptConfigType = None,
        init_cfg: OptMultiConfig = None,
        data_preprocessor: OptConfigType = None,
    ) -> None:
        super(TwoStage3DDetector, self).__init__(
            data_preprocessor=data_preprocessor, init_cfg=init_cfg)
        self.backbone = MODELS.build(backbone)

        if neck is not None:
            self.neck = MODELS.build(neck)

        if rpn_head is not None:
            rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
            rpn_head_ = rpn_head.copy()
            rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn)
            rpn_head_num_classes = rpn_head_.get('num_classes', None)
            if rpn_head_num_classes is None:
                rpn_head_.update(num_classes=1)
            self.rpn_head = MODELS.build(rpn_head_)

        if roi_head is not None:
            # update train and test cfg here for now
            rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None
            roi_head.update(train_cfg=rcnn_train_cfg)
            roi_head.update(test_cfg=test_cfg.rcnn)
            self.roi_head = MODELS.build(roi_head)

        self.train_cfg = train_cfg
        self.test_cfg = test_cfg

    @property
    def with_rpn(self) -> bool:
        """bool: whether the detector has RPN"""
        return hasattr(self, 'rpn_head') and self.rpn_head is not None

    @property
    def with_roi_head(self) -> bool:
        """bool: whether the detector has a RoI head"""
        return hasattr(self, 'roi_head') and self.roi_head is not None

    def loss(self, batch_inputs_dict: dict, batch_data_samples: SampleList,
             **kwargs) -> Union[dict, list]:
        """Calculate losses from a batch of inputs and data samples.

        Args:
            batch_inputs_dict (dict): The model input dict which include
                'points', 'imgs' keys.

                - points (list[torch.Tensor]): Point cloud of each sample.
                - imgs (torch.Tensor, optional): Image of each sample.

            batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                samples. It usually includes information such as
                `gt_instance_3d`, `gt_panoptic_seg_3d` and `gt_sem_seg_3d`.

        Returns:
            dict: A dictionary of loss components.
        """
        feats_dict = self.extract_feat(batch_inputs_dict)

        losses = dict()

        # RPN forward and loss
        if self.with_rpn:
            proposal_cfg = self.train_cfg.get('rpn_proposal',
                                              self.test_cfg.rpn)
            rpn_data_samples = copy.deepcopy(batch_data_samples)

            rpn_losses, rpn_results_list = self.rpn_head.loss_and_predict(
                feats_dict,
                rpn_data_samples,
                proposal_cfg=proposal_cfg,
                **kwargs)
            # avoid get same name with roi_head loss
            keys = rpn_losses.keys()
            for key in keys:
                if 'loss' in key and 'rpn' not in key:
                    rpn_losses[f'rpn_{key}'] = rpn_losses.pop(key)
            losses.update(rpn_losses)
        else:
            # TODO: Not support currently, should have a check at Fast R-CNN
            assert batch_data_samples[0].get('proposals', None) is not None
            # use pre-defined proposals in InstanceData for the second stage
            # to extract ROI features.
            rpn_results_list = [
                data_sample.proposals for data_sample in batch_data_samples
            ]

        roi_losses = self.roi_head.loss(feats_dict, rpn_results_list,
                                        batch_data_samples, **kwargs)
        losses.update(roi_losses)

        return losses

    def predict(self, batch_inputs_dict: dict, batch_data_samples: SampleList,
                **kwargs) -> SampleList:
        """Predict results from a batch of inputs and data samples with post-
        processing.

        Args:
            batch_inputs_dict (dict): The model input dict which include
                'points', 'imgs' keys.

                - points (list[torch.Tensor]): Point cloud of each sample.
                - imgs (torch.Tensor, optional): Image of each sample.

            batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                samples. It usually includes information such as
                `gt_instance_3d`, `gt_panoptic_seg_3d` and `gt_sem_seg_3d`.

        Returns:
            list[:obj:`Det3DDataSample`]: Detection results of the
            input samples. Each Det3DDataSample usually contain
            'pred_instances_3d'. And the ``pred_instances_3d`` usually
            contains following keys.

            - scores_3d (Tensor): Classification scores, has a shape
                (num_instance, )
            - labels_3d (Tensor): Labels of bboxes, has a shape
                (num_instances, ).
            - bbox_3d (Tensor): Contains a tensor with shape
                (num_instances, C) where C >=7.
        """
        feats_dict = self.extract_feat(batch_inputs_dict)

        if self.with_rpn:
            rpn_results_list = self.rpn_head.predict(feats_dict,
                                                     batch_data_samples)

        else:
            rpn_results_list = [
                data_sample.proposals for data_sample in batch_data_samples
            ]

        results_list = self.roi_head.predict(feats_dict, rpn_results_list,
                                             batch_data_samples)

        # connvert to Det3DDataSample
        results_list = self.convert_to_datasample(results_list)

        return results_list

    def _forward(self, batch_inputs_dict: dict, batch_data_samples: SampleList,
                 **kwargs) -> tuple:
        """Network forward process. Usually includes backbone, neck and head
        forward without any post-processing.

        Args:
            batch_inputs_dict (dict): The model input dict which include
                'points', 'img' keys.

                - points (list[torch.Tensor]): Point cloud of each sample.
                - imgs (torch.Tensor, optional): Image of each sample.

            batch_data_samples (List[:obj:`Det3DDataSample`]): The Data
                samples. It usually includes information such as
                `gt_instance_3d`, `gt_panoptic_seg_3d` and `gt_sem_seg_3d`.

        Returns:
            tuple: A tuple of features from ``rpn_head`` and ``roi_head``
            forward.
        """
        feats_dict = self.extract_feat(batch_inputs_dict)
        rpn_outs = self.rpn_head.forward(feats_dict['neck_feats'])

        # If there are no pre-defined proposals, use RPN to get proposals
        if batch_data_samples[0].get('proposals', None) is None:
            batch_input_metas = [
                data_samples.metainfo for data_samples in batch_data_samples
            ]
            rpn_results_list = self.rpn_head.predict_by_feat(
                *rpn_outs, batch_input_metas=batch_input_metas)
        else:
            # TODO: Not checked currently.
            rpn_results_list = [
                data_sample.proposals for data_sample in batch_data_samples
            ]

        # roi_head
        roi_outs = self.roi_head._forward(feats_dict, rpn_results_list)
        return rpn_outs + roi_outs
