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

import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmengine.model import BaseModule
from torch import Tensor

from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig


@MODELS.register_module()
class BFP(BaseModule):
    """BFP (Balanced Feature Pyramids)

    BFP takes multi-level features as inputs and gather them into a single one,
    then refine the gathered feature and scatter the refined results to
    multi-level features. This module is used in Libra R-CNN (CVPR 2019), see
    the paper `Libra R-CNN: Towards Balanced Learning for Object Detection
    <https://arxiv.org/abs/1904.02701>`_ for details.

    Args:
        in_channels (int): Number of input channels (feature maps of all levels
            should have the same channels).
        num_levels (int): Number of input feature levels.
        refine_level (int): Index of integration and refine level of BSF in
            multi-level features from bottom to top.
        refine_type (str): Type of the refine op, currently support
            [None, 'conv', 'non_local'].
        conv_cfg (:obj:`ConfigDict` or dict, optional): The config dict for
            convolution layers.
        norm_cfg (:obj:`ConfigDict` or dict, optional): The config dict for
            normalization layers.
        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or
            dict], optional): Initialization config dict.
    """

    def __init__(
        self,
        in_channels: int,
        num_levels: int,
        refine_level: int = 2,
        refine_type: str = None,
        conv_cfg: OptConfigType = None,
        norm_cfg: OptConfigType = None,
        init_cfg: OptMultiConfig = dict(
            type='Xavier', layer='Conv2d', distribution='uniform')
    ) -> None:
        super().__init__(init_cfg=init_cfg)
        assert refine_type in [None, 'conv', 'non_local']

        self.in_channels = in_channels
        self.num_levels = num_levels
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg

        self.refine_level = refine_level
        self.refine_type = refine_type
        assert 0 <= self.refine_level < self.num_levels

        if self.refine_type == 'conv':
            self.refine = ConvModule(
                self.in_channels,
                self.in_channels,
                3,
                padding=1,
                conv_cfg=self.conv_cfg,
                norm_cfg=self.norm_cfg)
        elif self.refine_type == 'non_local':
            self.refine = NonLocal2d(
                self.in_channels,
                reduction=1,
                use_scale=False,
                conv_cfg=self.conv_cfg,
                norm_cfg=self.norm_cfg)

    def forward(self, inputs: Tuple[Tensor]) -> Tuple[Tensor]:
        """Forward function."""
        assert len(inputs) == self.num_levels

        # step 1: gather multi-level features by resize and average
        feats = []
        gather_size = inputs[self.refine_level].size()[2:]
        for i in range(self.num_levels):
            if i < self.refine_level:
                gathered = F.adaptive_max_pool2d(
                    inputs[i], output_size=gather_size)
            else:
                gathered = F.interpolate(
                    inputs[i], size=gather_size, mode='nearest')
            feats.append(gathered)

        bsf = sum(feats) / len(feats)

        # step 2: refine gathered features
        if self.refine_type is not None:
            bsf = self.refine(bsf)

        # step 3: scatter refined features to multi-levels by a residual path
        outs = []
        for i in range(self.num_levels):
            out_size = inputs[i].size()[2:]
            if i < self.refine_level:
                residual = F.interpolate(bsf, size=out_size, mode='nearest')
            else:
                residual = F.adaptive_max_pool2d(bsf, output_size=out_size)
            outs.append(residual + inputs[i])

        return tuple(outs)
