# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmengine.logging import MMLogger
from mmengine.model import Sequential, constant_init, kaiming_init
from mmengine.runner.checkpoint import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm

from mmdet.registry import MODELS
from .resnet import BasicBlock
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet


class Bottleneck(_Bottleneck):
    r"""Bottleneck for the ResNet backbone in `DetectoRS
    <https://arxiv.org/pdf/2006.02334.pdf>`_.

    This bottleneck allows the users to specify whether to use
    SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid).

    Args:
         inplanes (int): The number of input channels.
         planes (int): The number of output channels before expansion.
         rfp_inplanes (int, optional): The number of channels from RFP.
             Default: None. If specified, an additional conv layer will be
             added for ``rfp_feat``. Otherwise, the structure is the same as
             base class.
         sac (dict, optional): Dictionary to construct SAC. Default: None.
         init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None
    """
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 rfp_inplanes=None,
                 sac=None,
                 init_cfg=None,
                 **kwargs):
        super(Bottleneck, self).__init__(
            inplanes, planes, init_cfg=init_cfg, **kwargs)

        assert sac is None or isinstance(sac, dict)
        self.sac = sac
        self.with_sac = sac is not None
        if self.with_sac:
            self.conv2 = build_conv_layer(
                self.sac,
                planes,
                planes,
                kernel_size=3,
                stride=self.conv2_stride,
                padding=self.dilation,
                dilation=self.dilation,
                bias=False)

        self.rfp_inplanes = rfp_inplanes
        if self.rfp_inplanes:
            self.rfp_conv = build_conv_layer(
                None,
                self.rfp_inplanes,
                planes * self.expansion,
                1,
                stride=1,
                bias=True)
            if init_cfg is None:
                self.init_cfg = dict(
                    type='Constant', val=0, override=dict(name='rfp_conv'))

    def rfp_forward(self, x, rfp_feat):
        """The forward function that also takes the RFP features as input."""

        def _inner_forward(x):
            identity = x

            out = self.conv1(x)
            out = self.norm1(out)
            out = self.relu(out)

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv1_plugin_names)

            out = self.conv2(out)
            out = self.norm2(out)
            out = self.relu(out)

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv2_plugin_names)

            out = self.conv3(out)
            out = self.norm3(out)

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv3_plugin_names)

            if self.downsample is not None:
                identity = self.downsample(x)

            out += identity

            return out

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        if self.rfp_inplanes:
            rfp_feat = self.rfp_conv(rfp_feat)
            out = out + rfp_feat

        out = self.relu(out)

        return out


class ResLayer(Sequential):
    """ResLayer to build ResNet style backbone for RPF in detectoRS.

    The difference between this module and base class is that we pass
    ``rfp_inplanes`` to the first block.

    Args:
        block (nn.Module): block used to build ResLayer.
        inplanes (int): inplanes of block.
        planes (int): planes of block.
        num_blocks (int): number of blocks.
        stride (int): stride of the first block. Default: 1
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        downsample_first (bool): Downsample at the first block or last block.
            False for Hourglass, True for ResNet. Default: True
        rfp_inplanes (int, optional): The number of channels from RFP.
            Default: None. If specified, an additional conv layer will be
            added for ``rfp_feat``. Otherwise, the structure is the same as
            base class.
    """

    def __init__(self,
                 block,
                 inplanes,
                 planes,
                 num_blocks,
                 stride=1,
                 avg_down=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 downsample_first=True,
                 rfp_inplanes=None,
                 **kwargs):
        self.block = block
        assert downsample_first, f'downsample_first={downsample_first} is ' \
                                 'not supported in DetectoRS'

        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = []
            conv_stride = stride
            if avg_down and stride != 1:
                conv_stride = 1
                downsample.append(
                    nn.AvgPool2d(
                        kernel_size=stride,
                        stride=stride,
                        ceil_mode=True,
                        count_include_pad=False))
            downsample.extend([
                build_conv_layer(
                    conv_cfg,
                    inplanes,
                    planes * block.expansion,
                    kernel_size=1,
                    stride=conv_stride,
                    bias=False),
                build_norm_layer(norm_cfg, planes * block.expansion)[1]
            ])
            downsample = nn.Sequential(*downsample)

        layers = []
        layers.append(
            block(
                inplanes=inplanes,
                planes=planes,
                stride=stride,
                downsample=downsample,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                rfp_inplanes=rfp_inplanes,
                **kwargs))
        inplanes = planes * block.expansion
        for _ in range(1, num_blocks):
            layers.append(
                block(
                    inplanes=inplanes,
                    planes=planes,
                    stride=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    **kwargs))

        super(ResLayer, self).__init__(*layers)


@MODELS.register_module()
class DetectoRS_ResNet(ResNet):
    """ResNet backbone for DetectoRS.

    Args:
        sac (dict, optional): Dictionary to construct SAC (Switchable Atrous
            Convolution). Default: None.
        stage_with_sac (list): Which stage to use sac. Default: (False, False,
            False, False).
        rfp_inplanes (int, optional): The number of channels from RFP.
            Default: None. If specified, an additional conv layer will be
            added for ``rfp_feat``. Otherwise, the structure is the same as
            base class.
        output_img (bool): If ``True``, the input image will be inserted into
            the starting position of output. Default: False.
    """

    arch_settings = {
        50: (Bottleneck, (3, 4, 6, 3)),
        101: (Bottleneck, (3, 4, 23, 3)),
        152: (Bottleneck, (3, 8, 36, 3))
    }

    def __init__(self,
                 sac=None,
                 stage_with_sac=(False, False, False, False),
                 rfp_inplanes=None,
                 output_img=False,
                 pretrained=None,
                 init_cfg=None,
                 **kwargs):
        assert not (init_cfg and pretrained), \
            'init_cfg and pretrained cannot be specified at the same time'
        self.pretrained = pretrained
        if init_cfg is not None:
            assert isinstance(init_cfg, dict), \
                f'init_cfg must be a dict, but got {type(init_cfg)}'
            if 'type' in init_cfg:
                assert init_cfg.get('type') == 'Pretrained', \
                    'Only can initialize module by loading a pretrained model'
            else:
                raise KeyError('`init_cfg` must contain the key "type"')
            self.pretrained = init_cfg.get('checkpoint')
        self.sac = sac
        self.stage_with_sac = stage_with_sac
        self.rfp_inplanes = rfp_inplanes
        self.output_img = output_img
        super(DetectoRS_ResNet, self).__init__(**kwargs)

        self.inplanes = self.stem_channels
        self.res_layers = []
        for i, num_blocks in enumerate(self.stage_blocks):
            stride = self.strides[i]
            dilation = self.dilations[i]
            dcn = self.dcn if self.stage_with_dcn[i] else None
            sac = self.sac if self.stage_with_sac[i] else None
            if self.plugins is not None:
                stage_plugins = self.make_stage_plugins(self.plugins, i)
            else:
                stage_plugins = None
            planes = self.base_channels * 2**i
            res_layer = self.make_res_layer(
                block=self.block,
                inplanes=self.inplanes,
                planes=planes,
                num_blocks=num_blocks,
                stride=stride,
                dilation=dilation,
                style=self.style,
                avg_down=self.avg_down,
                with_cp=self.with_cp,
                conv_cfg=self.conv_cfg,
                norm_cfg=self.norm_cfg,
                dcn=dcn,
                sac=sac,
                rfp_inplanes=rfp_inplanes if i > 0 else None,
                plugins=stage_plugins)
            self.inplanes = planes * self.block.expansion
            layer_name = f'layer{i + 1}'
            self.add_module(layer_name, res_layer)
            self.res_layers.append(layer_name)

        self._freeze_stages()

    # In order to be properly initialized by RFP
    def init_weights(self):
        # Calling this method will cause parameter initialization exception
        # super(DetectoRS_ResNet, self).init_weights()

        if isinstance(self.pretrained, str):
            logger = MMLogger.get_current_instance()
            load_checkpoint(self, self.pretrained, strict=False, logger=logger)
        elif self.pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    kaiming_init(m)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                    constant_init(m, 1)

            if self.dcn is not None:
                for m in self.modules():
                    if isinstance(m, Bottleneck) and hasattr(
                            m.conv2, 'conv_offset'):
                        constant_init(m.conv2.conv_offset, 0)

            if self.zero_init_residual:
                for m in self.modules():
                    if isinstance(m, Bottleneck):
                        constant_init(m.norm3, 0)
                    elif isinstance(m, BasicBlock):
                        constant_init(m.norm2, 0)
        else:
            raise TypeError('pretrained must be a str or None')

    def make_res_layer(self, **kwargs):
        """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS."""
        return ResLayer(**kwargs)

    def forward(self, x):
        """Forward function."""
        outs = list(super(DetectoRS_ResNet, self).forward(x))
        if self.output_img:
            outs.insert(0, x)
        return tuple(outs)

    def rfp_forward(self, x, rfp_feats):
        """Forward function for RFP."""
        if self.deep_stem:
            x = self.stem(x)
        else:
            x = self.conv1(x)
            x = self.norm1(x)
            x = self.relu(x)
        x = self.maxpool(x)
        outs = []
        for i, layer_name in enumerate(self.res_layers):
            res_layer = getattr(self, layer_name)
            rfp_feat = rfp_feats[i] if i > 0 else None
            for layer in res_layer:
                x = layer.rfp_forward(x, rfp_feat)
            if i in self.out_indices:
                outs.append(x)
        return tuple(outs)
