import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
                      constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm

from mmdet.utils import get_root_logger


from mmdet.models.backbones import ResNet
from mmdet.models import BACKBONES
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck

from mmdet.models.utils import ResLayer
from mmcv.runner import BaseModule


@BACKBONES.register_module()
class CustomResNet(ResNet):
    """ResNet backbone.

    Args:
        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
        stem_channels (int | None): Number of stem channels. If not specified,
            it will be the same as `base_channels`. Default: None.
        base_channels (int): Number of base channels of res layer. Default: 64.
        in_channels (int): Number of input image channels. Default: 3.
        num_stages (int): Resnet stages. Default: 4.
        strides (Sequence[int]): Strides of the first block of each stage.
        dilations (Sequence[int]): Dilation of each stage.
        out_indices (Sequence[int]): Output from which stages.
        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
            layer is the 3x3 conv layer, otherwise the stride-two layer is
            the first 1x1 conv layer.
        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
        norm_cfg (dict): Dictionary to construct and config norm layer.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only.
        plugins (list[dict]): List of plugins for stages, each dict contains:

            - cfg (dict, required): Cfg dict to build plugin.
            - position (str, required): Position inside block to insert
              plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
            - stages (tuple[bool], optional): Stages to apply plugin, length
              should be same as 'num_stages'.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity.
        pretrained (str, optional): model pretrained path. Default: None
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None

    Example:
        >>> from mmdet.models import ResNet
        >>> import torch
        >>> self = ResNet(depth=18)
        >>> self.eval()
        >>> inputs = torch.rand(1, 3, 32, 32)
        >>> level_outputs = self.forward(inputs)
        >>> for level_out in level_outputs:
        ...     print(tuple(level_out.shape))
        (1, 64, 8, 8)
        (1, 128, 4, 4)
        (1, 256, 2, 2)
        (1, 512, 1, 1)
    """

    arch_settings = {
        18: (BasicBlock, (2, 2, 2, 2)),
        34: (BasicBlock, (3, 4, 6, 3)),
        50: (Bottleneck, (3, 4, 6, 3)),
        101: (Bottleneck, (3, 4, 23, 3)),
        152: (Bottleneck, (3, 8, 36, 3))
    }

    def __init__(self,
                 depth,
                 in_channels=3,
                 stem_channels=None,
                 base_channels=64,
                 num_stages=4,
                 strides=(1, 2, 2, 2),
                 dilations=(1, 1, 1, 1),
                 out_indices=(0, 1, 2, 3),
                 style='pytorch',
                 deep_stem=False,
                 avg_down=False,
                 frozen_stages=-1,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN', requires_grad=True),
                 norm_eval=True,
                 dcn=None,
                 stage_with_dcn=(False, False, False, False),
                 plugins=None,
                 with_cp=False,
                 zero_init_residual=True,
                 pretrained=None,
                 num_channels_factor=None,
                 with_max_pool=True,
                 init_cfg=None):
        # super(CustomResNet, self).__init__(init_cfg)
        BaseModule.__init__(self, init_cfg)
        self.zero_init_residual = zero_init_residual
        if depth not in self.arch_settings:
            raise KeyError(f'invalid depth {depth} for resnet')

        block_init_cfg = None
        assert not (init_cfg and pretrained), \
            'init_cfg and pretrained cannot be specified at the same time'
        if isinstance(pretrained, str):
            warnings.warn('DeprecationWarning: pretrained is deprecated, '
                          'please use "init_cfg" instead')
            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
        elif pretrained is None:
            if init_cfg is None:
                self.init_cfg = [
                    dict(type='Kaiming', layer='Conv2d'),
                    dict(
                        type='Constant',
                        val=1,
                        layer=['_BatchNorm', 'GroupNorm'])
                ]
                block = self.arch_settings[depth][0]
                if self.zero_init_residual:
                    if block is BasicBlock:
                        block_init_cfg = dict(
                            type='Constant',
                            val=0,
                            override=dict(name='norm2'))
                    elif block is Bottleneck:
                        block_init_cfg = dict(
                            type='Constant',
                            val=0,
                            override=dict(name='norm3'))
        else:
            raise TypeError('pretrained must be a str or None')

        self.depth = depth
        if stem_channels is None:
            stem_channels = base_channels
        self.stem_channels = stem_channels
        self.base_channels = base_channels
        self.num_stages = num_stages
        assert num_stages >= 1 and num_stages <= 4
        self.strides = strides
        self.dilations = dilations
        assert len(strides) == len(dilations) == num_stages
        self.out_indices = out_indices
        assert max(out_indices) < num_stages
        self.style = style
        self.deep_stem = deep_stem
        self.avg_down = avg_down
        self.frozen_stages = frozen_stages
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.with_cp = with_cp
        self.norm_eval = norm_eval
        self.dcn = dcn
        self.stage_with_dcn = stage_with_dcn
        self.with_max_pool = with_max_pool
        self.num_channels_factor = num_channels_factor
        if dcn is not None:
            assert len(stage_with_dcn) == num_stages
        self.plugins = plugins
        self.block, stage_blocks = self.arch_settings[depth]
        self.stage_blocks = stage_blocks[:num_stages]
        self.inplanes = stem_channels

        self._make_stem_layer(in_channels, stem_channels)
        self.res_layers = []
        for i, num_blocks in enumerate(self.stage_blocks):
            stride = strides[i]
            dilation = dilations[i]
            dcn = self.dcn if self.stage_with_dcn[i] else None
            if plugins is not None:
                stage_plugins = self.make_stage_plugins(plugins, i)
            else:
                stage_plugins = None
            planes = base_channels * (2**i if self.num_channels_factor is None else self.num_channels_factor[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=with_cp,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                dcn=dcn,
                plugins=stage_plugins,
                init_cfg=block_init_cfg)
            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()

        self.feat_dim = self.block.expansion * base_channels * 2**(
            len(self.stage_blocks) - 1)



    def _make_stem_layer(self, in_channels, stem_channels):
        if self.deep_stem:
            self.stem = nn.Sequential(
                build_conv_layer(
                    self.conv_cfg,
                    in_channels,
                    stem_channels // 2,
                    kernel_size=3,
                    stride=2,
                    padding=1,
                    bias=False),
                build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
                nn.ReLU(inplace=True),
                build_conv_layer(
                    self.conv_cfg,
                    stem_channels // 2,
                    stem_channels // 2,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias=False),
                build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
                nn.ReLU(inplace=True),
                build_conv_layer(
                    self.conv_cfg,
                    stem_channels // 2,
                    stem_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias=False),
                build_norm_layer(self.norm_cfg, stem_channels)[1],
                nn.ReLU(inplace=True))
        else:
            self.conv1 = build_conv_layer(
                self.conv_cfg,
                in_channels,
                stem_channels,
                kernel_size=7,
                stride=2,
                padding=3,
                bias=False)
            self.norm1_name, norm1 = build_norm_layer(
                self.norm_cfg, stem_channels, postfix=1)
            self.add_module(self.norm1_name, norm1)
            self.relu = nn.ReLU(inplace=True)
        if self.with_max_pool:
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)


    def forward(self, x):
        """Forward function."""
        if self.deep_stem:
            x = self.stem(x)
        else:
            x = self.conv1(x)
            x = self.norm1(x)
            x = self.relu(x)
        if self.with_max_pool:
            x = self.maxpool(x)
        outs = []
        for i, layer_name in enumerate(self.res_layers):
            res_layer = getattr(self, layer_name)
            x = res_layer(x)
            if i in self.out_indices:
                outs.append(x)
        return tuple(outs)

    def train(self, mode=True):
        """Convert the model into training mode while keep normalization layer
        freezed."""
        super(ResNet, self).train(mode)
        self._freeze_stages()
        if mode and self.norm_eval:
            for m in self.modules():
                # trick: eval have effect on BatchNorm only
                if isinstance(m, _BatchNorm):
                    m.eval()
