# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_activation_layer, build_conv_layer,
                      build_norm_layer, constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm

from mmedit.utils import get_root_logger


class BasicBlock(nn.Module):
    """Basic block for ResNet."""

    expansion = 1

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False):
        super(BasicBlock, self).__init__()

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            3,
            stride=stride,
            padding=dilation,
            dilation=dilation,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            conv_cfg, planes, planes, 3, padding=1, bias=False)
        self.add_module(self.norm2_name, norm2)

        self.activate = build_activation_layer(act_cfg)
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation
        self.with_cp = with_cp

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    def forward(self, x):
        """Forward function."""

        def _inner_forward(x):
            identity = x

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

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

            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)

        out = self.activate(out)

        return out


class Bottleneck(nn.Module):
    """Bottleneck block for ResNet."""

    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False):
        super(Bottleneck, self).__init__()

        self.inplanes = inplanes
        self.planes = planes
        self.stride = stride
        self.dilation = dilation
        self.act_cfg = act_cfg
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.conv1_stride = 1
        self.conv2_stride = stride
        self.with_cp = with_cp

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, planes * self.expansion, postfix=3)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)

        self.conv2 = build_conv_layer(
            conv_cfg,
            planes,
            planes,
            kernel_size=3,
            stride=self.conv2_stride,
            padding=dilation,
            dilation=dilation,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            conv_cfg,
            planes,
            planes * self.expansion,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)

        self.activate = build_activation_layer(act_cfg)
        self.downsample = downsample

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    @property
    def norm3(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm3_name)

    def forward(self, x):
        identity = x

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

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

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

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

        out += identity
        out = self.activate(out)

        return out


class ResNet(nn.Module):
    """General ResNet.

    This class is adopted from
    https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/backbones/resnet.py.

    Args:
        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
        in_channels (int): Number of input image channels. Default" 3.
        stem_channels (int): Number of stem channels. Default: 64.
        base_channels (int): Number of base channels of res layer. Default: 64.
        num_stages (int): Resnet stages, normally 4.
        strides (Sequence[int]): Strides of the first block of each stage.
        dilations (Sequence[int]): Dilation of each stage.
        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.
        act_cfg (dict): Dictionary to construct and config activation layer.
        conv_cfg (dict): Dictionary to construct and config convolution layer.
        norm_cfg (dict): Dictionary to construct and config norm layer.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        multi_grid (Sequence[int]|None): Multi grid dilation rates of last
            stage. Default: None
        contract_dilation (bool): Whether contract first dilation of each layer
            Default: False
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity.
    """

    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,
                 stem_channels,
                 base_channels,
                 num_stages=4,
                 strides=(1, 2, 2, 2),
                 dilations=(1, 1, 2, 4),
                 deep_stem=False,
                 avg_down=False,
                 frozen_stages=-1,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False,
                 multi_grid=None,
                 contract_dilation=False,
                 zero_init_residual=True):
        super(ResNet, self).__init__()
        from functools import partial

        if depth not in self.arch_settings:
            raise KeyError(f'invalid depth {depth} for resnet')
        self.block, stage_blocks = self.arch_settings[depth]
        self.depth = depth
        self.inplanes = stem_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.deep_stem = deep_stem
        self.avg_down = avg_down
        self.frozen_stages = frozen_stages

        self.conv_cfg = conv_cfg
        self.act_cfg = act_cfg
        self.norm_cfg = norm_cfg

        self.with_cp = with_cp
        self.multi_grid = multi_grid
        self.contract_dilation = contract_dilation
        self.zero_init_residual = zero_init_residual

        self._make_stem_layer(in_channels, stem_channels)

        self.layer1 = self._make_layer(
            self.block, 64, stage_blocks[0], stride=strides[0])
        self.layer2 = self._make_layer(
            self.block, 128, stage_blocks[1], stride=strides[1])
        self.layer3 = self._make_layer(
            self.block, 256, stage_blocks[2], stride=strides[2])
        self.layer4 = self._make_layer(
            self.block, 512, stage_blocks[3], stride=strides[3])

        self.layer1.apply(partial(self._nostride_dilate, dilate=dilations[0]))
        self.layer2.apply(partial(self._nostride_dilate, dilate=dilations[1]))
        self.layer3.apply(partial(self._nostride_dilate, dilate=dilations[2]))
        self.layer4.apply(partial(self._nostride_dilate, dilate=dilations[3]))

        self._freeze_stages()

    def _make_stem_layer(self, in_channels, stem_channels):
        """Make stem layer for ResNet."""
        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],
                build_activation_layer(self.act_cfg),
                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],
                build_activation_layer(self.act_cfg),
                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],
                build_activation_layer(self.act_cfg))
        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.activate = build_activation_layer(self.act_cfg)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    @property
    def norm1(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm1_name)

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                build_conv_layer(
                    self.conv_cfg,
                    self.inplanes,
                    planes * block.expansion,
                    stride=stride,
                    kernel_size=1,
                    dilation=dilation,
                    bias=False),
                build_norm_layer(self.norm_cfg, planes * block.expansion)[1])

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

        return nn.Sequential(*layers)

    def _nostride_dilate(self, m, dilate):
        classname = m.__class__.__name__
        if classname.find('Conv') != -1 and dilate > 1:
            # the convolution with stride

            if m.stride == (2, 2):
                m.stride = (1, 1)
                if m.kernel_size == (3, 3):
                    m.dilation = (dilate // 2, dilate // 2)
                    m.padding = (dilate // 2, dilate // 2)
            # other convoluions
            else:
                if m.kernel_size == (3, 3):
                    m.dilation = (dilate, dilate)
                    m.padding = (dilate, dilate)

    def init_weights(self, pretrained=None):
        """Init weights for the model.

        Args:
            pretrained (str, optional): Path for pretrained weights. If given
                None, pretrained weights will not be loaded. Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif 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.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 _freeze_stages(self):
        """Freeze stages param and norm stats."""
        if self.frozen_stages >= 0:
            if self.deep_stem:
                self.stem.eval()
                for param in self.stem.parameters():
                    param.requires_grad = False
            else:
                self.norm1.eval()
                for m in [self.conv1, self.norm1]:
                    for param in m.parameters():
                        param.requires_grad = False

        for i in range(1, self.frozen_stages + 1):
            m = getattr(self, f'layer{i}')
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    def forward(self, x):
        """Forward function.

        Args:
            x (Tensor): Input tensor with shape (N, C, H, W).

        Returns:
            Tensor: Output tensor.
        """
        conv_out = [x]
        if self.deep_stem:
            x = self.stem(x)
        else:
            x = self.conv1(x)
            x = self.norm1(x)
            x = self.activate(x)
        conv_out.append(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        conv_out.append(x)
        x = self.layer2(x)
        conv_out.append(x)
        x = self.layer3(x)
        conv_out.append(x)
        x = self.layer4(x)
        conv_out.append(x)
        return conv_out

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_activation_layer, build_conv_layer,
                      build_norm_layer, constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm

from mmedit.utils import get_root_logger


class BasicBlock(nn.Module):
    """Basic block for ResNet."""

    expansion = 1

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False):
        super(BasicBlock, self).__init__()

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            3,
            stride=stride,
            padding=dilation,
            dilation=dilation,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            conv_cfg, planes, planes, 3, padding=1, bias=False)
        self.add_module(self.norm2_name, norm2)

        self.activate = build_activation_layer(act_cfg)
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation
        self.with_cp = with_cp

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    def forward(self, x):
        """Forward function."""

        def _inner_forward(x):
            identity = x

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

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

            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)

        out = self.activate(out)

        return out


class Bottleneck(nn.Module):
    """Bottleneck block for ResNet."""

    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False):
        super(Bottleneck, self).__init__()

        self.inplanes = inplanes
        self.planes = planes
        self.stride = stride
        self.dilation = dilation
        self.act_cfg = act_cfg
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.conv1_stride = 1
        self.conv2_stride = stride
        self.with_cp = with_cp

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, planes * self.expansion, postfix=3)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)

        self.conv2 = build_conv_layer(
            conv_cfg,
            planes,
            planes,
            kernel_size=3,
            stride=self.conv2_stride,
            padding=dilation,
            dilation=dilation,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            conv_cfg,
            planes,
            planes * self.expansion,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)

        self.activate = build_activation_layer(act_cfg)
        self.downsample = downsample

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    @property
    def norm3(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm3_name)

    def forward(self, x):
        identity = x

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

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

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

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

        out += identity
        out = self.activate(out)

        return out


class ResNet(nn.Module):
    """General ResNet.

    This class is adopted from
    https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/backbones/resnet.py.

    Args:
        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
        in_channels (int): Number of input image channels. Default" 3.
        stem_channels (int): Number of stem channels. Default: 64.
        base_channels (int): Number of base channels of res layer. Default: 64.
        num_stages (int): Resnet stages, normally 4.
        strides (Sequence[int]): Strides of the first block of each stage.
        dilations (Sequence[int]): Dilation of each stage.
        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.
        act_cfg (dict): Dictionary to construct and config activation layer.
        conv_cfg (dict): Dictionary to construct and config convolution layer.
        norm_cfg (dict): Dictionary to construct and config norm layer.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        multi_grid (Sequence[int]|None): Multi grid dilation rates of last
            stage. Default: None
        contract_dilation (bool): Whether contract first dilation of each layer
            Default: False
        zero_init_residual (bool): Whether to use zero init for last norm layer
            in resblocks to let them behave as identity.
    """

    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,
                 stem_channels,
                 base_channels,
                 num_stages=4,
                 strides=(1, 2, 2, 2),
                 dilations=(1, 1, 2, 4),
                 deep_stem=False,
                 avg_down=False,
                 frozen_stages=-1,
                 act_cfg=dict(type='ReLU'),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 with_cp=False,
                 multi_grid=None,
                 contract_dilation=False,
                 zero_init_residual=True):
        super(ResNet, self).__init__()
        from functools import partial

        if depth not in self.arch_settings:
            raise KeyError(f'invalid depth {depth} for resnet')
        self.block, stage_blocks = self.arch_settings[depth]
        self.depth = depth
        self.inplanes = stem_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.deep_stem = deep_stem
        self.avg_down = avg_down
        self.frozen_stages = frozen_stages

        self.conv_cfg = conv_cfg
        self.act_cfg = act_cfg
        self.norm_cfg = norm_cfg

        self.with_cp = with_cp
        self.multi_grid = multi_grid
        self.contract_dilation = contract_dilation
        self.zero_init_residual = zero_init_residual

        self._make_stem_layer(in_channels, stem_channels)

        self.layer1 = self._make_layer(
            self.block, 64, stage_blocks[0], stride=strides[0])
        self.layer2 = self._make_layer(
            self.block, 128, stage_blocks[1], stride=strides[1])
        self.layer3 = self._make_layer(
            self.block, 256, stage_blocks[2], stride=strides[2])
        self.layer4 = self._make_layer(
            self.block, 512, stage_blocks[3], stride=strides[3])

        self.layer1.apply(partial(self._nostride_dilate, dilate=dilations[0]))
        self.layer2.apply(partial(self._nostride_dilate, dilate=dilations[1]))
        self.layer3.apply(partial(self._nostride_dilate, dilate=dilations[2]))
        self.layer4.apply(partial(self._nostride_dilate, dilate=dilations[3]))

        self._freeze_stages()

    def _make_stem_layer(self, in_channels, stem_channels):
        """Make stem layer for ResNet."""
        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],
                build_activation_layer(self.act_cfg),
                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],
                build_activation_layer(self.act_cfg),
                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],
                build_activation_layer(self.act_cfg))
        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.activate = build_activation_layer(self.act_cfg)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    @property
    def norm1(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm1_name)

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                build_conv_layer(
                    self.conv_cfg,
                    self.inplanes,
                    planes * block.expansion,
                    stride=stride,
                    kernel_size=1,
                    dilation=dilation,
                    bias=False),
                build_norm_layer(self.norm_cfg, planes * block.expansion)[1])

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

        return nn.Sequential(*layers)

    def _nostride_dilate(self, m, dilate):
        classname = m.__class__.__name__
        if classname.find('Conv') != -1 and dilate > 1:
            # the convolution with stride

            if m.stride == (2, 2):
                m.stride = (1, 1)
                if m.kernel_size == (3, 3):
                    m.dilation = (dilate // 2, dilate // 2)
                    m.padding = (dilate // 2, dilate // 2)
            # other convoluions
            else:
                if m.kernel_size == (3, 3):
                    m.dilation = (dilate, dilate)
                    m.padding = (dilate, dilate)

    def init_weights(self, pretrained=None):
        """Init weights for the model.

        Args:
            pretrained (str, optional): Path for pretrained weights. If given
                None, pretrained weights will not be loaded. Defaults to None.
        """
        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint(self, pretrained, strict=False, logger=logger)
        elif 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.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 _freeze_stages(self):
        """Freeze stages param and norm stats."""
        if self.frozen_stages >= 0:
            if self.deep_stem:
                self.stem.eval()
                for param in self.stem.parameters():
                    param.requires_grad = False
            else:
                self.norm1.eval()
                for m in [self.conv1, self.norm1]:
                    for param in m.parameters():
                        param.requires_grad = False

        for i in range(1, self.frozen_stages + 1):
            m = getattr(self, f'layer{i}')
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    def forward(self, x):
        """Forward function.

        Args:
            x (Tensor): Input tensor with shape (N, C, H, W).

        Returns:
            Tensor: Output tensor.
        """
        conv_out = [x]
        if self.deep_stem:
            x = self.stem(x)
        else:
            x = self.conv1(x)
            x = self.norm1(x)
            x = self.activate(x)
        conv_out.append(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        conv_out.append(x)
        x = self.layer2(x)
        conv_out.append(x)
        x = self.layer3(x)
        conv_out.append(x)
        x = self.layer4(x)
        conv_out.append(x)
        return conv_out

# Copyright (c) OpenMMLab. All rights reserved. Basic block for ResNet. nn.Module: normalization layer after the first convolution layer nn.Module: normalization layer after the second convolution layer Forward function. Bottleneck block for ResNet. nn.Module: normalization layer after the first convolution layer nn.Module: normalization layer after the second convolution layer nn.Module: normalization layer after the second convolution layer General ResNet.      This class is adopted from     https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/backbones/resnet.py.      Args:         depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.         in_channels (int): Number of input image channels. Default" 3.         stem_channels (int): Number of stem channels. Default: 64.         base_channels (int): Number of base channels of res layer. Default: 64.         num_stages (int): Resnet stages, normally 4.         strides (Sequence[int]): Strides of the first block of each stage.         dilations (Sequence[int]): Dilation of each stage.         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.         act_cfg (dict): Dictionary to construct and config activation layer.         conv_cfg (dict): Dictionary to construct and config convolution layer.         norm_cfg (dict): Dictionary to construct and config norm layer.         with_cp (bool): Use checkpoint or not. Using checkpoint will save some             memory while slowing down the training speed.         multi_grid (Sequence[int]|None): Multi grid dilation rates of last             stage. Default: None         contract_dilation (bool): Whether contract first dilation of each layer             Default: False         zero_init_residual (bool): Whether to use zero init for last norm layer             in resblocks to let them behave as identity. Make stem layer for ResNet. nn.Module: normalization layer after the second convolution layer # the convolution with stride # other convoluions Init weights for the model.          Args:             pretrained (str, optional): Path for pretrained weights. If given                 None, pretrained weights will not be loaded. Defaults to None. Freeze stages param and norm stats. Forward function.          Args:             x (Tensor): Input tensor with shape (N, C, H, W).          Returns:             Tensor: Output tensor.