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# Source code for mmseg.models.backbones.resnest

    import math

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.checkpoint as cp
    from mmcv.cnn import build_conv_layer, build_norm_layer

    from ..builder import BACKBONES
    from ..utils import ResLayer
    from .resnet import Bottleneck as _Bottleneck
    from .resnet import ResNetV1d

    class RSoftmax(nn.Module):
        """Radix Softmax module in ``SplitAttentionConv2d``.

        Args:
            radix (int): Radix of input.
            groups (int): Groups of input.
        """

        def __init__(self, radix, groups):
            super().__init__()
            self.radix = radix
            self.groups = groups

        def forward(self, x):
            batch = x.size(0)
            if self.radix > 1:
                x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
                x = F.softmax(x, dim=1)
                x = x.reshape(batch, -1)
            else:
                x = torch.sigmoid(x)
            return x

    class SplitAttentionConv2d(nn.Module):
        """Split-Attention Conv2d in ResNeSt.

        Args:
            in_channels (int): Same as nn.Conv2d.
            out_channels (int): Same as nn.Conv2d.
            kernel_size (int | tuple[int]): Same as nn.Conv2d.
            stride (int | tuple[int]): Same as nn.Conv2d.
            padding (int | tuple[int]): Same as nn.Conv2d.
            dilation (int | tuple[int]): Same as nn.Conv2d.
            groups (int): Same as nn.Conv2d.
            radix (int): Radix of SpltAtConv2d. Default: 2
            reduction_factor (int): Reduction factor of inter_channels. Default: 4.
            conv_cfg (dict): Config dict for convolution layer. Default: None,
                which means using conv2d.
            norm_cfg (dict): Config dict for normalization layer. Default: None.
            dcn (dict): Config dict for DCN. Default: None.
        """

        def __init__(self,
                     in_channels,
                     channels,
                     kernel_size,
                     stride=1,
                     padding=0,
                     dilation=1,
                     groups=1,
                     radix=2,
                     reduction_factor=4,
                     conv_cfg=None,
                     norm_cfg=dict(type='BN'),
                     dcn=None):
            super(SplitAttentionConv2d, self).__init__()
            inter_channels = max(in_channels * radix // reduction_factor, 32)
            self.radix = radix
            self.groups = groups
            self.channels = channels
            self.with_dcn = dcn is not None
            self.dcn = dcn
            fallback_on_stride = False
            if self.with_dcn:
                fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
            if self.with_dcn and not fallback_on_stride:
                assert conv_cfg is None, 'conv_cfg must be None for DCN'
                conv_cfg = dcn
            self.conv = build_conv_layer(
                conv_cfg,
                in_channels,
                channels * radix,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                groups=groups * radix,
                bias=False)
            self.norm0_name, norm0 = build_norm_layer(
                norm_cfg, channels * radix, postfix=0)
            self.add_module(self.norm0_name, norm0)
            self.relu = nn.ReLU(inplace=True)
            self.fc1 = build_conv_layer(
                None, channels, inter_channels, 1, groups=self.groups)
            self.norm1_name, norm1 = build_norm_layer(
                norm_cfg, inter_channels, postfix=1)
            self.add_module(self.norm1_name, norm1)
            self.fc2 = build_conv_layer(
                None, inter_channels, channels * radix, 1, groups=self.groups)
            self.rsoftmax = RSoftmax(radix, groups)

        @property
        def norm0(self):
            """nn.Module: the normalization layer named "norm0" """
            return getattr(self, self.norm0_name)

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

        def forward(self, x):
            x = self.conv(x)
            x = self.norm0(x)
            x = self.relu(x)

            batch, rchannel = x.shape[:2]
            batch = x.size(0)
            if self.radix > 1:
                splits = x.view(batch, self.radix, -1, *x.shape[2:])
                gap = splits.sum(dim=1)
            else:
                gap = x
            gap = F.adaptive_avg_pool2d(gap, 1)
            gap = self.fc1(gap)

            gap = self.norm1(gap)
            gap = self.relu(gap)

            atten = self.fc2(gap)
            atten = self.rsoftmax(atten).view(batch, -1, 1, 1)

            if self.radix > 1:
                attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
                out = torch.sum(attens * splits, dim=1)
            else:
                out = atten * x
            return out.contiguous()

    class Bottleneck(_Bottleneck):
        """Bottleneck block for ResNeSt.

        Args:
            inplane (int): Input planes of this block.
            planes (int): Middle planes of this block.
            groups (int): Groups of conv2.
            width_per_group (int): Width per group of conv2. 64x4d indicates
                ``groups=64, width_per_group=4`` and 32x8d indicates
                ``groups=32, width_per_group=8``.
            radix (int): Radix of SpltAtConv2d. Default: 2
            reduction_factor (int): Reduction factor of inter_channels in
                SplitAttentionConv2d. Default: 4.
            avg_down_stride (bool): Whether to use average pool for stride in
                Bottleneck. Default: True.
            kwargs (dict): Key word arguments for base class.
        """
        expansion = 4

        def __init__(self,
                     inplanes,
                     planes,
                     groups=1,
                     base_width=4,
                     base_channels=64,
                     radix=2,
                     reduction_factor=4,
                     avg_down_stride=True,
                     **kwargs):
            """Bottleneck block for ResNeSt."""
            super(Bottleneck, self).__init__(inplanes, planes, **kwargs)

            if groups == 1:
                width = self.planes
            else:
                width = math.floor(self.planes *
                                   (base_width / base_channels)) * groups

            self.avg_down_stride = avg_down_stride and self.conv2_stride > 1

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

            self.conv1 = build_conv_layer(
                self.conv_cfg,
                self.inplanes,
                width,
                kernel_size=1,
                stride=self.conv1_stride,
                bias=False)
            self.add_module(self.norm1_name, norm1)
            self.with_modulated_dcn = False
            self.conv2 = SplitAttentionConv2d(
                width,
                width,
                kernel_size=3,
                stride=1 if self.avg_down_stride else self.conv2_stride,
                padding=self.dilation,
                dilation=self.dilation,
                groups=groups,
                radix=radix,
                reduction_factor=reduction_factor,
                conv_cfg=self.conv_cfg,
                norm_cfg=self.norm_cfg,
                dcn=self.dcn)
            delattr(self, self.norm2_name)

            if self.avg_down_stride:
                self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)

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

        def forward(self, x):

            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)

                if self.avg_down_stride:
                    out = self.avd_layer(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)

            out = self.relu(out)

            return out

    @BACKBONES.register_module()
    class ResNeSt(ResNetV1d):
        """ResNeSt backbone.

        Args:
            groups (int): Number of groups of Bottleneck. Default: 1
            base_width (int): Base width of Bottleneck. Default: 4
            radix (int): Radix of SpltAtConv2d. Default: 2
            reduction_factor (int): Reduction factor of inter_channels in
                SplitAttentionConv2d. Default: 4.
            avg_down_stride (bool): Whether to use average pool for stride in
                Bottleneck. Default: True.
            kwargs (dict): Keyword arguments for ResNet.
        """

        arch_settings = {
            50: (Bottleneck, (3, 4, 6, 3)),
            101: (Bottleneck, (3, 4, 23, 3)),
            152: (Bottleneck, (3, 8, 36, 3)),
            200: (Bottleneck, (3, 24, 36, 3))
        }

        def __init__(self,
                     groups=1,
                     base_width=4,
                     radix=2,
                     reduction_factor=4,
                     avg_down_stride=True,
                     **kwargs):
            self.groups = groups
            self.base_width = base_width
            self.radix = radix
            self.reduction_factor = reduction_factor
            self.avg_down_stride = avg_down_stride
            super(ResNeSt, self).__init__(**kwargs)

        def make_res_layer(self, **kwargs):
            """Pack all blocks in a stage into a ``ResLayer``."""
            return ResLayer(
                groups=self.groups,
                base_width=self.base_width,
                base_channels=self.base_channels,
                radix=self.radix,
                reduction_factor=self.reduction_factor,
                avg_down_stride=self.avg_down_stride,
                **kwargs)