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# Source code for mmseg.models.backbones.resnext

    import math

    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 ResNet

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

        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is
        "caffe", the stride-two layer is the first 1x1 conv layer.
        """

        def __init__(self,
                     inplanes,
                     planes,
                     groups=1,
                     base_width=4,
                     base_channels=64,
                     **kwargs):
            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.norm1_name, norm1 = build_norm_layer(
                self.norm_cfg, width, postfix=1)
            self.norm2_name, norm2 = build_norm_layer(
                self.norm_cfg, width, postfix=2)
            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)
            fallback_on_stride = False
            self.with_modulated_dcn = False
            if self.with_dcn:
                fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
            if not self.with_dcn or fallback_on_stride:
                self.conv2 = build_conv_layer(
                    self.conv_cfg,
                    width,
                    width,
                    kernel_size=3,
                    stride=self.conv2_stride,
                    padding=self.dilation,
                    dilation=self.dilation,
                    groups=groups,
                    bias=False)
            else:
                assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
                self.conv2 = build_conv_layer(
                    self.dcn,
                    width,
                    width,
                    kernel_size=3,
                    stride=self.conv2_stride,
                    padding=self.dilation,
                    dilation=self.dilation,
                    groups=groups,
                    bias=False)

            self.add_module(self.norm2_name, norm2)
            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)

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

        Args:
            depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
            in_channels (int): Number of input image channels. Normally 3.
            num_stages (int): Resnet stages, normally 4.
            groups (int): Group of resnext.
            base_width (int): Base width of resnext.
            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.
            frozen_stages (int): Stages to be frozen (all param fixed). -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.
            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.

        Example:
            >>> from mmseg.models import ResNeXt
            >>> import torch
            >>> self = ResNeXt(depth=50)
            >>> 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, 256, 8, 8)
            (1, 512, 4, 4)
            (1, 1024, 2, 2)
            (1, 2048, 1, 1)
        """

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

        def __init__(self, groups=1, base_width=4, **kwargs):
            self.groups = groups
            self.base_width = base_width
            super(ResNeXt, 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,
                **kwargs)