text: # Copyright (c) OpenMMLab. All rights reserved.
  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 .resnet import Bottleneck as _Bottleneck
  from .resnet import ResLayer, 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.
  
      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 SplitAttentionConv2d.
              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.
      """
  
      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')):
          super().__init__()
          inter_channels = max(in_channels * radix // reduction_factor, 32)
          self.radix = radix
          self.groups = groups
          self.channels = channels
          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):
          return getattr(self, self.norm0_name)
  
      @property
      def norm1(self):
          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]
          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:
          in_channels (int): Input channels of this block.
          out_channels (int): Output channels 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 SplitAttentionConv2d.
              Default: 4.
          avg_down_stride (bool): Whether to use average pool for stride in
              Bottleneck. Default: True.
          stride (int): stride of the block. Default: 1
          dilation (int): dilation of convolution. Default: 1
          downsample (nn.Module): downsample operation on identity branch.
              Default: None
          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.
          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')
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed.
      """
  
      def __init__(self,
                   in_channels,
                   out_channels,
                   groups=1,
                   width_per_group=4,
                   base_channels=64,
                   radix=2,
                   reduction_factor=4,
                   avg_down_stride=True,
                   **kwargs):
          super().__init__(in_channels, out_channels, **kwargs)
  
          self.groups = groups
          self.width_per_group = width_per_group
  
          # For ResNet bottleneck, middle channels are determined by expansion
          # and out_channels, but for ResNeXt bottleneck, it is determined by
          # groups and width_per_group and the stage it is located in.
          if groups != 1:
              assert self.mid_channels % base_channels == 0
              self.mid_channels = (
                  groups * width_per_group * self.mid_channels // base_channels)
  
          self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
  
          self.norm1_name, norm1 = build_norm_layer(
              self.norm_cfg, self.mid_channels, postfix=1)
          self.norm3_name, norm3 = build_norm_layer(
              self.norm_cfg, self.out_channels, postfix=3)
  
          self.conv1 = build_conv_layer(
              self.conv_cfg,
              self.in_channels,
              self.mid_channels,
              kernel_size=1,
              stride=self.conv1_stride,
              bias=False)
          self.add_module(self.norm1_name, norm1)
          self.conv2 = SplitAttentionConv2d(
              self.mid_channels,
              self.mid_channels,
              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)
          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,
              self.mid_channels,
              self.out_channels,
              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)
  
              out = self.conv2(out)
  
              if self.avg_down_stride:
                  out = self.avd_layer(out)
  
              out = self.conv3(out)
              out = self.norm3(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.relu(out)
  
          return out
  
  
  @BACKBONES.register_module()
  class ResNeSt(ResNetV1d):
      """ResNeSt backbone.
  
      Please refer to the `paper <https://arxiv.org/pdf/2004.08955.pdf>`_ for
      details.
  
      Args:
          depth (int): Network depth, from {50, 101, 152, 200}.
          groups (int): Groups of conv2 in Bottleneck. Default: 32.
          width_per_group (int): Width per group of conv2 in Bottleneck.
              Default: 4.
          radix (int): Radix of SpltAtConv2d. Default: 2
          reduction_factor (int): Reduction factor of SplitAttentionConv2d.
              Default: 4.
          avg_down_stride (bool): Whether to use average pool for stride in
              Bottleneck. Default: True.
          in_channels (int): Number of input image channels. Default: 3.
          stem_channels (int): Output channels of the stem layer. Default: 64.
          num_stages (int): Stages of the network. Default: 4.
          strides (Sequence[int]): Strides of the first block of each stage.
              Default: ``(1, 2, 2, 2)``.
          dilations (Sequence[int]): Dilation of each stage.
              Default: ``(1, 1, 1, 1)``.
          out_indices (Sequence[int]): Output from which stages. If only one
              stage is specified, a single tensor (feature map) is returned,
              otherwise multiple stages are specified, a tuple of tensors will
              be returned. Default: ``(3, )``.
          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.
              Default: False.
          avg_down (bool): Use AvgPool instead of stride conv when
              downsampling in the bottleneck. Default: False.
          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
              -1 means not freezing any parameters. Default: -1.
          conv_cfg (dict | None): The config dict for conv layers. Default: None.
          norm_cfg (dict): The config dict for norm layers.
          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. Default: False.
          with_cp (bool): Use checkpoint or not. Using checkpoint will save some
              memory while slowing down the training speed. Default: False.
          zero_init_residual (bool): Whether to use zero init for last norm layer
              in resblocks to let them behave as identity. Default: True.
      """
  
      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)),
          269: (Bottleneck, (3, 30, 48, 8))
      }
  
      def __init__(self,
                   depth,
                   groups=1,
                   width_per_group=4,
                   radix=2,
                   reduction_factor=4,
                   avg_down_stride=True,
                   **kwargs):
          self.groups = groups
          self.width_per_group = width_per_group
          self.radix = radix
          self.reduction_factor = reduction_factor
          self.avg_down_stride = avg_down_stride
          super().__init__(depth=depth, **kwargs)
  
      def make_res_layer(self, **kwargs):
          return ResLayer(
              groups=self.groups,
              width_per_group=self.width_per_group,
              base_channels=self.base_channels,
              radix=self.radix,
              reduction_factor=self.reduction_factor,
              avg_down_stride=self.avg_down_stride,
              **kwargs)
  