text: """ ResNeSt Models
  
  Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
  
  Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
  
  Modified for torchscript compat, and consistency with timm by Ross Wightman
  """
  import math
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.models.layers import DropBlock2d
  from .helpers import load_pretrained
  from .layers import SelectiveKernelConv, ConvBnAct, create_attn
  from .layers.split_attn import SplitAttnConv2d
  from .registry import register_model
  from .resnet import ResNet
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bilinear',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'conv1', 'classifier': 'fc',
          **kwargs
      }
  
  default_cfgs = {
      'resnest14d': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'),
      'resnest26d': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'),
      'resnest50d': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
      'resnest101e': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth',
          input_size=(3, 256, 256), pool_size=(8, 8)),
      'resnest200e': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth',
          input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=0.909, interpolation='bicubic'),
      'resnest269e': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth',
          input_size=(3, 416, 416), pool_size=(13, 13), crop_pct=0.928, interpolation='bicubic'),
      'resnest50d_4s2x40d': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
          interpolation='bicubic'),
      'resnest50d_1s4x24d': _cfg(
          url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_1s4x24d-d4a4f76f.pth',
          interpolation='bicubic')
  }
  
  
  class ResNestBottleneck(nn.Module):
      """ResNet Bottleneck
      """
      # pylint: disable=unused-argument
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None,
                   radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False,
                   reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
                   attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
          super(ResNestBottleneck, self).__init__()
          assert reduce_first == 1  # not supported
          assert attn_layer is None  # not supported
          assert aa_layer is None  # TODO not yet supported
          assert drop_path is None  # TODO not yet supported
  
          group_width = int(planes * (base_width / 64.)) * cardinality
          first_dilation = first_dilation or dilation
          if avd and (stride > 1 or is_first):
              avd_stride = stride
              stride = 1
          else:
              avd_stride = 0
          self.radix = radix
          self.drop_block = drop_block
  
          self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
          self.bn1 = norm_layer(group_width)
          self.act1 = act_layer(inplace=True)
          self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None
  
          if self.radix >= 1:
              self.conv2 = SplitAttnConv2d(
                  group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
                  dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_block=drop_block)
              self.bn2 = None  # FIXME revisit, here to satisfy current torchscript fussyness
              self.act2 = None
          else:
              self.conv2 = nn.Conv2d(
                  group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
                  dilation=first_dilation, groups=cardinality, bias=False)
              self.bn2 = norm_layer(group_width)
              self.act2 = act_layer(inplace=True)
          self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None
  
          self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
          self.bn3 = norm_layer(planes*4)
          self.act3 = act_layer(inplace=True)
          self.downsample = downsample
  
      def zero_init_last_bn(self):
          nn.init.zeros_(self.bn3.weight)
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          if self.drop_block is not None:
              out = self.drop_block(out)
          out = self.act1(out)
  
          if self.avd_first is not None:
              out = self.avd_first(out)
  
          out = self.conv2(out)
          if self.bn2 is not None:
              out = self.bn2(out)
              if self.drop_block is not None:
                  out = self.drop_block(out)
              out = self.act2(out)
  
          if self.avd_last is not None:
              out = self.avd_last(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
          if self.drop_block is not None:
              out = self.drop_block(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.act3(out)
          return out
  
  
  @register_model
  def resnest14d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-14d model. Weights ported from GluonCV.
      """
      default_cfg = default_cfgs['resnest14d']
      model = ResNet(
          ResNestBottleneck, [1, 1, 1, 1], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-26d model. Weights ported from GluonCV.
      """
      default_cfg = default_cfgs['resnest26d']
      model = ResNet(
          ResNestBottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
      Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
      """
      default_cfg = default_cfgs['resnest50d']
      model = ResNet(
          ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest101e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
       Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
      """
      default_cfg = default_cfgs['resnest101e']
      model = ResNet(
          ResNestBottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest200e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
      Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
      """
      default_cfg = default_cfgs['resnest200e']
      model = ResNet(
          ResNestBottleneck, [3, 24, 36, 3], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest269e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
      Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
      """
      default_cfg = default_cfgs['resnest269e']
      model = ResNet(
          ResNestBottleneck, [3, 30, 48, 8], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
          block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest50d_4s2x40d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
      """
      default_cfg = default_cfgs['resnest50d_4s2x40d']
      model = ResNet(
          ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2,
          block_args=dict(radix=4, avd=True, avd_first=True), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  
  
  @register_model
  def resnest50d_1s4x24d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      """ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
      """
      default_cfg = default_cfgs['resnest50d_1s4x24d']
      model = ResNet(
          ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
          stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4,
          block_args=dict(radix=1, avd=True, avd_first=True), **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
      return model
  