text: """ Swin Transformer
  A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
      - https://arxiv.org/pdf/2103.14030
  
  Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
  
  """
  # --------------------------------------------------------
  # Swin Transformer
  # Copyright (c) 2021 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ze Liu
  # --------------------------------------------------------
  import logging
  import math
  from copy import deepcopy
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.utils.checkpoint as checkpoint
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from .helpers import build_model_with_cfg, overlay_external_default_cfg
  from .layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_
  from .registry import register_model
  from .vision_transformer import checkpoint_filter_fn, _init_vit_weights
  
  _logger = logging.getLogger(__name__)
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
          'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'patch_embed.proj', 'classifier': 'head',
          **kwargs
      }
  
  
  default_cfgs = {
      # patch models (my experiments)
      'swin_base_patch4_window12_384': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
          input_size=(3, 384, 384), crop_pct=1.0),
  
      'swin_base_patch4_window7_224': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',
      ),
  
      'swin_large_patch4_window12_384': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
          input_size=(3, 384, 384), crop_pct=1.0),
  
      'swin_large_patch4_window7_224': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',
      ),
  
      'swin_small_patch4_window7_224': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',
      ),
  
      'swin_tiny_patch4_window7_224': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
      ),
  
      'swin_base_patch4_window12_384_in22k': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
          input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
  
      'swin_base_patch4_window7_224_in22k': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
          num_classes=21841),
  
      'swin_large_patch4_window12_384_in22k': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
          input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
  
      'swin_large_patch4_window7_224_in22k': _cfg(
          url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
          num_classes=21841),
  
  }
  
  
  def window_partition(x, window_size: int):
      """
      Args:
          x: (B, H, W, C)
          window_size (int): window size
  
      Returns:
          windows: (num_windows*B, window_size, window_size, C)
      """
      B, H, W, C = x.shape
      x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
      windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
      return windows
  
  
  def window_reverse(windows, window_size: int, H: int, W: int):
      """
      Args:
          windows: (num_windows*B, window_size, window_size, C)
          window_size (int): Window size
          H (int): Height of image
          W (int): Width of image
  
      Returns:
          x: (B, H, W, C)
      """
      B = int(windows.shape[0] / (H * W / window_size / window_size))
      x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
      return x
  
  
  class WindowAttention(nn.Module):
      r""" Window based multi-head self attention (W-MSA) module with relative position bias.
      It supports both of shifted and non-shifted window.
  
      Args:
          dim (int): Number of input channels.
          window_size (tuple[int]): The height and width of the window.
          num_heads (int): Number of attention heads.
          qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
          attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
          proj_drop (float, optional): Dropout ratio of output. Default: 0.0
      """
  
      def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.):
  
          super().__init__()
          self.dim = dim
          self.window_size = window_size  # Wh, Ww
          self.num_heads = num_heads
          head_dim = dim // num_heads
          self.scale = head_dim ** -0.5
  
          # define a parameter table of relative position bias
          self.relative_position_bias_table = nn.Parameter(
              torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
  
          # get pair-wise relative position index for each token inside the window
          coords_h = torch.arange(self.window_size[0])
          coords_w = torch.arange(self.window_size[1])
          coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
          coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
          relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
          relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
          relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
          relative_coords[:, :, 1] += self.window_size[1] - 1
          relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
          relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
          self.register_buffer("relative_position_index", relative_position_index)
  
          self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(dim, dim)
          self.proj_drop = nn.Dropout(proj_drop)
  
          trunc_normal_(self.relative_position_bias_table, std=.02)
          self.softmax = nn.Softmax(dim=-1)
  
      def forward(self, x, mask: Optional[torch.Tensor] = None):
          """
          Args:
              x: input features with shape of (num_windows*B, N, C)
              mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
          """
          B_, N, C = x.shape
          qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
          q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
  
          q = q * self.scale
          attn = (q @ k.transpose(-2, -1))
  
          relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
              self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
          relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
          attn = attn + relative_position_bias.unsqueeze(0)
  
          if mask is not None:
              nW = mask.shape[0]
              attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
              attn = attn.view(-1, self.num_heads, N, N)
              attn = self.softmax(attn)
          else:
              attn = self.softmax(attn)
  
          attn = self.attn_drop(attn)
  
          x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class SwinTransformerBlock(nn.Module):
      r""" Swin Transformer Block.
  
      Args:
          dim (int): Number of input channels.
          input_resolution (tuple[int]): Input resulotion.
          num_heads (int): Number of attention heads.
          window_size (int): Window size.
          shift_size (int): Shift size for SW-MSA.
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
          drop (float, optional): Dropout rate. Default: 0.0
          attn_drop (float, optional): Attention dropout rate. Default: 0.0
          drop_path (float, optional): Stochastic depth rate. Default: 0.0
          act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
      """
  
      def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                   mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
                   act_layer=nn.GELU, norm_layer=nn.LayerNorm):
          super().__init__()
          self.dim = dim
          self.input_resolution = input_resolution
          self.num_heads = num_heads
          self.window_size = window_size
          self.shift_size = shift_size
          self.mlp_ratio = mlp_ratio
          if min(self.input_resolution) <= self.window_size:
              # if window size is larger than input resolution, we don't partition windows
              self.shift_size = 0
              self.window_size = min(self.input_resolution)
          assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
  
          self.norm1 = norm_layer(dim)
          self.attn = WindowAttention(
              dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,
              attn_drop=attn_drop, proj_drop=drop)
  
          self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
          self.norm2 = norm_layer(dim)
          mlp_hidden_dim = int(dim * mlp_ratio)
          self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
  
          if self.shift_size > 0:
              # calculate attention mask for SW-MSA
              H, W = self.input_resolution
              img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
              h_slices = (slice(0, -self.window_size),
                          slice(-self.window_size, -self.shift_size),
                          slice(-self.shift_size, None))
              w_slices = (slice(0, -self.window_size),
                          slice(-self.window_size, -self.shift_size),
                          slice(-self.shift_size, None))
              cnt = 0
              for h in h_slices:
                  for w in w_slices:
                      img_mask[:, h, w, :] = cnt
                      cnt += 1
  
              mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
              mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
              attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
              attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
          else:
              attn_mask = None
  
          self.register_buffer("attn_mask", attn_mask)
  
      def forward(self, x):
          H, W = self.input_resolution
          B, L, C = x.shape
          assert L == H * W, "input feature has wrong size"
  
          shortcut = x
          x = self.norm1(x)
          x = x.view(B, H, W, C)
  
          # cyclic shift
          if self.shift_size > 0:
              shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
          else:
              shifted_x = x
  
          # partition windows
          x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
          x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
  
          # W-MSA/SW-MSA
          attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
  
          # merge windows
          attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
          shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
  
          # reverse cyclic shift
          if self.shift_size > 0:
              x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
          else:
              x = shifted_x
          x = x.view(B, H * W, C)
  
          # FFN
          x = shortcut + self.drop_path(x)
          x = x + self.drop_path(self.mlp(self.norm2(x)))
  
          return x
  
  
  class PatchMerging(nn.Module):
      r""" Patch Merging Layer.
  
      Args:
          input_resolution (tuple[int]): Resolution of input feature.
          dim (int): Number of input channels.
          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
      """
  
      def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
          super().__init__()
          self.input_resolution = input_resolution
          self.dim = dim
          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
          self.norm = norm_layer(4 * dim)
  
      def forward(self, x):
          """
          x: B, H*W, C
          """
          H, W = self.input_resolution
          B, L, C = x.shape
          assert L == H * W, "input feature has wrong size"
          assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
  
          x = x.view(B, H, W, C)
  
          x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
          x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
          x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
          x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
          x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
          x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
  
          x = self.norm(x)
          x = self.reduction(x)
  
          return x
  
      def extra_repr(self) -> str:
          return f"input_resolution={self.input_resolution}, dim={self.dim}"
  
      def flops(self):
          H, W = self.input_resolution
          flops = H * W * self.dim
          flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
          return flops
  
  
  class BasicLayer(nn.Module):
      """ A basic Swin Transformer layer for one stage.
  
      Args:
          dim (int): Number of input channels.
          input_resolution (tuple[int]): Input resolution.
          depth (int): Number of blocks.
          num_heads (int): Number of attention heads.
          window_size (int): Local window size.
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
          drop (float, optional): Dropout rate. Default: 0.0
          attn_drop (float, optional): Attention dropout rate. Default: 0.0
          drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
          norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
          downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
      """
  
      def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                   mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
                   drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
  
          super().__init__()
          self.dim = dim
          self.input_resolution = input_resolution
          self.depth = depth
          self.use_checkpoint = use_checkpoint
  
          # build blocks
          self.blocks = nn.ModuleList([
              SwinTransformerBlock(
                  dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size,
                  shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop,
                  drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer)
              for i in range(depth)])
  
          # patch merging layer
          if downsample is not None:
              self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
          else:
              self.downsample = None
  
      def forward(self, x):
          for blk in self.blocks:
              if not torch.jit.is_scripting() and self.use_checkpoint:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          if self.downsample is not None:
              x = self.downsample(x)
          return x
  
      def extra_repr(self) -> str:
          return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
  
  
  class SwinTransformer(nn.Module):
      r""" Swin Transformer
          A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
            https://arxiv.org/pdf/2103.14030
  
      Args:
          img_size (int | tuple(int)): Input image size. Default 224
          patch_size (int | tuple(int)): Patch size. Default: 4
          in_chans (int): Number of input image channels. Default: 3
          num_classes (int): Number of classes for classification head. Default: 1000
          embed_dim (int): Patch embedding dimension. Default: 96
          depths (tuple(int)): Depth of each Swin Transformer layer.
          num_heads (tuple(int)): Number of attention heads in different layers.
          window_size (int): Window size. Default: 7
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
          qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
          drop_rate (float): Dropout rate. Default: 0
          attn_drop_rate (float): Attention dropout rate. Default: 0
          drop_path_rate (float): Stochastic depth rate. Default: 0.1
          norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
          ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
          patch_norm (bool): If True, add normalization after patch embedding. Default: True
          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
      """
  
      def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                   embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
                   window_size=7, mlp_ratio=4., qkv_bias=True,
                   drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                   norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                   use_checkpoint=False, weight_init='', **kwargs):
          super().__init__()
  
          self.num_classes = num_classes
          self.num_layers = len(depths)
          self.embed_dim = embed_dim
          self.ape = ape
          self.patch_norm = patch_norm
          self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
          self.mlp_ratio = mlp_ratio
  
          # split image into non-overlapping patches
          self.patch_embed = PatchEmbed(
              img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
              norm_layer=norm_layer if self.patch_norm else None)
          num_patches = self.patch_embed.num_patches
          self.patch_grid = self.patch_embed.grid_size
  
          # absolute position embedding
          if self.ape:
              self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
              trunc_normal_(self.absolute_pos_embed, std=.02)
          else:
              self.absolute_pos_embed = None
  
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          # stochastic depth
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
  
          # build layers
          layers = []
          for i_layer in range(self.num_layers):
              layers += [BasicLayer(
                  dim=int(embed_dim * 2 ** i_layer),
                  input_resolution=(self.patch_grid[0] // (2 ** i_layer), self.patch_grid[1] // (2 ** i_layer)),
                  depth=depths[i_layer],
                  num_heads=num_heads[i_layer],
                  window_size=window_size,
                  mlp_ratio=self.mlp_ratio,
                  qkv_bias=qkv_bias,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                  norm_layer=norm_layer,
                  downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                  use_checkpoint=use_checkpoint)
              ]
          self.layers = nn.Sequential(*layers)
  
          self.norm = norm_layer(self.num_features)
          self.avgpool = nn.AdaptiveAvgPool1d(1)
          self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
  
          assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
          head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
          if weight_init.startswith('jax'):
              for n, m in self.named_modules():
                  _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
          else:
              self.apply(_init_vit_weights)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'absolute_pos_embed'}
  
      @torch.jit.ignore
      def no_weight_decay_keywords(self):
          return {'relative_position_bias_table'}
  
      def get_classifier(self):
          return self.head
  
      def reset_classifier(self, num_classes, global_pool=''):
          self.num_classes = num_classes
          self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          if self.absolute_pos_embed is not None:
              x = x + self.absolute_pos_embed
          x = self.pos_drop(x)
          x = self.layers(x)
          x = self.norm(x)  # B L C
          x = self.avgpool(x.transpose(1, 2))  # B C 1
          x = torch.flatten(x, 1)
          return x
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.head(x)
          return x
  
  
  def _create_swin_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
      if default_cfg is None:
          default_cfg = deepcopy(default_cfgs[variant])
      overlay_external_default_cfg(default_cfg, kwargs)
      default_num_classes = default_cfg['num_classes']
      default_img_size = default_cfg['input_size'][-2:]
  
      num_classes = kwargs.pop('num_classes', default_num_classes)
      img_size = kwargs.pop('img_size', default_img_size)
      if kwargs.get('features_only', None):
          raise RuntimeError('features_only not implemented for Vision Transformer models.')
  
      model = build_model_with_cfg(
          SwinTransformer, variant, pretrained,
          default_cfg=default_cfg,
          img_size=img_size,
          num_classes=num_classes,
          pretrained_filter_fn=checkpoint_filter_fn,
          **kwargs)
  
      return model
  
  
  
  @register_model
  def swin_base_patch4_window12_384(pretrained=False, **kwargs):
      """ Swin-B @ 384x384, pretrained ImageNet-22k, fine tune 1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
      return _create_swin_transformer('swin_base_patch4_window12_384', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_base_patch4_window7_224(pretrained=False, **kwargs):
      """ Swin-B @ 224x224, pretrained ImageNet-22k, fine tune 1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
      return _create_swin_transformer('swin_base_patch4_window7_224', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_large_patch4_window12_384(pretrained=False, **kwargs):
      """ Swin-L @ 384x384, pretrained ImageNet-22k, fine tune 1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
      return _create_swin_transformer('swin_large_patch4_window12_384', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_large_patch4_window7_224(pretrained=False, **kwargs):
      """ Swin-L @ 224x224, pretrained ImageNet-22k, fine tune 1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
      return _create_swin_transformer('swin_large_patch4_window7_224', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_small_patch4_window7_224(pretrained=False, **kwargs):
      """ Swin-S @ 224x224, trained ImageNet-1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
      return _create_swin_transformer('swin_small_patch4_window7_224', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_tiny_patch4_window7_224(pretrained=False, **kwargs):
      """ Swin-T @ 224x224, trained ImageNet-1k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
      return _create_swin_transformer('swin_tiny_patch4_window7_224', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_base_patch4_window12_384_in22k(pretrained=False, **kwargs):
      """ Swin-B @ 384x384, trained ImageNet-22k
      """
      model_kwargs = dict(
          patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
      return _create_swin_transformer('swin_base_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_base_patch4_window7_224_in22k(pretrained=False, **kwargs):
      """ Swin-B @ 224x224, trained ImageNet-22k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
      return _create_swin_transformer('swin_base_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_large_patch4_window12_384_in22k(pretrained=False, **kwargs):
      """ Swin-L @ 384x384, trained ImageNet-22k
      """
      model_kwargs = dict(
          patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
      return _create_swin_transformer('swin_large_patch4_window12_384_in22k', pretrained=pretrained, **model_kwargs)
  
  
  @register_model
  def swin_large_patch4_window7_224_in22k(pretrained=False, **kwargs):
      """ Swin-L @ 224x224, trained ImageNet-22k
      """
      model_kwargs = dict(
          patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
      return _create_swin_transformer('swin_large_patch4_window7_224_in22k', pretrained=pretrained, **model_kwargs)