text: # --------------------------------------------------------
  # Modified from Swin Transformer (https://github.com/microsoft/Swin-Transformer) 
  # Copyright (c) 2021 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ze Liu, Yutong Lin, Yixuan Wei
  # --------------------------------------------------------
  """
  Backbone modules.
  """
  import os
  from collections import OrderedDict
  import functools
  print = functools.partial(print, flush=True)
  import torch
  import torch.nn.functional as F
  import torchvision
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from typing import Dict, List
  from util.misc import NestedTensor, is_main_process
  import torch.utils.checkpoint as checkpoint
  import numpy as np
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from .position_encoding import build_position_encoding
  
  
  class Mlp(nn.Module):
      """ Multilayer perceptron."""
  
      def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
          super().__init__()
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          self.fc1 = nn.Linear(in_features, hidden_features)
          self.act = act_layer()
          self.fc2 = nn.Linear(hidden_features, out_features)
          self.drop = nn.Dropout(drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act(x)
          x = self.drop(x)
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  def window_partition(x, window_size):
      """
      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, H, W):
      """
      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):
      """ 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
          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
          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, qk_scale=None, 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 = qk_scale or 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=None):
          """ Forward function.
          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):
      """ Swin Transformer Block.
      Args:
          dim (int): Number of input channels.
          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
          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
          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, num_heads, window_size=7, shift_size=0,
                   mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                   act_layer=nn.GELU, norm_layer=nn.LayerNorm):
          super().__init__()
          self.dim = dim
          self.num_heads = num_heads
          self.window_size = window_size
          self.shift_size = shift_size
          self.mlp_ratio = mlp_ratio
          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, qk_scale=qk_scale, 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)
  
          self.H = None
          self.W = None
  
      def forward(self, x, mask_matrix):
          """ Forward function.
          Args:
              x: Input feature, tensor size (B, H*W, C).
              H, W: Spatial resolution of the input feature.
              mask_matrix: Attention mask for cyclic shift.
          """
          B, L, C = x.shape
          H, W = self.H, self.W
          assert L == H * W, "input feature has wrong size"
  
          shortcut = x
          x = self.norm1(x)
          x = x.view(B, H, W, C)
  
          # pad feature maps to multiples of window size
          pad_l = pad_t = 0
          pad_r = (self.window_size - W % self.window_size) % self.window_size
          pad_b = (self.window_size - H % self.window_size) % self.window_size
          x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
          _, Hp, Wp, _ = x.shape
  
          # cyclic shift
          if self.shift_size > 0:
              shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
              attn_mask = mask_matrix
          else:
              shifted_x = x
              attn_mask = None
  
          # 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=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, Hp, Wp)  # 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
  
          if pad_r > 0 or pad_b > 0:
              x = x[:, :H, :W, :].contiguous()
  
          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):
      """ Patch Merging Layer
      Args:
          dim (int): Number of input channels.
          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
      """
      def __init__(self, dim, norm_layer=nn.LayerNorm):
          super().__init__()
          self.dim = dim
          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
          self.norm = norm_layer(4 * dim)
  
      def forward(self, x, H, W):
          """ Forward function.
          Args:
              x: Input feature, tensor size (B, H*W, C).
              H, W: Spatial resolution of the input feature.
          """
          B, L, C = x.shape
          assert L == H * W, "input feature has wrong size"
  
          x = x.view(B, H, W, C)
  
          # padding
          pad_input = (H % 2 == 1) or (W % 2 == 1)
          if pad_input:
              x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
  
          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
  
  
  class BasicLayer(nn.Module):
      """ A basic Swin Transformer layer for one stage.
      Args:
          dim (int): Number of feature channels
          depth (int): Depths of this stage.
          num_heads (int): Number of attention head.
          window_size (int): Local window size. Default: 7.
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
          qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
          qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
          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,
                   depth,
                   num_heads,
                   window_size=7,
                   mlp_ratio=4.,
                   qkv_bias=True,
                   qk_scale=None,
                   drop=0.,
                   attn_drop=0.,
                   drop_path=0.,
                   norm_layer=nn.LayerNorm,
                   downsample=None,
                   use_checkpoint=False):
          super().__init__()
          self.window_size = window_size
          self.shift_size = window_size // 2
          self.depth = depth
          self.use_checkpoint = use_checkpoint
  
          # build blocks
          self.blocks = nn.ModuleList([
              SwinTransformerBlock(
                  dim=dim,
                  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,
                  qk_scale=qk_scale,
                  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(dim=dim, norm_layer=norm_layer)
          else:
              self.downsample = None
  
      def forward(self, x, H, W):
          """ Forward function.
          Args:
              x: Input feature, tensor size (B, H*W, C).
              H, W: Spatial resolution of the input feature.
          """
  
          # calculate attention mask for SW-MSA
          Hp = int(np.ceil(H / self.window_size)) * self.window_size
          Wp = int(np.ceil(W / self.window_size)) * self.window_size
          img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 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))
  
          for blk in self.blocks:
              blk.H, blk.W = H, W
              if self.use_checkpoint:
                  x = checkpoint.checkpoint(blk, x, attn_mask)
              else:
                  x = blk(x, attn_mask)
          if self.downsample is not None:
              x_down = self.downsample(x, H, W)
              Wh, Ww = (H + 1) // 2, (W + 1) // 2
              return x, H, W, x_down, Wh, Ww
          else:
              return x, H, W, x, H, W
  
  
  class PatchEmbed(nn.Module):
      """ Image to Patch Embedding
      Args:
          patch_size (int): Patch token size. Default: 4.
          in_chans (int): Number of input image channels. Default: 3.
          embed_dim (int): Number of linear projection output channels. Default: 96.
          norm_layer (nn.Module, optional): Normalization layer. Default: None
      """
  
      def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
          super().__init__()
          patch_size = to_2tuple(patch_size)
          self.patch_size = patch_size
  
          self.in_chans = in_chans
          self.embed_dim = embed_dim
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
          if norm_layer is not None:
              self.norm = norm_layer(embed_dim)
          else:
              self.norm = None
  
      def forward(self, x):
          """Forward function."""
          # padding
          _, _, H, W = x.size()
          if W % self.patch_size[1] != 0:
              x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
          if H % self.patch_size[0] != 0:
              x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
  
          x = self.proj(x)  # B C Wh Ww
          if self.norm is not None:
              Wh, Ww = x.size(2), x.size(3)
              x = x.flatten(2).transpose(1, 2)
              x = self.norm(x)
              x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
  
          return x
  
  
  class SwinTransformer(nn.Module):
      """ Swin Transformer backbone.
          A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
            https://arxiv.org/pdf/2103.14030
      Args:
          pretrain_img_size (int): Input image size for training the pretrained model,
              used in absolute postion embedding. Default 224.
          patch_size (int | tuple(int)): Patch size. Default: 4.
          in_chans (int): Number of input image channels. Default: 3.
          embed_dim (int): Number of linear projection output channels. Default: 96.
          depths (tuple[int]): Depths of each Swin Transformer stage.
          num_heads (tuple[int]): Number of attention head of each stage.
          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
          qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
          drop_rate (float): Dropout rate.
          attn_drop_rate (float): Attention dropout rate. Default: 0.
          drop_path_rate (float): Stochastic depth rate. Default: 0.2.
          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.
          out_indices (Sequence[int]): Output from which stages.
          frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
              -1 means not freezing any parameters.
          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
      """
  
      def __init__(self,
                   pretrain_img_size=224,
                   patch_size=4,
                   in_chans=3,
                   embed_dim=96,
                   depths=[2, 2, 6, 2],
                   num_heads=[3, 6, 12, 24],
                   window_size=7,
                   mlp_ratio=4.,
                   qkv_bias=True,
                   qk_scale=None,
                   drop_rate=0.,
                   attn_drop_rate=0.,
                   drop_path_rate=0.2,
                   norm_layer=nn.LayerNorm,
                   ape=False,
                   patch_norm=True,
                   out_indices=(0, 1, 2, 3),
                   frozen_stages=-1,
                   use_checkpoint=False):
          super().__init__()
  
          self.pretrain_img_size = pretrain_img_size
          self.num_layers = len(depths)
          self.embed_dim = embed_dim
          self.ape = ape
          self.patch_norm = patch_norm
          self.out_indices = out_indices
          self.frozen_stages = frozen_stages
  
          # split image into non-overlapping patches
          self.patch_embed = PatchEmbed(
              patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
              norm_layer=norm_layer if self.patch_norm else None)
  
          # absolute position embedding
          if self.ape:
              pretrain_img_size = to_2tuple(pretrain_img_size)
              patch_size = to_2tuple(patch_size)
              patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
  
              self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
              trunc_normal_(self.absolute_pos_embed, std=.02)
  
          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
          self.layers = nn.ModuleList()
          for i_layer in range(self.num_layers):
              layer = BasicLayer(
                  dim=int(embed_dim * 2 ** i_layer),
                  depth=depths[i_layer],
                  num_heads=num_heads[i_layer],
                  window_size=window_size,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  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.append(layer)
  
          num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
          self.num_features = num_features
  
          # add a norm layer for each output
          for i_layer in out_indices:
              layer = norm_layer(num_features[i_layer])
              layer_name = f'norm{i_layer}'
              self.add_module(layer_name, layer)
  
          self._freeze_stages()
  
      def _freeze_stages(self):
          if self.frozen_stages >= 0:
              self.patch_embed.eval()
              for param in self.patch_embed.parameters():
                  param.requires_grad = False
  
          if self.frozen_stages >= 1 and self.ape:
              self.absolute_pos_embed.requires_grad = False
  
          if self.frozen_stages >= 2:
              self.pos_drop.eval()
              for i in range(0, self.frozen_stages - 1):
                  m = self.layers[i]
                  m.eval()
                  for param in m.parameters():
                      param.requires_grad = False
  
      def init_weights(self, pretrained=None):
          """Initialize the weights in backbone.
          Args:
              pretrained (str, optional): Path to pre-trained weights.
                  Defaults to None.
          """
          def _init_weights(m):
              if isinstance(m, nn.Linear):
                  trunc_normal_(m.weight, std=.02)
                  if isinstance(m, nn.Linear) and m.bias is not None:
                      nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.LayerNorm):
                  nn.init.constant_(m.bias, 0)
                  nn.init.constant_(m.weight, 1.0)
  
          if isinstance(pretrained, str):
              self.apply(_init_weights)
              checkpoint = torch.load(pretrained, map_location='cpu')
              print(f'load from {pretrained}.') 
              self.load_state_dict(checkpoint['model'], strict=False)
          elif pretrained is None:
              self.apply(_init_weights)
          else:
              raise TypeError('pretrained must be a str or None')
  
      def forward(self, x):
          """Forward function."""
          x = self.patch_embed(x)
          Wh, Ww = x.size(2), x.size(3)
          if self.ape:
              # interpolate the position embedding to the corresponding size
              absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
              x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
          else:
              x = x.flatten(2).transpose(1, 2)
          x = self.pos_drop(x)
  
          outs = []
          for i in range(self.num_layers):
              layer = self.layers[i]
              x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
              if i in self.out_indices:
                  norm_layer = getattr(self, f'norm{i}')
                  x_out = norm_layer(x_out)
                  out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
                  outs.append(out)
          rets = {str(u): v for (u,v) in enumerate(outs)}
          return rets
  
      def train(self, mode=True):
          """Convert the model into training mode while keep layers freezed."""
          super(SwinTransformer, self).train(mode)
          self._freeze_stages()
  
  
  class BackboneBase(nn.Module):
      def __init__(self, backbone: nn.Module, strides=[4, 8, 16, 32], num_channels=[96, 192, 384, 768]):
          super().__init__()
          self.strides = strides
          self.num_channels = num_channels
          self.body = backbone
  
      def forward(self, tensor_list: NestedTensor):
          xs = self.body(tensor_list.tensors)
          out: Dict[str, NestedTensor] = {}
          for name, x in xs.items():
              m = tensor_list.mask
              assert m is not None
              mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
              out[name] = NestedTensor(x, mask)
          return out
  
  
  class Backbone(BackboneBase):
      """ResNet backbone with frozen BatchNorm."""
      def __init__(self, name: str,
                   checkpoint: bool = False,
                   pretrained: str = None):
          assert name in ['swin_t_p4w7', 'swin_s_p4w7', 'swin_b_p4w7', 'swin_l_p4w7']
          cfgs = configs[name]
          cfgs.update({'use_checkpoint': checkpoint})
          out_indices = cfgs['out_indices']
          strides = [int(2**(i+2)) for i in out_indices]
          num_channels = [int(cfgs['embed_dim'] * 2**i) for i in out_indices]
          backbone = SwinTransformer(**cfgs)
          backbone.init_weights(pretrained)
          super().__init__(backbone, strides, num_channels)
  
  
  class Joiner(nn.Sequential):
      def __init__(self, backbone, position_embedding):
          super().__init__(backbone, position_embedding)
          self.strides = backbone.strides
          self.num_channels = backbone.num_channels
  
      def forward(self, tensor_list: NestedTensor):
          xs = self[0](tensor_list)
          out: List[NestedTensor] = []
          pos = []
          for name, x in sorted(xs.items()):
              out.append(x)
          # position encoding
          for x in out:
              pos.append(self[1](x).to(x.tensors.dtype))
          return out, pos
  
      
  def build_swin_backbone(args):
      position_embedding = build_position_encoding(args)
      backbone = Backbone(args.backbone, args.checkpoint, args.pretrained)
      model = Joiner(backbone, position_embedding)
      return model
  
  
  configs = {
      'swin_t_p4w7': dict(embed_dim=96,
                      depths=[2, 2, 6, 2],
                      num_heads=[3, 6, 12, 24],
                      window_size=7,
                      mlp_ratio=4.,
                      qkv_bias=True,
                      qk_scale=None,
                      drop_rate=0.,
                      attn_drop_rate=0.,
                      drop_path_rate=0.2,
                      ape=False,
                      patch_norm=True,
                      out_indices=(1, 2, 3),
                      use_checkpoint=False),
      'swin_s_p4w7': dict(embed_dim=96,
                          depths=[2, 2, 18, 2],
                          num_heads=[3, 6, 12, 24],
                          window_size=7,
                          mlp_ratio=4.,
                          qkv_bias=True,
                          qk_scale=None,
                          drop_rate=0.,
                          attn_drop_rate=0.,
                          drop_path_rate=0.2,
                          ape=False,
                          patch_norm=True,
                          out_indices=(1, 2, 3),
                          use_checkpoint=False),
      'swin_b_p4w7': dict(embed_dim=128,
                          depths=[2, 2, 18, 2],
                          num_heads=[4, 8, 16, 32],
                          window_size=7,
                          mlp_ratio=4.,
                          qkv_bias=True,
                          qk_scale=None,
                          drop_rate=0.,
                          attn_drop_rate=0.,
                          drop_path_rate=0.3,
                          ape=False,
                          patch_norm=True,
                          out_indices=(1, 2, 3),
                          use_checkpoint=False),
      'swin_l_p4w7': dict(embed_dim=192,
                          depths=[2, 2, 18, 2],
                          num_heads=[6, 12, 24, 48],
                          window_size=7,
                          mlp_ratio=4.,
                          qkv_bias=True,
                          qk_scale=None,
                          drop_rate=0.,
                          attn_drop_rate=0.,
                          drop_path_rate=0.3,
                          ape=False,
                          patch_norm=True,
                          out_indices=(1, 2, 3),
                          use_checkpoint=False),
          
  }
  
  
  if __name__ == '__main__':
      model = SwinTransformer().cuda()
      inputs = torch.randn(2,3,384,224).cuda()
      out = model(inputs)
      