深度学习网络模型————Swin-Transformer详细讲解与代码实现

深度学习网络模型——Swin-Transformer详细讲解与代码实现

• 一、网路模型整体架构
• 二、Patch Partition模块详解
• 三、Patch Merging模块
• 四、W-MSA详解
• 五、SW-MSA详解
• • masked MSA详解
  • 六、 Relative Position Bias详解
  • 七、模型详细配置参数
  • 八、重要模块代码实现：
  • • 1、Patch Partition代码模块：
    • 2、Patch Merging代码模块：
    • 3、mask掩码生成代码模块：
    • 4、stage堆叠部分代码：
    • 5、SW-MSA或者W-MSA模块代码：
    • 九：模型整体流程代码实现：

      论文名称：Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

      原论文地址： https://arxiv.org/abs/2103.14030

      官方开源代码地址：https://github.com/microsoft/Swin-Transformer

      

      一、网路模型整体架构

      

      

      

      

      

      

      二、Patch Partition模块详解

      

      

      

      三、Patch Merging模块

      

      

      四、W-MSA详解

      

      

      五、SW-MSA详解

      

      

      

      

      

      

      

      masked MSA详解

      

      六、 Relative Position Bias详解

      

      

      七、模型详细配置参数

      

      八、重要模块代码实现：

      1、Patch Partition代码模块：

      class PatchEmbed(nn.Module):
          """
          2D Image to Patch Embedding
          split image into non-overlapping patches   即将图片划分成一个个没有重叠的patch
          """
          def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):
              super().__init__()
              patch_size = (patch_size, patch_size)
              self.patch_size = patch_size
              self.in_chans = in_c
              self.embed_dim = embed_dim
              self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)
              self.norm = norm_layer(embed_dim) if norm_layer else nn.IdEntity()
          def forward(self, x):
              _, _, H, W = x.shape
              # padding
              # 如果输入图片的H，W不是patch_size的整数倍，需要进行padding
              pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)
              if pad_input:
                  # to pad the last 3 dimensions,
                  # (W_left, W_right, H_top,H_bottom, C_front, C_back)
                  x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],   # 表示宽度方向右侧填充数
                                0, self.patch_size[0] - H % self.patch_size[0],   # 表示高度方向底部填充数
                                0, 0))
              # 下采样patch_size倍
              x = self.proj(x)
              _, _, H, W = x.shape
              # flatten: [B, C, H, W] -> [B, C, HW]
              # transpose: [B, C, HW] -> [B, HW, C]
              x = x.flatten(2).transpose(1, 2)
              x = self.norm(x)
              return x, H, W
      

      2、Patch Merging代码模块：

      class PatchMerging(nn.Module):
          r""" Patch Merging Layer.
              步长为2，间隔采样
          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):
              """
              x: B, H*W, C    即输入x的通道排列顺序
              """
              B, L, C = x.shape
              assert L == H * W, "input feature has wrong size"
              x = x.view(B, H, W, C)
              # padding
              # 如果输入feature map的H，W不是2的整数倍，需要进行padding
              pad_input = (H % 2 == 1) or (W % 2 == 1)
              if pad_input:
                  # to pad the last 3 dimensions, starting from the last dimension and moving forward.
                  # (C_front, C_back, W_left, W_right, H_top, H_bottom)
                  # 注意这里的Tensor通道是[B, H, W, C]，所以会和官方文档有些不同
                  x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
              # 以2为间隔进行采样
              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]   在channael维度上进行拼接
              x = x.view(B, -1, 4 * C)  # [B, H/2*W/2, 4*C]
              x = self.norm(x)
              x = self.reduction(x)  # [B, H/2*W/2, 2*C]
              return x
      

      3、mask掩码生成代码模块：

          def create_mask(self, x, H, W):
              # calculate attention mask for SW-MSA
              # 保证Hp和Wp是window_size的整数倍
              Hp = int(np.ceil(H / self.window_size)) * self.window_size
              Wp = int(np.ceil(W / self.window_size)) * self.window_size
              # 拥有和feature map一样的通道排列顺序，方便后续window_partition
              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
              # 将img_mask划分成一个一个窗口
              mask_windows = window_partition(img_mask, self.window_size)  # [nW, Mh, Mw, 1]           # 输出的是按照指定的window_size划分成一个一个窗口的数据
              mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]
              attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]  使用了广播机制
              # [nW, Mh*Mw, Mh*Mw]
              # 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得
              attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))   # 即对于不等于0的位置，赋值为-100；否则为0
              return attn_mask
      

      4、stage堆叠部分代码：

      class BasicLayer(nn.Module):
          """
          A basic Swin Transformer layer for one stage.
          Args:
              dim (int): Number of input channels.
              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, 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.depth = depth
              self.window_size = window_size
              self.use_checkpoint = use_checkpoint
              self.shift_size = window_size // 2  # 表示向右和向下偏移的窗口大小   即窗口大小除以2，然后向下取整
              # 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 self.shift_size,   # 通过判断shift_size是否等于0，来决定是使用W-MSA与SW-MSA
                      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    即：PatchMerging类
              if downsample is not None:
                  self.downsample = downsample(dim=dim, norm_layer=norm_layer)
              else:
                  self.downsample = None
          def create_mask(self, x, H, W):
              # calculate attention mask for SW-MSA
              # 保证Hp和Wp是window_size的整数倍
              Hp = int(np.ceil(H / self.window_size)) * self.window_size
              Wp = int(np.ceil(W / self.window_size)) * self.window_size
              # 拥有和feature map一样的通道排列顺序，方便后续window_partition
              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
              # 将img_mask划分成一个一个窗口
              mask_windows = window_partition(img_mask, self.window_size)  # [nW, Mh, Mw, 1]           # 输出的是按照指定的window_size划分成一个一个窗口的数据
              mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]
              attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]  使用了广播机制
              # [nW, Mh*Mw, Mh*Mw]
              # 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得
              attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))   # 即对于不等于0的位置，赋值为-100；否则为0
              return attn_mask
          def forward(self, x, H, W):
              attn_mask = self.create_mask(x, H, W)  # [nW, Mh*Mw, Mh*Mw]   # 制作mask蒙版
              for blk in self.blocks:
                  blk.H, blk.W = H, W
                  if not torch.jit.is_scripting() and self.use_checkpoint:
                      x = checkpoint.checkpoint(blk, x, attn_mask)
                  else:
                      x = blk(x, attn_mask)
              if self.downsample is not None:
                  x = self.downsample(x, H, W)
                  H, W = (H + 1) // 2, (W + 1) // 2
              return x, H, W
      

      5、SW-MSA或者W-MSA模块代码：

      class SwinTransformerBlock(nn.Module):
          r""" 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
              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, 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  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)
          def forward(self, x, attn_mask):
              H, W = self.H, self.W
              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)
              # pad feature maps to multiples of window size
              # 把feature map给pad到window size的整数倍
              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
              # 判断是进行SW-MSA或者是W-MSA模块
              if self.shift_size > 0:
                  # https://blog.csdn.net/ooooocj/article/details/126046858?ops_request_misc=&request_id=&biz_id=102&utm_term=torch.roll()%E7%94%A8%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-126046858.142^v73^control,201^v4^add_ask,239^v1^control&spm=1018.2226.3001.4187
                  shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))    #进行数据移动操作
              else:
                  shifted_x = x
                  attn_mask = None
              # partition windows
              # 将窗口按照window_size的大小进行划分，得到一个个窗口
              x_windows = window_partition(shifted_x, self.window_size)  # [nW*B, Mh, Mw, C]
              # 将数据进行展平操作
              x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # [nW*B, Mh*Mw, C]
              # W-MSA/SW-MSA
              """
                  # 进行多头自注意力机制操作
              """
              attn_windows = self.attn(x_windows, mask=attn_mask)  # [nW*B, Mh*Mw, C]
              # merge windows
              attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)  # [nW*B, Mh, Mw, C]
              # 将多窗口拼接回大的featureMap
              shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # [B, H', W', C]
              # reverse cyclic shift
              # 将移位的数据进行还原
              if self.shift_size > 0:
                  x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
              else:
                  x = shifted_x
              # 如果进行了padding操作，需要移出掉相应的pad
              if pad_r > 0 or pad_b > 0:
                  # 把前面pad的数据移除掉
                  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
      

      九：模型整体流程代码实现：

      """ 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
      """
      import torch
      import torch.nn as nn
      import torch.nn.functional as F
      import torch.utils.checkpoint as checkpoint
      import numpy as np
      from typing import Optional
      def drop_path_f(x, drop_prob: float = 0., training: bool = False):
          """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
          This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
          the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
          See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
          changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
          'survival rate' as the argument.
          """
          if drop_prob == 0. or not training:
              return x
          keep_prob = 1 - drop_prob
          shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
          random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
          random_tensor.floor_()  # binarize
          output = x.div(keep_prob) * random_tensor
          return output
      class DropPath(nn.Module):
          """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
          """
          def __init__(self, drop_prob=None):
              super(DropPath, self).__init__()
              self.drop_prob = drop_prob
          def forward(self, x):
              return drop_path_f(x, self.drop_prob, self.training)
      """
          将窗口按照window_size的大小进行划分，得到一个个窗口
      """
      def window_partition(x, window_size: int):
          """
          将feature map按照window_size划分成一个个没有重叠的window
          Args:
              x: (B, H, W, C)
              window_size (int): window size(M)
          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)
          # permute: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H//Mh, W//Mh, Mw, Mw, C]
          # view: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B*num_windows, Mh, Mw, C]
          windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)   # 输出的是按照指定的window_size划分成一个一个窗口的数据
          return windows
      def window_reverse(windows, window_size: int, H: int, W: int):
          """
          将一个个window还原成一个feature map
          Args:
              windows: (num_windows*B, window_size, window_size, C)
              window_size (int): Window size(M)
              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))
          # view: [B*num_windows, Mh, Mw, C] -> [B, H//Mh, W//Mw, Mh, Mw, C]
          x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
          # permute: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B, H//Mh, Mh, W//Mw, Mw, C]
          # view: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H, W, C]
          x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
          return x
      class PatchEmbed(nn.Module):
          """
          2D Image to Patch Embedding
          split image into non-overlapping patches   即将图片划分成一个个没有重叠的patch
          """
          def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):
              super().__init__()
              patch_size = (patch_size, patch_size)
              self.patch_size = patch_size
              self.in_chans = in_c
              self.embed_dim = embed_dim
              self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)
              self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
          def forward(self, x):
              _, _, H, W = x.shape
              # padding
              # 如果输入图片的H，W不是patch_size的整数倍，需要进行padding
              pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)
              if pad_input:
                  # to pad the last 3 dimensions,
                  # (W_left, W_right, H_top,H_bottom, C_front, C_back)
                  x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],   # 表示宽度方向右侧填充数
                                0, self.patch_size[0] - H % self.patch_size[0],   # 表示高度方向底部填充数
                                0, 0))
              # 下采样patch_size倍
              x = self.proj(x)
              _, _, H, W = x.shape
              # flatten: [B, C, H, W] -> [B, C, HW]
              # transpose: [B, C, HW] -> [B, HW, C]
              x = x.flatten(2).transpose(1, 2)
              x = self.norm(x)
              return x, H, W
      class PatchMerging(nn.Module):
          r""" Patch Merging Layer.
              步长为2，间隔采样
          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):
              """
              x: B, H*W, C    即输入x的通道排列顺序
              """
              B, L, C = x.shape
              assert L == H * W, "input feature has wrong size"
              x = x.view(B, H, W, C)
              # padding
              # 如果输入feature map的H，W不是2的整数倍，需要进行padding
              pad_input = (H % 2 == 1) or (W % 2 == 1)
              if pad_input:
                  # to pad the last 3 dimensions, starting from the last dimension and moving forward.
                  # (C_front, C_back, W_left, W_right, H_top, H_bottom)
                  # 注意这里的Tensor通道是[B, H, W, C]，所以会和官方文档有些不同
                  x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
              # 以2为间隔进行采样
              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]   在channael维度上进行拼接
              x = x.view(B, -1, 4 * C)  # [B, H/2*W/2, 4*C]
              x = self.norm(x)
              x = self.reduction(x)  # [B, H/2*W/2, 2*C]
              return x
      """
      MLP模块
      """
      class Mlp(nn.Module):
          """ MLP as used in Vision Transformer, MLP-Mixer and related networks
          """
          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.drop1 = nn.Dropout(drop)
              self.fc2 = nn.Linear(hidden_features, out_features)
              self.drop2 = nn.Dropout(drop)
          def forward(self, x):
              x = self.fc1(x)
              x = self.act(x)
              x = self.drop1(x)
              x = self.fc2(x)
              x = self.drop2(x)
              return x
      """
      WindowAttention即为：SW-MSA或者W-MSA模块
      """
      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  # [Mh, Mw]
              self.num_heads = num_heads
              head_dim = dim // num_heads
              self.scale = head_dim ** -0.5
              # define a parameter table of relative position bias
              # 创建偏置bias项矩阵
              self.relative_position_bias_table = nn.Parameter(
                  torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # [2*Mh-1 * 2*Mw-1, nH]    其元素的个数===>>[(2*Mh-1) * (2*Mw-1)]
              # get pair-wise relative position index for each token inside the window
              coords_h = torch.arange(self.window_size[0])  # 如果此处的self.window_size[0]为2的话，则生成的coords_h为[0,1]
              coords_w = torch.arange(self.window_size[1])  # 同理得
              coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # [2, Mh, Mw]
              coords_flatten = torch.flatten(coords, 1)  # [2, Mh*Mw]
              # [2, Mh*Mw, 1] - [2, 1, Mh*Mw]
              relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # [2, Mh*Mw, Mh*Mw]
              relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # [Mh*Mw, Mh*Mw, 2]
              relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0  行标+（M-1）
              relative_coords[:, :, 1] += self.window_size[1] - 1     # 列表标+（M-1）
              relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
              relative_position_index = relative_coords.sum(-1)  # [Mh*Mw, Mh*Mw]
              self.register_buffer("relative_position_index", relative_position_index)   # 将relative_position_index放入到模型的缓存当中
              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)
              nn.init.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, Mh*Mw, C)
                  mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
              """
              # [batch_size*num_windows, Mh*Mw, total_embed_dim]
              B_, N, C = x.shape
              # qkv(): -> [batch_size*num_windows, Mh*Mw, 3 * total_embed_dim]
              # reshape: -> [batch_size*num_windows, Mh*Mw, 3, num_heads, embed_dim_per_head]
              # permute: -> [3, batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
              qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
              # [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
              q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
              # transpose: -> [batch_size*num_windows, num_heads, embed_dim_per_head, Mh*Mw]
              # @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]
              q = q * self.scale
              attn = (q @ k.transpose(-2, -1))
              # relative_position_bias_table.view: [Mh*Mw*Mh*Mw,nH] -> [Mh*Mw,Mh*Mw,nH]
              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)
              relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # [nH, Mh*Mw, Mh*Mw]
              attn = attn + relative_position_bias.unsqueeze(0)
              # 进行mask，相同区域使用0表示；不同区域使用-100表示
              if mask is not None:
                  # mask: [nW, Mh*Mw, Mh*Mw]
                  nW = mask.shape[0]  # num_windows
                  # attn.view: [batch_size, num_windows, num_heads, Mh*Mw, Mh*Mw]
                  # mask.unsqueeze: [1, nW, 1, Mh*Mw, Mh*Mw]
                  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)
              # @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
              # transpose: -> [batch_size*num_windows, Mh*Mw, num_heads, embed_dim_per_head]
              # reshape: -> [batch_size*num_windows, Mh*Mw, total_embed_dim]
              x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
              x = self.proj(x)
              x = self.proj_drop(x)
              return x
      """
          SwinTransformerBlock
      """
      class SwinTransformerBlock(nn.Module):
          r""" 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
              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, 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  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)
          def forward(self, x, attn_mask):
              H, W = self.H, self.W
              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)
              # pad feature maps to multiples of window size
              # 把feature map给pad到window size的整数倍
              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
              # 判断是进行SW-MSA或者是W-MSA模块
              if self.shift_size > 0:
                  # https://blog.csdn.net/ooooocj/article/details/126046858?ops_request_misc=&request_id=&biz_id=102&utm_term=torch.roll()%E7%94%A8%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-126046858.142^v73^control,201^v4^add_ask,239^v1^control&spm=1018.2226.3001.4187
                  shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))    #进行数据移动操作
              else:
                  shifted_x = x
                  attn_mask = None
              # partition windows
              # 将窗口按照window_size的大小进行划分，得到一个个窗口
              x_windows = window_partition(shifted_x, self.window_size)  # [nW*B, Mh, Mw, C]
              # 将数据进行展平操作
              x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # [nW*B, Mh*Mw, C]
              # W-MSA/SW-MSA
              """
                  # 进行多头自注意力机制操作
              """
              attn_windows = self.attn(x_windows, mask=attn_mask)  # [nW*B, Mh*Mw, C]
              # merge windows
              attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)  # [nW*B, Mh, Mw, C]
              # 将多窗口拼接回大的featureMap
              shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # [B, H', W', C]
              # reverse cyclic shift
              # 将移位的数据进行还原
              if self.shift_size > 0:
                  x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
              else:
                  x = shifted_x
              # 如果进行了padding操作，需要移出掉相应的pad
              if pad_r > 0 or pad_b > 0:
                  # 把前面pad的数据移除掉
                  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 BasicLayer(nn.Module):
          """
          A basic Swin Transformer layer for one stage.
          Args:
              dim (int): Number of input channels.
              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, 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.depth = depth
              self.window_size = window_size
              self.use_checkpoint = use_checkpoint
              self.shift_size = window_size // 2  # 表示向右和向下偏移的窗口大小   即窗口大小除以2，然后向下取整
              # 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 self.shift_size,   # 通过判断shift_size是否等于0，来决定是使用W-MSA与SW-MSA
                      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    即：PatchMerging类
              if downsample is not None:
                  self.downsample = downsample(dim=dim, norm_layer=norm_layer)
              else:
                  self.downsample = None
          def create_mask(self, x, H, W):
              # calculate attention mask for SW-MSA
              # 保证Hp和Wp是window_size的整数倍
              Hp = int(np.ceil(H / self.window_size)) * self.window_size
              Wp = int(np.ceil(W / self.window_size)) * self.window_size
              # 拥有和feature map一样的通道排列顺序，方便后续window_partition
              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
              # 将img_mask划分成一个一个窗口
              mask_windows = window_partition(img_mask, self.window_size)  # [nW, Mh, Mw, 1]           # 输出的是按照指定的window_size划分成一个一个窗口的数据
              mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]
              attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]  使用了广播机制
              # [nW, Mh*Mw, Mh*Mw]
              # 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得
              attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))   # 即对于不等于0的位置，赋值为-100；否则为0
              return attn_mask
          def forward(self, x, H, W):
              attn_mask = self.create_mask(x, H, W)  # [nW, Mh*Mw, Mh*Mw]   # 制作mask蒙版
              for blk in self.blocks:
                  blk.H, blk.W = H, W
                  if not torch.jit.is_scripting() and self.use_checkpoint:
                      x = checkpoint.checkpoint(blk, x, attn_mask)
                  else:
                      x = blk(x, attn_mask)
              if self.downsample is not None:
                  x = self.downsample(x, H, W)
                  H, W = (H + 1) // 2, (W + 1) // 2
              return x, H, W
      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:
              patch_size (int | tuple(int)): Patch size. Default: 4   表示通过Patch Partition层后，下采样几倍
              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.
              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, patch_size=4,  # 表示通过Patch Partition层后，下采样几倍
                       in_chans=3,           # 输入图像通道
                       num_classes=1000,     # 类别数
                       embed_dim=96,         # Patch partition层后的LinearEmbedding层映射后的维度，之后的几层都是该数的整数倍  分别是 C、2C、4C、8C
                       depths=(2, 2, 6, 2),  # 表示每一个Stage模块内，Swin Transformer Block重复的次数
                       num_heads=(3, 6, 12, 24),  # 表示每一个Stage模块内，Swin Transformer Block中采用的Multi-Head self-Attention的head的个数
                       window_size=7,         # 表示W-MSA与SW-MSA所采用的window的大小
                       mlp_ratio=4.,          # 表示MLP模块中，第一个全连接层增大的倍数
                       qkv_bias=True,
                       drop_rate=0.,          # 对应的PatchEmbed层后面的
                       attn_drop_rate=0.,     # 对应于Multi-Head self-Attention模块中对应的dropRate
                       drop_path_rate=0.1,    # 对应于每一个Swin-Transformer模块中采用的DropRate   其是慢慢的递增的，从0增长到drop_path_rate
                       norm_layer=nn.LayerNorm,
                       patch_norm=True,
                       use_checkpoint=False, **kwargs):
              super().__init__()
              self.num_classes = num_classes
              self.num_layers = len(depths)  # depths:表示重复的Swin Transoformer Block模块的次数  表示每一个Stage模块内，Swin Transformer Block重复的次数
              self.embed_dim = embed_dim
              self.patch_norm = patch_norm
              # stage4输出特征矩阵的channels
              self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
              self.mlp_ratio = mlp_ratio
              # split image into non-overlapping patches   即将图片划分成一个个没有重叠的patch
              self.patch_embed = PatchEmbed(
                  patch_size=patch_size, in_c=in_chans, embed_dim=embed_dim,
                  norm_layer=norm_layer if self.patch_norm else None)
              self.pos_drop = nn.Dropout(p=drop_rate)   # PatchEmbed层后面的Dropout层
              # 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):
                  # 注意这里构建的stage和论文图中有些差异
                  # 这里的stage不包含该stage的patch_merging层，包含的是下个stage的
                  layers = BasicLayer(dim=int(embed_dim * 2 ** i_layer),  # 传入特征矩阵的维度，即channel方向的深度
                                      depth=depths[i_layer],              # 表示当前stage中需要堆叠的多少Swin Transformer Block
                                      num_heads=num_heads[i_layer],       # 表示每一个Stage模块内，Swin Transformer Block中采用的Multi-Head self-Attention的head的个数
                                      window_size=window_size,            # 表示W-MSA与SW-MSA所采用的window的大小
                                      mlp_ratio=self.mlp_ratio,           # 表示MLP模块中，第一个全连接层增大的倍数
                                      qkv_bias=qkv_bias,
                                      drop=drop_rate,                     # 对应的PatchEmbed层后面的
                                      attn_drop=attn_drop_rate,           # 对应于Multi-Head self-Attention模块中对应的dropRate
                                      drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],     # 对应于每一个Swin-Transformer模块中采用的DropRate   其是慢慢的递增的，从0增长到drop_path_rate
                                      norm_layer=norm_layer,
                                      downsample=PatchMerging if (i_layer  0 else nn.Identity()
              self.apply(self._init_weights)
          def _init_weights(self, m):
              if isinstance(m, nn.Linear):
                  nn.init.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)
          def forward(self, x):
              # x: [B, L, C]
              x, H, W = self.patch_embed(x)  # 对图像下采样4倍
              x = self.pos_drop(x)
              # 依次传入各个stage中
              for layer in self.layers:
                  x, H, W = layer(x, H, W)
              x = self.norm(x)  # [B, L, C]
              x = self.avgpool(x.transpose(1, 2))  # [B, C, 1]
              x = torch.flatten(x, 1)
              x = self.head(x)   # 经过全连接层，得到输出
              return x
      def swin_tiny_patch4_window7_224(num_classes: int = 1000, **kwargs):
          # trained ImageNet-1K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=7,
                                  embed_dim=96,
                                  depths=(2, 2, 6, 2),
                                  num_heads=(3, 6, 12, 24),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_small_patch4_window7_224(num_classes: int = 1000, **kwargs):
          # trained ImageNet-1K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=7,
                                  embed_dim=96,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(3, 6, 12, 24),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_base_patch4_window7_224(num_classes: int = 1000, **kwargs):
          # trained ImageNet-1K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=7,
                                  embed_dim=128,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(4, 8, 16, 32),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_base_patch4_window12_384(num_classes: int = 1000, **kwargs):
          # trained ImageNet-1K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=12,
                                  embed_dim=128,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(4, 8, 16, 32),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_base_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):
          # trained ImageNet-22K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=7,
                                  embed_dim=128,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(4, 8, 16, 32),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_base_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):
          # trained ImageNet-22K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=12,
                                  embed_dim=128,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(4, 8, 16, 32),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_large_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):
          # trained ImageNet-22K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=7,
                                  embed_dim=192,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(6, 12, 24, 48),
                                  num_classes=num_classes,
                                  **kwargs)
          return model
      def swin_large_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):
          # trained ImageNet-22K
          # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth
          model = SwinTransformer(in_chans=3,
                                  patch_size=4,
                                  window_size=12,
                                  embed_dim=192,
                                  depths=(2, 2, 18, 2),
                                  num_heads=(6, 12, 24, 48),
                                  num_classes=num_classes,
                                  **kwargs)
          return model