code: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from mmcv.cnn.bricks.transformer import build_dropout
  from mmcv.cnn.utils.weight_init import trunc_normal_
  from mmcv.runner.base_module import BaseModule
  
  from ..builder import ATTENTION
  from .helpers import to_2tuple
  
  
  class WindowMSA(BaseModule):
      """Window based multi-head self-attention (W-MSA) module with relative
      position bias.
  
      Args:
          embed_dims (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 q, k, v.
              Defaults to True.
          qk_scale (float | None, optional): Override default qk scale of
              head_dim ** -0.5 if set. Defaults to None.
          attn_drop (float, optional): Dropout ratio of attention weight.
              Defaults to 0.
          proj_drop (float, optional): Dropout ratio of output. Defaults to 0.
          init_cfg (dict, optional): The extra config for initialization.
              Defaults to None.
      """
  
      def __init__(self,
                   embed_dims,
                   window_size,
                   num_heads,
                   qkv_bias=True,
                   qk_scale=None,
                   attn_drop=0.,
                   proj_drop=0.,
                   init_cfg=None):
  
          super().__init__(init_cfg)
          self.embed_dims = embed_dims
          self.window_size = window_size  # Wh, Ww
          self.num_heads = num_heads
          head_embed_dims = embed_dims // num_heads
          self.scale = qk_scale or head_embed_dims**-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
  
          # About 2x faster than original impl
          Wh, Ww = self.window_size
          rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
          rel_position_index = rel_index_coords + rel_index_coords.T
          rel_position_index = rel_position_index.flip(1).contiguous()
          self.register_buffer('relative_position_index', rel_position_index)
  
          self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(embed_dims, embed_dims)
          self.proj_drop = nn.Dropout(proj_drop)
  
          self.softmax = nn.Softmax(dim=-1)
  
      def init_weights(self):
          super(WindowMSA, self).init_weights()
  
          trunc_normal_(self.relative_position_bias_table, std=0.02)
  
      def forward(self, x, mask=None):
          """
          Args:
  
              x (tensor): input features with shape of (num_windows*B, N, C)
              mask (tensor, Optional): mask with shape of (num_windows, Wh*Ww,
                  Wh*Ww), value should be between (-inf, 0].
          """
          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
  
      @staticmethod
      def double_step_seq(step1, len1, step2, len2):
          seq1 = torch.arange(0, step1 * len1, step1)
          seq2 = torch.arange(0, step2 * len2, step2)
          return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
  
  
  @ATTENTION.register_module()
  class ShiftWindowMSA(BaseModule):
      """Shift Window Multihead Self-Attention Module.
  
      Args:
          embed_dims (int): Number of input channels.
          input_resolution (Tuple[int, int]): The resolution of the input feature
              map.
          num_heads (int): Number of attention heads.
          window_size (int): The height and width of the window.
          shift_size (int, optional): The shift step of each window towards
              right-bottom. If zero, act as regular window-msa. Defaults to 0.
          qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
              Default: True
          qk_scale (float | None, optional): Override default qk scale of
              head_dim ** -0.5 if set. Defaults to None.
          attn_drop (float, optional): Dropout ratio of attention weight.
              Defaults to 0.0.
          proj_drop (float, optional): Dropout ratio of output. Defaults to 0.
          dropout_layer (dict, optional): The dropout_layer used before output.
              Defaults to dict(type='DropPath', drop_prob=0.).
          auto_pad (bool, optional): Auto pad the feature map to be divisible by
              window_size, Defaults to False.
          init_cfg (dict, optional): The extra config for initialization.
              Default: None.
      """
  
      def __init__(self,
                   embed_dims,
                   input_resolution,
                   num_heads,
                   window_size,
                   shift_size=0,
                   qkv_bias=True,
                   qk_scale=None,
                   attn_drop=0,
                   proj_drop=0,
                   dropout_layer=dict(type='DropPath', drop_prob=0.),
                   auto_pad=False,
                   init_cfg=None):
          super().__init__(init_cfg)
  
          self.embed_dims = embed_dims
          self.input_resolution = input_resolution
          self.shift_size = shift_size
          self.window_size = window_size
          if min(self.input_resolution) <= self.window_size:
              # if window size is larger than input resolution, don't partition
              self.shift_size = 0
              self.window_size = min(self.input_resolution)
  
          self.w_msa = WindowMSA(embed_dims, to_2tuple(self.window_size),
                                 num_heads, qkv_bias, qk_scale, attn_drop,
                                 proj_drop)
  
          self.drop = build_dropout(dropout_layer)
  
          H, W = self.input_resolution
          # Handle auto padding
          self.auto_pad = auto_pad
          if self.auto_pad:
              self.pad_r = (self.window_size -
                            W % self.window_size) % self.window_size
              self.pad_b = (self.window_size -
                            H % self.window_size) % self.window_size
              self.H_pad = H + self.pad_b
              self.W_pad = W + self.pad_r
          else:
              H_pad, W_pad = self.input_resolution
              assert H_pad % self.window_size + W_pad % self.window_size == 0,\
                  f'input_resolution({self.input_resolution}) is not divisible '\
                  f'by window_size({self.window_size}). Please check feature '\
                  f'map shape or set `auto_pad=True`.'
              self.H_pad, self.W_pad = H_pad, W_pad
              self.pad_r, self.pad_b = 0, 0
  
          if self.shift_size > 0:
              # calculate attention mask for SW-MSA
              img_mask = torch.zeros((1, self.H_pad, self.W_pad, 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
  
              # nW, window_size, window_size, 1
              mask_windows = self.window_partition(img_mask)
              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, query):
          H, W = self.input_resolution
          B, L, C = query.shape
          assert L == H * W, 'input feature has wrong size'
          query = query.view(B, H, W, C)
  
          if self.pad_r or self.pad_b:
              query = F.pad(query, (0, 0, 0, self.pad_r, 0, self.pad_b))
  
          # cyclic shift
          if self.shift_size > 0:
              shifted_query = torch.roll(
                  query,
                  shifts=(-self.shift_size, -self.shift_size),
                  dims=(1, 2))
          else:
              shifted_query = query
  
          # nW*B, window_size, window_size, C
          query_windows = self.window_partition(shifted_query)
          # nW*B, window_size*window_size, C
          query_windows = query_windows.view(-1, self.window_size**2, C)
  
          # W-MSA/SW-MSA (nW*B, window_size*window_size, C)
          attn_windows = self.w_msa(query_windows, mask=self.attn_mask)
  
          # merge windows
          attn_windows = attn_windows.view(-1, self.window_size,
                                           self.window_size, C)
  
          # B H' W' C
          shifted_x = self.window_reverse(attn_windows, self.H_pad, self.W_pad)
          # 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 self.pad_r or self.pad_b:
              x = x[:, :H, :W, :].contiguous()
  
          x = x.view(B, H * W, C)
  
          x = self.drop(x)
          return x
  
      def window_reverse(self, windows, H, W):
          window_size = self.window_size
          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
  
      def window_partition(self, x):
          B, H, W, C = x.shape
          window_size = self.window_size
          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()
          windows = windows.view(-1, window_size, window_size, C)
          return windows
  
apis: torch.nn.Linear torch.roll torch.nn.Softmax torch.nn.functional.pad torch.arange torch.zeros torch.nn.Dropout