1.通用stem

输入图片卷积后为（b,256,56,56）。四倍下采样后为（b，384，14，14），再加上（1，384）维度的class_token成为（b， 197，384）

for i in range(2, self.fin_stage):
            x, x_t = eval('self.conv_trans_' + str(i))(x, x_t)
# 这里要重复2-12次，输入是特征图x（b,256,56,56）与token x_t(b,197,384)，输出也是这两个部分。下面拆开讲解循环部分：

2.初始下采样

 def forward(self, x, x_t):
        x, x2 = self.cnn_block(x)        
 # 第一次后维度x(b,256,56,56) x2(b,64,56,56)  i=5时为 (512,28,28)(128, 28, 28)
 # self.cnn_block作用是下采样，在i循环中（2-12），2-4不变，5-8不变，9不变，12-12不变

3.CNN–>Trans

        _, _, H, W = x2.shape

        x_st = self.squeeze_block(x2, x_t) 
        x_t = self.trans_block(x_st + x_t)
 # 特征图x2经过2次卷积-->(b，196,384)，叠加x_t的第一维，成为 x_st(b, 197, 384)。x_st 与 x_t相加，输入Trans模块，得到 x_t维度不变。

self.squeeze_block：(conv_project): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1))
                    (sample_pooling): AvgPool2d(kernel_size=2, stride=2, padding=0)
                    (ln): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
                    (act): GELU()
   def forward(self, x, x_t):
       x = self.conv_project(x)  # [N, C, H, W]

       x = self.sample_pooling(x).flatten(2).transpose(1, 2)
       x = self.ln(x)
       x = self.act(x)

       x = torch.cat([x_t[:, 0][:, None, :], x], dim=1)

       return x

4.Trans–>CNN

  x_t_r = self.expand_block(x_t, H // self.dw_stride, W // self.dw_stride)    
  x = self.fusion_block(x, x_t_r, return_x_2=False)
# 将token embed 进行双线性插值，得到 x_t_r(b， 64, 56, 56])，增大了分辨率
# 变成矩阵的 x_t_r 再加回到特征图x，得到x
        return x, x_t

 self.expand_block： (conv_project): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1))
                     (bn): BatchNorm2d(128, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
                     (act): ReLU()
      def forward(self, x, H, W):
        B, _, C = x.shape
        # [N, 197, 384] -> [N, 196, 384] -> [N, 384, 196] -> [N, 384, 14, 14]
        x_r = x[:, 1:].transpose(1, 2).reshape(B, C, H, W)
        x_r = self.act(self.bn(self.conv_project(x_r)))

        return F.interpolate(x_r, size=(H * self.up_stride, W * self.up_stride))

self.fusion_block：
  (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (bn1): BatchNorm2d(128, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
  (act1): ReLU(inplace=True)
  (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (bn2): BatchNorm2d(128, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
  (act2): ReLU(inplace=True)
  (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (bn3): BatchNorm2d(512, eps=1e-06, momentum=0.1, affine=True, track_running_stats=True)
  (act3): ReLU(inplace=True

def forward(self, x, x_t=None, return_x_2=True):
        residual = x              # (b,256, 56, 56)

        x = self.conv1(x)
        x = self.bn1(x)
        if self.drop_block is not None:
            x = self.drop_block(x)
        x = self.act1(x)

        self.conv2(x + x_t)x = self.conv2(x) if x_t is not None else x = self.conv2(x)
        x = self.bn2(x)
        if self.drop_block is not None:
            x = self.drop_block(x)
        x2 = self.act2(x)

        x = self.conv3(x2)
        x = self.bn3(x)
        if self.drop_block is not None:
            x = self.drop_block(x)

        if self.drop_path is not None:
            x = self.drop_path(x)

        if self.res_conv:
            residual = self.residual_conv(residual)
            residual = self.residual_bn(residual)

        x += residual
        x = self.act3(x)
        
            return x

5. 分类阶段

在i=2-12时，输出维度依次为:
(1) x :(b,256,56,56) (b,512,28,28) (b,1024,14,14) (b,1024,7,7)
(2)x_t: (b,197,384) …(b,197,384)
x:(b,1024,7,7) —avgPool–>(b,1024)----conv_cls_head—>(b,1000)
x_t: (b,197,384)—取第一维—>（b,1,384）—trans_cls_head–>(b,1000)
最后结果取两个的平均