Swin Transformer相比其他Transformer架构有哪些优势？

Swin Transformer是一种新型的Transformer架构，相比其他Transformer架构，它有以下优势：
1.更高的计算效率：Swin Transformer使用了分层的结构，将图像分成多个小块，每个小块内部进行自注意力计算，然后再将小块组合起来进行全局自注意力计算，这种分层的结构使得计算效率更高。

# Swin Transformer中的分层结构
class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, window_size, shift_size=0, mlp_ratio=4., qkv_bias=False, 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
        self.qkv_bias = qkv_bias
        self.qk_scale = qk_scale
        self.drop = drop
        self.attn_drop = attn_drop
        self.drop_path = drop_path
        self.act_layer = act_layer
        self.norm_layer = norm_layer
        self.init_layers()

    def init_layers(self):
        self.norm1 = self.norm_layer(self.dim)
        self.attn = WindowAttention(
            dim=self.dim, window_size=self.window_size,
            num_heads=self.num_heads, qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
            attn_drop=self.attn_drop, proj_drop=self.drop)
        self.drop_path = DropPath(self.drop_path) if self.drop_path > 0. else nn.Identity()
        self.norm2 = self.norm_layer(self.dim)
        mlp_hidden_dim = int(self.dim * self.mlp_ratio)
        self.mlp = Mlp(in_features=self.dim, hidden_features=mlp_hidden_dim, act_layer=self.act_layer, drop=self.drop)

    def forward(self, x):
        H, W = x.shape[-2:]
        # cyclic shift
        if self.shift_size > 0:
            x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        # partition windows
        x_windows = x.unfold(1, self.window_size, self.window_size).unfold(2, self.window_size, self.window_size)
        x_windows = x_windows.contiguous().view(-1, self.dim, self.window_size, self.window_size)
        # W-MSA/SW-MSA
        attn_windows = self.attn(self.norm1(x_windows))
        # merge windows
        attn_windows = attn_windows.view(-1, self.num_heads, self.window_size * self.window_size, attn_windows.shape[-1])
        attn_windows = attn_windows.permute(0, 2, 1, 3).reshape(-1, self.window_size, self.window_size, self.dim)
        # reverse partition
        x_out = torch.nn.functional.fold(attn_windows, output_size=(H, W), kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size))
        # FFN
        x_out = x + self.drop_path(x_out)
        x_out = x_out + self.drop_path(self.mlp(self.norm2(x_out)))
        return x_out

2.更好的性能表现：Swin Transformer在多个图像分类任务上都取得了最先进的性能表现，例如ImageNet、CIFAR-10等。
3.更好的可扩展性：Swin Transformer可以很容易地扩展到更大的图像尺寸和更深的网络结构，而不会导致计算效率下降。