论文阅读笔记：Swin-Transformer

1. Swin-Transformer

Liu, Ze, et al. “Swin transformer: Hierarchical vision transformer using shifted windows.” Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

本文是一篇奠定了Transformer在图像领域地位的论文，它不同于ViT(Vision Transformer)，提出了一种层次化的结构，因为ViT一开始就固定了patch的划分，因此感受野不会变化，而Swin Transformer采用了传统CNN下采样的设计，在不同的阶段采用不同的感受野尺度，最终得到了比ViT更好的性能表现。

1.1 patch划分

论文代码提供了一种用卷积来进行初始划分patch的方法，就是用kernerl_size，stride与patch_size的卷积核做卷积操作。

class PatchEmbed(nn.Module):

    def __init__(self, patch_size=4, in_c=1, embed_dim=96, norm_layer=None):
        super(PatchEmbed, self).__init__()
        patch_size = (patch_size, patch_size)
        self.patch_size = patch_size
        self.in_channels = in_c
        self.embed_dim = embed_dim
        self.proj = nn.Conv2d(
            in_channels = in_c,
            out_channels = embed_dim,
            kernel_size=patch_size,
            stride= patch_size
        )  # 用卷积做patch的划分，kernel_size和stride一致即可
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        # x [batch_size, c, h, w]
        _, _, H, W = x.shape
        # 若H,W不是patch_size的整数倍，则进行填充
        pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)
        # pad函数的作用是填充图像，pad(input, tuple)
        # input 输入的图像
        # tuple 例如(1, 2) 最后一维左边填充1列，右边填充2列 (1, 2, 3, 4) w左边填充1列，右边填充2列，h上边填充3行，下边填充4行
        if pad_input:
            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],
                      0, self.patch_size[0] - H % self.patch_size[0], 0, 0))

        x = self.proj(x)  # [batch_size, embed_dim, h//patch_size, w//patch_size]
        _, _, H, W = x.shape  # H,W为feature map的高宽
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)
        # flatten: [B, C, H, W] -> [B, C, HW]
        # transpose: [B, C, HW] -> [B, HW, C]
        # 代表了最终的输出是 HW个patch，每个patch的通道数是embed_dim
        return x, H, W

1.2 DropPath

http://www.manongjc.com/detail/24-jjgknxdkdzormze.html

论文代码中为了减少过拟合的影响，引入了DropPath的方法，具体参考上面的链接，代码如下：

class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    DropPath是将深度学习模型中的多分支结构随机”删除“
    """
    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)

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

1.3 窗口划分和恢复

Swin Transformer和ViT最大的不同就是引入了窗口的概念，对一个窗口中的像素/patch做自注意力，而不是整张图片所有的像素/patch做自注意力，因此计算效率更高。

def window_partition(x,window_size):
    """
    划分Feature Map, 划分成一个个没有重叠的Window；
    这个window_partition与ViT Patch的划分方法如出一辙;
    若干patch组合成一个window
    :param x: (B,H,W,C)
    :param window_size: (M)
    :return: 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)  # 使得一个window中的patch做MSA
    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

1.4 PatchMerge

https://blog.csdn.net/qq_37541097/article/details/121119988?spm=1001.2014.3001.5502

上面这篇博文对patchmerge概括的很好，具体来说就是做了一个跟cnn类似的事情，通道翻倍，宽高减半，从而可以替代cnn。文章里的patch划分基本就是在做这件事，因此要和window_size区分开。

class PatchMerging(nn.Module):
    """
    用来在每个Stage开始前进行DownSample，以缩小分辨率，并调整通道数量，以达到分层和高效的作用。
    - 类似于CNN内，通过调整Stride来降低分辨率的作用。
    Step1: 行列间隔2选取元素
    Step2: 拼接为一整个Tensor(通道数变为4倍）
    Step3: 通过FC Layer调整通道数
    """
    def __init__(self,dim,norm_layer=nn.LayerNorm):
        # dim : 输入的通道数
        super(PatchMerging, self).__init__()
        self.dim = dim
        # 4倍通道->2倍通道
        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
        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)

        # padding
        # 非2整数倍，需要对Feature Map进行padding operation
        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)) # 3维扩充

        # 间隔2取元素
        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)

        # 降低通道数
        x = self.reduction(x)  # [B, H/2*W/2, 2*C]

        return x

1.5 WindowAttention

文章采用了两种window attention的方式：W-MSA和SW-MSA，前者很容易，就是简单的在每个窗口内做attention就可以了，但是这种方式每个窗口之间都是孤立的，没有信息的交互，为了解决这一问题，作者提出了SW-MSA，采用一个shifted window使得窗口之间能够有信息的交互，W-MSA和SW-MSA是成对出现，图中左侧是W-MSA右侧是SW-MSA，可以看到做完shifted window之后，图片变成了9块，因此需要对每一块都做一次MSA，这带来了很大的计算开销，所以作者又提出了一种高效的计算方式，即将图片通过一系列变换，从而变成W-MSA那样的块数。

论文代码中还采用了一种Relative Position Bias的技术，这个技术能够一定程度上提高一些精度，但是文章中并未有文字阐述。具体可以参考：

https://blog.csdn.net/qq_37541097/article/details/121119988?spm=1001.2014.3001.5502

class WindowsAttention(nn.Module):

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.):
        """
        在一个window中做MSA
        :param dim: 输入通道数
        :param window_size: 窗口尺寸
        :param num_heads:
        :param qkv_bias:
        :param attn_drop:
        :param proj_drop:
        """
        super(WindowsAttention, self).__init__()
        self.dim = dim
        self.window_size = window_size  # [Mh, Mw]
        self.num_heads = num_heads
        head_dim = dim // num_heads # 每个head的dim
        self.scale = head_dim ** -0.5 # scale

        # 定义一个parameter table来存放relative position bias
        # 相对位置偏置
        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]

        # 相对位置索引获得方法
        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])  # [Mh]
        coords_w = torch.arange(self.window_size[1])  # [Mw]
        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing="ij"))  # [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
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # [Mh*Mw, Mh*Mw]

        # Register_buffer: 应该就是在内存中定一个常量，同时，模型保存和加载的时候可以写入和读出。
        # 不需要学习，但是可以灵活读写
        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)

        nn.init.trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self,x,mask=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
        x的输入维度是(num_windows窗口数*Batch Size)
        在窗口内进行Attention Op
        """
        # [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)

        # QK^T/sqrt(d)
        # 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))  # [batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]

        # QK^T/sqrt(d) + B
        # B:
        # 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]
        # [Bs*nW, nH, Mh*Mw, Mh*Mw]
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            # SW-MSA 需要做attention Mask
            # mask: [nW, Mh*Mw, Mh*Mw]
            # 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

2. 搭建网络

2.1 SwinTransformerBlock

包含了若干对W-MSA和SW-MSA，所以数目必须是偶数。

class SwinTransformerBlock(nn.Module):
    """
    Swin Transformer Block包括：
    Feature Map Input -> LayerNorm -> SW-MSA/W-MSA -> LayerNorm-> MLP -------->
            |--------------------------------------||----------------------|
    """

    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):
        """
        Args参数定义:
        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
        """
        super(SwinTransformerBlock, self).__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio

        # shift_size必须小于windows_size
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0~window_size"

        # LN1
        self.norm1 = norm_layer(dim)

        # Windows_Multi-head Self Attention
        self.attn = WindowsAttention(
            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,
            attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        # LN2
        self.norm2 = norm_layer(dim)

        # MLP Layer
        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):
        # feature map的Height & Width,对应的是之前patch embedding后的输出
        H, W = self.H, self.W
        # Batch, length, channel
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        # Skip Connect
        shortcut = x
        x = self.norm1(x)
        # reshape feature map
        x = x.view(B, H, W, C)  # 恢复成feature map

        # 对feature map进行pad,pad到windows size的整数倍
        pad_l = 0
        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代表pad后的feature map的Height和Width
        _, Hp, Wp, _ = x.shape

        # 是W-MSA 还是 SW-MSA ？
        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))  # 向上向左循环移位
        else:
            shifted_x = x
            attn_mask = None

        # 窗口划分
        # Windows Partition
        x_windows = window_partition(shifted_x,self.window_size) #[nW*B, Mh, Mw, C]
        x_windows = x_windows.view(-1, self.window_size*self.window_size,C) # [nW*B, Mh*Mw, C]

        # W-MSA / SW-MSA
        attn_windows = self.attn(x_windows, mask=attn_mask)  # [nW*B, Mh*Mw, C]

        # 将分割的Windows进行还原
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)  # [nW*B, Mh, Mw, C]
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # [B, H', W', C]

        # 如果是SW-MSA，需要逆shift过程
        # 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


        # 移除Pad数据
        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
        # 两个Skip Connect
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

2.2 BasicLayer

"""一个Stage内的基本SwinTransformer模块"""
class BasicLayer(nn.Module):
    """
    One Stage SwinTransformer Layer包括:
    """
    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):
        """
        Args:
        dim (int): Number of input channels.
        depth (int): Number of blocks. block数量
        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.
        """
        super(BasicLayer, self).__init__()
        self.dim = dim
        self.depth = depth
        self.window_size = window_size
        self.use_checkpoint = use_checkpoint  # pre-trained
        self.shift_size = window_size // 2

        # 构建SwinTransformer Block
        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, #当i为偶，就是W-MSA，i为奇，就是SW-MSA，与论文一致, 保证窗口之间通信
                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 类似于Pooling下采样
        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):
        """
        SW-MSA后，对于移位后左上角的窗口（也就是移位前最中间的窗口）来说，里面的元素都是互相紧挨着的，
        他们之间可以互相两两做自注意力，但是对于剩下几个窗口来说，它们里面的元素是从别的很远的地方搬过来的，
        所以他们之间，按道理来说是不应该去做自注意力，也就是说他们之间不应该有什么太大的联系
        以14x14个patch为例进行
        H: Feature Map Height
        W: Feature Map Width
        x: Feature Map
        """
        # 为SW-MSA计算Attention Mask.
        # 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]

        # 准备进行区域生成，方便生成Mask
        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))

        # 区域编码
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        # Shift Window 混合区域的窗口分割
        mask_windows = window_partition(img_mask, self.window_size)  # [nW, Mh, Mw, 1]
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]

        # 掩码生成
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]
        # [nW, Mh*Mw, Mh*Mw]
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        return attn_mask

    def forward(self,x,H,W):
        # [nW, Mh*Mw, Mh*Mw] nW:窗口数
        attn_mask = self.create_mask(x,H,W)
        for blk in self.blocks:
            blk.H, blk.W = H, W  # self.H = H, self.W = 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 # DownSample之后，H,W应该减半
        return x, H, W

2.3 SwinTransformer

"""Swin Transformer"""
class SwinTransformer(nn.Module):
    """Swin Transformer结构
    这里有个不同之处，就是每个Stage Layer中，
    """
    def __init__(self, patch_size=4, in_chans=3, num_classes=1000,
                 embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
                 window_size=7, mlp_ratio=4., qkv_bias=True,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
                 norm_layer=nn.LayerNorm, patch_norm=True,
                 use_checkpoint=False, **kwargs):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm

        # 输出特征矩阵的Channels (C)
        # H/4 x W/4 x 48 -> H/4 x W/4 x C(Stage1) -> H/8 x W/8 x 2C(Stage2) -> H/16 x W/16 x 4C(stage3) ...
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # 将image切分为不重合的Patches
        # input: (Bs, 224, 224, 3)
        # output: (e.g patch_size=4: Bs, 56x56, 4x4x3)
        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)

        # stochastic depth
        # Drop Path
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # bulid 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),
                                depth=depths[i_layer],
                                num_heads=num_heads[i_layer],
                                window_size=window_size,
                                mlp_ratio=self.mlp_ratio,
                                qkv_bias=qkv_bias,
                                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(layers)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 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)
        x = self.pos_drop(x)

        # 多尺度分层Multi-Stage
        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) # 分类头
        return x