# Swin Transformer

源自: [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/pdf/2103.14030)


## 原理
### Hierarchical Architecture
>Swin Transformer, which constructs hierarchical feature maps and has linear computational complexity to image size.

Swin Transformer 則建構了分層特徵圖 (hierarchical feature maps)，使其能夠像傳統的卷積神經網絡（CNNs，如 VGG 和 ResNet）一樣，產生具有不同解析度的特徵表示

### Patch Partition and Embedding
> It first splits an input RGB image into non-overlapping patches by a patch splitting module, like ViT. Each patch is treated as a “token” and its feature is set as a concatenation of the raw pixel RGB values.

- 模型首先透過圖像塊分割模組 (patch splitting module) 將輸入的 RGB 圖像分割成不重疊的圖像塊 (non-overlapping patches)。
- 在實作中，每個 4×4 的圖像塊被視為一個「token」，其特徵是原始像素 RGB 值的串聯，維度為 4×4×3=48。
- 接著，應用線性嵌入層 (linear embedding layer) 將此原始 feature 投影到任意維度 C。


### Patch Merging
- 隨著網路深入，模型透過圖像塊合併層 (patch merging layers) 來減少 tokens 的數量，從而建立分層表示。
>To produce a hierarchical representation, the number of tokens is reduced by patch merging layers as the network gets deeper.
- 第一個合併層會將相鄰的 2×2 個圖像塊的特徵進行串聯，形成 4C 維度的 feature。
>The first patch merging layer concatenates the features of each group of 2 × 2 neighboring patches, and applies a linear layer on the 4C-dimensional concatenated features.

- 隨後，應用一個線性層，使 tokens 數量減少 2×2=4 倍（解析度降低一半），但輸出維度增加到 2C
>This reduces the number of tokens by a multiple of 2×2 = 4 (2× downsampling of resolution), and the out-put dimension is set to 2C.

reference: [Swin Transformer解读](https://datawhalechina.github.io/thorough-pytorch/%E7%AC%AC%E5%8D%81%E7%AB%A0/Swin-Transformer%E8%A7%A3%E8%AF%BB.html)

### Shifted Window based Self-Attention (SW-MSA)
>To intro-duce cross-window connections while maintaining the effi-cient computation of non-overlapping windows, we propose a shifted window partitioning approach which alternates be-tween two partitioning configurations in consecutive Swin Transformer blocks.

因為在僅在固定窗口內計算 attention 會導致窗口之間缺乏連接，限制了模型的建模能力

所以在各自 window 中算完 MSA(Multi-Head Self-Attention) 後，再用 Shifted Window MSA，增加模型 cross-window connection的能力

reference: [使用动图深入解释微软的Swin Transformer](https://cloud.tencent.com/developer/article/2015888)

## Source Code
reference: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py

### `MLP`

class Mlp(nn.Module):
    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.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

1. 為什麼 `fc` 不能用只用一個?

**MLP 數學本質**
$$y=W_2 \cdot \sigma(W_1 \cdot + b_1) + b_2$$

基本上 $W_1$ 和 $W_2$ 是兩個不同矩陣，中間還有個 $\sigma$ 是激活函數 (activation function)

如果共用一個 `fc`
$$y=W\cdot \sigma(W\cdot x)$$

表達能力下降，可能造成 underfitting，且兩次線性轉換的目的不同

    x = fc1(x)   # in_features → hidden_features
    x = GELU(x)
    x = fc2(x)   # hidden_features → out_features

第一次線性轉換: 展開特徵，改變座標系，feature projection
激活函數: 打破線性限制

第二次線性轉換: 篩選出已經被非線性處理過的高維特徵，feature mixing，對齊模型需要的輸出格式

### `window partition`

def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    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)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows

比較不同維度操作的差別

| 操作 | 做什麼| 改變資料順序？| 需要 contiguous？ | 常見用途 |
| -| -| -| - | - |
| `view`| 改 shape| ❌ 否| ✅ 必須| 最快，純 reshape |
| `reshape` | 改 shape| ❌ 否*| ❌（必要時會 copy）| 安全版 view |
| `permute` | 換維度順序 | ❌（只是換 index） | ❌| NCHW ↔ NHWC |
| `transpose` | 交換兩個維度 | ❌ | ❌| 矩陣轉置 |

所以，如果要避免出錯，可以先用 `reshape`，否則一定要改成 `contiguous()`

x = x.permute(0, 2, 1).contiguous().view(...)
# 或
x = x.permute(0, 2, 1).reshape(...)

所以 `window_partition` 不能直接

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

因為 `view` 不能不能改變資料在記憶體中的排列順序，只能重新解讀線性連續的一維記憶體

### `window_reverse`

def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        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))
    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

從 partition 後的 window，轉回原本的維度 `(B,H,W,C)`

### `WindowAttention`

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
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        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, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -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

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 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)  # Wh*Ww, Wh*Ww
        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)

        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, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        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

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops

就是論文中的 $W-MSA$

#### `relative_position`

可以參考: https://datawhalechina.github.io/thorough-pytorch/%E7%AC%AC%E5%8D%81%E7%AB%A0/Swin-Transformer%E8%A7%A3%E8%AF%BB.html#id3

透過 relative position bias 可以讓模型學習 **距離感知** 的權重

- 為什麼不用絕對位置? 會對每個 token 的固定位置產生 bias
- 保留相對空間信息: 讓模型知道哪個 patch 在上下左右的相對距離有多遠
- 平移不變: 這樣對於之後要做 shifted windows(SW-MSA) 很重要
- 提升注意力經度: 模型能更合理分配注意力給空間上相近或相關的 patch

#### `QKV`

1. `q`, `k`, `v` : `(B_, nH, N, d)`
`B_`: $\cfrac{H \times W}{window_size}$
`nH`: num of head
`N` : $H \times W$
`d` : $\cfrac{C}{n\_H}$

2. `q = q * self.scale`

`(B_, nH, N, d)` $\to$ `(B_, nH, N, d)`
$$scale = \cfrac{1}{\sqrt{d}}$$

3. `attn = (q @ k.transpose(-2, -1))`

`k.transpose(-2, -1)` : `(B_, nH, N, d)` $\to$ `(B_, hH, d, N)`

`attn` : `(B_, nH, N, d)` $\otimes$ `(B_, nH, d, N)` $=$ `(B_, nH, N, N)`

4. attn = attn + relative_position_bias.unsqueeze(0)

`relative_position_bias` : $(N \times N, nH)$
reshape $\to$ `(N, N, nH)`
permute $\to$ `(nH, N, N)`
unsqueeze $\to$ `(1, nH, N, N)`

所以
attn : `(B_, nH, N, N)`

5. `x = (attn @ v).transpose(1, 2).reshape(B_, N, C)`

`(attn @ v)`: `(B_, nH, N, N)` $\otimes$ `(B_, nH, N, d)` $\to$ `(B_, nH, N, d)`

transpose $\to$ `(B_, N, nH, d)`
reshape $\to$ `(B_, N, C)`

### `SwinTransformerBlock`

class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resulotion.
        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
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        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
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 fused_window_process=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 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)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 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

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            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)
        self.fused_window_process = fused_window_process

    def forward(self, x):
        H, W = self.input_resolution
        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)

        # cyclic shift
        if self.shift_size > 0:
            if not self.fused_window_process:
                shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
                # partition windows
                x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
            else:
                x_windows = WindowProcess.apply(x, B, H, W, C, -self.shift_size, self.window_size)
        else:
            shifted_x = x
            # partition windows
            x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C

        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

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

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)

        # reverse cyclic shift
        if self.shift_size > 0:
            if not self.fused_window_process:
                shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
                x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
            else:
                x = WindowProcessReverse.apply(attn_windows, B, H, W, C, self.shift_size, self.window_size)
        else:
            shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
            x = shifted_x
        x = x.view(B, H * W, C)
        x = shortcut + self.drop_path(x)

        # FFN
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


輸入: `x`
$\to$ `(B, L, C)` \# $L = H \times W$

$\to$ `(B, H, W, C)`

#### Cyclic Shift

shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))

`torch.roll` : 對張量沿指定維度平移（循環移動）元素
1 維平移

x = torch.tensor([1, 2, 3, 4, 5])
y = torch.roll(x, shifts=2)
print(y)  # tensor([4, 5, 1, 2, 3])

2 維平移

x = torch.tensor([[1, 2, 3],
                  [4, 5, 6]])
y = torch.roll(x, shifts=1, dims=0)
print(y)
# tensor([[4, 5, 6],
#         [1, 2, 3]])

在 SwinTransformer 是這樣運作的

在維度 1, 2: `H`(Height), `W`(Width) 進行平移

dims=(1, 2)

平移範圍

shifts=(-self.shift_size, -self.shift_size)

也就是，如果 `shift_size` 是 1

原始矩陣 x:
[[1, 2, 3],
 [4, 5, 6],
 [7, 8, 9]]

torch.roll(x, shifts=(-1, -1), dims=(0,1)) => 
[[5, 6, 4],
 [8, 9, 7],
 [2, 3, 1]]

- 高度方向平移 `-shift_size`，寬度方向也平移 `-shift_size`
- 負號表示向上和向左平移
向上平移

    [[4, 5, 6],
     [7, 8, 9],
     [1, 2, 3]]

向左平移

    [[5, 6, 4],
     [8, 9, 7],
     [2, 3, 1]]

- 循環移動：溢出的元素從另一端回來

#### Partition windows
把整個 image 切成多個 patch
`(B, H, W, C)` $\to$ `(B * H/window_size * W/window_size, window_size, window_size, C )`

這樣看來是 shift 完才 partition 的

#### Attention

attn_windows = self.attn(x_windows, mask=self.attn_mask)

用 [WindowAttention](https://hackmd.io/@clh/BkQ0dg2Nkl/https%3A%2F%2Fhackmd.io%2F%40clh%2FBypDcKbZ-g#WindowAttention) 來計算每個 window 的 attention

現在維度: `(B * W/window_size * H/window_size, window_size * window_size, C)`

#### Merge Windows $\to$ Reverse Cyclic Shift
進行 merge

attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)

其實只是轉換維度
`(B * W/window_size * H/window_size, window_size * window_size, C)`
$\to$ `(nW, window_size, window_size, C)`

`nW` : `B * W/window_size * H/window_size`

最後 reverse 回去

shifted_x = window_reverse(attn_windows, self.window_size, H, W)

$\to$ `(B, H, W, C)`

cyclic shift 也要平移回去

x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))

#### Residual Connection

shortcut = x
...
x = x.view(B, H * W, C)
x = shortcut + self.drop_path(x)

原本輸入的 `x` 維度: `(B, H*W, C)` 再加上 `MSA` 的 attention

加上 Residual Connection 的好處是
- 梯度流暢：殘差連接可以幫助梯度直接傳回，避免深層網路訓練困難。
- 保留原始訊息：原始輸入訊息不會完全被改變，注意力只負責補充特徵。
- 提升模型穩定性：避免注意力輸出過度影響網路表現。

#### Block

最後經過 MLP

x = x + self.drop_path(self.mlp(self.norm2(x)))

維度: `(B, H*W, C)`

### `PatchMerging`

class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

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

        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)
        x = self.reduction(x)

        return x

它的作用類似於卷積神經網絡（CNN）中的 Pooling（池化） 層，目的是降低解析度（下採樣）並增加通道數，從而讓模型能夠捕捉到更大範圍的特徵（增大感受野）。

x: `(B, H*W, C)`
view $\to$ `(B, H, W, C)`
這樣做是為了在降低計算量（減少 Token 數量）的同時，極大化保留影像資訊並增加特徵維度。
$\to$ `(B, H/2, W/2, 4C)`
view $\to$ `(B, H/2 * W/2, 4C)`

### `BasicLayer`

lass BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        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
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        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.
        fused_window_process (bool, optional): If True, use one kernel to fused window shift & window partition for acceleration, similar for the reversed part. Default: False
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 fused_window_process=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer,
                                 fused_window_process=fused_window_process)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

指這每一塊

### `PatchEmbed`

class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

把 image 進行 patch embedding: 將一張連續的影像（像素）轉換成一個個離散的序列（Tokens），這樣 Transformer 才能處理。

假設輸入影像是 $224 \times 224 \times 3$，patch_size=4，embed_dim=96：
|步驟|程式碼操作|Shape|說明|
|-|-|-|-|
|輸入|`x`|$(B, 3, 224, 224)$|原始影像 $(C, H, W)$|
|卷積投影|`self.proj(x)`|$(B, 96, 56, 56)$|$224/4 = 56$。影像縮小，深度變厚|
|展平|`.flatten(2)`|$(B, 96, 3136)$|將 $56 \times 56$ 的空間維度拉直成 $3136$ 個點|
|轉置|`.transpose(1, 2)`|$(B, 3136, 96)$|最終格式：$(B, L, C)$，符合 Transformer 要求|

### `SwinTransformer`