# 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`
```python
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`
```python
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()`
```python
x = x.permute(0, 2, 1).contiguous().view(...)
# 或
x = x.permute(0, 2, 1).reshape(...)
```
所以 `window_partition` 不能直接
```python
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`
```python
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`
```python
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`
```python
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
```python
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
```python
dims=(1, 2)
```
平移範圍
```python
shifts=(-self.shift_size, -self.shift_size)
```
也就是，如果 `shift_size` 是 1
```python
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
```python
[[4, 5, 6],
[7, 8, 9],
[1, 2, 3]]
```
向左平移
```python
[[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
```python
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
```python
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
```pytyhon
shifted_x = window_reverse(attn_windows, self.window_size, H, W)
```
$\to$ `(B, H, W, C)`
cyclic shift 也要平移回去
```python
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
```
#### Residual Connection
```python
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
```python
x = x + self.drop_path(self.mlp(self.norm2(x)))
```
維度: `(B, H*W, C)`
### `PatchMerging`
```python
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`
```python
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`
```python
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`
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