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Swin Transformer
Swin Transformer架构在2021年的论文Swin Transformer：使用移位窗口的分层视觉Transformer中引入，它采用移位窗口（而不是滑动窗口）方法来优化延迟和性能，从而减少了所需的操作数量。Swin被认为是计算机视觉的分层骨干网络。Swin可用于图像分类等任务。
在深度学习中，骨干网络是神经网络中执行特征提取的部分。可以在骨干网络上添加额外的层来执行各种视觉任务。分层骨干网络具有分层结构，有时具有不同的分辨率。这与VitDet模型中的非分层平面骨干网络形成对比。
主要亮点
移位窗口
在原始ViT中，注意力是在每个补丁与所有其他补丁之间进行的，这在计算上是密集型的。Swin通过将ViT的通常二次复杂度降低为线性复杂度（相对于图像大小）来优化此过程。Swin使用类似于CNN的技术实现了这一点，其中补丁仅关注同一窗口中的其他补丁，而不是所有其他补丁，然后逐渐与相邻补丁合并。这就是Swin成为分层模型的原因。
图片摘自Swin Transformer论文
优势
计算效率
Swin比完全基于补丁的方法（如ViT）性能更好。
大型数据集
SwinV2是首批30亿参数模型之一。随着训练规模的增加，Swin超越了CNN。大量的参数使得学习能力和更复杂的表示能力得以提升。
Swin Transformer V2 (论文)
Swin Transformer V2是一个大型视觉模型，可支持高达30亿参数，并能用高分辨率图像进行训练。它通过稳定训练、将低分辨率图像预训练模型迁移到高分辨率任务，以及使用SimMIM（一种自监督训练方法，可减少训练所需的标记图像数量）来改进原始Swin Transformer。
图像修复中的应用
SwinIR (论文)
SwinIR是一个基于Swin Transformer的模型，用于将低分辨率图像转换为高分辨率图像。
Swin2SR (论文)
Swin2SR是另一个图像修复模型。它通过结合Swin Transformer V2，应用Swin V2的优势（如训练稳定性和更高图像分辨率能力）对SwinIR进行了改进。
Swin的PyTorch实现概述
下面概述了原始论文中Swin的实现的关键部分
Swin Transformer类
初始化参数。除了各种dropout和归一化参数之外，这些参数还包括
window_size：用于局部自注意力的窗口大小。ape (bool)：如果为True，则将绝对位置嵌入添加到补丁嵌入中。fused_window_process：可选的硬件优化。
应用补丁嵌入：与ViT类似，图像被分割成不重叠的补丁，并使用
Conv2D进行线性嵌入。应用位置嵌入：
SwinTransformer可选地使用绝对位置嵌入（ape），添加到补丁嵌入中。绝对位置嵌入通常有助于模型学习使用每个补丁的位置信息以进行更明智的预测。应用深度衰减：深度衰减有助于正则化和防止过拟合。深度衰减通常通过在训练期间跳过层来完成。在此Swin实现中，使用随机深度衰减，这意味着层越深，跳过的可能性越大。
层构建:
- 模型由多个
SwinTransformerBlock层（BasicLayer）组成，每个层都使用PatchMerging对特征图进行下采样以进行分层处理。 - 特征的维度和特征图的分辨率在不同层之间变化。
- 模型由多个
分类头：与ViT类似，它使用多层感知器（MLP）头进行分类任务，在最后一步定义为
self.head。
class SwinTransformer(nn.Module):
def __init__(
self,
img_size=224,
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=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
use_checkpoint=False,
fused_window_process=False,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None,
)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=0.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2**i_layer),
input_resolution=(
patches_resolution[0] // (2**i_layer),
patches_resolution[1] // (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,
qk_scale=qk_scale,
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,
fused_window_process=fused_window_process,
)
self.layers.append(layer)
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):
trunc_normal_(m.weight, std=0.02)
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)
@torch.jit.ignore
def no_weight_decay(self):
return {"absolute_pos_embed"}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {"relative_position_bias_table"}
def forward_features(self, x):
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = torch.flatten(x, 1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return xSwin Transformer块
SwinTransformerBlock封装了Swin Transformer的核心操作：局部窗口注意力（local windowed attention）和随后的MLP处理。它通过关注局部补丁同时保持学习全局表示的能力，在Swin Transformer高效处理大图像方面发挥了关键作用。
层组件:
- 归一化层1 (
self.norm1)：在注意力机制之前应用。 - 窗口注意力 (
self.attn)：在局部窗口内计算自注意力。 - 丢弃路径 (
self.drop_path)：实现随机深度以进行正则化。 - 归一化层2 (
self.norm2)：在MLP层之前应用。 - MLP (
mlp)：一个多层感知器，用于处理注意力后的特征。 - 注意力掩码 (
self.register_buffer)：注意力掩码用于自注意力计算期间，以控制窗口化输入中的哪些元素被允许相互交互（即相互关注）。移位窗口方法通过允许一些跨窗口交互来帮助模型捕获更广泛的上下文信息。
Swin Transformer块的初始化
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=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
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 > 0.0 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
### New cell ###
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)
# Feed-forward network (FFN)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return xSwin Transformer块的前向传播
共有4个关键步骤
- 循环移位：特征图通过
window_partition被划分为窗口。然后对这些分区应用循环移位。循环移位是通过将序列中的元素（在此例中为分区）向左或向右移动，并将超出边界的元素循环回到另一端来完成的。这个过程改变了元素彼此之间的相对位置，但保持了序列的完整性。例如，如果将序列A, B, C, D向右循环移位一个位置，它将变为D, A, B, C。
循环移位允许模型捕获相邻窗口之间的关系，从而增强其学习超出单个窗口局部范围的空间上下文的能力。
窗口注意力：使用基于窗口的多头自注意力（W-MSA）模块执行注意力。
合并补丁：通过
PatchMerging合并补丁。逆向循环移位：注意力完成后，通过
reverse_window撤销窗口划分，并反转循环移位操作，使特征图恢复其原始形式。
class WindowAttention(nn.Module):
"""
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=0.0,
proj_drop=0.0,
):
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=0.02)
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窗口注意力
WindowAttention是一个基于窗口的多头自注意力（W-MSA）模块，带有相对位置偏置。它既可用于移位窗口，也可用于非移位窗口。
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补丁合并层
补丁合并方法用于下采样。它用于减少特征图的空间维度，类似于传统卷积神经网络（CNN）中的池化。它通过逐步增加感受野和降低空间分辨率来帮助构建分层特征表示。
from datasets import load_dataset
from transformers import AutoImageProcessor, SwinForImageClassification
import torch
model = SwinForImageClassification.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224"
)
image_processor = AutoImageProcessor.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224"
)
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_label_id = logits.argmax(-1).item()
predicted_label_text = model.config.id2label[predicted_label_id]
print(predicted_label_text)试一试
您可以在这里找到Swin的🤗文档。
使用预训练Swin模型进行分类
以下是使用Swin模型将猫图像分类到1000个ImageNet类别之一的方法
from datasets import load_dataset
from transformers import AutoImageProcessor, SwinForImageClassification
import torch
model = SwinForImageClassification.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224"
)
image_processor = AutoImageProcessor.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224"
)
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_label_id = logits.argmax(-1).item()
predicted_label_text = model.config.id2label[predicted_label_id]
print(predicted_label_text)