深度学习网络模型——Swin-Transformer详细讲解与代码实现

一、网路模型整体架构二、Patch Partition模块详解三、Patch Merging模块四、W-MSA详解五、SW-MSA详解masked MSA详解

六、 Relative Position Bias详解七、模型详细配置参数八、重要模块代码实现：1、Patch Partition代码模块：2、Patch Merging代码模块：3、mask掩码生成代码模块：4、stage堆叠部分代码：5、SW-MSA或者W-MSA模块代码：

九：模型整体流程代码实现：

论文名称：Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

原论文地址： https://arxiv.org/abs/2103.14030

官方开源代码地址：https://github.com/microsoft/Swin-Transformer

一、网路模型整体架构

二、Patch Partition模块详解

三、Patch Merging模块

四、W-MSA详解

五、SW-MSA详解

masked MSA详解

六、 Relative Position Bias详解

七、模型详细配置参数

八、重要模块代码实现：

1、Patch Partition代码模块：

<code>class PatchEmbed(nn.Module):

"""

2D Image to Patch Embedding

split image into non-overlapping patches 即将图片划分成一个个没有重叠的patch

"""

def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):

super().__init__()

patch_size = (patch_size, patch_size)

self.patch_size = patch_size

self.in_chans = in_c

self.embed_dim = embed_dim

self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)

self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

def forward(self, x):

_, _, H, W = x.shape

# padding

# 如果输入图片的H，W不是patch_size的整数倍，需要进行padding

pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)

if pad_input:

# to pad the last 3 dimensions,

# (W_left, W_right, H_top,H_bottom, C_front, C_back)

x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1], # 表示宽度方向右侧填充数

0, self.patch_size[0] - H % self.patch_size[0], # 表示高度方向底部填充数

0, 0))

# 下采样patch_size倍

x = self.proj(x)

_, _, H, W = x.shape

# flatten: [B, C, H, W] -> [B, C, HW]

# transpose: [B, C, HW] -> [B, HW, C]

x = x.flatten(2).transpose(1, 2)

x = self.norm(x)

return x, H, W

2、Patch Merging代码模块：

class PatchMerging(nn.Module):

r""" Patch Merging Layer.

步长为2，间隔采样

Args:

dim (int): Number of input channels.

norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

"""

def __init__(self, dim, norm_layer=nn.LayerNorm):

super().__init__()

self.dim = dim

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 即输入x的通道排列顺序

"""

B, L, C = x.shape

assert L == H * W, "input feature has wrong size"

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

# padding

# 如果输入feature map的H，W不是2的整数倍，需要进行padding

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))

# 以2为间隔进行采样

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] 在channael维度上进行拼接

x = x.view(B, -1, 4 * C) # [B, H/2*W/2, 4*C]

x = self.norm(x)

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

return x

3、mask掩码生成代码模块：

def create_mask(self, x, H, W):

# calculate attention mask for SW-MSA

# 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]

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

# 将img_mask划分成一个一个窗口

mask_windows = window_partition(img_mask, self.window_size) # [nW, Mh, Mw, 1] # 输出的是按照指定的window_size划分成一个一个窗口的数据

mask_windows = mask_windows.view(-1, self.window_size * self.window_size) # [nW, Mh*Mw]

attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1] 使用了广播机制

# [nW, Mh*Mw, Mh*Mw]

# 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得

attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) # 即对于不等于0的位置，赋值为-100；否则为0

return attn_mask

4、stage堆叠部分代码：

class BasicLayer(nn.Module):

"""

A basic Swin Transformer layer for one stage.

Args:

dim (int): Number of input channels.

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

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.

"""

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):

super().__init__()

self.dim = dim

self.depth = depth

self.window_size = window_size

self.use_checkpoint = use_checkpoint

self.shift_size = window_size // 2 # 表示向右和向下偏移的窗口大小 即窗口大小除以2，然后向下取整

# build blocks

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, # 通过判断shift_size是否等于0，来决定是使用W-MSA与SW-MSA

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 即：PatchMerging类

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):

# calculate attention mask for SW-MSA

# 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]

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

# 将img_mask划分成一个一个窗口

mask_windows = window_partition(img_mask, self.window_size) # [nW, Mh, Mw, 1] # 输出的是按照指定的window_size划分成一个一个窗口的数据

mask_windows = mask_windows.view(-1, self.window_size * self.window_size) # [nW, Mh*Mw]

attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1] 使用了广播机制

# [nW, Mh*Mw, Mh*Mw]

# 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得

attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) # 即对于不等于0的位置，赋值为-100；否则为0

return attn_mask

def forward(self, x, H, W):

attn_mask = self.create_mask(x, H, W) # [nW, Mh*Mw, Mh*Mw] # 制作mask蒙版

for blk in self.blocks:

blk.H, blk.W = H, 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

return x, H, W

5、SW-MSA或者W-MSA模块代码：

class SwinTransformerBlock(nn.Module):

r""" Swin Transformer Block.

Args:

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

"""

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):

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

assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

self.norm1 = norm_layer(dim) # 先经过层归一化处理

# WindowAttention即为：SW-MSA或者W-MSA模块

self.attn = WindowAttention(

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()

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)

def forward(self, x, attn_mask):

H, W = self.H, self.W

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)

# pad feature maps to multiples of window size

# 把feature map给pad到window size的整数倍

pad_l = 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, _ = x.shape

# cyclic shift

# 判断是进行SW-MSA或者是W-MSA模块

if self.shift_size > 0:

# https://blog.csdn.net/ooooocj/article/details/126046858?ops_request_misc=&request_id=&biz_id=102&utm_term=torch.roll()%E7%94%A8%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-126046858.142^v73^control,201^v4^add_ask,239^v1^control&spm=1018.2226.3001.4187

shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) #进行数据移动操作

else:

shifted_x = x

attn_mask = None

# partition windows

# 将窗口按照window_size的大小进行划分，得到一个个窗口

x_windows = window_partition(shifted_x, self.window_size) # [nW*B, Mh, Mw, C]

# 将数据进行展平操作

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

# W-MSA/SW-MSA

"""

# 进行多头自注意力机制操作

"""

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

# merge windows

attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) # [nW*B, Mh, Mw, C]

# 将多窗口拼接回大的featureMap

shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # [B, H', W', C]

# reverse cyclic shift

# 将移位的数据进行还原

if self.shift_size > 0:

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

else:

x = shifted_x

# 如果进行了padding操作，需要移出掉相应的pad

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

x = shortcut + self.drop_path(x)

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

return x

九：模型整体流程代码实现：

""" Swin Transformer

A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`

- https://arxiv.org/pdf/2103.14030

Code/weights from https://github.com/microsoft/Swin-Transformer

"""

import torch

import torch.nn as nn

import torch.nn.functional as F

import torch.utils.checkpoint as checkpoint

import numpy as np

from typing import Optional

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

class DropPath(nn.Module):

"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

"""

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)

"""

将窗口按照window_size的大小进行划分，得到一个个窗口

"""

def window_partition(x, window_size: int):

"""

将feature map按照window_size划分成一个个没有重叠的window

Args:

x: (B, H, W, C)

window_size (int): window size(M)

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)

# 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) # 输出的是按照指定的window_size划分成一个一个窗口的数据

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

class PatchEmbed(nn.Module):

"""

2D Image to Patch Embedding

split image into non-overlapping patches 即将图片划分成一个个没有重叠的patch

"""

def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):

super().__init__()

patch_size = (patch_size, patch_size)

self.patch_size = patch_size

self.in_chans = in_c

self.embed_dim = embed_dim

self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)

self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

def forward(self, x):

_, _, H, W = x.shape

# padding

# 如果输入图片的H，W不是patch_size的整数倍，需要进行padding

pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)

if pad_input:

# to pad the last 3 dimensions,

# (W_left, W_right, H_top,H_bottom, C_front, C_back)

x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1], # 表示宽度方向右侧填充数

0, self.patch_size[0] - H % self.patch_size[0], # 表示高度方向底部填充数

0, 0))

# 下采样patch_size倍

x = self.proj(x)

_, _, H, W = x.shape

# flatten: [B, C, H, W] -> [B, C, HW]

# transpose: [B, C, HW] -> [B, HW, C]

x = x.flatten(2).transpose(1, 2)

x = self.norm(x)

return x, H, W

class PatchMerging(nn.Module):

r""" Patch Merging Layer.

步长为2，间隔采样

Args:

dim (int): Number of input channels.

norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

"""

def __init__(self, dim, norm_layer=nn.LayerNorm):

super().__init__()

self.dim = dim

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 即输入x的通道排列顺序

"""

B, L, C = x.shape

assert L == H * W, "input feature has wrong size"

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

# padding

# 如果输入feature map的H，W不是2的整数倍，需要进行padding

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))

# 以2为间隔进行采样

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] 在channael维度上进行拼接

x = x.view(B, -1, 4 * C) # [B, H/2*W/2, 4*C]

x = self.norm(x)

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

return x

"""

MLP模块

"""

class Mlp(nn.Module):

""" MLP as used in Vision Transformer, MLP-Mixer and related networks

"""

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.drop1 = nn.Dropout(drop)

self.fc2 = nn.Linear(hidden_features, out_features)

self.drop2 = nn.Dropout(drop)

def forward(self, x):

x = self.fc1(x)

x = self.act(x)

x = self.drop1(x)

x = self.fc2(x)

x = self.drop2(x)

return x

"""

WindowAttention即为：SW-MSA或者W-MSA模块

"""

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

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, attn_drop=0., proj_drop=0.):

super().__init__()

self.dim = dim

self.window_size = window_size # [Mh, Mw]

self.num_heads = num_heads

head_dim = dim // num_heads

self.scale = head_dim ** -0.5

# define a parameter table of relative position bias

# 创建偏置bias项矩阵

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] 其元素的个数===>>[(2*Mh-1) * (2*Mw-1)]

# get pair-wise relative position index for each token inside the window

coords_h = torch.arange(self.window_size[0]) # 如果此处的self.window_size[0]为2的话，则生成的coords_h为[0,1]

coords_w = torch.arange(self.window_size[1]) # 同理得

coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # [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 行标+（M-1）

relative_coords[:, :, 1] += self.window_size[1] - 1 # 列表标+（M-1）

relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1

relative_position_index = relative_coords.sum(-1) # [Mh*Mw, Mh*Mw]

self.register_buffer("relative_position_index", relative_position_index) # 将relative_position_index放入到模型的缓存当中

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: Optional[torch.Tensor] = 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

"""

# [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) # make torchscript happy (cannot use tensor as tuple)

# 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))

# 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]

attn = attn + relative_position_bias.unsqueeze(0)

# 进行mask，相同区域使用0表示；不同区域使用-100表示

if mask is not None:

# mask: [nW, Mh*Mw, Mh*Mw]

nW = mask.shape[0] # num_windows

# 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

"""

SwinTransformerBlock

"""

class SwinTransformerBlock(nn.Module):

r""" Swin Transformer Block.

Args:

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

"""

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):

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

assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

self.norm1 = norm_layer(dim) # 先经过层归一化处理

# WindowAttention即为：SW-MSA或者W-MSA模块

self.attn = WindowAttention(

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()

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)

def forward(self, x, attn_mask):

H, W = self.H, self.W

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)

# pad feature maps to multiples of window size

# 把feature map给pad到window size的整数倍

pad_l = 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, _ = x.shape

# cyclic shift

# 判断是进行SW-MSA或者是W-MSA模块

if self.shift_size > 0:

# https://blog.csdn.net/ooooocj/article/details/126046858?ops_request_misc=&request_id=&biz_id=102&utm_term=torch.roll()%E7%94%A8%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-126046858.142^v73^control,201^v4^add_ask,239^v1^control&spm=1018.2226.3001.4187

shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) #进行数据移动操作

else:

shifted_x = x

attn_mask = None

# partition windows

# 将窗口按照window_size的大小进行划分，得到一个个窗口

x_windows = window_partition(shifted_x, self.window_size) # [nW*B, Mh, Mw, C]

# 将数据进行展平操作

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

# W-MSA/SW-MSA

"""

# 进行多头自注意力机制操作

"""

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

# merge windows

attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) # [nW*B, Mh, Mw, C]

# 将多窗口拼接回大的featureMap

shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # [B, H', W', C]

# reverse cyclic shift

# 将移位的数据进行还原

if self.shift_size > 0:

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

else:

x = shifted_x

# 如果进行了padding操作，需要移出掉相应的pad

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

x = shortcut + self.drop_path(x)

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

return x

class BasicLayer(nn.Module):

"""

A basic Swin Transformer layer for one stage.

Args:

dim (int): Number of input channels.

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

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.

"""

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):

super().__init__()

self.dim = dim

self.depth = depth

self.window_size = window_size

self.use_checkpoint = use_checkpoint

self.shift_size = window_size // 2 # 表示向右和向下偏移的窗口大小 即窗口大小除以2，然后向下取整

# build blocks

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, # 通过判断shift_size是否等于0，来决定是使用W-MSA与SW-MSA

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 即：PatchMerging类

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):

# calculate attention mask for SW-MSA

# 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]

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

# 将img_mask划分成一个一个窗口

mask_windows = window_partition(img_mask, self.window_size) # [nW, Mh, Mw, 1] # 输出的是按照指定的window_size划分成一个一个窗口的数据

mask_windows = mask_windows.view(-1, self.window_size * self.window_size) # [nW, Mh*Mw]

attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1] 使用了广播机制

# [nW, Mh*Mw, Mh*Mw]

# 因为需要求得的是自身注意力机制，所以，所以相同的区域使用0表示，；不同的区域不等于0，填入-100，这样，在求得

attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) # 即对于不等于0的位置，赋值为-100；否则为0

return attn_mask

def forward(self, x, H, W):

attn_mask = self.create_mask(x, H, W) # [nW, Mh*Mw, Mh*Mw] # 制作mask蒙版

for blk in self.blocks:

blk.H, blk.W = H, 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

return x, H, W

class SwinTransformer(nn.Module):

r""" Swin Transformer

A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -

https://arxiv.org/pdf/2103.14030

Args:

patch_size (int | tuple(int)): Patch size. Default: 4 表示通过Patch Partition层后，下采样几倍

in_chans (int): Number of input image channels. Default: 3

num_classes (int): Number of classes for classification head. Default: 1000

embed_dim (int): Patch embedding dimension. Default: 96

depths (tuple(int)): Depth of each Swin Transformer layer.

num_heads (tuple(int)): Number of attention heads in different layers.

window_size (int): Window size. Default: 7

mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4

qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True

drop_rate (float): Dropout rate. Default: 0

attn_drop_rate (float): Attention dropout rate. Default: 0

drop_path_rate (float): Stochastic depth rate. Default: 0.1

norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.

patch_norm (bool): If True, add normalization after patch embedding. Default: True

use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False

"""

def __init__(self, patch_size=4, # 表示通过Patch Partition层后，下采样几倍

in_chans=3, # 输入图像通道

num_classes=1000, # 类别数

embed_dim=96, # Patch partition层后的LinearEmbedding层映射后的维度，之后的几层都是该数的整数倍 分别是 C、2C、4C、8C

depths=(2, 2, 6, 2), # 表示每一个Stage模块内，Swin Transformer Block重复的次数

num_heads=(3, 6, 12, 24), # 表示每一个Stage模块内，Swin Transformer Block中采用的Multi-Head self-Attention的head的个数

window_size=7, # 表示W-MSA与SW-MSA所采用的window的大小

mlp_ratio=4., # 表示MLP模块中，第一个全连接层增大的倍数

qkv_bias=True,

drop_rate=0., # 对应的PatchEmbed层后面的

attn_drop_rate=0., # 对应于Multi-Head self-Attention模块中对应的dropRate

drop_path_rate=0.1, # 对应于每一个Swin-Transformer模块中采用的DropRate 其是慢慢的递增的，从0增长到drop_path_rate

norm_layer=nn.LayerNorm,

patch_norm=True,

use_checkpoint=False, **kwargs):

super().__init__()

self.num_classes = num_classes

self.num_layers = len(depths) # depths:表示重复的Swin Transoformer Block模块的次数 表示每一个Stage模块内，Swin Transformer Block重复的次数

self.embed_dim = embed_dim

self.patch_norm = patch_norm

# stage4输出特征矩阵的channels

self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))

self.mlp_ratio = mlp_ratio

# split image into non-overlapping patches 即将图片划分成一个个没有重叠的patch

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) # PatchEmbed层后面的Dropout层

# 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):

# 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), # 传入特征矩阵的维度，即channel方向的深度

depth=depths[i_layer], # 表示当前stage中需要堆叠的多少Swin Transformer Block

num_heads=num_heads[i_layer], # 表示每一个Stage模块内，Swin Transformer Block中采用的Multi-Head self-Attention的head的个数

window_size=window_size, # 表示W-MSA与SW-MSA所采用的window的大小

mlp_ratio=self.mlp_ratio, # 表示MLP模块中，第一个全连接层增大的倍数

qkv_bias=qkv_bias,

drop=drop_rate, # 对应的PatchEmbed层后面的

attn_drop=attn_drop_rate, # 对应于Multi-Head self-Attention模块中对应的dropRate

drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # 对应于每一个Swin-Transformer模块中采用的DropRate 其是慢慢的递增的，从0增长到drop_path_rate

norm_layer=norm_layer,

downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, # 判断是否是第四个，因为第四个Stage是没有PatchMerging层的

use_checkpoint=use_checkpoint)

self.layers.append(layers)

self.norm = norm_layer(self.num_features)

self.avgpool = nn.AdaptiveAvgPool1d(1) # 自适应的全局平均池化

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) # 对图像下采样4倍

x = self.pos_drop(x)

# 依次传入各个stage中

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) # 经过全连接层，得到输出

return x

def swin_tiny_patch4_window7_224(num_classes: int = 1000, **kwargs):

# trained ImageNet-1K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=7,

embed_dim=96,

depths=(2, 2, 6, 2),

num_heads=(3, 6, 12, 24),

num_classes=num_classes,

**kwargs)

return model

def swin_small_patch4_window7_224(num_classes: int = 1000, **kwargs):

# trained ImageNet-1K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=7,

embed_dim=96,

depths=(2, 2, 18, 2),

num_heads=(3, 6, 12, 24),

num_classes=num_classes,

**kwargs)

return model

def swin_base_patch4_window7_224(num_classes: int = 1000, **kwargs):

# trained ImageNet-1K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=7,

embed_dim=128,

depths=(2, 2, 18, 2),

num_heads=(4, 8, 16, 32),

num_classes=num_classes,

**kwargs)

return model

def swin_base_patch4_window12_384(num_classes: int = 1000, **kwargs):

# trained ImageNet-1K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=12,

embed_dim=128,

depths=(2, 2, 18, 2),

num_heads=(4, 8, 16, 32),

num_classes=num_classes,

**kwargs)

return model

def swin_base_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):

# trained ImageNet-22K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=7,

embed_dim=128,

depths=(2, 2, 18, 2),

num_heads=(4, 8, 16, 32),

num_classes=num_classes,

**kwargs)

return model

def swin_base_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):

# trained ImageNet-22K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=12,

embed_dim=128,

depths=(2, 2, 18, 2),

num_heads=(4, 8, 16, 32),

num_classes=num_classes,

**kwargs)

return model

def swin_large_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):

# trained ImageNet-22K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=7,

embed_dim=192,

depths=(2, 2, 18, 2),

num_heads=(6, 12, 24, 48),

num_classes=num_classes,

**kwargs)

return model

def swin_large_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):

# trained ImageNet-22K

# https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth

model = SwinTransformer(in_chans=3,

patch_size=4,

window_size=12,

embed_dim=192,

depths=(2, 2, 18, 2),

num_heads=(6, 12, 24, 48),

num_classes=num_classes,

**kwargs)

return model