Swin-Transformer（2021-08）

简介

一直到写下这篇笔记开始，基于Swin的模型仍然霸榜Object Detection等多个榜单。
很多博客都已经介绍的非常详细了，这里只记录一下自己学习过程中遇到的困惑。

Swin与ViT的对比，ViT将image划分为固定大小的patch，以patch为单位进行attention计算，计算过程中的feature map 分辨率是保持不变的，并且ViT为了保持与NLP的一致性，添加了一个额外的class token，最后用于分类。因此ViT不太适用于检测等下游任务，无法提取多尺度特征。

想要transformer用于检测等视觉任务，一种就是模仿CNN，将transformer改造为层级机构，一种就是使用纯transformer结构进行探索。

Swin显然属于前者，采用局部窗口自注意力和shift window的方式巧妙构造出了层级结构，可以作为视觉领域的通用backbone使用。

上图是Swin-T的结构图，输入图像首先通过Patch Partitiion 和Linear Embedding 转化为token形式的向量序列，然后输入Swin Transformer Block中，每个Block都是由一个window–Multi-Head self Attention 和Shift-Window Multi-head self-attention 组成，因此总是偶数。

window partition 窗口划分

将(B, H, W, C) 划分为(num_windows*B, window_size, window_size, C)的windows

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)
    #view() 必须针对contiguous数据存放格式
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows
#恢复原始输入 x
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

测试运行如下

x = torch.randn(1,224,224,3)
p =window_partition(x,7)
print(p.size())
o = window_reverse(p,7,224,224)
print(o.size())

***output

 torch.Size([1024, 7, 7, 3])
torch.Size([1, 224, 224, 3])***

partition就是将输入image转换为指定window size的patch向量，这里就是将（1 ，224，224，3）的batch转换为1024个窗口尺寸为(7,7)的patch。

reverse函数是partition的反函数

PatchEmbedding

[B C H W]->[B ,Ph*Pw,96]

import torch 
from torch import nn
from timm.models.layers import to_2tuple

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) #(224,224)
        patch_size = to_2tuple(patch_size)  #patch size (4,4)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] #(56,56)
        self.img_size = img_size 
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution #分辨率指patch数
        self.num_patches = patches_resolution[0] * patches_resolution[1] #56*56=3136

        self.in_chans = in_chans
        self.embed_dim = embed_dim
        #使用2d卷积进行patch 划分，输入channe默认为3
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) #output:(batch,96,56,56)
        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
        #限制图像尺寸必须为224*224
        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]})." 
                   
        #B C h W -> b embed h*w ->b h*W embed 得到 Patch Embeding 形状
        x = self.proj(x) #Patch 划分 [1,96,56,56]
        x=x.flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops
    
x =  torch.randn(1,3,224,224)
PM  = PatchEmbed()
out = PM(x)
print(out.shape)

**output

torch.Size([1, 3136, 96])**

PatchMerging

[B,H*W,C]->[B,H/2,W/2,2C]
PatchMerging 相当于卷积中的下采样，降低feature map的分辨率，同时增加channel的维度，这里分辨率H，W都降为原来的一半(整体减少4倍)，channel数变为原来的两倍。

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" #限制输入x的第二个维度与H,W匹配
        assert H % 2 == 0 and W % 2 == 0, f"x size ({
      H}*{
      W}) are not even."

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

        #按照 H W 间隔为2 ，两两组合 共4组
        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
        
        print("\n",x0,"\n",x1,"\n",x2,"\n",x3)
        
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C #特征图分辨率降低一半
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C 此时通道维度变为原来的4倍

        x = self.norm(x)
        x = self.reduction(x)  #4C -> 2C 通过linear 通道维度由4倍变为原来的2倍

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={
      self.input_resolution}, dim={
      self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops
    
PM = PatchMerging(input_resolution=(4,4),dim=3)
x = torch.arange(48,dtype=torch.float).view(1,16,3)
o=PM(x)
print(o.shape)

output：

***tensor([[[[ 0.,  1.,  2.],
          [ 6.,  7.,  8.]],
         [[24., 25., 26.],
          [30., 31., 32.]]]])*** 
          
 ***tensor([[[[12., 13., 14.],
          [18., 19., 20.]],
         [[36., 37., 38.],
          [42., 43., 44.]]]])*** 
          
 ***tensor([[[[ 3.,  4.,  5.],
          [ 9., 10., 11.]],
         [[27., 28., 29.],
          [33., 34., 35.]]]])*** 
          
 ***tensor([[[[15., 16., 17.],
          [21., 22., 23.]],
         [[39., 40., 41.],
          [45., 46., 47.]]]])***
          
**torch.Size([1, 4, 6])**

Window Attention

窗口自注意力计算，限制在一个window中，公式相比与之前的attention添加了一个额外的B-相对位置偏移(relative postional bias)

从论文给出的结果来看，采用相对位置偏置后的结果要比其它方法更好。

mask shift-window 可视化

更直观的方式，有颜色的结果是需要自注意力计算保留的，其他白色部分是需要忽略(mask)的地方，可以参考从后边代码生成的结果与这个图是一致的。

 #attn mask 部分测试代码

#假设输入image 为4X4大小，划分为4个window，每个window为2X2大小
input_resolution =(4,4)
window_size =2
shift_size=1  #shift_size 也就是[M/2]下取整

def window_partition(x, window_size):#[B H W C]->[BHW/(window size)^2 , window size,winsow size,C ]
    """ 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


if shift_size > 0:  
     
    # calculate attention mask for SW-MSA
    H, W = input_resolution
    img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
    
    # 提前规划好的分片位置索引-slice(start,stop,step)
    h_slices = (slice(0, -window_size),
                slice(-window_size, -shift_size),
                slice(-shift_size, None))
    w_slices = (slice(0, -window_size),
                slice(-window_size, -shift_size),
                slice(-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, window_size)  # nW, window_size, window_size, 1
    mask_windows = mask_windows.view(-1, window_size * 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


print(attn_mask)

output

tensor([[[   0.,    0.,    0.,    0.],
         [   0.,    0.,    0.,    0.],
         [   0.,    0.,    0.,    0.],
         [   0.,    0.,    0.,    0.]],

        [[   0., -100.,    0., -100.],
         [-100.,    0., -100.,    0.],
         [   0., -100.,    0., -100.],
         [-100.,    0., -100.,    0.]],

        [[   0.,    0., -100., -100.],
         [   0.,    0., -100., -100.],
         [-100., -100.,    0.,    0.],
         [-100., -100.,    0.,    0.]],

        [[   0., -100., -100., -100.],
         [-100.,    0., -100., -100.],
         [-100., -100.,    0., -100.],
         [-100., -100., -100.,    0.]]])

#mask shift window 可视化代码

import torch

import matplotlib.pyplot as plt


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


window_size = 7
shift_size = 3
H, W = 14, 14
img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
h_slices = (slice(0, -window_size),
            slice(-window_size, -shift_size),
            slice(-shift_size, None))
w_slices = (slice(0, -window_size),
            slice(-window_size, -shift_size),
            slice(-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, window_size)  # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, window_size * 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))

plt.matshow(img_mask[0, :, :, 0].numpy())
plt.matshow(attn_mask[0].numpy())
plt.matshow(attn_mask[1].numpy())
plt.matshow(attn_mask[2].numpy())
plt.matshow(attn_mask[3].numpy())

plt.show()

Swin-X 参数配置列表

包含关系：Swin_transformer（Basic_layer(Swin_Block)）

reference(推荐阅读)：

1.https://zhuanlan.zhihu.com/p/367111046
2. https://hub.fastgit.xyz/microsoft/Swin-Transformer/issues/38
3. https://zhuanlan.zhihu.com/p/430047908 -很清楚
4. https://blog.csdn.net/qq_37541097/article/details/121119988
5. https://blog.csdn.net/qq_39478403/article/details/120042232