1 摘要

1.1 核心

通过将图像切成patch线形层编码成token特征编码的方法，用transformer的encoder来做图像分类

2 模型架构

2.1 概览

2.2 对应CV的特定修改和相关理解

解决问题：

1. transformer输入限制: 由于自注意力+backbone，算法复杂度为o(n²)，token长度一般要<512才足够运算
   解决：a) 将图片转为token输入 b) 将特征图转为token输入 c)√ 切patch转为token输入
2. transformer无先验知识：卷积存在平移不变性(同特征同卷积核同结果)和局部相似性(相邻特征相似结果)，
   而transformer无卷积核概念，只有整个编解码器，需要从头学
   解决：大量数据训练
3. cv的各种自注意力机制需要复杂工程实现：
   解决：直接用整个transformer模块
4. 分类head：
   解决：直接沿用transformer cls token
5. position编码：
   解决：1D编码

pipeline：
224x224输入切成16x16patch进行位置编码和线性编码后增加cls token 一起输入的encoder encoder中有L个selfattention模块
输出的cls token为目标类别

3 代码

如果理解了transformer，看完这个结构感觉真的很简单，这篇论文也只是开山之作，没有特别复杂的结构，所以想到代码里看看。

import torch
from torch import nnfrom einops import rearrange, repeat
from einops.layers.torch import Rearrange# helpersdef pair(t):return t if isinstance(t, tuple) else (t, t)# classesclass FeedForward(nn.Module):def __init__(self, dim, hidden_dim, dropout = 0.):super().__init__()self.net = nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim, hidden_dim),nn.GELU(),nn.Dropout(dropout),nn.Linear(hidden_dim, dim),nn.Dropout(dropout))def forward(self, x):return self.net(x)class Attention(nn.Module):def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):super().__init__()inner_dim = dim_head *  headsproject_out = not (heads == 1 and dim_head == dim)self.heads = headsself.scale = dim_head ** -0.5self.norm = nn.LayerNorm(dim)self.attend = nn.Softmax(dim = -1)self.dropout = nn.Dropout(dropout)# linear(1024 , 3072)self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)self.to_out = nn.Sequential(nn.Linear(inner_dim, dim),nn.Dropout(dropout)) if project_out else nn.Identity()def forward(self, x):# [1, 65, 1024]x = self.norm(x)# [1, 65, 1024]qkv = self.to_qkv(x).chunk(3, dim = -1)# self.to_qkv(x)                [1, 65, 3072]# self.to_qkv(x).chunk(3,-1)    [3, 1, 65, 1024]q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)# q,k,v                         [1, 65, 1024] -> [1, 16, 65, 64]# 把 65个1024的特征分为 heads个65个d维的特征 然后每个heads去分别有自己要处理的隐藏层，对不同的特征建立不同学习能力dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale# [1, 16, 65, 64] * [1, 16, 64, 65] -> [1, 16, 65, 65]# scale 保证在softmax前所有的值都不太大attn = self.attend(dots)# softmax [1, 16, 65, 65]attn = self.dropout(attn)# dropout [1, 16, 65, 65]out = torch.matmul(attn, v)# out [1, 16, 65, 64]out = rearrange(out, 'b h n d -> b n (h d)')# out [1, 65, 1024]return self.to_out(out)# out [1, 65, 1024]class Transformer(nn.Module):def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):super().__init__()self.norm = nn.LayerNorm(dim)self.layers = nn.ModuleList([])for _ in range(depth):self.layers.append(nn.ModuleList([Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),FeedForward(dim, mlp_dim, dropout = dropout)]))def forward(self, x):# [1, 65, 1024]for attn, ff in self.layers:# [1, 65, 1024]x = attn(x) + x# [1, 65, 1024]x = ff(x) + x# [1, 65, 1024]return self.norm(x)# shape不会改变class ViT(nn.Module):def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):super().__init__()image_height, image_width = pair(image_size)patch_height, patch_width = pair(patch_size)assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'num_patches = (image_height // patch_height) * (image_width // patch_width)patch_dim = channels * patch_height * patch_widthassert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'# num_patches   64# patch_dim     3072# dim           1024self.to_patch_embedding = nn.Sequential(#Rearrange是einops中的一个方法# einops：灵活和强大的张量操作，可读性强和可靠性好的代码。支持numpy、pytorch、tensorflow等。# 代码中Rearrage的意思是将传入的image（3，224，224），按照（3，（h,p1）,(w,p2))也就是224=hp1,224 = wp2，接着把shape变成b (h w) (p1 p2 c)格式的，这样把图片分成了每个patch并且将patch拉长，方便下一步的全连接层# 还有一种方法是采用窗口为16*16，stride 16的卷积核提取每个patch，然后再flatten送入全连接层。Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),nn.LayerNorm(patch_dim),nn.Linear(patch_dim, dim),nn.LayerNorm(dim),)self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))self.cls_token = nn.Parameter(torch.randn(1, 1, dim))self.dropout = nn.Dropout(emb_dropout)self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)self.pool = poolself.to_latent = nn.Identity()self.mlp_head = nn.Linear(dim, num_classes)def forward(self, img):# 1. [1, 3, 256, 256]       输入imgx = self.to_patch_embedding(img)# 2. [1, 64, 1024]          patch embdb, n, _ = x.shape# 3. [1, 1, 1024]           cls_tokenscls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)# 4. [1, 65, 1024]          cat [cls_tokens, x]x = torch.cat((cls_tokens, x), dim=1)# 5. [1, 65, 1024]          add [x] [pos_embedding]x += self.pos_embedding[:, :(n + 1)]# 6. [1, 65, 1024]          dropoutx = self.dropout(x)# 7. [1, 65, 1024]          N * transformerx = self.transformer(x)# 8. [1,1024]               cls_x outputx = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]# 9. [1,1024]               cls_x output meanx = self.to_latent(x)# 10.[1,1024]               nn.Identity()不改变输入和输出 占位层return self.mlp_head(x)# 11.[1,cls]                mlp_cls_head

4 总结

multihead和我原有的理解偏差修正。
我以为的是QKV会有N块相同的copy()，每一份去做后续的linear等操作。
代码里是直接用linear将QKV分为一整个大块，用permute/rearrange的操作切成了N块，f(Q,K)之后再恢复成一整个大块，很强。