论文题目：《AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE》
会议时间：International Conference on Learning Representations, 2021 (ICLR, 2021)
论文地址：https://openreview.net/pdf?id=YicbFdNTTy
论文源码：https://github.com/lucidrains/vit-pytorch（非官方）
常见规格：
| Model | Layers | Hidden size D | MLP size | Heads | Params |
|---|---|---|---|---|---|
| ViT-Base | 12 | 768 | 3072 | 12 | 86M |
| ViT-Large | 24 | 1024 | 4096 | 16 | 307M |
| ViT-Huge | 32 | 1280 | 5120 | 16 | 632M |
另外，还会添加patch大小，例如：ViT-L/16表示使用 16 × 16 16\times16 16×16的patch大小切分图片。
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_width
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = nn.Sequential(
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),
)
# 位置编码
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
# 类别token（类似类别查询向量）
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 = pool
self.to_latent = nn.Identity()
self.mlp_head = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, num_classes)
)
def forward(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
# 类别token先与特征合并（沿序列方向合并）
cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=b)
x = torch.cat((cls_tokens, x), dim=1)
# 加上位置编码，用于表示图片patch的位置
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
x = self.to_latent(x)
return self.mlp_head(x)
注：以上仅是笔者个人见解，若有问题，欢迎指正。