### 关于Vision Transformer (ViT) 的伪代码实现
为了更好地理解如何构建和训练 Vision Transformer (ViT)，以下是基于现有研究[^1]的一个简化版伪代码实现：
```python
import torch.nn as nn
class PatchEmbedding(nn.Module):
""" 将图像分割成多个patch并嵌入 """
def __init__(self, img_size=224, patch_size=16, embed_dim=768):
super().__init__()
self.proj = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class MultiHeadSelfAttention(nn.Module):
""" 多头自注意力机制 """
def __init__(self, dim, num_heads=12):
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3)
self.proj = nn.Linear(dim, dim)
def forward(self, x):
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.unbind(0)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(1, 2).reshape(B, N, C)
out = self.proj(out)
return out
class MLPBlock(nn.Module):
""" 多层感知机模块 """
def __init__(self, in_features, hidden_features=None, out_features=None):
super().__init__()
hidden_features = hidden_features or in_features
out_features = out_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_features, out_features)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
class EncoderLayer(nn.Module):
""" 编码器层，由多头自注意机制和MLP组成 """
def __init__(self, dim, mlp_ratio=4., drop_rate=0.1, num_heads=12):
super().__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = MultiHeadSelfAttention(dim, num_heads=num_heads)
self.drop_path = DropPath(drop_rate) if drop_rate > 0. else nn.Identity()
self.norm2 = nn.LayerNorm(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLPBlock(in_features=dim, hidden_features=mlp_hidden_dim)
def forward(self, x):
residual = x
x = self.norm1(x)
x = self.attn(x)
x = residual + self.drop_path(x)
y = self.norm2(x)
y = self.mlp(y)
x = x + self.drop_path(y)
return x
class VisionTransformer(nn.Module):
""" 整合上述组件形成完整的ViT结构 """
def __init__(self, img_size=224, patch_size=16, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., class_token=True, distillation=False):
super().__init__()
self.patch_embed = PatchEmbedding(img_size=img_size, patch_size=patch_size, embed_dim=embed_dim)
num_patches = (img_size // patch_size)**2
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.randn(1, num_patches + 1, embed_dim))
self.blocks = nn.Sequential(*[
EncoderLayer(
dim=embed_dim,
mlp_ratio=mlp_ratio,
drop_rate=drop_rate,
num_heads=num_heads
) for _ in range(depth)])
self.class_token = class_token
self.distillation = distillation
def forward(self, x):
batch_size = x.size(0)
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embed[:, :x.size(1)]
x = self.blocks(x)
if not self.distillation:
x = x[:, 0]
else:
x = x.mean(dim=1)
return x
```
此段代码展示了如何创建一个基本版本的 Vision Transformer 架构。具体来说，这段代码定义了几种核心组件——`PatchEmbedding`, `MultiHeadSelfAttention`, `MLPBlock` 和 `EncoderLayer` —— 并最终通过组合这些部分来建立整个网络架构。
值得注意的是，在实际应用中可能还需要考虑更多细节，比如位置编码的具体形式、正则化方法的选择以及优化策略等。此外，不同论文可能会对某些超参数做出特定调整以适应不同的任务需求。
超级会员免费看
MINILLM是一种新的知识蒸馏方法，用于从大型语言模型（LLM）中提取知识，以训练更小的模型。它通过反向Kullback-Leibler散度（KLD）来优化，避免学生模型高估教师分布的低概率区域。MINILLM通过策略梯度优化、教师混合采样和长度归一化来提高训练效果，适用于不同大小的模型。实验表明，MINILLM在指令跟随任务中优于标准KD方法，具有更低的曝光偏差、更好的校准和长文本生成性能。
订阅专栏 解锁全文
扫一扫
220
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
