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| import torch
import torch.nn as nn
class CrossModalTransformerFusion(nn.Module):
"""
跨模态Transformer融合层
论文核心：捕获模态间细粒度时空依赖
如：EEG theta波变化 ↔ 眼动注视丢失
"""
def __init__(self, d_model: int = 128,
n_heads: int = 8,
n_modalities: int = 5):
super().__init__()
self.modality_proj = nn.ModuleList([
nn.Linear(d_model, d_model)
for _ in range(n_modalities)
])
self.cross_attention = nn.MultiheadAttention(
embed_dim=d_model,
num_heads=n_heads,
dropout=0.1,
batch_first=True
)
self.ffn = nn.Sequential(
nn.Linear(d_model, d_model * 4),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(d_model * 4, d_model),
)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
def forward(self, modality_features: list) -> torch.Tensor:
"""
Args:
modality_features: [B, T, D] × n_modalities
Returns:
fused: [B, T, D] 融合后特征
"""
projected = [
proj(feat) for proj, feat in zip(
self.modality_proj, modality_features
)
]
all_features = torch.stack(projected, dim=1)
B, M, T, D = all_features.shape
all_features = all_features.view(B, M * T, D)
attended, _ = self.cross_attention(
all_features, all_features, all_features
)
x = self.norm1(all_features + attended)
ffn_out = self.ffn(x)
x = self.norm2(x + ffn_out)
x = x.view(B, M, T, D)
fused = x.mean(dim=1)
return fused
class AdaptiveMetaLearner(nn.Module):
"""
个性化元学习：MAML-based
论文Section 3.3:
- ≤5个样本适配新驾驶员
- 元学习初始化参数
"""
def __init__(self, model: nn.Module,
inner_lr: float = 0.01,
outer_lr: float = 0.001,
inner_steps: int = 5):
super().__init__()
self.model = model
self.inner_lr = inner_lr
self.outer_lr = outer_lr
self.inner_steps = inner_steps
def meta_train_step(self, support_set, query_set):
"""
MAML元训练步骤
Args:
support_set: 新驾驶员的少量样本（≤5个）
query_set: 查询集用于评估
"""
fast_weights = {}
for name, param in self.model.named_parameters():
fast_weights[name] = param.clone()
for step in range(self.inner_steps):
support_loss = self._compute_loss(
self.model, support_set, fast_weights
)
grads = torch.autograd.grad(
support_loss, fast_weights.values()
)
fast_weights = {
name: w - self.inner_lr * g
for (name, w), g in zip(fast_weights.items(), grads)
}
query_loss = self._compute_loss(
self.model, query_set, fast_weights
)
return query_loss
def _compute_loss(self, model, data, weights):
"""用fast weights计算损失"""
x, y = data
output = model.forward_with_weights(x, weights)
return nn.functional.cross_entropy(output, y)
class FederatedOptimizer:
"""
联邦优化器
论文Section 3.4:
- 去中心化训练
- 自适应梯度压缩
- 非IID数据处理
"""
def __init__(self, global_model: nn.Module,
n_clients: int,
compression_ratio: float = 0.1):
self.global_model = global_model
self.n_clients = n_clients
self.compression_ratio = compression_ratio
def client_update(self, client_id: int,
local_data, epochs: int = 5) -> dict:
"""客户端本地训练"""
local_model = copy.deepcopy(self.global_model)
local_model.train()
optimizer = torch.optim.Adam(
local_model.parameters(), lr=1e-3
)
for epoch in range(epochs):
for batch in local_data:
optimizer.zero_grad()
loss = self._compute_loss(local_model, batch)
loss.backward()
optimizer.step()
global_state = self.global_model.state_dict()
local_state = local_model.state_dict()
updates = {
k: local_state[k] - global_state[k]
for k in global_state
}
compressed = self._compress(updates)
return compressed
def _compress(self, updates: dict) -> dict:
"""Top-k梯度压缩"""
all_vals = torch.cat([
v.flatten() for v in updates.values()
])
k = int(len(all_vals) * self.compression_ratio)
topk_vals, topk_idx = torch.topk(all_vals.abs(), k)
mask = torch.zeros_like(all_vals)
mask[topk_idx] = 1.0
compressed = {}
offset = 0
for name, update in updates.items():
n = update.numel()
compressed[name] = update * mask[offset:offset+n].view_as(update)
offset += n
return compressed
def aggregate(self, client_updates: list,
client_weights: list) -> dict:
"""FedAvg聚合"""
global_state = self.global_model.state_dict()
aggregated = {k: torch.zeros_like(v)
for k, v in global_state.items()}
for update, weight in zip(client_updates, client_weights):
for k in aggregated:
aggregated[k] += update[k] * weight
for k in global_state:
global_state[k] += aggregated[k]
self.global_model.load_state_dict(global_state)
if __name__ == "__main__":
import copy
fusion = CrossModalTransformerFusion(d_model=128, n_modalities=5)
modalities = [torch.randn(4, 30, 128) for _ in range(5)]
fused = fusion(modalities)
print(f"输入: 5个模态 × {modalities[0].shape}")
print(f"融合输出: {fused.shape}")
print(f"参数量: {sum(p.numel() for p in fusion.parameters()):,}")
|