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| import torch
import torch.nn as nn
from mobilenetv3 import MobileNetV3
class GazeLNN(nn.Module):
"""
GazeLNN: 基于液态神经网络的视线预测模型
架构：
1. MobileNetV3 特征提取器（轻量级）
2. Liquid Neural Network 循环核心（自适应时序）
3. 自回归预测（auto-regressive）
特点：
- 输入依赖的时间动态（LNN核心优势）
- 计算量仅 0.61 GFLOPs
- 支持88个注视点的序列预测
"""
def __init__(self, config: dict):
super().__init__()
self.feature_extractor = MobileNetV3(
width_mult=0.75,
output_stride=16
)
self.lnn_core = LiquidNeuralNetwork(
input_dim=256,
hidden_dim=128,
output_dim=64,
num_layers=2,
solver='rk4'
)
self.fixation_head = nn.Sequential(
nn.Linear(64, 128),
nn.ReLU(),
nn.Linear(128, 88 * 2)
)
self.history_encoder = nn.GRU(
input_size=2,
hidden_size=64,
num_layers=1,
batch_first=True
)
def forward(self, image: torch.Tensor, history_fixations: torch.Tensor = None):
"""
前向传播
Args:
image: 输入图像，shape=(B, 3, H, W)
history_fixations: 历史注视点，shape=(B, T, 2)
Returns:
predicted_fixations: 预测注视点序列，shape=(B, 88, 2)
"""
B = image.shape[0]
features = self.feature_extractor(image)
features = features.mean(dim=[2, 3])
if history_fixations is not None:
history_emb, _ = self.history_encoder(history_fixations)
history_emb = history_emb[:, -1, :]
features = features + history_emb
lnn_output = self.lnn_core(features)
fixations = self.fixation_head(lnn_output)
fixations = fixations.view(B, 88, 2)
return fixations
class LiquidNeuralNetwork(nn.Module):
"""
液态神经网络（Liquid Neural Network, LNN）
核心原理：
- 连续时间动态：基于ODE求解
- 输入依赖时间常数：自适应响应速度
- 状态空间模型：类似RNN但更灵活
优势：
- 计算效率高：仅需 0.61 GFLOPs
- 实时性强：推理速度 6.84 ms
- 自适应：输入相关的时间尺度
"""
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int,
num_layers: int = 1, solver: str = 'rk4'):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.solver = solver
self.time_constant = nn.Linear(input_dim, hidden_dim)
self.W_hh = nn.Linear(hidden_dim, hidden_dim)
self.W_xh = nn.Linear(input_dim, hidden_dim)
self.output_proj = nn.Linear(hidden_dim, output_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
前向传播（连续时间积分）
Args:
x: 输入特征，shape=(B, input_dim)
Returns:
output: shape=(B, output_dim)
"""
B = x.shape[0]
h = torch.zeros(B, self.hidden_dim, device=x.device)
tau = torch.sigmoid(self.time_constant(x))
T = 10
for t in range(T):
if self.solver == 'euler':
dh = (-h / tau + torch.tanh(self.W_hh(h) + self.W_xh(x)))
h = h + dh * (1.0 / T)
elif self.solver == 'rk4':
k1 = self._derivative(h, x, tau)
k2 = self._derivative(h + 0.5 * k1, x, tau)
k3 = self._derivative(h + 0.5 * k2, x, tau)
k4 = self._derivative(h + k3, x, tau)
h = h + (k1 + 2*k2 + 2*k3 + k4) / 6.0
output = self.output_proj(h)
return output
def _derivative(self, h: torch.Tensor, x: torch.Tensor, tau: torch.Tensor):
"""计算状态导数"""
return (-h / tau + torch.tanh(self.W_hh(h) + self.W_xh(x)))
if __name__ == "__main__":
model = GazeLNN({})
num_params = sum(p.numel() for p in model.parameters())
print(f"参数量: {num_params / 1e6:.2f} M")
image = torch.randn(1, 3, 224, 224)
history = torch.randn(1, 10, 2)
import time
start = time.time()
output = model(image, history)
elapsed = time.time() - start
print(f"输出形状: {output.shape}")
print(f"推理时间: {elapsed * 1000:.2f} ms")
|