轻量级行人属性识别算法设计

轻量级行人属性识别算法设计

一、算法框架设计

我们提出一种基于注意力机制与特征解耦的轻量级网络（ALFD-Net），整体架构包含三个核心模块：

import torch
import torch.nn as nn
from torchvision.models import mobilenet_v3_small

class ALFD_Net(nn.Module):
    def __init__(self, num_attributes, groups):
        super(ALFD_Net, self).__init__()
        # 轻量级骨干网络
        self.backbone = mobilenet_v3_small(pretrained=True)
        backbone_out = 576
        
        # 创新点1：多尺度特征融合模块
        self.msf = MultiScaleFusion(backbone_out)
        
        # 创新点2：属性感知注意力模块
        self.aa_attention = AttributeAwareAttention(backbone_out, groups)
        
        # 创新点3：动态权重解耦分类器
        self.classifier = DynamicDecouplingClassifier(backbone_out, num_attributes, groups)

    def forward(self, x):
        x = self.backbone.features(x)
        x = self.msf(x)  # 多尺度特征融合
        attn_map = self.aa_attention(x)  # 属性感知注意力
        x = x * attn_map  # 注意力加权
        logits = self.classifier(x)  # 动态解耦分类
        return logits

二、核心创新点

创新点1：多尺度特征融合模块（MSF）

class MultiScaleFusion(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, in_channels//4, 1),
            nn.BatchNorm2d(in_channels//4),
            nn.ReLU()
        )
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, in_channels//4, 3, padding=1),
            nn.BatchNorm2d(in_channels//4),
            nn.ReLU()
        )
        self.branch3