import torch
import torch.nn as nn


# 轻量双通道注意力
class LiteDualAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.channel_attn = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_channels, in_channels // 16, 1, bias=False),
            nn.SiLU(),
            nn.Conv2d(in_channels // 16, in_channels, 1, bias=False),
            nn.Sigmoid()
        )

        self.spatial_attn = nn.Sequential(
            nn.Conv2d(2, 1, 5, padding=2, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        # 通道加权
        ca = self.channel_attn(x) * x

        # 空间特征统计（均值+标准差）
        stats = torch.cat([
            x.mean(dim=1, keepdim=True),
            x.std(dim=1, keepdim=True)
        ], dim=1)

        # 空间加权（5x5卷积生成单通道注意力）
        sa = self.spatial_attn(stats) * ca
        return sa


class CNNClassifier(nn.Module):
    def __init__(self, num_classes):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False),
            LiteDualAttention(32),
            nn.BatchNorm2d(32),
            nn.Hardswish(inplace=True),

            nn.Conv2d(32, 48, 3, stride=2, padding=1, bias=False),
            LiteDualAttention(48),
            nn.BatchNorm2d(48),
            nn.Hardswish(inplace=True),

            nn.Conv2d(48, 64, 3, stride=2, padding=1, bias=False),
            LiteDualAttention(64),
            nn.BatchNorm2d(64),
            nn.Hardswish(inplace=True),

            nn.Conv2d(64, 96, 3, padding=2, dilation=2, bias=False),
            LiteDualAttention(96),
            nn.BatchNorm2d(96),
            nn.Hardswish(inplace=True)
        )

        self.classifier = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(96, 256),
            nn.Hardswish(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        return self.classifier(self.features(x))