Initial commit: SmartCar Framework v0.1 — 龙芯2K0300智能车开发框架\n\n- HAL: GPIO/PWM/Encoder/Framebuffer 驱动\n- Control: PID/IMU/Motor/Servo 控制\n- Vision: HSV双Otsu→4点标定IPM→洪泛填充→逐行搜线\n- Strategy: 三区前瞻偏差+速度策略\n- Debug: 文件热调参+LCD预览+cv截帧\n- Scheduler: 5ms timerfd+epoll 中央调度器

model/model_heat_v4.py

@@ -0,0 +1,100 @@
+"""
+NanoDetHeat v4: 残差 + SE注意力 + 扩通道
+MACs: ~8.7M, 参数: ~18K, 目标: 20 TPS @ 0.44 GOPS (79%)
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+def conv_bn_relu(in_c, out_c, kernel, stride=1, groups=1):
+    pad = kernel // 2
+    return nn.Sequential(
+        nn.Conv2d(in_c, out_c, kernel, stride, pad, groups=groups, bias=False),
+        nn.BatchNorm2d(out_c),
+        nn.ReLU(inplace=True),
+    )
+
+
+class SEResDWBlock(nn.Module):
+    """残差DW + SE通道注意力"""
+    def __init__(self, in_c, out_c, stride=1, reduction=4):
+        super().__init__()
+        self.dw = conv_bn_relu(in_c, in_c, 3, stride=stride, groups=in_c)
+        self.pw = conv_bn_relu(in_c, out_c, 1)
+        
+        self.skip = nn.Identity() if (stride == 1 and in_c == out_c) else nn.Sequential(
+            nn.Conv2d(in_c, out_c, 1, stride, bias=False),
+            nn.BatchNorm2d(out_c),
+        )
+        
+        self.se = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(out_c, out_c // reduction, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_c // reduction, out_c, 1),
+            nn.Sigmoid(),
+        )
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        out = self.pw(self.dw(x))
+        out = out + self.skip(x)
+        return self.relu(out * self.se(out))
+
+
+class NanoDetHeatV4(nn.Module):
+    def __init__(self, num_classes=3):
+        super().__init__()
+        self.stem   = conv_bn_relu(3, 8, 3, stride=2)
+        self.block1 = SEResDWBlock(8, 16, stride=1)
+        self.block2 = SEResDWBlock(16, 28, stride=2)
+        self.block3 = SEResDWBlock(28, 40, stride=1)
+        self.block4 = SEResDWBlock(40, 56, stride=2)
+        self.block5 = SEResDWBlock(56, 64, stride=1)
+
+        self.shared = conv_bn_relu(64, 24, 1)
+        self.cls_head = nn.Conv2d(24, num_classes + 1, 1, bias=True)
+        self.size_head = nn.Conv2d(24, 2, 1, bias=True)
+        self._init_weights()
+
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None: nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1); nn.init.constant_(m.bias, 0)
+        self.cls_head.bias.data[-1] = 0.5  # 轻微背景偏置,焦点损失自动调整
+
+    def forward(self, x):
+        x = self.stem(x); x = self.block1(x); x = self.block2(x)
+        x = self.block3(x); x = self.block4(x); x = self.block5(x)
+        x = self.shared(x)
+        return torch.cat([self.cls_head(x), self.size_head(x)], dim=1)
+
+
+OUT_H, OUT_W = 15, 20
+STRIDE = 8
+
+
+if __name__ == "__main__":
+    m = NanoDetHeatV4()
+    p = sum(p.numel() for p in m.parameters())
+    
+    macs = (3*3*3*8*80*60 +                                        # stem
+            8*9*80*60 + 8*16*80*60 + 8*16*80*60 +                 # b1
+            16*9*40*30 + 16*28*40*30 + 16*28*40*30 +              # b2
+            28*9*40*30 + 28*40*40*30 +                             # b3
+            40*9*20*15 + 40*56*20*15 + 40*56*20*15 +              # b4
+            56*9*20*15 + 56*64*20*15 +                             # b5
+            64*24*20*15 + 24*4*20*15 + 24*2*20*15)                # heads
+    
+    print(f"Params: {p:,} = ~{p/1000:.1f}K")
+    print(f"MACs: {macs:,} = ~{macs/1e6:.2f}M")
+    print(f"20 TPS: {macs*20*2/1e9:.2f} GOPS ({(macs*20*2/0.44e9)*100:.0f}%)")
+    print(f"25 TPS: {macs*25*2/1e9:.2f} GOPS ({(macs*25*2/0.44e9)*100:.0f}%)")
+    x = torch.randn(1,3,120,160); y = m(x)
+    print(f"Output: {y.shape}")