feat: 添加模型转换工具(Mild v13 PyTorch->bin导出)

- tools/export_mild_v13.py: PyTorch .pth -> 自定义 .bin 导出脚本
- tools/inspect_pth.py: 检查 .pth 权重文件结构
- tools/best_v13_mild_r2.pth: 训练好的 PyTorch 权重
- .gitignore: 追加 build 变体目录和生成文件

.gitignore

@@ -8,7 +8,15 @@
 build/*
 build2/*
 build3/*
+build4/*
+build_lsx/*
+build_new/*
 test/
 
+ZebraCrossing_Detection-master/
+ZebraCrossing_Detection-master.zip
+smartcar2_code.txt
+smartcar_test
+
 # Local History for Visual Studio Code
 .history/

tools/export_mild_v13.py

@@ -0,0 +1,123 @@
+"""
+Mild Mega r2 → mild_v13.bin 导出
+架构: 3→9→9→13→13→19→19→19→44→44→64→64 → head(dw,64→64) → out(64→9)
+无残差连接, 纯顺序 conv_bn_relu + SE
+"""
+import sys, struct, os
+import torch
+import torch.nn as nn
+import numpy as np
+
+class MildMegaR2(nn.Module):
+    def __init__(self, num_classes=4):
+        super().__init__()
+        channels = [3, 9, 9, 13, 13, 19, 19, 19, 44, 44, 64, 64]
+        strides  = [2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1]
+
+        for i in range(11):
+            in_c, out_c, s = channels[i], channels[i + 1], strides[i]
+            if s == 2:
+                conv = nn.Conv2d(in_c, out_c, 3, s, 1, bias=False)
+            elif in_c == out_c:
+                conv = nn.Conv2d(in_c, out_c, 3, 1, 1, groups=in_c, bias=False)
+            else:
+                conv = nn.Conv2d(in_c, out_c, 1, 1, 0, bias=False)
+
+            setattr(self, f'b{i + 1}', nn.Sequential(
+                conv,
+                nn.BatchNorm2d(out_c),
+                nn.ReLU(inplace=True),
+            ))
+
+            mid = out_c // 2
+            setattr(self, f'se_{i}', nn.Sequential(
+                nn.AdaptiveAvgPool2d(1),
+                nn.Conv2d(out_c, mid, 1, bias=True),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(mid, out_c, 1, bias=True),
+                nn.Sigmoid(),
+            ))
+
+        # head: depthwise 3x3, 64→64
+        self.head = nn.Sequential(
+            nn.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+        )
+        # output: 1x1 conv, 64→(4 class + 1 bg + 4 bbox) = 9
+        self.out = nn.Conv2d(64, 9, 1, bias=True)
+
+    def forward(self, x):
+        for i in range(11):
+            x = getattr(self, f'b{i + 1}')(x)
+            se_out = getattr(self, f'se_{i}')(x)
+            x = x * se_out
+        x = self.head(x)
+        x = self.out(x)
+        return x
+
+
+def export(pth_path, bin_path):
+    model = MildMegaR2(num_classes=4)
+    ckpt = torch.load(pth_path, map_location='cpu', weights_only=False)
+
+    # Handle wrapped checkpoint
+    if isinstance(ckpt, dict) and 'model' in ckpt:
+        ckpt = ckpt['model']
+    if isinstance(ckpt, dict) and 'state_dict' in ckpt:
+        ckpt = ckpt['state_dict']
+
+    # Remove 'module.' prefix (from DataParallel/DDP)
+    cleaned = {}
+    for k, v in ckpt.items():
+        nk = k.replace('module.', '')
+        cleaned[nk] = v
+
+    model.load_state_dict(cleaned, strict=False)
+    model.eval()
+
+    params = {}
+    for name, param in model.named_parameters():
+        params[name] = param.detach().cpu().numpy().astype(np.float32)
+    for name, buf in model.named_buffers():
+        params[name] = buf.detach().cpu().numpy().astype(np.float32)
+
+    total = sum(v.size for v in params.values())
+    print(f"Layers: {len(params)}, params: {total:,} ≈ {total / 1000:.1f}K")
+
+    for k, v in sorted(params.items()):
+        sh = str(list(v.shape))
+        print(f"  {k:50s} {sh:25s} {v.size:,}")
+
+    with open(bin_path, 'wb') as f:
+        f.write(struct.pack('i', len(params)))
+        for name in sorted(params.keys()):
+            arr = params[name]
+            nb = name.encode('utf-8')
+            f.write(struct.pack('i', len(nb)))
+            f.write(nb)
+            f.write(struct.pack('i', arr.ndim))
+            for d in arr.shape:
+                f.write(struct.pack('i', d))
+            f.write(arr.tobytes())
+
+    size_kb = os.path.getsize(bin_path) / 1024
+    print(f"\nExported: {bin_path} ({size_kb:.0f} KB)")
+
+    # Quick inference test
+    x = torch.randn(1, 3, 120, 160)
+    with torch.no_grad():
+        y = model(x)
+    print(f"Input: {list(x.shape)} → Output: {list(y.shape)} (OK)")
+
+    return True
+
+
+if __name__ == '__main__':
+    import os
+    os.chdir(os.path.dirname(os.path.abspath(__file__)))
+    pth = 'best_v13_mild_r2.pth'
+    if not os.path.exists(pth):
+        print(f"ERROR: {pth} not found")
+        sys.exit(1)
+    export(pth, 'mild_v13.bin')

tools/inspect_pth.py

@@ -0,0 +1,18 @@
+import torch, sys, os
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+pth = 'best_v13_mild_r2.pth'
+ckpt = torch.load(pth, map_location='cpu', weights_only=False)
+print(f"Type: {type(ckpt).__name__}")
+if isinstance(ckpt, dict):
+    if 'model' in ckpt:
+        print("Has 'model' key")
+        ckpt = ckpt['model']
+    if 'state_dict' in ckpt:
+        print("Has 'state_dict' key")
+        ckpt = ckpt['state_dict']
+    print(f"\nTotal keys: {len(ckpt)}")
+    for k, v in sorted(ckpt.items()):
+        shape = tuple(v.shape) if hasattr(v, 'shape') else 'scalar'
+        print(f"  {k:50s} {str(shape)}")
+else:
+    print("Not a dict, top-level keys unknown")