Add async model thread, vision FPS throttle, configurable FPS, .gitignore __pycache__

model/export_weights.py

@@ -0,0 +1,51 @@
+"""
+NanoDetHeatV4 权重导出: PyTorch. pth → 二进制
+"""
+import sys, struct, os
+sys.path.insert(0, os.path.dirname(__file__))
+import torch
+import numpy as np
+from model_heat_v4 import NanoDetHeatV4
+
+def export(pth_path, out_path):
+    model = NanoDetHeatV4(num_classes=3)
+    ckpt = torch.load(pth_path, map_location='cpu', weights_only=False)
+    if 'model' in ckpt:
+        ckpt = ckpt['model']
+    model.load_state_dict(ckpt, strict=False)
+    model.eval()
+
+    layers = {}
+    for name, param in model.named_parameters():
+        layers[name] = param.detach().cpu().numpy().astype(np.float32)
+    for name, buf in model.named_buffers():
+        layers[name] = buf.detach().cpu().numpy().astype(np.float32)
+
+    total = sum(v.size for v in layers.values())
+    print(f"层数: {len(layers)}, 总参数: {total:,} ≈ {total/1000:.1f}K")
+
+    for k, v in sorted(layers.items()):
+        print(f"  {k:50s}  {list(v.shape)}  {v.size:,}")
+
+    with open(out_path, 'wb') as f:
+        f.write(struct.pack('I', len(layers)))
+        for name in sorted(layers.keys()):
+            data = layers[name]
+            nb = name.encode('utf-8')
+            f.write(struct.pack('I', len(nb)))
+            f.write(nb)
+            f.write(struct.pack('I', data.ndim))
+            f.write(struct.pack('i' * data.ndim, *data.shape))
+            f.write(data.tobytes())
+
+    print(f"\n导出: {out_path}")
+
+    # 测试推理
+    x = torch.randn(1, 3, 120, 160)
+    with torch.no_grad():
+        y = model(x)
+    print(f"输入: {x.shape} → 输出: {y.shape}")
+
+if __name__ == '__main__':
+    base = os.path.dirname(os.path.abspath(__file__))
+    export(os.path.join(base, 'best.pth.1'), os.path.join(base, 'nanodet.bin'))