feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-25 16:59:59 +08:00
commit 7c43b44c57
1619 changed files with 373355 additions and 0 deletions
--- a/algorithms/lane_ufld/code/UFLD/pth_to_onnx.py
+++ b/algorithms/lane_ufld/code/UFLD/pth_to_onnx.py
@@ -0,0 +1,99 @@
+"""Export UFLD .pth checkpoint to ONNX (matches tusimple_res18_4lane_v1 / best.pth)."""
+
+import argparse
+import os
+
+import torch
+
+from model.model import parsingNet
+from utils.common import checkpoint_state_dict
+from utils.config import Config
+
+
+def export_onnx(
+    model_path,
+    output_path=None,
+    backbone='18',
+    griding_num=100,
+    num_lanes=4,
+    opset=11,
+):
+    if output_path is None:
+        base, _ = os.path.splitext(model_path)
+        output_path = base + '.onnx'
+
+    cls_num_per_lane = 56  # TuSimple row anchors
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    net = parsingNet(
+        pretrained=False,
+        backbone=backbone,
+        cls_dim=(griding_num + 1, cls_num_per_lane, num_lanes),
+        use_aux=False,
+    ).to(device)
+    net.load_state_dict(checkpoint_state_dict(model_path, map_location=device), strict=False)
+    net.eval()
+
+    dummy = torch.randn(1, 3, 288, 800, device=device)
+    os.makedirs(os.path.dirname(output_path) or '.', exist_ok=True)
+    torch.onnx.export(
+        net,
+        dummy,
+        output_path,
+        opset_version=opset,
+        input_names=['input'],
+        output_names=['output'],
+        dynamic_axes={'input': {0: 'batch'}, 'output': {0: 'batch'}},
+        dynamo=False,  # legacy exporter embeds weights (PyTorch 2.9+ default may omit)
+    )
+    print('saved:', os.path.abspath(output_path))
+    print('input:  [1, 3, 288, 800]')
+    print('output:', tuple(net(dummy).shape))
+    return output_path
+
+
+def main():
+    default_pth = 'log/20250702_165153_lr_1e-05_b_32_ufld_2lanes_res18/best.pth'
+    parser = argparse.ArgumentParser(description='Export UFLD checkpoint to ONNX')
+    parser.add_argument('--config', default=None, help='optional py config (overrides defaults)')
+    parser.add_argument('--model_path', default=default_pth, help='path to .pth')
+    parser.add_argument('--output', default=None, help='output .onnx path')
+    parser.add_argument('--backbone', default=None)
+    parser.add_argument('--griding_num', type=int, default=None)
+    parser.add_argument('--num_lanes', type=int, default=None)
+    parser.add_argument('--opset', type=int, default=11)
+    args = parser.parse_args()
+
+    backbone = '18'
+    griding_num = 100
+    num_lanes = 4
+    model_path = args.model_path
+    output = args.output
+
+    if args.config:
+        cfg = Config.fromfile(args.config)
+        backbone = cfg.backbone
+        griding_num = cfg.griding_num
+        num_lanes = cfg.num_lanes
+        if getattr(cfg, 'test_model', None):
+            model_path = cfg.test_model
+
+    if args.backbone is not None:
+        backbone = args.backbone
+    if args.griding_num is not None:
+        griding_num = args.griding_num
+    if args.num_lanes is not None:
+        num_lanes = args.num_lanes
+
+    export_onnx(
+        model_path,
+        output_path=output,
+        backbone=backbone,
+        griding_num=griding_num,
+        num_lanes=num_lanes,
+        opset=args.opset,
+    )
+
+
+if __name__ == '__main__':
+    main()