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/test0.py
+++ b/algorithms/lane_ufld/code/UFLD/test0.py
@@ -0,0 +1,54 @@
+import torch, os
+from model.model import parsingNet
+from utils.common import merge_config
+from utils.dist_utils import dist_print
+from evaluation.eval_wrapper import eval_lane
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if __name__ == "__main__":
+    torch.backends.cudnn.benchmark = True
+
+    args, cfg = merge_config()
+
+    print(cfg)
+
+    distributed = False
+    if 'WORLD_SIZE' in os.environ:
+        distributed = int(os.environ['WORLD_SIZE']) > 1
+
+    if distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method='env://')
+    dist_print('start testing...')
+    assert cfg.backbone in ['18','34','50','101','152','50next','101next','50wide','101wide']
+
+    if cfg.dataset == 'CULane':
+        cls_num_per_lane = 18
+    elif cfg.dataset == 'Tusimple':
+        cls_num_per_lane = 56
+    else:
+        raise NotImplementedError
+
+    net = parsingNet(pretrained=False, backbone=cfg.backbone, cls_dim=(cfg.griding_num+1, cls_num_per_lane,
+                                                                       cfg.num_lanes), use_aux=False).to(device)
+                                                                       # cfg.num_lanes), use_aux=False).cuda()
+    # we don't need auxiliary segmentation in testing
+
+    state_dict = torch.load(cfg.test_model, map_location='cuda')['model']
+    compatible_state_dict = {}
+    for k, v in state_dict.items():
+        if 'module.' in k:
+            compatible_state_dict[k[7:]] = v
+        else:
+            compatible_state_dict[k] = v
+
+    net.load_state_dict(compatible_state_dict, strict=False)
+
+    if distributed:
+        net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.local_rank])
+
+    if not os.path.exists(cfg.test_work_dir):
+        os.mkdir(cfg.test_work_dir)
+
+    eval_lane(net, cfg.dataset, cfg.data_root, cfg.test_work_dir, cfg.griding_num, False, distributed)