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>

algorithms/lane_ufld/code/pytorch-auto-drive-master/utils/common.py

@@ -0,0 +1,69 @@
+import torch
+from collections import OrderedDict
+
+from .ddp_utils import save_on_master
+
+
+def get_warnings():
+    # Get rid of the extra line of code printing
+    # https://stackoverflow.com/a/26433913/15449902
+    import warnings
+
+    def warning_on_one_line(message, category, filename, lineno, file=None, line=None):
+        return '%s:%s: %s: %s\n' % (filename, lineno, category.__name__, message)
+
+    warnings.formatwarning = warning_on_one_line
+
+    return warnings
+
+
+warnings = get_warnings()
+
+
+# Save model checkpoints (supports amp)
+def save_checkpoint(net, optimizer, lr_scheduler, filename='temp.pt'):
+    checkpoint = {
+        'model': net.state_dict(),
+        'optimizer': optimizer.state_dict() if optimizer is not None else None,
+        'lr_scheduler': lr_scheduler.state_dict() if lr_scheduler is not None else None
+    }
+    save_on_master(checkpoint, filename)
+
+
+# Load model checkpoints (supports amp)
+def load_checkpoint(net, optimizer, lr_scheduler, filename, strict=True):
+    try:
+        checkpoint = torch.load(filename, map_location='cpu')
+    except:
+        warnings.warn('Model not saved as on cpu, could be a legacy trained weight, trying loading on saved device...')
+        checkpoint = torch.load(filename)
+        print('Loaded on saved device.')
+
+    # To keep BC while having a acceptable variable name for lane detection
+    checkpoint['model'] = OrderedDict((k.replace('aux_head', 'lane_classifier') if 'aux_head' in k else k, v)
+                                      for k, v in checkpoint['model'].items())
+    # state_dict = checkpoint['model']
+    # self_state_dict = net.state_dict()
+    # self_keys = list(self_state_dict.keys())
+    # for i, (_, v) in enumerate(state_dict.items()):
+    #     if i > len(self_keys) - 1:
+    #         break
+    #     self_state_dict[self_keys[i]] = v
+    #
+    # # for k, v in state_dict.items():
+    # #     print(k)
+    # # quit(0)
+    net.load_state_dict(checkpoint['model'], strict=strict)
+
+    if optimizer is not None:
+        try:  # Shouldn't be necessary, but just in case
+            optimizer.load_state_dict(checkpoint['optimizer'])
+        except RuntimeError:
+            warnings.warn('Incorrect optimizer state dict, maybe you are using old code with aux_head?')
+            pass
+    if lr_scheduler is not None:
+        try:  # Shouldn't be necessary, but just in case
+            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+        except RuntimeError:
+            warnings.warn('Incorrect lr scheduler state dict, maybe you are using old code with aux_head?')
+            pass