diff --git a/pcdet/models/__init__.py b/pcdet/models/__init__.py
new file mode 100644
index 0000000..7049bb4
--- /dev/null
+++ b/pcdet/models/__init__.py
@@ -0,0 +1,54 @@
+from collections import namedtuple
+
+import numpy as np
+import torch
+
+from .detectors import build_detector
+
+try:
+    import kornia
+except:
+    pass 
+    # print('Warning: kornia is not installed. This package is only required by CaDDN')
+
+
+
+def build_network(model_cfg, num_class, dataset):
+    model = build_detector(
+        model_cfg=model_cfg, num_class=num_class, dataset=dataset
+    )
+    return model
+
+
+def load_data_to_gpu(batch_dict):
+    for key, val in batch_dict.items():
+        if key == 'camera_imgs':
+            batch_dict[key] = val.cuda()
+        elif not isinstance(val, np.ndarray):
+            continue
+        elif key in ['frame_id', 'metadata', 'calib', 'image_paths','ori_shape','img_process_infos']:
+            continue
+        elif key in ['images']:
+            batch_dict[key] = kornia.image_to_tensor(val).float().cuda().contiguous()
+        elif key in ['image_shape']:
+            batch_dict[key] = torch.from_numpy(val).int().cuda()
+        else:
+            batch_dict[key] = torch.from_numpy(val).float().cuda()
+
+
+def model_fn_decorator():
+    ModelReturn = namedtuple('ModelReturn', ['loss', 'tb_dict', 'disp_dict'])
+
+    def model_func(model, batch_dict):
+        load_data_to_gpu(batch_dict)
+        ret_dict, tb_dict, disp_dict = model(batch_dict)
+
+        loss = ret_dict['loss'].mean()
+        if hasattr(model, 'update_global_step'):
+            model.update_global_step()
+        else:
+            model.module.update_global_step()
+
+        return ModelReturn(loss, tb_dict, disp_dict)
+
+    return model_func