fix: fix the return type of DeepEval.get_model_def_script() for all backends

deepmd/dpmodel/infer/deep_eval.py

@@ -90,6 +90,7 @@ def __init__(
 
         model_data = load_dp_model(model_file)
         self.dp = BaseModel.deserialize(model_data["model"])
+        self.dp.model_def_script = json.dumps(model_data.get("model_def_script", {}))
         self.rcut = self.dp.get_rcut()
         self.type_map = self.dp.get_type_map()
         if isinstance(auto_batch_size, bool):

deepmd/jax/infer/deep_eval.py

@@ -104,7 +104,7 @@ def __init__(
                 stablehlo_atomic_virial_no_ghost=model_data["@variables"][
                     "stablehlo_atomic_virial_no_ghost"
                 ].tobytes(),
-                model_def_script=model_data["model_def_script"],
+                model_def_script=json.dumps(model_data["model_def_script"]),
                 **model_data["constants"],
             )
         elif model_file.endswith(".savedmodel"):

deepmd/pd/infer/deep_eval.py

@@ -726,7 +726,7 @@ def eval_typeebd(self) -> np.ndarray:
         typeebd = paddle.concat(out, axis=1)
         return to_numpy_array(typeebd)
 
-    def get_model_def_script(self) -> str:
+    def get_model_def_script(self) -> dict:
         """Get model definition script."""
         return self.model_def_script
 

deepmd/pt/infer/deep_eval.py

@@ -684,7 +684,7 @@ def eval_typeebd(self) -> np.ndarray:
         typeebd = torch.cat(out, dim=1)
         return to_numpy_array(typeebd)
 
-    def get_model_def_script(self) -> str:
+    def get_model_def_script(self) -> dict:
         """Get model definition script."""
         return self.model_def_script
 

source/tests/consistent/io/test_io.py

@@ -158,6 +158,7 @@ def test_deep_eval(self) -> None:
                 prefix + backend.suffixes[suffix_idx], reference_data
             )
             deep_eval = DeepEval(prefix + backend.suffixes[suffix_idx])
+            self.assertIsInstance(deep_eval.get_model_def_script(), dict)
             if deep_eval.get_dim_fparam() > 0:
                 fparam = np.ones((nframes, deep_eval.get_dim_fparam()))
             else: