From e74a6ac99351fd88d2e2ec020855e934f5db571a Mon Sep 17 00:00:00 2001
From: root <2000011006@stu.pku.edu.cn>
Date: Mon, 23 Dec 2024 13:15:37 +0800
Subject: [PATCH] change var_name to property_name

---
 deepmd/pt/infer/deep_eval.py            |  2 +-
 deepmd/pt/model/model/property_model.py |  2 +-
 deepmd/pt/model/task/fitting.py         |  4 +---
 deepmd/pt/model/task/property.py        |  4 +---
 deepmd/utils/out_stat.py                | 20 ++++++++++++--------
 5 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/deepmd/pt/infer/deep_eval.py b/deepmd/pt/infer/deep_eval.py
index 233a8e7943..3259f8c6a2 100644
--- a/deepmd/pt/infer/deep_eval.py
+++ b/deepmd/pt/infer/deep_eval.py
@@ -204,7 +204,7 @@ def model_type(self) -> type["DeepEvalWrapper"]:
             return DeepGlobalPolar
         elif "wfc" in model_output_type:
             return DeepWFC
-        elif "property" in model_output_type:
+        elif self.dp.model["Default"].get_property_name() in model_output_type:
             return DeepProperty
         else:
             raise RuntimeError("Unknown model type")
diff --git a/deepmd/pt/model/model/property_model.py b/deepmd/pt/model/model/property_model.py
index 2da7995c80..e0db502aaf 100644
--- a/deepmd/pt/model/model/property_model.py
+++ b/deepmd/pt/model/model/property_model.py
@@ -76,7 +76,7 @@ def get_task_dim(self) -> int:
     @torch.jit.export
     def get_intensive(self) -> bool:
         """Get whether the property is intensive."""
-        return self.model_output_def()["property"].intensive
+        return self.model_output_def()[self.get_property_name()].intensive
 
     @torch.jit.export
     def get_property_name(self) -> str:
diff --git a/deepmd/pt/model/task/fitting.py b/deepmd/pt/model/task/fitting.py
index 6eee56c8a0..4a4b8c0a5a 100644
--- a/deepmd/pt/model/task/fitting.py
+++ b/deepmd/pt/model/task/fitting.py
@@ -368,9 +368,7 @@ def set_case_embd(self, case_idx: int):
         ]
 
     def get_compute_stats_do_not_distinguish_types(self) -> bool:
-        """
-        Get whether the fitting net computes stats which are not distinguished between different types of atoms.
-        """
+        """Get whether the fitting net computes stats which are not distinguished between different types of atoms."""
         return False
 
     def __setitem__(self, key, value) -> None:
diff --git a/deepmd/pt/model/task/property.py b/deepmd/pt/model/task/property.py
index f0cfa6f5d9..90bd66c0fb 100644
--- a/deepmd/pt/model/task/property.py
+++ b/deepmd/pt/model/task/property.py
@@ -127,9 +127,7 @@ def output_def(self) -> FittingOutputDef:
         )
 
     def get_compute_stats_do_not_distinguish_types(self) -> bool:
-        """
-        Get whether the fitting net computes stats which are not distinguished between different types of atoms.
-        """
+        """Get whether the fitting net computes stats which are not distinguished between different types of atoms."""
         return True
 
     @classmethod
diff --git a/deepmd/utils/out_stat.py b/deepmd/utils/out_stat.py
index 4198427414..5a64138a40 100644
--- a/deepmd/utils/out_stat.py
+++ b/deepmd/utils/out_stat.py
@@ -175,13 +175,17 @@ def compute_stats_do_not_distinguish_types(
     assert natoms.ndim == 2
     assert output_redu.shape[0] == natoms.shape[0]  # [nf,1]
 
-    computed_output_bias = np.repeat(
-        np.mean(output_redu, axis=0)[np.newaxis, :], ntypes, axis=0
-    )
-    output_std = np.std(output_redu, axis=0)
-
-    computed_output_bias = computed_output_bias.reshape([natoms.shape[1]] + var_shape)  # noqa: RUF005
-    output_std = output_std.reshape(var_shape)
-    output_std = np.tile(output_std, (computed_output_bias.shape[0], 1))
+    if intensive:
+        computed_output_bias = np.repeat(
+            np.mean(output_redu, axis=0)[np.newaxis, :], ntypes, axis=0
+        )
+        output_std = np.std(output_redu, axis=0)
+
+        computed_output_bias = computed_output_bias.reshape([natoms.shape[1]] + var_shape)  # noqa: RUF005
+        output_std = output_std.reshape(var_shape)
+        output_std = np.tile(output_std, (computed_output_bias.shape[0], 1))
+    else:
+        # TODO: implement extensive
+        pass
 
     return computed_output_bias, output_std