resolve comments

Author: ChiahsinChu

deepmd/dpmodel/fitting/general_fitting.py

@@ -155,15 +155,17 @@ def __init__(
             self.fparam_inv_std = np.ones(self.numb_fparam)  # pylint: disable=no-explicit-dtype
         else:
             self.fparam_avg, self.fparam_inv_std = None, None
-        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
+        if self.numb_aparam > 0:
             self.aparam_avg = np.zeros(self.numb_aparam)  # pylint: disable=no-explicit-dtype
             self.aparam_inv_std = np.ones(self.numb_aparam)  # pylint: disable=no-explicit-dtype
         else:
             self.aparam_avg, self.aparam_inv_std = None, None
         # init networks
-        in_dim = self.dim_descrpt + self.numb_fparam
-        if not self.use_aparam_as_mask:
-            in_dim += self.numb_aparam
+        in_dim = (
+            self.dim_descrpt
+            + self.numb_fparam
+            + (0 if self.use_aparam_as_mask else self.numb_aparam)
+        )
         self.nets = NetworkCollection(
             1 if not self.mixed_types else 0,
             self.ntypes,
@@ -391,7 +393,7 @@ def _call_common(
                     axis=-1,
                 )
         # check aparam dim, concate to input descriptor
-        if not self.use_aparam_as_mask and self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             assert aparam is not None, "aparam should not be None"
             if aparam.shape[-1] != self.numb_aparam:
                 raise ValueError(

deepmd/pt/model/task/fitting.py

@@ -198,7 +198,7 @@ def __init__(
             )
         else:
             self.fparam_avg, self.fparam_inv_std = None, None
-        if not self.use_aparam_as_mask and self.numb_aparam > 0:
+        if self.numb_aparam > 0:
             self.register_buffer(
                 "aparam_avg",
                 torch.zeros(self.numb_aparam, dtype=self.prec, device=device),
@@ -210,9 +210,11 @@ def __init__(
         else:
             self.aparam_avg, self.aparam_inv_std = None, None
 
-        in_dim = self.dim_descrpt + self.numb_fparam
-        if not self.use_aparam_as_mask:
-            in_dim += self.numb_aparam
+        in_dim = (
+            self.dim_descrpt
+            + self.numb_fparam
+            + (0 if self.use_aparam_as_mask else self.numb_aparam)
+        )
 
         self.filter_layers = NetworkCollection(
             1 if not self.mixed_types else 0,
@@ -444,7 +446,7 @@ def _forward_common(
                     dim=-1,
                 )
         # check aparam dim, concate to input descriptor
-        if not self.use_aparam_as_mask and self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             assert aparam is not None, "aparam should not be None"
             assert self.aparam_avg is not None
             assert self.aparam_inv_std is not None

deepmd/tf/fit/ener.py

@@ -340,7 +340,7 @@ def compute_input_stats(self, all_stat: dict, protection: float = 1e-2) -> None:
                     self.fparam_std[ii] = protection
             self.fparam_inv_std = 1.0 / self.fparam_std
         # stat aparam
-        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
+        if self.numb_aparam > 0:
             sys_sumv = []
             sys_sumv2 = []
             sys_sumn = []
@@ -505,7 +505,7 @@ def build(
                 self.fparam_avg = 0.0
             if self.fparam_inv_std is None:
                 self.fparam_inv_std = 1.0
-        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
+        if self.numb_aparam > 0:
             if self.aparam_avg is None:
                 self.aparam_avg = 0.0
             if self.aparam_inv_std is None:
@@ -561,7 +561,7 @@ def build(
                     trainable=False,
                     initializer=tf.constant_initializer(self.fparam_inv_std),
                 )
-            if self.numb_aparam > 0 and not self.use_aparam_as_mask:
+            if self.numb_aparam > 0:
                 t_aparam_avg = tf.get_variable(
                     "t_aparam_avg",
                     self.numb_aparam,
@@ -602,7 +602,7 @@ def build(
             fparam = (fparam - t_fparam_avg) * t_fparam_istd
 
         aparam = None
-        if not self.use_aparam_as_mask and self.numb_aparam > 0:
+        if self.numb_aparam > 0 and not self.use_aparam_as_mask:
             aparam = input_dict["aparam"]
             aparam = tf.reshape(aparam, [-1, self.numb_aparam])
             aparam = (aparam - t_aparam_avg) * t_aparam_istd
@@ -895,9 +895,6 @@ def serialize(self, suffix: str = "") -> dict:
         dict
             The serialized data
         """
-        in_dim = self.dim_descrpt + self.numb_fparam
-        if not self.use_aparam_as_mask:
-            in_dim += self.numb_aparam
         data = {
             "@class": "Fitting",
             "type": "ener",
@@ -924,7 +921,11 @@ def serialize(self, suffix: str = "") -> dict:
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
                 ndim=0 if self.mixed_types else 1,
-                in_dim=in_dim,
+                in_dim=(
+                    self.dim_descrpt
+                    + self.numb_fparam
+                    + (0 if self.use_aparam_as_mask else self.numb_aparam)
+                ),
                 neuron=self.n_neuron,
                 activation_function=self.activation_function_name,
                 resnet_dt=self.resnet_dt,