fix: fix type hint of sel (deepmodeling#3624)

The wrong type hint appeared in
f5c67af, very old...

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -95,7 +95,7 @@ class DescrptSeA(NativeOP, BaseDescriptor):
             The cut-off radius :math:`r_c`
     rcut_smth
             From where the environment matrix should be smoothed :math:`r_s`
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
@@ -144,7 +144,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/dpmodel/descriptor/se_r.py

@@ -55,7 +55,7 @@ class DescrptSeR(NativeOP, BaseDescriptor):
             The cut-off radius :math:`r_c`
     rcut_smth
             From where the environment matrix should be smoothed :math:`r_s`
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
@@ -100,7 +100,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         resnet_dt: bool = False,
         trainable: bool = True,

deepmd/tf/descriptor/loc_frame.py

@@ -35,11 +35,11 @@ class DescrptLocFrame(Descriptor):
     ----------
     rcut
             The cut-off radius
-    sel_a : list[str]
+    sel_a : list[int]
             The length of the list should be the same as the number of atom types in the system.
             `sel_a[i]` gives the selected number of type-i neighbors.
             The full relative coordinates of the neighbors are used by the descriptor.
-    sel_r : list[str]
+    sel_r : list[int]
             The length of the list should be the same as the number of atom types in the system.
             `sel_r[i]` gives the selected number of type-i neighbors.
             Only relative distance of the neighbors are used by the descriptor.

deepmd/tf/descriptor/se_a.py

@@ -126,7 +126,7 @@ class DescrptSeA(DescrptSe):
             The cut-off radius :math:`r_c`
     rcut_smth
             From where the environment matrix should be smoothed :math:`r_s`
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
@@ -169,7 +169,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/tf/descriptor/se_a_ebd.py

@@ -38,7 +38,7 @@ class DescrptSeAEbd(DescrptSeA):
             The cut-off radius
     rcut_smth
             From where the environment matrix should be smoothed
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net
@@ -74,7 +74,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/tf/descriptor/se_a_ebd_v2.py

@@ -31,7 +31,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/tf/descriptor/se_a_ef.py

@@ -42,7 +42,7 @@ class DescrptSeAEf(DescrptSe):
             The cut-off radius
     rcut_smth
             From where the environment matrix should be smoothed
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net
@@ -74,7 +74,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,
@@ -305,7 +305,7 @@ def __init__(
         op,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/tf/descriptor/se_a_mask.py

@@ -82,7 +82,7 @@ class DescrptSeAMask(DescrptSeA):
 
     Parameters
     ----------
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the neighbor list.
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
@@ -117,7 +117,7 @@ class DescrptSeAMask(DescrptSeA):
 
     def __init__(
         self,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         axis_neuron: int = 8,
         resnet_dt: bool = False,

deepmd/tf/descriptor/se_atten.py

@@ -82,7 +82,7 @@ class DescrptSeAtten(DescrptSeA):
             The cut-off radius :math:`r_c`
     rcut_smth
             From where the environment matrix should be smoothed :math:`r_s`
-    sel : list[str]
+    sel : int
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`

deepmd/tf/descriptor/se_atten_v2.py

@@ -25,7 +25,7 @@ class DescrptSeAttenV2(DescrptSeAtten):
             The cut-off radius :math:`r_c`
     rcut_smth
             From where the environment matrix should be smoothed :math:`r_s`
-    sel : list[str]
+    sel : int
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`

deepmd/tf/descriptor/se_r.py

@@ -63,7 +63,7 @@ class DescrptSeR(DescrptSe):
             The cut-off radius
     rcut_smth
             From where the environment matrix should be smoothed
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net
@@ -91,7 +91,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         resnet_dt: bool = False,
         trainable: bool = True,

deepmd/tf/descriptor/se_t.py

@@ -56,7 +56,7 @@ class DescrptSeT(DescrptSe):
             The cut-off radius
     rcut_smth
             From where the environment matrix should be smoothed
-    sel : list[str]
+    sel : list[int]
             sel[i] specifies the maxmum number of type i atoms in the cut-off radius
     neuron : list[int]
             Number of neurons in each hidden layers of the embedding net
@@ -81,7 +81,7 @@ def __init__(
         self,
         rcut: float,
         rcut_smth: float,
-        sel: List[str],
+        sel: List[int],
         neuron: List[int] = [24, 48, 96],
         resnet_dt: bool = False,
         trainable: bool = True,