[pre-commit.ci] auto fixes from pre-commit.com hooks

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deepmd/pt/model/descriptor/__init__.py

@@ -13,7 +13,6 @@
 )
 from .env_mat import (
     prod_env_mat,
-
 )
 from .gaussian_lcc import (
     DescrptGaussianLcc,

deepmd/pt/model/descriptor/env_mat.py

@@ -1,13 +1,15 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
-from typing import Optional
+
 import torch
 
 from deepmd.pt.utils.preprocess import (
     compute_smooth_weight,
 )
 
 
-def _make_env_mat(nlist, coord, rcut: float, ruct_smth: float, radial_only: bool=False):
+def _make_env_mat(
+    nlist, coord, rcut: float, ruct_smth: float, radial_only: bool = False
+):
     """Make smooth environment matrix."""
     bsz, natoms, nnei = nlist.shape
     coord = coord.view(bsz, -1, 3)
@@ -35,7 +37,14 @@ def _make_env_mat(nlist, coord, rcut: float, ruct_smth: float, radial_only: bool
 
 
 def prod_env_mat(
-    extended_coord, nlist, atype, mean, stddev, rcut: float, rcut_smth: float, radial_only: bool=False
+    extended_coord,
+    nlist,
+    atype,
+    mean,
+    stddev,
+    rcut: float,
+    rcut_smth: float,
+    radial_only: bool = False,
 ):
     """Generate smooth environment matrix from atom coordinates and other context.
 

deepmd/pt/model/descriptor/se_r.py

@@ -18,9 +18,6 @@
     EmbeddingNet,
     NetworkCollection,
 )
-from deepmd.pt.model.network.network import (
-    TypeFilter,
-)
 from deepmd.pt.utils import (
     env,
 )
@@ -89,7 +86,6 @@ def __init__(
         self.filter_layers_old = None
         self.filter_layers = None
 
-
         filter_layers = NetworkCollection(
             ndim=1, ntypes=len(sel), network_type="embedding_network"
         )

deepmd/pt/utils/env_mat_stat.py

@@ -68,7 +68,9 @@ class EnvMatStatSe(EnvMatStat):
     def __init__(self, descriptor: "DescriptorBlock"):
         super().__init__()
         self.descriptor = descriptor
-        self.last_dim = self.descriptor.ndescrpt// self.descriptor.nnei # se_r=1, se_a=4
+        self.last_dim = (
+            self.descriptor.ndescrpt // self.descriptor.nnei
+        )  # se_r=1, se_a=4
 
     def iter(
         self, data: List[Dict[str, torch.Tensor]]
@@ -132,7 +134,9 @@ def iter(
             # reshape to nframes * nloc at the atom level,
             # so nframes/mixed_type do not matter
             env_mat = env_mat.view(
-                coord.shape[0] * coord.shape[1], self.descriptor.get_nsel(), self.last_dim
+                coord.shape[0] * coord.shape[1],
+                self.descriptor.get_nsel(),
+                self.last_dim,
             )
             atype = atype.view(coord.shape[0] * coord.shape[1])
             # (1, nloc) eq (ntypes, 1), so broadcast is possible
@@ -179,7 +183,7 @@ def __call__(self):
 
         all_davg = []
         all_dstd = []
-        
+
         for type_i in range(self.descriptor.get_ntypes()):
             if self.last_dim == 4:
                 davgunit = [[avgs[f"r_{type_i}"], 0, 0, 0]]
@@ -205,4 +209,4 @@ def __call__(self):
 
         mean = np.stack(all_davg)
         stddev = np.stack(all_dstd)
-        return mean, stddev
+        return mean, stddev