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

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deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -1,12 +1,12 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import copy
 from typing import (
+    Callable,
     Dict,
     List,
     Optional,
     Tuple,
     Union,
-    Callable,
 )
 
 import torch
@@ -297,7 +297,8 @@ def _compute_weight(
         nmodels = len(self.models)
         nframes, nloc, _ = nlists_[0].shape
         return [
-            torch.ones((nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE) / nmodels
+            torch.ones((nframes, nloc, 1), dtype=torch.float64, device=env.DEVICE)
+            / nmodels
             for _ in range(nmodels)
         ]
 
@@ -336,7 +337,7 @@ def is_aparam_nall(self) -> bool:
         If False, the shape is (nframes, nloc, ndim).
         """
         return False
-    
+
     def compute_or_load_out_stat(
         self,
         merged: Union[Callable[[], List[dict]], List[dict]],
@@ -360,7 +361,7 @@ def compute_or_load_out_stat(
         """
         for md in self.models:
             md.compute_or_load_out_stat(merged, stat_file_path)
-    
+
     def compute_or_load_stat(
         self,
         sampled_func,

source/tests/pt/model/test_linear_atomic_model_stat.py

@@ -17,7 +17,6 @@
     OutputVariableDef,
 )
 from deepmd.pt.model.atomic_model import (
-    BaseAtomicModel,
     DPAtomicModel,
     LinearEnergyAtomicModel,
 )
@@ -85,9 +84,10 @@ def forward(
             .to(env.GLOBAL_PT_FLOAT_PRECISION)
             .to(env.DEVICE)
         )
-        
+
         return ret
 
+
 class FooFittingB(torch.nn.Module, BaseFitting):
     def output_def(self):
         return FittingOutputDef(
@@ -128,9 +128,10 @@ def forward(
             .to(env.GLOBAL_PT_FLOAT_PRECISION)
             .to(env.DEVICE)
         )
-        
+
         return ret
 
+
 class TestAtomicModelStat(unittest.TestCase, TestCaseSingleFrameWithNlist):
     def tearDown(self):
         self.tempdir.cleanup()
@@ -153,7 +154,6 @@ def setUp(self):
                 ),
                 # bias of foo: 1, 3
                 "energy": to_torch_tensor(np.array([5.0, 7.0]).reshape(2, 1)),
-
             }
         ]
         self.tempdir = tempfile.TemporaryDirectory()
@@ -183,9 +183,9 @@ def test_linear_atomic_model_stat_with_bias(self):
             ft_b,
             type_map=type_map,
         ).to(env.DEVICE)
-        linear_model = LinearEnergyAtomicModel(
-            [md0,md1],type_map=type_map
-        ).to(env.DEVICE)
+        linear_model = LinearEnergyAtomicModel([md0, md1], type_map=type_map).to(
+            env.DEVICE
+        )
 
         args = [
             to_torch_tensor(ii) for ii in [self.coord_ext, self.atype_ext, self.nlist]
@@ -196,7 +196,7 @@ def test_linear_atomic_model_stat_with_bias(self):
         # 1. test run without bias
         # nf x na x odim
         ret0 = linear_model.forward_common_atomic(*args)
-        
+
         ret0 = to_numpy_array(ret0["energy"])
         ret_no_bias = []
         for md in linear_model.models:
@@ -207,7 +207,7 @@ def test_linear_atomic_model_stat_with_bias(self):
                 [7.0, 8.0, 9.0],
             ]
         ).reshape([nf, nloc] + linear_model.fitting_output_def()["energy"].shape)
-        
+
         np.testing.assert_almost_equal(ret0, expected_ret0)
 
         # 2. test bias is applied
@@ -222,8 +222,8 @@ def test_linear_atomic_model_stat_with_bias(self):
             ret = to_numpy_array(ret["energy"])
             linear_ret.append(ret_no_bias[idx] + ener_bias[at])
             np.testing.assert_almost_equal((ret_no_bias[idx] + ener_bias[at]), ret)
-        
+
         # linear model not adding bias again
         ret1 = linear_model.forward_common_atomic(*args)
         ret1 = to_numpy_array(ret1["energy"])
-        np.testing.assert_almost_equal(torch.mean(torch.stack(linear_ret),dim=0), ret1)
+        np.testing.assert_almost_equal(torch.mean(torch.stack(linear_ret), dim=0), ret1)