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

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

source/tests/pd/common.py

@@ -80,7 +80,9 @@ def eval_model(
             assert isinstance(spins, paddle.Tensor), err_msg
         assert isinstance(atom_types, paddle.Tensor) or isinstance(atom_types, list)
         if isinstance(atom_types, paddle.Tensor):
-            atom_types = atom_types.clone().detach().to(dtype=paddle.int32, place=DEVICE)
+            atom_types = (
+                atom_types.clone().detach().to(dtype=paddle.int32, place=DEVICE)
+            )
         else:
             atom_types = paddle.to_tensor(atom_types, dtype=paddle.int32, place=DEVICE)
     elif isinstance(coords, np.ndarray):
@@ -105,15 +107,27 @@ def eval_model(
         natoms = len(atom_types[0])
 
     if isinstance(coords, paddle.Tensor):
-        coord_input = coords.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+        coord_input = (
+            coords.reshape([-1, natoms, 3])
+            .clone()
+            .detach()
+            .to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+        )
     else:
         coord_input = paddle.to_tensor(
-            coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE
+            coords.reshape([-1, natoms, 3]),
+            dtype=GLOBAL_PD_FLOAT_PRECISION,
+            place=DEVICE,
         )
     spin_input = None
     if spins is not None:
         if isinstance(spins, paddle.Tensor):
-            spin_input = spins.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+            spin_input = (
+                spins.reshape([-1, natoms, 3])
+                .clone()
+                .detach()
+                .to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+            )
         else:
             spin_input = paddle.to_tensor(
                 spins.reshape([-1, natoms, 3]),
@@ -133,7 +147,12 @@ def eval_model(
     else:
         pbc = True
         if isinstance(cells, paddle.Tensor):
-            box_input = cells.reshape([-1, 3, 3]).clone().detach().to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+            box_input = (
+                cells.reshape([-1, 3, 3])
+                .clone()
+                .detach()
+                .to(dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE)
+            )
         else:
             box_input = paddle.to_tensor(
                 cells.reshape([-1, 3, 3]), dtype=GLOBAL_PD_FLOAT_PRECISION, place=DEVICE

source/tests/pt/common.py

@@ -80,7 +80,9 @@ def eval_model(
             assert isinstance(spins, torch.Tensor), err_msg
         assert isinstance(atom_types, torch.Tensor) or isinstance(atom_types, list)
         if isinstance(atom_types, torch.Tensor):
-            atom_types = atom_types.clone().detach().to(dtype=torch.int32, device=DEVICE)
+            atom_types = (
+                atom_types.clone().detach().to(dtype=torch.int32, device=DEVICE)
+            )
         else:
             atom_types = torch.tensor(atom_types, dtype=torch.int32, device=DEVICE)
     elif isinstance(coords, np.ndarray):
@@ -105,15 +107,27 @@ def eval_model(
         natoms = len(atom_types[0])
 
     if isinstance(coords, torch.Tensor):
-        coord_input = coords.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+        coord_input = (
+            coords.reshape([-1, natoms, 3])
+            .clone()
+            .detach()
+            .to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+        )
     else:
         coord_input = torch.tensor(
-            coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
+            coords.reshape([-1, natoms, 3]),
+            dtype=GLOBAL_PT_FLOAT_PRECISION,
+            device=DEVICE,
         )
     spin_input = None
     if spins is not None:
         if isinstance(spins, torch.Tensor):
-            spin_input = spins.reshape([-1, natoms, 3]).clone().detach().to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+            spin_input = (
+                spins.reshape([-1, natoms, 3])
+                .clone()
+                .detach()
+                .to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+            )
         else:
             spin_input = torch.tensor(
                 spins.reshape([-1, natoms, 3]),
@@ -133,10 +147,17 @@ def eval_model(
     else:
         pbc = True
         if isinstance(cells, torch.Tensor):
-            box_input = cells.reshape([-1, 3, 3]).clone().detach().to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+            box_input = (
+                cells.reshape([-1, 3, 3])
+                .clone()
+                .detach()
+                .to(dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE)
+            )
         else:
             box_input = torch.tensor(
-                cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
+                cells.reshape([-1, 3, 3]),
+                dtype=GLOBAL_PT_FLOAT_PRECISION,
+                device=DEVICE,
             )
     num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size)