./runtests.sh --autofix

Signed-off-by: jirka <jirka.borovec@seznam.cz>

Author: Borda

monai/losses/ds_loss.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn.functional as F
 from torch.nn.modules.loss import _Loss

monai/losses/perceptual.py

@@ -95,7 +95,9 @@ def __init__(
 
         if network_type.lower() not in list(PercetualNetworkType):
             raise ValueError(
-                "Unrecognised criterion entered for Perceptual Loss. Must be one in: {}".format(", ".join(PercetualNetworkType))
+                "Unrecognised criterion entered for Perceptual Loss. Must be one in: {}".format(
+                    ", ".join(PercetualNetworkType)
+                )
             )
 
         if cache_dir:

monai/metrics/utils.py

@@ -13,7 +13,7 @@
 
 import warnings
 from collections.abc import Iterable, Sequence
-from functools import partial, cache
+from functools import cache, partial
 from types import ModuleType
 from typing import Any
 

monai/networks/blocks/attention_utils.py

@@ -9,7 +9,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn.functional as F
 from torch import nn

monai/networks/blocks/crossattention.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn as nn
 

monai/networks/blocks/mlp.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch.nn as nn
 
 from monai.networks.layers import get_act_layer

monai/networks/blocks/selfattention.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

monai/networks/blocks/spatialattention.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn as nn
 

monai/networks/blocks/transformerblock.py

@@ -11,7 +11,6 @@
 
 from __future__ import annotations
 
-
 import torch
 import torch.nn as nn
 

monai/networks/layers/simplelayers.py

@@ -12,8 +12,8 @@
 from __future__ import annotations
 
 import math
-from copy import deepcopy
 from collections.abc import Sequence
+from copy import deepcopy
 
 import torch
 import torch.nn.functional as F