refactor: adjust acts to take in arbitrary kwargs

Author: okunator

cellseg_models_pytorch/modules/act/gated_gelu.py

@@ -26,7 +26,7 @@
 
 
 class GEGLU(nn.Module):
-    def __init__(self, dim_in: int, dim_out: int):
+    def __init__(self, dim_in: int, dim_out: int, **kwargs) -> None:
         """Apply a variant of the gated linear unit activation function.
 
         https://arxiv.org/abs/2002.05202.
@@ -52,7 +52,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class ApproximateGELU(nn.Module):
-    def __init__(self, dim_in: int, dim_out: int):
+    def __init__(self, dim_in: int, dim_out: int, **kwargs) -> None:
         """Apply the approximate form of Gaussian Error Linear Unit (GELU).
 
         https://arxiv.org/abs/1606.08415

cellseg_models_pytorch/modules/act/mish.py

@@ -50,7 +50,7 @@ def mish(x: torch.Tensor):
 
 
 class Mish(nn.Module):
-    def __init__(self, inplace: bool = False) -> None:
+    def __init__(self, inplace: bool = False, **kwargs) -> None:
         """
         Element-wise mish.
 

cellseg_models_pytorch/modules/act/star_relu.py

@@ -12,6 +12,7 @@ def __init__(
         scale_learnable: bool = True,
         bias_learnable: bool = True,
         inplace: bool = False,
+        **kwargs
     ) -> None:
         """Apply StarReLU activation.
 

cellseg_models_pytorch/modules/act/swish.py

@@ -39,7 +39,7 @@ def swish(x: torch.Tensor) -> torch.Tensor:
 
 
 class Swish(nn.Module):
-    def __init__(self, inplace: bool = False) -> None:
+    def __init__(self, inplace: bool = False, **kwargs) -> None:
         """Apply the element-wise swish function.
 
         Parameters

cellseg_models_pytorch/utils/img_utils.py

@@ -131,7 +131,7 @@ def normalize(
     im = im - im.mean(axis=axis, keepdims=True)
 
     if standardize:
-        im /= im.std(axis=axis, keepdims=True)
+        im = im / (im.std(axis=axis, keepdims=True) + 1e-8)
 
     # clamp
     if not any(x is None for x in (amin, amax)):
@@ -170,7 +170,7 @@ def minmax_normalize(
         )
 
     im = img.copy()
-    im = (im - im.min()) / (im.max() - im.min())
+    im = (im - im.min()) / (im.max() - im.min() + 1e-8)
 
     # clamp
     if not any(x is None for x in (amin, amax)):