batch norms fix for 3d core

Author: pollytur

neuralpredictors/layers/cores/base.py

@@ -1,3 +1,4 @@
+import warnings
 from abc import ABC, abstractmethod
 from collections import OrderedDict
 
@@ -77,7 +78,8 @@ def add_bn_layer(self, layer: OrderedDict, layer_idx: int):
                 raise NotImplementedError(f"Subclasses must have a `{attr}` attribute.")
         for attr in ["batch_norm", "hidden_channels", "bias", "batch_norm_scale"]:
             if not isinstance(getattr(self, attr), list):
-                raise ValueError(f"`{attr}` must be a list.")
+                setattr(self, attr, [getattr(self, attr)] * self.layers)
+                warnings.warn(f"The {attr} is applied to all layers", UserWarning)
 
         if self.batch_norm[layer_idx]:
             hidden_channels = self.hidden_channels[layer_idx]

neuralpredictors/layers/cores/conv3d.py

@@ -160,7 +160,7 @@ def __init__(
             padding=(0, input_kernel[1] // 2, input_kernel[2] // 2) if self.padding else 0,
         )
 
-        self.add_bn_layer(layer=layer, hidden_channels=self.hidden_channels[0])
+        self.add_bn_layer(layer=layer, layer_idx=0)
 
         if layers > 1 or self.final_nonlinearity:
             if hidden_nonlinearities == "adaptive_elu":
@@ -185,7 +185,7 @@ def __init__(
                 padding=(0, self.hidden_kernel[l][1] // 2, self.hidden_kernel[l][2] // 2) if self.padding else 0,
             )
 
-            self.add_bn_layer(layer=layer, hidden_channels=self.hidden_channels[l + 1])
+            self.add_bn_layer(layer=layer, layer_idx=l + 1)
 
             if self.final_nonlinearity or l < self.layers:
                 if hidden_nonlinearities == "adaptive_elu":
@@ -363,7 +363,10 @@ def __init__(
             dilation=(self.temporal_dilation, 1, 1),
         )
 
-        self.add_bn_layer(layer=layer, hidden_channels=self.hidden_channels[0])
+        self.add_bn_layer(
+            layer=layer,
+            layer_idx=0,
+        )
 
         if layers > 1 or final_nonlin:
             if hidden_nonlinearities == "adaptive_elu":
@@ -394,7 +397,7 @@ def __init__(
                 dilation=(self.hidden_temporal_dilation[l], 1, 1),
             )
 
-            self.add_bn_layer(layer=layer, hidden_channels=self.hidden_channels[l + 1])
+            self.add_bn_layer(layer=layer, layer_idx=l + 1)
 
             if final_nonlin or l < self.layers:
                 if hidden_nonlinearities == "adaptive_elu":