Kurz vorm durchdrehen

lib/modules/blocks.py

@@ -4,11 +4,11 @@ import torch
 from torch import nn
 from lib.modules.utils import AutoPad, Interpolate
 
+
 #
 # Sub - Modules
 ###################
 
-
 class ConvModule(nn.Module):
 
     @property
@@ -60,7 +60,7 @@ class DeConvModule(nn.Module):
     def __init__(self, in_shape, conv_filters=3, conv_kernel=5, conv_stride=1, conv_padding=0,
                  dropout: Union[int, float] = 0, autopad=False,
                  activation: Union[None, nn.Module] = nn.ReLU, interpolation_scale=None,
-                 use_bias=True, normalize=False):
+                 use_bias=True, use_norm=False):
         super(DeConvModule, self).__init__()
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
         self.padding = conv_padding
@@ -70,7 +70,7 @@ class DeConvModule(nn.Module):
 
         self.autopad = AutoPad() if autopad else lambda x: x
         self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
-        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
+        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if use_norm else lambda x: x
         self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
         self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, conv_kernel, bias=use_bias,
                                           padding=self.padding, stride=self.stride)