fixed if-bug in reset parameters

BrainMapperAE.py

@@ -209,18 +209,20 @@ class BrainMapperAE3D(nn.Module):
             for _, submodule in module.named_children():
                 if isinstance(submodule, (torch.nn.ConvTranspose3d, torch.nn.Conv3d, torch.nn.InstanceNorm3d)) == True:
                     submodule.reset_parameters()
-                    if custom_weight_reset_flag == True & isinstance(submodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
-                        gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
-                        fan, _ = calculate_fan(submodule.weight)
-                        std = np.divide(gain, np.sqrt(fan))
-                        submodule.weight.data.normal_(0, std)
+                    if custom_weight_reset_flag == True:
+                        if isinstance(submodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):                        
+                            gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
+                            fan, _ = calculate_fan(submodule.weight)
+                            std = np.divide(gain, np.sqrt(fan))
+                            submodule.weight.data.normal_(0, std)
                 for _, subsubmodule in submodule.named_children():
                     if isinstance(subsubmodule, (torch.nn.ConvTranspose3d, torch.nn.Conv3d, torch.nn.InstanceNorm3d)) == True:
                         subsubmodule.reset_parameters()
-                        if custom_weight_reset_flag == True & isinstance(subsubmodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
-                            gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
-                            fan, _ = calculate_fan(subsubmodule.weight)
-                            std = np.divide(gain, np.sqrt(fan))
-                            subsubmodule.weight.data.normal_(0, std)
+                        if custom_weight_reset_flag == True:
+                            if isinstance(subsubmodule, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
+                                gain = np.sqrt(np.divide(2, 1 + np.power(0.25, 2)))
+                                fan, _ = calculate_fan(subsubmodule.weight)
+                                std = np.divide(gain, np.sqrt(fan))
+                                subsubmodule.weight.data.normal_(0, std)
 
         print("Initialized network parameters!")