From c5e0539473dd7735b277a459034f44f7c58d597e Mon Sep 17 00:00:00 2001
From: Dirk Schut <dirk_schut@live.nl>
Date: Wed, 16 Oct 2024 15:11:48 +0200
Subject: [PATCH] Add conv_bias parameter in LPD (Issue #139)

---
 LION/models/iterative_unrolled/LPD.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/LION/models/iterative_unrolled/LPD.py b/LION/models/iterative_unrolled/LPD.py
index 33158a5..9f143d1 100644
--- a/LION/models/iterative_unrolled/LPD.py
+++ b/LION/models/iterative_unrolled/LPD.py
@@ -32,7 +32,7 @@ class dataProximal(nn.Module):
     CNN block of the dual variable
     """
 
-    def __init__(self, layers, channels, instance_norm=False):
+    def __init__(self, layers, channels, conv_bias, instance_norm=False):
 
         super().__init__()
         # imput parsing
@@ -50,12 +50,12 @@ def __init__(self, layers, channels, instance_norm=False):
             # PReLUs and 3x3 kernels all the way except the last
             if ii < layers - 1:
                 layer_list.append(
-                    nn.Conv2d(channels[ii], channels[ii + 1], 3, padding=1, bias=False)
+                    nn.Conv2d(channels[ii], channels[ii + 1], 3, padding=1, bias=conv_bias)
                 )
                 layer_list.append(nn.PReLU())
             else:
                 layer_list.append(
-                    nn.Conv2d(channels[ii], channels[ii + 1], 1, padding=0, bias=False)
+                    nn.Conv2d(channels[ii], channels[ii + 1], 1, padding=0, bias=conv_bias)
                 )
         self.block = nn.Sequential(*layer_list)
 
@@ -68,7 +68,7 @@ class RegProximal(nn.Module):
     CNN block of the primal variable
     """
 
-    def __init__(self, layers, channels, instance_norm=False):
+    def __init__(self, layers, channels, conv_bias, instance_norm=False):
         super().__init__()
         if len(channels) != layers + 1:
             raise ValueError(
@@ -84,12 +84,12 @@ def __init__(self, layers, channels, instance_norm=False):
             # PReLUs and 3x3 kernels all the way except the last
             if ii < layers - 1:
                 layer_list.append(
-                    nn.Conv2d(channels[ii], channels[ii + 1], 3, padding=1, bias=False)
+                    nn.Conv2d(channels[ii], channels[ii + 1], 3, padding=1, bias=conv_bias)
                 )
                 layer_list.append(nn.PReLU())
             else:
                 layer_list.append(
-                    nn.Conv2d(channels[ii], channels[ii + 1], 1, padding=0, bias=False)
+                    nn.Conv2d(channels[ii], channels[ii + 1], 1, padding=0, bias=conv_bias)
                 )
         self.block = nn.Sequential(*layer_list)
 
@@ -122,6 +122,7 @@ def __init__(
                 RegProximal(
                     layers=len(self.model_parameters.reg_channels) - 1,
                     channels=self.model_parameters.reg_channels,
+                    conv_bias=self.model_parameters.conv_bias,
                     instance_norm=self.model_parameters.instance_norm,
                 ),
             )
@@ -130,6 +131,7 @@ def __init__(
                 dataProximal(
                     layers=len(self.model_parameters.data_channels) - 1,
                     channels=self.model_parameters.data_channels,
+                    conv_bias=self.model_parameters.conv_bias,
                     instance_norm=self.model_parameters.instance_norm,
                 ),
             )
@@ -199,6 +201,7 @@ def default_parameters():
         LPD_params.step_positive = False
         LPD_params.mode = "ct"
         LPD_params.instance_norm = False
+        LPD_params.conv_bias = True
         return LPD_params
 
     @staticmethod
@@ -212,6 +215,7 @@ def continous_LPD_paper():
         LPD_params.step_positive = True
         LPD_params.mode = "ct"
         LPD_params.instance_norm = True
+        LPD_params.conv_bias = False
         return LPD_params
 
     @staticmethod