created constructor function for UNet

BrainMapperUNet.py

@@ -17,14 +17,57 @@ To use this module, import it and instantiate is as you wish:
 import numpy as np
 import torch
 import torch.nn as nn
+import utils.modules as modules
 
-class BrainMapperUnet(nn.Module):
-    """
-    Description
+class BrainMapperUNet(nn.Module):
+    """Architecture class BrainMapper U-net.
+
+    This class contains the pytorch implementation of the U-net architecture underpinning the BrainMapper project.
+
+    Args:
+        parameters (dict): Contains information relevant parameters
+        parameters = {
+            'kernel_heigth': 5
+            'kernel_width': 5
+            'kernel_classification': 1
+            'input_channels': 1
+            'output_channels': 64
+            'convolution_stride': 1
+            'dropout': 0.2
+            'pool_kernel_size': 2
+            'pool_stride': 2
+            'up_mode': 'upconv'
+            'number_of_classes': 1
+        }
+    
+    Returns:
+        probability_map (torch.tensor): Output forward passed tensor through the U-net block
+
+    Raises:
+        None
     """
     
     def __init__(self, parameters):
-        pass
+        super(BrainMapperUNet, self).__init__()
+
+        # TODO: currently, architecture based on QuickNAT - need to adjust parameter values accordingly!
+
+        self.encoderBlock1 = modules.EncoderBlock(parameters)
+        parameters['input_channels'] = parameters['output_channels']
+        self.encoderBlock2 = modules.EncoderBlock(parameters)
+        self.encoderBlock3 = modules.EncoderBlock(parameters)
+        self.encoderBlock4 = modules.EncoderBlock(parameters)
+
+        self.bottleneck = modules.ConvolutionalBlock(parameters)
+
+        parameters['input_channels'] = parameters['output_channels'] * 2.0
+        self.decoderBlock1 = modules.DecoderBlock(parameters)
+        self.decoderBlock2 = modules.DecoderBlock(parameters)
+        self.decoderBlock3 = modules.DecoderBlock(parameters)
+        self.decoderBlock4 = modules.DecoderBlock(parameters)
+
+        parameters['input_channels'] = parameters['output_channels']
+        self.classifier = modules.ClassifierBlock(parameters)
 
     def forward(self, X):
         """