Merge branch 'magnus-branch' into main

Author: Seilmast

.gitignore

@@ -8,6 +8,9 @@ bin/
 wandb/
 wandb_api.py
 
+#Magnus specific
+docker/*
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

main.py

@@ -24,8 +24,10 @@ def main():
     ------
 
     """
+
     args = get_args()
 
+
     createfolders(args.datafolder, args.resultfolder, args.modelfolder)
 
     device = args.device

utils/metrics/EntropyPred.py

@@ -5,5 +5,8 @@ class EntropyPrediction(nn.Module):
     def __init__(self):
         super().__init__()
 
-    def __call__(self, y_true, y_false):
+    def __call__(self, y_true, y_false_logits):
         return
+
+    def __reset__(self):
+        pass

utils/models/magnus_model.py

@@ -2,8 +2,63 @@
 
 
 class MagnusModel(nn.Module):
-    def __init__(self):
+    def __init__(self,
+                 imagesize: int,
+                 imagechannels: int,
+                 n_classes:int=10):
+        
+        """
+        Magnus model contains the model for Magnus' part of the homeexam. 
+        This class contains a neural network consisting of three linear layers of 133 neurons each,
+        with ReLU activation between each layer.
+
+        Args
+        ----
+            imagesize (int): Expected size of input image. This is needed to scale first layer input
+            imagechannels (int): Expected number of image channels. This is needed to scale first layer input
+            n_classes (int): Number of classes we are to provide. 
+
+        Returns
+        -------
+            MagnusModel (nn.Module): Neural network as described above in this docstring.
+        """
+        
+        
         super().__init__()
+        self.imagesize = imagesize 
+        self.imagechannels = imagechannels
+        
+        self.layer1 = nn.Sequential(*([
+            nn.Linear(self.imagechannels*self.imagesize*self.imagesize, 133),
+            nn.ReLU()
+        ]))
+        self.layer2 = nn.Sequential(*([
+            nn.Linear(133, 133),
+            nn.ReLU()
+        ]))
+        self.layer3 = nn.Sequential(*([
+            nn.Linear(133, n_classes),
+            nn.ReLU()
+        ]))
 
     def forward(self, x):
-        return
+        """
+        Forward pass of MagnusModel
+
+        Args
+        ----
+            x (th.Tensor): Four-dimensional tensor in the form (Batch Size x Channels x Image Height x Image Width)
+                
+        Returns
+        -------
+            out (th.Tensor): Class-logits of network given input x
+        """
+        assert len(x.size) == 4
+        
+        x = x.view(x.size(0), -1)
+        
+        x = self.layer1(x)
+        x = self.layer2(x)
+        out = self.layer3(x)
+    
+        return out