ruffed

Author: hzavadil98

main.py

@@ -27,7 +27,6 @@ def main():
 
     args = get_args()
 
-
     createfolders(args.datafolder, args.resultfolder, args.modelfolder)
 
     device = args.device

utils/arg_parser.py

@@ -44,7 +44,13 @@ def get_args():
         "--modelname",
         type=str,
         default="MagnusModel",
-        choices=["MagnusModel", "ChristianModel", "SolveigModel", "JanModel", "JohanModel"],
+        choices=[
+            "MagnusModel",
+            "ChristianModel",
+            "SolveigModel",
+            "JanModel",
+            "JohanModel",
+        ],
         help="Model which to be trained on",
     )
     parser.add_argument(
@@ -96,12 +102,9 @@ def get_args():
         help="If true, the code will not run the training loop.",
     )
     args = parser.parse_args()
-    
-    
+
     assert args.epoch > 0, "Epoch should be a positive integer."
     assert args.learning_rate > 0, "Learning rate should be a positive float."
     assert args.batchsize > 0, "Batch size should be a positive integer."
-   
-    
-    
+
     return args

utils/load_metric.py

@@ -7,7 +7,6 @@
 
 
 class MetricWrapper(nn.Module):
-
     """
     Wrapper class for metrics, that runs multiple metrics on the same data.
 
@@ -46,9 +45,7 @@ class MetricWrapper(nn.Module):
     {'entropy': [], 'f1': [], 'precision': []}
     """
 
-
     def __init__(self, *metrics, num_classes):
-
         super().__init__()
         self.metrics = {}
         self.num_classes = num_classes

utils/metrics/EntropyPred.py

@@ -9,4 +9,4 @@ def __call__(self, y_true, y_false_logits):
         return
 
     def __reset__(self):
-        pass
+        pass

utils/models/magnus_model.py

@@ -2,44 +2,39 @@
 
 
 class MagnusModel(nn.Module):
-    def __init__(self,
-                 imagesize: int,
-                 imagechannels: int,
-                 n_classes:int=10):
-        
+    def __init__(self, imagesize: int, imagechannels: int, n_classes: int = 10):
         """
-        Magnus model contains the model for Magnus' part of the homeexam. 
+        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. 
+            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.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()
-        ]))
+
+        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):
         """
@@ -48,17 +43,17 @@ def forward(self, x):
         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