added two modfels

architectures/Model.py

@@ -1,3 +1,4 @@
+
 from abc import ABC, abstractmethod
 import torch
 import torch.nn as nn
@@ -85,10 +86,17 @@ class Model(ABC):
             case Architecture.DENSENET121:
                 from architectures.DenseNet121 import DenseNet121
                 return DenseNet121(device, size)
+            # googleNet
+            case Architecture.GOOGLENET:
+                from architectures.GoogleNet import GoogleNet
+                return GoogleNet(device, size)
+            # EfficientNet
+            case Architecture.EFFICIENTNET:
+                from architectures.EfficentNet import EfficientNet
+                return EfficientNet(device, size)
             case _:
                 raise ValueError(f"Unknown model: {arch}")
 
- 
 # model architectures
 from enum import Enum, auto
 
@@ -96,4 +104,6 @@ class Architecture(Enum):
     RESNET18 = auto()
     RESNET50 = auto()
     INCEPTION = auto()
-    DENSENET121 = auto()
+    DENSENET121 = auto()
+    GOOGLENET = auto()
+    EFFICIENTNET = auto()

architectures/ResNet18.py

@@ -11,12 +11,12 @@ class ResNet18(Model):
         m = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
 
         # freez all layers
-        for param in m.parameters():
-            param.requires_grad = False
+        #for param in m.parameters():
+        #    param.requires_grad = False
 
         # unfreez the last two
-        for param in m.layer3.parameters(): param.requires_grad = True
-        for param in m.layer4.parameters(): param.requires_grad = True
+        #for param in m.layer3.parameters(): param.requires_grad = True
+        #for param in m.layer4.parameters(): param.requires_grad = True
         
         m.fc = nn.Linear(m.fc.in_features, self.size)
         return m.to(self.device)