Update main.py

main.py

@@ -213,7 +213,8 @@ def main():
     )
 
     # Setup optimizer
-    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
+    optimizer = optim.Adam(model.parameters(), lr=args.lr)
+    print("optim Adam")
 
     # Loop over the epochs
     best_val_loss = 1e8
@@ -240,4 +241,5 @@ def main():
 
 
 if __name__ == "__main__":
+    print("optim en cours")
     main()

model.py

@@ -1,23 +1,25 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torchvision import transforms, models
+
 
 nclasses = 250
 
 
 class Net(nn.Module):
-    def __init__(self):
+    def __init__(self,num_classes=nclasses):
         super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv3 = nn.Conv2d(20, 20, kernel_size=5)
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, nclasses)
+        resnet = models.resnet18(pretrained=True)
+
+        # Remove the fully connected layer of ResNet-18
+        self.features = nn.Sequential(*list(resnet.children())[:-1])
+
+        # Add your custom fully connected layer
+        self.fc = nn.Linear(resnet.fc.in_features, num_classes)
 
     def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), 2))
-        x = F.relu(F.max_pool2d(self.conv2(x), 2))
-        x = F.relu(F.max_pool2d(self.conv3(x), 2))
-        x = x.view(-1, 320)
-        x = F.relu(self.fc1(x))
-        return self.fc2(x)
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x