Performance improvements on ResNet

README.md

@@ -19,7 +19,7 @@ $ python main.py --logdir outputs --trained outputs/best_model.pt --slope 0.2
 
 For training on Office dataset using ResNet-50
 ```
-$ python core/train_source_rn50.py --n_classes 31 --lr 1e-4 --src_cat amazon --tgt_cat webcam
+$ python core/train_source_rn50.py --n_classes 31 --lr 1e-5 --src_cat amazon --tgt_cat webcam
 $ python main.py --n_classes 31 --trained outputs/garbage/best_model.pt --lr 1e-5 --d_lr 1e-4 --logdir outputs --model resnet50 --src-cat amazon --tgt-cat webcam
 ```
 

models/resnet50off.py

@@ -45,7 +45,7 @@ class Encoder(nn.Module):
                 param.requires_grad = False
 
     def forward(self, x):
-        x = x.expand(x.data.shape[0], 3, 227, 227)
+        x = x.expand(x.data.shape[0], 3, 224, 224)
         x = self.feature_extractor(x)
         ###
         x = self.layer4(x)
@@ -60,9 +60,9 @@ class Classifier(nn.Module):
         super(Classifier, self).__init__()
         self.l1 = nn.Linear(2048, n_classes)
 
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                nn.init.kaiming_normal_(m.weight)
+        # for m in self.modules():
+        #     if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+        #         nn.init.kaiming_normal_(m.weight)
 
     def forward(self, x):
         x = self.l1(x)
@@ -90,14 +90,20 @@ class Discriminator(nn.Module):
         self.l1 = nn.Linear(2048, h)
         self.l2 = nn.Linear(h, h)
         self.l3 = nn.Linear(h, 2)
+        ###
+        self.l4 = nn.LogSoftmax(dim=1)
+        ###
         self.slope = args.slope
 
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                nn.init.kaiming_normal_(m.weight)
+        # for m in self.modules():
+        #     if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+        #         nn.init.kaiming_normal_(m.weight)
 
     def forward(self, x):
         x = F.leaky_relu(self.l1(x), self.slope)
         x = F.leaky_relu(self.l2(x), self.slope)
         x = self.l3(x)
+        ###
+        x = self.l4(x)
+        ###
         return x

utils/altutils.py

@@ -40,7 +40,7 @@ def get_office(dataset_root, batch_size, category):
     """Get Office datasets loader."""
     # image pre-processing
     pre_process = transforms.Compose([
-        transforms.Resize(227),
+        transforms.Resize(224),
         transforms.ToTensor(),
         transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
     ])