Delete unnecessary comment lines

core/train_source_rn50.py

@@ -25,12 +25,7 @@ def main(args):
 
     # train source CNN
     source_cnn = CNN(in_channels=args.in_channels, srcTrain=True).to(args.device)
-    # for param in source_cnn.encoder.parameters():
-    #     param.requires_grad = False
     criterion = nn.CrossEntropyLoss()
-    # optimizer = optim.Adam(
-    #     source_cnn.classifier.parameters(),
-    #     lr=args.lr, weight_decay=args.weight_decay)
     optimizer = optim.Adam(
         source_cnn.parameters(),
         lr=args.lr, weight_decay=args.weight_decay)

models/resnet50off.py

@@ -32,10 +32,6 @@ class Encoder(nn.Module):
     def __init__(self, in_channels=3, h=256, dropout=0.5, srcTrain=False):
         super(Encoder, self).__init__()
         
-        # resnetModel = models.resnet50(pretrained=True)
-        # feature_map = list(resnetModel.children())
-        # feature_map.pop()
-        # self.feature_extractor = nn.Sequential(*feature_map)
         rnMod = ResNet50Mod()
         self.feature_extractor = rnMod.freezed_rn50
         self.layer4 = rnMod.layer4
@@ -47,10 +43,8 @@ class Encoder(nn.Module):
     def forward(self, x):
         x = x.expand(x.data.shape[0], 3, 224, 224)
         x = self.feature_extractor(x)
-        ###
         x = self.layer4(x)
         x = self.avgpool(x)
-        ###
         x = x.view(x.size(0), -1)
         return x
 
@@ -60,10 +54,6 @@ 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)
-
     def forward(self, x):
         x = self.l1(x)
         return x
@@ -90,20 +80,12 @@ 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)
-
     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

@@ -37,7 +37,16 @@ def setLogger(logFilePath):
     return logger
 
 def get_office(dataset_root, batch_size, category):
-    """Get Office datasets loader."""
+    """Get Office datasets loader
+
+    Args:
+        dataset_root (str): path to the dataset folder
+        batch_size (int): batch size
+        category (str): category of Office dataset (amazon, webcam, dslr)
+
+    Returns:
+        obj: dataloader object for Office dataset
+    """    
     # image pre-processing
     pre_process = transforms.Compose([
         transforms.Resize(224),