refactor

experiment.py

@@ -26,8 +26,6 @@ def run(args):
     ])
     source_dataset_train = SVHN(
         './input', 'train', transform=source_transform, download=True)
-    source_dataset_test = SVHN(
-        './input', 'test', transform=source_transform, download=True)
     target_dataset_train = MNIST(
         './input', 'train', transform=target_transform, download=True)
     target_dataset_test = MNIST(
@@ -36,9 +34,6 @@ def run(args):
         source_dataset_train, args.batch_size, shuffle=True,
         drop_last=True,
         num_workers=args.n_workers)
-    source_test_loader = DataLoader(
-        source_dataset_test, args.batch_size, shuffle=False,
-        num_workers=args.n_workers)
     target_train_loader = DataLoader(
         target_dataset_train, args.batch_size, shuffle=True,
         drop_last=True,
@@ -52,27 +47,19 @@ def run(args):
     if os.path.isfile(args.trained):
         c = torch.load(args.trained)
         source_cnn.load_state_dict(c['model'])
-        print('Loaded `{}`'.format(args.trained))
-    else:
-        criterion = nn.CrossEntropyLoss()
-        optimizer = optim.Adam(
-            source_cnn.parameters(),
-            lr=args.lr, weight_decay=args.weight_decay)
-        source_cnn = train_source_cnn(
-            source_cnn, source_train_loader, source_test_loader,
-            criterion, optimizer, args=args)
+        logger.info('Loaded `{}`'.format(args.trained))
 
     # train target CNN
     target_cnn = CNN(in_channels=args.in_channels, target=True).to(args.device)
     target_cnn.load_state_dict(source_cnn.state_dict())
     discriminator = Discriminator(args=args).to(args.device)
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.RMSprop(  # optim.Adam(
+    optimizer = optim.Adam(
         target_cnn.encoder.parameters(),
-        lr=args.lr, weight_decay=args.weight_decay)
-    d_optimizer = optim.RMSprop(  # optim.Adam(
+        lr=args.lr, betas=args.betas, weight_decay=args.weight_decay)
+    d_optimizer = optim.Adam(
         discriminator.parameters(),
-        lr=args.lr, weight_decay=args.weight_decay)
+        lr=args.lr, betas=args.betas, weight_decay=args.weight_decay)
     train_target_cnn(
         source_cnn, target_cnn, discriminator,
         criterion, optimizer, d_optimizer,

train_source.py

@@ -0,0 +1,62 @@
+import argparse
+import os
+
+from torch import nn, optim
+from torch.utils.data import DataLoader
+from torchvision.datasets import SVHN
+from torchvision import transforms
+
+from models import CNN
+from trainer import train_source_cnn
+
+
+def main(args):
+    if not os.path.exists(args.logdir):
+        os.makedirs(args.logdir)
+
+    # data
+    source_transform = transforms.Compose([
+        transforms.ToTensor()]
+    )
+    source_dataset_train = SVHN(
+        './input', 'train', transform=source_transform, download=True)
+    source_dataset_test = SVHN(
+        './input', 'test', transform=source_transform, download=True)
+    source_train_loader = DataLoader(
+        source_dataset_train, args.batch_size, shuffle=True,
+        drop_last=True,
+        num_workers=args.n_workers)
+    source_test_loader = DataLoader(
+        source_dataset_test, args.batch_size, shuffle=False,
+        num_workers=args.n_workers)
+
+    # train source CNN
+    source_cnn = CNN(in_channels=args.in_channels).to(args.device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(
+        source_cnn.parameters(),
+        lr=args.lr, weight_decay=args.weight_decay)
+    source_cnn = train_source_cnn(
+        source_cnn, source_train_loader, source_test_loader,
+        criterion, optimizer, args=args)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    # NN
+    parser.add_argument('--in_channels', type=int, default=3)
+    parser.add_argument('--n_classes', type=int, default=10)
+    parser.add_argument('--trained', type=str, default='')
+    parser.add_argument('--slope', type=float, default=0.2)
+    # train
+    parser.add_argument('--lr', type=float, default=1e-3)
+    parser.add_argument('--weight_decay', type=float, default=2.5e-5)
+    parser.add_argument('--epochs', type=int, default=50)
+    parser.add_argument('--batch_size', type=int, default=128)
+    # misc
+    parser.add_argument('--device', type=str, default='cuda:0')
+    parser.add_argument('--n_workers', type=int, default=0)
+    parser.add_argument('--logdir', type=str, default='outputs/garbage')
+    parser.add_argument('--message', '-m',  type=str, default='')
+    args, unknown = parser.parse_known_args()
+    main(args)