Changes for new file organization

README.md

@@ -3,6 +3,9 @@ implement Adversarial Discriminative Domain Adapation in PyTorch
 
 This repo is mostly based on https://github.com/Fujiki-Nakamura/ADDA.PyTorch
 
+## Note
+Before running the training code, make sure that `DATASETDIR` environment variable is set to dataset directory.
+
 ## Example
 ```
 $ python train_source.py --logdir outputs

experiment.py → core/experiment.py

@@ -1,14 +1,14 @@
 import os
-
+import sys
+sys.path.append(os.path.abspath('.'))
 import torch
 from torch import nn, optim
 from torch.utils.data import DataLoader
 from torchvision.datasets import SVHN, MNIST
 from torchvision import transforms
-
-from models import CNN, Discriminator
-from trainer import train_target_cnn
-from utils import get_logger
+from models.models import CNN, Discriminator
+from core.trainer import train_target_cnn
+from utils.utils import get_logger
 
 
 def run(args):
@@ -17,6 +17,8 @@ def run(args):
     logger = get_logger(os.path.join(args.logdir, 'main.log'))
     logger.info(args)
 
+    dataset_root = os.environ["DATASETDIR"]
+
     # data
     source_transform = transforms.Compose([
         # transforms.Grayscale(),
@@ -28,11 +30,11 @@ def run(args):
         transforms.Lambda(lambda x: x.repeat(3, 1, 1))
     ])
     source_dataset_train = SVHN(
-        './input', 'train', transform=source_transform, download=True)
+        'input/', 'train', transform=source_transform, download=True)
     target_dataset_train = MNIST(
-        './input', 'train', transform=target_transform, download=True)
+        'input/', 'train', transform=target_transform, download=True)
     target_dataset_test = MNIST(
-        './input', 'test', transform=target_transform, download=True)
+        'input/', 'test', transform=target_transform, download=True)
     source_train_loader = DataLoader(
         source_dataset_train, args.batch_size, shuffle=True,
         drop_last=True,

train_source.py → core/train_source.py

@@ -1,14 +1,14 @@
 import argparse
 import os
-
+import sys
+sys.path.append(os.path.abspath('.'))
 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
-from utils import get_logger
+from models.models import CNN
+from core.trainer import train_source_cnn
+from utils.utils import get_logger
 
 
 def main(args):
@@ -17,14 +17,18 @@ def main(args):
     logger = get_logger(os.path.join(args.logdir, 'train_source.log'))
     logger.info(args)
 
+    dataset_root = os.environ["DATASETDIR"]
+
     # data
     source_transform = transforms.Compose([
         transforms.ToTensor()]
     )
+    # source_dataset_train = SVHN(
+    #     dataset_root, 'train', transform=source_transform, download=True)
     source_dataset_train = SVHN(
-        './input', 'train', transform=source_transform, download=True)
+        'input/', 'train', transform=source_transform, download=True)
     source_dataset_test = SVHN(
-        './input', 'test', transform=source_transform, download=True)
+        'input/', 'test', transform=source_transform, download=True)
     source_train_loader = DataLoader(
         source_dataset_train, args.batch_size, shuffle=True,
         drop_last=True,

trainer.py → core/trainer.py

@@ -1,12 +1,12 @@
+import os
+import sys
+sys.path.append(os.path.abspath('.'))
 from logging import getLogger
 from time import time
-
 import numpy as np
 from sklearn.metrics import accuracy_score
-from tensorboardX import SummaryWriter
 import torch
-
-from utils import AverageMeter, save
+from utils.utils import AverageMeter, save
 
 
 logger = getLogger('adda.trainer')
@@ -54,7 +54,6 @@ def train_target_cnn(
     validation = validate(source_cnn, target_test_loader, criterion, args=args)
     log_source = 'Source/Acc {:.3f} '.format(validation['acc'])
 
-    writer = SummaryWriter(args.logdir)
     best_score = None
     for epoch_i in range(1, 1 + args.epochs):
         start_time = time()
@@ -91,13 +90,6 @@ def train_target_cnn(
         }
         save(args.logdir, state_dict, is_best)
 
-        # tensorboard
-        writer.add_scalar('Adv/D/Loss', training['d/loss'], epoch_i)
-        writer.add_scalar('Adv/Target/Loss', training['target/loss'], epoch_i)
-        writer.add_scalar('Val/Target/Loss', validation['loss'], epoch_i)
-        writer.add_scalar('Val/Target/Acc', validation['acc'], epoch_i)
-        writer.add_scalar('Train/Target/Acc', validation2['acc'], epoch_i)
-
 
 def adversarial(
     source_cnn, target_cnn, discriminator,

main.py

@@ -1,5 +1,5 @@
 import argparse
-import experiment
+from core.experiment import run
 
 
 if __name__ == '__main__':
@@ -21,4 +21,4 @@ if __name__ == '__main__':
     parser.add_argument('--logdir', type=str, default='outputs/garbage')
     parser.add_argument('--message', '-m',  type=str, default='')
     args, unknown = parser.parse_known_args()
-    experiment.run(args)
+    run(args)