logger

experiment.py

@@ -7,12 +7,15 @@ from torchvision.datasets import SVHN, MNIST
 from torchvision import transforms
 
 from models import CNN, Discriminator
-from trainer import train_source_cnn, train_target_cnn
+from trainer import train_target_cnn
+from utils import get_logger
 
 
 def run(args):
     if not os.path.exists(args.logdir):
         os.makedirs(args.logdir)
+    logger = get_logger(os.path.join(args.logdir, 'main.log'))
+    logger.info(args)
 
     # data
     source_transform = transforms.Compose([

trainer.py

@@ -1,3 +1,4 @@
+from logging import getLogger
 from time import time
 
 import numpy as np
@@ -7,6 +8,9 @@ import torch
 from utils import AverageMeter, save
 
 
+logger = getLogger('adda.trainer')
+
+
 def train_source_cnn(
     source_cnn, train_loader, test_loader, criterion, optimizer,
     args=None
@@ -24,7 +28,7 @@ def train_source_cnn(
         log += 'Val/Loss {:.3f} Val/Acc {:.3f} '.format(
             validation['loss'], validation['acc'])
         log += 'Time {:.2f}s'.format(time() - start_time)
-        print(log)
+        logger.info(log)
 
         # save
         is_best = (best_score is None or validation['acc'] > best_score)
@@ -68,7 +72,7 @@ def train_target_cnn(
             validation['loss'], validation['acc'])
         log += log_source
         log += 'Time {:.2f}s'.format(time() - start_time)
-        print(log)
+        logger.info(log)
 
 
 def adversarial(

utils.py

@@ -11,6 +11,22 @@ def save(log_dir, state_dict, is_best):
         shutil.copyfile(checkpoint_path, best_model_path)
 
 
+def get_logger(log_file):
+    from logging import getLogger, FileHandler, StreamHandler, Formatter, DEBUG, INFO  # noqa
+    fh = FileHandler(log_file)
+    fh.setLevel(DEBUG)
+    sh = StreamHandler()
+    sh.setLevel(INFO)
+    for handler in [fh, sh]:
+        formatter = Formatter('%(asctime)s - %(message)s')
+        handler.setFormatter(formatter)
+    logger = getLogger('adda')
+    logger.setLevel(INFO)
+    logger.addHandler(fh)
+    logger.addHandler(sh)
+    return logger
+
+
 class AverageMeter(object):
     """Computes and stores the average and current value
        https://github.com/pytorch/examples/blob/master/imagenet/main.py#L296