Looging improvements

core/test.py

@@ -1,7 +1,7 @@
 import torch.utils.data
 import torch.nn as nn
 
-def test(model, data_loader, device, flag):
+def test(model, data_loader, device, loggi, flag):
     """Evaluate model for dataset."""
     # set eval state for Dropout and BN layers
     model.eval()
@@ -39,6 +39,6 @@ def test(model, data_loader, device, flag):
     acc = acc_ / n_total
     acc_domain = acc_domain_ / n_total
 
-    print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_, n_total, acc_domain))
+    loggi.info("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_, n_total, acc_domain))
 
     return loss, acc, acc_domain

core/train.py

@@ -1,7 +1,7 @@
 """Train dann."""
 
 import numpy as np
-
+import os
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -113,7 +113,7 @@ def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_e
 
     return model
 
-def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger=None):
+def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, loggi, logger=None):
     """Train dann."""
     ####################
     # 1. setup network #
@@ -215,8 +215,8 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
 
         # eval model
         if ((epoch + 1) % params.eval_step == 0):
-            tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, flag='target')
-            src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, flag='source')
+            tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, loggi, flag='target')
+            src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, loggi, flag='source')
             if tgt_acc > bestAcc:
                 bestAcc = tgt_acc
                 bestAccS = src_acc
@@ -232,9 +232,9 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
 
     # save final model
     save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt")
-    print('============ Summary ============= \n')
-    print('Accuracy of the %s dataset: %f' % (params.src_dataset, bestAccS))
-    print('Accuracy of the %s dataset: %f' % (params.tgtc_dataset, bestAcc))
+    loggi.info('============ Summary ============= \n')
+    loggi.info('Accuracy of the %s dataset: %f' % (params.src_dataset, bestAccS))
+    loggi.info('Accuracy of the %s dataset: %f' % (params.tgtc_dataset, bestAcc))
 
     return model
 

experiments/mnist_mnistm.py

@@ -5,6 +5,7 @@ import torch
 from models.model import MNISTmodel, MNISTmodel_plain
 from core.train import train_dann
 from utils.utils import get_data_loader, init_model, init_random_seed
+from utils.altutils import setLogger
 
 
 class Config(object):
@@ -63,6 +64,10 @@ class Config(object):
 
 params = Config()
 
+currentDir = os.path.dirname(os.path.realpath(__file__))
+logFile = os.path.join(currentDir+'/../', 'dann-{}-{}.log'.format(params.src_dataset, params.tgt_dataset))
+loggi = setLogger(logFile)
+
 # init random seed
 init_random_seed(params.manual_seed)
 
@@ -81,4 +86,4 @@ dann = init_model(net=MNISTmodel(), restore=None)
 # train dann model
 print("Training dann model")
 if not (dann.restored and params.dann_restore):
-    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device)
+    dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, loggi)

utils/altutils.py

@@ -0,0 +1,33 @@
+import configparser
+import logging
+
+
+def readConfigFile(filePath):
+    """
+    Read config file
+
+    Args:
+        filePath ([str]): path to config file
+
+    Returns:
+        [Obj]: config object
+    """    
+    config = configparser.ConfigParser()
+    config.read(filePath)
+    return config
+
+
+def setLogger(logFilePath):
+    """
+    Set logger
+
+    Args:
+        logFilePath ([str]): path to log file
+
+    Returns:
+        [obj]: logger object
+    """    
+    logHandler = [logging.FileHandler(logFilePath), logging.StreamHandler()]
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s", handlers=logHandler)
+    logger = logging.getLogger()
+    return logger