[engine] Set logger on module level

bob/ip/binseg/engine/inferencer.py

@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 
 import os
-import logging
 import time
 import datetime
 import numpy as np
@@ -17,6 +16,9 @@ from bob.ip.binseg.utils.metric import base_metrics
 from bob.ip.binseg.utils.plot import precision_recall_f1iso_confintval
 from bob.ip.binseg.utils.summary import summary
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 def batch_metrics(predictions, ground_truths, names, output_folder, logger):
     """
@@ -165,7 +167,7 @@ def do_inference(model, data_loader, device, output_folder=None):
         device to use ``'cpu'`` or ``'cuda'``
     output_folder : str
     """
-    logger = logging.getLogger("bob.ip.binseg.engine.inference")
+
     logger.info("Start evaluation")
     logger.info("Output folder: {}, Device: {}".format(output_folder, device))
     results_subfolder = os.path.join(output_folder, "results")

bob/ip/binseg/engine/predicter.py

@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 
 import os
-import logging
 import time
 import datetime
 import numpy as np
@@ -12,6 +11,9 @@ from tqdm import tqdm
 from bob.ip.binseg.engine.inferencer import save_probability_images
 from bob.ip.binseg.engine.inferencer import save_hdf
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 def do_predict(model, data_loader, device, output_folder=None):
 
@@ -27,7 +29,6 @@ def do_predict(model, data_loader, device, output_folder=None):
         device to use ``'cpu'`` or ``'cuda'``
     output_folder : str
     """
-    logger = logging.getLogger("bob.ip.binseg.engine.inference")
     logger.info("Start evaluation")
     logger.info("Output folder: {}, Device: {}".format(output_folder, device))
     results_subfolder = os.path.join(output_folder, "results")

bob/ip/binseg/engine/ssltrainer.py

@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 
 import os
-import logging
 import time
 import datetime
 import torch
@@ -13,6 +12,9 @@ import numpy as np
 from bob.ip.binseg.utils.metric import SmoothedValue
 from bob.ip.binseg.utils.plot import loss_curve
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 def sharpen(x, T):
     temp = x ** (1 / T)
@@ -204,7 +206,6 @@ def do_ssltrain(
         rampup epochs
 
     """
-    logger = logging.getLogger("bob.ip.binseg.engine.trainer")
     logger.info("Start training")
     start_epoch = arguments["epoch"]
     max_epoch = arguments["max_epoch"]

bob/ip/binseg/engine/trainer.py

@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 
 import os
-import logging
 import time
 import datetime
 import torch
@@ -12,6 +11,9 @@ from tqdm import tqdm
 from bob.ip.binseg.utils.metric import SmoothedValue
 from bob.ip.binseg.utils.plot import loss_curve
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 def do_train(
     model,
@@ -25,12 +27,12 @@ def do_train(
     arguments,
     output_folder,
 ):
-    """ 
+    """
     Train model and save to disk.
-    
+
     Parameters
     ----------
-    model : :py:class:`torch.nn.Module` 
+    model : :py:class:`torch.nn.Module`
         Network (e.g. DRIU, HED, UNet)
     data_loader : :py:class:`torch.utils.data.DataLoader`
     optimizer : :py:mod:`torch.optim`
@@ -42,14 +44,13 @@ def do_train(
         checkpointer
     checkpoint_period : int
         save a checkpoint every n epochs
-    device : str  
+    device : str
         device to use ``'cpu'`` or ``'cuda'``
     arguments : dict
         start end end epochs
-    output_folder : str 
+    output_folder : str
         output path
     """
-    logger = logging.getLogger("bob.ip.binseg.engine.trainer")
     logger.info("Start training")
     start_epoch = arguments["epoch"]
     max_epoch = arguments["max_epoch"]