change default logger to dedicated one

Author: cmpute

pytorch_lightning/callbacks/early_stopping.py

@@ -5,13 +5,12 @@
 
 """
 
-import logging as log
 import warnings
 
 import numpy as np
 
 from .base import Callback
-
+from ..core import root_logger
 
 class EarlyStopping(Callback):
     r"""

pytorch_lightning/callbacks/model_checkpoint.py

@@ -7,12 +7,12 @@
 
 import os
 import glob
-import logging as log
 import warnings
 
 import numpy as np
 
 from .base import Callback
+from ..core import root_logger
 
 
 class ModelCheckpoint(Callback):

pytorch_lightning/core/__init__.py

@@ -102,6 +102,9 @@ def test_dataloader(self):
 
 """
 
+import logging
+root_logger = logging.getLogger("lightning")
+
 from .decorators import data_loader
 from .lightning import LightningModule
 

pytorch_lightning/core/lightning.py

@@ -1,6 +1,5 @@
 import collections
 import inspect
-import logging as log
 import os
 import warnings
 from abc import ABC, abstractmethod
@@ -11,6 +10,7 @@
 import torch.distributed as dist
 from torch.optim import Adam
 
+from pytorch_lightning.core import root_logger as log
 from pytorch_lightning.core.decorators import data_loader
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
@@ -26,7 +26,6 @@
 except ImportError:
     XLA_AVAILABLE = False
 
-
 class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
     def __init__(self, *args, **kwargs):

pytorch_lightning/core/memory.py

@@ -3,14 +3,14 @@
 '''
 
 import gc
-import logging as log
 import os
 import subprocess
 from subprocess import PIPE
 
 import numpy as np
 import torch
 
+from ..core import root_logger as log
 
 class ModelSummary(object):
 

pytorch_lightning/core/saving.py

@@ -1,8 +1,9 @@
-import os
 import csv
-import logging as log
+import os
 from argparse import Namespace
 
+from ..core import root_logger as log
+
 
 class ModelIO(object):
 

pytorch_lightning/profiler/profiler.py

@@ -6,7 +6,7 @@
 import pstats
 import io
 from abc import ABC, abstractmethod
-import logging as log
+from ..core import root_logger as log
 
 
 class BaseProfiler(ABC):

pytorch_lightning/trainer/auto_mix_precision.py

@@ -1,13 +1,14 @@
 
 from abc import ABC
 
+from ..core import root_logger as log
+
 try:
     from apex import amp
 
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
-import logging as log
 
 
 class TrainerAMPMixin(ABC):

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -113,14 +113,14 @@ def train_fx(trial_hparams, cluster_manager, _):
 
 """
 
-import logging as log
 import os
 import re
 import warnings
 from abc import ABC, abstractmethod
 from typing import Union
 
 import torch
+from pytorch_lightning.core import root_logger as log
 from pytorch_lightning.loggers import LightningLoggerBase
 
 from pytorch_lightning.utilities.debugging import MisconfigurationException

pytorch_lightning/trainer/distrib_parts.py

@@ -335,12 +335,12 @@
 """
 
 from abc import ABC, abstractmethod
-import logging as log
 import os
 import signal
 
 import torch
 
+from pytorch_lightning.core import root_logger as log
 from pytorch_lightning.overrides.data_parallel import (
     LightningDistributedDataParallel,
     LightningDataParallel,