change default logger to dedicated one (#1064)

Fix test


Fix format

Update pytorch_lightning/__init__.py
Separate imports

pl_examples/basic_examples/lightning_module_template.py

@@ -1,7 +1,6 @@
 """
 Example template for defining a system
 """
-import logging as log
 import os
 from argparse import ArgumentParser
 from collections import OrderedDict
@@ -14,6 +13,7 @@ from torch import optim
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.core import LightningModule
 
 

pytorch_lightning/__init__.py

@@ -24,6 +24,9 @@ if __LIGHTNING_SETUP__:
     # We are not importing the rest of the scikit during the build
     # process, as it may not be compiled yet
 else:
+    from logging import getLogger
+    _logger = getLogger("lightning")
+
     from .core import LightningModule
     from .trainer import Trainer
     from .callbacks import Callback

pytorch_lightning/callbacks/early_stopping.py

@@ -5,12 +5,12 @@ Stop training when a monitored quantity has stopped improving.
 
 """
 
-import logging as log
 import warnings
 
 import numpy as np
 
 from .base import Callback
+from pytorch_lightning import _logger as log
 
 
 class EarlyStopping(Callback):

pytorch_lightning/callbacks/model_checkpoint.py

@@ -5,7 +5,6 @@ Model Checkpointing
 Automatically save model checkpoints during training.
 """
 
-import logging as log
 import os
 import shutil
 import warnings
@@ -14,6 +13,7 @@ import re
 import numpy as np
 
 from pytorch_lightning.callbacks.base import Callback
+from pytorch_lightning import _logger as log
 
 
 class ModelCheckpoint(Callback):

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
@@ -15,6 +14,7 @@ from torch.optim import Adam
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
 from pytorch_lightning.core.memory import ModelSummary

pytorch_lightning/core/memory.py

@@ -3,7 +3,6 @@ Generates a summary of a model's layers and dimensionality
 """
 
 import gc
-import logging as log
 import os
 import subprocess
 from subprocess import PIPE
@@ -15,6 +14,8 @@ from torch.nn import Module
 
 import pytorch_lightning as pl
 
+from pytorch_lightning import _logger as log
+
 
 class ModelSummary(object):
 

pytorch_lightning/core/saving.py

@@ -1,9 +1,10 @@
 import csv
-import logging as log
 import os
 from argparse import Namespace
 from typing import Union, Dict, Any
 
+from pytorch_lightning import _logger as log
+
 
 class ModelIO(object):
 

pytorch_lightning/loggers/comet.py

@@ -6,7 +6,6 @@ CometLogger
 -------------
 """
 
-import logging as log
 from argparse import Namespace
 from typing import Optional, Dict, Union, Any
 
@@ -27,8 +26,9 @@ except ImportError:
 import torch
 from torch import is_tensor
 
+from pytorch_lightning import _logger as log
+from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 from pytorch_lightning.utilities.debugging import MisconfigurationException
-from .base import LightningLoggerBase, rank_zero_only
 
 
 class CometLogger(LightningLoggerBase):

pytorch_lightning/loggers/mlflow.py

@@ -23,7 +23,6 @@ Use the logger anywhere in you LightningModule as follows:
         self.logger.experiment.whatever_ml_flow_supports(...)
 
 """
-import logging as log
 from argparse import Namespace
 from time import time
 from typing import Optional, Dict, Any, Union
@@ -34,7 +33,8 @@ except ImportError:
     raise ImportError('You want to use `mlflow` logger which is not installed yet,'
                       ' install it with `pip install mlflow`.')
 
-from .base import LightningLoggerBase, rank_zero_only
+from pytorch_lightning import _logger as log
+from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 
 
 class MLFlowLogger(LightningLoggerBase):

pytorch_lightning/loggers/neptune.py

@@ -6,7 +6,6 @@ Log using `neptune-logger <https://neptune.ai>`_
 NeptuneLogger
 --------------
 """
-import logging as log
 from argparse import Namespace
 from typing import Optional, List, Dict, Any, Union, Iterable
 
@@ -20,6 +19,7 @@ except ImportError:
 import torch
 from torch import is_tensor
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 
 

pytorch_lightning/loggers/trains.py

@@ -24,7 +24,6 @@ Use the logger anywhere in you LightningModule as follows:
 
 """
 
-import logging as log
 from argparse import Namespace
 from pathlib import Path
 from typing import Any, Dict, Optional, Union
@@ -38,7 +37,8 @@ except ImportError:
     raise ImportError('You want to use `TRAINS` logger which is not installed yet,'
                       ' install it with `pip install trains`.')
 
-from .base import LightningLoggerBase, rank_zero_only
+from pytorch_lightning import _logger as log
+from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 
 
 class TrainsLogger(LightningLoggerBase):

pytorch_lightning/profiler/profiler.py

@@ -1,6 +1,5 @@
 import cProfile
 import io
-import logging as log
 import pstats
 import time
 from abc import ABC, abstractmethod
@@ -9,6 +8,8 @@ from contextlib import contextmanager
 
 import numpy as np
 
+from pytorch_lightning import _logger as log
+
 
 class BaseProfiler(ABC):
     """

pytorch_lightning/trainer/auto_mix_precision.py

@@ -1,6 +1,7 @@
-import logging as log
 from abc import ABC
 
+from pytorch_lightning import _logger as log
+
 try:
     from apex import amp
 except ImportError:

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -113,7 +113,6 @@ When the script starts again, Lightning will:
 
 """
 
-import logging as log
 import os
 import re
 import warnings
@@ -121,7 +120,7 @@ from abc import ABC, abstractmethod
 from typing import Union
 
 import torch
-
+from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 

pytorch_lightning/trainer/distrib_parts.py

@@ -334,12 +334,12 @@ Here lightning distributes parts of your module across available GPUs to optimiz
 
 """
 
-import logging as log
 import os
 from abc import ABC, abstractmethod
 
 import torch
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.overrides.data_parallel import (
     LightningDistributedDataParallel,
     LightningDataParallel,

pytorch_lightning/trainer/trainer.py

@@ -1,5 +1,4 @@
 import inspect
-import logging as log
 import os
 import sys
 import warnings
@@ -14,8 +13,8 @@ from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
-from pytorch_lightning.callbacks import Callback
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+from pytorch_lightning import _logger as log
+from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, Callback
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.profiler import Profiler, PassThroughProfiler
 from pytorch_lightning.profiler.profiler import BaseProfiler

pytorch_lightning/trainer/training_io.py

@@ -89,7 +89,6 @@ At a rough level, here's what happens inside Trainer :py:mod:`pytorch_lightning.
 
 """
 
-import logging as log
 import os
 import re
 import signal
@@ -102,6 +101,7 @@ from typing import Union
 import torch
 import torch.distributed as torch_distrib
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.overrides.data_parallel import (

pytorch_lightning/trainer/training_loop.py

@@ -122,7 +122,6 @@ When this flag is enabled each batch is split into sequences of size truncated_b
 """
 
 import copy
-import logging as log
 import warnings
 from abc import ABC, abstractmethod
 from typing import Callable
@@ -131,6 +130,7 @@ from typing import Union, List
 import numpy as np
 from torch.utils.data import DataLoader
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase

pytorch_lightning/trainer/training_tricks.py

@@ -1,9 +1,9 @@
-import logging as log
 import math
 from abc import ABC, abstractmethod
 
 import torch
 
+from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks import GradientAccumulationScheduler
 
 EPSILON = 1e-6