Minor imports cleaning (#402)

* code cleaning

* drop unused imports

* optimize imports

pytorch_lightning/__init__.py

@@ -1,6 +1,6 @@
-from .trainer.trainer import Trainer
-from .root_module.root_module import LightningModule
 from .root_module.decorators import data_loader
+from .root_module.root_module import LightningModule
+from .trainer.trainer import Trainer
 
 __all__ = [
     'Trainer',

pytorch_lightning/callbacks/pt_callbacks.py

@@ -1,6 +1,5 @@
 import os
 import shutil
-import warnings
 
 import numpy as np
 
@@ -260,6 +259,7 @@ class GradientAccumulationScheduler(Callback):
     # Arguments
         scheduling: dict, scheduling in format {epoch: accumulation_factor}
     """
+
     def __init__(self, scheduling: dict):
         if scheduling == {}:  # empty dict error
             raise TypeError("Empty dict cannot be interpreted correct")

pytorch_lightning/logging/comet_logger.py

@@ -1,7 +1,7 @@
-from time import time
-from logging import getLogger
 from os import environ
+
 from comet_ml import Experiment as CometExperiment
+
 from .base import LightningLoggerBase, rank_zero_only
 
 # needed to prevent ImportError and duplicated logs.

pytorch_lightning/logging/mlflow_logger.py

@@ -1,5 +1,5 @@
-from time import time
 from logging import getLogger
+from time import time
 
 import mlflow
 

pytorch_lightning/logging/test_tube_logger.py

@@ -1,16 +1,13 @@
-import os.path
-from copy import copy
+from test_tube import Experiment
 
 from .base import LightningLoggerBase, rank_zero_only
 
-from test_tube import Experiment
-
 
 class TestTubeLogger(LightningLoggerBase):
     __test__ = False
 
     def __init__(
-        self, save_dir, name="default", description=None, debug=False,
+            self, save_dir, name="default", description=None, debug=False,
             version=None, create_git_tag=False
     ):
         super().__init__()

pytorch_lightning/pt_overrides/override_data_parallel.py

@@ -1,11 +1,11 @@
-from torch.nn import DataParallel
-from torch.nn.parallel import DistributedDataParallel
 import itertools
+import threading
 from itertools import chain
 
-import threading
 import torch
 from torch.cuda._utils import _get_device_index
+from torch.nn import DataParallel
+from torch.nn.parallel import DistributedDataParallel
 
 
 def _find_tensors(obj):  # pragma: no cover

pytorch_lightning/root_module/decorators.py

@@ -17,9 +17,9 @@ def data_loader(fn):
             try:
                 value = fn(self)  # Lazy evaluation, done only once.
                 if (
-                    value is not None and
-                    not isinstance(value, list) and
-                    fn.__name__ in['test_dataloader', 'val_dataloader']
+                        value is not None and
+                        not isinstance(value, list) and
+                        fn.__name__ in ['test_dataloader', 'val_dataloader']
                 ):
                     value = [value]
             except AttributeError as e:

pytorch_lightning/root_module/memory.py

@@ -3,11 +3,11 @@ Generates a summary of a model's layers and dimensionality
 '''
 
 import gc
-
-import torch
 import subprocess
+
 import numpy as np
 import pandas as pd
+import torch
 
 
 class ModelSummary(object):
@@ -146,7 +146,6 @@ class ModelSummary(object):
         df['Params'] = df['Params'].map(get_human_readable_count)
 
         if self.model.example_input_array is not None:
-
             df['In_sizes'] = self.in_sizes
             df['Out_sizes'] = self.out_sizes
 

pytorch_lightning/root_module/root_module.py

@@ -1,13 +1,13 @@
+import warnings
+
 import torch
 
-from pytorch_lightning.root_module.memory import ModelSummary
-from pytorch_lightning.root_module.grads import GradInformation
-from pytorch_lightning.trainer.trainer_io import load_hparams_from_tags_csv
-from pytorch_lightning.root_module.model_saving import ModelIO
-from pytorch_lightning.root_module.hooks import ModelHooks
 from pytorch_lightning.root_module.decorators import data_loader
-
-import warnings
+from pytorch_lightning.root_module.grads import GradInformation
+from pytorch_lightning.root_module.hooks import ModelHooks
+from pytorch_lightning.root_module.memory import ModelSummary
+from pytorch_lightning.root_module.model_saving import ModelIO
+from pytorch_lightning.trainer.trainer_io import load_hparams_from_tags_csv
 
 
 class LightningModule(GradInformation, ModelIO, ModelHooks):

pytorch_lightning/testing/lm_test_module.py

@@ -1,18 +1,4 @@
-import os
-from collections import OrderedDict
-
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-from torchvision import transforms
-from test_tube import HyperOptArgumentParser
-
-from pytorch_lightning.root_module.root_module import LightningModule
-from pytorch_lightning import data_loader
 
 from .lm_test_module_base import LightningTestModelBase
 from .lm_test_module_mixins import LightningValidationMixin, LightningTestMixin

pytorch_lightning/testing/lm_test_module_base.py

@@ -4,15 +4,15 @@ from collections import OrderedDict
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from test_tube import HyperOptArgumentParser
 from torch import optim
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
 from torchvision import transforms
-from test_tube import HyperOptArgumentParser
+from torchvision.datasets import MNIST
 
-from pytorch_lightning.root_module.root_module import LightningModule
 from pytorch_lightning import data_loader
+from pytorch_lightning.root_module.root_module import LightningModule
 
 
 class LightningTestModelBase(LightningModule):

pytorch_lightning/testing/lm_test_module_mixins.py

@@ -1,17 +1,7 @@
-import os
 from collections import OrderedDict
 
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-from torchvision import transforms
-from test_tube import HyperOptArgumentParser
 
-from pytorch_lightning.root_module.root_module import LightningModule
 from pytorch_lightning import data_loader
 
 

pytorch_lightning/trainer/amp_mixin.py

@@ -1,5 +1,6 @@
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False

pytorch_lightning/trainer/data_loading_mixin.py

@@ -1,13 +1,14 @@
 import warnings
 
-from torch.utils.data.distributed import DistributedSampler
 import torch.distributed as dist
 from torch.utils.data import IterableDataset
+from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False

pytorch_lightning/trainer/ddp_mixin.py

@@ -4,12 +4,13 @@ import warnings
 
 import torch
 import torch.distributed as dist
+
 from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
-
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False

pytorch_lightning/trainer/dp_mixin.py

@@ -1,17 +1,12 @@
-import os
-import re
-import signal
-import pdb
-from subprocess import call
-
 import torch
-import torch.distributed as dist
+
 from pytorch_lightning.pt_overrides.override_data_parallel import (
     LightningDistributedDataParallel, LightningDataParallel)
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False

pytorch_lightning/trainer/evaluation_loop_mixin.py

@@ -1,4 +1,5 @@
 import torch
+
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 

pytorch_lightning/trainer/logging_mixin.py

@@ -1,4 +1,5 @@
 import torch
+
 from pytorch_lightning.root_module import memory
 
 

pytorch_lightning/trainer/train_loop_mixin.py

@@ -2,6 +2,7 @@ import numpy as np
 
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
@@ -112,7 +113,6 @@ class TrainerTrainLoopMixin(object):
             # when metrics should be logged
             should_log_metrics = batch_nb % self.row_log_interval == 0 or early_stop_epoch
             if should_log_metrics or self.fast_dev_run:
-
                 # logs user requested information to logger
                 self.log_metrics(batch_step_metrics, grad_norm_dic)
 

pytorch_lightning/trainer/trainer.py

@@ -5,29 +5,28 @@ The trainer handles all the logic for running a val loop, training loop, distrib
 import os
 import warnings
 
-import tqdm
 import torch
-import torch.multiprocessing as mp
 import torch.distributed as dist
+import torch.multiprocessing as mp
+import tqdm
 from torch.optim.optimizer import Optimizer
 
-from pytorch_lightning.trainer.trainer_io import TrainerIOMixin
+from pytorch_lightning.trainer.amp_mixin import TrainerAMPMixin
+from pytorch_lightning.trainer.callback_config_mixin import TrainerCallbackConfigMixin
+from pytorch_lightning.trainer.data_loading_mixin import TrainerDataLoadingMixin
 from pytorch_lightning.trainer.ddp_mixin import TrainerDDPMixin
 from pytorch_lightning.trainer.dp_mixin import TrainerDPMixin
-from pytorch_lightning.trainer.amp_mixin import TrainerAMPMixin
-from pytorch_lightning.trainer.data_loading_mixin import TrainerDataLoadingMixin
 from pytorch_lightning.trainer.evaluation_loop_mixin import TrainerEvaluationLoopMixin
-from pytorch_lightning.trainer.train_loop_mixin import TrainerTrainLoopMixin
 from pytorch_lightning.trainer.logging_mixin import TrainerLoggingMixin
-from pytorch_lightning.trainer.training_tricks_mixin import TrainerTrainingTricksMixin
-from pytorch_lightning.trainer.callback_config_mixin import TrainerCallbackConfigMixin
 from pytorch_lightning.trainer.model_hooks_mixin import TrainerModelHooksMixin
-
+from pytorch_lightning.trainer.train_loop_mixin import TrainerTrainLoopMixin
+from pytorch_lightning.trainer.trainer_io import TrainerIOMixin
+from pytorch_lightning.trainer.training_tricks_mixin import TrainerTrainingTricksMixin
 from pytorch_lightning.utilities.debugging import MisconfigurationException
-import pdb
 
 try:
     from apex import amp
+
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
@@ -332,7 +331,7 @@ class Trainer(TrainerIOMixin,
                 task = int(os.environ['SLURM_LOCALID'])
                 self.ddp_train(task, model)
             else:
-                mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model, ))
+                mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))
 
         # 1 gpu or dp option triggers training using DP module
         # easier to avoid NCCL issues

pytorch_lightning/trainer/trainer_io.py

@@ -1,11 +1,11 @@
 import os
 import re
 import signal
-import pdb
 from subprocess import call
 
 import torch
 import torch.distributed as dist
+
 from pytorch_lightning.pt_overrides.override_data_parallel import (
     LightningDistributedDataParallel, LightningDataParallel)
 

pytorch_lightning/utilities/arg_parse.py

@@ -7,7 +7,6 @@ import os
 
 
 def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):
-
     # training, test, val check intervals
     parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',
                         help='true = run test set also')