cleaning imports (#1032)

pl_examples/full_examples/semantic_segmentation/models/unet/model.py

@@ -1,6 +1,4 @@
-import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 from models.unet.parts import DoubleConv, Down, Up
 

pl_examples/full_examples/semantic_segmentation/semseg.py

@@ -1,18 +1,15 @@
 import os
 from argparse import ArgumentParser
-from collections import OrderedDict
-from PIL import Image
 
 import numpy as np
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
-import torchvision
 import torchvision.transforms as transforms
+from PIL import Image
+from models.unet.model import UNet
 from torch.utils.data import DataLoader, Dataset
 
 import pytorch_lightning as pl
-from models.unet.model import UNet
 
 
 class KITTI(Dataset):

pytorch_lightning/callbacks/__init__.py

@@ -1,8 +1,7 @@
 from .base import Callback
 from .early_stopping import EarlyStopping
-from .model_checkpoint import ModelCheckpoint
 from .gradient_accumulation_scheduler import GradientAccumulationScheduler
-
+from .model_checkpoint import ModelCheckpoint
 
 __all__ = [
     'Callback',

pytorch_lightning/callbacks/model_checkpoint.py

@@ -5,9 +5,9 @@ Model Checkpointing
 Automatically save model checkpoints during training.
 """
 
+import logging as log
 import os
 import shutil
-import logging as log
 import warnings
 import re
 

pytorch_lightning/core/decorators.py

@@ -1,5 +1,3 @@
-import traceback
-from functools import wraps
 import warnings
 
 

pytorch_lightning/core/lightning.py

@@ -13,8 +13,8 @@ from torch.optim import Adam
 
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
-from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
 from pytorch_lightning.core.memory import ModelSummary
+from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 

pytorch_lightning/core/saving.py

@@ -1,6 +1,6 @@
-import os
 import csv
 import logging as log
+import os
 from argparse import Namespace
 
 

pytorch_lightning/profiler/profiler.py

@@ -1,12 +1,13 @@
-from contextlib import contextmanager
-from collections import defaultdict
-import time
-import numpy as np
 import cProfile
-import pstats
 import io
-from abc import ABC, abstractmethod
 import logging as log
+import pstats
+import time
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from contextlib import contextmanager
+
+import numpy as np
 
 
 class BaseProfiler(ABC):

pytorch_lightning/trainer/callback_hook.py

@@ -1,5 +1,5 @@
-from typing import Callable
 from abc import ABC
+from typing import Callable
 
 from pytorch_lightning.callbacks import Callback
 

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -121,8 +121,8 @@ from abc import ABC, abstractmethod
 from typing import Union
 
 import torch
-from pytorch_lightning.loggers import LightningLoggerBase
 
+from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:

pytorch_lightning/trainer/distrib_parts.py

@@ -334,10 +334,9 @@ Here lightning distributes parts of your module across available GPUs to optimiz
 
 """
 
-from abc import ABC, abstractmethod
 import logging as log
 import os
-import signal
+from abc import ABC, abstractmethod
 
 import torch
 

pytorch_lightning/trainer/evaluation_loop.py

@@ -123,10 +123,9 @@ In this second case, the options you pass to trainer will be used when running
 
 """
 
-from typing import Callable
-
 import sys
 from abc import ABC, abstractmethod
+from typing import Callable
 
 import torch
 from torch.utils.data import DataLoader

pytorch_lightning/trainer/logging.py

@@ -1,4 +1,4 @@
-from abc import ABC, abstractmethod
+from abc import ABC
 from typing import Union, Iterable
 
 import torch

pytorch_lightning/trainer/trainer.py

@@ -1,20 +1,22 @@
+import logging as log
 import os
 import sys
 import warnings
-import logging as log
-from typing import Union, Optional, List, Dict, Tuple, Iterable
 from argparse import ArgumentParser
+from typing import Union, Optional, List, Dict, Tuple, Iterable
 
 import torch
 from torch import optim
 import torch.distributed as dist
 import torch.multiprocessing as mp
+from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
-from torch.optim.optimizer import Optimizer
 
+from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 from pytorch_lightning.loggers import LightningLoggerBase
+from pytorch_lightning.profiler import Profiler, PassThroughProfiler
 from pytorch_lightning.profiler.profiler import BaseProfiler
 from pytorch_lightning.trainer.auto_mix_precision import TrainerAMPMixin
 from pytorch_lightning.trainer.callback_config import TrainerCallbackConfigMixin
@@ -35,9 +37,6 @@ from pytorch_lightning.trainer.training_io import TrainerIOMixin
 from pytorch_lightning.trainer.training_loop import TrainerTrainLoopMixin
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
 from pytorch_lightning.utilities.debugging import MisconfigurationException
-from pytorch_lightning.profiler import Profiler, PassThroughProfiler
-from pytorch_lightning.callbacks import Callback
-
 
 try:
     from apex import amp

pytorch_lightning/trainer/training_loop.py

@@ -121,21 +121,20 @@ When this flag is enabled each batch is split into sequences of size truncated_b
 
 """
 
-from typing import Callable
-
 import copy
-import warnings
 import logging as log
+import warnings
 from abc import ABC, abstractmethod
+from typing import Callable
 from typing import Union, List
 
 import numpy as np
 from torch.utils.data import DataLoader
 
+from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.utilities.debugging import MisconfigurationException
-from pytorch_lightning.callbacks.base import Callback
 
 try:
     from apex import amp

pytorch_lightning/trainer/training_tricks.py

@@ -1,8 +1,8 @@
 import logging as log
+import math
 from abc import ABC, abstractmethod
 
 import torch
-import math
 
 from pytorch_lightning.callbacks import GradientAccumulationScheduler
 

tests/loggers/test_base.py

@@ -1,5 +1,4 @@
 import pickle
-
 from unittest.mock import MagicMock
 
 import tests.models.utils as tutils

tests/loggers/test_comet.py

@@ -1,16 +1,14 @@
 import os
 import pickle
-
-import torch
-
 from unittest.mock import patch
 
 import pytest
+import torch
 
 import tests.models.utils as tutils
 from pytorch_lightning import Trainer
-from pytorch_lightning.utilities.debugging import MisconfigurationException
 from pytorch_lightning.loggers import CometLogger
+from pytorch_lightning.utilities.debugging import MisconfigurationException
 from tests.models import LightningTestModel
 
 

tests/loggers/test_neptune.py

@@ -1,14 +1,13 @@
 import pickle
-
 from unittest.mock import patch
 
+import torch
+
 import tests.models.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import NeptuneLogger
 from tests.models import LightningTestModel
 
-import torch
-
 
 def test_neptune_logger(tmpdir):
     """Verify that basic functionality of neptune logger works."""

tests/loggers/test_wandb.py

@@ -1,8 +1,8 @@
 import os
 import pickle
+from unittest.mock import patch
 
 import pytest
-from unittest.mock import patch
 
 import tests.models.utils as tutils
 from pytorch_lightning import Trainer

tests/test_amp.py

@@ -4,10 +4,10 @@ import pytest
 
 import tests.models.utils as tutils
 from pytorch_lightning import Trainer
+from pytorch_lightning.utilities.debugging import MisconfigurationException
 from tests.models import (
     LightningTestModel,
 )
-from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 
 def test_amp_single_gpu(tmpdir):

tests/test_gpu_models.py

@@ -9,14 +9,14 @@ from pytorch_lightning.callbacks import (
     ModelCheckpoint,
 )
 from pytorch_lightning.core import memory
-from tests.models import (
-    LightningTestModel,
-)
 from pytorch_lightning.trainer.distrib_parts import (
     parse_gpu_ids,
     determine_root_gpu_device,
 )
 from pytorch_lightning.utilities.debugging import MisconfigurationException
+from tests.models import (
+    LightningTestModel,
+)
 
 PRETEND_N_OF_GPUS = 16
 

tests/test_profiler.py

@@ -1,4 +1,5 @@
 import time
+
 import numpy as np
 import pytest
 

tests/trainer/test_callbacks.py

@@ -1,6 +1,7 @@
 import os
 
 import tests.models.utils as tutils
+from pytorch_lightning import Callback
 from pytorch_lightning import Trainer, LightningModule
 from pytorch_lightning.callbacks import ModelCheckpoint
 from tests.models import (
@@ -10,8 +11,6 @@ from tests.models import (
     LightTestMixin
 )
 
-from pytorch_lightning import Callback
-
 
 def test_trainer_callback_system(tmpdir):
     """Test the callback system."""

tests/trainer/test_dataloaders.py

@@ -2,6 +2,7 @@ import pytest
 
 import tests.models.utils as tutils
 from pytorch_lightning import Trainer
+from pytorch_lightning.utilities.debugging import MisconfigurationException
 from tests.models import (
     TestModelBase,
     LightningTestModel,
@@ -14,7 +15,6 @@ from tests.models import (
     LightValStepFitSingleDataloaderMixin,
     LightTrainDataloader,
 )
-from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 
 def test_multiple_val_dataloader(tmpdir):

tests/trainer/test_trainer.py

@@ -1,17 +1,21 @@
 import glob
 import math
 import os
+from argparse import ArgumentParser, Namespace
+from unittest import mock
+
 import pytest
 import torch
-from argparse import ArgumentParser, Namespace
 
 import tests.models.utils as tutils
-from unittest import mock
-from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import (
     EarlyStopping,
     ModelCheckpoint,
 )
+from pytorch_lightning.core.lightning import load_hparams_from_tags_csv
+from pytorch_lightning.trainer.logging import TrainerLoggingMixin
+from pytorch_lightning.utilities.debugging import MisconfigurationException
 from tests.models import (
     TestModelBase,
     DictHparamsModel,
@@ -22,9 +26,6 @@ from tests.models import (
     LightTrainDataloader,
     LightTestDataloader,
 )
-from pytorch_lightning.core.lightning import load_hparams_from_tags_csv
-from pytorch_lightning.trainer.logging import TrainerLoggingMixin
-from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 
 def test_hparams_save_load(tmpdir):