Apply isort to `pl_examples/` (#5291)

* Remove examples from isort ignore list

* Apply isort

(cherry picked from commit 0c7c9e8540)

pl_examples/basic_examples/autoencoder.py

@@ -17,15 +17,14 @@ from argparse import ArgumentParser
 import torch
 import torch.nn.functional as F
 from torch import nn
-from torch.utils.data import DataLoader
-from torch.utils.data import random_split
+from torch.utils.data import DataLoader, random_split
 
 import pytorch_lightning as pl
 from pl_examples import _TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if _TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 

pl_examples/basic_examples/backbone_image_classifier.py

@@ -22,8 +22,8 @@ import pytorch_lightning as pl
 from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if _TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 

pl_examples/basic_examples/conv_sequential_example.py

@@ -20,16 +20,16 @@ to balance across your GPUs.
 To run:
 python conv_model_sequential_example.py --accelerator ddp --gpus 4 --max_epochs 1  --batch_size 256 --use_ddp_sequential
 """
-import math
 from argparse import ArgumentParser
+import math
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning import Trainer
 from pytorch_lightning.metrics.functional import accuracy
 from pytorch_lightning.plugins.ddp_sequential_plugin import DDPSequentialPlugin

pl_examples/basic_examples/dali_image_classifier.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 from abc import ABC
 from argparse import ArgumentParser
+from distutils.version import LooseVersion
 from random import shuffle
 from warnings import warn
-from distutils.version import LooseVersion
 
 import numpy as np
 import torch
@@ -26,16 +26,15 @@ import pytorch_lightning as pl
 from pl_examples import _TORCHVISION_AVAILABLE, _DALI_AVAILABLE, cli_lightning_logo
 
 if _TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 
 if _DALI_AVAILABLE:
-    from nvidia.dali import ops
+    from nvidia.dali import ops, __version__ as dali_version
     from nvidia.dali.pipeline import Pipeline
     from nvidia.dali.plugin.pytorch import DALIClassificationIterator
-    from nvidia.dali import __version__ as dali_version
 
     NEW_DALI_API = LooseVersion(dali_version) >= LooseVersion('0.28.0')
     if NEW_DALI_API:

pl_examples/basic_examples/simple_image_classifier.py

@@ -18,9 +18,9 @@ from pprint import pprint
 import torch
 from torch.nn import functional as F
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
+import pytorch_lightning as pl
 
 
 class LitClassifier(pl.LightningModule):

pl_examples/bug_report_model.py

@@ -20,11 +20,12 @@
 # --------------------------------------------
 # --------------------------------------------
 import os
+
 import torch
 from torch.utils.data import Dataset
 
 from pl_examples import cli_lightning_logo
-from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning import LightningModule, Trainer
 
 
 class RandomDataset(Dataset):

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -38,22 +38,21 @@ import argparse
 from collections import OrderedDict
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, Generator, Union
+from typing import Generator, Optional, Union
 
 import torch
-import torch.nn.functional as F
 from torch import optim
 from torch.nn import Module
+import torch.nn.functional as F
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
-from torchvision import models
-from torchvision import transforms
+from torchvision import models, transforms
 from torchvision.datasets import ImageFolder
 from torchvision.datasets.utils import download_and_extract_archive
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning import _logger as log
 
 BN_TYPES = (torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)

pl_examples/domain_templates/generative_adversarial_net.py

@@ -19,20 +19,20 @@ After a few epochs, launch TensorBoard to see the images being generated at ever
 
 tensorboard --logdir default
 """
-import os
 from argparse import ArgumentParser, Namespace
+import os
 
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F  # noqa
-import torchvision
-import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
+import torchvision
 from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
 
 from pl_examples import cli_lightning_logo
-from pytorch_lightning.core import LightningModule, LightningDataModule
+from pytorch_lightning.core import LightningDataModule, LightningModule
 from pytorch_lightning.trainer import Trainer
 
 

pl_examples/domain_templates/imagenet.py

@@ -30,8 +30,8 @@ or show all options you can change:
     python imagenet.py --help
 
 """
-import os
 from argparse import ArgumentParser, Namespace
+import os
 
 import torch
 import torch.nn.functional as F
@@ -44,8 +44,8 @@ import torchvision.datasets as datasets
 import torchvision.models as models
 import torchvision.transforms as transforms
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning.core import LightningModule
 
 

pl_examples/domain_templates/reinforce_learn_Qnet.py

@@ -33,8 +33,8 @@ Second-Edition/blob/master/Chapter06/02_dqn_pong.py
 """
 
 import argparse
-from collections import OrderedDict, deque, namedtuple
-from typing import Tuple, List
+from collections import deque, namedtuple, OrderedDict
+from typing import List, Tuple
 
 import gym
 import numpy as np
@@ -45,8 +45,8 @@ from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import IterableDataset
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 
 
 class DQN(nn.Module):

pl_examples/domain_templates/semantic_segmentation.py

@@ -12,20 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from argparse import ArgumentParser, Namespace
 import os
 import random
-from argparse import ArgumentParser, Namespace
 
 import numpy as np
+from PIL import Image
 import torch
 import torch.nn.functional as F
-import torchvision.transforms as transforms
-from PIL import Image
 from torch.utils.data import DataLoader, Dataset
+import torchvision.transforms as transforms
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
 from pl_examples.domain_templates.unet import UNet
+import pytorch_lightning as pl
 from pytorch_lightning.loggers import WandbLogger
 
 DEFAULT_VOID_LABELS = (0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1)

pyproject.toml

@@ -23,7 +23,6 @@ known_first_party = [
     "tests",
 ]
 skip_glob = [
-    "pl_examples/*",
     "pytorch_lightning/accelerators/*",
     "pytorch_lightning/callbacks/*",
     "pytorch_lightning/cluster_environments/*",