Remove some ignored errors (pytorch#1605)

* Removed ignored errors and warnings

* autopep8 fix

* Fixed import errors

* autopep8 fix

* More fixes

* Fixed code-formatting tools call order

* autopep8 fix

* Fix isort

* Fixed code-style action

* Updated isort to >5.0.0
and reformatted files

* Fixed isort version and reverted code-style install

Co-authored-by: vfdev-5 <vfdev-5@users.noreply.github.com>

.github/workflows/code-style.yml

@@ -27,10 +27,11 @@ jobs:
         with:
           python-version: "3.8"
       - run: |
-          python -m pip install autopep8 "black==19.10b0" "isort==4.3.21"
-          isort -rc .
+          python -m pip install autopep8 "black==19.10b0" "isort==5.7.0"
           autopep8 --recursive --in-place --aggressive --aggressive .
           black .
+          isort .
+
       - name: Commit and push changes
         uses: stefanzweifel/git-auto-commit-action@v4
         with:

.github/workflows/unit-tests.yml

@@ -79,10 +79,10 @@ jobs:
       - name: Check code formatting
         shell: bash -l {0}
         run: |
-          pip install flake8 "black==19.10b0" "isort==4.3.21"
+          pip install flake8 "black==19.10b0" "isort==5.7.0"
           flake8 ignite/ tests/ examples/
           black --check .
-          isort -rc -c .
+          isort -c .
 
       - name: Run Mypy
         shell: bash -l {0}

CONTRIBUTING.md

@@ -65,7 +65,7 @@ git clone https://github.com/pytorch/ignite.git
 cd ignite
 python setup.py develop
 pip install -r requirements-dev.txt
-pip install flake8 "black==19.10b0" "isort==4.3.21" mypy
+pip install flake8 "black==19.10b0" "isort==5.7.0" mypy
 ```
 
 ### Code development

examples/contrib/cifar10/main.py

@@ -1,23 +1,20 @@
-from pathlib import Path
 from datetime import datetime
+from pathlib import Path
 
 import fire
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
+import utils
 
 import ignite
 import ignite.distributed as idist
-from ignite.engine import Events, Engine, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
-from ignite.handlers import Checkpoint, DiskSaver
-from ignite.utils import manual_seed, setup_logger
-
 from ignite.contrib.engines import common
 from ignite.contrib.handlers import PiecewiseLinear
-
-import utils
+from ignite.engine import Engine, Events, create_supervised_evaluator
+from ignite.handlers import Checkpoint, DiskSaver
+from ignite.metrics import Accuracy, Loss
+from ignite.utils import manual_seed, setup_logger
 
 
 def training(local_rank, config):

examples/contrib/cifar10/utils.py

@@ -1,8 +1,7 @@
 import os
 
-from torchvision import models
-from torchvision import datasets
-from torchvision.transforms import Compose, ToTensor, Normalize, Pad, RandomCrop, RandomHorizontalFlip
+from torchvision import datasets, models
+from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
 
 train_transform = Compose(
     [

examples/contrib/cifar100_amp_benchmark/benchmark_fp32.py

@@ -1,17 +1,14 @@
 import fire
-
 import torch
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
-
 from torchvision.models import wide_resnet50_2
+from utils import get_train_eval_loaders
 
-from ignite.engine import Events, Engine, create_supervised_evaluator, convert_tensor
-from ignite.metrics import Accuracy, Loss
-from ignite.handlers import Timer
 from ignite.contrib.handlers import ProgressBar
-
-from utils import get_train_eval_loaders
+from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.handlers import Timer
+from ignite.metrics import Accuracy, Loss
 
 
 def main(dataset_path, batch_size=256, max_epochs=10):

examples/contrib/cifar100_amp_benchmark/benchmark_nvidia_apex.py

@@ -1,19 +1,15 @@
 import fire
-
 import torch
+from apex import amp
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
-
 from torchvision.models import wide_resnet50_2
+from utils import get_train_eval_loaders
 
-from apex import amp
-
-from ignite.engine import Events, Engine, create_supervised_evaluator, convert_tensor
-from ignite.metrics import Accuracy, Loss
-from ignite.handlers import Timer
 from ignite.contrib.handlers import ProgressBar
-
-from utils import get_train_eval_loaders
+from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.handlers import Timer
+from ignite.metrics import Accuracy, Loss
 
 
 def main(dataset_path, batch_size=256, max_epochs=10, opt="O1"):

examples/contrib/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py

@@ -1,20 +1,15 @@
 import fire
-
 import torch
+from torch.cuda.amp import GradScaler, autocast
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
-
-# Creates a GradScaler once at the beginning of training.
-from torch.cuda.amp import GradScaler, autocast
-
 from torchvision.models import wide_resnet50_2
+from utils import get_train_eval_loaders
 
-from ignite.engine import Events, Engine, create_supervised_evaluator, convert_tensor
-from ignite.metrics import Accuracy, Loss
-from ignite.handlers import Timer
 from ignite.contrib.handlers import ProgressBar
-
-from utils import get_train_eval_loaders
+from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.handlers import Timer
+from ignite.metrics import Accuracy, Loss
 
 
 def main(dataset_path, batch_size=256, max_epochs=10):

examples/contrib/cifar100_amp_benchmark/utils.py

@@ -1,10 +1,8 @@
 import random
 
+from torch.utils.data import DataLoader, Subset
 from torchvision.datasets.cifar import CIFAR100
-from torchvision.transforms import Compose, RandomCrop, Pad, RandomHorizontalFlip
-from torchvision.transforms import ToTensor, Normalize, RandomErasing
-
-from torch.utils.data import Subset, DataLoader
+from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomErasing, RandomHorizontalFlip, ToTensor
 
 
 def get_train_eval_loaders(path, batch_size=256):

examples/contrib/cifar10_qat/main.py

@@ -5,10 +5,10 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
+import utils
 
 import ignite
 import ignite.distributed as idist
-import utils
 from ignite.contrib.engines import common
 from ignite.contrib.handlers import PiecewiseLinear
 from ignite.engine import Engine, Events, create_supervised_evaluator

examples/contrib/cifar10_qat/utils.py

@@ -5,11 +5,10 @@
 import torch
 import torch.nn as nn
 import torchvision
+from pact import PACTReLU
 from torchvision import datasets, models
 from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
 
-from pact import PACTReLU
-
 train_transform = Compose(
     [
         Pad(4),

examples/contrib/mnist/mnist_with_clearml_logger.py

@@ -19,10 +19,18 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, ToTensor, Normalize
-
-from ignite.contrib.handlers.clearml_logger import *
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+from ignite.contrib.handlers.clearml_logger import (
+    ClearMLLogger,
+    ClearMLSaver,
+    GradsHistHandler,
+    GradsScalarHandler,
+    WeightsHistHandler,
+    WeightsScalarHandler,
+    global_step_from_engine,
+)
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.handlers import Checkpoint
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger

examples/contrib/mnist/mnist_with_neptune_logger.py

@@ -25,12 +25,18 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, ToTensor, Normalize
-
-from ignite.contrib.handlers.neptune_logger import *
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+from ignite.contrib.handlers.neptune_logger import (
+    GradsScalarHandler,
+    NeptuneLogger,
+    NeptuneSaver,
+    WeightsScalarHandler,
+    global_step_from_engine,
+)
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.handlers import Checkpoint
+from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 
 

examples/contrib/mnist/mnist_with_tensorboard_logger.py

@@ -26,12 +26,19 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, ToTensor, Normalize
-
-from ignite.contrib.handlers.tensorboard_logger import *
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+from ignite.contrib.handlers.tensorboard_logger import (
+    GradsHistHandler,
+    GradsScalarHandler,
+    TensorboardLogger,
+    WeightsHistHandler,
+    WeightsScalarHandler,
+    global_step_from_engine,
+)
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.handlers import ModelCheckpoint
+from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 
 

examples/contrib/mnist/mnist_with_tqdm_logger.py

@@ -1,15 +1,15 @@
 from argparse import ArgumentParser
 
+import torch
+import torch.nn.functional as F
 from torch import nn
 from torch.optim import SGD
 from torch.utils.data import DataLoader
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import Compose, ToTensor, Normalize
 from torchvision.datasets import MNIST
+from torchvision.transforms import Compose, Normalize, ToTensor
 
 from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.metrics import Accuracy, Loss, RunningAverage
 
 

examples/contrib/mnist/mnist_with_visdom_logger.py

@@ -20,17 +20,22 @@
 from argparse import ArgumentParser
 
 import torch
-from torch.utils.data import DataLoader
-from torch import nn
 import torch.nn.functional as F
+from torch import nn
 from torch.optim import SGD
+from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, ToTensor, Normalize
-
-from ignite.contrib.handlers.visdom_logger import *
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+from ignite.contrib.handlers.visdom_logger import (
+    GradsScalarHandler,
+    VisdomLogger,
+    WeightsScalarHandler,
+    global_step_from_engine,
+)
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.handlers import ModelCheckpoint
+from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 
 

examples/contrib/mnist/mnist_with_wandb_logger.py

@@ -23,12 +23,12 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, ToTensor, Normalize
+from torchvision.transforms import Compose, Normalize, ToTensor
 
-from ignite.contrib.handlers.wandb_logger import *
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
+from ignite.contrib.handlers.wandb_logger import WandBLogger, global_step_from_engine
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.handlers import ModelCheckpoint
+from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 
 

examples/fast_neural_style/neural_style.py

@@ -1,25 +1,22 @@
 # coding: utf-8
 import argparse
 import os
+import random
 import sys
+from collections import OrderedDict
 
 import numpy as np
-import random
 import torch
+import utils
+from handlers import Progbar
 from torch.optim import Adam
 from torch.utils.data import DataLoader
-from torchvision import datasets
-from torchvision import transforms
-
-from ignite.engine import Engine, Events
-from ignite.handlers import ModelCheckpoint
-
-import utils
+from torchvision import datasets, transforms
 from transformer_net import TransformerNet
 from vgg import Vgg16
-from handlers import Progbar
 
-from collections import OrderedDict
+from ignite.engine import Engine, Events
+from ignite.handlers import ModelCheckpoint
 
 
 def check_paths(args):

examples/gan/dcgan.py

@@ -366,9 +366,9 @@ def create_plots(engine):
 
             mpl.use("agg")
 
+            import matplotlib.pyplot as plt
             import numpy as np
             import pandas as pd
-            import matplotlib.pyplot as plt
 
         except ImportError:
             warnings.warn("Loss plots will not be generated -- pandas or matplotlib not found")

examples/mnist/mnist.py

@@ -1,19 +1,18 @@
 from argparse import ArgumentParser
 
 import torch
+import torch.nn.functional as F
 from torch import nn
 from torch.optim import SGD
 from torch.utils.data import DataLoader
-import torch.nn.functional as F
-from torchvision.transforms import Compose, ToTensor, Normalize
 from torchvision.datasets import MNIST
+from torchvision.transforms import Compose, Normalize, ToTensor
+from tqdm import tqdm
 
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 
-from tqdm import tqdm
-
 
 class Net(nn.Module):
     def __init__(self):