Revert "Removed ignored errors and warnings (#1602)"

This reverts commit c26bfc5.

examples/contrib/cifar10/main.py

@@ -1,21 +1,24 @@
-from datetime import datetime
 from pathlib import Path
+from datetime import datetime
+
+import fire
 
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-import fire
 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
-from ignite.handlers import Checkpoint, DiskSaver
+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
+
 
 def training(local_rank, config):
 

examples/contrib/cifar10/utils.py

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

examples/contrib/cifar100_amp_benchmark/benchmark_fp32.py

@@ -1,13 +1,16 @@
+import fire
+
 import torch
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
+
 from torchvision.models import wide_resnet50_2
 
-import fire
-from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
-from ignite.handlers import Timer
+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
 
 

examples/contrib/cifar100_amp_benchmark/benchmark_nvidia_apex.py

@@ -1,14 +1,18 @@
+import fire
+
 import torch
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
+
 from torchvision.models import wide_resnet50_2
 
-import fire
 from apex import amp
-from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
-from ignite.handlers import Timer
+
+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
 
 

examples/contrib/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py

@@ -1,16 +1,19 @@
+import fire
+
 import torch
+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 torch.nn import CrossEntropyLoss
-from torch.optim import SGD
+
 from torchvision.models import wide_resnet50_2
 
-import fire
-from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
-from ignite.handlers import Timer
+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
 
 

examples/contrib/cifar100_amp_benchmark/utils.py

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

examples/contrib/cifar10_qat/main.py

@@ -1,11 +1,11 @@
 from datetime import datetime
 from pathlib import Path
 
+import fire
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
-import fire
 import ignite
 import ignite.distributed as idist
 import utils

examples/contrib/cifar10_qat/utils.py

@@ -1,13 +1,13 @@
 import os
 
+import brevitas
+import brevitas.nn as qnn
 import torch
 import torch.nn as nn
 import torchvision
 from torchvision import datasets, models
 from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
 
-import brevitas
-import brevitas.nn as qnn
 from pact import PACTReLU
 
 train_transform = Compose(

examples/contrib/mnist/mnist_with_clearml_logger.py

@@ -19,10 +19,10 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, Normalize, ToTensor
+from torchvision.transforms import Compose, ToTensor, Normalize
 
 from ignite.contrib.handlers.clearml_logger import *
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
 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 +25,12 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, Normalize, ToTensor
+from torchvision.transforms import Compose, ToTensor, Normalize
 
 from ignite.contrib.handlers.neptune_logger import *
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
-from ignite.handlers import Checkpoint
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
 from ignite.metrics import Accuracy, Loss
+from ignite.handlers import Checkpoint
 from ignite.utils import setup_logger
 
 

examples/contrib/mnist/mnist_with_tensorboard_logger.py

@@ -26,12 +26,12 @@
 from torch.optim import SGD
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, Normalize, ToTensor
+from torchvision.transforms import Compose, ToTensor, Normalize
 
 from ignite.contrib.handlers.tensorboard_logger import *
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
-from ignite.handlers import ModelCheckpoint
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
 from ignite.metrics import Accuracy, Loss
+from ignite.handlers import ModelCheckpoint
 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_evaluator, create_supervised_trainer
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
 from ignite.metrics import Accuracy, Loss, RunningAverage
 
 

examples/contrib/mnist/mnist_with_visdom_logger.py

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

examples/fast_neural_style/neural_style.py

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

examples/fast_neural_style/utils.py

@@ -1,5 +1,4 @@
 import torch
-
 from PIL import Image
 
 

examples/mnist/mnist.py

@@ -1,16 +1,17 @@
 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 ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
+
 from tqdm import tqdm
 
 

examples/mnist/mnist_save_resume_engine.py

@@ -1,19 +1,16 @@
-from argparse import ArgumentParser
 from pathlib import Path
+from argparse import ArgumentParser
 
 import torch
-import torch.nn.functional as F
 from torch import nn
 from torch.optim import SGD
 from torch.optim.lr_scheduler import StepLR
 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 ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
-from ignite.handlers import Checkpoint, DiskSaver
-from ignite.metrics import Accuracy, Loss
-from ignite.utils import manual_seed
 from tqdm import tqdm
 
 try:
@@ -28,6 +25,11 @@
             "or upgrade PyTorch using your package manager of choice (pip or conda)."
         )
 
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.metrics import Accuracy, Loss
+from ignite.handlers import Checkpoint, DiskSaver
+from ignite.utils import manual_seed
+
 
 # Basic model's definition
 class Net(nn.Module):

examples/mnist/mnist_with_tensorboard.py

@@ -16,17 +16,13 @@
 """
 
 from argparse import ArgumentParser
-
 import torch
-import torch.nn.functional as F
+from torch.utils.data import DataLoader
 from torch import nn
+import torch.nn.functional as F
 from torch.optim import SGD
-from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-from torchvision.transforms import Compose, Normalize, ToTensor
-
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
-from ignite.metrics import Accuracy, Loss
+from torchvision.transforms import Compose, ToTensor, Normalize
 
 try:
     from tensorboardX import SummaryWriter
@@ -40,6 +36,9 @@
             "or upgrade PyTorch using your package manager of choice (pip or conda)."
         )
 
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.metrics import Accuracy, Loss
+
 
 class Net(nn.Module):
     def __init__(self):