style: formatted with isort black

templates/_base/_argparse.py

@@ -1,5 +1,6 @@
 {% block imports %}
 from argparse import ArgumentParser
+
 {% endblock %}
 
 {% block defaults %}

templates/_base/_handlers.py

@@ -2,12 +2,13 @@
 Ignite handlers
 """
 from typing import Any, Dict, Iterable, Mapping, Optional, Tuple, Union
+
 from ignite.contrib.engines import common
 from ignite.contrib.handlers.base_logger import BaseLogger
 from ignite.contrib.handlers.param_scheduler import LRScheduler
 from ignite.engine.engine import Engine
 from ignite.engine.events import Events
-from ignite.handlers import TimeLimit, Timer, Checkpoint, EarlyStopping
+from ignite.handlers import Checkpoint, EarlyStopping, TimeLimit, Timer
 from torch.nn import Module
 from torch.optim.optimizer import Optimizer
 from torch.utils.data.distributed import DistributedSampler

templates/gan/_test_internal.py

@@ -23,7 +23,14 @@
 from test_all import set_up
 from torch import nn, optim
 from trainers import create_trainers
-from utils import hash_checkpoint, log_metrics, resume_from, setup_logging, get_handlers, get_logger
+from utils import (
+    get_handlers,
+    get_logger,
+    hash_checkpoint,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 
 def test_get_handlers(tmp_path):

templates/gan/datasets.py

@@ -1,6 +1,6 @@
-from torchvision import transforms as T
-from torchvision import datasets as dset
 import ignite.distributed as idist
+from torchvision import datasets as dset
+from torchvision import transforms as T
 
 
 def get_datasets(dataset, dataroot):

templates/gan/main.py

@@ -7,17 +7,24 @@
 from pathlib import Path
 from typing import Any
 
-import torch
 import ignite.distributed as idist
+import torch
+from config import get_default_parser
+from datasets import get_datasets
+from ignite.contrib.handlers.wandb_logger import WandBLogger
 from ignite.engine.events import Events
 from ignite.utils import manual_seed
 from torchvision import utils as vutils
-
-from datasets import get_datasets
 from trainers import create_trainers
-from utils import setup_logging, log_metrics, log_basic_info, initialize, resume_from, get_handlers, get_logger
-from config import get_default_parser
-
+from utils import (
+    get_handlers,
+    get_logger,
+    initialize,
+    log_basic_info,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 FAKE_IMG_FNAME = "fake_sample_epoch_{:04d}.png"
 REAL_IMG_FNAME = "real_sample_epoch_{:04d}.png"

templates/gan/trainers.py

@@ -3,13 +3,12 @@
 """
 from typing import Any
 
-import torch
 import ignite.distributed as idist
+import torch
 from ignite.engine import Engine
 from torch.cuda.amp import autocast
 from torch.optim.optimizer import Optimizer
 
-
 # Edit below functions the way how the model will be training
 
 # train_function is how the model will be learning with given batch

templates/gan/utils.py

@@ -8,19 +8,19 @@
 from pathlib import Path
 from pprint import pformat
 from typing import Any, Mapping, Optional, Tuple, Union
-from ignite.contrib.handlers.param_scheduler import ParamScheduler
 
 import ignite.distributed as idist
 import torch
-from torch import nn, optim
+from ignite.contrib.handlers.param_scheduler import ParamScheduler
 from ignite.engine import Engine
 from ignite.handlers.checkpoint import Checkpoint
 from ignite.utils import setup_logger
+from models import Discriminator, Generator
+from torch import nn, optim
 from torch.nn import Module
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.optim.optimizer import Optimizer
 
-from models import Generator, Discriminator
 {% include "_handlers.py" %}
 
 

templates/image_classification/_test_internal.py

@@ -30,7 +30,14 @@
     train_events_to_attr,
     train_function,
 )
-from utils import hash_checkpoint, log_metrics, resume_from, setup_logging, get_handlers, get_logger
+from utils import (
+    get_handlers,
+    get_logger,
+    hash_checkpoint,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 
 def test_get_handlers(tmp_path):

templates/image_classification/datasets.py

@@ -1,6 +1,13 @@
-from torchvision import datasets
-from torchvision.transforms import Compose, Normalize, Pad, RandomCrop, RandomHorizontalFlip, ToTensor
 import ignite.distributed as idist
+from torchvision import datasets
+from torchvision.transforms import (
+    Compose,
+    Normalize,
+    Pad,
+    RandomCrop,
+    RandomHorizontalFlip,
+    ToTensor,
+)
 
 train_transform = Compose(
     [

templates/image_classification/main.py

@@ -7,14 +7,22 @@
 from typing import Any
 
 import ignite.distributed as idist
+from config import get_default_parser
+from datasets import get_datasets
+from ignite.contrib.handlers.wandb_logger import WandBLogger
 from ignite.engine.events import Events
-from ignite.utils import manual_seed
 from ignite.metrics import Accuracy, Loss
-
-from datasets import get_datasets
+from ignite.utils import manual_seed
 from trainers import create_trainers
-from utils import setup_logging, log_metrics, log_basic_info, initialize, resume_from, get_handlers, get_logger
-from config import get_default_parser
+from utils import (
+    get_handlers,
+    get_logger,
+    initialize,
+    log_basic_info,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 
 def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):

templates/image_classification/test_all.py

@@ -11,9 +11,7 @@
 from torch.functional import Tensor
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import Dataset
-from trainers import (
-    evaluate_function,
-)
+from trainers import evaluate_function
 from utils import initialize
 
 

templates/image_classification/trainers.py

@@ -2,12 +2,13 @@
 `trainer` and `evaluator` like trainer and evaluator
 """
 from typing import Any, Tuple
-from ignite.metrics import loss
 
 import torch
 from ignite.engine import Engine
+from ignite.metrics import loss
 from torch.cuda.amp import autocast
 from torch.optim.optimizer import Optimizer
+
 {% include "_events.py" %}
 
 

templates/image_classification/utils.py

@@ -8,19 +8,19 @@
 from pathlib import Path
 from pprint import pformat
 from typing import Any, Mapping, Optional, Tuple, Union
-from ignite.contrib.handlers.param_scheduler import ParamScheduler
 
 import ignite.distributed as idist
 import torch
+from ignite.contrib.handlers import PiecewiseLinear
+from ignite.contrib.handlers.param_scheduler import ParamScheduler
 from ignite.engine import Engine
 from ignite.handlers.checkpoint import Checkpoint
 from ignite.utils import setup_logger
-from ignite.contrib.handlers import PiecewiseLinear
-from torch.nn import Module, CrossEntropyLoss
+from models import get_model
+from torch.nn import CrossEntropyLoss, Module
+from torch.optim import SGD, Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
-from torch.optim import Optimizer, SGD
 
-from models import get_model
 {% include "_handlers.py" %}
 
 

templates/single/_test_internal.py

@@ -8,7 +8,6 @@
 import pytest
 import torch
 from config import get_default_parser
-
 from ignite.contrib.handlers import (
     ClearMLLogger,
     MLflowLogger,
@@ -32,7 +31,14 @@
     train_events_to_attr,
     train_function,
 )
-from utils import hash_checkpoint, log_metrics, resume_from, setup_logging, get_handlers, get_logger
+from utils import (
+    get_handlers,
+    get_logger,
+    hash_checkpoint,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 
 def set_up():

templates/single/main.py

@@ -7,13 +7,21 @@
 from typing import Any
 
 import ignite.distributed as idist
+from config import get_default_parser
+from datasets import get_datasets
+from ignite.contrib.handlers.wandb_logger import WandBLogger
 from ignite.engine.events import Events
 from ignite.utils import manual_seed
-
-from datasets import get_datasets
-from trainers import create_trainers, TrainEvents
-from utils import setup_logging, log_metrics, log_basic_info, initialize, resume_from, get_handlers, get_logger
-from config import get_default_parser
+from trainers import TrainEvents, create_trainers
+from utils import (
+    get_handlers,
+    get_logger,
+    initialize,
+    log_basic_info,
+    log_metrics,
+    resume_from,
+    setup_logging,
+)
 
 
 def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):

templates/single/trainers.py

@@ -7,6 +7,7 @@
 from ignite.engine import Engine
 from torch.cuda.amp import autocast
 from torch.optim.optimizer import Optimizer
+
 {% include "_events.py" %}
 
 

templates/single/utils.py

@@ -8,16 +8,17 @@
 from pathlib import Path
 from pprint import pformat
 from typing import Any, Mapping, Optional, Tuple, Union
-from ignite.contrib.handlers.param_scheduler import ParamScheduler
 
 import ignite.distributed as idist
 import torch
+from ignite.contrib.handlers.param_scheduler import ParamScheduler
 from ignite.engine import Engine
 from ignite.handlers.checkpoint import Checkpoint
 from ignite.utils import setup_logger
 from torch.nn import Module
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.optim.optimizer import Optimizer
+
 {% include "_handlers.py" %}