Switch formatting from black+isort to µfmt (black+µsort) (#2460)

* Switch to usort

* Apply usort to covered files

* Update contributing docs for usort

* use usort diff instead of check

.gitignore

@@ -39,3 +39,7 @@ coverage.xml
 /docs/src/
 /docs/build/
 /docs/source/generated/
+
+# Virtualenv
+.venv/
+.python-version

.pre-commit-config.yaml

@@ -14,18 +14,13 @@ repos:
       - id: prettier
         exclude_types: ["python", "jupyter", "shell", "gitignore"]
 
-  - repo: https://github.com/python/black
-    rev: 21.12b0
+  - repo: https://github.com/omnilib/ufmt
+    rev: v1.3.1
     hooks:
-      - id: black
-        language_version: python3.8
-        args: ["--config", "pyproject.toml"]
-
-  - repo: https://github.com/timothycrosley/isort
-    rev: 5.7.0
-    hooks:
-      - id: isort
-        args: ["--settings", "setup.cfg"]
+      - id: ufmt
+        additional_dependencies:
+          - black == 21.12b0
+          - usort == 1.0.1
 
   - repo: https://gitlab.com/pycqa/flake8
     rev: 3.8.4

CONTRIBUTING.md

@@ -110,33 +110,34 @@ If you modify the code, you will most probably also need to code some tests to e
 - naming convention for files `test_*.py`, e.g. `test_precision.py`
 - naming of testing functions `def test_*`, e.g. `def test_precision_on_random_data()`
   - if test function should run on GPU, please **make sure to add `cuda`** in the test name, e.g. `def test_something_on_cuda()`.
-  Additionally, we may want to decorate it with `@pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU")`.
-  For more examples, please see https://github.com/pytorch/ignite/blob/master/tests/ignite/engine/test_create_supervised.py
+    Additionally, we may want to decorate it with `@pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU")`.
+    For more examples, please see https://github.com/pytorch/ignite/blob/master/tests/ignite/engine/test_create_supervised.py
   - if test function checks distributed configuration, we have to mark the test as `@pytest.mark.distributed` and additional
-  conditions depending on the intended checks. For example, please see
-  https://github.com/pytorch/ignite/blob/master/tests/ignite/metrics/test_accuracy.py
-
+    conditions depending on the intended checks. For example, please see
+    https://github.com/pytorch/ignite/blob/master/tests/ignite/metrics/test_accuracy.py
 
 New code should be compatible with Python 3.X versions. Once you finish implementing a feature or bugfix and tests,
 please run lint checking and tests:
 
 #### Formatting Code
 
-To ensure the codebase complies with a style guide, we use [flake8](https://flake8.pycqa.org/en/latest/),
-[black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/) tools to
-format and check codebase for compliance with PEP8.
+To ensure the codebase complies with a style guide, we use [flake8](https://flake8.pycqa.org/en/latest/)
+and [ufmt](https://ufmt.omnilib.dev/) ([black](https://black.readthedocs.io/en/stable/) and
+[usort](https://usort.readthedocs.io/en/stable/)) to format and check codebase for compliance with PEP8.
 
 ##### Formatting without pre-commit
 
 If you choose not to use pre-commit, you can take advantage of IDE extensions configured to black format or invoke
 black manually to format files and commit them.
 
-To install `flake8`, `black==21.12b0`, `isort==5.7.0` and `mypy`, please run
+To install `flake8`, `ufmt` and `mypy`, please run
+
 ```bash
 bash ./tests/run_code_style.sh install
 ```
 
 To format files and commit changes:
+
 ```bash
 # This should autoformat the files
 bash ./tests/run_code_style.sh fmt
@@ -147,27 +148,27 @@ git commit -m "Added awesome feature"
 
 ##### Formatting with pre-commit
 
-To automate the process, we have configured the repo with [pre-commit hooks](https://pre-commit.com/) to use black to autoformat the staged files to ensure every commit complies with a style guide. This requires some setup, which is described below:
+To automate the process, we have configured the repo with [pre-commit hooks](https://pre-commit.com/) to use µfmt to autoformat the staged files to ensure every commit complies with a style guide. This requires some setup, which is described below:
 
 1. Install pre-commit in your python environment.
-2. Run pre-commit install that configures a virtual environment to invoke black, isort and flake8 on commits.
+2. Run pre-commit install that configures a virtual environment to invoke ufmt and flake8 on commits.
 
 ```bash
 pip install pre-commit
 pre-commit install
 ```
 
 3. When files are committed:
-   - If the stages files are not compliant with black, black will autoformat the staged files. If this were to happen, files should be staged and committed again. See example code below.
+   - If the stages files are not compliant with black or µsort, µfmt will autoformat the staged files. If this were to happen, files should be staged and committed again. See example code below.
    - If the staged files are not compliant with flake8, errors will be raised. These errors should be fixed and the files should be committed again. See example code below.
 
 ```bash
 git add .
 git commit -m "Added awesome feature"
 # DONT'T WORRY IF ERRORS ARE RAISED.
-# YOUR CODE IS NOT COMPLIANT WITH flake8, isort or black
+# YOUR CODE IS NOT COMPLIANT WITH flake8, µsort or black
 # Fix any flake8 errors by following their suggestions
-# isort and black will automatically format the files so they might look different, but you'll need to stage the files
+# µfmt will automatically format the files so they might look different, but you'll need to stage the files
 # again for committing
 # After fixing any flake8 errors
 git add .

examples/contrib/cifar10/main.py

@@ -6,7 +6,7 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.cuda.amp import GradScaler, autocast
+from torch.cuda.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist

examples/contrib/cifar100_amp_benchmark/benchmark_fp32.py

@@ -6,7 +6,7 @@
 from utils import get_train_eval_loaders
 
 from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events
 from ignite.handlers import Timer
 from ignite.metrics import Accuracy, Loss
 

examples/contrib/cifar100_amp_benchmark/benchmark_nvidia_apex.py

@@ -7,7 +7,7 @@
 from utils import get_train_eval_loaders
 
 from ignite.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events
 from ignite.handlers import Timer
 from ignite.metrics import Accuracy, Loss
 

examples/contrib/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py

@@ -1,13 +1,13 @@
 import fire
 import torch
-from torch.cuda.amp import GradScaler, autocast
+from torch.cuda.amp import autocast, GradScaler
 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.contrib.handlers import ProgressBar
-from ignite.engine import Engine, Events, convert_tensor, create_supervised_evaluator
+from ignite.engine import convert_tensor, create_supervised_evaluator, Engine, Events
 from ignite.handlers import Timer
 from ignite.metrics import Accuracy, Loss
 

examples/contrib/cifar10_qat/main.py

@@ -6,13 +6,13 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.cuda.amp import GradScaler, autocast
+from torch.cuda.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist
 from ignite.contrib.engines import common
 from ignite.contrib.handlers import PiecewiseLinear
-from ignite.engine import Engine, Events, create_supervised_evaluator
+from ignite.engine import create_supervised_evaluator, Engine, Events
 from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import manual_seed, setup_logger

examples/contrib/mnist/mnist_with_clearml_logger.py

@@ -24,13 +24,13 @@
 from ignite.contrib.handlers.clearml_logger import (
     ClearMLLogger,
     ClearMLSaver,
+    global_step_from_engine,
     GradsHistHandler,
     GradsScalarHandler,
     WeightsHistHandler,
     WeightsScalarHandler,
-    global_step_from_engine,
 )
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 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

@@ -28,13 +28,13 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 from ignite.contrib.handlers.neptune_logger import (
+    global_step_from_engine,
     GradsScalarHandler,
     NeptuneLogger,
     NeptuneSaver,
     WeightsScalarHandler,
-    global_step_from_engine,
 )
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 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

@@ -29,14 +29,14 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 from ignite.contrib.handlers.tensorboard_logger import (
+    global_step_from_engine,
     GradsHistHandler,
     GradsScalarHandler,
     TensorboardLogger,
     WeightsHistHandler,
     WeightsScalarHandler,
-    global_step_from_engine,
 )
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 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

@@ -9,7 +9,7 @@
 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 create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.metrics import Accuracy, Loss, RunningAverage
 
 

examples/contrib/mnist/mnist_with_visdom_logger.py

@@ -28,12 +28,12 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 from ignite.contrib.handlers.visdom_logger import (
+    global_step_from_engine,
     GradsScalarHandler,
     VisdomLogger,
     WeightsScalarHandler,
-    global_step_from_engine,
 )
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 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

@@ -25,8 +25,8 @@
 from torchvision.datasets import MNIST
 from torchvision.transforms import Compose, Normalize, ToTensor
 
-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.contrib.handlers.wandb_logger import global_step_from_engine, WandBLogger
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.handlers import ModelCheckpoint
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger

examples/contrib/transformers/main.py

@@ -7,7 +7,7 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.cuda.amp import GradScaler, autocast
+from torch.cuda.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist

examples/mnist/mnist.py

@@ -9,7 +9,7 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 from tqdm import tqdm
 
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import setup_logger
 

examples/mnist/mnist_save_resume_engine.py

@@ -11,7 +11,7 @@
 from torchvision.transforms import Compose, Normalize, ToTensor
 from tqdm import tqdm
 
-from ignite.engine import Events, create_supervised_evaluator, create_supervised_trainer
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.handlers import Checkpoint, DiskSaver
 from ignite.metrics import Accuracy, Loss
 from ignite.utils import manual_seed

examples/mnist/mnist_with_tensorboard.py

@@ -25,7 +25,7 @@
 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 create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.metrics import Accuracy, Loss
 
 try:

examples/mnist/mnist_with_tensorboard_on_tpu.py

@@ -25,7 +25,7 @@
 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 create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.metrics import Accuracy, Loss, RunningAverage
 
 try:

examples/mnist/mnist_with_visdom.py

@@ -9,7 +9,7 @@
 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 create_supervised_evaluator, create_supervised_trainer, Events
 from ignite.metrics import Accuracy, Loss
 
 try:

examples/references/classification/imagenet/code/scripts/training.py

@@ -5,15 +5,15 @@
 
 import torch
 from apex import amp
-from py_config_runner.config_utils import TRAINVAL_CONFIG, assert_config, get_params
+from py_config_runner.config_utils import assert_config, get_params, TRAINVAL_CONFIG
 from py_config_runner.utils import set_seed
 from utils import exp_tracking
 from utils.handlers import predictions_gt_images_handler
 
 import ignite
 import ignite.distributed as idist
 from ignite.contrib.engines import common
-from ignite.engine import Engine, Events, _prepare_batch, create_supervised_evaluator
+from ignite.engine import _prepare_batch, create_supervised_evaluator, Engine, Events
 from ignite.metrics import Accuracy, TopKCategoricalAccuracy
 from ignite.utils import setup_logger
 

examples/references/segmentation/pascal_voc2012/main.py

@@ -6,14 +6,14 @@
 import torch
 
 try:
-    from torch.cuda.amp import GradScaler, autocast
+    from torch.cuda.amp import autocast, GradScaler
 except ImportError:
     raise RuntimeError("Please, use recent PyTorch version, e.g. >=1.6.0")
 
 import dataflow as data
 import utils
 import vis
-from py_config_runner import ConfigObject, InferenceConfigSchema, TrainvalConfigSchema, get_params
+from py_config_runner import ConfigObject, get_params, InferenceConfigSchema, TrainvalConfigSchema
 
 import ignite.distributed as idist
 from ignite.contrib.engines import common

ignite/contrib/engines/__init__.py

@@ -1 +1 @@
-from ignite.contrib.engines.tbptt import Tbptt_Events, create_supervised_tbptt_trainer
+from ignite.contrib.engines.tbptt import create_supervised_tbptt_trainer, Tbptt_Events

ignite/contrib/engines/common.py

@@ -1,7 +1,7 @@
 import numbers
 import warnings
 from functools import partial
-from typing import Any, Callable, Dict, Iterable, Mapping, Optional, Sequence, Union, cast
+from typing import Any, Callable, cast, Dict, Iterable, Mapping, Optional, Sequence, Union
 
 import torch
 import torch.nn as nn
@@ -12,6 +12,7 @@
 import ignite.distributed as idist
 from ignite.contrib.handlers import (
     ClearMLLogger,
+    global_step_from_engine,
     LRScheduler,
     MLflowLogger,
     NeptuneLogger,
@@ -20,7 +21,6 @@
     TensorboardLogger,
     VisdomLogger,
     WandBLogger,
-    global_step_from_engine,
 )
 from ignite.contrib.handlers.base_logger import BaseLogger
 from ignite.contrib.metrics import GpuInfo

ignite/contrib/engines/tbptt.py

@@ -6,7 +6,7 @@
 import torch.nn as nn
 from torch.optim.optimizer import Optimizer
 
-from ignite.engine import Engine, EventEnum, _prepare_batch
+from ignite.engine import _prepare_batch, Engine, EventEnum
 from ignite.utils import apply_to_tensor
 
 

ignite/contrib/handlers/__init__.py

@@ -7,16 +7,15 @@
 from ignite.contrib.handlers.trains_logger import TrainsLogger
 from ignite.contrib.handlers.visdom_logger import VisdomLogger
 from ignite.contrib.handlers.wandb_logger import WandBLogger
-from ignite.handlers import EpochOutputStore  # ref
-from ignite.handlers import global_step_from_engine  # ref
+from ignite.handlers import EpochOutputStore, global_step_from_engine  # ref  # ref
 from ignite.handlers.lr_finder import FastaiLRFinder
 from ignite.handlers.param_scheduler import (
     ConcatScheduler,
     CosineAnnealingScheduler,
+    create_lr_scheduler_with_warmup,
     LinearCyclicalScheduler,
     LRScheduler,
     ParamGroupScheduler,
     PiecewiseLinear,
-    create_lr_scheduler_with_warmup,
 )
 from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler