Chenglu/black (#2378)

* Updated black to 21.12b0 and fixed codebase for others folders (#2367)

* Updated black to 21.12b0 and fixed codebase for others folders

* Updated black

* formatting fixed (#2372)

* fix: remove tailing commas that would mislead black formating

* autopep8 fix

* fix: left out tailing comma

* chg: change black version

* fix: test_deprecated assert failed with additional newline

* fix: typo

* fix: fix func_no_reasons test assert failed

Co-authored-by: louis-she <[email protected]>

Co-authored-by: vfdev <[email protected]>
Co-authored-by: louis-she <[email protected]>

.github/workflows/trigger_circle_ci.py

@@ -24,7 +24,7 @@ def trigger_new_pipeline(data, headers):
         "https://circleci.com/api/v2/project/gh/pytorch/ignite/pipeline", data=json.dumps(data), headers=headers
     )
     assert_result(result, 201)
-    output = get_output(result.text, ["id",])
+    output = get_output(result.text, ["id"])
     return output["id"]
 
 
@@ -46,7 +46,7 @@ def get_workflow_id(pipeline_id, headers):
     while True:
         result = requests.get(f"https://circleci.com/api/v2/pipeline/{pipeline_id}/workflow", headers=headers)
         assert_result(result, 200)
-        output = get_output(result.text, ["items",])
+        output = get_output(result.text, ["items"])
         items = output["items"]
         if len(items) > 1:
             raise RuntimeError(f"Incorrect number of workflow ids: {len(items)} != 1\n" f"items: {items}")

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
         exclude_types: ["python", "jupyter", "shell", "gitignore"]
 
   - repo: https://github.com/python/black
-    rev: 19.10b0
+    rev: 21.12b0
     hooks:
       - id: black
         language_version: python3.8

CONTRIBUTING.md

@@ -109,11 +109,11 @@ 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()`. 
+  - 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
-  - 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 
+  - 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
 
 
@@ -131,7 +131,7 @@ format and check codebase for compliance with PEP8.
 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==19.10b0`, `isort==5.7.0` and `mypy`, please run
+To install `flake8`, `black==21.12b0`, `isort==5.7.0` and `mypy`, please run
 ```bash
 bash ./tests/run_code_style.sh install
 ```

docker/test_image.py

@@ -33,7 +33,7 @@ def main():
     print(traceback.format_exc())
     """
     try:
-        out = client.containers.run(args.image, f"python -c '{try_except_cmd}'", auto_remove=True, stderr=True,)
+        out = client.containers.run(args.image, f"python -c '{try_except_cmd}'", auto_remove=True, stderr=True)
         assert isinstance(out, bytes), type(out)
         out = out.decode("utf-8").strip()
 

examples/contrib/cifar10/main.py

@@ -223,11 +223,11 @@ def get_dataflow(config):
 
     # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu
     train_loader = idist.auto_dataloader(
-        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True,
+        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True
     )
 
     test_loader = idist.auto_dataloader(
-        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False,
+        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False
     )
     return train_loader, test_loader
 

examples/contrib/cifar10/utils.py

@@ -13,7 +13,7 @@
     ]
 )
 
-test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),])
+test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
 
 
 def get_train_test_datasets(path):

examples/contrib/cifar10_qat/main.py

@@ -208,11 +208,11 @@ def get_dataflow(config):
 
     # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu
     train_loader = idist.auto_dataloader(
-        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True,
+        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True
     )
 
     test_loader = idist.auto_dataloader(
-        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False,
+        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False
     )
     return train_loader, test_loader
 

examples/contrib/cifar10_qat/utils.py

@@ -17,7 +17,7 @@
     ]
 )
 
-test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),])
+test_transform = Compose([ToTensor(), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
 
 
 def get_train_test_datasets(path):

examples/contrib/transformers/main.py

@@ -228,11 +228,11 @@ def get_dataflow(config):
 
     # Setup data loader also adapted to distributed config: nccl, gloo, xla-tpu
     train_loader = idist.auto_dataloader(
-        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True,
+        train_dataset, batch_size=config["batch_size"], num_workers=config["num_workers"], shuffle=True, drop_last=True
     )
 
     test_loader = idist.auto_dataloader(
-        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False,
+        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False
     )
     return train_loader, test_loader
 
@@ -246,7 +246,7 @@ def initialize(config):
     # Adapt model for distributed settings if configured
     model = idist.auto_model(model)
 
-    optimizer = optim.AdamW(model.parameters(), lr=config["learning_rate"], weight_decay=config["weight_decay"],)
+    optimizer = optim.AdamW(model.parameters(), lr=config["learning_rate"], weight_decay=config["weight_decay"])
     optimizer = idist.auto_optim(optimizer)
     criterion = nn.BCEWithLogitsLoss()
 

examples/gan/dcgan.py

@@ -35,7 +35,7 @@
 
 
 class Net(nn.Module):
-    """ A base class for both generator and the discriminator.
+    """A base class for both generator and the discriminator.
     Provides a common weight initialization scheme.
 
     """
@@ -56,7 +56,7 @@ def forward(self, x):
 
 
 class Generator(Net):
-    """ Generator network.
+    """Generator network.
 
     Args:
         nf (int): Number of filters in the second-to-last deconv layer
@@ -95,7 +95,7 @@ def forward(self, x):
 
 
 class Discriminator(Net):
-    """ Discriminator network.
+    """Discriminator network.
 
     Args:
         nf (int): Number of filters in the first conv layer.
@@ -133,9 +133,7 @@ def forward(self, x):
 
 
 def check_manual_seed(seed):
-    """ If manual seed is not specified, choose a random one and communicate it to the user.
-
-    """
+    """If manual seed is not specified, choose a random one and communicate it to the user."""
 
     seed = seed or random.randint(1, 10000)
     random.seed(seed)
@@ -311,8 +309,8 @@ def step(engine, batch):
     @trainer.on(Events.ITERATION_COMPLETED(every=PRINT_FREQ))
     def print_logs(engine):
         fname = os.path.join(output_dir, LOGS_FNAME)
-        columns = ["iteration",] + list(engine.state.metrics.keys())
-        values = [str(engine.state.iteration),] + [str(round(value, 5)) for value in engine.state.metrics.values()]
+        columns = ["iteration"] + list(engine.state.metrics.keys())
+        values = [str(engine.state.iteration)] + [str(round(value, 5)) for value in engine.state.metrics.values()]
 
         with open(fname, "a") as f:
             if f.tell() == 0:

examples/mnist/mnist_save_resume_engine.py

@@ -50,8 +50,7 @@ def forward(self, x):
 
 
 def get_data_loaders(train_batch_size, val_batch_size):
-    """Method to setup data loaders: train_loader and val_loader
-    """
+    """Method to setup data loaders: train_loader and val_loader"""
     data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
 
     train_loader = DataLoader(
@@ -71,8 +70,7 @@ def get_data_loaders(train_batch_size, val_batch_size):
 
 
 def log_model_weights(engine, model=None, fp=None, **kwargs):
-    """Helper method to log norms of model weights: print and dump into a file
-    """
+    """Helper method to log norms of model weights: print and dump into a file"""
     assert model and fp
     output = {"total": 0.0}
     max_counter = 5
@@ -92,8 +90,7 @@ def log_model_weights(engine, model=None, fp=None, **kwargs):
 
 
 def log_model_grads(engine, model=None, fp=None, **kwargs):
-    """Helper method to log norms of model gradients: print and dump into a file
-    """
+    """Helper method to log norms of model gradients: print and dump into a file"""
     assert model and fp
     output = {"grads/total": 0.0}
     max_counter = 5
@@ -116,8 +113,7 @@ def log_model_grads(engine, model=None, fp=None, **kwargs):
 
 
 def log_data_stats(engine, fp=None, **kwargs):
-    """Helper method to log mean/std of input batch of images and median of batch of targets.
-    """
+    """Helper method to log mean/std of input batch of images and median of batch of targets."""
     assert fp
     x, y = engine.state.batch
     output = {

examples/mnist/mnist_with_tensorboard_on_tpu.py

@@ -84,7 +84,7 @@ def run(train_batch_size, val_batch_size, epochs, lr, momentum, log_interval, lo
 
     # Create trainer and evaluator
     trainer = create_supervised_trainer(
-        model, optimizer, criterion, device=device, output_transform=lambda x, y, y_pred, loss: [loss.item(),]
+        model, optimizer, criterion, device=device, output_transform=lambda x, y, y_pred, loss: [loss.item()]
     )
 
     val_metrics = {"accuracy": Accuracy(), "nll": Loss(criterion)}

examples/references/classification/imagenet/code/dataflow/dataloaders.py

@@ -52,16 +52,16 @@ def get_train_val_loaders(
         train_eval_ds = train_ds
 
     train_loader = idist.auto_dataloader(
-        train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True,
+        train_ds, shuffle=True, batch_size=batch_size, num_workers=num_workers, drop_last=True
     )
 
     val_batch_size = batch_size * 4 if val_batch_size is None else val_batch_size
     val_loader = idist.auto_dataloader(
-        val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False,
+        val_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False
     )
 
     train_eval_loader = idist.auto_dataloader(
-        train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False,
+        train_eval_ds, shuffle=False, batch_size=val_batch_size, num_workers=num_workers, drop_last=False
     )
 
     return train_loader, val_loader, train_eval_loader

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

@@ -61,7 +61,7 @@ def train_update_function(engine, batch):
             "supervised batch loss": loss.item(),
         }
 
-    output_names = getattr(config, "output_names", ["supervised batch loss",])
+    output_names = getattr(config, "output_names", ["supervised batch loss"])
     lr_scheduler = config.lr_scheduler
 
     trainer = Engine(train_update_function)
@@ -97,7 +97,7 @@ def create_evaluators(model, metrics, config):
         device=config.device,
         non_blocking=True,
         prepare_batch=config.prepare_batch,
-        output_transform=lambda x, y, y_pred: (model_output_transform(y_pred), y,),
+        output_transform=lambda x, y, y_pred: (model_output_transform(y_pred), y),
     )
     train_evaluator = create_supervised_evaluator(**evaluator_args)
     evaluator = create_supervised_evaluator(**evaluator_args)

examples/references/classification/imagenet/code/utils/exp_tracking.py

@@ -69,9 +69,7 @@ def _plx_log_params(params_dict):
     from polyaxon_client.tracking import Experiment
 
     plx_exp = Experiment()
-    plx_exp.log_params(
-        **{"pytorch version": torch.__version__, "ignite version": ignite.__version__,}
-    )
+    plx_exp.log_params(**{"pytorch version": torch.__version__, "ignite version": ignite.__version__})
     plx_exp.log_params(**params_dict)
 
 
@@ -86,9 +84,7 @@ def _mlflow_log_artifact(fp):
 
 @idist.one_rank_only()
 def _mlflow_log_params(params_dict):
-    mlflow.log_params(
-        {"pytorch version": torch.__version__, "ignite version": ignite.__version__,}
-    )
+    mlflow.log_params({"pytorch version": torch.__version__, "ignite version": ignite.__version__})
     mlflow.log_params(params_dict)
 
 

examples/references/segmentation/pascal_voc2012/configs/baseline_dplv3_resnet101.py

@@ -106,7 +106,7 @@ def model_output_transform(output):
 nesterov = False
 
 optimizer = optim.SGD(
-    [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()},],
+    [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()}],
     lr=1.0,
     momentum=momentum,
     weight_decay=weight_decay,

examples/references/segmentation/pascal_voc2012/configs/baseline_dplv3_resnet101_sbd.py

@@ -110,7 +110,7 @@ def model_output_transform(output):
 nesterov = False
 
 optimizer = optim.SGD(
-    [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()},],
+    [{"params": model.backbone.parameters()}, {"params": model.classifier.parameters()}],
     lr=1.0,
     momentum=momentum,
     weight_decay=weight_decay,

examples/references/segmentation/pascal_voc2012/dataflow.py

@@ -66,7 +66,7 @@ def __getitem__(self, index):
             return {
                 "image": img,
                 "mask": mask,
-                "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index],},
+                "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index]},
             }
 
         return {"image": img, "mask": mask}
@@ -93,20 +93,20 @@ def __getitem__(self, index):
             return {
                 "image": img,
                 "mask": mask,
-                "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index],},
+                "meta": {"index": index, "image_path": self.images[index], "mask_path": self.masks[index]},
             }
 
         return {"image": img, "mask": mask}
 
 
 def get_train_dataset(root_path, return_meta=False):
     return VOCSegmentationOpencv(
-        root=root_path, year="2012", image_set="train", download=False, return_meta=return_meta,
+        root=root_path, year="2012", image_set="train", download=False, return_meta=return_meta
     )
 
 
 def get_val_dataset(root_path, return_meta=False):
-    return VOCSegmentationOpencv(root=root_path, year="2012", image_set="val", download=False, return_meta=return_meta,)
+    return VOCSegmentationOpencv(root=root_path, year="2012", image_set="val", download=False, return_meta=return_meta)
 
 
 def get_train_noval_sbdataset(root_path, return_meta=False):
@@ -187,7 +187,7 @@ def get_train_val_loaders(
 
 
 def get_inference_dataloader(
-    root_path, mode, transforms, batch_size=16, num_workers=8, pin_memory=True, limit_num_samples=None,
+    root_path, mode, transforms, batch_size=16, num_workers=8, pin_memory=True, limit_num_samples=None
 ):
     assert mode in ("train", "test"), "Mode should be 'train' or 'test'"
 

examples/references/segmentation/pascal_voc2012/main.py

@@ -138,7 +138,7 @@ def custom_event_filter(_, val_iteration):
         tb_logger.attach(
             evaluator,
             log_handler=vis.predictions_gt_images_handler(
-                img_denormalize_fn=img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation",
+                img_denormalize_fn=img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation"
             ),
             event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter),
         )
@@ -296,7 +296,7 @@ def setup_experiment_tracking(config, with_clearml, task_type="training"):
 
             schema = TrainvalConfigSchema if task_type == "training" else InferenceConfigSchema
 
-            task = Task.init("Pascal-VOC12 Training", config.config_filepath.stem, task_type=task_type,)
+            task = Task.init("Pascal-VOC12 Training", config.config_filepath.stem, task_type=task_type)
             task.connect_configuration(config.config_filepath.as_posix())
 
             task.upload_artifact(config.script_filepath.name, config.script_filepath.as_posix())
@@ -313,12 +313,8 @@ def setup_experiment_tracking(config, with_clearml, task_type="training"):
             output_path = output_path / datetime.now().strftime("%Y%m%d-%H%M%S")
             output_path.mkdir(parents=True, exist_ok=True)
 
-            shutil.copyfile(
-                config.script_filepath.as_posix(), output_path / config.script_filepath.name,
-            )
-            shutil.copyfile(
-                config.config_filepath.as_posix(), output_path / config.config_filepath.name,
-            )
+            shutil.copyfile(config.script_filepath.as_posix(), output_path / config.script_filepath.name)
+            shutil.copyfile(config.config_filepath.as_posix(), output_path / config.config_filepath.name)
 
         output_path = output_path.as_posix()
     return Path(idist.broadcast(output_path, src=0))
@@ -419,9 +415,7 @@ def evaluation(local_rank, config, logger, with_clearml):
     # Setup Tensorboard logger
     if rank == 0:
         tb_logger = common.TensorboardLogger(log_dir=config.output_path.as_posix())
-        tb_logger.attach_output_handler(
-            evaluator, event_name=Events.COMPLETED, tag="validation", metric_names="all",
-        )
+        tb_logger.attach_output_handler(evaluator, event_name=Events.COMPLETED, tag="validation", metric_names="all")
 
     # Log confusion matrix to ClearML:
     if with_clearml:
@@ -473,6 +467,4 @@ def run_evaluation(config_filepath, backend="nccl", with_clearml=True):
 
 if __name__ == "__main__":
 
-    fire.Fire(
-        {"download": download_datasets, "training": run_training, "eval": run_evaluation,}
-    )
+    fire.Fire({"download": download_datasets, "training": run_training, "eval": run_evaluation})