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 <louis-she@users.noreply.github.com>

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

Author: 2 people

.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: