Updated versions for black usort and ufmt and reformatted codebase (#…

…2930)
pytorch · Apr 28, 2023 · 96433ce · 96433ce
1 parent 9d38754
commit 96433ce
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Showing 149 changed files with 40 additions and 821 deletions.
diff --git a/.github/workflows/trigger_circle_ci.py b/.github/workflows/trigger_circle_ci.py
@@ -42,7 +42,6 @@ def assert_pipeline_created(pipeline_id, headers):
 
 
 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)
@@ -59,7 +58,6 @@ def get_workflow_id(pipeline_id, headers):
 
 
 def assert_workflows_successful(pipeline_id, headers):
-
     workflow_id = get_workflow_id(pipeline_id, headers)
 
     base_url = "https://app.circleci.com/pipelines/github/pytorch/ignite"
@@ -84,7 +82,6 @@ def assert_workflows_successful(pipeline_id, headers):
 
 
 if __name__ == "__main__":
-
     print("Trigger new pipeline on Circle-CI")
 
     if "CIRCLE_TOKEN" not in os.environ:

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -15,12 +15,12 @@ repos:
         exclude_types: ["python", "jupyter", "shell", "gitignore"]
 
   - repo: https://github.com/omnilib/ufmt
-    rev: v1.3.1
+    rev: v2.1.0
     hooks:
       - id: ufmt
         additional_dependencies:
-          - black == 21.12b0
-          - usort == 1.0.1
+          - black == 23.3.0
+          - usort == 1.0.6
 
   - repo: https://github.com/pycqa/flake8
     rev: 6.0.0

diff --git a/docker/test_image.py b/docker/test_image.py
@@ -21,7 +21,6 @@
 
 
 def run_python_cmd(cmd):
-
     try_except_cmd = f"""
 import warnings
 warnings.filterwarnings("ignore")
@@ -65,7 +64,6 @@ def main():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser("Check docker image script")
     parser.add_argument("image", type=str, help="Docker image to check")
     args = parser.parse_args()

diff --git a/examples/contrib/cifar10/main.py b/examples/contrib/cifar10/main.py
@@ -20,7 +20,6 @@
 
 
 def training(local_rank, config):
-
     rank = idist.get_rank()
     manual_seed(config["seed"] + rank)
     device = idist.device()
@@ -205,7 +204,6 @@ def run(
         raise RuntimeError("The value of with_amp should be False if backend is xla")
 
     with idist.Parallel(backend=backend, **spawn_kwargs) as parallel:
-
         parallel.run(training, config)
 
 
@@ -283,7 +281,6 @@ def log_basic_info(logger, config):
 
 
 def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger):
-
     device = idist.device()
 
     # Setup Ignite trainer:
@@ -299,7 +296,6 @@ def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, con
     scaler = GradScaler(enabled=with_amp)
 
     def train_step(engine, batch):
-
         x, y = batch[0], batch[1]
 
         if x.device != device:

diff --git a/examples/contrib/cifar10_qat/main.py b/examples/contrib/cifar10_qat/main.py
@@ -19,7 +19,6 @@
 
 
 def training(local_rank, config):
-
     rank = idist.get_rank()
     manual_seed(config["seed"] + rank)
     device = idist.device()
@@ -189,7 +188,6 @@ def run(
     spawn_kwargs["nproc_per_node"] = nproc_per_node
 
     with idist.Parallel(backend=backend, **spawn_kwargs) as parallel:
-
         parallel.run(training, config)
 
 
@@ -267,7 +265,6 @@ def log_basic_info(logger, config):
 
 
 def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger):
-
     device = idist.device()
 
     # Setup Ignite trainer:
@@ -283,7 +280,6 @@ def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, con
     scaler = GradScaler(enabled=with_amp)
 
     def train_step(engine, batch):
-
         x, y = batch[0], batch[1]
 
         if x.device != device:

diff --git a/examples/contrib/transformers/main.py b/examples/contrib/transformers/main.py
@@ -22,7 +22,6 @@
 
 
 def training(local_rank, config):
-
     rank = idist.get_rank()
     manual_seed(config["seed"] + rank)
     device = idist.device()
@@ -33,7 +32,6 @@ def training(local_rank, config):
 
     output_path = config["output_dir"]
     if rank == 0:
-
         now = datetime.now().strftime("%Y%m%d-%H%M%S")
         folder_name = f"{config['model']}_backend-{idist.backend()}-{idist.get_world_size()}_{now}"
         output_path = Path(output_path) / folder_name
@@ -207,7 +205,6 @@ def run(
     spawn_kwargs["nproc_per_node"] = nproc_per_node
 
     with idist.Parallel(backend=backend, **spawn_kwargs) as parallel:
-
         parallel.run(training, config)
 
 
@@ -293,7 +290,6 @@ def log_basic_info(logger, config):
 
 
 def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, config, logger):
-
     device = idist.device()
 
     # Setup Ignite trainer:
@@ -309,7 +305,6 @@ def create_trainer(model, optimizer, criterion, lr_scheduler, train_sampler, con
     scaler = GradScaler(enabled=with_amp)
 
     def train_step(engine, batch):
-
         input_batch = batch[0]
         labels = batch[1].view(-1, 1)
 

diff --git a/examples/fast_neural_style/neural_style.py b/examples/fast_neural_style/neural_style.py
@@ -78,7 +78,6 @@ def train(args):
     running_avgs = OrderedDict()
 
     def step(engine, batch):
-
         x, _ = batch
         x = x.to(device)
 

diff --git a/examples/gan/dcgan.py b/examples/gan/dcgan.py
@@ -207,7 +207,6 @@ def main(
     alpha,
     output_dir,
 ):
-
     # seed
     check_manual_seed(seed)
 
@@ -243,7 +242,6 @@ def get_noise():
 
     # The main function, processing a batch of examples
     def step(engine, batch):
-
         # unpack the batch. It comes from a dataset, so we have <images, labels> pairs. Discard labels.
         real, _ = batch
         real = real.to(device)

diff --git a/examples/references/classification/imagenet/dataflow.py b/examples/references/classification/imagenet/dataflow.py
@@ -19,7 +19,6 @@ def opencv_loader(path):
 
 
 def get_dataloader(dataset, sampler=None, shuffle=False, limit_num_samples=None, **kwargs):
-
     if limit_num_samples is not None:
         g = torch.Generator().manual_seed(limit_num_samples)
         indices = torch.randperm(len(dataset), generator=g)[:limit_num_samples]
@@ -38,7 +37,6 @@ def get_train_val_loaders(
     limit_train_num_samples: Optional[int] = None,
     limit_val_num_samples: Optional[int] = None,
 ) -> Tuple[DataLoader, DataLoader, DataLoader]:
-
     train_ds = ImageFolder(
         Path(root_path) / "train",
         transform=lambda sample: train_transforms(image=sample)["image"],

diff --git a/examples/references/classification/imagenet/main.py b/examples/references/classification/imagenet/main.py
@@ -24,7 +24,6 @@
 
 
 def training(local_rank, config, logger, with_clearml):
-
     rank = idist.get_rank()
     manual_seed(config.seed + local_rank)
 
@@ -305,7 +304,6 @@ def run_training(config_filepath, backend="nccl", with_clearml=True):
     assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found"
 
     with idist.Parallel(backend=backend) as parallel:
-
         logger = setup_logger(name="ImageNet Training", distributed_rank=idist.get_rank())
 
         config = ConfigObject(config_filepath)
@@ -327,7 +325,6 @@ def run_training(config_filepath, backend="nccl", with_clearml=True):
 
 
 def get_model_weights(config, logger, with_clearml):
-
     path = ""
     if with_clearml:
         from clearml import Model
@@ -352,7 +349,6 @@ def get_model_weights(config, logger, with_clearml):
 
 
 def evaluation(local_rank, config, logger, with_clearml):
-
     rank = idist.get_rank()
     device = idist.device()
     manual_seed(config.seed + local_rank)
@@ -428,5 +424,4 @@ def run_evaluation(config_filepath, backend="nccl", with_clearml=True):
 
 
 if __name__ == "__main__":
-
     fire.Fire({"training": run_training, "eval": run_evaluation})
diff --git a/examples/references/classification/imagenet/utils.py b/examples/references/classification/imagenet/utils.py
@@ -6,7 +6,6 @@
 
 
 def initialize(config):
-
     device = idist.device()
 
     model = config.model.to(device)

diff --git a/examples/references/segmentation/pascal_voc2012/dataflow.py b/examples/references/segmentation/pascal_voc2012/dataflow.py
@@ -27,7 +27,6 @@ def __getitem__(self, index):
 
 
 class VOCSegmentationOpencv(VOCSegmentation):
-
     target_names = [
         "background",
         "aeroplane",
@@ -114,7 +113,6 @@ def get_train_noval_sbdataset(root_path, return_meta=False):
 
 
 def get_dataloader(dataset, sampler=None, shuffle=False, limit_num_samples=None, **kwargs):
-
     if limit_num_samples is not None:
         g = torch.Generator().manual_seed(limit_num_samples)
         indices = torch.randperm(len(dataset), generator=g)[:limit_num_samples]
@@ -135,7 +133,6 @@ def get_train_val_loaders(
     limit_train_num_samples=None,
     limit_val_num_samples=None,
 ):
-
     train_ds = get_train_dataset(root_path)
     val_ds = get_val_dataset(root_path)
 

diff --git a/examples/references/segmentation/pascal_voc2012/main.py b/examples/references/segmentation/pascal_voc2012/main.py
@@ -49,7 +49,6 @@ def download_datasets(output_path):
 
 
 def training(local_rank, config, logger, with_clearml):
-
     rank = idist.get_rank()
     manual_seed(config.seed + local_rank)
 
@@ -342,7 +341,6 @@ def run_training(config_filepath, backend="nccl", with_clearml=True):
     assert config_filepath.exists(), f"File '{config_filepath.as_posix()}' is not found"
 
     with idist.Parallel(backend=backend) as parallel:
-
         logger = setup_logger(name="Pascal-VOC12 Training", distributed_rank=idist.get_rank())
 
         config = ConfigObject(config_filepath)
@@ -364,7 +362,6 @@ def run_training(config_filepath, backend="nccl", with_clearml=True):
 
 
 def get_model_weights(config, logger, with_clearml):
-
     path = ""
     if with_clearml:
         from clearml import Model
@@ -389,7 +386,6 @@ def get_model_weights(config, logger, with_clearml):
 
 
 def evaluation(local_rank, config, logger, with_clearml):
-
     rank = idist.get_rank()
     device = idist.device()
     manual_seed(config.seed + local_rank)
@@ -472,5 +468,4 @@ def run_evaluation(config_filepath, backend="nccl", with_clearml=True):
 
 
 if __name__ == "__main__":
-
     fire.Fire({"download": download_datasets, "training": run_training, "eval": run_evaluation})
diff --git a/examples/references/segmentation/pascal_voc2012/utils.py b/examples/references/segmentation/pascal_voc2012/utils.py
@@ -6,7 +6,6 @@
 
 
 def initialize(config):
-
     device = idist.device()
 
     model = config.model.to(device)

diff --git a/examples/reinforcement_learning/actor_critic.py b/examples/reinforcement_learning/actor_critic.py
@@ -122,7 +122,6 @@ def finish_episode(policy, optimizer, gamma):
 
 
 def main(env, args):
-
     policy = Policy()
     optimizer = optim.Adam(policy.parameters(), lr=3e-2)
     timesteps = range(10000)
@@ -185,7 +184,6 @@ def should_finish_training():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser(description="Ignite actor-critic example")
     parser.add_argument("--gamma", type=float, default=0.99, metavar="G", help="discount factor (default: 0.99)")
     parser.add_argument("--seed", type=int, default=543, metavar="N", help="random seed (default: 1)")

diff --git a/examples/reinforcement_learning/reinforce.py b/examples/reinforcement_learning/reinforce.py
@@ -70,7 +70,6 @@ def finish_episode(policy, optimizer, gamma):
 
 
 def main(env, args):
-
     policy = Policy()
     optimizer = optim.Adam(policy.parameters(), lr=1e-2)
     timesteps = range(10000)
@@ -123,7 +122,6 @@ def should_finish_training():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser(description="PyTorch REINFORCE example")
     parser.add_argument("--gamma", type=float, default=0.99, metavar="G", help="discount factor (default: 0.99)")
     parser.add_argument("--seed", type=int, default=543, metavar="N", help="random seed (default: 543)")

diff --git a/examples/siamese_network/siamese_network.py b/examples/siamese_network/siamese_network.py
@@ -65,7 +65,6 @@ def forward_once(self, x):
         return output
 
     def forward(self, input1, input2, input3):
-
         # pass the input through resnet
         output1 = self.forward_once(input1)
         output2 = self.forward_once(input2)
@@ -180,7 +179,6 @@ def calculate_loss(input1, input2):
 
 
 def run(args, model, device, optimizer, train_loader, test_loader, lr_scheduler):
-
     # using Triplet Margin Loss
     criterion = nn.TripletMarginLoss(p=2, margin=2.8)
 

diff --git a/examples/super_resolution/model.py b/examples/super_resolution/model.py
@@ -10,7 +10,7 @@ def __init__(self, upscale_factor):
         self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
         self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
         self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
-        self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))
+        self.conv4 = nn.Conv2d(32, upscale_factor**2, (3, 3), (1, 1), (1, 1))
         self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
 
         self._initialize_weights()

diff --git a/ignite/base/mixins.py b/ignite/base/mixins.py
@@ -4,7 +4,6 @@
 
 
 class Serializable:
-
     _state_dict_all_req_keys: Tuple = ()
     _state_dict_one_of_opt_keys: Tuple = ()
 

diff --git a/ignite/contrib/engines/common.py b/ignite/contrib/engines/common.py
@@ -176,7 +176,6 @@ def _setup_common_training_handlers(
         trainer.add_event_handler(Events.EPOCH_COMPLETED, empty_cuda_cache)
 
     if to_save is not None:
-
         if output_path is None and save_handler is None:
             raise ValueError(
                 "If to_save argument is provided then output_path or save_handler arguments should be also defined"
@@ -242,7 +241,6 @@ def _setup_common_distrib_training_handlers(
     save_handler: Optional[Union[Callable, BaseSaveHandler]] = None,
     **kwargs: Any,
 ) -> None:
-
     _setup_common_training_handlers(
         trainer,
         to_save=to_save,
Original file line number	Diff line number	Diff line change
Expand Up		@@ -6,7 +6,6 @@


		def initialize(config):

		device = idist.device()

		model = config.model.to(device)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,7 +4,6 @@


		class Serializable:

		_state_dict_all_req_keys: Tuple = ()
		_state_dict_one_of_opt_keys: Tuple = ()

Expand Down