Updated versions for black usort and ufmt and reformatted codebase #2930

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Merged

vfdev-5 merged 1 commit into pytorch:master from vfdev-5:update-code-formatting-tools

Apr 28, 2023

.github/workflows/trigger_circle_ci.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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)
@@ Expand All / @@ -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"
@@ Expand All / @@ -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:
@@ Expand Down @@

.pre-commit-config.yaml

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -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

docker/test_image.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -21,7 +21,6 @@ @@
     def run_python_cmd(cmd):
         try_except_cmd = f"""
     import warnings
     warnings.filterwarnings("ignore")
@@ Expand Down Expand Up / @@ -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()
@@ Expand Down @@

examples/contrib/cifar10/main.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -20,7 +20,6 @@ @@
     def training(local_rank, config):
         rank = idist.get_rank()
         manual_seed(config["seed"] + rank)
         device = idist.device()
@@ Expand Down Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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:
@@ Expand All @@
         scaler = GradScaler(enabled=with_amp)
         def train_step(engine, batch):
             x, y = batch[0], batch[1]
             if x.device != device:
@@ Expand Down @@

examples/contrib/cifar10_qat/main.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -19,7 +19,6 @@ @@
     def training(local_rank, config):
         rank = idist.get_rank()
         manual_seed(config["seed"] + rank)
         device = idist.device()
@@ Expand Down Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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:
@@ Expand All @@
         scaler = GradScaler(enabled=with_amp)
         def train_step(engine, batch):
             x, y = batch[0], batch[1]
             if x.device != device:
@@ Expand Down @@

examples/contrib/transformers/main.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -22,7 +22,6 @@ @@
     def training(local_rank, config):
         rank = idist.get_rank()
         manual_seed(config["seed"] + rank)
         device = idist.device()
@@ Expand All / @@ -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
@@ Expand Down Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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:
@@ Expand All @@
         scaler = GradScaler(enabled=with_amp)
         def train_step(engine, batch):
             input_batch = batch[0]
             labels = batch[1].view(-1, 1)
@@ Expand Down @@

examples/fast_neural_style/neural_style.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -78,7 +78,6 @@ def train(args): @@
         running_avgs = OrderedDict()
         def step(engine, batch):
             x, _ = batch
             x = x.to(device)
@@ Expand Down @@

examples/gan/dcgan.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -207,7 +207,6 @@ def main( @@
         alpha,
         output_dir,
     ):
         # seed
         check_manual_seed(seed)
@@ Expand Down Expand Up / @@ -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)
@@ Expand Down @@

examples/references/classification/imagenet/dataflow.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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]
@@ Expand All / @@ -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"],
@@ Expand Down @@

examples/references/classification/imagenet/main.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -24,7 +24,6 @@ @@
     def training(local_rank, config, logger, with_clearml):
         rank = idist.get_rank()
         manual_seed(config.seed + local_rank)
@@ Expand Down Expand Up @@
         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)
@@ Expand All @@
     def get_model_weights(config, logger, with_clearml):
         path = ""
         if with_clearml:
             from clearml import Model
@@ Expand All / @@ -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)
@@ Expand Down Expand Up @@
     if __name__ == "__main__":
         fire.Fire({"training": run_training, "eval": run_evaluation})

examples/references/classification/imagenet/utils.py

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

examples/references/segmentation/pascal_voc2012/dataflow.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -27,7 +27,6 @@ def __getitem__(self, index): @@
     class VOCSegmentationOpencv(VOCSegmentation):
         target_names = [
             "background",
             "aeroplane",
@@ Expand Down Expand Up @@
     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]
@@ Expand All / @@ -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)
@@ Expand Down @@

examples/references/segmentation/pascal_voc2012/main.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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)
@@ Expand Down Expand Up @@
         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)
@@ Expand All @@
     def get_model_weights(config, logger, with_clearml):
         path = ""
         if with_clearml:
             from clearml import Model
@@ Expand All / @@ -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)
@@ Expand Down Expand Up @@
     if __name__ == "__main__":
         fire.Fire({"download": download_datasets, "training": run_training, "eval": run_evaluation})

examples/references/segmentation/pascal_voc2012/utils.py

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

examples/reinforcement_learning/actor_critic.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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)")
@@ Expand Down @@

examples/reinforcement_learning/reinforce.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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)")
@@ Expand Down @@

examples/siamese_network/siamese_network.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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)
@@ Expand Down Expand Up / @@ -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)
@@ Expand Down @@

examples/super_resolution/model.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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()
@@ Expand Down @@

ignite/base/mixins.py

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

ignite/contrib/engines/common.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -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"
@@ Expand Down Expand Up / @@ -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,
@@ Expand Down @@

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