[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

.pre-commit-config.yaml

@@ -20,4 +20,4 @@ repos:
     hooks:
       - id: black
         exclude: ^tests/
-        args: [ --safe, --quiet ]
+        args: [ --safe, --quiet ]

docs/source/conf.py

@@ -34,7 +34,7 @@
 author = "Atharva Phatak"
 
 # The full version, including alpha/beta/rc tags
-with open("../../version.txt", "r") as f:
+with open("../../version.txt") as f:
     release = str(f.readline().strip())
 
 

examples/Advanced-Tutorials/KD/vanilla-kd.py

@@ -11,7 +11,7 @@
 
 class KDExperiment(Experiment):
     def __init__(self, temperature, alpha, **kwargs):
-        super(KDExperiment, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self.temperature = temperature
         self.alpha = alpha
 

examples/Advanced-Tutorials/autoencoders/mnist-vae.py

@@ -15,13 +15,13 @@ def __init__(self, d):
         super().__init__()
         self.d = d
         self.encoder = nn.Sequential(
-            nn.Linear(784, self.d ** 2), nn.ReLU(), nn.Linear(self.d ** 2, self.d * 2)
+            nn.Linear(784, self.d**2), nn.ReLU(), nn.Linear(self.d**2, self.d * 2)
         )
 
         self.decoder = nn.Sequential(
-            nn.Linear(self.d, self.d ** 2),
+            nn.Linear(self.d, self.d**2),
             nn.ReLU(),
-            nn.Linear(self.d ** 2, 784),
+            nn.Linear(self.d**2, 784),
             nn.Sigmoid(),
         )
 

examples/Advanced-Tutorials/gans/dcgan.py

@@ -20,7 +20,7 @@ def __init__(self, latent_dim, batchnorm=True):
             latent_dim (int): latent dimension ("noise vector")
             batchnorm (bool): Whether or not to use batch normalization
         """
-        super(Generator, self).__init__()
+        super().__init__()
         self.latent_dim = latent_dim
         self.batchnorm = batchnorm
         self._init_modules()
@@ -77,7 +77,7 @@ def __init__(self, output_dim):
         Images must be single-channel and 28x28 pixels.
         Output activation is Sigmoid.
         """
-        super(Discriminator, self).__init__()
+        super().__init__()
         self.output_dim = output_dim
         self._init_modules()  # I know this is overly-organized. Fight me.
 
@@ -126,15 +126,13 @@ def forward(self, input_tensor):
 # Defining Custom Loop for training
 class DCGANExperiment(Experiment):
     def __init__(self, latent_dim, batch_size, **kwargs):
-
-        super(DCGANExperiment, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
         self.noise_fn = lambda x: torch.randn((x, latent_dim), device=self.device)
         self.target_ones = torch.ones((batch_size, 1), device=self.device)
         self.target_zeros = torch.zeros((batch_size, 1), device=self.device)
 
     def train_step(self):
-
         latent_vec = self.noise_fn(self.batch[self.input_key].shape[0])
 
         # self.backend has methods like zero_grad, etc to handle the backward pass, optimizer_step and zero_grad.

examples/Advanced-Tutorials/self-supervision/ssl_byol.py

@@ -65,7 +65,7 @@ def default_augmentation(image_size: Tuple[int, int] = (224, 224)) -> nn.Module:
 # Defining the models.
 class MLPHead(nn.Module):
     def __init__(self, in_channels: int, projection_size: int = 256, hidden_size: int = 4096):
-        super(MLPHead, self).__init__()
+        super().__init__()
 
         self.net = nn.Sequential(
             nn.Linear(in_channels, hidden_size),
@@ -81,7 +81,7 @@ def forward(self, x):
 # Defining resnet encoders.
 class ResnetEncoder(nn.Module):
     def __init__(self, pretrained, mlp_params):
-        super(ResnetEncoder, self).__init__()
+        super().__init__()
         resnet = torchvision.models.resnet18(pretrained=pretrained)
         self.encoder = torch.nn.Sequential(*list(resnet.children())[:-1])
         self.projector = MLPHead(in_channels=resnet.fc.in_features, **mlp_params)
@@ -95,7 +95,7 @@ def forward(self, x):
 # Defining custom training method required as required by Bootstrap your own latent.(SSL)
 class BYOLExperiment(Experiment):
     def __init__(self, momentum, augmentation_fn, image_size, **kwargs):
-        super(BYOLExperiment, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self.momentum = momentum
         self.augmentation_fn = augmentation_fn(image_size)
 
@@ -117,7 +117,6 @@ def update_target_network(self):
             )
 
     def train_step(self) -> Dict:
-
         self.backend.zero_grad(optimizer=self.state.optimizer)
         x = self.batch[self.input_key]
         view_1, view_2 = self.augmentation_fn(x), self.augmentation_fn(x)

examples/Basic-Tutorials/fit_methods.py

@@ -14,7 +14,7 @@
 
 class Net(torch.nn.Module):
     def __init__(self, out_features):
-        super(Net, self).__init__()
+        super().__init__()
 
         self.conv1 = nn.Sequential(
             nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, padding=1),

examples/Basic-Tutorials/image_classification.py

@@ -31,7 +31,6 @@ def forward(self, x):
 
 
 if __name__ == "__main__":
-
     # Defining Dataloaders.
 
     data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])

examples/Basic-Tutorials/text_classification.py

@@ -18,8 +18,7 @@
 # Defining The model
 class Model(torch.nn.Module):
     def __init__(self, dropout, out_features):
-
-        super(Model, self).__init__()
+        super().__init__()
         self.bert = transformers.BertModel.from_pretrained("prajjwal1/bert-tiny", return_dict=False)
         self.bert_drop = nn.Dropout(dropout)
         self.out = nn.Linear(128, out_features)

setup.py

@@ -15,15 +15,15 @@
 
 
 readme_file_path = os.path.join(current_file_path, "README.md")
-with open(readme_file_path, "r", encoding="utf-8") as f:
+with open(readme_file_path, encoding="utf-8") as f:
     readme = f.read()
 
 
 version_file_path = os.path.join(current_file_path, "version.txt")
-with open(version_file_path, "r", encoding="utf-8") as f:
+with open(version_file_path, encoding="utf-8") as f:
     version = f.read().strip()
 
-with open(os.path.join(current_file_path, "requirements.txt"), "r") as f:
+with open(os.path.join(current_file_path, "requirements.txt")) as f:
     requirements = f.read().splitlines()
 
 

tests/experiment/test_experiment.py

@@ -13,7 +13,7 @@
 
 class Model(torch.nn.Module):
     def __init__(self, num_features, num_classes):
-        super(Model, self).__init__()
+        super().__init__()
         self.model = torch.nn.Linear(num_features, num_classes)
 
     def forward(self, x):

torchflare/callbacks/callback_decorators.py

@@ -18,7 +18,7 @@ class FunctionalCallback(Callbacks):
     """
 
     def __init__(self, func, order):
-        super(FunctionalCallback, self).__init__(order=order)
+        super().__init__(order=order)
         self.func = func
         functools.update_wrapper(self, func)
 

torchflare/callbacks/comet_logger.py

@@ -54,7 +54,7 @@ def __init__(
         tags: List[str],
     ):
         """Constructor for CometLogger class."""
-        super(CometLogger, self).__init__(order=CallbackOrder.LOGGING)
+        super().__init__(order=CallbackOrder.LOGGING)
         self.api_token = api_token
         self.project_name = project_name
         self.workspace = workspace

torchflare/callbacks/criterion_callback.py

@@ -11,7 +11,7 @@ class AvgLoss(Callbacks):
     """Class for averaging the loss."""
 
     def __init__(self):
-        super(AvgLoss, self).__init__(order=CallbackOrder.LOSS)
+        super().__init__(order=CallbackOrder.LOSS)
         self.accum_loss, self.count = {}, 0
         self.reset()
 

torchflare/callbacks/early_stopping.py

@@ -47,7 +47,7 @@ def __init__(
         min_delta: float = 1e-7,
     ):
         """Constructor for EarlyStopping class."""
-        super(EarlyStopping, self).__init__(order=CallbackOrder.STOPPING)
+        super().__init__(order=CallbackOrder.STOPPING)
 
         if monitor.startswith("train_") or monitor.startswith("val_"):
             self.monitor = monitor

torchflare/callbacks/extra_utils.py

@@ -22,12 +22,10 @@ def init_improvement(mode: str, min_delta: float):
         The scoring function and best value according to mode.
     """
     if mode == "min":
-
         improvement = partial(_is_min, min_delta=min_delta)
         best_score = math.inf
 
     else:
-
         improvement = partial(_is_max, min_delta=min_delta)
         best_score = -math.inf
 

torchflare/callbacks/load_checkpoint.py

@@ -16,7 +16,7 @@ class LoadCheckpoint(Callbacks, ABC):
 
     def __init__(self, path_to_model: str = None):
         """Constructor method for LoadCheckpoint Class."""
-        super(LoadCheckpoint, self).__init__(order=CallbackOrder.MODEL_INIT)
+        super().__init__(order=CallbackOrder.MODEL_INIT)
         self.path = path_to_model
 
     @staticmethod

torchflare/callbacks/lr_schedulers.py

@@ -21,7 +21,7 @@ def __init__(self, scheduler, step_on_batch: bool):
             scheduler: A pytorch scheduler
             step_on_batch: Whether the scheduler steps after batch or not.
         """
-        super(LRSchedulerCallback, self).__init__(order=CallbackOrder.SCHEDULER)
+        super().__init__(order=CallbackOrder.SCHEDULER)
         self._scheduler = scheduler
         self.step_on_batch = step_on_batch
         self.scheduler = None

torchflare/callbacks/message_notifiers.py

@@ -42,7 +42,7 @@ class SlackNotifierCallback(Callbacks, ABC):
 
     def __init__(self, webhook_url: str):
         """Constructor method for SlackNotifierCallback."""
-        super(SlackNotifierCallback, self).__init__(order=CallbackOrder.EXTERNAL)
+        super().__init__(order=CallbackOrder.EXTERNAL)
         self.webhook_url = webhook_url
 
     def on_epoch_end(self, experiment: "Experiment"):
@@ -81,7 +81,7 @@ class DiscordNotifierCallback(Callbacks, ABC):
 
     def __init__(self, exp_name: str, webhook_url: str):
         """Constructor method for DiscordNotifierCallback."""
-        super(DiscordNotifierCallback, self).__init__(order=CallbackOrder.EXTERNAL)
+        super().__init__(order=CallbackOrder.EXTERNAL)
         self.exp_name = exp_name
         self.webhook_url = webhook_url
 

torchflare/callbacks/metric_utils.py

@@ -23,7 +23,7 @@ def __init__(self, metrics: List = None):
         Args:
             metrics: The list of metrics
         """
-        super(MetricCallback, self).__init__(CallbackOrder.METRICS)
+        super().__init__(CallbackOrder.METRICS)
         metrics = MetricCollection(metrics)
         self.metrics = {
             "train": metrics.clone(prefix="train_"),

torchflare/callbacks/model_checkpoint.py

@@ -62,7 +62,7 @@ class ModelCheckpoint(Callbacks, ABC):
 
     def __init__(self, mode: str, monitor: str, save_dir: str = "./", file_name: str = "model.bin"):
         """Constructor for ModelCheckpoint class."""
-        super(ModelCheckpoint, self).__init__(order=CallbackOrder.CHECKPOINT)
+        super().__init__(order=CallbackOrder.CHECKPOINT)
         if monitor.startswith("train_") or monitor.startswith("val_"):
             self.monitor = monitor
         else:
@@ -86,7 +86,6 @@ def on_epoch_end(self, experiment: "Experiment"):
         val = experiment.exp_logs.get(self.monitor)
 
         if self.improvement(score=val, best=self.best_val):
-
             self.checkpoint(
                 model=experiment.state.model,
                 optimizer=experiment.state.optimizer,

torchflare/callbacks/model_history.py

@@ -15,7 +15,7 @@ class History(Callbacks, ABC):
 
     def __init__(self):
         """Constructor class for History Class."""
-        super(History, self).__init__(order=CallbackOrder.LOGGING)
+        super().__init__(order=CallbackOrder.LOGGING)
         self.history = None
 
     def on_experiment_start(self, experiment: "Experiment"):

torchflare/callbacks/neptune_logger.py

@@ -55,7 +55,7 @@ def __init__(
         tags: List[str] = None,
     ):
         """Constructor for NeptuneLogger Class."""
-        super(NeptuneLogger, self).__init__(order=CallbackOrder.LOGGING)
+        super().__init__(order=CallbackOrder.LOGGING)
         self.project_dir = project_dir
         self.api_token = api_token
         self.params = params
@@ -74,7 +74,6 @@ def on_experiment_start(self, experiment: "Experiment"):
         self.experiment["params"] = self.params
 
     def _log_metrics(self, name, value, epoch):
-
         self.experiment[name].log(value=value, step=epoch)
 
     def on_epoch_end(self, experiment: "Experiment"):

torchflare/callbacks/progress_bar.py

@@ -26,7 +26,7 @@ def __init__(
         unit_name: str = "step",
     ):
         """Constructor class for ProgressBar."""
-        super(ProgressBar, self).__init__(order=CallbackOrder.EXTERNAL)
+        super().__init__(order=CallbackOrder.EXTERNAL)
         self.num_epochs = None
         self.width = width
         self.interval = interval
@@ -127,7 +127,6 @@ def _update(self, current_step: int, values: Dict[str, float]):
                 info += " {:.0f}us/{}".format(time_per_unit * 1e6, self.unit_name)
 
         for k, v in values.items():
-
             info += f" - {k}:"
             if abs(v) > 1e-3:
                 info += f" {v:.4f}"

torchflare/callbacks/tensorboard_logger.py

@@ -26,7 +26,7 @@ class TensorboardLogger(Callbacks, ABC):
 
     def __init__(self, log_dir: str):
         """Constructor for TensorboardLogger class."""
-        super(TensorboardLogger, self).__init__(order=CallbackOrder.LOGGING)
+        super().__init__(order=CallbackOrder.LOGGING)
         self.log_dir = log_dir
         self._experiment = None
 

torchflare/callbacks/wandb_logger.py

@@ -62,7 +62,7 @@ def __init__(
         directory: str = None,
     ):
         """Constructor of WandbLogger."""
-        super(WandbLogger, self).__init__(order=CallbackOrder.LOGGING)
+        super().__init__(order=CallbackOrder.LOGGING)
         self.entity = entity
         self.project = project
         self.name = name

torchflare/criterion/cross_entropy.py

@@ -45,7 +45,7 @@ class LabelSmoothingCrossEntropy(nn.Module):
 
     def __init__(self, smoothing: float = 0.1):
         """Constructor method for LabelSmoothingCrossEntropy."""
-        super(LabelSmoothingCrossEntropy, self).__init__()
+        super().__init__()
         if smoothing > 1.0:
             raise ValueError("Smoothing value must be less than 1.")
         self.smoothing = smoothing
@@ -82,7 +82,7 @@ class SymmetricCE(nn.Module):
 
     def __init__(self, num_classes, alpha: float = 1.0, beta: float = 1.0):
         """Constructor method for symmetric CE."""
-        super(SymmetricCE, self).__init__()
+        super().__init__()
         self.alpha = alpha
         self.beta = beta
         self.num_classes = num_classes

torchflare/criterion/focal_loss.py

@@ -19,7 +19,7 @@ def __init__(self, gamma=0, eps=1e-7, reduction="mean"):
             eps : Constant for computational stability.
             reduction: The reduction parameter for Cross Entropy Loss.
         """
-        super(BCEFocalLoss, self).__init__()
+        super().__init__()
         self.gamma = gamma
         self.reduction = reduction
         self.eps = eps
@@ -63,7 +63,7 @@ class FocalLoss(nn.Module):
 
     def __init__(self, gamma=0, eps=1e-7, reduction="mean"):
         """Constructor Method for FocalLoss class."""
-        super(FocalLoss, self).__init__()
+        super().__init__()
         self.gamma = gamma
         self.reduction = reduction
         self.eps = eps
@@ -100,7 +100,7 @@ class FocalCosineLoss(nn.Module):
 
     def __init__(self, alpha: float = 1, gamma: float = 2, xent: float = 0.1, reduction="mean"):
         """Constructor for FocalCosineLoss."""
-        super(FocalCosineLoss, self).__init__()
+        super().__init__()
         self.alpha = alpha
         self.gamma = gamma
         self.xent = xent