[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,23 +1,23 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v6.0.0
     hooks:
       - id: debug-statements
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-merge-conflict
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.4.1
+    rev: v3.21.2
     hooks:
       - id: pyupgrade
   - repo: https://github.com/pycqa/isort
-    rev: 5.8.0
+    rev: 7.0.0
     hooks:
       - id: isort
         exclude: ^tests/|torchflare/callbacks/__init__.py
-  - repo: https://github.com/python/black
-    rev: 21.4b2
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 25.11.0
     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

@@ -1,6 +1,7 @@
 """Vanilla Knowledge distillation using Torchflare.
 This example only shows how to modify the training script for KD.
 """
+
 from typing import Dict
 
 import torch
@@ -11,7 +12,7 @@
 
 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

@@ -1,4 +1,5 @@
 """Generating MNIST digits using Variational Autoencoders."""
+
 import torch
 import torch.nn.functional as F
 from torch import nn
@@ -15,13 +16,13 @@ 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

@@ -1,4 +1,5 @@
 """Generating MNIST Digits using DCGAN."""
+
 import os
 
 import torch
@@ -20,7 +21,7 @@ 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 +78,7 @@ 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.
 
@@ -127,7 +128,7 @@ def forward(self, input_tensor):
 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)

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)
 

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/text_classification.py

@@ -19,7 +19,7 @@
 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

@@ -1,4 +1,5 @@
 """Setup.py for torchflare."""
+
 # flake8: noqa
 import os
 
@@ -15,15 +16,15 @@
 
 
 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.py

@@ -1,4 +1,5 @@
 """Implementation of Callbacks and CallbackRunner."""
+
 from typing import TYPE_CHECKING, List
 
 if TYPE_CHECKING:

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

@@ -1,4 +1,5 @@
 """Implementation of Early stopping."""
+
 import math
 from abc import ABC
 from typing import TYPE_CHECKING
@@ -47,7 +48,7 @@ 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

@@ -1,4 +1,5 @@
 """Implements extra utilities required."""
+
 import math
 from functools import partial
 

torchflare/callbacks/load_checkpoint.py

@@ -1,4 +1,5 @@
 """Implements Load checkpoint."""
+
 from abc import ABC
 from typing import TYPE_CHECKING
 
@@ -16,7 +17,7 @@ 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

@@ -1,4 +1,5 @@
 """Implements LrScheduler callbacks."""
+
 from abc import ABC
 from typing import TYPE_CHECKING, Callable, Iterable, List, Optional, Union
 
@@ -21,7 +22,7 @@ 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

@@ -1,4 +1,5 @@
 """Implements notifiers for slack and discord."""
+
 import json
 from abc import ABC
 from typing import TYPE_CHECKING
@@ -42,7 +43,7 @@ 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 +82,7 @@ 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

@@ -1,4 +1,5 @@
 """Implements container for loss and metric computation."""
+
 from typing import TYPE_CHECKING, Dict, List
 
 from torchmetrics import MetricCollection
@@ -23,7 +24,7 @@ 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

@@ -1,4 +1,5 @@
 """Implements Model Checkpoint Callback."""
+
 import os
 from abc import ABC
 from typing import TYPE_CHECKING
@@ -62,7 +63,7 @@ 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:

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

@@ -1,4 +1,5 @@
 """Implements Neptune Logger."""
+
 from abc import ABC
 from typing import TYPE_CHECKING, List
 
@@ -55,7 +56,7 @@ 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

torchflare/callbacks/progress_bar.py

@@ -1,4 +1,5 @@
 """Implementation of Progress Bar."""
+
 import math
 import sys
 import time
@@ -26,7 +27,7 @@ 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

torchflare/callbacks/states.py

@@ -1,4 +1,5 @@
 """Definitions of experiment states and Callback order."""
+
 from enum import IntEnum
 
 

torchflare/callbacks/tensorboard_logger.py

@@ -1,4 +1,5 @@
 """Implements Tensorboard Logger."""
+
 from abc import ABC
 from typing import TYPE_CHECKING
 
@@ -26,7 +27,7 @@ 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