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

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

src/pl_bolts/callbacks/__init__.py

@@ -1,4 +1,5 @@
 """Collection of PyTorchLightning callbacks."""
+
 from pl_bolts.callbacks.byol_updates import BYOLMAWeightUpdate
 from pl_bolts.callbacks.data_monitor import ModuleDataMonitor, TrainingDataMonitor
 from pl_bolts.callbacks.printing import PrintTableMetricsCallback

src/pl_bolts/callbacks/data_monitor.py

@@ -73,6 +73,7 @@ def log_histograms(self, batch: Any, group: str = "") -> None:
                 Otherwise the histograms get labelled with an integer index.
                 Each label also has the tensors's shape as suffix.
             group: Name under which the histograms will be grouped.
+
         """
         if not self._log or (self._train_batch_idx + 1) % self._log_every_n_steps != 0:  # type: ignore[operator]
             return
@@ -112,7 +113,7 @@ def _is_logger_available(self, logger: Logger) -> bool:
         if not isinstance(logger, self.supported_loggers):
             rank_zero_warn(
                 f"{self.__class__.__name__} does not support logging with {logger.__class__.__name__}."
-                f" Supported loggers are: {', '.join((str(x.__name__) for x in self.supported_loggers))}"
+                f" Supported loggers are: {', '.join(str(x.__name__) for x in self.supported_loggers)}"
             )
             available = False
         return available
@@ -220,6 +221,7 @@ def __init__(self, log_every_n_steps: int = None) -> None:
 
                 # log histogram of training data passed to `LightningModule.training_step`
                 trainer = Trainer(callbacks=[TrainingDataMonitor()])
+
         """
         super().__init__(log_every_n_steps=log_every_n_steps)
 

src/pl_bolts/callbacks/verification/base.py

@@ -77,6 +77,7 @@ def _model_forward(self, input_array: Any) -> Any:
 
         Returns:
             The output of the model.
+
         """
         if isinstance(input_array, tuple):
             return self.model(*input_array)
@@ -105,15 +106,16 @@ def __init__(self, warn: bool = True, error: bool = False) -> None:
         self._raise_error = error
 
     def message(self, *args: Any, **kwargs: Any) -> str:
-        """The message to be printed when the model does not pass the verification. If the message for warning and
-        error differ, override the :meth:`warning_message` and :meth:`error_message` methods directly.
+        """The message to be printed when the model does not pass the verification. If the message for warning and error
+        differ, override the :meth:`warning_message` and :meth:`error_message` methods directly.
 
         Arguments:
             *args: Any positional arguments that are needed to construct the message.
             **kwargs: Any keyword arguments that are needed to construct the message.
 
         Returns:
             The message as a string.
+
         """
 
     def warning_message(self, *args: Any, **kwargs: Any) -> str:

src/pl_bolts/callbacks/verification/batch_gradient.py

@@ -91,6 +91,7 @@ class BatchGradientVerificationCallback(VerificationCallbackBase):
     """The callback version of the :class:`BatchGradientVerification` test.
 
     Verification is performed right before training begins.
+
     """
 
     def __init__(
@@ -211,12 +212,13 @@ def collect_batches(tensor: Tensor) -> Tensor:
 @under_review()
 @contextmanager
 def selective_eval(model: nn.Module, layer_types: Iterable[Type[nn.Module]]) -> None:
-    """A context manager that sets all requested types of layers to eval mode. This method uses an ``isinstance``
-    check, so all subclasses are also affected.
+    """A context manager that sets all requested types of layers to eval mode. This method uses an ``isinstance`` check,
+    so all subclasses are also affected.
 
     Args:
         model: A model which has layers that need to be set to eval mode.
         layer_types: The list of class objects for which all layers of that type will be set to eval mode.
+
     """
     to_revert = []
     try:

src/pl_bolts/callbacks/vision/image_generation.py

@@ -31,6 +31,7 @@ class TensorboardGenerativeModelImageSampler(Callback):
         from pl_bolts.callbacks import TensorboardGenerativeModelImageSampler
 
         trainer = Trainer(callbacks=[TensorboardGenerativeModelImageSampler()])
+
     """
 
     def __init__(

src/pl_bolts/callbacks/vision/sr_image_logger.py

@@ -17,8 +17,8 @@
 
 @under_review()
 class SRImageLoggerCallback(Callback):
-    """Logs low-res, generated high-res, and ground truth high-res images to TensorBoard Your model must implement
-    the ``forward`` function for generation.
+    """Logs low-res, generated high-res, and ground truth high-res images to TensorBoard Your model must implement the
+    ``forward`` function for generation.
 
     Requirements::
 
@@ -30,6 +30,7 @@ class SRImageLoggerCallback(Callback):
         from pl_bolts.callbacks import SRImageLoggerCallback
 
         trainer = Trainer(callbacks=[SRImageLoggerCallback()])
+
     """
 
     def __init__(self, log_interval: int = 1000, scale_factor: int = 4, num_samples: int = 5) -> None:

src/pl_bolts/datamodules/experience_source.py

@@ -1,5 +1,6 @@
 """Datamodules for RL models that rely on experiences generated during training Based on implementations found
 here: https://github.com/Shmuma/ptan/blob/master/ptan/experience.py."""
+
 from abc import ABC
 from collections import deque, namedtuple
 from typing import Callable, Iterator, List, Tuple

src/pl_bolts/datamodules/imagenet_datamodule.py

@@ -167,12 +167,12 @@ def train_dataloader(self) -> DataLoader:
         return loader
 
     def val_dataloader(self) -> DataLoader:
-        """Uses the part of the train split of imagenet2012  that was not used for training via
-        `num_imgs_per_val_class`
+        """Uses the part of the train split of imagenet2012  that was not used for training via `num_imgs_per_val_class`
 
         Args:
             batch_size: the batch size
             transforms: the transforms
+
         """
         transforms = self.val_transform() if self.val_transforms is None else self.val_transforms
 

src/pl_bolts/datamodules/stl10_datamodule.py

@@ -139,6 +139,7 @@ def train_dataloader_mixed(self) -> DataLoader:
 
             batch_size: the batch size
             transforms: a sequence of transforms
+
         """
         transforms = self._default_transforms() if self.train_transforms is None else self.train_transforms
 

src/pl_bolts/datamodules/vocdetection_datamodule.py

@@ -166,6 +166,7 @@ def train_dataloader(self, image_transforms: Optional[Callable] = None) -> DataL
 
         Args:
             image_transforms: custom image-only transforms
+
         """
         transforms = [
             _prepare_voc_instance,
@@ -181,6 +182,7 @@ def val_dataloader(self, image_transforms: Optional[Callable] = None) -> DataLoa
 
         Args:
             image_transforms: custom image-only transforms
+
         """
         transforms = [
             _prepare_voc_instance,

src/pl_bolts/datasets/kitti_dataset.py

@@ -86,6 +86,7 @@ def encode_segmap(self, mask):
 
         It also sets all of the valid pixels to the appropriate value between 0 and `len(valid_labels)` (the number of
         valid classes), so it can be used properly by the loss function when comparing with the output.
+
         """
         for voidc in self.void_labels:
             mask[mask == voidc] = self.ignore_index

src/pl_bolts/datasets/sr_dataset_mixin.py

@@ -1,4 +1,5 @@
 """Adapted from: https://github.com/https-deeplearning-ai/GANs-Public."""
+
 from typing import Any, Tuple
 
 import torch

src/pl_bolts/datasets/utils.py

@@ -60,6 +60,7 @@ def to_tensor(arrays: TArrays) -> torch.Tensor:
 
     Returns:
         Tensor of the integers
+
     """
     return torch.tensor(arrays)
 

src/pl_bolts/models/__init__.py

@@ -1,4 +1,5 @@
 """Collection of PyTorchLightning models."""
+
 from pl_bolts.models.autoencoders.basic_ae.basic_ae_module import AE
 from pl_bolts.models.autoencoders.basic_vae.basic_vae_module import VAE
 from pl_bolts.models.mnist_module import LitMNIST

src/pl_bolts/models/detection/yolo/darknet_network.py

@@ -145,6 +145,7 @@ def read(tensor: Tensor) -> int:
             """Reads the contents of ``tensor`` from the current position of ``weight_file``.
 
             Returns the number of elements read. If there's no more data in ``weight_file``, returns 0.
+
             """
             np_array = np.fromfile(weight_file, count=tensor.numel(), dtype=np.float32)
             num_elements = np_array.size
@@ -275,8 +276,8 @@ def convert(key: str, value: str) -> Union[str, int, float, List[Union[str, int,
 
 
 def _create_layer(config: CONFIG, num_inputs: List[int], **kwargs: Any) -> CREATE_LAYER_OUTPUT:
-    """Calls one of the ``_create_<layertype>(config, num_inputs)`` functions to create a PyTorch module from the
-    layer config.
+    """Calls one of the ``_create_<layertype>(config, num_inputs)`` functions to create a PyTorch module from the layer
+    config.
 
     Args:
         config: Dictionary of configuration options for this layer.
@@ -285,6 +286,7 @@ def _create_layer(config: CONFIG, num_inputs: List[int], **kwargs: Any) -> CREAT
     Returns:
         module (:class:`~torch.nn.Module`), num_outputs (int): The created PyTorch module and the number of channels in
         its output.
+
     """
     create_func: Dict[str, Callable[..., CREATE_LAYER_OUTPUT]] = {
         "convolutional": _create_convolutional,

src/pl_bolts/models/detection/yolo/loss.py

@@ -205,6 +205,7 @@ def _target_labels_to_probs(
 
     Returns:
         An ``[M, C]`` matrix of target class probabilities.
+
     """
     if targets.ndim == 1:
         # The data may contain a different number of classes than what the model predicts. In case a label is

src/pl_bolts/models/detection/yolo/torch_networks.py

@@ -31,6 +31,7 @@ def run_detection(
         detections: A list where a tensor containing the detections will be appended to.
         losses: A list where a tensor containing the losses will be appended to, if ``targets`` is given.
         hits: A list where the number of targets that matched this layer will be appended to, if ``targets`` is given.
+
     """
     output, preds = detection_layer(layer_input, image_size)
     detections.append(output)
@@ -69,6 +70,7 @@ def run_detection_with_aux_head(
         detections: A list where a tensor containing the detections will be appended to.
         losses: A list where a tensor containing the losses will be appended to, if ``targets`` is given.
         hits: A list where the number of targets that matched this layer will be appended to, if ``targets`` is given.
+
     """
     output, preds = detection_layer(layer_input, image_size)
     detections.append(output)
@@ -1132,8 +1134,8 @@ def forward(self, x: Tensor, targets: Optional[TARGETS] = None) -> NETWORK_OUTPU
 
 
 class YOLOV5Network(nn.Module):
-    """The YOLOv5 network architecture. Different variants (n/s/m/l/x) can be achieved by adjusting the ``depth``
-    and ``width`` parameters.
+    """The YOLOv5 network architecture. Different variants (n/s/m/l/x) can be achieved by adjusting the ``depth`` and
+    ``width`` parameters.
 
     Args:
         num_classes: Number of different classes that this model predicts.
@@ -1176,6 +1178,7 @@ class YOLOV5Network(nn.Module):
         class_loss_multiplier: Classification loss will be scaled by this value.
         xy_scale: Eliminate "grid sensitivity" by scaling the box coordinates by this factor. Using a value > 1.0 helps
             to produce coordinate values close to one.
+
     """
 
     def __init__(
@@ -1613,8 +1616,8 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class YOLOXNetwork(nn.Module):
-    """The YOLOX network architecture. Different variants (nano/tiny/s/m/l/x) can be achieved by adjusting the
-    ``depth`` and ``width`` parameters.
+    """The YOLOX network architecture. Different variants (nano/tiny/s/m/l/x) can be achieved by adjusting the ``depth``
+    and ``width`` parameters.
 
     Args:
         num_classes: Number of different classes that this model predicts.
@@ -1657,6 +1660,7 @@ class YOLOXNetwork(nn.Module):
         class_loss_multiplier: Classification loss will be scaled by this value.
         xy_scale: Eliminate "grid sensitivity" by scaling the box coordinates by this factor. Using a value > 1.0 helps
             to produce coordinate values close to one.
+
     """
 
     def __init__(

src/pl_bolts/models/detection/yolo/utils.py

@@ -102,8 +102,8 @@ def aligned_iou(wh1: Tensor, wh2: Tensor) -> Tensor:
 
 
 def iou_below(pred_boxes: Tensor, target_boxes: Tensor, threshold: float) -> Tensor:
-    """Creates a binary mask whose value will be ``True``, unless the predicted box overlaps any target
-    significantly (IoU greater than ``threshold``).
+    """Creates a binary mask whose value will be ``True``, unless the predicted box overlaps any target significantly
+    (IoU greater than ``threshold``).
 
     Args:
         pred_boxes: The predicted corner coordinates. Tensor of size ``[height, width, boxes_per_cell, 4]``.
@@ -112,6 +112,7 @@ def iou_below(pred_boxes: Tensor, target_boxes: Tensor, threshold: float) -> Ten
     Returns:
         A boolean tensor sized ``[height, width, boxes_per_cell]``, with ``False`` where the predicted box overlaps a
         target significantly and ``True`` elsewhere.
+
     """
     shape = pred_boxes.shape[:-1]
     pred_boxes = pred_boxes.view(-1, 4)

src/pl_bolts/models/detection/yolo/yolo_module.py

@@ -144,8 +144,8 @@ def __init__(
     def forward(
         self, images: Union[Tensor, IMAGES], targets: Optional[TARGETS] = None
     ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
-        """Runs a forward pass through the network (all layers listed in ``self.network``), and if training targets
-        are provided, computes the losses from the detection layers.
+        """Runs a forward pass through the network (all layers listed in ``self.network``), and if training targets are
+        provided, computes the losses from the detection layers.
 
         Detections are concatenated from the detection layers. Each detection layer will produce a number of detections
         that depends on the size of the feature map and the number of anchors per feature map cell.
@@ -161,6 +161,7 @@ def forward(
             provided, a dictionary of losses. Detections are shaped ``[batch_size, anchors, classes + 5]``, where
             ``anchors`` is the feature map size (width * height) times the number of anchors per cell. The predicted box
             coordinates are in `(x1, y1, x2, y2)` format and scaled to the input image size.
+
         """
         self.validate_batch(images, targets)
         images_tensor = images if isinstance(images, Tensor) else torch.stack(images)
@@ -185,6 +186,7 @@ def configure_optimizers(self) -> Tuple[List[optim.Optimizer], List[LRScheduler]
         If weight decay is specified, it will be applied only to convolutional layer weights, as they contain much more
         parameters than the biases and batch normalization parameters. Regularizing all parameters could lead to
         underfitting.
+
         """
         if ("weight_decay" in self.optimizer_params) and (self.optimizer_params["weight_decay"] != 0):
             defaults = copy(self.optimizer_params)
@@ -574,12 +576,13 @@ def __init__(
 
 
 class ResizedVOCDetectionDataModule(VOCDetectionDataModule):
-    """A subclass of ``VOCDetectionDataModule`` that resizes the images to a specific size. YOLO expectes the image
-    size to be divisible by the ratio in which the network downsamples the image.
+    """A subclass of ``VOCDetectionDataModule`` that resizes the images to a specific size. YOLO expectes the image size
+    to be divisible by the ratio in which the network downsamples the image.
 
     Args:
         width: Resize images to this width.
         height: Resize images to this height.
+
     """
 
     def __init__(self, width: int = 608, height: int = 608, **kwargs: Any):
@@ -609,6 +612,7 @@ def _resize(self, image: Tensor, target: TARGET) -> Tuple[Tensor, TARGET]:
 
         Returns:
             Resized image tensor.
+
         """
         device = target["boxes"].device
         height, width = image.shape[-2:]

src/pl_bolts/models/gans/srgan/components.py

@@ -1,4 +1,5 @@
 """Adapted from: https://github.com/https-deeplearning-ai/GANs-Public."""
+
 import torch
 import torch.nn as nn
 

src/pl_bolts/models/gans/srgan/srgan_module.py

@@ -1,4 +1,5 @@
 """Adapted from: https://github.com/https-deeplearning-ai/GANs-Public."""
+
 from argparse import ArgumentParser
 from pathlib import Path
 from typing import Any, List, Optional, Tuple

src/pl_bolts/models/gans/srgan/srresnet_module.py

@@ -1,4 +1,5 @@
 """Adapted from: https://github.com/https-deeplearning-ai/GANs-Public."""
+
 from argparse import ArgumentParser
 from typing import Any, Tuple
 

src/pl_bolts/models/rl/advantage_actor_critic_model.py

@@ -1,4 +1,5 @@
 """Advantage Actor Critic (A2C)"""
+
 from argparse import ArgumentParser
 from collections import OrderedDict
 from typing import Any, Iterator, List, Tuple

src/pl_bolts/models/rl/common/agents.py

@@ -2,6 +2,7 @@
 
 https://github.com/Shmuma/ptan/blob/master/ptan/agent.py.
 """
+
 from abc import ABC
 from typing import List
 

src/pl_bolts/models/rl/common/distributions.py

@@ -1,4 +1,5 @@
 """Distributions used in some continuous RL algorithms."""
+
 import torch
 
 from pl_bolts.utils.stability import under_review

src/pl_bolts/models/rl/common/gym_wrappers.py

@@ -1,5 +1,6 @@
 """Set of wrapper functions for gym environments taken from
 https://github.com/Shmuma/ptan/blob/master/ptan/common/wrappers.py."""
+
 import collections
 
 import numpy as np

src/pl_bolts/models/rl/common/networks.py

@@ -1,4 +1,5 @@
 """Series of networks used Based on implementations found here:"""
+
 import math
 from typing import Tuple
 

src/pl_bolts/models/rl/double_dqn_model.py

@@ -1,4 +1,5 @@
 """Double DQN."""
+
 import argparse
 from collections import OrderedDict
 from typing import Tuple