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Add docformatter to pre-commit (#5279)

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Glenn Jocher 2023-10-09 02:25:22 +02:00 • committed by GitHub
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90 changed files with 1396 additions and 497 deletions
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4
ultralytics/engine/exporter.py
View file

@ -986,9 +986,7 @@ class Exporter:
return model
def add_callback(self, event: str, callback):
"""
Appends the given callback.
"""
"""Appends the given callback."""
self.callbacks[event].append(callback)
def run_callbacks(self, event: str):

12
ultralytics/engine/model.py
View file

@ -159,9 +159,7 @@ class Model(nn.Module):
self.overrides['task'] = self.task
def _check_is_pytorch_model(self):
"""
Raises TypeError is model is not a PyTorch model
"""
"""Raises TypeError is model is not a PyTorch model."""
pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
pt_module = isinstance(self.model, nn.Module)
if not (pt_module or pt_str):
@ -173,9 +171,7 @@ class Model(nn.Module):
f"argument directly in your inference command, i.e. 'model.predict(source=..., device=0)'")
def reset_weights(self):
"""
Resets the model modules parameters to randomly initialized values, losing all training information.
"""
"""Resets the model modules parameters to randomly initialized values, losing all training information."""
self._check_is_pytorch_model()
for m in self.model.modules():
if hasattr(m, 'reset_parameters'):
@ -185,9 +181,7 @@ class Model(nn.Module):
return self
def load(self, weights='yolov8n.pt'):
"""
Transfers parameters with matching names and shapes from 'weights' to model.
"""
"""Transfers parameters with matching names and shapes from 'weights' to model."""
self._check_is_pytorch_model()
if isinstance(weights, (str, Path)):
weights, self.ckpt = attempt_load_one_weight(weights)

16
ultralytics/engine/predictor.py
View file

@ -58,7 +58,7 @@ Example:
class BasePredictor:
"""
BasePredictor
BasePredictor.
A base class for creating predictors.
@ -109,7 +109,8 @@ class BasePredictor:
callbacks.add_integration_callbacks(self)
def preprocess(self, im):
"""Prepares input image before inference.
"""
Prepares input image before inference.
Args:
im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
@ -128,6 +129,7 @@ class BasePredictor:
return im
def inference(self, im, *args, **kwargs):
"""Runs inference on a given image using the specified model and arguments."""
visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem,
mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
return self.model(im, augment=self.args.augment, visualize=visualize)
@ -194,7 +196,11 @@ class BasePredictor:
return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one
def predict_cli(self, source=None, model=None):
"""Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode."""
"""
Method used for CLI prediction.
It uses always generator as outputs as not required by CLI mode.
"""
gen = self.stream_inference(source, model)
for _ in gen: # running CLI inference without accumulating any outputs (do not modify)
pass
@ -352,7 +358,5 @@ class BasePredictor:
callback(self)
def add_callback(self, event: str, func):
"""
Add callback
"""
"""Add callback."""
self.callbacks[event].append(func)

26
ultralytics/engine/results.py
View file

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Ultralytics Results, Boxes and Masks classes for handling inference results
Ultralytics Results, Boxes and Masks classes for handling inference results.
Usage: See https://docs.ultralytics.com/modes/predict/
"""
@ -19,12 +19,11 @@ from ultralytics.utils.torch_utils import smart_inference_mode
class BaseTensor(SimpleClass):
"""
Base tensor class with additional methods for easy manipulation and device handling.
"""
"""Base tensor class with additional methods for easy manipulation and device handling."""
def __init__(self, data, orig_shape) -> None:
"""Initialize BaseTensor with data and original shape.
"""
Initialize BaseTensor with data and original shape.
Args:
data (torch.Tensor | np.ndarray): Predictions, such as bboxes, masks and keypoints.
@ -126,6 +125,18 @@ class Results(SimpleClass):
self.probs = probs
def _apply(self, fn, *args, **kwargs):
"""
Applies a function to all non-empty attributes and returns a new Results object with modified attributes. This
function is internally called by methods like .to(), .cuda(), .cpu(), etc.
Args:
fn (str): The name of the function to apply.
*args: Variable length argument list to pass to the function.
**kwargs: Arbitrary keyword arguments to pass to the function.
Returns:
Results: A new Results object with attributes modified by the applied function.
"""
r = self.new()
for k in self._keys:
v = getattr(self, k)
@ -250,9 +261,7 @@ class Results(SimpleClass):
return annotator.result()
def verbose(self):
"""
Return log string for each task.
"""
"""Return log string for each task."""
log_string = ''
probs = self.probs
boxes = self.boxes
@ -537,6 +546,7 @@ class Probs(BaseTensor):
"""
def __init__(self, probs, orig_shape=None) -> None:
"""Initialize the Probs class with classification probabilities and optional original shape of the image."""
super().__init__(probs, orig_shape)
@property

50
ultralytics/engine/trainer.py
View file

@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Train a model on a dataset
Train a model on a dataset.
Usage:
$ yolo mode=train model=yolov8n.pt data=coco128.yaml imgsz=640 epochs=100 batch=16
@ -37,7 +37,7 @@ from ultralytics.utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel,
class BaseTrainer:
"""
BaseTrainer
BaseTrainer.
A base class for creating trainers.
@ -143,15 +143,11 @@ class BaseTrainer:
callbacks.add_integration_callbacks(self)
def add_callback(self, event: str, callback):
"""
Appends the given callback.
"""
"""Appends the given callback."""
self.callbacks[event].append(callback)
def set_callback(self, event: str, callback):
"""
Overrides the existing callbacks with the given callback.
"""
"""Overrides the existing callbacks with the given callback."""
self.callbacks[event] = [callback]
def run_callbacks(self, event: str):
@ -207,9 +203,7 @@ class BaseTrainer:
world_size=world_size)
def _setup_train(self, world_size):
"""
Builds dataloaders and optimizer on correct rank process.
"""
"""Builds dataloaders and optimizer on correct rank process."""
# Model
self.run_callbacks('on_pretrain_routine_start')
@ -450,14 +444,14 @@ class BaseTrainer:
@staticmethod
def get_dataset(data):
"""
Get train, val path from data dict if it exists. Returns None if data format is not recognized.
Get train, val path from data dict if it exists.
Returns None if data format is not recognized.
"""
return data['train'], data.get('val') or data.get('test')
def setup_model(self):
"""
load/create/download model for any task.
"""
"""Load/create/download model for any task."""
if isinstance(self.model, torch.nn.Module): # if model is loaded beforehand. No setup needed
return
@ -482,14 +476,14 @@ class BaseTrainer:
self.ema.update(self.model)
def preprocess_batch(self, batch):
"""
Allows custom preprocessing model inputs and ground truths depending on task type.
"""
"""Allows custom preprocessing model inputs and ground truths depending on task type."""
return batch
def validate(self):
"""
Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
Runs validation on test set using self.validator.
The returned dict is expected to contain "fitness" key.
"""
metrics = self.validator(self)
fitness = metrics.pop('fitness', -self.loss.detach().cpu().numpy()) # use loss as fitness measure if not found
@ -506,26 +500,20 @@ class BaseTrainer:
raise NotImplementedError('get_validator function not implemented in trainer')
def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
"""
Returns dataloader derived from torch.data.Dataloader.
"""
"""Returns dataloader derived from torch.data.Dataloader."""
raise NotImplementedError('get_dataloader function not implemented in trainer')
def build_dataset(self, img_path, mode='train', batch=None):
"""Build dataset"""
"""Build dataset."""
raise NotImplementedError('build_dataset function not implemented in trainer')
def label_loss_items(self, loss_items=None, prefix='train'):
"""
Returns a loss dict with labelled training loss items tensor
"""
"""Returns a loss dict with labelled training loss items tensor."""
# Not needed for classification but necessary for segmentation & detection
return {'loss': loss_items} if loss_items is not None else ['loss']
def set_model_attributes(self):
"""
To set or update model parameters before training.
"""
"""To set or update model parameters before training."""
self.model.names = self.data['names']
def build_targets(self, preds, targets):
@ -632,8 +620,8 @@ class BaseTrainer:
def build_optimizer(self, model, name='auto', lr=0.001, momentum=0.9, decay=1e-5, iterations=1e5):
"""
Constructs an optimizer for the given model, based on the specified optimizer name, learning rate,
momentum, weight decay, and number of iterations.
Constructs an optimizer for the given model, based on the specified optimizer name, learning rate, momentum,
weight decay, and number of iterations.
Args:
model (torch.nn.Module): The model for which to build an optimizer.

40
ultralytics/engine/tuner.py
View file

@ -31,32 +31,32 @@ from ultralytics.utils.plotting import plot_tune_results
class Tuner:
"""
Class responsible for hyperparameter tuning of YOLO models.
Class responsible for hyperparameter tuning of YOLO models.
The class evolves YOLO model hyperparameters over a given number of iterations
by mutating them according to the search space and retraining the model to evaluate their performance.
The class evolves YOLO model hyperparameters over a given number of iterations
by mutating them according to the search space and retraining the model to evaluate their performance.
Attributes:
space (dict): Hyperparameter search space containing bounds and scaling factors for mutation.
tune_dir (Path): Directory where evolution logs and results will be saved.
tune_csv (Path): Path to the CSV file where evolution logs are saved.
Attributes:
space (dict): Hyperparameter search space containing bounds and scaling factors for mutation.
tune_dir (Path): Directory where evolution logs and results will be saved.
tune_csv (Path): Path to the CSV file where evolution logs are saved.
Methods:
_mutate(hyp: dict) -> dict:
Mutates the given hyperparameters within the bounds specified in `self.space`.
Methods:
_mutate(hyp: dict) -> dict:
Mutates the given hyperparameters within the bounds specified in `self.space`.
__call__():
Executes the hyperparameter evolution across multiple iterations.
__call__():
Executes the hyperparameter evolution across multiple iterations.
Example:
Tune hyperparameters for YOLOv8n on COCO8 at imgsz=640 and epochs=30 for 300 tuning iterations.
```python
from ultralytics import YOLO
Example:
Tune hyperparameters for YOLOv8n on COCO8 at imgsz=640 and epochs=30 for 300 tuning iterations.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
```
"""
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
```
"""
def __init__(self, args=DEFAULT_CFG, _callbacks=None):
"""

7
ultralytics/engine/validator.py
View file

@ -36,7 +36,7 @@ from ultralytics.utils.torch_utils import de_parallel, select_device, smart_infe
class BaseValidator:
"""
BaseValidator
BaseValidator.
A base class for creating validators.
@ -102,8 +102,7 @@ class BaseValidator:
@smart_inference_mode()
def __call__(self, trainer=None, model=None):
"""
Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer
"""Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer
gets priority).
"""
self.training = trainer is not None
@ -260,7 +259,7 @@ class BaseValidator:
raise NotImplementedError('get_dataloader function not implemented for this validator')
def build_dataset(self, img_path):
"""Build dataset"""
"""Build dataset."""
raise NotImplementedError('build_dataset function not implemented in validator')
def preprocess(self, batch):