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Implement all missing docstrings (#5298)

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Glenn Jocher 2023-10-10 20:07:13 +02:00 • committed by GitHub
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26 changed files with 649 additions and 79 deletions
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35
ultralytics/models/fastsam/predict.py
View file

@ -9,14 +9,45 @@ from ultralytics.utils import DEFAULT_CFG, ops
class FastSAMPredictor(DetectionPredictor):
"""
FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics
YOLO framework.
This class extends the DetectionPredictor, customizing the prediction pipeline specifically for fast SAM.
It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing
for single-class segmentation.
Attributes:
cfg (dict): Configuration parameters for prediction.
overrides (dict, optional): Optional parameter overrides for custom behavior.
_callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initializes FastSAMPredictor class by inheriting from DetectionPredictor and setting task to 'segment'."""
"""
Initializes the FastSAMPredictor class, inheriting from DetectionPredictor and setting the task to 'segment'.
Args:
cfg (dict): Configuration parameters for prediction.
overrides (dict, optional): Optional parameter overrides for custom behavior.
_callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
"""
super().__init__(cfg, overrides, _callbacks)
self.args.task = 'segment'
def postprocess(self, preds, img, orig_imgs):
"""Postprocesses the predictions, applies non-max suppression, scales the boxes, and returns the results."""
"""
Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image
size, and returns the final results.
Args:
preds (list): The raw output predictions from the model.
img (torch.Tensor): The processed image tensor.
orig_imgs (list | torch.Tensor): The original image or list of images.
Returns:
(list): A list of Results objects, each containing processed boxes, masks, and other metadata.
"""
p = ops.non_max_suppression(
preds[0],
self.args.conf,

37
ultralytics/models/fastsam/prompt.py
View file

@ -13,6 +13,15 @@ from ultralytics.utils import TQDM
class FastSAMPrompt:
"""
Fast Segment Anything Model class for image annotation and visualization.
Attributes:
device (str): Computing device ('cuda' or 'cpu').
results: Object detection or segmentation results.
source: Source image or image path.
clip: CLIP model for linear assignment.
"""
def __init__(self, source, results, device='cuda') -> None:
"""Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
@ -92,6 +101,20 @@ class FastSAMPrompt:
better_quality=True,
retina=False,
with_contours=True):
"""
Plots annotations, bounding boxes, and points on images and saves the output.
Args:
annotations (list): Annotations to be plotted.
output (str or Path): Output directory for saving the plots.
bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
points (list, optional): Points to be plotted. Defaults to None.
point_label (list, optional): Labels for the points. Defaults to None.
mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
better_quality (bool, optional): Whether to apply morphological transformations for better mask quality. Defaults to True.
retina (bool, optional): Whether to use retina mask. Defaults to False.
with_contours (bool, optional): Whether to plot contours. Defaults to True.
"""
pbar = TQDM(annotations, total=len(annotations))
for ann in pbar:
result_name = os.path.basename(ann.path)
@ -160,6 +183,20 @@ class FastSAMPrompt:
target_height=960,
target_width=960,
):
"""
Quickly shows the mask annotations on the given matplotlib axis.
Args:
annotation (array-like): Mask annotation.
ax (matplotlib.axes.Axes): Matplotlib axis.
random_color (bool, optional): Whether to use random color for masks. Defaults to False.
bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
points (list, optional): Points to be plotted. Defaults to None.
pointlabel (list, optional): Labels for the points. Defaults to None.
retinamask (bool, optional): Whether to use retina mask. Defaults to True.
target_height (int, optional): Target height for resizing. Defaults to 960.
target_width (int, optional): Target width for resizing. Defaults to 960.
"""
n, h, w = annotation.shape # batch, height, width
areas = np.sum(annotation, axis=(1, 2))

28
ultralytics/models/fastsam/val.py
View file

@ -5,9 +5,35 @@ from ultralytics.utils.metrics import SegmentMetrics
class FastSAMValidator(SegmentationValidator):
"""
Custom validation class for fast SAM (Segment Anything Model) segmentation in Ultralytics YOLO framework.
Extends the SegmentationValidator class, customizing the validation process specifically for fast SAM. This class
sets the task to 'segment' and uses the SegmentMetrics for evaluation. Additionally, plotting features are disabled
to avoid errors during validation.
Attributes:
dataloader: The data loader object used for validation.
save_dir (str): The directory where validation results will be saved.
pbar: A progress bar object.
args: Additional arguments for customization.
_callbacks: List of callback functions to be invoked during validation.
"""
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
"""
Initialize the FastSAMValidator class, setting the task to 'segment' and metrics to SegmentMetrics.
Args:
dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
save_dir (Path, optional): Directory to save results.
pbar (tqdm.tqdm): Progress bar for displaying progress.
args (SimpleNamespace): Configuration for the validator.
_callbacks (dict): Dictionary to store various callback functions.
Notes:
Plots for ConfusionMatrix and other related metrics are disabled in this class to avoid errors.
"""
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
self.args.task = 'segment'
self.args.plots = False # disable ConfusionMatrix and other plots to avoid errors