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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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64
ultralytics/utils/metrics.py
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@ -1,7 +1,5 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Model validation metrics
"""
"""Model validation metrics."""
import math
import warnings
@ -195,7 +193,7 @@ class ConfusionMatrix:
def process_cls_preds(self, preds, targets):
"""
Update confusion matrix for classification task
Update confusion matrix for classification task.
Args:
preds (Array[N, min(nc,5)]): Predicted class labels.
@ -308,9 +306,7 @@ class ConfusionMatrix:
on_plot(plot_fname)
def print(self):
"""
Print the confusion matrix to the console.
"""
"""Print the confusion matrix to the console."""
for i in range(self.nc + 1):
LOGGER.info(' '.join(map(str, self.matrix[i])))
@ -440,7 +436,6 @@ def ap_per_class(tp,
f1 (np.ndarray): F1-score values at each confidence threshold.
ap (np.ndarray): Average precision for each class at different IoU thresholds.
unique_classes (np.ndarray): An array of unique classes that have data.
"""
# Sort by objectness
@ -498,32 +493,33 @@ def ap_per_class(tp,
class Metric(SimpleClass):
"""
Class for computing evaluation metrics for YOLOv8 model.
Class for computing evaluation metrics for YOLOv8 model.
Attributes:
p (list): Precision for each class. Shape: (nc,).
r (list): Recall for each class. Shape: (nc,).
f1 (list): F1 score for each class. Shape: (nc,).
all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
ap_class_index (list): Index of class for each AP score. Shape: (nc,).
nc (int): Number of classes.
Attributes:
p (list): Precision for each class. Shape: (nc,).
r (list): Recall for each class. Shape: (nc,).
f1 (list): F1 score for each class. Shape: (nc,).
all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10).
ap_class_index (list): Index of class for each AP score. Shape: (nc,).
nc (int): Number of classes.
Methods:
ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
mp(): Mean precision of all classes. Returns: Float.
mr(): Mean recall of all classes. Returns: Float.
map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float.
map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float.
map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float.
mean_results(): Mean of results, returns mp, mr, map50, map.
class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i].
maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,).
fitness(): Model fitness as a weighted combination of metrics. Returns: Float.
update(results): Update metric attributes with new evaluation results.
"""
Methods:
ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or [].
mp(): Mean precision of all classes. Returns: Float.
mr(): Mean recall of all classes. Returns: Float.
map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float.
map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float.
map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float.
mean_results(): Mean of results, returns mp, mr, map50, map.
class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i].
maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,).
fitness(): Model fitness as a weighted combination of metrics. Returns: Float.
update(results): Update metric attributes with new evaluation results.
"""
def __init__(self) -> None:
"""Initializes a Metric instance for computing evaluation metrics for the YOLOv8 model."""
self.p = [] # (nc, )
self.r = [] # (nc, )
self.f1 = [] # (nc, )
@ -606,12 +602,12 @@ class Metric(SimpleClass):
return [self.mp, self.mr, self.map50, self.map]
def class_result(self, i):
"""class-aware result, return p[i], r[i], ap50[i], ap[i]."""
"""Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
return self.p[i], self.r[i], self.ap50[i], self.ap[i]
@property
def maps(self):
"""mAP of each class."""
"""MAP of each class."""
maps = np.zeros(self.nc) + self.map
for i, c in enumerate(self.ap_class_index):
maps[c] = self.ap[i]
@ -672,6 +668,7 @@ class DetMetrics(SimpleClass):
"""
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
"""Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names."""
self.save_dir = save_dir
self.plot = plot
self.on_plot = on_plot
@ -756,6 +753,7 @@ class SegmentMetrics(SimpleClass):
"""
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
"""Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names."""
self.save_dir = save_dir
self.plot = plot
self.on_plot = on_plot
@ -865,6 +863,7 @@ class PoseMetrics(SegmentMetrics):
"""
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None:
"""Initialize the PoseMetrics class with directory path, class names, and plotting options."""
super().__init__(save_dir, plot, names)
self.save_dir = save_dir
self.plot = plot
@ -954,6 +953,7 @@ class ClassifyMetrics(SimpleClass):
"""
def __init__(self) -> None:
"""Initialize a ClassifyMetrics instance."""
self.top1 = 0
self.top5 = 0
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}