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Update .pre-commit-config.yaml (#1026)

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Glenn Jocher 2023-02-17 22:26:40 +01:00 • committed by GitHub
parent 9047d737f4
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76 changed files with 928 additions and 935 deletions
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32
ultralytics/yolo/utils/metrics.py
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@ -238,14 +238,14 @@ class ConfusionMatrix:
nc, nn = self.nc, len(names) # number of classes, names
sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
ticklabels = (names + ['background']) if labels else "auto"
ticklabels = (names + ['background']) if labels else 'auto'
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
sn.heatmap(array,
ax=ax,
annot=nc < 30,
annot_kws={
"size": 8},
'size': 8},
cmap='Blues',
fmt='.2f',
square=True,
@ -287,7 +287,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
ax.set_ylabel('Precision')
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
ax.set_title('Precision-Recall Curve')
fig.savefig(save_dir, dpi=250)
plt.close(fig)
@ -309,7 +309,7 @@ def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confi
ax.set_ylabel(ylabel)
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
ax.set_title(f'{ylabel}-Confidence Curve')
fig.savefig(save_dir, dpi=250)
plt.close(fig)
@ -343,7 +343,7 @@ def compute_ap(recall, precision):
return ap, mpre, mrec
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=Path(), names=(), eps=1e-16, prefix=""):
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=Path(), names=(), eps=1e-16, prefix=''):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
@ -507,7 +507,7 @@ class Metric:
class DetMetrics:
def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
def __init__(self, save_dir=Path('.'), plot=False, names=()) -> None:
self.save_dir = save_dir
self.plot = plot
self.names = names
@ -521,7 +521,7 @@ class DetMetrics:
@property
def keys(self):
return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
return ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
def mean_results(self):
return self.box.mean_results()
@ -543,12 +543,12 @@ class DetMetrics:
@property
def results_dict(self):
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
class SegmentMetrics:
def __init__(self, save_dir=Path("."), plot=False, names=()) -> None:
def __init__(self, save_dir=Path('.'), plot=False, names=()) -> None:
self.save_dir = save_dir
self.plot = plot
self.names = names
@ -563,7 +563,7 @@ class SegmentMetrics:
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix="Mask")[2:]
prefix='Mask')[2:]
self.seg.nc = len(self.names)
self.seg.update(results_mask)
results_box = ap_per_class(tp_b,
@ -573,15 +573,15 @@ class SegmentMetrics:
plot=self.plot,
save_dir=self.save_dir,
names=self.names,
prefix="Box")[2:]
prefix='Box')[2:]
self.box.nc = len(self.names)
self.box.update(results_box)
@property
def keys(self):
return [
"metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)",
"metrics/precision(M)", "metrics/recall(M)", "metrics/mAP50(M)", "metrics/mAP50-95(M)"]
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)',
'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)']
def mean_results(self):
return self.box.mean_results() + self.seg.mean_results()
@ -604,7 +604,7 @@ class SegmentMetrics:
@property
def results_dict(self):
return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness]))
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness]))
class ClassifyMetrics:
@ -626,8 +626,8 @@ class ClassifyMetrics:
@property
def results_dict(self):
return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness]))
return dict(zip(self.keys + ['fitness'], [self.top1, self.top5, self.fitness]))
@property
def keys(self):
return ["metrics/accuracy_top1", "metrics/accuracy_top5"]
return ['metrics/accuracy_top1', 'metrics/accuracy_top5']