Update .pre-commit-config.yaml (#11009)

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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

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

* Update __init__.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update .pre-commit-config.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Pre-commit updates

* Pre-commit updates

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/metrics.py

@@ -28,7 +28,7 @@ def smooth(y, f=0.05):
     return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
 
 
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=""):
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
     """ Compute the average precision, given the recall and precision curves.
     Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
@@ -194,14 +194,14 @@ 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,
@@ -331,7 +331,7 @@ 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)
@@ -354,7 +354,7 @@ 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)