PyUpgrade 3.8 updates (#13289)

* PyUpgrade 3.8 updates

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

* Auto-format by https://ultralytics.com/actions

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

classify/predict.py

@@ -147,7 +147,7 @@ def run(
             save_path = str(save_dir / p.name)  # im.jpg
             txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
 
-            s += "%gx%g " % im.shape[2:]  # print string
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
             annotator = Annotator(im0, example=str(names), pil=True)
 
             # Print results

detect.py

@@ -237,7 +237,7 @@ def run(
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # im.jpg
             txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
-            s += "%gx%g " % im.shape[2:]  # print string
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             imc = im0.copy() if save_crop else im0  # for save_crop
             annotator = Annotator(im0, line_width=line_thickness, example=str(names))

segment/predict.py

@@ -164,7 +164,7 @@ def run(
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # im.jpg
             txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt
-            s += "%gx%g " % im.shape[2:]  # print string
+            s += "{:g}x{:g} ".format(*im.shape[2:])  # print string
             imc = im0.copy() if save_crop else im0  # for save_crop
             annotator = Annotator(im0, line_width=line_thickness, example=str(names))
             if len(det):

utils/metrics.py

@@ -347,7 +347,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
     else:
         ax.plot(px, py, linewidth=1, color="grey")  # plot(recall, precision)
 
-    ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean())
+    ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5")
     ax.set_xlabel("Recall")
     ax.set_ylabel("Precision")
     ax.set_xlim(0, 1)