Ankle/yolov5_birdproject
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PyUpgrade 3.8 updates (#13289)

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* 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>
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Glenn JocherandUltralyticsAssistant authored and GitHub committed 2024-09-01 17:35:08 +02:00
1 parent e8a30cf820
commit f44db01005
4 files changed
+4 -4
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classify/predict.py
+1 -1
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@@ -147,7 +147,7 @@ def run(
save_path = str(save_dir / p.name) # im.jpg 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 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) annotator = Annotator(im0, example=str(names), pil=True)
# Print results # Print results
detect.py
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@@ -237,7 +237,7 @@ def run(
p = Path(p) # to Path p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg 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 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 gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names)) annotator = Annotator(im0, line_width=line_thickness, example=str(names))
segment/predict.py
+1 -1
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@@ -164,7 +164,7 @@ def run(
p = Path(p) # to Path p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg 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 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 imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names)) annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det): if len(det):
utils/metrics.py
+1 -1
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@@ -347,7 +347,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
else: else:
ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) 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_xlabel("Recall")
ax.set_ylabel("Precision") ax.set_ylabel("Precision")
ax.set_xlim(0, 1) ax.set_xlim(0, 1)