Ankle/yolov5_ZWgra
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Apply ruff==0.9.0 formatting (#13486)

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* Refactor code for speed and clarity

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

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn JocherandUltralyticsAssistant authored and GitHub committed 2025-01-10 01:42:04 +01:00
1 parent 6981c2742a
commit 86fd1ab270
20 files changed
+53 -84
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classify/predict.py
+1 -1
View File
@@ -192,7 +192,7 @@ def run(
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
classify/train.py
+8 -8
View File
@@ -201,10 +201,10 @@ def train(opt, device):
scaler = amp.GradScaler(enabled=cuda)
val = test_dir.stem # 'val' or 'test'
LOGGER.info(
f'Image sizes {imgsz} train, {imgsz} test\n'
f'Using {nw * WORLD_SIZE} dataloader workers\n'
f"Image sizes {imgsz} train, {imgsz} test\n"
f"Using {nw * WORLD_SIZE} dataloader workers\n"
f"Logging results to {colorstr('bold', save_dir)}\n"
f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
)
for epoch in range(epochs): # loop over the dataset multiple times
@@ -290,13 +290,13 @@ def train(opt, device):
# Train complete
if RANK in {-1, 0} and final_epoch:
LOGGER.info(
f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
f"\nResults saved to {colorstr('bold', save_dir)}"
f'\nPredict: python classify/predict.py --weights {best} --source im.jpg'
f'\nValidate: python classify/val.py --weights {best} --data {data_dir}'
f'\nExport: python export.py --weights {best} --include onnx'
f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
f"\nExport: python export.py --weights {best} --include onnx"
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
f'\nVisualize: https://netron.app\n'
f"\nVisualize: https://netron.app\n"
)
# Plot examples