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59 lines
2.1 KiB
59 lines
2.1 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import subprocess |
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from pathlib import Path |
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import pytest |
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from ultralytics.utils import ONLINE, ROOT, SETTINGS |
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WEIGHT_DIR = Path(SETTINGS['weights_dir']) |
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TASK_ARGS = [ # (task, model, data) |
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('detect', 'yolov8n', 'coco8.yaml'), ('segment', 'yolov8n-seg', 'coco8-seg.yaml'), |
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('classify', 'yolov8n-cls', 'imagenet10'), ('pose', 'yolov8n-pose', 'coco8-pose.yaml')] |
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EXPORT_ARGS = [ # (model, format) |
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('yolov8n', 'torchscript'), ('yolov8n-seg', 'torchscript'), ('yolov8n-cls', 'torchscript'), |
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('yolov8n-pose', 'torchscript')] |
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def run(cmd): |
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# Run a subprocess command with check=True |
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subprocess.run(cmd.split(), check=True) |
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def test_special_modes(): |
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run('yolo checks') |
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run('yolo settings') |
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run('yolo help') |
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@pytest.mark.parametrize('task,model,data', TASK_ARGS) |
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def test_train(task, model, data): |
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run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 cache=disk') |
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@pytest.mark.parametrize('task,model,data', TASK_ARGS) |
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def test_val(task, model, data): |
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run(f'yolo val {task} model={model}.pt data={data} imgsz=32') |
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@pytest.mark.parametrize('task,model,data', TASK_ARGS) |
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def test_predict(task, model, data): |
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run(f"yolo predict model={model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt") |
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if ONLINE: |
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32') |
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32') |
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32') |
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@pytest.mark.parametrize('model,format', EXPORT_ARGS) |
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def test_export(model, format): |
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run(f'yolo export model={model}.pt format={format}') |
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# Slow Tests |
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@pytest.mark.slow |
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@pytest.mark.parametrize('task,model,data', TASK_ARGS) |
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def test_train_gpu(task, model, data): |
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run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 device="0"') # single GPU |
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run(f'yolo train {task} model={model}.pt data={data} imgsz=32 epochs=1 device="0,1"') # Multi GPU
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