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118 lines
3.8 KiB
118 lines
3.8 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 ROOT, SETTINGS |
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WEIGHTS_DIR = Path(SETTINGS['weights_dir']) |
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TASK_ARGS = [ |
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('detect', 'yolov8n', 'coco8.yaml'), |
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('segment', 'yolov8n-seg', 'coco8-seg.yaml'), |
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('classify', 'yolov8n-cls', 'imagenet10'), |
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('pose', 'yolov8n-pose', 'coco8-pose.yaml'), ] # (task, model, data) |
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EXPORT_ARGS = [ |
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('yolov8n', 'torchscript'), |
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('yolov8n-seg', 'torchscript'), |
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('yolov8n-cls', 'torchscript'), |
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('yolov8n-pose', 'torchscript'), ] # (model, format) |
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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 help') |
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run('yolo checks') |
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run('yolo version') |
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run('yolo settings reset') |
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run('yolo cfg') |
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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={WEIGHTS_DIR / 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={WEIGHTS_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt") |
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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={WEIGHTS_DIR / model}.pt format={format} imgsz=32') |
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def test_rtdetr(task='detect', model='yolov8n-rtdetr.yaml', data='coco8.yaml'): |
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# Warning: MUST use imgsz=640 |
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run(f'yolo train {task} model={model} data={data} imgsz=640 epochs=1 cache=disk') |
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run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt") |
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def test_fastsam(task='segment', model=WEIGHTS_DIR / 'FastSAM-s.pt', data='coco8-seg.yaml'): |
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source = ROOT / 'assets/bus.jpg' |
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run(f'yolo segment val {task} model={model} data={data} imgsz=32') |
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run(f'yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt') |
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from ultralytics import FastSAM |
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from ultralytics.models.fastsam import FastSAMPrompt |
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# Create a FastSAM model |
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sam_model = FastSAM(model) # or FastSAM-x.pt |
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# Run inference on an image |
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everything_results = sam_model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9) |
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# Everything prompt |
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prompt_process = FastSAMPrompt(source, everything_results, device='cpu') |
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ann = prompt_process.everything_prompt() |
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# Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2] |
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ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300]) |
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# Text prompt |
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ann = prompt_process.text_prompt(text='a photo of a dog') |
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# Point prompt |
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# points default [[0,0]] [[x1,y1],[x2,y2]] |
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# point_label default [0] [1,0] 0:background, 1:foreground |
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ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1]) |
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prompt_process.plot(annotations=ann, output='./') |
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def test_mobilesam(): |
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from ultralytics import SAM |
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# Load the model |
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model = SAM(WEIGHTS_DIR / 'mobile_sam.pt') |
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# Source |
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source = ROOT / 'assets/zidane.jpg' |
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# Predict a segment based on a point prompt |
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model.predict(source, points=[900, 370], labels=[1]) |
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# Predict a segment based on a box prompt |
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model.predict(source, bboxes=[439, 437, 524, 709]) |
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# Predict all |
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# model(source) |
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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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