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128 lines
4.5 KiB
128 lines
4.5 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 = [
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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 copy-cfg')
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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={WEIGHT_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={WEIGHT_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
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@pytest.mark.skipif(not ONLINE, reason='environment is offline')
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_predict_online(task, model, data):
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mode = 'track' if task in ('detect', 'segment', 'pose') else 'predict' # mode for video inference
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run(f'yolo predict model={WEIGHT_DIR / model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
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run(f'yolo {mode} model={WEIGHT_DIR / model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32'
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)
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# Run Python YouTube tracking because CLI is broken. TODO: fix CLI YouTube
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# run(f'yolo {mode} model={model}.pt source=https://youtu.be/G17sBkb38XQ imgsz=32 tracker=bytetrack.yaml')
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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={WEIGHT_DIR / model}.pt format={format} imgsz=32')
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# Test SAM, RTDETR Models
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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 val {task} model={model} data={data} imgsz=640')
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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='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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model = FastSAM('FastSAM-s.pt') # or FastSAM-x.pt
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# Run inference on an image
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everything_results = 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('mobile_sam.pt')
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# Predict a segment based on a point prompt
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model.predict(ROOT / 'assets/zidane.jpg', points=[900, 370], labels=[1])
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# Predict a segment based on a box prompt
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model.predict(ROOT / 'assets/zidane.jpg', bboxes=[439, 437, 524, 709])
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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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