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101 lines
2.8 KiB
101 lines
2.8 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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from pathlib import Path
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, SETTINGS
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from ultralytics.yolo.v8 import classify, detect, segment
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CFG_DET = 'yolov8n.yaml'
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CFG_SEG = 'yolov8n-seg.yaml'
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CFG_CLS = 'squeezenet1_0'
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CFG = get_cfg(DEFAULT_CFG)
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MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
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SOURCE = ROOT / 'assets'
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def test_detect():
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overrides = {'data': 'coco8.yaml', 'model': CFG_DET, 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'coco8.yaml'
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# Trainer
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trainer = detect.DetectionTrainer(overrides=overrides)
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trainer.train()
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# Validator
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val = detect.DetectionValidator(args=CFG)
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
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result = pred(source=SOURCE, model=f'{MODEL}.pt')
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assert len(result), 'predictor test failed'
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overrides['resume'] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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try:
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trainer.train()
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except Exception as e:
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print(f'Expected exception caught: {e}')
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return
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Exception('Resume test failed!')
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def test_segment():
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overrides = {'data': 'coco8-seg.yaml', 'model': CFG_SEG, 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'coco8-seg.yaml'
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CFG.v5loader = False
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# YOLO(CFG_SEG).train(**overrides) # works
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# trainer
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trainer = segment.SegmentationTrainer(overrides=overrides)
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trainer.train()
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# Validator
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val = segment.SegmentationValidator(args=CFG)
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
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result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
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assert len(result), 'predictor test failed'
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# Test resume
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overrides['resume'] = trainer.last
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trainer = segment.SegmentationTrainer(overrides=overrides)
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try:
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trainer.train()
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except Exception as e:
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print(f'Expected exception caught: {e}')
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return
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Exception('Resume test failed!')
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def test_classify():
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overrides = {
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'data': 'imagenet10',
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'model': 'yolov8n-cls.yaml',
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'imgsz': 32,
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'epochs': 1,
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'batch': 64,
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'save': False}
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CFG.data = 'imagenet10'
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CFG.imgsz = 32
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CFG.batch = 64
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# YOLO(CFG_SEG).train(**overrides) # works
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# Trainer
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trainer = classify.ClassificationTrainer(overrides=overrides)
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trainer.train()
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# Validator
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val = classify.ClassificationValidator(args=CFG)
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val(model=trainer.best)
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# Predictor
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pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
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result = pred(source=SOURCE, model=trainer.best)
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assert len(result), 'predictor test failed'
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