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124 lines
4.2 KiB
124 lines
4.2 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from pathlib import Path |
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from ultralytics import YOLO |
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from ultralytics.cfg import get_cfg |
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from ultralytics.engine.exporter import Exporter |
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from ultralytics.models.yolo import classify, detect, segment |
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from ultralytics.utils import DEFAULT_CFG, ROOT, SETTINGS |
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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_func(model=None): |
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print('callback test passed') |
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def test_export(): |
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exporter = Exporter() |
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exporter.add_callback('on_export_start', test_func) |
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assert test_func in exporter.callbacks['on_export_start'], 'callback test failed' |
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f = exporter(model=YOLO(CFG_DET).model) |
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YOLO(f)(SOURCE) # exported model inference |
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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.add_callback('on_train_start', test_func) |
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed' |
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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.add_callback('on_val_start', test_func) |
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assert test_func in val.callbacks['on_val_start'], 'callback test failed' |
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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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pred.add_callback('on_predict_start', test_func) |
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed' |
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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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# 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.add_callback('on_train_start', test_func) |
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed' |
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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.add_callback('on_val_start', test_func) |
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assert test_func in val.callbacks['on_val_start'], 'callback test failed' |
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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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pred.add_callback('on_predict_start', test_func) |
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed' |
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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 = {'data': 'imagenet10', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'save': False} |
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CFG.data = 'imagenet10' |
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CFG.imgsz = 32 |
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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.add_callback('on_train_start', test_func) |
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed' |
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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.add_callback('on_val_start', test_func) |
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assert test_func in val.callbacks['on_val_start'], 'callback test failed' |
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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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pred.add_callback('on_predict_start', test_func) |
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed' |
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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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