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# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
from ultralytics import YOLO
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, SETTINGS
from ultralytics.yolo.v8 import classify, detect, segment
CFG_DET = 'yolov8n.yaml'
CFG_SEG = 'yolov8n-seg.yaml'
CFG_CLS = 'squeezenet1_0'
CFG = get_cfg(DEFAULT_CFG)
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
SOURCE = ROOT / 'assets'
def test_func(model=None):
print('callback test passed')
def test_export():
exporter = Exporter()
exporter.add_callback('on_export_start', test_func)
assert test_func in exporter.callbacks['on_export_start'], 'callback test failed'
f = exporter(model=YOLO(CFG_DET).model)
YOLO(f)(SOURCE) # exported model inference
def test_detect():
overrides = {'data': 'coco8.yaml', 'model': CFG_DET, 'imgsz': 32, 'epochs': 1, 'save': False}
CFG.data = 'coco8.yaml'
# Trainer
trainer = detect.DetectionTrainer(overrides=overrides)
trainer.add_callback('on_train_start', test_func)
assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
trainer.train()
# Validator
val = detect.DetectionValidator(args=CFG)
val.add_callback('on_val_start', test_func)
assert test_func in val.callbacks['on_val_start'], 'callback test failed'
val(model=trainer.best) # validate best.pt
# Predictor
pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=f'{MODEL}.pt')
assert len(result), 'predictor test failed'
overrides['resume'] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f'Expected exception caught: {e}')
return
Exception('Resume test failed!')
def test_segment():
overrides = {'data': 'coco8-seg.yaml', 'model': CFG_SEG, 'imgsz': 32, 'epochs': 1, 'save': False}
CFG.data = 'coco8-seg.yaml'
CFG.v5loader = False
# YOLO(CFG_SEG).train(**overrides) # works
# trainer
trainer = segment.SegmentationTrainer(overrides=overrides)
trainer.add_callback('on_train_start', test_func)
assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
trainer.train()
# Validator
val = segment.SegmentationValidator(args=CFG)
val.add_callback('on_val_start', test_func)
assert test_func in val.callbacks['on_val_start'], 'callback test failed'
val(model=trainer.best) # validate best.pt
# Predictor
pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
assert len(result), 'predictor test failed'
# Test resume
overrides['resume'] = trainer.last
trainer = segment.SegmentationTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f'Expected exception caught: {e}')
return
Exception('Resume test failed!')
def test_classify():
overrides = {'data': 'imagenet10', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'save': False}
CFG.data = 'imagenet10'
CFG.imgsz = 32
# YOLO(CFG_SEG).train(**overrides) # works
# Trainer
trainer = classify.ClassificationTrainer(overrides=overrides)
trainer.add_callback('on_train_start', test_func)
assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
trainer.train()
# Validator
val = classify.ClassificationValidator(args=CFG)
val.add_callback('on_val_start', test_func)
assert test_func in val.callbacks['on_val_start'], 'callback test failed'
val(model=trainer.best)
# Predictor
pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=trainer.best)
assert len(result), 'predictor test failed'
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