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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import sys
from unittest import mock
from tests import MODEL
from ultralytics import YOLO
from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
def test_func(*args): # noqa
"""Test function callback for evaluating YOLO model performance metrics."""
print("callback test passed")
def test_export():
"""Tests the model exporting function by adding a callback and asserting its execution."""
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("yolo11n.yaml").model)
YOLO(f)(ASSETS) # exported model inference
def test_detect():
"""Test YOLO object detection training, validation, and prediction functionality."""
overrides = {"data": "coco8.yaml", "model": "yolo11n.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8.yaml"
cfg.imgsz = 32
# 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"
# Confirm there is no issue with sys.argv being empty.
with mock.patch.object(sys, "argv", []):
result = pred(source=ASSETS, model=MODEL)
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():
"""Tests image segmentation training, validation, and prediction pipelines using YOLO models."""
overrides = {"data": "coco8-seg.yaml", "model": "yolo11n-seg.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8-seg.yaml"
cfg.imgsz = 32
# 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=ASSETS, model=WEIGHTS_DIR / "yolo11n-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():
"""Test image classification including training, validation, and prediction phases."""
overrides = {"data": "imagenet10", "model": "yolo11n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
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=ASSETS, model=trainer.best)
assert len(result), "predictor test failed"
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