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95 lines
2.6 KiB
95 lines
2.6 KiB
from ultralytics import YOLO
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT
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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_config(DEFAULT_CONFIG)
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SOURCE = ROOT / "assets"
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def test_detect():
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overrides = {"data": "coco128.yaml", "model": CFG_DET, "imgsz": 32, "epochs": 1, "save": False}
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CFG.data = "coco128.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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trained_model = trainer.best
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# Validator
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val = detect.DetectionValidator(args=CFG)
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val(model=trained_model)
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# predictor
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pred = detect.DetectionPredictor(overrides={"imgsz": [640, 640]})
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pred(source=SOURCE, model=trained_model)
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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": "coco128-seg.yaml", "model": CFG_SEG, "imgsz": 32, "epochs": 1, "save": False}
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CFG.data = "coco128-seg.yaml"
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CFG.v5loader = False
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# YOLO(CFG_SEG).train(**overrides) # This 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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trained_model = trainer.best
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# Validator
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val = segment.SegmentationValidator(args=CFG)
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val(model=trained_model)
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# predictor
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pred = segment.SegmentationPredictor(overrides={"imgsz": [640, 640]})
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pred(source=SOURCE, model=trained_model)
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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": "imagenette160",
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"model": "squeezenet1_0",
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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 = "imagenette160"
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CFG.imgsz = 32
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CFG.batch = 64
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# YOLO(CFG_SEG).train(**overrides) # This 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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trained_model = trainer.best
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# Validator
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val = classify.ClassificationValidator(args=CFG)
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val(model=trained_model)
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# predictor
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pred = classify.ClassificationPredictor(overrides={"imgsz": [640, 640]})
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pred(source=SOURCE, model=trained_model)
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