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99 lines
2.8 KiB
99 lines
2.8 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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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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p = pred(source=SOURCE, model="yolov8n.pt") |
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assert len(p) == 2, "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": "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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p = pred(source=SOURCE, model="yolov8n-seg.pt") |
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assert len(p) == 2, "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": "imagenette160", |
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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 = "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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p = pred(source=SOURCE, model=trained_model) |
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assert len(p) == 2, "Predictor test failed!"
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