Pip debug fixes (#139)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -3,56 +3,50 @@ import torch
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from ultralytics import YOLO
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from ultralytics.yolo.utils import ROOT
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MODEL = ROOT / 'weights/yolov8n.pt'
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CFG = 'yolov8n.yaml'
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def test_model_forward():
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model = YOLO("yolov8n.yaml")
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model = YOLO(CFG)
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img = torch.rand(1, 3, 320, 320)
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model.forward(img)
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model(img)
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def test_model_info():
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model = YOLO("yolov8n.yaml")
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model = YOLO(CFG)
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model.info()
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model = YOLO("yolov8n.pt")
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model = YOLO(MODEL)
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model.info(verbose=True)
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def test_model_fuse():
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model = YOLO("yolov8n.yaml")
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model = YOLO(CFG)
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model.fuse()
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model = YOLO("yolov8n.pt")
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model = YOLO(MODEL)
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model.fuse()
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def test_predict_dir():
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model = YOLO("yolov8n.pt")
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model = YOLO(MODEL)
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model.predict(source=ROOT / "assets")
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def test_val():
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model = YOLO("yolov8n.pt")
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model = YOLO(MODEL)
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model.val(data="coco128.yaml", imgsz=32)
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def test_train_resume():
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model = YOLO("yolov8n.yaml")
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model.train(epochs=1, imgsz=32, data="coco128.yaml")
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try:
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model.resume(task="detect")
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except AssertionError:
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print("Successfully caught resume assert!")
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def test_train_scratch():
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model = YOLO("yolov8n.yaml")
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model = YOLO(CFG)
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model.train(data="coco128.yaml", epochs=1, imgsz=32)
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img = torch.rand(1, 3, 320, 320)
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model(img)
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def test_train_pretrained():
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model = YOLO("yolov8n.pt")
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model = YOLO(MODEL)
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model.train(data="coco128.yaml", epochs=1, imgsz=32)
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img = torch.rand(1, 3, 320, 320)
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model(img)
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@ -77,27 +71,27 @@ def test_export_torchscript():
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from ultralytics.yolo.engine.exporter import export_formats
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print(export_formats())
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model = YOLO("yolov8n.yaml")
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model = YOLO(MODEL)
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model.export(format='torchscript')
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def test_export_onnx():
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model = YOLO("yolov8n.yaml")
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model = YOLO(MODEL)
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model.export(format='onnx')
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def test_export_openvino():
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model = YOLO("yolov8n.yaml")
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model = YOLO(MODEL)
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model.export(format='openvino')
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def test_export_coreml():
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model = YOLO("yolov8n.yaml")
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model = YOLO(MODEL)
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model.export(format='coreml')
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def test_export_paddle():
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model = YOLO("yolov8n.yaml")
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model = YOLO(MODEL)
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model.export(format='paddle')
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