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Update .pre-commit-config.yaml (#1026)

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This commit is contained in:
Glenn Jocher
2023-02-17 22:26:40 +01:00
committed by GitHub
parent 9047d737f4
commit edd3ff1669
76 changed files with 928 additions and 935 deletions
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6
tests/test_cli.py
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@ -49,9 +49,9 @@ def test_val_classify():
# Predict checks -------------------------------------------------------------------------------------------------------
def test_predict_detect():
run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32")
run(f"yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32")
run(f"yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32")
run(f"yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32")
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32')
def test_predict_segment():

42
tests/test_engine.py
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@ -11,12 +11,12 @@ CFG_SEG = 'yolov8n-seg.yaml'
CFG_CLS = 'squeezenet1_0'
CFG = get_cfg(DEFAULT_CFG)
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
SOURCE = ROOT / "assets"
SOURCE = ROOT / 'assets'
def test_detect():
overrides = {"data": "coco8.yaml", "model": CFG_DET, "imgsz": 32, "epochs": 1, "save": False}
CFG.data = "coco8.yaml"
overrides = {'data': 'coco8.yaml', 'model': CFG_DET, 'imgsz': 32, 'epochs': 1, 'save': False}
CFG.data = 'coco8.yaml'
# Trainer
trainer = detect.DetectionTrainer(overrides=overrides)
@ -27,24 +27,24 @@ def test_detect():
val(model=trainer.best) # validate best.pt
# Predictor
pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
result = pred(source=SOURCE, model=f"{MODEL}.pt")
assert len(result), "predictor test failed"
pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=f'{MODEL}.pt')
assert len(result), 'predictor test failed'
overrides["resume"] = trainer.last
overrides['resume'] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f"Expected exception caught: {e}")
print(f'Expected exception caught: {e}')
return
Exception("Resume test failed!")
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"
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
@ -57,25 +57,25 @@ def test_segment():
val(model=trainer.best) # validate best.pt
# Predictor
pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
result = pred(source=SOURCE, model=f"{MODEL}-seg.pt")
assert len(result) == 2, "predictor test failed"
pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
assert len(result) == 2, 'predictor test failed'
# Test resume
overrides["resume"] = trainer.last
overrides['resume'] = trainer.last
trainer = segment.SegmentationTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f"Expected exception caught: {e}")
print(f'Expected exception caught: {e}')
return
Exception("Resume test failed!")
Exception('Resume test failed!')
def test_classify():
overrides = {"data": "mnist160", "model": "yolov8n-cls.yaml", "imgsz": 32, "epochs": 1, "batch": 64, "save": False}
CFG.data = "mnist160"
overrides = {'data': 'mnist160', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'batch': 64, 'save': False}
CFG.data = 'mnist160'
CFG.imgsz = 32
CFG.batch = 64
# YOLO(CFG_SEG).train(**overrides) # works
@ -89,6 +89,6 @@ def test_classify():
val(model=trainer.best)
# Predictor
pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=trainer.best)
assert len(result) == 2, "predictor test failed"
assert len(result) == 2, 'predictor test failed'

32
tests/test_python.py
View File

@ -37,24 +37,24 @@ def test_model_fuse():
def test_predict_dir():
model = YOLO(MODEL)
model(source=ROOT / "assets")
model(source=ROOT / 'assets')
def test_predict_img():
model = YOLO(MODEL)
img = Image.open(str(SOURCE))
output = model(source=img, save=True, verbose=True) # PIL
assert len(output) == 1, "predict test failed"
assert len(output) == 1, 'predict test failed'
img = cv2.imread(str(SOURCE))
output = model(source=img, save=True, save_txt=True) # ndarray
assert len(output) == 1, "predict test failed"
assert len(output) == 1, 'predict test failed'
output = model(source=[img, img], save=True, save_txt=True) # batch
assert len(output) == 2, "predict test failed"
assert len(output) == 2, 'predict test failed'
output = model(source=[img, img], save=True, stream=True) # stream
assert len(list(output)) == 2, "predict test failed"
assert len(list(output)) == 2, 'predict test failed'
tens = torch.zeros(320, 640, 3)
output = model(tens.numpy())
assert len(output) == 1, "predict test failed"
assert len(output) == 1, 'predict test failed'
# test multiple source
imgs = [
SOURCE, # filename
@ -64,23 +64,23 @@ def test_predict_img():
Image.open(SOURCE), # PIL
np.zeros((320, 640, 3))] # numpy
output = model(imgs)
assert len(output) == 6, "predict test failed!"
assert len(output) == 6, 'predict test failed!'
def test_val():
model = YOLO(MODEL)
model.val(data="coco8.yaml", imgsz=32)
model.val(data='coco8.yaml', imgsz=32)
def test_train_scratch():
model = YOLO(CFG)
model.train(data="coco8.yaml", epochs=1, imgsz=32)
model.train(data='coco8.yaml', epochs=1, imgsz=32)
model(SOURCE)
def test_train_pretrained():
model = YOLO(MODEL)
model.train(data="coco8.yaml", epochs=1, imgsz=32)
model.train(data='coco8.yaml', epochs=1, imgsz=32)
model(SOURCE)
@ -139,10 +139,10 @@ def test_all_model_yamls():
def test_workflow():
model = YOLO(MODEL)
model.train(data="coco8.yaml", epochs=1, imgsz=32)
model.train(data='coco8.yaml', epochs=1, imgsz=32)
model.val()
model.predict(SOURCE)
model.export(format="onnx") # export a model to ONNX format
model.export(format='onnx') # export a model to ONNX format
def test_predict_callback_and_setup():
@ -154,8 +154,8 @@ def test_predict_callback_and_setup():
bs = [predictor.dataset.bs for _ in range(len(path))]
predictor.results = zip(predictor.results, im0s, bs)
model = YOLO("yolov8n.pt")
model.add_callback("on_predict_batch_end", on_predict_batch_end)
model = YOLO('yolov8n.pt')
model.add_callback('on_predict_batch_end', on_predict_batch_end)
dataset = load_inference_source(source=SOURCE, transforms=model.transforms)
bs = dataset.bs # noqa access predictor properties
@ -168,8 +168,8 @@ def test_predict_callback_and_setup():
def test_result():
model = YOLO("yolov8n-seg.pt")
img = str(ROOT / "assets/bus.jpg")
model = YOLO('yolov8n-seg.pt')
img = str(ROOT / 'assets/bus.jpg')
res = model([img, img])
res[0].numpy()
res[0].cpu().numpy()