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

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Glenn Jocher 2023-02-17 22:26:40 +01:00 • committed by GitHub
parent 9047d737f4
commit edd3ff1669
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76 changed files with 928 additions and 935 deletions
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32
tests/test_python.py
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@ -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()