xdobro23/YOLOv8-16bit
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Simplify usage, remove YOLO.new() and YOLO.load()

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This commit is contained in:
Glenn Jocher
2023-01-02 15:42:01 +01:00
parent 7b21a87da3
commit d76d7af566
5 changed files with 33 additions and 42 deletions
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2
tests/check_flops.py
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@ -4,7 +4,7 @@ from ultralytics.yolo.utils import ROOT
if __name__ == "__main__":
for m in list((ROOT / 'yolo/v8/models').rglob('*.yaml')):
try:
YOLO.new(m.name, verbose=True)
YOLO(m.name, verbose=True)
except Exception as e:
print(f'ERROR for {m}: {e}')

18
tests/test_model.py
View File

@ -4,7 +4,7 @@ from ultralytics import YOLO
def test_model_init():
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
model.info()
try:
YOLO()
@ -14,38 +14,38 @@ def test_model_init():
def test_model_forward():
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512)
model.forward(img)
model(img)
def test_model_info():
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
model.info()
model = model.load("best.pt")
model.info(verbose=True)
def test_model_fuse():
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
model.fuse()
model.load("best.pt")
model.fuse()
def test_visualize_preds():
model = YOLO.load("best.pt")
model = YOLO("best.pt")
model.predict(source="ultralytics/assets")
def test_val():
model = YOLO.load("best.pt")
model = YOLO("best.pt")
model.val(data="coco128.yaml", imgsz=32)
def test_model_resume():
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
model.train(epochs=1, imgsz=32, data="coco128.yaml")
try:
model.resume(task="detect")
@ -54,7 +54,7 @@ def test_model_resume():
def test_model_train_pretrained():
model = YOLO.load("best.pt")
model = YOLO("best.pt")
model.train(data="coco128.yaml", epochs=1, imgsz=32)
model = model.new("yolov8n.yaml")
model.train(data="coco128.yaml", epochs=1, imgsz=32)
@ -83,7 +83,7 @@ def test_exports():
print(export_formats())
model = YOLO.new("yolov8n.yaml")
model = YOLO("yolov8n.yaml")
model.export(format='torchscript')
model.export(format='onnx')
model.export(format='openvino')