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Omit ultralytics/utils/callbacks from coverage (#4345)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-08-14 03:25:51 +02:00
committed by GitHub
parent d47718c367
commit c940d29d4f
18 changed files with 63 additions and 36 deletions
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24
tests/test_cli.py
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@ -53,9 +53,9 @@ def test_predict(task, model, data):
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_predict_online(task, model, data):
mode = 'track' if task in ('detect', 'segment', 'pose') else 'predict' # mode for video inference
run(f'yolo predict model={WEIGHT_DIR / model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo {mode} model={WEIGHT_DIR / model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32'
)
model = WEIGHT_DIR / model
run(f'yolo predict model={model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo {mode} model={model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
# Run Python YouTube tracking because CLI is broken. TODO: fix CLI YouTube
# run(f'yolo {mode} model={model}.pt source=https://youtu.be/G17sBkb38XQ imgsz=32 tracker=bytetrack.yaml')
@ -74,7 +74,7 @@ def test_rtdetr(task='detect', model='yolov8n-rtdetr.yaml', data='coco8.yaml'):
run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt")
def test_fastsam(task='segment', model='FastSAM-s.pt', data='coco8-seg.yaml'):
def test_fastsam(task='segment', model=WEIGHT_DIR / 'FastSAM-s.pt', data='coco8-seg.yaml'):
source = ROOT / 'assets/bus.jpg'
run(f'yolo segment val {task} model={model} data={data} imgsz=32')
@ -84,10 +84,10 @@ def test_fastsam(task='segment', model='FastSAM-s.pt', data='coco8-seg.yaml'):
from ultralytics.models.fastsam import FastSAMPrompt
# Create a FastSAM model
model = FastSAM('FastSAM-s.pt') # or FastSAM-x.pt
sam_model = FastSAM(model) # or FastSAM-x.pt
# Run inference on an image
everything_results = model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
everything_results = sam_model(source, device='cpu', retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
# Everything prompt
prompt_process = FastSAMPrompt(source, everything_results, device='cpu')
@ -110,13 +110,19 @@ def test_mobilesam():
from ultralytics import SAM
# Load the model
model = SAM('mobile_sam.pt')
model = SAM(WEIGHT_DIR / 'mobile_sam.pt')
# Source
source = ROOT / 'assets/zidane.jpg'
# Predict a segment based on a point prompt
model.predict(ROOT / 'assets/zidane.jpg', points=[900, 370], labels=[1])
model.predict(source, points=[900, 370], labels=[1])
# Predict a segment based on a box prompt
model.predict(ROOT / 'assets/zidane.jpg', bboxes=[439, 437, 524, 709])
model.predict(source, bboxes=[439, 437, 524, 709])
# Predict all
# model(source)
# Slow Tests

4
tests/test_python.py
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@ -212,8 +212,8 @@ def test_results():
for r in results:
r = r.cpu().numpy()
r = r.to(device='cpu', dtype=torch.float32)
r.save_txt(txt_file='label.txt', save_conf=True)
r.save_crop(save_dir='crops/')
r.save_txt(txt_file='runs/tests/label.txt', save_conf=True)
r.save_crop(save_dir='runs/tests/crops/')
r.tojson(normalize=True)
r.plot(pil=True)
r.plot(conf=True, boxes=True)