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ultralytics 8.0.43 optimized Results class and fixes (#1069)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-02-21 20:37:59 +01:00
committed by GitHub
parent f2a7a29e53
commit fe61018975
22 changed files with 180 additions and 102 deletions
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4
tests/test_engine.py
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@ -59,7 +59,7 @@ def test_segment():
# Predictor
pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
assert len(result) == 2, 'predictor test failed'
assert len(result), 'predictor test failed'
# Test resume
overrides['resume'] = trainer.last
@ -97,4 +97,4 @@ def test_classify():
# Predictor
pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
result = pred(source=SOURCE, model=trainer.best)
assert len(result) == 2, 'predictor test failed'
assert len(result), 'predictor test failed'

18
tests/test_python.py
View File

@ -14,6 +14,13 @@ from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n.pt'
CFG = 'yolov8n.yaml'
SOURCE = ROOT / 'assets/bus.jpg'
SOURCE_GREYSCALE = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
SOURCE_RGBA = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
# Convert SOURCE to greyscale and 4-ch
im = Image.open(SOURCE)
im.convert('L').save(SOURCE_GREYSCALE) # greyscale
im.convert('RGBA').save(SOURCE_RGBA) # 4-ch PNG with alpha
def test_model_forward():
@ -42,8 +49,7 @@ def test_predict_dir():
def test_predict_img():
model = YOLO(MODEL)
img = Image.open(str(SOURCE))
output = model(source=img, save=True, verbose=True) # PIL
output = model(source=Image.open(SOURCE), save=True, verbose=True) # PIL
assert len(output) == 1, 'predict test failed'
img = cv2.imread(str(SOURCE))
output = model(source=img, save=True, save_txt=True) # ndarray
@ -67,6 +73,13 @@ def test_predict_img():
assert len(output) == 6, 'predict test failed!'
def test_predict_grey_and_4ch():
model = YOLO(MODEL)
for f in SOURCE_RGBA, SOURCE_GREYSCALE:
for source in Image.open(f), cv2.imread(str(f)), f:
model(source, save=True, verbose=True)
def test_val():
model = YOLO(MODEL)
model.val(data='coco8.yaml', imgsz=32)
@ -151,6 +164,7 @@ def test_predict_callback_and_setup():
# results -> List[batch_size]
path, _, im0s, _, _ = predictor.batch
# print('on_predict_batch_end', im0s[0].shape)
im0s = im0s if isinstance(im0s, list) else [im0s]
bs = [predictor.dataset.bs for _ in range(len(path))]
predictor.results = zip(predictor.results, im0s, bs)