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ultralytics 8.0.44 export and task fixes (#1088)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-02-24 03:11:25 +01:00
committed by GitHub
parent fe61018975
commit 3ea659411b
32 changed files with 439 additions and 480 deletions
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9
tests/test_cli.py
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@ -3,7 +3,7 @@
import subprocess
from pathlib import Path
from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS
from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS, checks
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
CFG = 'yolov8n'
@ -49,9 +49,10 @@ 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')
if checks.check_online():
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():

41
tests/test_python.py
View File

@ -9,7 +9,7 @@ from PIL import Image
from ultralytics import YOLO
from ultralytics.yolo.data.build import load_inference_source
from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS
from ultralytics.yolo.utils import LINUX, ROOT, SETTINGS, checks
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n.pt'
CFG = 'yolov8n.yaml'
@ -49,28 +49,20 @@ def test_predict_dir():
def test_predict_img():
model = YOLO(MODEL)
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
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'
output = model(source=[img, img], save=True, stream=True) # stream
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'
# test multiple source
imgs = [
SOURCE, # filename
im = cv2.imread(str(SOURCE))
assert len(model(source=Image.open(SOURCE), save=True, verbose=True)) == 1 # PIL
assert len(model(source=im, save=True, save_txt=True)) == 1 # ndarray
assert len(model(source=[im, im], save=True, save_txt=True)) == 2 # batch
assert len(list(model(source=[im, im], save=True, stream=True))) == 2 # stream
assert len(model(torch.zeros(320, 640, 3).numpy())) == 1 # tensor to numpy
batch = [
str(SOURCE), # filename
Path(SOURCE), # Path
'https://ultralytics.com/images/zidane.jpg', # URI
'https://ultralytics.com/images/zidane.jpg' if checks.check_online() else SOURCE, # URI
cv2.imread(str(SOURCE)), # OpenCV
Image.open(SOURCE), # PIL
np.zeros((320, 640, 3))] # numpy
output = model(imgs)
assert len(output) == 6, 'predict test failed!'
assert len(model(batch)) == len(batch) # multiple sources in a batch
def test_predict_grey_and_4ch():
@ -85,6 +77,11 @@ def test_val():
model.val(data='coco8.yaml', imgsz=32)
def test_val_scratch():
model = YOLO(CFG)
model.val(data='coco8.yaml', imgsz=32)
def test_train_scratch():
model = YOLO(CFG)
model.train(data='coco8.yaml', epochs=1, imgsz=32)
@ -103,6 +100,12 @@ def test_export_torchscript():
YOLO(f)(SOURCE) # exported model inference
def test_export_torchscript_scratch():
model = YOLO(CFG)
f = model.export(format='torchscript')
YOLO(f)(SOURCE) # exported model inference
def test_export_onnx():
model = YOLO(MODEL)
f = model.export(format='onnx')