Add new @Retry() decorator (#7854)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
5f00fbd227
commit
1435f0e9de
9 changed files with 372 additions and 269 deletions
|
|
@ -14,15 +14,27 @@ from torchvision.transforms import ToTensor
|
|||
from ultralytics import RTDETR, YOLO
|
||||
from ultralytics.cfg import TASK2DATA
|
||||
from ultralytics.data.build import load_inference_source
|
||||
from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_PATH, LINUX, MACOS, ONLINE, ROOT, WEIGHTS_DIR, WINDOWS,
|
||||
checks, is_dir_writeable)
|
||||
from ultralytics.utils import (
|
||||
ASSETS,
|
||||
DEFAULT_CFG,
|
||||
DEFAULT_CFG_PATH,
|
||||
LINUX,
|
||||
MACOS,
|
||||
ONLINE,
|
||||
ROOT,
|
||||
WEIGHTS_DIR,
|
||||
WINDOWS,
|
||||
Retry,
|
||||
checks,
|
||||
is_dir_writeable,
|
||||
)
|
||||
from ultralytics.utils.downloads import download
|
||||
from ultralytics.utils.torch_utils import TORCH_1_9
|
||||
|
||||
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
|
||||
CFG = 'yolov8n.yaml'
|
||||
SOURCE = ASSETS / 'bus.jpg'
|
||||
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
|
||||
MODEL = WEIGHTS_DIR / "path with spaces" / "yolov8n.pt" # test spaces in path
|
||||
CFG = "yolov8n.yaml"
|
||||
SOURCE = ASSETS / "bus.jpg"
|
||||
TMP = (ROOT / "../tests/tmp").resolve() # temp directory for test files
|
||||
IS_TMP_WRITEABLE = is_dir_writeable(TMP)
|
||||
|
||||
|
||||
|
|
@ -40,9 +52,9 @@ def test_model_methods():
|
|||
model.info(verbose=True, detailed=True)
|
||||
model = model.reset_weights()
|
||||
model = model.load(MODEL)
|
||||
model.to('cpu')
|
||||
model.to("cpu")
|
||||
model.fuse()
|
||||
model.clear_callback('on_train_start')
|
||||
model.clear_callback("on_train_start")
|
||||
model.reset_callbacks()
|
||||
|
||||
# Model properties
|
||||
|
|
@ -61,23 +73,23 @@ def test_model_profile():
|
|||
_ = model.predict(im, profile=True)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason='directory is not writeable')
|
||||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
|
||||
def test_predict_txt():
|
||||
"""Test YOLO predictions with sources (file, dir, glob, recursive glob) specified in a text file."""
|
||||
txt_file = TMP / 'sources.txt'
|
||||
with open(txt_file, 'w') as f:
|
||||
for x in [ASSETS / 'bus.jpg', ASSETS, ASSETS / '*', ASSETS / '**/*.jpg']:
|
||||
f.write(f'{x}\n')
|
||||
txt_file = TMP / "sources.txt"
|
||||
with open(txt_file, "w") as f:
|
||||
for x in [ASSETS / "bus.jpg", ASSETS, ASSETS / "*", ASSETS / "**/*.jpg"]:
|
||||
f.write(f"{x}\n")
|
||||
_ = YOLO(MODEL)(source=txt_file, imgsz=32)
|
||||
|
||||
|
||||
def test_predict_img():
|
||||
"""Test YOLO prediction on various types of image sources."""
|
||||
model = YOLO(MODEL)
|
||||
seg_model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
|
||||
cls_model = YOLO(WEIGHTS_DIR / 'yolov8n-cls.pt')
|
||||
pose_model = YOLO(WEIGHTS_DIR / 'yolov8n-pose.pt')
|
||||
obb_model = YOLO(WEIGHTS_DIR / 'yolov8n-obb.pt')
|
||||
seg_model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
|
||||
cls_model = YOLO(WEIGHTS_DIR / "yolov8n-cls.pt")
|
||||
pose_model = YOLO(WEIGHTS_DIR / "yolov8n-pose.pt")
|
||||
obb_model = YOLO(WEIGHTS_DIR / "yolov8n-obb.pt")
|
||||
im = cv2.imread(str(SOURCE))
|
||||
assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
|
||||
assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1 # ndarray
|
||||
|
|
@ -87,10 +99,11 @@ def test_predict_img():
|
|||
batch = [
|
||||
str(SOURCE), # filename
|
||||
Path(SOURCE), # Path
|
||||
'https://ultralytics.com/images/zidane.jpg' if ONLINE else SOURCE, # URI
|
||||
"https://ultralytics.com/images/zidane.jpg" if ONLINE else SOURCE, # URI
|
||||
cv2.imread(str(SOURCE)), # OpenCV
|
||||
Image.open(SOURCE), # PIL
|
||||
np.zeros((320, 640, 3))] # numpy
|
||||
np.zeros((320, 640, 3)),
|
||||
] # numpy
|
||||
assert len(model(batch, imgsz=32)) == len(batch) # multiple sources in a batch
|
||||
|
||||
# Test tensor inference
|
||||
|
|
@ -113,16 +126,16 @@ def test_predict_img():
|
|||
def test_predict_grey_and_4ch():
|
||||
"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images."""
|
||||
im = Image.open(SOURCE)
|
||||
directory = TMP / 'im4'
|
||||
directory = TMP / "im4"
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
source_greyscale = directory / 'greyscale.jpg'
|
||||
source_rgba = directory / '4ch.png'
|
||||
source_non_utf = directory / 'non_UTF_测试文件_tést_image.jpg'
|
||||
source_spaces = directory / 'image with spaces.jpg'
|
||||
source_greyscale = directory / "greyscale.jpg"
|
||||
source_rgba = directory / "4ch.png"
|
||||
source_non_utf = directory / "non_UTF_测试文件_tést_image.jpg"
|
||||
source_spaces = directory / "image with spaces.jpg"
|
||||
|
||||
im.convert('L').save(source_greyscale) # greyscale
|
||||
im.convert('RGBA').save(source_rgba) # 4-ch PNG with alpha
|
||||
im.convert("L").save(source_greyscale) # greyscale
|
||||
im.convert("RGBA").save(source_rgba) # 4-ch PNG with alpha
|
||||
im.save(source_non_utf) # non-UTF characters in filename
|
||||
im.save(source_spaces) # spaces in filename
|
||||
|
||||
|
|
@ -136,7 +149,8 @@ def test_predict_grey_and_4ch():
|
|||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
@Retry(times=3, delay=10)
|
||||
def test_youtube():
|
||||
"""
|
||||
Test YouTube inference.
|
||||
|
|
@ -144,11 +158,11 @@ def test_youtube():
|
|||
Marked --slow to reduce YouTube API rate limits risk.
|
||||
"""
|
||||
model = YOLO(MODEL)
|
||||
model.predict('https://youtu.be/G17sBkb38XQ', imgsz=96, save=True)
|
||||
model.predict("https://youtu.be/G17sBkb38XQ", imgsz=96, save=True)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason='directory is not writeable')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
|
||||
def test_track_stream():
|
||||
"""
|
||||
Test streaming tracking (short 10 frame video) with non-default ByteTrack tracker.
|
||||
|
|
@ -157,56 +171,56 @@ def test_track_stream():
|
|||
"""
|
||||
import yaml
|
||||
|
||||
video_url = 'https://ultralytics.com/assets/decelera_portrait_min.mov'
|
||||
video_url = "https://ultralytics.com/assets/decelera_portrait_min.mov"
|
||||
model = YOLO(MODEL)
|
||||
model.track(video_url, imgsz=160, tracker='bytetrack.yaml')
|
||||
model.track(video_url, imgsz=160, tracker='botsort.yaml', save_frames=True) # test frame saving also
|
||||
model.track(video_url, imgsz=160, tracker="bytetrack.yaml")
|
||||
model.track(video_url, imgsz=160, tracker="botsort.yaml", save_frames=True) # test frame saving also
|
||||
|
||||
# Test Global Motion Compensation (GMC) methods
|
||||
for gmc in 'orb', 'sift', 'ecc':
|
||||
with open(ROOT / 'cfg/trackers/botsort.yaml', encoding='utf-8') as f:
|
||||
for gmc in "orb", "sift", "ecc":
|
||||
with open(ROOT / "cfg/trackers/botsort.yaml", encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
tracker = TMP / f'botsort-{gmc}.yaml'
|
||||
data['gmc_method'] = gmc
|
||||
with open(tracker, 'w', encoding='utf-8') as f:
|
||||
tracker = TMP / f"botsort-{gmc}.yaml"
|
||||
data["gmc_method"] = gmc
|
||||
with open(tracker, "w", encoding="utf-8") as f:
|
||||
yaml.safe_dump(data, f)
|
||||
model.track(video_url, imgsz=160, tracker=tracker)
|
||||
|
||||
|
||||
def test_val():
|
||||
"""Test the validation mode of the YOLO model."""
|
||||
YOLO(MODEL).val(data='coco8.yaml', imgsz=32, save_hybrid=True)
|
||||
YOLO(MODEL).val(data="coco8.yaml", imgsz=32, save_hybrid=True)
|
||||
|
||||
|
||||
def test_train_scratch():
|
||||
"""Test training the YOLO model from scratch."""
|
||||
model = YOLO(CFG)
|
||||
model.train(data='coco8.yaml', epochs=2, imgsz=32, cache='disk', batch=-1, close_mosaic=1, name='model')
|
||||
model.train(data="coco8.yaml", epochs=2, imgsz=32, cache="disk", batch=-1, close_mosaic=1, name="model")
|
||||
model(SOURCE)
|
||||
|
||||
|
||||
def test_train_pretrained():
|
||||
"""Test training the YOLO model from a pre-trained state."""
|
||||
model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
|
||||
model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5, name=0)
|
||||
model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
|
||||
model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
|
||||
model(SOURCE)
|
||||
|
||||
|
||||
def test_export_torchscript():
|
||||
"""Test exporting the YOLO model to TorchScript format."""
|
||||
f = YOLO(MODEL).export(format='torchscript', optimize=False)
|
||||
f = YOLO(MODEL).export(format="torchscript", optimize=False)
|
||||
YOLO(f)(SOURCE) # exported model inference
|
||||
|
||||
|
||||
def test_export_onnx():
|
||||
"""Test exporting the YOLO model to ONNX format."""
|
||||
f = YOLO(MODEL).export(format='onnx', dynamic=True)
|
||||
f = YOLO(MODEL).export(format="onnx", dynamic=True)
|
||||
YOLO(f)(SOURCE) # exported model inference
|
||||
|
||||
|
||||
def test_export_openvino():
|
||||
"""Test exporting the YOLO model to OpenVINO format."""
|
||||
f = YOLO(MODEL).export(format='openvino')
|
||||
f = YOLO(MODEL).export(format="openvino")
|
||||
YOLO(f)(SOURCE) # exported model inference
|
||||
|
||||
|
||||
|
|
@ -214,10 +228,10 @@ def test_export_coreml():
|
|||
"""Test exporting the YOLO model to CoreML format."""
|
||||
if not WINDOWS: # RuntimeError: BlobWriter not loaded with coremltools 7.0 on windows
|
||||
if MACOS:
|
||||
f = YOLO(MODEL).export(format='coreml')
|
||||
f = YOLO(MODEL).export(format="coreml")
|
||||
YOLO(f)(SOURCE) # model prediction only supported on macOS for nms=False models
|
||||
else:
|
||||
YOLO(MODEL).export(format='coreml', nms=True)
|
||||
YOLO(MODEL).export(format="coreml", nms=True)
|
||||
|
||||
|
||||
def test_export_tflite(enabled=False):
|
||||
|
|
@ -228,7 +242,7 @@ def test_export_tflite(enabled=False):
|
|||
"""
|
||||
if enabled and LINUX:
|
||||
model = YOLO(MODEL)
|
||||
f = model.export(format='tflite')
|
||||
f = model.export(format="tflite")
|
||||
YOLO(f)(SOURCE)
|
||||
|
||||
|
||||
|
|
@ -240,7 +254,7 @@ def test_export_pb(enabled=False):
|
|||
"""
|
||||
if enabled and LINUX:
|
||||
model = YOLO(MODEL)
|
||||
f = model.export(format='pb')
|
||||
f = model.export(format="pb")
|
||||
YOLO(f)(SOURCE)
|
||||
|
||||
|
||||
|
|
@ -251,20 +265,20 @@ def test_export_paddle(enabled=False):
|
|||
Note Paddle protobuf requirements conflicting with onnx protobuf requirements.
|
||||
"""
|
||||
if enabled:
|
||||
YOLO(MODEL).export(format='paddle')
|
||||
YOLO(MODEL).export(format="paddle")
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_export_ncnn():
|
||||
"""Test exporting the YOLO model to NCNN format."""
|
||||
f = YOLO(MODEL).export(format='ncnn')
|
||||
f = YOLO(MODEL).export(format="ncnn")
|
||||
YOLO(f)(SOURCE) # exported model inference
|
||||
|
||||
|
||||
def test_all_model_yamls():
|
||||
"""Test YOLO model creation for all available YAML configurations."""
|
||||
for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
|
||||
if 'rtdetr' in m.name:
|
||||
for m in (ROOT / "cfg" / "models").rglob("*.yaml"):
|
||||
if "rtdetr" in m.name:
|
||||
if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
|
||||
_ = RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
|
||||
else:
|
||||
|
|
@ -274,10 +288,10 @@ def test_all_model_yamls():
|
|||
def test_workflow():
|
||||
"""Test the complete workflow including training, validation, prediction, and exporting."""
|
||||
model = YOLO(MODEL)
|
||||
model.train(data='coco8.yaml', epochs=1, imgsz=32, optimizer='SGD')
|
||||
model.train(data="coco8.yaml", epochs=1, imgsz=32, optimizer="SGD")
|
||||
model.val(imgsz=32)
|
||||
model.predict(SOURCE, imgsz=32)
|
||||
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():
|
||||
|
|
@ -291,34 +305,34 @@ def test_predict_callback_and_setup():
|
|||
predictor.results = zip(predictor.results, im0s, bs) # results is List[batch_size]
|
||||
|
||||
model = YOLO(MODEL)
|
||||
model.add_callback('on_predict_batch_end', on_predict_batch_end)
|
||||
model.add_callback("on_predict_batch_end", on_predict_batch_end)
|
||||
|
||||
dataset = load_inference_source(source=SOURCE)
|
||||
bs = dataset.bs # noqa access predictor properties
|
||||
results = model.predict(dataset, stream=True, imgsz=160) # source already setup
|
||||
for r, im0, bs in results:
|
||||
print('test_callback', im0.shape)
|
||||
print('test_callback', bs)
|
||||
print("test_callback", im0.shape)
|
||||
print("test_callback", bs)
|
||||
boxes = r.boxes # Boxes object for bbox outputs
|
||||
print(boxes)
|
||||
|
||||
|
||||
def test_results():
|
||||
"""Test various result formats for the YOLO model."""
|
||||
for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
|
||||
for m in "yolov8n-pose.pt", "yolov8n-seg.pt", "yolov8n.pt", "yolov8n-cls.pt":
|
||||
results = YOLO(WEIGHTS_DIR / m)([SOURCE, SOURCE], imgsz=160)
|
||||
for r in results:
|
||||
r = r.cpu().numpy()
|
||||
r = r.to(device='cpu', dtype=torch.float32)
|
||||
r.save_txt(txt_file=TMP / 'runs/tests/label.txt', save_conf=True)
|
||||
r.save_crop(save_dir=TMP / 'runs/tests/crops/')
|
||||
r = r.to(device="cpu", dtype=torch.float32)
|
||||
r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
|
||||
r.save_crop(save_dir=TMP / "runs/tests/crops/")
|
||||
r.tojson(normalize=True)
|
||||
r.plot(pil=True)
|
||||
r.plot(conf=True, boxes=True)
|
||||
print(r, len(r), r.path)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_data_utils():
|
||||
"""Test utility functions in ultralytics/data/utils.py."""
|
||||
from ultralytics.data.utils import HUBDatasetStats, autosplit
|
||||
|
|
@ -327,25 +341,25 @@ def test_data_utils():
|
|||
# from ultralytics.utils.files import WorkingDirectory
|
||||
# with WorkingDirectory(ROOT.parent / 'tests'):
|
||||
|
||||
for task in 'detect', 'segment', 'pose', 'classify':
|
||||
file = Path(TASK2DATA[task]).with_suffix('.zip') # i.e. coco8.zip
|
||||
download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False, dir=TMP)
|
||||
for task in "detect", "segment", "pose", "classify":
|
||||
file = Path(TASK2DATA[task]).with_suffix(".zip") # i.e. coco8.zip
|
||||
download(f"https://github.com/ultralytics/hub/raw/main/example_datasets/{file}", unzip=False, dir=TMP)
|
||||
stats = HUBDatasetStats(TMP / file, task=task)
|
||||
stats.get_json(save=True)
|
||||
stats.process_images()
|
||||
|
||||
autosplit(TMP / 'coco8')
|
||||
zip_directory(TMP / 'coco8/images/val') # zip
|
||||
autosplit(TMP / "coco8")
|
||||
zip_directory(TMP / "coco8/images/val") # zip
|
||||
|
||||
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_data_converter():
|
||||
"""Test dataset converters."""
|
||||
from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
|
||||
|
||||
file = 'instances_val2017.json'
|
||||
download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}', dir=TMP)
|
||||
convert_coco(labels_dir=TMP, save_dir=TMP / 'yolo_labels', use_segments=True, use_keypoints=False, cls91to80=True)
|
||||
file = "instances_val2017.json"
|
||||
download(f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}", dir=TMP)
|
||||
convert_coco(labels_dir=TMP, save_dir=TMP / "yolo_labels", use_segments=True, use_keypoints=False, cls91to80=True)
|
||||
coco80_to_coco91_class()
|
||||
|
||||
|
||||
|
|
@ -353,10 +367,12 @@ def test_data_annotator():
|
|||
"""Test automatic data annotation."""
|
||||
from ultralytics.data.annotator import auto_annotate
|
||||
|
||||
auto_annotate(ASSETS,
|
||||
det_model=WEIGHTS_DIR / 'yolov8n.pt',
|
||||
sam_model=WEIGHTS_DIR / 'mobile_sam.pt',
|
||||
output_dir=TMP / 'auto_annotate_labels')
|
||||
auto_annotate(
|
||||
ASSETS,
|
||||
det_model=WEIGHTS_DIR / "yolov8n.pt",
|
||||
sam_model=WEIGHTS_DIR / "mobile_sam.pt",
|
||||
output_dir=TMP / "auto_annotate_labels",
|
||||
)
|
||||
|
||||
|
||||
def test_events():
|
||||
|
|
@ -366,7 +382,7 @@ def test_events():
|
|||
events = Events()
|
||||
events.enabled = True
|
||||
cfg = copy(DEFAULT_CFG) # does not require deepcopy
|
||||
cfg.mode = 'test'
|
||||
cfg.mode = "test"
|
||||
events(cfg)
|
||||
|
||||
|
||||
|
|
@ -375,10 +391,10 @@ def test_cfg_init():
|
|||
from ultralytics.cfg import check_dict_alignment, copy_default_cfg, smart_value
|
||||
|
||||
with contextlib.suppress(SyntaxError):
|
||||
check_dict_alignment({'a': 1}, {'b': 2})
|
||||
check_dict_alignment({"a": 1}, {"b": 2})
|
||||
copy_default_cfg()
|
||||
(Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')).unlink(missing_ok=False)
|
||||
[smart_value(x) for x in ['none', 'true', 'false']]
|
||||
(Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")).unlink(missing_ok=False)
|
||||
[smart_value(x) for x in ["none", "true", "false"]]
|
||||
|
||||
|
||||
def test_utils_init():
|
||||
|
|
@ -393,12 +409,12 @@ def test_utils_init():
|
|||
|
||||
def test_utils_checks():
|
||||
"""Test various utility checks."""
|
||||
checks.check_yolov5u_filename('yolov5n.pt')
|
||||
checks.check_yolov5u_filename("yolov5n.pt")
|
||||
checks.git_describe(ROOT)
|
||||
checks.check_requirements() # check requirements.txt
|
||||
checks.check_imgsz([600, 600], max_dim=1)
|
||||
checks.check_imshow()
|
||||
checks.check_version('ultralytics', '8.0.0')
|
||||
checks.check_version("ultralytics", "8.0.0")
|
||||
checks.print_args()
|
||||
# checks.check_imshow(warn=True)
|
||||
|
||||
|
|
@ -407,7 +423,7 @@ def test_utils_benchmarks():
|
|||
"""Test model benchmarking."""
|
||||
from ultralytics.utils.benchmarks import ProfileModels
|
||||
|
||||
ProfileModels(['yolov8n.yaml'], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
|
||||
ProfileModels(["yolov8n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
|
||||
|
||||
|
||||
def test_utils_torchutils():
|
||||
|
|
@ -423,18 +439,29 @@ def test_utils_torchutils():
|
|||
time_sync()
|
||||
|
||||
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_utils_downloads():
|
||||
"""Test file download utilities."""
|
||||
from ultralytics.utils.downloads import get_google_drive_file_info
|
||||
|
||||
get_google_drive_file_info('https://drive.google.com/file/d/1cqT-cJgANNrhIHCrEufUYhQ4RqiWG_lJ/view?usp=drive_link')
|
||||
get_google_drive_file_info("https://drive.google.com/file/d/1cqT-cJgANNrhIHCrEufUYhQ4RqiWG_lJ/view?usp=drive_link")
|
||||
|
||||
|
||||
def test_utils_ops():
|
||||
"""Test various operations utilities."""
|
||||
from ultralytics.utils.ops import (ltwh2xywh, ltwh2xyxy, make_divisible, xywh2ltwh, xywh2xyxy, xywhn2xyxy,
|
||||
xywhr2xyxyxyxy, xyxy2ltwh, xyxy2xywh, xyxy2xywhn, xyxyxyxy2xywhr)
|
||||
from ultralytics.utils.ops import (
|
||||
ltwh2xywh,
|
||||
ltwh2xyxy,
|
||||
make_divisible,
|
||||
xywh2ltwh,
|
||||
xywh2xyxy,
|
||||
xywhn2xyxy,
|
||||
xywhr2xyxyxyxy,
|
||||
xyxy2ltwh,
|
||||
xyxy2xywh,
|
||||
xyxy2xywhn,
|
||||
xyxyxyxy2xywhr,
|
||||
)
|
||||
|
||||
make_divisible(17, torch.tensor([8]))
|
||||
|
||||
|
|
@ -455,9 +482,9 @@ def test_utils_files():
|
|||
|
||||
file_age(SOURCE)
|
||||
file_date(SOURCE)
|
||||
get_latest_run(ROOT / 'runs')
|
||||
get_latest_run(ROOT / "runs")
|
||||
|
||||
path = TMP / 'path/with spaces'
|
||||
path = TMP / "path/with spaces"
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
with spaces_in_path(path) as new_path:
|
||||
print(new_path)
|
||||
|
|
@ -471,9 +498,9 @@ def test_utils_patches_torch_save():
|
|||
|
||||
mock = MagicMock(side_effect=RuntimeError)
|
||||
|
||||
with patch('ultralytics.utils.patches._torch_save', new=mock):
|
||||
with patch("ultralytics.utils.patches._torch_save", new=mock):
|
||||
with pytest.raises(RuntimeError):
|
||||
torch_save(torch.zeros(1), TMP / 'test.pt')
|
||||
torch_save(torch.zeros(1), TMP / "test.pt")
|
||||
|
||||
assert mock.call_count == 4, "torch_save was not attempted the expected number of times"
|
||||
|
||||
|
|
@ -512,7 +539,7 @@ def test_nn_modules_block():
|
|||
BottleneckCSP(c1, c2)(x)
|
||||
|
||||
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_hub():
|
||||
"""Test Ultralytics HUB functionalities."""
|
||||
from ultralytics.hub import export_fmts_hub, logout
|
||||
|
|
@ -520,7 +547,7 @@ def test_hub():
|
|||
|
||||
export_fmts_hub()
|
||||
logout()
|
||||
smart_request('GET', 'https://github.com', progress=True)
|
||||
smart_request("GET", "https://github.com", progress=True)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -529,12 +556,13 @@ def image():
|
|||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
'auto_augment, erasing, force_color_jitter',
|
||||
"auto_augment, erasing, force_color_jitter",
|
||||
[
|
||||
(None, 0.0, False),
|
||||
('randaugment', 0.5, True),
|
||||
('augmix', 0.2, False),
|
||||
('autoaugment', 0.0, True), ],
|
||||
("randaugment", 0.5, True),
|
||||
("augmix", 0.2, False),
|
||||
("autoaugment", 0.0, True),
|
||||
],
|
||||
)
|
||||
def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):
|
||||
import torchvision.transforms as T
|
||||
|
|
@ -566,17 +594,17 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
|
|||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
|
||||
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
|
||||
def test_model_tune():
|
||||
"""Tune YOLO model for performance."""
|
||||
YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
|
||||
YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
|
||||
YOLO("yolov8n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
|
||||
YOLO("yolov8n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
|
||||
|
||||
|
||||
def test_model_embeddings():
|
||||
"""Test YOLO model embeddings."""
|
||||
model_detect = YOLO(MODEL)
|
||||
model_segment = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
|
||||
model_segment = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
|
||||
|
||||
for batch in [SOURCE], [SOURCE, SOURCE]: # test batch size 1 and 2
|
||||
assert len(model_detect.embed(source=batch, imgsz=32)) == len(batch)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue