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Fixed import errors
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@Mindstan
Mindstan committed Nov 18, 2024
1 parent 3999b02 commit 961bdfd
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Showing 3 changed files with 181 additions and 59 deletions.
184 changes: 140 additions & 44 deletions tests/test_python.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,9 @@
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9

IS_TMP_WRITEABLE = is_dir_writeable(TMP) # WARNING: must be run once tests start as TMP does not exist on tests/init
IS_TMP_WRITEABLE = is_dir_writeable(
TMP
) # WARNING: must be run once tests start as TMP does not exist on tests/init


def test_model_forward():
Expand Down Expand Up @@ -112,16 +114,28 @@ def test_predict_img(model_name):
"""Test YOLO model predictions on various image input types and sources, including online images."""
model = YOLO(WEIGHTS_DIR / model_name)
im = cv2.imread(str(SOURCE)) # uint8 numpy array
assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
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
assert len(model(torch.rand((2, 3, 32, 32)), imgsz=32)) == 2 # batch-size 2 Tensor, FP32 0.0-1.0 RGB order
assert (
len(model(torch.rand((2, 3, 32, 32)), imgsz=32)) == 2
) # batch-size 2 Tensor, FP32 0.0-1.0 RGB order
assert len(model(source=[im, im], save=True, save_txt=True, imgsz=32)) == 2 # batch
assert len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2 # stream
assert len(model(torch.zeros(320, 640, 3).numpy().astype(np.uint8), imgsz=32)) == 1 # tensor to numpy
assert (
len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2
) # stream
assert (
len(model(torch.zeros(320, 640, 3).numpy().astype(np.uint8), imgsz=32)) == 1
) # tensor to numpy
batch = [
str(SOURCE), # filename
Path(SOURCE), # Path
"https://github.com/ultralytics/assets/releases/download/v0.0.0/zidane.jpg" if ONLINE else SOURCE, # URI
(
"https://github.com/ultralytics/assets/releases/download/v0.0.0/zidane.jpg"
if ONLINE
else SOURCE
), # URI
cv2.imread(str(SOURCE)), # OpenCV
Image.open(SOURCE), # PIL
np.zeros((320, 640, 3), dtype=np.uint8), # numpy
Expand Down Expand Up @@ -162,7 +176,10 @@ def test_predict_grey_and_4ch():

@pytest.mark.slow
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
@pytest.mark.skipif(is_github_action_running(), reason="No auth https://github.com/JuanBindez/pytubefix/issues/166")
@pytest.mark.skipif(
is_github_action_running(),
reason="No auth https://github.com/JuanBindez/pytubefix/issues/166",
)
def test_youtube():
"""Test YOLO model on a YouTube video stream, handling potential network-related errors."""
model = YOLO(MODEL)
Expand All @@ -184,7 +201,9 @@ def test_track_stream():
video_url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/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="botsort.yaml", save_frames=True
) # test frame saving also

# Test Global Motion Compensation (GMC) methods
for gmc in "orb", "sift", "ecc":
Expand All @@ -205,28 +224,49 @@ def test_val():
def test_train_scratch():
"""Test training the YOLO model from scratch using the provided configuration."""
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 of the YOLO model starting from a pre-trained checkpoint."""
model = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
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_all_model_yamls():
"""Test YOLO model creation for all available YAML configurations in the `cfg/models` directory."""
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'
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:
YOLO(m.name)


@pytest.mark.skipif(WINDOWS, reason="Windows slow CI export bug https://github.com/ultralytics/ultralytics/pull/16003")
@pytest.mark.skipif(
WINDOWS,
reason="Windows slow CI export bug https://github.com/ultralytics/ultralytics/pull/16003",
)
def test_workflow():
"""Test the complete workflow including training, validation, prediction, and exporting."""
model = YOLO(MODEL)
Expand All @@ -244,7 +284,9 @@ def on_predict_batch_end(predictor):
path, im0s, _ = predictor.batch
im0s = im0s if isinstance(im0s, list) else [im0s]
bs = [predictor.dataset.bs for _ in range(len(path))]
predictor.results = zip(predictor.results, im0s, bs) # results is List[batch_size]
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)
Expand Down Expand Up @@ -281,18 +323,24 @@ def test_results(model):
def test_labels_and_crops():
"""Test output from prediction args for saving YOLO detection labels and crops; ensures accurate saving."""
imgs = [SOURCE, ASSETS / "zidane.jpg"]
results = YOLO(WEIGHTS_DIR / "yolo11n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
results = YOLO(WEIGHTS_DIR / "yolo11n.pt")(
imgs, imgsz=160, save_txt=True, save_crop=True
)
save_path = Path(results[0].save_dir)
for r in results:
im_name = Path(r.path).stem
cls_idxs = r.boxes.cls.int().tolist()
# Check correct detections
assert cls_idxs == ([0, 7, 0, 0] if r.path.endswith("bus.jpg") else [0, 0, 0]) # bus.jpg and zidane.jpg classes
assert cls_idxs == (
[0, 7, 0, 0] if r.path.endswith("bus.jpg") else [0, 0, 0]
) # bus.jpg and zidane.jpg classes
# Check label path
labels = save_path / f"labels/{im_name}.txt"
assert labels.exists()
# Check detections match label count
assert len(r.boxes.data) == len([line for line in labels.read_text().splitlines() if line])
assert len(r.boxes.data) == len(
[line for line in labels.read_text().splitlines() if line]
)
# Check crops path and files
crop_dirs = list((save_path / "crops").iterdir())
crop_files = [f for p in crop_dirs for f in p.glob("*")]
Expand All @@ -313,7 +361,11 @@ def test_data_utils():

for task in TASKS:
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)
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()
Expand All @@ -328,8 +380,17 @@ def test_data_converter():
from ultralytics.data.converter import coco80_to_coco91_class, convert_coco

file = "instances_val2017.json"
download(f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{file}", dir=TMP)
convert_coco(labels_dir=TMP, save_dir=TMP / "yolo_labels", use_segments=True, use_keypoints=False, cls91to80=True)
download(
f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{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()


Expand All @@ -345,31 +406,27 @@ def test_data_annotator():
)


def test_events():
"""Test event sending functionality."""
from ultralytics.hub.utils import Events

events = Events()
events.enabled = True
cfg = copy(DEFAULT_CFG) # does not require deepcopy
cfg.mode = "test"
events(cfg)


def test_cfg_init():
"""Test configuration initialization utilities from the 'ultralytics.cfg' module."""
from ultralytics.cfg import check_dict_alignment, copy_default_cfg, smart_value

with contextlib.suppress(SyntaxError):
check_dict_alignment({"a": 1}, {"b": 2})
copy_default_cfg()
(Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")).unlink(missing_ok=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():
"""Test initialization utilities in the Ultralytics library."""
from ultralytics.utils import get_git_branch, get_git_origin_url, get_ubuntu_version, is_github_action_running
from ultralytics.utils import (
get_git_branch,
get_git_origin_url,
get_ubuntu_version,
is_github_action_running,
)

get_ubuntu_version()
is_github_action_running()
Expand All @@ -388,18 +445,26 @@ def test_utils_checks():
checks.print_args()


@pytest.mark.skipif(WINDOWS, reason="Windows profiling is extremely slow (cause unknown)")
@pytest.mark.skipif(
WINDOWS, reason="Windows profiling is extremely slow (cause unknown)"
)
def test_utils_benchmarks():
"""Benchmark model performance using 'ProfileModels' from 'ultralytics.utils.benchmarks'."""
from ultralytics.utils.benchmarks import ProfileModels

ProfileModels(["yolo11n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
ProfileModels(
["yolo11n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1
).profile()


def test_utils_torchutils():
"""Test Torch utility functions including profiling and FLOP calculations."""
from ultralytics.nn.modules.conv import Conv
from ultralytics.utils.torch_utils import get_flops_with_torch_profiler, profile, time_sync
from ultralytics.utils.torch_utils import (
get_flops_with_torch_profiler,
profile,
time_sync,
)

x = torch.randn(1, 64, 20, 20)
m = Conv(64, 64, k=1, s=2)
Expand All @@ -415,7 +480,9 @@ def test_utils_downloads():
"""Test file download utilities from ultralytics.utils.downloads."""
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():
Expand Down Expand Up @@ -449,7 +516,12 @@ def test_utils_ops():

def test_utils_files():
"""Test file handling utilities including file age, date, and paths with spaces."""
from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
from ultralytics.utils.files import (
file_age,
file_date,
get_latest_run,
spaces_in_path,
)

file_age(SOURCE)
file_date(SOURCE)
Expand All @@ -474,12 +546,20 @@ def test_utils_patches_torch_save():
with pytest.raises(RuntimeError):
torch_save(torch.zeros(1), TMP / "test.pt")

assert mock.call_count == 4, "torch_save was not attempted the expected number of times"
assert (
mock.call_count == 4
), "torch_save was not attempted the expected number of times"


def test_nn_modules_conv():
"""Test Convolutional Neural Network modules including CBAM, Conv2, and ConvTranspose."""
from ultralytics.nn.modules.conv import CBAM, Conv2, ConvTranspose, DWConvTranspose2d, Focus
from ultralytics.nn.modules.conv import (
CBAM,
Conv2,
ConvTranspose,
DWConvTranspose2d,
Focus,
)

c1, c2 = 8, 16 # input and output channels
x = torch.zeros(4, c1, 10, 10) # BCHW
Expand Down Expand Up @@ -557,7 +637,9 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
erasing=erasing,
)

transformed_image = transform(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)))
transformed_image = transform(
Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
)

assert transformed_image.shape == (3, 224, 224)
assert torch.is_tensor(transformed_image)
Expand All @@ -568,8 +650,17 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
def test_model_tune():
"""Tune YOLO model for performance improvement."""
YOLO("yolo11n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
YOLO("yolo11n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
YOLO("yolo11n-pose.pt").tune(
data="coco8-pose.yaml",
plots=False,
imgsz=32,
epochs=1,
iterations=2,
device="cpu",
)
YOLO("yolo11n-cls.pt").tune(
data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu"
)


def test_model_embeddings():
Expand All @@ -582,7 +673,9 @@ def test_model_embeddings():
assert len(model_segment.embed(source=batch, imgsz=32)) == len(batch)


@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
@pytest.mark.skipif(
checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12"
)
def test_yolo_world():
"""Tests YOLO world models with CLIP support, including detection and training scenarios."""
model = YOLO("yolov8s-world.pt") # no YOLO11n-world model yet
Expand All @@ -605,7 +698,10 @@ def test_yolo_world():

model = YOLO("yolov8s-worldv2.yaml") # no YOLO11n-world model yet
model.train(
data={"train": {"yolo_data": ["dota8.yaml"]}, "val": {"yolo_data": ["dota8.yaml"]}},
data={
"train": {"yolo_data": ["dota8.yaml"]},
"val": {"yolo_data": ["dota8.yaml"]},
},
epochs=1,
imgsz=32,
cache="disk",
Expand Down
2 changes: 1 addition & 1 deletion ultralytics/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license

__version__ = "8.3.4b1+psycle"
__version__ = "8.3.4"

import os

Expand Down
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