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New ASSETS and trackers GMC cleanup (#4425)

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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
2023-08-17 18:19:05 +02:00
committed by GitHub
parent aaba14e6b2
commit 9d27e7ada4
32 changed files with 222 additions and 201 deletions
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12
tests/test_cli.py
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@ -5,7 +5,7 @@ from pathlib import Path
import pytest
from ultralytics.utils import ROOT, SETTINGS
from ultralytics.utils import ASSETS, SETTINGS
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
TASK_ARGS = [
@ -40,12 +40,12 @@ def test_train(task, model, data):
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_val(task, model, data):
run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32')
run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json')
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_predict(task, model, data):
run(f"yolo predict model={WEIGHTS_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
run(f'yolo predict model={WEIGHTS_DIR / model}.pt source={ASSETS} imgsz=32 save save_crop save_txt')
@pytest.mark.parametrize('model,format', EXPORT_ARGS)
@ -56,11 +56,11 @@ def test_export(model, format):
def test_rtdetr(task='detect', model='yolov8n-rtdetr.yaml', data='coco8.yaml'):
# Warning: MUST use imgsz=640
run(f'yolo train {task} model={model} data={data} imgsz=640 epochs=1 cache=disk')
run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt")
run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=640 save save_crop save_txt")
def test_fastsam(task='segment', model=WEIGHTS_DIR / 'FastSAM-s.pt', data='coco8-seg.yaml'):
source = ROOT / 'assets/bus.jpg'
source = ASSETS / 'bus.jpg'
run(f'yolo segment val {task} model={model} data={data} imgsz=32')
run(f'yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt')
@ -98,7 +98,7 @@ def test_mobilesam():
model = SAM(WEIGHTS_DIR / 'mobile_sam.pt')
# Source
source = ROOT / 'assets/zidane.jpg'
source = ASSETS / 'zidane.jpg'
# Predict a segment based on a point prompt
model.predict(source, points=[900, 370], labels=[1])

11
tests/test_engine.py
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@ -6,14 +6,13 @@ from ultralytics import YOLO
from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import DEFAULT_CFG, ROOT, SETTINGS
from ultralytics.utils import ASSETS, DEFAULT_CFG, SETTINGS
CFG_DET = 'yolov8n.yaml'
CFG_SEG = 'yolov8n-seg.yaml'
CFG_CLS = 'yolov8n-cls.yaml' # or 'squeezenet1_0'
CFG = get_cfg(DEFAULT_CFG)
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
SOURCE = ROOT / 'assets'
def test_func(*args): # noqa
@ -25,7 +24,7 @@ def test_export():
exporter.add_callback('on_export_start', test_func)
assert test_func in exporter.callbacks['on_export_start'], 'callback test failed'
f = exporter(model=YOLO(CFG_DET).model)
YOLO(f)(SOURCE) # exported model inference
YOLO(f)(ASSETS) # exported model inference
def test_detect():
@ -49,7 +48,7 @@ def test_detect():
pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=f'{MODEL}.pt')
result = pred(source=ASSETS, model=f'{MODEL}.pt')
assert len(result), 'predictor test failed'
overrides['resume'] = trainer.last
@ -85,7 +84,7 @@ def test_segment():
pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
result = pred(source=ASSETS, model=f'{MODEL}-seg.pt')
assert len(result), 'predictor test failed'
# Test resume
@ -122,5 +121,5 @@ def test_classify():
pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
pred.add_callback('on_predict_start', test_func)
assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
result = pred(source=SOURCE, model=trainer.best)
result = pred(source=ASSETS, model=trainer.best)
assert len(result), 'predictor test failed'

99
tests/test_python.py
View File

@ -13,14 +13,14 @@ from torchvision.transforms import ToTensor
from ultralytics import RTDETR, YOLO
from ultralytics.data.build import load_inference_source
from ultralytics.utils import DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS
from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
CFG = 'yolov8n.yaml'
SOURCE = ROOT / 'assets/bus.jpg'
SOURCE = ASSETS / 'bus.jpg'
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
@ -29,9 +29,14 @@ def test_model_forward():
model(SOURCE, imgsz=32, augment=True)
def test_model_info():
def test_model_methods():
model = YOLO(MODEL)
model.info(verbose=True)
model.info(verbose=True, detailed=True)
model = model.reset_weights()
model = model.load(MODEL)
model.to('cpu')
_ = model.names
_ = model.device
def test_model_fuse():
@ -41,7 +46,7 @@ def test_model_fuse():
def test_predict_dir():
model = YOLO(MODEL)
model(source=ROOT / 'assets', imgsz=32)
model(source=ASSETS, imgsz=32)
def test_predict_img():
@ -102,11 +107,23 @@ def test_predict_grey_and_4ch():
def test_track_stream():
# Test YouTube streaming inference (short 10 frame video) with non-default ByteTrack tracker
# imgsz=160 required for tracking for higher confidence and better matches
import yaml
model = YOLO(MODEL)
model.predict('https://youtu.be/G17sBkb38XQ', imgsz=96)
model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='bytetrack.yaml')
model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='botsort.yaml')
# Test Global Motion Compensation (GMC) methods
for gmc in 'orb', 'sift', 'ecc':
with open(ROOT / 'cfg/trackers/botsort.yaml') as f:
data = yaml.safe_load(f)
tracker = TMP / f'botsort-{gmc}.yaml'
data['gmc_method'] = gmc
with open(tracker, 'w') as f:
yaml.safe_dump(data, f)
model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker=tracker)
def test_val():
model = YOLO(MODEL)
@ -133,7 +150,7 @@ def test_export_torchscript():
def test_export_onnx():
model = YOLO(MODEL)
f = model.export(format='onnx')
f = model.export(format='onnx', dynamic=True)
YOLO(f)(SOURCE) # exported model inference
@ -173,6 +190,12 @@ def test_export_paddle(enabled=False):
model.export(format='paddle')
def test_export_ncnn(enabled=False):
model = YOLO(MODEL)
f = model.export(format='ncnn')
YOLO(f)(SOURCE) # exported model inference
def test_all_model_yamls():
for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
if 'rtdetr' in m.name:
@ -251,12 +274,13 @@ def test_data_utils():
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
def test_data_converter():
# Test dataset converters
from ultralytics.data.converter import convert_coco
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}')
shutil.move(file, TMP)
convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
coco80_to_coco91_class()
def test_events():
@ -270,9 +294,64 @@ def test_events():
events(cfg)
def test_utils_checks():
from ultralytics.utils.checks import check_yolov5u_filename, git_describe
def test_utils_init():
from ultralytics.utils import (get_git_branch, get_git_origin_url, get_ubuntu_version, is_github_actions_ci,
is_ubuntu)
check_yolov5u_filename('yolov5.pt')
is_ubuntu()
get_ubuntu_version()
is_github_actions_ci()
get_git_origin_url()
get_git_branch()
def test_utils_checks():
from ultralytics.utils.checks import check_requirements, check_yolov5u_filename, git_describe
check_yolov5u_filename('yolov5n.pt')
# check_imshow(warn=True)
git_describe(ROOT)
check_requirements() # check requirements.txt
def test_utils_benchmarks():
from ultralytics.utils.benchmarks import ProfileModels
ProfileModels(['yolov8n.yaml'], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
def test_utils_torchutils():
from ultralytics.nn.modules.conv import Conv
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)
profile(x, [m], n=3)
get_flops_with_torch_profiler(m)
time_sync()
def test_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')
def test_utils_ops():
from ultralytics.utils.ops import make_divisible
make_divisible(17, 8)
def test_utils_files():
from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
file_age(SOURCE)
file_date(SOURCE)
get_latest_run(ROOT / 'runs')
path = TMP / 'path/with spaces'
path.mkdir(parents=True, exist_ok=True)
with spaces_in_path(path) as new_path:
print(new_path)