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ultralytics 8.0.181 RTDETR, MLFlow fixes and Examples updates (#4927)

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Co-authored-by: Jordane Sikati <jordanesikati@pusan.ac.kr>
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Co-authored-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com>
Co-authored-by: Lukas Hennies <45569834+Gornoka@users.noreply.github.com>
Co-authored-by: 唐洁 <tangjie1953479@tongji.edu.cn>
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Glenn Jocher 2023-09-18 14:12:34 +02:00 • committed by GitHub
parent 5702b2dccd
commit 742ec7fb1d
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9 changed files with 46 additions and 26 deletions
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5
tests/test_cli.py
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@ -6,7 +6,10 @@ from pathlib import Path
import pytest
from ultralytics.utils import ASSETS, SETTINGS
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
CUDA_IS_AVAILABLE = cuda_is_available()
CUDA_DEVICE_COUNT = cuda_device_count()
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
TASK_ARGS = [
('detect', 'yolov8n', 'coco8.yaml'),
@ -117,6 +120,8 @@ def test_mobilesam():
# Slow Tests -----------------------------------------------------------------------------------------------------------
@pytest.mark.slow
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason='DDP is not available')
def test_train_gpu(task, model, data):
run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 device=0') # single GPU
run(f'yolo train {task} model={model}.pt data={data} imgsz=32 epochs=1 device=0,1') # multi GPU

11
tests/test_cuda.py
View file

@ -7,9 +7,10 @@ import torch
from ultralytics import YOLO, download
from ultralytics.utils import ASSETS, SETTINGS
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
CUDA_IS_AVAILABLE = torch.cuda.is_available()
CUDA_DEVICE_COUNT = torch.cuda.device_count()
CUDA_IS_AVAILABLE = cuda_is_available()
CUDA_DEVICE_COUNT = cuda_device_count()
DATASETS_DIR = Path(SETTINGS['datasets_dir'])
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
@ -18,10 +19,8 @@ DATA = 'coco8.yaml'
def test_checks():
from ultralytics.utils.checks import cuda_device_count, cuda_is_available
assert cuda_device_count() == CUDA_DEVICE_COUNT
assert cuda_is_available() == CUDA_IS_AVAILABLE
assert torch.cuda.is_available() == CUDA_IS_AVAILABLE
assert torch.cuda.device_count() == CUDA_DEVICE_COUNT
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')

5
tests/test_python.py
View file

@ -14,7 +14,7 @@ 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, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS
from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS, is_dir_writeable
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9
@ -23,6 +23,7 @@ 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)
def test_model_forward():
@ -58,6 +59,7 @@ def test_model_profile():
_ = model.predict(im, profile=True)
@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason='directory is not writeable')
def test_predict_txt():
# Write a list of sources (file, dir, glob, recursive glob) to a txt file
txt_file = TMP / 'sources.txt'
@ -128,6 +130,7 @@ def test_predict_grey_and_4ch():
@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 YouTube streaming inference (short 10 frame video) with non-default ByteTrack tracker
# imgsz=160 required for tracking for higher confidence and better matches