mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-11-04 08:56:11 +08:00
b4dca690d4
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Maia Numerosky <17316848+maianumerosky@users.noreply.github.com>
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
|
import subprocess
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from ultralytics import YOLO
|
|
from ultralytics.utils import ASSETS, SETTINGS
|
|
|
|
CUDA_IS_AVAILABLE = torch.cuda.is_available()
|
|
CUDA_DEVICE_COUNT = torch.cuda.device_count()
|
|
|
|
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
|
|
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
|
|
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
|
|
|
|
|
|
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
|
|
def test_train():
|
|
YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, batch=-1, device=0) # also test AutoBatch, requires imgsz>=64
|
|
|
|
|
|
@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason=f'DDP is not available, {CUDA_DEVICE_COUNT} device(s) found')
|
|
def test_train_ddp():
|
|
YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=[0, 1]) # requires imgsz>=64
|
|
|
|
|
|
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
|
|
def test_utils_benchmarks():
|
|
from ultralytics.utils.benchmarks import ProfileModels
|
|
|
|
YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1) # pre-export engine model, auto-device
|
|
ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
|
|
|
|
|
|
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
|
|
def test_predict_sam():
|
|
from ultralytics import SAM
|
|
|
|
# Load a model
|
|
model = SAM(WEIGHTS_DIR / 'sam_b.pt')
|
|
|
|
# Display model information (optional)
|
|
model.info()
|
|
|
|
# Run inference
|
|
model(ASSETS / 'bus.jpg', device=0)
|
|
|
|
# Run inference with bboxes prompt
|
|
model(ASSETS / 'zidane.jpg', bboxes=[439, 437, 524, 709], device=0)
|
|
|
|
# Run inference with points prompt
|
|
model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
|
|
|
|
|
|
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
|
|
def test_model_tune():
|
|
subprocess.run('pip install ray[tune]'.split(), check=True)
|
|
YOLO('yolov8n-cls.yaml').tune(data='imagenet10',
|
|
grace_period=1,
|
|
max_samples=1,
|
|
imgsz=32,
|
|
epochs=1,
|
|
plots=False,
|
|
device='cpu')
|