ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
19
tests/conftest.py
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19
tests/conftest.py
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@ -0,0 +1,19 @@
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import pytest
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def pytest_addoption(parser):
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parser.addoption('--runslow', action='store_true', default=False, help='run slow tests')
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def pytest_configure(config):
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config.addinivalue_line('markers', 'slow: mark test as slow to run')
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def pytest_collection_modifyitems(config, items):
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if config.getoption('--runslow'):
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# --runslow given in cli: do not skip slow tests
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return
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skip_slow = pytest.mark.skip(reason='need --runslow option to run')
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for item in items:
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if 'slow' in item.keywords:
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item.add_marker(skip_slow)
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@ -3,10 +3,17 @@
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import subprocess
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from pathlib import Path
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from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
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import pytest
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MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
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CFG = 'yolov8n'
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from ultralytics.yolo.utils import ONLINE, ROOT, SETTINGS
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WEIGHT_DIR = Path(SETTINGS['weights_dir'])
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TASK_ARGS = [ # (task, model, data)
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('detect', 'yolov8n', 'coco8.yaml'), ('segment', 'yolov8n-seg', 'coco8-seg.yaml'),
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('classify', 'yolov8n-cls', 'imagenet10'), ('pose', 'yolov8n-pose', 'coco8-pose.yaml')]
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EXPORT_ARGS = [ # (model, format)
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('yolov8n', 'torchscript'), ('yolov8n-seg', 'torchscript'), ('yolov8n-cls', 'torchscript'),
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('yolov8n-pose', 'torchscript')]
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def run(cmd):
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@ -20,78 +27,33 @@ def test_special_modes():
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run('yolo help')
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# Train checks ---------------------------------------------------------------------------------------------------------
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def test_train_det():
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run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1 v5loader')
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_train(task, model, data):
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run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1')
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def test_train_seg():
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run(f'yolo train segment model={CFG}-seg.yaml data=coco8-seg.yaml imgsz=32 epochs=1')
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_val(task, model, data):
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run(f'yolo val {task} model={model}.pt data={data} imgsz=32')
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def test_train_cls():
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run(f'yolo train classify model={CFG}-cls.yaml data=imagenet10 imgsz=32 epochs=1')
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def test_train_pose():
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run(f'yolo train pose model={CFG}-pose.yaml data=coco8-pose.yaml imgsz=32 epochs=1')
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# Val checks -----------------------------------------------------------------------------------------------------------
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def test_val_detect():
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run(f'yolo val detect model={MODEL}.pt data=coco8.yaml imgsz=32')
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def test_val_segment():
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run(f'yolo val segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32')
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def test_val_classify():
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run(f'yolo val classify model={MODEL}-cls.pt data=imagenet10 imgsz=32')
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def test_val_pose():
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run(f'yolo val pose model={MODEL}-pose.pt data=coco8-pose.yaml imgsz=32')
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# Predict checks -------------------------------------------------------------------------------------------------------
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def test_predict_detect():
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run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_predict(task, model, data):
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run(f"yolo predict model={model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
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if ONLINE:
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32')
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
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run(f'yolo predict model={model}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32')
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def test_predict_segment():
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run(f"yolo predict model={MODEL}-seg.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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@pytest.mark.parametrize('model,format', EXPORT_ARGS)
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def test_export(model, format):
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run(f'yolo export model={model}.pt format={format}')
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def test_predict_classify():
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run(f"yolo predict model={MODEL}-cls.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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def test_predict_pose():
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run(f"yolo predict model={MODEL}-pose.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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# Export checks --------------------------------------------------------------------------------------------------------
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def test_export_detect_torchscript():
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run(f'yolo export model={MODEL}.pt format=torchscript')
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def test_export_segment_torchscript():
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run(f'yolo export model={MODEL}-seg.pt format=torchscript')
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def test_export_classify_torchscript():
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run(f'yolo export model={MODEL}-cls.pt format=torchscript')
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def test_export_classify_pose():
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run(f'yolo export model={MODEL}-pose.pt format=torchscript')
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def test_export_detect_edgetpu(enabled=False):
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if enabled and LINUX:
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run(f'yolo export model={MODEL}.pt format=edgetpu')
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# Slow Tests
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@pytest.mark.slow
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_train_gpu(task, model, data):
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run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 device="0"') # single GPU
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run(f'yolo train {task} model={model}.pt data={data} imgsz=32 epochs=1 device="0,1"') # Multi GPU
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