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Fix LoadStreams final frame bug (#4387)

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Co-authored-by: Nadim Bou Alwan <64587372+nadinator@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-08-16 19:00:03 +02:00
committed by GitHub
parent 17e6b9c270
commit fb1ae9bfad
11 changed files with 97 additions and 67 deletions
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25
tests/conftest.py
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@ -1,7 +1,14 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import shutil
from pathlib import Path
import pytest
from ultralytics.utils import ROOT
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
def pytest_addoption(parser):
parser.addoption('--runslow', action='store_true', default=False, help='run slow tests')
@ -19,3 +26,21 @@ def pytest_collection_modifyitems(config, items):
for item in items:
if 'slow' in item.keywords:
item.add_marker(skip_slow)
def pytest_sessionstart(session):
"""
Called after the 'Session' object has been created and before performing test collection.
"""
shutil.rmtree(TMP, ignore_errors=True) # delete any existing tests/tmp directory
TMP.mkdir(parents=True, exist_ok=True) # create a new empty directory
def pytest_terminal_summary(terminalreporter, exitstatus, config):
# Remove files
for file in ['bus.jpg', 'decelera_landscape_min.mov']:
Path(file).unlink(missing_ok=True)
# Remove directories
for directory in ['.pytest_cache/', TMP]:
shutil.rmtree(directory, ignore_errors=True)

17
tests/test_cli.py
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@ -5,7 +5,7 @@ from pathlib import Path
import pytest
from ultralytics.utils import ONLINE, ROOT, SETTINGS
from ultralytics.utils import ROOT, SETTINGS
WEIGHT_DIR = Path(SETTINGS['weights_dir'])
TASK_ARGS = [
@ -30,7 +30,6 @@ def test_special_modes():
run('yolo checks')
run('yolo version')
run('yolo settings reset')
run('yolo copy-cfg')
run('yolo cfg')
@ -49,28 +48,14 @@ def test_predict(task, model, data):
run(f"yolo predict model={WEIGHT_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_predict_online(task, model, data):
mode = 'track' if task in ('detect', 'segment', 'pose') else 'predict' # mode for video inference
model = WEIGHT_DIR / model
run(f'yolo predict model={model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
run(f'yolo {mode} model={model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=96')
# Run Python YouTube tracking because CLI is broken. TODO: fix CLI YouTube
# run(f'yolo {mode} model={model}.pt source=https://youtu.be/G17sBkb38XQ imgsz=32 tracker=bytetrack.yaml')
@pytest.mark.parametrize('model,format', EXPORT_ARGS)
def test_export(model, format):
run(f'yolo export model={WEIGHT_DIR / model}.pt format={format} imgsz=32')
# Test SAM, RTDETR Models
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 val {task} model={model} data={data} imgsz=640')
run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt")

76
tests/test_python.py
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@ -1,17 +1,20 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import shutil
from copy import copy
from pathlib import Path
import cv2
import numpy as np
import pytest
import torch
from PIL import Image
from torchvision.transforms import ToTensor
from ultralytics import RTDETR, YOLO
from ultralytics.data.build import load_inference_source
from ultralytics.utils import LINUX, MACOS, ONLINE, ROOT, SETTINGS
from ultralytics.utils import 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'])
@ -19,13 +22,6 @@ MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
CFG = 'yolov8n.yaml'
SOURCE = ROOT / 'assets/bus.jpg'
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
SOURCE_GREYSCALE = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
SOURCE_RGBA = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
# Convert SOURCE to greyscale and 4-ch
im = Image.open(SOURCE)
im.convert('L').save(SOURCE_GREYSCALE) # greyscale
im.convert('RGBA').save(SOURCE_RGBA) # 4-ch PNG with alpha
def test_model_forward():
@ -84,16 +80,32 @@ def test_predict_img():
def test_predict_grey_and_4ch():
# Convert SOURCE to greyscale and 4-ch
im = Image.open(SOURCE)
source_greyscale = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
source_rgba = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
im.convert('L').save(source_greyscale) # greyscale
im.convert('RGBA').save(source_rgba) # 4-ch PNG with alpha
# Inference
model = YOLO(MODEL)
for f in SOURCE_RGBA, SOURCE_GREYSCALE:
for f in source_rgba, source_greyscale:
for source in Image.open(f), cv2.imread(str(f)), f:
model(source, save=True, verbose=True, imgsz=32)
# Cleanup
source_greyscale.unlink()
source_rgba.unlink()
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
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
model = YOLO(MODEL)
model.track('https://youtu.be/G17sBkb38XQ', imgsz=96, tracker='bytetrack.yaml')
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')
def test_val():
@ -101,13 +113,6 @@ def test_val():
model.val(data='coco8.yaml', imgsz=32)
def test_amp():
if torch.cuda.is_available():
from ultralytics.utils.checks import check_amp
model = YOLO(MODEL).model.cuda()
assert check_amp(model)
def test_train_scratch():
model = YOLO(CFG)
model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='disk', batch=-1) # test disk caching with AutoBatch
@ -133,10 +138,9 @@ def test_export_onnx():
def test_export_openvino():
if not MACOS:
model = YOLO(MODEL)
f = model.export(format='openvino')
YOLO(f)(SOURCE) # exported model inference
model = YOLO(MODEL)
f = model.export(format='openvino')
YOLO(f)(SOURCE) # exported model inference
def test_export_coreml(): # sourcery skip: move-assign
@ -173,7 +177,7 @@ def test_all_model_yamls():
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'
RTDETR(m.name)
RTDETR(m.name)(SOURCE, imgsz=640)
else:
YOLO(m.name)
@ -225,17 +229,14 @@ def test_results():
print(getattr(r, k))
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
def test_data_utils():
# Test functions in ultralytics/data/utils.py
from ultralytics.data.utils import HUBDatasetStats, autosplit, zip_directory
from ultralytics.utils.downloads import download
# from ultralytics.utils.files import WorkingDirectory
# with WorkingDirectory(ROOT.parent / 'tests'):
shutil.rmtree(TMP, ignore_errors=True)
TMP.mkdir(parents=True)
download('https://github.com/ultralytics/hub/raw/master/example_datasets/coco8.zip', unzip=False)
shutil.move('coco8.zip', TMP)
stats = HUBDatasetStats(TMP / 'coco8.zip', task='detect')
@ -244,4 +245,25 @@ def test_data_utils():
autosplit(TMP / 'coco8')
zip_directory(TMP / 'coco8/images/val') # zip
shutil.rmtree(TMP)
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
def test_data_converter():
# Test dataset converters
from ultralytics.data.converter import 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)
def test_events():
# Test event sending
from ultralytics.hub.utils import Events
events = Events()
events.enabled = True
cfg = copy(DEFAULT_CFG) # does not require deepcopy
cfg.mode = 'test'
events(cfg)