You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
279 lines
8.9 KiB
279 lines
8.9 KiB
# 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 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'
|
|
TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
|
|
|
|
|
|
def test_model_forward():
|
|
model = YOLO(CFG)
|
|
model(SOURCE, imgsz=32, augment=True)
|
|
|
|
|
|
def test_model_info():
|
|
model = YOLO(MODEL)
|
|
model.info(verbose=True)
|
|
|
|
|
|
def test_model_fuse():
|
|
model = YOLO(MODEL)
|
|
model.fuse()
|
|
|
|
|
|
def test_predict_dir():
|
|
model = YOLO(MODEL)
|
|
model(source=ROOT / 'assets', imgsz=32)
|
|
|
|
|
|
def test_predict_img():
|
|
model = YOLO(MODEL)
|
|
seg_model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
|
|
cls_model = YOLO(WEIGHTS_DIR / 'yolov8n-cls.pt')
|
|
pose_model = YOLO(WEIGHTS_DIR / 'yolov8n-pose.pt')
|
|
im = cv2.imread(str(SOURCE))
|
|
assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
|
|
assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1 # ndarray
|
|
assert len(model(source=[im, im], save=True, save_txt=True, imgsz=32)) == 2 # batch
|
|
assert len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2 # stream
|
|
assert len(model(torch.zeros(320, 640, 3).numpy(), imgsz=32)) == 1 # tensor to numpy
|
|
batch = [
|
|
str(SOURCE), # filename
|
|
Path(SOURCE), # Path
|
|
'https://ultralytics.com/images/zidane.jpg' if ONLINE else SOURCE, # URI
|
|
cv2.imread(str(SOURCE)), # OpenCV
|
|
Image.open(SOURCE), # PIL
|
|
np.zeros((320, 640, 3))] # numpy
|
|
assert len(model(batch, imgsz=32)) == len(batch) # multiple sources in a batch
|
|
|
|
# Test tensor inference
|
|
im = cv2.imread(str(SOURCE)) # OpenCV
|
|
t = cv2.resize(im, (32, 32))
|
|
t = ToTensor()(t)
|
|
t = torch.stack([t, t, t, t])
|
|
results = model(t, imgsz=32)
|
|
assert len(results) == t.shape[0]
|
|
results = seg_model(t, imgsz=32)
|
|
assert len(results) == t.shape[0]
|
|
results = cls_model(t, imgsz=32)
|
|
assert len(results) == t.shape[0]
|
|
results = pose_model(t, imgsz=32)
|
|
assert len(results) == t.shape[0]
|
|
|
|
|
|
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 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.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():
|
|
model = YOLO(MODEL)
|
|
model.val(data='coco8.yaml', imgsz=32)
|
|
|
|
|
|
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
|
|
model(SOURCE)
|
|
|
|
|
|
def test_train_pretrained():
|
|
model = YOLO(WEIGHTS_DIR / 'yolov8n-seg.pt')
|
|
model.train(data='coco8-seg.yaml', epochs=1, imgsz=32, cache='ram', copy_paste=0.5, mixup=0.5) # test RAM caching
|
|
model(SOURCE)
|
|
|
|
|
|
def test_export_torchscript():
|
|
model = YOLO(MODEL)
|
|
f = model.export(format='torchscript')
|
|
YOLO(f)(SOURCE) # exported model inference
|
|
|
|
|
|
def test_export_onnx():
|
|
model = YOLO(MODEL)
|
|
f = model.export(format='onnx')
|
|
YOLO(f)(SOURCE) # exported model inference
|
|
|
|
|
|
def test_export_openvino():
|
|
model = YOLO(MODEL)
|
|
f = model.export(format='openvino')
|
|
YOLO(f)(SOURCE) # exported model inference
|
|
|
|
|
|
def test_export_coreml(): # sourcery skip: move-assign
|
|
model = YOLO(MODEL)
|
|
model.export(format='coreml', nms=True)
|
|
# if MACOS:
|
|
# YOLO(f)(SOURCE) # model prediction only supported on macOS
|
|
|
|
|
|
def test_export_tflite(enabled=False):
|
|
# TF suffers from install conflicts on Windows and macOS
|
|
if enabled and LINUX:
|
|
model = YOLO(MODEL)
|
|
f = model.export(format='tflite')
|
|
YOLO(f)(SOURCE)
|
|
|
|
|
|
def test_export_pb(enabled=False):
|
|
# TF suffers from install conflicts on Windows and macOS
|
|
if enabled and LINUX:
|
|
model = YOLO(MODEL)
|
|
f = model.export(format='pb')
|
|
YOLO(f)(SOURCE)
|
|
|
|
|
|
def test_export_paddle(enabled=False):
|
|
# Paddle protobuf requirements conflicting with onnx protobuf requirements
|
|
if enabled:
|
|
model = YOLO(MODEL)
|
|
model.export(format='paddle')
|
|
|
|
|
|
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)(SOURCE, imgsz=640)
|
|
else:
|
|
YOLO(m.name)
|
|
|
|
|
|
def test_workflow():
|
|
model = YOLO(MODEL)
|
|
model.train(data='coco8.yaml', epochs=1, imgsz=32)
|
|
model.val()
|
|
model.predict(SOURCE)
|
|
model.export(format='onnx') # export a model to ONNX format
|
|
|
|
|
|
def test_predict_callback_and_setup():
|
|
# Test callback addition for prediction
|
|
def on_predict_batch_end(predictor): # results -> List[batch_size]
|
|
path, im0s, _, _ = predictor.batch
|
|
im0s = im0s if isinstance(im0s, list) else [im0s]
|
|
bs = [predictor.dataset.bs for _ in range(len(path))]
|
|
predictor.results = zip(predictor.results, im0s, bs)
|
|
|
|
model = YOLO(MODEL)
|
|
model.add_callback('on_predict_batch_end', on_predict_batch_end)
|
|
|
|
dataset = load_inference_source(source=SOURCE)
|
|
bs = dataset.bs # noqa access predictor properties
|
|
results = model.predict(dataset, stream=True) # source already setup
|
|
for r, im0, bs in results:
|
|
print('test_callback', im0.shape)
|
|
print('test_callback', bs)
|
|
boxes = r.boxes # Boxes object for bbox outputs
|
|
print(boxes)
|
|
|
|
|
|
def test_results():
|
|
for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
|
|
model = YOLO(m)
|
|
results = model([SOURCE, SOURCE])
|
|
for r in results:
|
|
r = r.cpu().numpy()
|
|
r = r.to(device='cpu', dtype=torch.float32)
|
|
r.save_txt(txt_file='runs/tests/label.txt', save_conf=True)
|
|
r.save_crop(save_dir='runs/tests/crops/')
|
|
r.tojson(normalize=True)
|
|
r.plot(pil=True)
|
|
r.plot(conf=True, boxes=True)
|
|
print(r)
|
|
print(r.path)
|
|
for k in r.keys:
|
|
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
|
|
from ultralytics.utils.downloads import zip_directory
|
|
|
|
# from ultralytics.utils.files import WorkingDirectory
|
|
# with WorkingDirectory(ROOT.parent / 'tests'):
|
|
|
|
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')
|
|
stats.get_json(save=True)
|
|
stats.process_images()
|
|
|
|
autosplit(TMP / 'coco8')
|
|
zip_directory(TMP / 'coco8/images/val') # zip
|
|
|
|
|
|
@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)
|
|
|
|
|
|
def test_utils_checks():
|
|
from ultralytics.utils.checks import check_yolov5u_filename, git_describe
|
|
|
|
check_yolov5u_filename('yolov5.pt')
|
|
# check_imshow(warn=True)
|
|
git_describe(ROOT)
|