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# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
import cv2
import numpy as np
import torch
from PIL import Image
from torchvision.transforms import ToTensor
from ultralytics import RTDETR, YOLO
from ultralytics.yolo.data.build import load_inference_source
from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n.pt'
CFG = 'yolov8n.yaml'
SOURCE = ROOT / 'assets/bus.jpg'
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():
model = YOLO(CFG)
model(SOURCE)
def test_model_info():
model = YOLO(CFG)
model.info()
model = YOLO(MODEL)
model.info(verbose=True)
def test_model_fuse():
model = YOLO(CFG)
model.fuse()
model = YOLO(MODEL)
model.fuse()
def test_predict_dir():
model = YOLO(MODEL)
model(source=ROOT / 'assets')
def test_predict_img():
model = YOLO(MODEL)
seg_model = YOLO('yolov8n-seg.pt')
cls_model = YOLO('yolov8n-cls.pt')
pose_model = YOLO('yolov8n-pose.pt')
im = cv2.imread(str(SOURCE))
assert len(model(source=Image.open(SOURCE), save=True, verbose=True)) == 1 # PIL
assert len(model(source=im, save=True, save_txt=True)) == 1 # ndarray
assert len(model(source=[im, im], save=True, save_txt=True)) == 2 # batch
assert len(list(model(source=[im, im], save=True, stream=True))) == 2 # stream
assert len(model(torch.zeros(320, 640, 3).numpy())) == 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, visualize=True)) == 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, visualize=True)
assert len(results) == t.shape[0]
results = seg_model(t, visualize=True)
assert len(results) == t.shape[0]
results = cls_model(t, visualize=True)
assert len(results) == t.shape[0]
results = pose_model(t, visualize=True)
assert len(results) == t.shape[0]
def test_predict_grey_and_4ch():
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)
def test_val():
model = YOLO(MODEL)
model.val(data='coco8.yaml', imgsz=32)
def test_val_scratch():
model = YOLO(CFG)
model.val(data='coco8.yaml', imgsz=32)
def test_amp():
if torch.cuda.is_available():
from ultralytics.yolo.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') # test disk caching
model(SOURCE)
def test_train_pretrained():
model = YOLO(MODEL)
model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='ram') # 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_torchscript_scratch():
model = YOLO(CFG)
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')
# 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 list((ROOT / 'models').rglob('yolo*.yaml')):
if m.name == 'yolov8-rtdetr.yaml': # except the rtdetr model
RTDETR(m.name)
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
# print('on_predict_batch_end', im0s[0].shape)
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 _, (result, im0, bs) in enumerate(results):
print('test_callback', im0.shape)
print('test_callback', bs)
boxes = result.boxes # Boxes object for bbox outputs
print(boxes)
def _test_results_api(res):
# General apis except plot
res = res.cpu().numpy()
# res = res.cuda()
res = res.to(device='cpu', dtype=torch.float32)
res.save_txt('label.txt', save_conf=False)
res.save_txt('label.txt', save_conf=True)
res.save_crop('crops/')
res.tojson(normalize=False)
res.tojson(normalize=True)
res.plot(pil=True)
res.plot(conf=True, boxes=False)
res.plot()
print(res)
print(res.path)
for k in res.keys:
print(getattr(res, k))
def test_results():
for m in ['yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt']:
model = YOLO(m)
res = model([SOURCE, SOURCE])
_test_results_api(res[0])
def test_track():
im = cv2.imread(str(SOURCE))
for m in ['yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt']:
model = YOLO(m)
res = model.track(source=im)
_test_results_api(res[0])
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