YOLOv8-16bit/tests/test_python.py

# 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.data.build import load_inference_source
from ultralytics.utils import LINUX, MACOS, ONLINE, ROOT, SETTINGS
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'
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, imgsz=32)


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():
    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)


def test_track_stream():
    # Test YouTube streaming inference (short 10 frame video) with non-default ByteTrack tracker
    model = YOLO(MODEL)
    model.track('https://youtu.be/G17sBkb38XQ', imgsz=32, tracker='bytetrack.yaml')


def test_val():
    model = YOLO(MODEL)
    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
    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_onnx():
    model = YOLO(MODEL)
    f = model.export(format='onnx')
    YOLO(f)(SOURCE)  # exported model inference


def test_export_openvino():
    if not MACOS:
        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)
        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='label.txt', save_conf=True)
            r.save_crop(save_dir='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))