torchlm: Can't use cuda in pipnet

I want use cuda in pipnet, so I run the following code:

import torchlm
from torchlm.tools import faceboxesv2
from torchlm.models import pipnet
import cv2
image_path = '../rgb/image0/1.png'
image = cv2.imread(image_path)
torchlm.runtime.bind(faceboxesv2())
torchlm.runtime.bind(
    pipnet(backbone="resnet18", pretrained=True,
        num_nb=10, num_lms=98, net_stride=32, input_size=256,
        meanface_type="wflw", checkpoint=None, map_location="cuda")
)
torchlm.runtime.forward(image)

Then I get a error that say:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor

I know it due to my image data is still stay in cpu instead of gpu, and I need load my data to gpu. So I add a line code like following:

image = cv2.imread(image_path)
image = torch.tensor(image).cuda()

But now I get another error:

  File "C:\Home\Development\Anaconda\envs\DeepLearning\lib\site-packages\torchlm\tools\_faceboxesv2.py", line 305, in apply_detecting
    image_scale = cv2.resize(
cv2.error: OpenCV(4.5.5) :-1: error: (-5:Bad argument) in function 'resize'
> Overload resolution failed:
>  - src is not a numpy array, neither a scalar
>  - Expected Ptr<cv::UMat> for argument 'src'

It means I need pass a ndarray array instead of a torch tensor. But if I pass a ndarray, its data will stay in cpu, and I will get the first error again.

What shold I do? Hava anyone get the same error?