LibTorch,使用deeplab模型在转发时提供segfault

我正试图通过Libtorch的DeepLab模型来分割图像。使用pytorch,我正在转换Deeplabv3模型,如下所示:

import torch
import torchvision
from torchvision import models

deeplap_model = models.segmentation.deeplabv3_resnet101(pretrained=True)
deeplap_model.eval()

class wrapper(torch.nn.Module):
    def __init__(self, model):
        super(wrapper, self).__init__()
        self.model = model

    def forward(self, input):
        results = []
        output = self.model(input)
        for k, v in output.items():
            results.append(v)
        return tuple(results)

model = wrapper(deeplap_model)

example = torch.rand(1, 3, 224, 224)
# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)

traced_script_module.save("model.pt")

现在,在带有LibTorch的c++中,我试图加载模型并通过它运行数据。然而,这一点失败了:

std::shared_ptr<torch::jit::script::Module> module = torch::jit::load("model.pt");
module->to(torch::kCUDA);
assert(module != nullptr);
std::cout << "ok\n";


std::vector<torch::jit::IValue> inputs;
cv::Mat image;
image = cv::imread("pic.jpeg", 1);
cv::Mat image_resized;
cv::resize(image, image_resized, cv::Size(224, 224));
cv::cvtColor(image_resized, image_resized, cv::COLOR_BGR2RGB);
cv::Mat image_resized_float;
image_resized.convertTo(image_resized_float, CV_32F, 1.0 / 255);

auto img_tensor = torch::from_blob(image_resized_float.data, { 1, 224, 224, 3 }, torch::kFloat32);
cout << "img tensor loaded..\n";
img_tensor = img_tensor.permute({ 0, 3, 1, 2 });
img_tensor[0][0] = img_tensor[0][0].sub(0.485).div(0.229);
img_tensor[0][1] = img_tensor[0][1].sub(0.456).div(0.224);
img_tensor[0][2] = img_tensor[0][2].sub(0.406).div(0.225);

// to GPU
img_tensor = img_tensor.to(at::kCUDA);

torch::Tensor out_tensor2 = module->forward({ img_tensor }).toTensor(); //SEGFAULT

我哪里做错了?