使用数组或某一段内存来初始化张量时,通常调用torch::from_blob函数来实现: //使用数组来初始化张量内容intaa[4] = {3,4,6,7};autoaaaaa = torch::from_blob(aa, {2,2}, torch::kInt); cout << aaaaa << endl;//使用vector迭代容器来初始化张量内容vector<float> aaaa = {3,4,6};autoaaa = ...
LibTorch中的固定内存可以通过使用torch::from_blob函数来创建固定内存的张量。例如,以下代码片段演示了如何使用固定内存创建一个大小为(3, 3)的张量: 代码语言:txt 复制 float data[9] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0}; torch::Tensor tensor = torch::from_blob(data, {3, 3},...
{ 3,4,6 }; auto ac = torch::from_blob(aa, { 3 }, torch::kFloat); std::cout << "from_blob init:" << std::endl << ac << std::endl; auto ah = torch::from_blob(av.data(), { 3 }, torch::kFloat); std::cout << "from_blob init:" << std::endl << ah << ...
调用PyTorch模型的步骤: // 1.实例化一个模型对象torch::jit::script::Modulemodule;// 2.载入PyTorch模型module=torch::jit::load("/home/book/Share/MPG_model.pt");// 3.新建输入向量std::vector<torch::jit::IValue>inputs;// 4.向新建的输入向量载入数据inputs.push_back(torch::from_blob(arr,{...
auto input_tensor = torch::from_blob(image.data, { image.rows, image.cols,3}, torch::kByte).permute({2,0,1}).unsqueeze(0).to(torch::kFloat32) /225.0; //加载模型 auto model = torch::jit::load("your path to\resnet34.pt"); ...
cv::Mat input; while(1) { stream>>frame; image = resize_with_ratio(frame); imshow("resized image",image); //显示摄像头的数据 cv::cvtColor(image, input, cv::COLOR_BGR2RGB); // 下方的代码即将图像转化为Tensor,随后导入模型进行预测 torch::Tensor tensor_image = torch::from_blob(input....
1Mat frame = cv::imread("1.png", IMREAD_COLOR);2cvtColor(frame, frame, CV_BGR2RGB);//转换色彩通道3frame.convertTo(frame, CV_32FC3,1.0f/255.0f);4auto tensor_image =torch::from_blob(frame.data, {frame.rows, frame.cols, frame.channels()});5//换chw;6tensor_image = tensor_image...
tensorFromBlob”: 不是“at::DeprecatedTypeProperties”的成员 (1)libtorch找不到GPU 通过python import torch时,返回为True。所以cuda、cudnn和torch都没问题。 在“属性 --> 链接器 --> 命令行 --> 其他选项”中添加: /INCLUDE:?warp_size@cuda@at@@YAHXZ ...
convertTo(float_image, CV_32F, 1.0 / 255); auto img_tensor = torch::from_blob(float_image.data, {1, img_size, img_size, 3}, torch::kFloat32); img_tensor = img_tensor.permute({0, 3, 1, 2}); // 参照自己数据集的均值和方差 img_tensor[0][0] = img_tensor[0][0].sub_(...
一 张量初始化 1.torch::zeros #include <torch/torch.h> usingnamespacestd; intmain() { torch::Tensorb=torch::zeros({2,3}); cout<<b<<endl; system("pause"); return0; } 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 2.torch::ones ...