在pytorch中,通过掩码Mask对张量进行筛选是容易的直接Tensor[Mask]即可。但是c++中无法直接这样使用,需要index函数实现,代码如下: auto c = torch::randn({3,4}); auto mask = torch::zeros({3,4}); mask[0][0] = 1; std::cout<<c; std::cout<<c.index({mask.t
index需要单独说明用途。在pytorch中,通过掩码Mask对张量进行筛选是容易的直接Tensor[Mask]即可。但是c++中无法直接这样使用,需要index函数实现,代码如下: autoc = torch::randn({3,4});automask = torch::zeros({3,4}); mask[0][0] =1; std::cout<<c; std::cout<<c.index({mask.to(torch::kBool)...
2.index_add_(dim,index,tensor) → Tensor 按参数index中的索引数确定的顺序,将参数tensor中的元素加到原来的tensor中。附属尺寸必须具有与索引长度相同的大小(必须是向量),所有其他维度必须与原tensor匹配,否则会引起错误。 Example: >>> x = torch.ones(5, 3) >>> t = torch.tensor([[1, 2, 3], ...
四、Expected integer literal for index 问题和解决方法类似第三个 五、Arguments for call are not valid. The following variants are available Expected a value of type 'List[Tensor]' for argument 'indices' but instead found type 'List[Optional[Tensor]]' 问题 赋值类型不对,需求是tensor,但给的是in...
"read src image ok" << endl; cout << "module forward" << endl; auto output = module.forward({ srcTensor, bgrTensor }).toTensor(); cout << "module forward over" << endl; Tensor mask = output.permute({ 0, 2, 3, 1 }).detach().index({ 0, "...", 0 }); mask.mul_(255...
auto max_index = std::get(max_result).item(); std::cout << max_index << std::endl; } void Classfier(cv::Mat &image){ torch::Tensor img_tensor = torch::from_blob(image.data, {1, image.rows, image.cols, 3}, torch::kByte); ...
‘float* at::Tensor::data<float>() const’中: /search/odin/boqin/mmdetection_ocr_split/cpp/roi_aligin/libtorch/include/ATen/core/TensorMethods.h:1386:对‘c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)...
index需要单独说明用途。在pytorch中,通过掩码Mask对张量进行筛选是容易的直接Tensor[Mask]即可。但是c++中无法直接这样使用,需要index函数实现,代码如下: auto c = torch::randn({3,4}); auto mask = torch::zeros({3,4}); mask[0][0] = 1;
{public:CSPdarknet53_tinyImpl();std::vector<torch::Tensor>forward(torch::Tensor x);private: BasicConv conv1{nullptr}; BasicConv conv2{nullptr}; Resblock_body resblock_body1{nullptr}; Resblock_body resblock_body2{nullptr}; Resblock_body resblock_body3{nullptr}; ...
mask_resize = resize_no_new_pixel(mask,out_h,out_w); //mask_resize是mask经过resize的图 torch::Tensor label_tensor = torch::from_blob(mask_resize.data, { mask_resize.rows, mask_resize.cols, 1 }, torch::kByte).permute({ 2, 0, 1 }); ...