🍉 移动端TNN部署学习笔记，支持Android与iOS。

input_cvt_param.scale = {1.0 / 255, 1.0 / 255, 1.0 / 255, 0.0}; input_cvt_param.bias = {0.0, 0.0, 0.0, 0.0}; why scale is 1/255 ,and the last is 0.0? bias is ?

@cmdbug

大神，我的模型可以正常运行 ，但是检测的类别依然按照coco类别显示的，类别索引是对的，我把label数组改成自己的了，但是依然显示的是coco的label。

# yolov5.h文件
    std::vector<std::string> labels{"prohibitory", "warning", "mandatory"};
private:
    std::shared_ptr<tnn::TNN> net;
    std::shared_ptr<TNN_NS::Instance> instance;
    TNN_NS::DeviceType device_type;

    int net_width = 640;
    int net_height = 640;
    int num_class = 3;

比如，索引号检测是0，应该显示prohibitory，结果显示的是coco中的person，求大神解答一下

Hi. I am running your demo and feel confused about your YOLOv5 demo.

There is a "layers" parameter in the Yolov5.h: https://github.com/cmdbug/TNN_Demo/blob/f361ba092924902854f336784f7b8fb9bfd21347/iOS_TNN_Demo/TNNDemo/Yolov5.h#L87 std::vector layers{ {"output", 32, {{116, 90}, {156, 198}, {373, 326}}}, {"413", 16, {{30, 61}, {62, 45}, {59, 119}}}, {"431", 8, {{10, 13}, {16, 30}, {33, 23}}}, };

I wonder what's the meanings of these values?

Hello. I trained a nanodet model from https://github.com/RangiLyu/nanodet, and an error occurred to me when processing auto status = NanoDet::instance->GetCommandQueue(&command_queue); :

2021-10-03 22:27:42.800142+0800 TNNDemo[7059:1059735] new NanoDet E/tnn: virtual tnn::Status tnn::DefaultNetwork::InitLayers(tnn::NetStructure *, tnn::NetResource *) [File /Users/TURUI/Desktop/TNN/source/tnn/core/default_network.cc][Line 236] Error Init layer Conv_0 (err: 4096 or 0x1000) 2021-10-03 22:27:42.839470+0800 TNNDemo[7059:1059735] GPU initialization failed, switch to CPU E/tnn: virtual tnn::Status tnn::DefaultNetwork::InitLayers(tnn::NetStructure *, tnn::NetResource *) [File /Users/TURUI/Desktop/TNN/source/tnn/core/default_network.cc][Line 236] Error Init layer Conv_0 (err: 4096 or 0x1000) 2021-10-03 22:27:42.845144+0800 TNNDemo[7059:1059735] TNN init failed 4096 (lldb)

It seems that the error is caused by initialization. Do you have any ideas to fix it?

使用的模型：https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt 转换onnx命令：python models/export.py --weights yolov5s.pt --img 640 --batch 1 转换TNN命令：python converter.py onnx2tnn yolov5s.onnx -o . -in 1,3,448,640 转换后的模型：https://www.lanzoui.com/iSktTqkfote 运行demo时报错：