Ncnn version demo of [CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search (ncnn)

The official implementation by pytorch:

https://github.com/researchmm/LightTrack

How to run it?

modify your own CMakeList.txt

build

$ mkdir build && cd build
$ cmake .. && make -j && make install

run

$ cd install/lighttrack_demo
$ ./LightTrack [videopath(file or camera)]

TODO

Model Quantization

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Comments
  • 你好大佬,我打开跑这个模型在rk3588开发板上。

    你好大佬,我打开跑这个模型在rk3588开发板上。

    请问能加个联系方式吗。 想问下,官方训练好的模型是那个模型。

    We have uploaded the pre-trained weights of the SuperNets(for both ImageNet classification and object tracking) to Google Drive. Users can use them as initialization for future research on efficient object tracking.

    是网盘提供的这个文件吗。

  • 按照教程转pth为onnx格式,报错Process finished with exit code 132 (interrupted by signal 4: SIGILL)

    按照教程转pth为onnx格式,报错Process finished with exit code 132 (interrupted by signal 4: SIGILL)

    您好,我在替换了lib/models/super_model_DP.py和tracking/torch2onnx.py后,无论是转换backbone还是neck_head模型,程序都会在torch.onnx.export内突然结束,没有明显的报错,只有Process finished with exit code 132 (interrupted by signal 4: SIGILL),前面的读模型都是正常的,也能print(siam_net) super_model_DP.py代码 Screenshot from 2022-10-27 22-31-41 torch2onnx.py代码 Screenshot from 2022-10-27 22-33-05 运行结果 Screenshot from 2022-10-27 22-34-13

    我通过单步调试发现程序在F.conv2d()函数内突然中止了,不清楚是否跟我的LightTrack环境有关,我没有完全安装官方的安装脚本的库,只装了pytorch,cuda,cudnn,torchvision等转换格式必要的库,因为按照官方脚本有些库装不上。 如能解答,不胜感激

  • 怎么用gpu进行推理?

    怎么用gpu进行推理?

    #if NCNN_VULKAN and USE_GPU std::cout << NCNN_VULKAN << std::endl; ex_backbone.set_vulkan_compute(true); #endif

    // net_init.opt.use_vulkan_compute= true; // net_init.set_vulkan_device(0); net_init.opt.use_fp16_packed = true; net_init.opt.use_fp16_storage = true; net_init.opt.use_fp16_arithmetic = true; net_init.opt.use_int8_storage = true; net_backbone.opt.use_fp16_packed = true; net_backbone.opt.use_fp16_storage = true; net_backbone.opt.use_fp16_arithmetic = true; net_backbone.opt.use_int8_storage = true; net_neck_head.opt.use_fp16_packed = true; net_neck_head.opt.use_fp16_storage = true; net_neck_head.opt.use_fp16_arithmetic = true; net_neck_head.opt.use_int8_storage = true;

    我增加了这些部分,但gpu推理失败了 错误信息为 compile spir-v module failed ERROR: 0:10: 'constant_id' : only allowed when generating SPIR-V ERROR: 0:10: '' : compilation terminated ERROR: 2 compilation errors. No code generated.

    出错位置为 ex_backbone.extract("output.1", xf);

  • pscore_window max score一直为nan

    pscore_window max score一直为nan

    你好,我这里遇到如下的报错: Update stage ---- output cls_score and bbox_pred extracting cost time : 6 ms Update stage ---- postprocess cost time : 0 ms pscore_window max score is: nan

    通过单步调试发现, cls_score_data, bbox_pred_data1,bbox_pred_data2, bbox_pred_data3, bbox_pred_data4都是 NaN

    ` float* cls_score_data = (float*)cls_score.data; cls_score_sigmoid.clear();

    int cols = cls_score.w;
    int rows = cls_score.h;
    
    for (int i = 0; i < cols*rows; i++)   // 18 * 18
    {
        cls_score_sigmoid.push_back(sigmoid(cls_score_data[i]));
    }
    
    std::vector<float> pred_x1(cols*rows, 0), pred_y1(cols*rows, 0), pred_x2(cols*rows, 0), pred_y2(cols*rows, 0);
    
    float* bbox_pred_data1 = bbox_pred.channel(0);
    float* bbox_pred_data2 = bbox_pred.channel(1);
    float* bbox_pred_data3 = bbox_pred.channel(2);
    float* bbox_pred_data4 = bbox_pred.channel(3);`
    

    难道是在网络的输出地方就出错了? ex_neck_head.extract("output.1", cls_score); // [c, w, h] = [1, 18, 18] ex_neck_head.extract("output.2", bbox_pred); // [c, w, h] = [4, 18, 18]

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