Repository: linyicheng1/LET-NET
Branch: main
Commit: 64dfae986d45
Files: 10
Total size: 73.2 KB
Directory structure:
gitextract_15a7jtc0/
├── CMakeLists.txt
├── README.md
├── main.cpp
├── model/
│ ├── letnet-gray.onnx
│ ├── letnet-gray.pt
│ ├── letnet.onnx
│ ├── letnet.pt
│ └── model.param
├── tracking.cpp
└── tracking.h
================================================
FILE CONTENTS
================================================
================================================
FILE: CMakeLists.txt
================================================
cmake_minimum_required(VERSION 3.10)
project(demo)
set(CMAKE_CXX_STANDARD 14)
SET(CMAKE_BUILD_TYPE Release)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(ncnn_DIR "/home/c211/lyc/ncnn/alexnet_demo/ncnn/build/install/lib/cmake/ncnn" CACHE PATH "Directory that contains ncnnConfig.cmake")
find_package(OpenCV REQUIRED)
find_package(ncnn REQUIRED)
add_executable(demo main.cpp tracking.cpp)
target_link_libraries(demo
ncnn
${OpenCV_LIBS}
-fopenmp
)
================================================
FILE: README.md
================================================
# LET-NET: A lightweight CNN network for sparse corners extraction and tracking
LET-NET implements an extremely lightweight network for feature point extraction and image consistency computation. The network can process a 240 x 320 image on a CPU in about 5ms. Combined with LK optical flow, it breaks the assumption of brightness consistency and performs well on dynamic lighting as well as blurred images.
<div display:inline>
<a href="https://www.bilibili.com/video/BV1gz4y1V77M/?vd_source=4dd69fa6d40221a0fa0733def5c4708a#reply576226249"><img src="assets/bilibili3.png" width=30% /></a>
<a href="https://www.bilibili.com/video/BV1gz4y1V77M/?vd_source=4dd69fa6d40221a0fa0733def5c4708a#reply576226249"><img src="assets/bilibili2.png" width=30% /></a>
<a href="https://www.bilibili.com/video/BV1gz4y1V77M/?vd_source=4dd69fa6d40221a0fa0733def5c4708a#reply576226249"><img src="assets/bilibili.png" width=30% /></a>
</div>
## News
！！！ **A new [version](https://github.com/linyicheng1/LET-NET2) with end-to-end training has been made publicly available.**
1. The LET-NET training code is released at https://github.com/linyicheng1/LET-NET-Train.
2. Gray Image is also suport in LET-NET, you can get pytorch and onnx model tpye in `./model/`
3. [LET-VINS](https://github.com/linyicheng1/LET-NET/blob/main/assets/VINS-Mono.zip) Demo run on UMA-VI dataset is released.
4. Our proposed LET-VINS won the second place in the VIO track of the ICCV2023SLAM Challenge, which is the best performance among the traditional methods.
5. The preprinted paper was posted at [here](https://arxiv.org/abs/2310.15655).
<img src=https://github.com/linyicheng1/LET-NET/assets/50650063/f4e96620-925e-4ff8-a2a9-c8af202c3b99 border=10 width=40%>
## Related Paper
- **Breaking of brightness consistency in optical flow with a lightweight CNN network**,Yicheng Lin, Shuo Wang, Yunlong Jiang, Bin Han, arXiv:2310.15655, [pdf](https://arxiv.org/pdf/2310.15655.pdf)
## 1. Prerequisites
- OpenCV (https://docs.opencv.org/3.4/d7/d9f/tutorial_linux_install.html)
- ncnn (https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-linux)
> Notes: After installing ncnn, you need to change the path in CMakeLists.txt
```
set(ncnn_DIR "<your_path>/install/lib/cmake/ncnn" CACHE PATH "Directory that contains ncnnConfig.cmake")
```
## 2. Build
```
mkdir build && cd build
cmake .. && make -j4
```
## 3. Run demo
You can enter the path to a video or two images.
```
./build/demo <path_param> <path_bin> <path_video>
```
or
```
./build/demo <path_param> <path_bin> <path_img_1> <path_img_2>
```
For example using the data we provide:
```
./build/demo ./model/model.param ./model/model.bin ./assets/nyu_snippet.mp4
```
You should see the following output from the NYU sequence snippet:
<img src="assets/demo.gif" width="260">
## 4. Examples
### Dynamic lighting
The left is ours and the right is the original optical flow algorithm.
<table><tr>
<td><img src=assets/3.png border=0 width="260"></td>
<td><img src=assets/4.png border=0 width="260"></td>
</tr></table>
### Underwater
The left is ours and the right is the original optical flow algorithm.
<table><tr>
<td><img src=assets/1.png border=0 width="260"></td>
<td><img src=assets/2.png border=0 width="260"></td>
</tr></table>
### Active light source
The left is ours and the right is the original optical flow algorithm.
<table><tr>
<td><img src=assets/7.png border=0 width="260"></td>
<td><img src=assets/8.png border=0 width="260"></td>
</tr></table>
## 5. Cite
```
@ARTICLE{let-net,
author={Lin, Yicheng and Wang, Shuo and Jiang, Yunlong and Han, Bin},
journal={IEEE Robotics and Automation Letters},
title={{Breaking of brightness consistency in optical flow with a lightweight CNN network}},
year={2024},
pages={1-8}
}
```
================================================
FILE: main.cpp
================================================
#include "net.h"
#include "mat.h"
#include "opencv2/opencv.hpp"
#include "chrono"
#include "tracking.h"
#define IMG_H 240
#define IMG_W 320
int main(int argc, char** argv) {
if (argc != 4 && argc != 5) {
std::cout<<" Usage: ./demo <model_param> <model_bin> <video_path> <video_path> or ./demo <model_param> <model_bin> <image_path_1> <image_path_2>"<<std::endl;
return -1;
}
cv::VideoCapture capture;
cv::VideoWriter writer("output.avi", cv::VideoWriter::fourcc('M', 'J', 'P', 'G'), 10, cv::Size(320, 240));
cv::Mat img1, img2;
bool is_video = false;
if (argc == 4) {
is_video = true;
std::string video_path = argv[3];
capture.open(video_path);
if (!capture.isOpened()) {
std::cout<<" Error opening video file !"<<std::endl;
return -1;
}
} else {
std::string img_path_1 = argv[3];
std::string img_path_2 = argv[4];
img1 = cv::imread(img_path_1);
img2 = cv::imread(img_path_2);
cv::resize(img1, img1, cv::Size(IMG_W, IMG_H));
cv::resize(img2, img2, cv::Size(IMG_W, IMG_H));
if (img1.empty() || img2.empty()) {
std::cout << " Error opening image file !" << std::endl;
return -1;
}
}
const float mean_vals[3] = {0, 0, 0};
const float norm_vals[3] = {1.0/255.0, 1.0/255.0, 1.0/255.0};
const float mean_vals_inv[3] = {0, 0, 0};
const float norm_vals_inv[3] = {255.f, 255.f, 255.f};
ncnn::Net net;
net.load_param(argv[1]);
net.load_model(argv[2]);
cv::Mat score(IMG_H, IMG_W, CV_8UC1);
cv::Mat desc(IMG_H, IMG_W, CV_8UC3);
cv::Mat frame;
ncnn::Mat in;
ncnn::Mat out1, out2;
corner_tracking tracker;
while (true) {
ncnn::Extractor ex = net.create_extractor();
ex.set_light_mode(true);
ex.set_num_threads(1);
if (is_video) {
capture >> frame;
} else {
static int i = 0;
if (i == 0)
frame = img1;
else if (i == 1)
frame = img2;
else
break;
i++;
}
if (frame.empty())
break;
cv::resize(frame, frame, cv::Size(IMG_W, IMG_H));
////////////////////////// opencv image to ncnn mat //////////////////////////
std::chrono::high_resolution_clock::time_point t1 = std::chrono::high_resolution_clock::now();
in = ncnn::Mat::from_pixels(frame.data, ncnn::Mat::PIXEL_BGR, frame.cols, frame.rows);
in.substract_mean_normalize(mean_vals, norm_vals);
////////////////////////// ncnn forward //////////////////////////
std::chrono::high_resolution_clock::time_point t2 = std::chrono::high_resolution_clock::now();
ex.input("input", in);
ex.extract("score", out1);
ex.extract("descriptor", out2);
////////////////////////// ncnn mat to opencv image //////////////////////////
std::chrono::high_resolution_clock::time_point t3 = std::chrono::high_resolution_clock::now();
out1.substract_mean_normalize(mean_vals_inv, norm_vals_inv);
out2.substract_mean_normalize(mean_vals_inv, norm_vals_inv);
// memcpy((uchar*)score.data, out1.data, sizeof(float) * out1.w * out1.h);
out1.to_pixels(score.data, ncnn::Mat::PIXEL_GRAY);
out2.to_pixels(desc.data, ncnn::Mat::PIXEL_BGR);
std::chrono::high_resolution_clock::time_point t4 = std::chrono::high_resolution_clock::now();
////////////////////////// show times //////////////////////////
std::chrono::duration<double> time_used_1 = std::chrono::duration_cast<std::chrono::duration<double>>(t2-t1);
std::chrono::duration<double> time_used_2 = std::chrono::duration_cast<std::chrono::duration<double>>(t3-t2);
std::chrono::duration<double> time_used_3 = std::chrono::duration_cast<std::chrono::duration<double>>(t4-t3);
std::cout<<"time_used 1 : "<<time_used_1.count()*1000<<"ms"<<std::endl;
std::cout<<"time_used 2 : "<<time_used_2.count()*1000<<"ms"<<std::endl;
std::cout<<"time_used 3 : "<<time_used_3.count()*1000<<"ms"<<std::endl;
////////////////////////// show result //////////////////////////
cv::Mat new_desc = desc.clone();
tracker.update(score, new_desc);
tracker.show(frame);
if (is_video) {
writer << frame;
}
cv::waitKey(500);
}
writer.release();
capture.release();
return 0;
}
================================================
FILE: model/letnet-gray.onnx
================================================
pytorch2.0.1:
input.1
onnx::Conv_35
onnx::Conv_36/block1/conv1/Conv_output_0/block1/conv1/Conv"Conv*
dilations@@*
group*
kernel_shape@@*
pads@@@@*
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
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onnx::Conv_38
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dilations@@*
group*
kernel_shape@@*
pads@@@@*
strides@@2/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
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group*
kernel_shape@@*
pads@ @ @ @ *
strides@@2
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(167): forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/conv1/Conv_output_0/gate/Relu_output_0
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onnx::Conv_39J x?]?fR???
2H>a>Z#
input.1
b
27
b
33
B
================================================
FILE: model/letnet.onnx
================================================
pytorch2.0.1:
input.1
onnx::Conv_35
onnx::Conv_36/block1/conv1/Conv_output_0/block1/conv1/Conv"Conv*
dilations@@*
group*
kernel_shape@@*
pads@@@@*
strides@@2/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(463): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(49): forward
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/home/server/wangshuo/ALIKE_code/nets/alnet.py(166): forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
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/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/activation.py(103): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(49): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(166): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/block1/gate/Relu_output_0
onnx::Conv_38
onnx::Conv_39/block1/conv2/Conv_output_0/block1/conv2/Conv"Conv*
dilations@@*
group*
kernel_shape@@*
pads@@@@*
strides@@2/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(463): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(50): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(166): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/block1/conv2/Conv_output_0 /block1/gate_1/Relu_output_0/block1/gate_1/Relu"Relu2/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/functional.py(1455): relu
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/activation.py(103): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(50): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(166): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/block1/gate_1/Relu_output_0
conv1.weight/conv1/Conv_output_0 /conv1/Conv"Conv*
dilations@@*
group*
kernel_shape@@*
pads@ @ @ @ *
strides@@2
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(463): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(167): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/conv1/Conv_output_0/gate/Relu_output_0
/gate/Relu"Relu2
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/functional.py(1455): relu
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/activation.py(103): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(167): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
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group*
kernel_shape@@*
pads@ @ @ @ *
strides@@2
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(459): _conv_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/conv.py(463): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/nets/alnet.py(169): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
E/Constant_output_0 /Constant"Constant*
value* J 2
/conv_head/Conv_output_0
/Constant_output_0/Gather_output_0/Gather"Gather*
axis2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(170): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Gather_output_0/Sigmoid_output_0/Sigmoid"Sigmoid2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(170): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Sigmoid_output_027
/Unsqueeze" Unsqueeze*
axes@2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(170): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Constant_1_output_0 /Constant_1"Constant*
value*J 2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Constant_2_output_0 /Constant_2"Constant*
value*J 2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Constant_3_output_0 /Constant_3"Constant*
value*J2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Constant_4_output_0 /Constant_4"Constant*
value*J 2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/conv_head/Conv_output_0
/Constant_2_output_0
/Constant_3_output_0
/Constant_1_output_0
/Constant_4_output_0/Slice_output_0/Slice"Slice2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
/Slice_output_033
/Sigmoid_1"Sigmoid2
/home/server/wangshuo/ALIKE_code/nets/alnet.py(171): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1488): _slow_forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/nn/modules/module.py(1501): _call_impl
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/jit/_trace.py(1268): _get_trace_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(893): _trace_and_get_graph_from_model
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(989): _create_jit_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1113): _model_to_graph
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(1548): _export
/home/server/wangshuo/ALIKE_code/py38/lib/python3.8/site-packages/torch/onnx/utils.py(506): export
/home/server/wangshuo/ALIKE_code/nets/alnet.py(318): <module>
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================================================
FILE: model/model.param
================================================
7767517
14 15
Input input 0 1 input
Convolution Conv_0 1 1 input input.4 0=8 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=216
ReLU Relu_1 1 1 input.4 onnx::Conv_17
Convolution Conv_2 1 1 onnx::Conv_17 input.12 0=8 1=3 11=3 2=1 12=1 3=1 13=1 4=1 14=1 15=1 16=1 5=1 6=576
ReLU Relu_3 1 1 input.12 onnx::Conv_20
Convolution Conv_4 1 1 onnx::Conv_20 input.16 0=16 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=0 6=128
ReLU Relu_5 1 1 input.16 onnx::Conv_22
Convolution Conv_6 1 1 onnx::Conv_22 onnx::Gather_23 0=4 1=1 11=1 2=1 12=1 3=1 13=1 4=0 14=0 15=0 16=0 5=0 6=64
Split splitncnn_0 1 2 onnx::Gather_23 onnx::Gather_23_splitncnn_0 onnx::Gather_23_splitncnn_1
Crop Gather_8 1 1 onnx::Gather_23_splitncnn_1 onnx::Sigmoid_25 -23309=1,3 -23310=1,4 -23311=1,0
Sigmoid Sigmoid_9 1 1 onnx::Sigmoid_25 onnx::Unsqueeze_26
ExpandDims Unsqueeze_10 1 1 onnx::Unsqueeze_26 score -23303=1,0
Crop Slice_15 1 1 onnx::Gather_23_splitncnn_0 onnx::Sigmoid_32 -23309=1,0 -23310=1,-1 -23311=1,0
Sigmoid Sigmoid_16 1 1 onnx::Sigmoid_32 descriptor
================================================
FILE: tracking.cpp
================================================
#include "tracking.h"
void corner_tracking::update(const cv::Mat& score, const cv::Mat& desc) {
if (trackedPoints.empty()) {// first frame
trackedPoints = extractFeature(score);
trackedPointsHistory.resize(trackedPoints.size());
for (size_t i = 0; i < trackedPoints.size(); i++) {
trackedPointsHistory[i].push_back(trackedPoints[i]);
}
} else {
std::vector<cv::Point2f> trackedPointsNew;
std::vector<uchar> status;
std::vector<float> err;
cv::calcOpticalFlowPyrLK(
prevDesc,
desc,
trackedPoints,
trackedPointsNew,
status,
err);
std::vector<cv::Point2f> tracked = {};
std::vector<std::vector<cv::Point2f>> trackedHistory = {};
for (size_t i = 0; i < status.size(); i++) {
if (status[i]) {
tracked.push_back(trackedPointsNew[i]);
trackedPointsHistory[i].push_back(trackedPointsNew[i]);
if (trackedPointsHistory[i].size() > 5) {
trackedPointsHistory[i].erase(trackedPointsHistory[i].begin());
}
trackedHistory.push_back(trackedPointsHistory[i]);
}
}
std::vector<cv::Point2f> add = extractFeature(score, 20, tracked);
std::vector<std::vector<cv::Point2f>> add_history(add.size());
for (size_t i = 0; i < add.size(); i++) {
add_history[i].push_back(add[i]);
}
trackedPoints.clear();
trackedPointsHistory.clear();
trackedPoints.insert(trackedPoints.end(), tracked.begin(), tracked.end());
trackedPoints.insert(trackedPoints.end(), add.begin(), add.end());
trackedPointsHistory.insert(trackedPointsHistory.end(), trackedHistory.begin(), trackedHistory.end());
trackedPointsHistory.insert(trackedPointsHistory.end(), add_history.begin(), add_history.end());
}
prevDesc = desc;
}
void corner_tracking::show(cv::Mat &img) {
for (auto& p : trackedPoints) {
cv::circle(img, p, 2, cv::Scalar(0, 255, 0), -1);
}
for (auto& history : trackedPointsHistory) {
for (size_t i = 1; i < history.size(); i++) {
cv::line(img, history[i - 1], history[i], cv::Scalar(0, 0, 255), 1);
}
}
cv::imshow("tracking", img);
}
std::vector<cv::Point2f> corner_tracking::extractFeature(
const cv::Mat& score,
int ncellsize,
const std::vector<cv::Point2f>& vcurkps)
{
if (score.empty()) {
return std::vector<cv::Point2f>();
}
size_t ncols = score.cols;
size_t nrows = score.rows;
size_t nhalfcell = ncellsize / 4;
size_t nhcells = nrows / ncellsize;
size_t nwcells = ncols / ncellsize;
size_t nbcells = nhcells * nwcells;
std::vector<cv::Point2f> vdetectedpx;
vdetectedpx.reserve(nbcells);
std::vector<std::vector<bool>> voccupcells(
nhcells + 1,
std::vector<bool>(nwcells + 1, false)
);
cv::Mat mask = cv::Mat::ones(score.rows, score.cols, CV_8UC1);
for (const auto& px : vcurkps) {
voccupcells[px.y / ncellsize][px.x / ncellsize] = true;
cv::circle(mask, px, nhalfcell, cv::Scalar(0.), -1);
}
size_t nboccup = 0;
std::vector<std::vector<cv::Point2f>> vvdetectedpx(nbcells);
std::vector<std::vector<cv::Point2f>> vvsecdetectionspx(nbcells);
auto cvrange = cv::Range(0, nbcells);
parallel_for_(cvrange, [&](const cv::Range& range)
{
for (int i = range.start; i < range.end; i ++) {
size_t r = floor(i / nwcells);
size_t c = i % nwcells;
if( voccupcells[r][c] ) {
nboccup++;
continue;
}
size_t x = c*ncellsize;
size_t y = r*ncellsize;
cv::Rect hroi(x,y,ncellsize,ncellsize);
if( x+ncellsize < ncols-1 && y+ncellsize < nrows-1 ) {
double dminval, dmaxval;
cv::Point minpx, maxpx;
cv::minMaxLoc(score(hroi).mul(mask(hroi)), &dminval, &dmaxval, &minpx, &maxpx);
maxpx.x += x;
maxpx.y += y;
if( dmaxval >= 0.2) {
vvdetectedpx.at(i).push_back(maxpx);
cv::circle(mask, maxpx, nhalfcell, cv::Scalar(0.), -1);
}
cv::minMaxLoc(score(hroi).mul(mask(hroi)), &dminval, &dmaxval, &minpx, &maxpx);
maxpx.x += x;
maxpx.y += y;
if( dmaxval >= 0.2)
{
vvsecdetectionspx.at(i).push_back(maxpx);
cv::circle(mask, maxpx, nhalfcell, cv::Scalar(0.), -1);
}
}
}
});
for (const auto& vpx:vvdetectedpx) {
if (!vpx.empty()) {
vdetectedpx.insert(vdetectedpx.end(), vpx.begin(), vpx.end());
}
}
size_t nbkps = vdetectedpx.size();
if (nbkps + nboccup < nbcells) {
size_t nbsec = nbcells - nbkps - nboccup;
size_t k = 0;
for (const auto &vseckp : vvsecdetectionspx) {
if (!vseckp.empty()) {
vdetectedpx.push_back(vseckp.back());
k ++;
if (k == nbsec) {
break;
}
}
}
}
return vdetectedpx;
}
================================================
FILE: tracking.h
================================================
#ifndef __TRACKING_H_
#define __TRACKING_H_
#include <vector>
#include "opencv2/opencv.hpp"
class corner_tracking
{
public:
corner_tracking() = default;
~corner_tracking() = default;
void update(const cv::Mat& score, const cv::Mat& desc);
void show(cv::Mat& img);
private:
std::vector<cv::Point2f> extractFeature(
const cv::Mat& score,
int ncellsize = 20,
const std::vector<cv::Point2f>& points = std::vector<cv::Point2f>());
std::vector<cv::Point2f> trackedPoints;
std::vector<cv::Point2f> prevTrackedPoints;
cv::Mat prevScore;
cv::Mat prevDesc;
std::vector<std::vector<cv::Point2f>> trackedPointsHistory;
};
#endif //__TRACKING_H_
gitextract_15a7jtc0/ ├── CMakeLists.txt ├── README.md ├── main.cpp ├── model/ │ ├── letnet-gray.onnx │ ├── letnet-gray.pt │ ├── letnet.onnx │ ├── letnet.pt │ └── model.param ├── tracking.cpp └── tracking.h
SYMBOL INDEX (2 symbols across 2 files)
FILE: main.cpp
function main (line 10) | int main(int argc, char** argv) {
FILE: tracking.h
function class (line 7) | class corner_tracking
Condensed preview — 10 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (84K chars).
[
{
"path": "CMakeLists.txt",
"chars": 505,
"preview": "cmake_minimum_required(VERSION 3.10)\nproject(demo)\n\nset(CMAKE_CXX_STANDARD 14)\nSET(CMAKE_BUILD_TYPE Release)\nset(CMAKE_C"
},
{
"path": "README.md",
"chars": 3801,
"preview": "# LET-NET: A lightweight CNN network for sparse corners extraction and tracking\n\nLET-NET implements an extremely lightwe"
},
{
"path": "main.cpp",
"chars": 4569,
"preview": "#include \"net.h\"\n#include \"mat.h\"\n#include \"opencv2/opencv.hpp\"\n#include \"chrono\"\n#include \"tracking.h\"\n\n#define IMG_H 2"
},
{
"path": "model/letnet-gray.onnx",
"chars": 29086,
"preview": "\b\u0006\u0012\u0007pytorch\u001a\u00052.0.1:\u0001\n\u0011\n\u0007input.1\n\ronnx::Conv_35\n\ronnx::Conv_36\u0012\u001b/block1/conv1/Conv_output_0\u001a\u0012/block1/conv1/Conv\"\u0004Conv*\u0012\n\t"
},
{
"path": "model/letnet.onnx",
"chars": 29443,
"preview": "\b\u0006\u0012\u0007pytorch\u001a\u00052.0.1:\u0001\n\u0011\n\u0007input.1\n\ronnx::Conv_35\n\ronnx::Conv_36\u0012\u001b/block1/conv1/Conv_output_0\u001a\u0012/block1/conv1/Conv\"\u0004Conv*\u0012\n\t"
},
{
"path": "model/model.param",
"chars": 1391,
"preview": "7767517\n14 15\nInput input 0 1 input\nConvolution Conv_0 1 1 input in"
},
{
"path": "tracking.cpp",
"chars": 5445,
"preview": "#include \"tracking.h\"\n\nvoid corner_tracking::update(const cv::Mat& score, const cv::Mat& desc) {\n\n if (trackedPoints."
},
{
"path": "tracking.h",
"chars": 713,
"preview": "#ifndef __TRACKING_H_\n#define __TRACKING_H_\n#include <vector>\n#include \"opencv2/opencv.hpp\"\n\n\nclass corner_tracking\n{\npu"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the linyicheng1/LET-NET GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 10 files (73.2 KB), approximately 27.4k tokens, and a symbol index with 2 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.