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retinaface tested on 1080/trt7

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This commit is contained in:
wang-xinyu 2020-04-09 21:36:52 +08:00
parent 1fd0f2259e
commit d106bb5702
6 changed files with 23 additions and 12 deletions
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9
README.md
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@ -14,9 +14,9 @@ There is a guide for quickly getting started, taking lenet5 as a demo. [Getting_
## Test Environment
1. Jetson TX1 / Ubuntu16.04 / cuda9.0 / cudnn7.1.5 / tensorrt4.0.2 / nvinfer4.1.3
1. Jetson TX1 / Ubuntu16.04 / cuda9.0 / cudnn7.1.5 / tensorrt4.0.2 / nvinfer4.1.3 / opencv3.3
2. GTX1080 / Ubuntu16.04 / cuda10.0 / cudnn7.6.5 / tensorrt7.0.0 / nvinfer7.0.0
2. GTX1080 / Ubuntu16.04 / cuda10.0 / cudnn7.6.5 / tensorrt7.0.0 / nvinfer7.0.0 / opencv3.3
Currently, TX1 ans x86 GTX1080 were tested. trt4 api were using, some api are deprecated in trt7, but still can compile successfully.
@ -67,8 +67,11 @@ Some tricky operations encountered in these models, already solved, but might ha
| Models | Device | BatchSize | Mode | Input Shape(HxW) | FPS |
|-|-|:-:|:-:|:-:|:-:|
| YOLOv3(darknet53) | Xavier | 1 | FP16 | 320x320 | 55 |
| YOLOv3-spp(darknet53) | GTX1080 | 1 | FP32 | 256x416 | 94 |
| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 256x416 | 94 |
| RetinaFace(resnet50) | TX2 | 1 | FP16 | 384x640 | 15 |
| RetinaFace(resnet50) | Xeon E5-2620/GTX1080 | 1 | FP32 | 928x1600 | 15 |
Detection net FPS test including inference and nms time, excluding image preprocess time.
Help wanted, if you got speed results, please add an issue or PR.

12
retinaface/CMakeLists.txt
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@ -12,9 +12,15 @@ find_package(CUDA REQUIRED)
set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_30;code=sm_30)
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(/usr/local/cuda-9.0/targets/aarch64-linux/include)
link_directories(/usr/local/cuda-9.0/targets/aarch64-linux/lib)
if (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
message("embed_platform on")
include_directories(/usr/local/cuda/targets/aarch64-linux/include)
link_directories(/usr/local/cuda/targets/aarch64-linux/lib)
else()
message("embed_platform off")
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
endif()
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")

2
retinaface/README.md
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@ -2,7 +2,7 @@
## Notice
- Only tested on TensorRT4
- Tested on TX2/TensorRT4 and GTX1080/TensorRT7
- The pytorch implementation is [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface), I forked it into
[wang-xinyu/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) and add genwts.py

1
retinaface/decode.cu
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@ -102,6 +102,7 @@ namespace nvinfer1
int base_step = 8;
int base_anchor = 16;
int thread_count;
cudaMemset(output, 0, sizeof(float));
for (unsigned int i = 0; i < 3; ++i)
{
num_elem = decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step;

4
retinaface/decode.h
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@ -10,8 +10,8 @@ namespace decodeplugin
float class_confidence;
float landmark[10];
};
static const int INPUT_H = 384;
static const int INPUT_W = 640;
static const int INPUT_H = 928;
static const int INPUT_W = 1600;
}

7
retinaface/retina_r50.cpp
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@ -12,7 +12,7 @@
#include "decode.h"
#include <opencv2/opencv.hpp>
#define USE_FP16 // comment out this if want to use FP32
//#define USE_FP16 // comment out this if want to use FP32
#define DEVICE 0 // GPU id
// stuff we know about the network and the input/output blobs
@ -101,6 +101,7 @@ void nms(std::vector<decodeplugin::Detection>& res, float *output, float nms_thr
dets.push_back(det);
}
std::sort(dets.begin(), dets.end(), cmp);
if (dets.size() > 5000) dets.erase(dets.begin() + 5000, dets.end());
for (size_t m = 0; m < dets.size(); ++m) {
auto& item = dets[m];
res.push_back(item);
@ -497,7 +498,7 @@ int main(int argc, char** argv) {
}
// prepare input data ---------------------------
float data[3 * INPUT_H * INPUT_W];
static float data[3 * INPUT_H * INPUT_W];
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
// data[i] = 1.0;
@ -536,7 +537,7 @@ int main(int argc, char** argv) {
if (res[j].class_confidence < 0.1) continue;
cv::Rect r = get_rect_adapt_landmark(img, res[j].bbox, res[j].landmark);
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
cv::putText(img, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);
//cv::putText(img, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);
for (int k = 0; k < 10; k += 2) {
cv::circle(img, cv::Point(res[j].landmark[k], res[j].landmark[k + 1]), 1, cv::Scalar(255 * (k > 2), 255 * (k > 0 && k < 8), 255 * (k < 6)), 4);
}