add retinaface(mobilenet0.25)
This commit is contained in:
wang-xinyu
parent
efb809c043
commit
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30
README.md
30
README.md
@ -59,7 +59,7 @@ Following models are implemented.
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|[yolov3-spp](./yolov3-spp)| darknet-53, weights and pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
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|[yolov4](./yolov4)| CSPDarknet53, weights from [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet#pre-trained-models), pytorch implementation from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) |
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|[yolov5](./yolov5)| yolov5-s/m/l/x v1.0 v2.0 v3.0, pytorch implementation from [ultralytics/yolov5](https://github.com/ultralytics/yolov5) |
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|[retinaface](./retinaface)| resnet-50, weights from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) |
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|[retinaface](./retinaface)| resnet50 and mobilnet0.25, weights from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) |
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|[arcface](./arcface)| LResNet50E-IR, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface) |
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|[retinafaceAntiCov](./retinafaceAntiCov)| mobilenet0.25, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface), retinaface anti-COVID-19, detect face and mask attribute |
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|[dbnet](./dbnet)| Scene Text Detection, weights from [BaofengZan/DBNet.pytorch](https://github.com/BaofengZan/DBNet.pytorch) |
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@ -91,20 +91,20 @@ Some tricky operations encountered in these models, already solved, but might ha
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| Models | Device | BatchSize | Mode | Input Shape(HxW) | FPS |
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|-|-|:-:|:-:|:-:|:-:|
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| YOLOv3-tiny | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 333 |
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| YOLOv3(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 39.2 |
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| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 38.5 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 35.7 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 40.9 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 41.3 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 142 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 173 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 190 |
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| YOLOv5-m | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 71 |
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| YOLOv5-l | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 43 |
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| YOLOv5-x | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 29 |
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| RetinaFace(resnet50) | TX2 | 1 | FP16 | 384x640 | 15 |
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| RetinaFace(resnet50) | Xeon E5-2620/GTX1080 | 1 | FP32 | 928x1600 | 15 |
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| YOLOv3-tiny | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 333 |
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| YOLOv3(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 39.2 |
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| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 38.5 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 35.7 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 4 | FP32 | 608x608 | 40.9 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 8 | FP32 | 608x608 | 41.3 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 142 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 4 | FP32 | 608x608 | 173 |
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| YOLOv5-s | Xeon E5-2620/GTX1080 | 8 | FP32 | 608x608 | 190 |
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| YOLOv5-m | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 71 |
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| YOLOv5-l | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 43 |
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| YOLOv5-x | Xeon E5-2620/GTX1080 | 1 | FP32 | 608x608 | 29 |
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| RetinaFace(resnet50) | Xeon E5-2620/GTX1080 | 1 | FP32 | 480x640 | 90 |
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| RetinaFace(mobilenet0.25) | Xeon E5-2620/GTX1080 | 1 | FP32 | 480x640 | 333 |
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| ArcFace(LResNet50E-IR) | Xeon E5-2620/GTX1080 | 1 | FP32 | 112x112 | 333 |
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Help wanted, if you got speed results, please add an issue or PR.
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@ -30,11 +30,17 @@ target_link_libraries(decodeplugin nvinfer cudart)
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find_package(OpenCV)
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include_directories(OpenCV_INCLUDE_DIRS)
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add_executable(retina_50 ${PROJECT_SOURCE_DIR}/retina_r50.cpp)
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target_link_libraries(retina_50 nvinfer)
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target_link_libraries(retina_50 cudart)
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target_link_libraries(retina_50 decodeplugin)
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target_link_libraries(retina_50 ${OpenCV_LIBRARIES})
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add_executable(retina_r50 ${PROJECT_SOURCE_DIR}/retina_r50.cpp)
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target_link_libraries(retina_r50 nvinfer)
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target_link_libraries(retina_r50 cudart)
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target_link_libraries(retina_r50 decodeplugin)
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target_link_libraries(retina_r50 ${OpenCV_LIBRARIES})
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add_executable(retina_mnet ${PROJECT_SOURCE_DIR}/retina_mnet.cpp)
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target_link_libraries(retina_mnet nvinfer)
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target_link_libraries(retina_mnet cudart)
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target_link_libraries(retina_mnet decodeplugin)
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target_link_libraries(retina_mnet ${OpenCV_LIBRARIES})
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add_definitions(-O2 -pthread)
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@ -1,7 +1,7 @@
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# RetinaFace
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The pytorch implementation is [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface), I forked it into
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[wang-xinyu/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) and add genwts.py
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[wang-xinyu/Pytorch_Retinaface](https://github.com/wang-xinyu/Pytorch_Retinaface) and add genwts.py
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This branch is using TensorRT 7 API, branch [trt4->retinaface](https://github.com/wang-xinyu/tensorrtx/tree/trt4/retinaface) is using TensorRT 4.
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@ -12,8 +12,8 @@ namespace decodeplugin
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float class_confidence;
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float landmark[10];
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};
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static const int INPUT_H = 928;
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static const int INPUT_W = 1600;
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static const int INPUT_H = 480;
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static const int INPUT_W = 640;
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}
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namespace nvinfer1
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563
retinaface/retina_mnet.cpp
Normal file
563
retinaface/retina_mnet.cpp
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@ -0,0 +1,563 @@
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <chrono>
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#include <opencv2/opencv.hpp>
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include "decode.h"
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#include "logging.h"
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#define CHECK(status) \
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do\
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{\
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auto ret = (status);\
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if (ret != 0)\
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{\
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std::cerr << "Cuda failure: " << ret << std::endl;\
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abort();\
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}\
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} while (0)
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#define USE_FP16 // comment out this if want to use FP32
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#define DEVICE 0 // GPU id
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#define BATCH_SIZE 1
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#define TOP_K 5000
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#define VIS_THRESH 0.6
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = decodeplugin::INPUT_H; // H, W must be able to be divided by 32.
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static const int INPUT_W = decodeplugin::INPUT_W;;
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static const int OUTPUT_SIZE = (INPUT_H / 8 * INPUT_W / 8 + INPUT_H / 16 * INPUT_W / 16 + INPUT_H / 32 * INPUT_W / 32) * 2 * 15 + 1;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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using namespace nvinfer1;
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static Logger gLogger;
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cv::Mat preprocess_img(cv::Mat& img) {
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int w, h, x, y;
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float r_w = INPUT_W / (img.cols*1.0);
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float r_h = INPUT_H / (img.rows*1.0);
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if (r_h > r_w) {
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w = INPUT_W;
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h = r_w * img.rows;
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x = 0;
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y = (INPUT_H - h) / 2;
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} else {
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w = r_h* img.cols;
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h = INPUT_H;
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x = (INPUT_W - w) / 2;
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y = 0;
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}
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cv::Mat re(h, w, CV_8UC3);
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cv::resize(img, re, re.size(), 0, 0, cv::INTER_LINEAR);
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cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
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re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
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return out;
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}
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cv::Rect get_rect_adapt_landmark(cv::Mat& img, float bbox[4], float lmk[10]) {
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int l, r, t, b;
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float r_w = INPUT_W / (img.cols * 1.0);
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float r_h = INPUT_H / (img.rows * 1.0);
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if (r_h > r_w) {
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l = bbox[0] / r_w;
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r = bbox[2] / r_w;
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t = (bbox[1] - (INPUT_H - r_w * img.rows) / 2) / r_w;
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b = (bbox[3] - (INPUT_H - r_w * img.rows) / 2) / r_w;
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for (int i = 0; i < 10; i += 2) {
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lmk[i] /= r_w;
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lmk[i + 1] = (lmk[i + 1] - (INPUT_H - r_w * img.rows) / 2) / r_w;
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}
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} else {
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l = (bbox[0] - (INPUT_W - r_h * img.cols) / 2) / r_h;
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r = (bbox[2] - (INPUT_W - r_h * img.cols) / 2) / r_h;
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t = bbox[1] / r_h;
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b = bbox[3] / r_h;
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for (int i = 0; i < 10; i += 2) {
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lmk[i] = (lmk[i] - (INPUT_W - r_h * img.cols) / 2) / r_h;
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lmk[i + 1] /= r_h;
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}
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}
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return cv::Rect(l, t, r-l, b-t);
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}
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float iou(float lbox[4], float rbox[4]) {
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float interBox[] = {
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std::max(lbox[0], rbox[0]), //left
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std::min(lbox[2], rbox[2]), //right
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std::max(lbox[1], rbox[1]), //top
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std::min(lbox[3], rbox[3]), //bottom
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};
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if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
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return 0.0f;
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float interBoxS = (interBox[1] - interBox[0]) * (interBox[3] - interBox[2]);
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return interBoxS / ((lbox[2] - lbox[0]) * (lbox[3] - lbox[1]) + (rbox[2] - rbox[0]) * (rbox[3] - rbox[1]) -interBoxS + 0.000001f);
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}
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bool cmp(decodeplugin::Detection& a, decodeplugin::Detection& b) {
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return a.class_confidence > b.class_confidence;
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}
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void nms(std::vector<decodeplugin::Detection>& res, float *output, float nms_thresh = 0.4) {
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std::vector<decodeplugin::Detection> dets;
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for (int i = 0; i < output[0]; i++) {
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if (output[15 * i + 1 + 4] <= 0.1) continue;
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decodeplugin::Detection det;
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memcpy(&det, &output[15 * i + 1], sizeof(decodeplugin::Detection));
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dets.push_back(det);
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}
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std::sort(dets.begin(), dets.end(), cmp);
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if (dets.size() > TOP_K) dets.erase(dets.begin() + TOP_K, dets.end());
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for (size_t m = 0; m < dets.size(); ++m) {
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auto& item = dets[m];
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res.push_back(item);
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//std::cout << item.class_confidence << " bbox " << item.bbox[0] << ", " << item.bbox[1] << ", " << item.bbox[2] << ", " << item.bbox[3] << std::endl;
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for (size_t n = m + 1; n < dets.size(); ++n) {
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if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
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dets.erase(dets.begin()+n);
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--n;
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}
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}
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}
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}
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// Load weights from files
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// TensorRT weight files have a simple space delimited format:
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// [type] [size] <data x size in hex>
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std::map<std::string, Weights> loadWeights(const std::string file) {
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std::cout << "Loading weights: " << file << std::endl;
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std::map<std::string, Weights> weightMap;
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// Open weights file
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std::ifstream input(file);
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assert(input.is_open() && "Unable to load weight file.");
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// Read number of weight blobs
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int32_t count;
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input >> count;
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assert(count > 0 && "Invalid weight map file.");
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while (count--)
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{
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Weights wt{DataType::kFLOAT, nullptr, 0};
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uint32_t size;
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// Read name and type of blob
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std::string name;
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input >> name >> std::dec >> size;
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wt.type = DataType::kFLOAT;
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// Load blob
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uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
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for (uint32_t x = 0, y = size; x < y; ++x)
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{
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input >> std::hex >> val[x];
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}
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wt.values = val;
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wt.count = size;
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weightMap[name] = wt;
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}
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return weightMap;
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}
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Weights getWeights(std::map<std::string, Weights>& weightMap, std::string key) {
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if (weightMap.count(key) != 1) {
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std::cerr << key << " not existed in weight map, fatal error!!!" << std::endl;
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exit(-1);
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}
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return weightMap[key];
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}
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IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
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float *gamma = (float*)weightMap[lname + ".weight"].values;
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float *beta = (float*)weightMap[lname + ".bias"].values;
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float *mean = (float*)weightMap[lname + ".running_mean"].values;
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float *var = (float*)weightMap[lname + ".running_var"].values;
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int len = weightMap[lname + ".running_var"].count;
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float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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scval[i] = gamma[i] / sqrt(var[i] + eps);
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}
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Weights scale{DataType::kFLOAT, scval, len};
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float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
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}
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Weights shift{DataType::kFLOAT, shval, len};
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float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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pval[i] = 1.0;
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}
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Weights power{DataType::kFLOAT, pval, len};
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weightMap[lname + ".scale"] = scale;
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weightMap[lname + ".shift"] = shift;
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weightMap[lname + ".power"] = power;
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IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
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assert(scale_1);
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return scale_1;
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}
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ILayer* conv_bn(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1, float leaky = 0.1) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{1, 1});
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
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auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
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lr->setAlpha(leaky);
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assert(lr);
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return lr;
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}
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ILayer* conv_bn_no_relu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{1, 1});
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
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return bn1;
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}
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ILayer* conv_bn1X1(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1, float leaky = 0.1) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{1, 1}, getWeights(weightMap, lname + ".0.weight"), emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{0, 0});
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||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
|
||||
auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
|
||||
lr->setAlpha(leaky);
|
||||
assert(lr);
|
||||
return lr;
|
||||
}
|
||||
|
||||
ILayer* conv_dw(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int inp, int oup, int s = 1, float leaky = 0.1) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, inp, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{s, s});
|
||||
conv1->setPaddingNd(DimsHW{1, 1});
|
||||
conv1->setNbGroups(inp);
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
|
||||
auto lr1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
|
||||
lr1->setAlpha(leaky);
|
||||
assert(lr1);
|
||||
IConvolutionLayer* conv2 = network->addConvolutionNd(*lr1->getOutput(0), oup, DimsHW{1, 1}, getWeights(weightMap, lname + ".3.weight"), emptywts);
|
||||
assert(conv2);
|
||||
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".4", 1e-5);
|
||||
auto lr2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU);
|
||||
lr2->setAlpha(leaky);
|
||||
assert(lr2);
|
||||
return lr2;
|
||||
}
|
||||
|
||||
IActivationLayer* ssh(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup) {
|
||||
auto conv3x3 = conv_bn_no_relu(network, weightMap, input, lname + ".conv3X3", oup / 2);
|
||||
auto conv5x5_1 = conv_bn(network, weightMap, input, lname + ".conv5X5_1", oup / 4);
|
||||
auto conv5x5 = conv_bn_no_relu(network, weightMap, *conv5x5_1->getOutput(0), lname + ".conv5X5_2", oup / 4);
|
||||
auto conv7x7 = conv_bn(network, weightMap, *conv5x5_1->getOutput(0), lname + ".conv7X7_2", oup / 4);
|
||||
conv7x7 = conv_bn_no_relu(network, weightMap, *conv7x7->getOutput(0), lname + ".conv7x7_3", oup / 4);
|
||||
ITensor* inputTensors[] = {conv3x3->getOutput(0), conv5x5->getOutput(0), conv7x7->getOutput(0)};
|
||||
auto cat = network->addConcatenation(inputTensors, 3);
|
||||
IActivationLayer* relu1 = network->addActivation(*cat->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
return relu1;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create input tensor with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
|
||||
assert(data);
|
||||
|
||||
std::map<std::string, Weights> weightMap = loadWeights("../retinaface.wts");
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// ------------- backbone mobilenet0.25 ---------------
|
||||
// stage 1
|
||||
auto x = conv_bn(network, weightMap, *data, "body.stage1.0", 8, 2);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.1", 8, 16);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.2", 16, 32, 2);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.3", 32, 32);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.4", 32, 64, 2);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.5", 64, 64);
|
||||
auto stage1 = x;
|
||||
|
||||
// stage 2
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.0", 64, 128, 2);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.1", 128, 128);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.2", 128, 128);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.3", 128, 128);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.4", 128, 128);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.5", 128, 128);
|
||||
auto stage2 = x;
|
||||
|
||||
// stage 3
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage3.0", 128, 256, 2);
|
||||
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage3.1", 256, 256);
|
||||
auto stage3 = x;
|
||||
|
||||
//Dims d1 = stage1->getOutput(0)->getDimensions();
|
||||
//std::cout << d1.d[0] << " " << d1.d[1] << " " << d1.d[2] << std::endl;
|
||||
// ------------- FPN ---------------
|
||||
auto output1 = conv_bn1X1(network, weightMap, *stage1->getOutput(0), "fpn.output1", 64);
|
||||
auto output2 = conv_bn1X1(network, weightMap, *stage2->getOutput(0), "fpn.output2", 64);
|
||||
auto output3 = conv_bn1X1(network, weightMap, *stage3->getOutput(0), "fpn.output3", 64);
|
||||
|
||||
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 64 * 2 * 2));
|
||||
for (int i = 0; i < 64 * 2 * 2; i++) {
|
||||
deval[i] = 1.0;
|
||||
}
|
||||
Weights deconvwts{DataType::kFLOAT, deval, 64 * 2 * 2};
|
||||
IDeconvolutionLayer* up3 = network->addDeconvolutionNd(*output3->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts);
|
||||
assert(up3);
|
||||
up3->setStrideNd(DimsHW{2, 2});
|
||||
up3->setNbGroups(64);
|
||||
weightMap["up3"] = deconvwts;
|
||||
|
||||
output2 = network->addElementWise(*output2->getOutput(0), *up3->getOutput(0), ElementWiseOperation::kSUM);
|
||||
output2 = conv_bn(network, weightMap, *output2->getOutput(0), "fpn.merge2", 64);
|
||||
|
||||
IDeconvolutionLayer* up2 = network->addDeconvolutionNd(*output2->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts);
|
||||
assert(up2);
|
||||
up2->setStrideNd(DimsHW{2, 2});
|
||||
up2->setNbGroups(64);
|
||||
output1 = network->addElementWise(*output1->getOutput(0), *up2->getOutput(0), ElementWiseOperation::kSUM);
|
||||
output1 = conv_bn(network, weightMap, *output1->getOutput(0), "fpn.merge1", 64);
|
||||
|
||||
// ------------- SSH ---------------
|
||||
auto ssh1 = ssh(network, weightMap, *output1->getOutput(0), "ssh1", 64);
|
||||
auto ssh2 = ssh(network, weightMap, *output2->getOutput(0), "ssh2", 64);
|
||||
auto ssh3 = ssh(network, weightMap, *output3->getOutput(0), "ssh3", 64);
|
||||
|
||||
//// ------------- Head ---------------
|
||||
auto bbox_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.0.conv1x1.weight"], weightMap["BboxHead.0.conv1x1.bias"]);
|
||||
auto bbox_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.1.conv1x1.weight"], weightMap["BboxHead.1.conv1x1.bias"]);
|
||||
auto bbox_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.2.conv1x1.weight"], weightMap["BboxHead.2.conv1x1.bias"]);
|
||||
|
||||
auto cls_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.0.conv1x1.weight"], weightMap["ClassHead.0.conv1x1.bias"]);
|
||||
auto cls_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.1.conv1x1.weight"], weightMap["ClassHead.1.conv1x1.bias"]);
|
||||
auto cls_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.2.conv1x1.weight"], weightMap["ClassHead.2.conv1x1.bias"]);
|
||||
|
||||
auto lmk_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.0.conv1x1.weight"], weightMap["LandmarkHead.0.conv1x1.bias"]);
|
||||
auto lmk_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.1.conv1x1.weight"], weightMap["LandmarkHead.1.conv1x1.bias"]);
|
||||
auto lmk_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.2.conv1x1.weight"], weightMap["LandmarkHead.2.conv1x1.bias"]);
|
||||
|
||||
//// ------------- Decode bbox, conf, landmark ---------------
|
||||
ITensor* inputTensors1[] = {bbox_head1->getOutput(0), cls_head1->getOutput(0), lmk_head1->getOutput(0)};
|
||||
auto cat1 = network->addConcatenation(inputTensors1, 3);
|
||||
ITensor* inputTensors2[] = {bbox_head2->getOutput(0), cls_head2->getOutput(0), lmk_head2->getOutput(0)};
|
||||
auto cat2 = network->addConcatenation(inputTensors2, 3);
|
||||
ITensor* inputTensors3[] = {bbox_head3->getOutput(0), cls_head3->getOutput(0), lmk_head3->getOutput(0)};
|
||||
auto cat3 = network->addConcatenation(inputTensors3, 3);
|
||||
|
||||
auto creator = getPluginRegistry()->getPluginCreator("Decode_TRT", "1");
|
||||
PluginFieldCollection pfc;
|
||||
IPluginV2 *pluginObj = creator->createPlugin("decode", &pfc);
|
||||
ITensor* inputTensors[] = {cat1->getOutput(0), cat2->getOutput(0), cat3->getOutput(0)};
|
||||
auto decodelayer = network->addPluginV2(inputTensors, 3, *pluginObj);
|
||||
assert(decodelayer);
|
||||
|
||||
decodelayer->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*decodelayer->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
config->setMaxWorkspaceSize(1 << 20);
|
||||
#ifdef USE_FP16
|
||||
config->setFlag(BuilderFlag::kFP16);
|
||||
#endif
|
||||
std::cout << "Building engine, please wait for a while..." << std::endl;
|
||||
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
|
||||
std::cout << "Build engine successfully!" << std::endl;
|
||||
|
||||
// Don't need the network any more
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*)(mem.second.values));
|
||||
mem.second.values = NULL;
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
IBuilderConfig* config = builder->createBuilderConfig();
|
||||
|
||||
// Create model to populate the network, then set the outputs and create an engine
|
||||
ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
// Engine requires exactly IEngine::getNbBindings() number of buffers.
|
||||
assert(engine.getNbBindings() == 2);
|
||||
void* buffers[2];
|
||||
|
||||
// In order to bind the buffers, we need to know the names of the input and output tensors.
|
||||
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
|
||||
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
|
||||
// Create GPU buffers on device
|
||||
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
|
||||
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CHECK(cudaFree(buffers[inputIndex]));
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./retina_mnet -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./retina_mnet -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
cudaSetDevice(DEVICE);
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(BATCH_SIZE, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p("retina_mnet.engine", std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 1;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file("retina_mnet.engine", std::ios::binary);
|
||||
if (file.good()) {
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
}
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
|
||||
// prepare input data ---------------------------
|
||||
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
|
||||
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
||||
// data[i] = 1.0;
|
||||
|
||||
cv::Mat img = cv::imread("worlds-largest-selfie.jpg");
|
||||
cv::Mat pr_img = preprocess_img(img);
|
||||
//cv::imwrite("preprocessed.jpg", pr_img);
|
||||
|
||||
// For multi-batch, I feed the same image multiple times.
|
||||
// If you want to process different images in a batch, you need adapt it.
|
||||
for (int b = 0; b < BATCH_SIZE; b++) {
|
||||
float *p_data = &data[b * 3 * INPUT_H * INPUT_W];
|
||||
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
|
||||
p_data[i] = pr_img.at<cv::Vec3b>(i)[0] - 104.0;
|
||||
p_data[i + INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[1] - 117.0;
|
||||
p_data[i + 2 * INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[2] - 123.0;
|
||||
}
|
||||
}
|
||||
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
||||
//ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
|
||||
// Run inference
|
||||
static float prob[BATCH_SIZE * OUTPUT_SIZE];
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, data, prob, BATCH_SIZE);
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
||||
|
||||
for (int b = 0; b < BATCH_SIZE; b++) {
|
||||
std::vector<decodeplugin::Detection> res;
|
||||
nms(res, &prob[b * OUTPUT_SIZE]);
|
||||
std::cout << "number of detections -> " << prob[b * OUTPUT_SIZE] << std::endl;
|
||||
std::cout << " -> " << prob[b * OUTPUT_SIZE + 10] << std::endl;
|
||||
std::cout << "after nms -> " << res.size() << std::endl;
|
||||
cv::Mat tmp = img.clone();
|
||||
for (size_t j = 0; j < res.size(); j++) {
|
||||
if (res[j].class_confidence < VIS_THRESH) continue;
|
||||
cv::Rect r = get_rect_adapt_landmark(tmp, res[j].bbox, res[j].landmark);
|
||||
cv::rectangle(tmp, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
|
||||
//cv::putText(tmp, 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(tmp, 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);
|
||||
}
|
||||
}
|
||||
cv::imwrite(std::to_string(b) + "_result.jpg", tmp);
|
||||
}
|
||||
|
||||
// Destroy the engine
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
// Print histogram of the output distribution
|
||||
//std::cout << "\nOutput:\n\n";
|
||||
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
//{
|
||||
// std::cout << prob[i] << ", ";
|
||||
// if (i % 10 == 0) std::cout << i / 10 << std::endl;
|
||||
//}
|
||||
//std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user