add arcface
This commit is contained in:
wang-xinyu
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.gitignore
vendored
1
.gitignore
vendored
@ -1,2 +1,3 @@
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*/build
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*/*.wts
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*/*.ppm
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@ -8,9 +8,10 @@ I wrote this project to get familiar with tensorrt API, and also to share and le
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All the models are implemented in pytorch first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo [Pytorchx](https://github.com/wang-xinyu/pytorchx), the remaining are from polular open-source pytorch implementations.
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# News
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## News
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- `22 May 2020`. A new branch [trt4](https://github.com/wang-xinyu/tensorrtx/tree/trt4) created, which is using TensorRT 4 API. Now the master branch is using TensorRT 7 API. But only `yolov4` has been migrated to TensorRT 7 API for now. The rest will be migrated soon. And a tutorial for `migarating from TensorRT 4 to 7` provided.
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- `28 May 2020`. arcface LResNet50E-IR model from [deepinsight/insightface](https://github.com/deepinsight/insightface) implemented. We got 333fps on GTX1080.
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## Tutorials
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@ -47,6 +48,7 @@ Following models are implemented.
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|[yolov3-spp](./yolov3-spp)| darknet-53, weights 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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|[retinaface](./retinaface)| resnet-50, 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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## Tricky Operations
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@ -68,6 +70,7 @@ Some tricky operations encountered in these models, already solved, but might ha
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|hsigmoid| hard sigmoid is implemented as a plugin, hsigmoid and hswish are used in mobilenetv3 |
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|retinaface output decode| implement a plugin to decode bbox, confidence and landmarks, see retinaface. |
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|mish| mish activation is implemented as a plugin, mish is used in yolov4 |
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|prelu| mxnet's prelu activation with trainable gamma is implemented as a plugin, used in arcface |
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## Speed Benchmark
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@ -80,6 +83,7 @@ Some tricky operations encountered in these models, already solved, but might ha
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 41.3 |
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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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| ArcFace(LResNet50E-IR) | Xeon E5-2620/GTX1080 | 1 | FP32 | 112x112 | 333 |
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Detection net FPS test including inference and nms time, excluding image preprocess time.
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41
arcface/CMakeLists.txt
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41
arcface/CMakeLists.txt
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cmake_minimum_required(VERSION 2.6)
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project(arcface)
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add_definitions(-std=c++11)
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_BUILD_TYPE Debug)
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find_package(CUDA REQUIRED)
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set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_30;code=sm_30)
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include_directories(${PROJECT_SOURCE_DIR}/include)
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if (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64")
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message("embed_platform on")
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include_directories(/usr/local/cuda/targets/aarch64-linux/include)
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link_directories(/usr/local/cuda/targets/aarch64-linux/lib)
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else()
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message("embed_platform off")
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include_directories(/usr/local/cuda/include)
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link_directories(/usr/local/cuda/lib64)
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endif()
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
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cuda_add_library(myplugins SHARED ${PROJECT_SOURCE_DIR}/prelu.cu)
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find_package(OpenCV)
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include_directories(OpenCV_INCLUDE_DIRS)
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add_executable(arcface-r50 ${PROJECT_SOURCE_DIR}/arcface-r50.cpp)
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target_link_libraries(arcface-r50 nvinfer)
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target_link_libraries(arcface-r50 cudart)
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target_link_libraries(arcface-r50 myplugins)
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target_link_libraries(arcface-r50 ${OpenCV_LIBS})
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add_definitions(-O2 -pthread)
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47
arcface/README.md
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47
arcface/README.md
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# arcface
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The mxnet implementation is from [deepinsight/insightface.](https://github.com/deepinsight/insightface)
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The pretrained model is [LResNet50E-IR,ArcFace@ms1m-refine-v1.](https://github.com/deepinsight/insightface/wiki/Model-Zoo#32-lresnet50e-irarcfacems1m-refine-v1)
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The two images used in this project are joey0.ppm and joey1.ppm, download them from [Google Drive.](https://drive.google.com/drive/folders/1ctqpkRCRKyBZRCNwo9Uq4eUoMRLtFq1e)
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<p align="center">
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</p>
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## Run
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```
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1. generate arcface-r50.wts from mxnet implementation with LResNet50E-IR,ArcFace@ms1m-refine-v1 pretrained model
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git clone https://github.com/deepinsight/insightface
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cd insightface/deploy
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// copy tensorrtx/arcface/gen_wts.py to here(insightface/deploy)
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// download model-r50-am-lfw.zip and unzip here(insightface/deploy)
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python gen_wts.py
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// a file 'arcface-r50.wts' will be generated.
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// the master branch of insightface should work, if not, you can checkout 94ad870abb3203d6f31b049b70dd080dc8f33fca
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2. put arcface-r50.wts into tensorrtx/arcface, build and run
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cd tensorrtx/arcface
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// download joey0.ppm and joey1.ppm, and put here(tensorrtx/arcface)
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mkdir build
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cd build
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cmake ..
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make
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sudo ./arcface-r50 -s // serialize model to plan file i.e. 'arcface-r50.engine'
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sudo ./arcface-r50 -d // deserialize plan file and run inference
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3. check the output log, latency and similarity score.
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```
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## Config
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- FP16/FP32 can be selected by the macro `USE_FP16` in arcface-r50.cpp
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- GPU id can be selected by the macro `DEVICE` in arcface-r50.cpp
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## More Information
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See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
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403
arcface/arcface-r50.cpp
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403
arcface/arcface-r50.cpp
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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 <dirent.h>
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include "logging.h"
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#include "prelu.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 // currently, only support BATCH=1
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using namespace nvinfer1;
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 112;
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static const int INPUT_W = 112;
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static const int OUTPUT_SIZE = 512;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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static Logger gLogger;
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REGISTER_TENSORRT_PLUGIN(PReluPluginCreator);
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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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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 + "_gamma"].values;
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float *beta = (float*)weightMap[lname + "_beta"].values;
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float *mean = (float*)weightMap[lname + "_moving_mean"].values;
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float *var = (float*)weightMap[lname + "_moving_var"].values;
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int len = weightMap[lname + "_moving_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* addPRelu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname) {
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auto creator = getPluginRegistry()->getPluginCreator("PRelu_TRT", "1");
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PluginFieldCollection pfc;
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PluginField pf("gamma", weightMap[lname + "_gamma"].values, PluginFieldType::kFLOAT32, weightMap[lname + "_gamma"].count);
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pfc.nbFields = 1;
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pfc.fields = &pf;
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IPluginV2 *pluginObj = creator->createPlugin(lname.c_str(), &pfc);
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ITensor* inputTensors[] = {&input};
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auto prelu = network->addPluginV2(&inputTensors[0], 1, *pluginObj);
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assert(prelu);
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return prelu;
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}
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ILayer* resUnit(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int num_filters, int s, bool dim_match, std::string lname) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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auto bn1 = addBatchNorm2d(network, weightMap, input, lname + "_bn1", 2e-5);
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IConvolutionLayer* conv1 = network->addConvolutionNd(*bn1->getOutput(0), num_filters, DimsHW{3, 3}, weightMap[lname + "_conv1_weight"], emptywts);
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assert(conv1);
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conv1->setPaddingNd(DimsHW{1, 1});
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auto bn2 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "_bn2", 2e-5);
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auto act1 = addPRelu(network, weightMap, *bn2->getOutput(0), lname + "_relu1");
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IConvolutionLayer* conv2 = network->addConvolutionNd(*act1->getOutput(0), num_filters, DimsHW{3, 3}, weightMap[lname + "_conv2_weight"], emptywts);
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assert(conv2);
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conv2->setStrideNd(DimsHW{s, s});
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conv2->setPaddingNd(DimsHW{1, 1});
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auto bn3 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "_bn3", 2e-5);
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IElementWiseLayer* ew1;
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if (dim_match) {
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ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
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} else {
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IConvolutionLayer* conv1sc = network->addConvolutionNd(input, num_filters, DimsHW{1, 1}, weightMap[lname + "_conv1sc_weight"], emptywts);
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assert(conv1sc);
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conv1sc->setStrideNd(DimsHW{s, s});
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auto bn1sc = addBatchNorm2d(network, weightMap, *conv1sc->getOutput(0), lname + "_sc", 2e-5);
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ew1 = network->addElementWise(*bn1sc->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
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}
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assert(ew1);
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return ew1;
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}
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// Creat the engine using only the API and not any parser.
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ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("../arcface-r50.wts");
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv0 = network->addConvolutionNd(*data, 64, DimsHW{3, 3}, weightMap["conv0_weight"], emptywts);
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assert(conv0);
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conv0->setPaddingNd(DimsHW{1, 1});
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auto bn0 = addBatchNorm2d(network, weightMap, *conv0->getOutput(0), "bn0", 2e-5);
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auto relu0 = addPRelu(network, weightMap, *bn0->getOutput(0), "relu0");
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auto s1u1 = resUnit(network, weightMap, *relu0->getOutput(0), 64, 2, false, "stage1_unit1");
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auto s1u2 = resUnit(network, weightMap, *s1u1->getOutput(0), 64, 1, true, "stage1_unit2");
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auto s1u3 = resUnit(network, weightMap, *s1u2->getOutput(0), 64, 1, true, "stage1_unit3");
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auto s2u1 = resUnit(network, weightMap, *s1u3->getOutput(0), 128, 2, false, "stage2_unit1");
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auto s2u2 = resUnit(network, weightMap, *s2u1->getOutput(0), 128, 1, true, "stage2_unit2");
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auto s2u3 = resUnit(network, weightMap, *s2u2->getOutput(0), 128, 1, true, "stage2_unit3");
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auto s2u4 = resUnit(network, weightMap, *s2u3->getOutput(0), 128, 1, true, "stage2_unit4");
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auto s3u1 = resUnit(network, weightMap, *s2u4->getOutput(0), 256, 2, false, "stage3_unit1");
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auto s3u2 = resUnit(network, weightMap, *s3u1->getOutput(0), 256, 1, true, "stage3_unit2");
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auto s3u3 = resUnit(network, weightMap, *s3u2->getOutput(0), 256, 1, true, "stage3_unit3");
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auto s3u4 = resUnit(network, weightMap, *s3u3->getOutput(0), 256, 1, true, "stage3_unit4");
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auto s3u5 = resUnit(network, weightMap, *s3u4->getOutput(0), 256, 1, true, "stage3_unit5");
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auto s3u6 = resUnit(network, weightMap, *s3u5->getOutput(0), 256, 1, true, "stage3_unit6");
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auto s3u7 = resUnit(network, weightMap, *s3u6->getOutput(0), 256, 1, true, "stage3_unit7");
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auto s3u8 = resUnit(network, weightMap, *s3u7->getOutput(0), 256, 1, true, "stage3_unit8");
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auto s3u9 = resUnit(network, weightMap, *s3u8->getOutput(0), 256, 1, true, "stage3_unit9");
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auto s3u10 = resUnit(network, weightMap, *s3u9->getOutput(0), 256, 1, true, "stage3_unit10");
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auto s3u11 = resUnit(network, weightMap, *s3u10->getOutput(0), 256, 1, true, "stage3_unit11");
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auto s3u12 = resUnit(network, weightMap, *s3u11->getOutput(0), 256, 1, true, "stage3_unit12");
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auto s3u13 = resUnit(network, weightMap, *s3u12->getOutput(0), 256, 1, true, "stage3_unit13");
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auto s3u14 = resUnit(network, weightMap, *s3u13->getOutput(0), 256, 1, true, "stage3_unit14");
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auto s4u1 = resUnit(network, weightMap, *s3u14->getOutput(0), 512, 2, false, "stage4_unit1");
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auto s4u2 = resUnit(network, weightMap, *s4u1->getOutput(0), 512, 1, true, "stage4_unit2");
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auto s4u3 = resUnit(network, weightMap, *s4u2->getOutput(0), 512, 1, true, "stage4_unit3");
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auto bn1 = addBatchNorm2d(network, weightMap, *s4u3->getOutput(0), "bn1", 2e-5);
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IFullyConnectedLayer* fc1 = network->addFullyConnected(*bn1->getOutput(0), 512, weightMap["pre_fc1_weight"], weightMap["pre_fc1_bias"]);
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assert(fc1);
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auto bn2 = addBatchNorm2d(network, weightMap, *fc1->getOutput(0), "fc1", 2e-5);
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bn2->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*bn2->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#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));
|
||||
}
|
||||
|
||||
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 read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
|
||||
DIR *p_dir = opendir(p_dir_name);
|
||||
if (p_dir == nullptr) {
|
||||
return -1;
|
||||
}
|
||||
|
||||
struct dirent* p_file = nullptr;
|
||||
while ((p_file = readdir(p_dir)) != nullptr) {
|
||||
if (strcmp(p_file->d_name, ".") != 0 &&
|
||||
strcmp(p_file->d_name, "..") != 0) {
|
||||
//std::string cur_file_name(p_dir_name);
|
||||
//cur_file_name += "/";
|
||||
//cur_file_name += p_file->d_name;
|
||||
std::string cur_file_name(p_file->d_name);
|
||||
file_names.push_back(cur_file_name);
|
||||
}
|
||||
}
|
||||
|
||||
closedir(p_dir);
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
cudaSetDevice(DEVICE);
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (argc == 2 && std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(BATCH_SIZE, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
std::ofstream p("arcface-r50.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 0;
|
||||
} else if (argc == 2 && std::string(argv[1]) == "-d") {
|
||||
std::ifstream file("arcface-r50.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 {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./arcface-r50 -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./arcface-r50 -d // deserialize plan file and run inference" << std::endl;
|
||||
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;
|
||||
static float prob[BATCH_SIZE * OUTPUT_SIZE];
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
|
||||
cv::Mat img = cv::imread("../joey0.ppm");
|
||||
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
|
||||
data[i] = ((float)img.at<cv::Vec3b>(i)[0] - 127.5) * 0.0078125;
|
||||
data[i + INPUT_H * INPUT_W] = ((float)img.at<cv::Vec3b>(i)[1] - 127.5) * 0.0078125;
|
||||
data[i + 2 * INPUT_H * INPUT_W] = ((float)img.at<cv::Vec3b>(i)[2] - 127.5) * 0.0078125;
|
||||
}
|
||||
|
||||
// Run inference
|
||||
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;
|
||||
|
||||
cv::Mat out(512, 1, CV_32FC1, prob);
|
||||
cv::Mat out_norm;
|
||||
cv::normalize(out, out_norm);
|
||||
|
||||
img = cv::imread("../joey1.ppm");
|
||||
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
|
||||
data[i] = ((float)img.at<cv::Vec3b>(i)[0] - 127.5) * 0.0078125;
|
||||
data[i + INPUT_H * INPUT_W] = ((float)img.at<cv::Vec3b>(i)[1] - 127.5) * 0.0078125;
|
||||
data[i + 2 * INPUT_H * INPUT_W] = ((float)img.at<cv::Vec3b>(i)[2] - 127.5) * 0.0078125;
|
||||
}
|
||||
|
||||
// Run inference
|
||||
start = std::chrono::system_clock::now();
|
||||
doInference(*context, data, prob, BATCH_SIZE);
|
||||
end = std::chrono::system_clock::now();
|
||||
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
||||
|
||||
cv::Mat out1(1, 512, CV_32FC1, prob);
|
||||
cv::Mat out_norm1;
|
||||
cv::normalize(out1, out_norm1);
|
||||
|
||||
cv::Mat res = out_norm1 * out_norm;
|
||||
|
||||
std::cout << "similarity score: " << *(float*)res.data << std::endl;
|
||||
|
||||
// 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 << p_out_norm[i] << ", ";
|
||||
// if (i % 10 == 0) std::cout << i / 10 << std::endl;
|
||||
//}
|
||||
//std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
503
arcface/logging.h
Normal file
503
arcface/logging.h
Normal file
@ -0,0 +1,503 @@
|
||||
/*
|
||||
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef TENSORRT_LOGGING_H
|
||||
#define TENSORRT_LOGGING_H
|
||||
|
||||
#include "NvInferRuntimeCommon.h"
|
||||
#include <cassert>
|
||||
#include <ctime>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
|
||||
using Severity = nvinfer1::ILogger::Severity;
|
||||
|
||||
class LogStreamConsumerBuffer : public std::stringbuf
|
||||
{
|
||||
public:
|
||||
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mOutput(stream)
|
||||
, mPrefix(prefix)
|
||||
, mShouldLog(shouldLog)
|
||||
{
|
||||
}
|
||||
|
||||
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
|
||||
: mOutput(other.mOutput)
|
||||
{
|
||||
}
|
||||
|
||||
~LogStreamConsumerBuffer()
|
||||
{
|
||||
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr())
|
||||
{
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync()
|
||||
{
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput()
|
||||
{
|
||||
if (mShouldLog)
|
||||
{
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
std::cout << "[";
|
||||
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
|
||||
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
|
||||
// std::stringbuf::str() gets the string contents of the buffer
|
||||
// insert the buffer contents pre-appended by the appropriate prefix into the stream
|
||||
mOutput << mPrefix << str();
|
||||
// set the buffer to empty
|
||||
str("");
|
||||
// flush the stream
|
||||
mOutput.flush();
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog)
|
||||
{
|
||||
mShouldLog = shouldLog;
|
||||
}
|
||||
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase
|
||||
{
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog)
|
||||
{
|
||||
}
|
||||
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumer
|
||||
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
|
||||
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
|
||||
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
|
||||
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
|
||||
{
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(severity <= reportableSeverity)
|
||||
, mSeverity(severity)
|
||||
{
|
||||
}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(other.mShouldLog)
|
||||
, mSeverity(other.mSeverity)
|
||||
{
|
||||
}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity)
|
||||
{
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
bool mShouldLog;
|
||||
Severity mSeverity;
|
||||
};
|
||||
|
||||
//! \class Logger
|
||||
//!
|
||||
//! \brief Class which manages logging of TensorRT tools and samples
|
||||
//!
|
||||
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
|
||||
//! and supports logging two types of messages:
|
||||
//!
|
||||
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
|
||||
//! - Test pass/fail messages
|
||||
//!
|
||||
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
|
||||
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
|
||||
//!
|
||||
//! In the future, this class could be extended to support dumping test results to a file in some standard format
|
||||
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
|
||||
//!
|
||||
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
|
||||
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
|
||||
//! library and messages coming from the sample.
|
||||
//!
|
||||
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger
|
||||
{
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING)
|
||||
: mReportableSeverity(severity)
|
||||
{
|
||||
}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult
|
||||
{
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
|
||||
//! \return The nvinfer1::ILogger associated with this Logger
|
||||
//!
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger()
|
||||
{
|
||||
return *this;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
//!
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) override
|
||||
{
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Method for controlling the verbosity of logging output
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity)
|
||||
{
|
||||
mReportableSeverity = severity;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
//!
|
||||
//! This object is an opaque handle to information used by the Logger to print test results.
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom
|
||||
{
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started)
|
||||
, mName(name)
|
||||
, mCmdline(cmdline)
|
||||
{
|
||||
}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
std::string mCmdline;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Define a test for logging
|
||||
//!
|
||||
//! \param[in] name The name of the test. This should be a string starting with
|
||||
//! "TensorRT" and containing dot-separated strings containing
|
||||
//! the characters [A-Za-z0-9_].
|
||||
//! For example, "TensorRT.sample_googlenet"
|
||||
//! \param[in] cmdline The command line used to reproduce the test
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
|
||||
{
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
|
||||
//! as input
|
||||
//!
|
||||
//! \param[in] name The name of the test
|
||||
//! \param[in] argc The number of command-line arguments
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
|
||||
{
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom)
|
||||
{
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass)
|
||||
{
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const
|
||||
{
|
||||
return mReportableSeverity;
|
||||
}
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
210
arcface/prelu.cu
Normal file
210
arcface/prelu.cu
Normal file
@ -0,0 +1,210 @@
|
||||
#include <cmath>
|
||||
#include <stdio.h>
|
||||
#include <cassert>
|
||||
#include <iostream>
|
||||
#include "prelu.h"
|
||||
|
||||
namespace nvinfer1
|
||||
{
|
||||
PReluPlugin::PReluPlugin(const std::vector<float>& gamma) : gamma_(gamma)
|
||||
{
|
||||
}
|
||||
|
||||
PReluPlugin::~PReluPlugin()
|
||||
{
|
||||
}
|
||||
|
||||
// create the plugin at runtime from a byte stream
|
||||
PReluPlugin::PReluPlugin(const void* data, size_t length)
|
||||
{
|
||||
char *p = (char*)data;
|
||||
input_size_ = reinterpret_cast<const int*>(p)[0];
|
||||
p += sizeof(int);
|
||||
gamma_.assign((float*)p, (float*)p + (length - sizeof(int)) / sizeof(float));
|
||||
}
|
||||
|
||||
void PReluPlugin::serialize(void* buffer) const
|
||||
{
|
||||
*reinterpret_cast<int*>(buffer) = input_size_;
|
||||
char *p = reinterpret_cast<char*>(buffer);
|
||||
p += sizeof(int);
|
||||
memcpy(p, gamma_.data(), gamma_.size() * sizeof(float));
|
||||
}
|
||||
|
||||
size_t PReluPlugin::getSerializationSize() const
|
||||
{
|
||||
return sizeof(input_size_) + gamma_.size() * sizeof(float);
|
||||
}
|
||||
|
||||
int PReluPlugin::initialize()
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
|
||||
Dims PReluPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
|
||||
{
|
||||
assert(nbInputDims == 1);
|
||||
assert(index == 0);
|
||||
input_size_ = inputs[0].d[0] * inputs[0].d[1] * inputs[0].d[2];
|
||||
// Output dimensions
|
||||
return Dims3(inputs[0].d[0], inputs[0].d[1], inputs[0].d[2]);
|
||||
}
|
||||
|
||||
// Set plugin namespace
|
||||
void PReluPlugin::setPluginNamespace(const char* pluginNamespace)
|
||||
{
|
||||
mPluginNamespace = pluginNamespace;
|
||||
}
|
||||
|
||||
const char* PReluPlugin::getPluginNamespace() const
|
||||
{
|
||||
return mPluginNamespace;
|
||||
}
|
||||
|
||||
// Return the DataType of the plugin output at the requested index
|
||||
DataType PReluPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
|
||||
{
|
||||
return DataType::kFLOAT;
|
||||
}
|
||||
|
||||
// Return true if output tensor is broadcast across a batch.
|
||||
bool PReluPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Return true if plugin can use input that is broadcast across batch without replication.
|
||||
bool PReluPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
void PReluPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
|
||||
{
|
||||
}
|
||||
|
||||
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
|
||||
void PReluPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
|
||||
{
|
||||
}
|
||||
|
||||
// Detach the plugin object from its execution context.
|
||||
void PReluPlugin::detachFromContext() {}
|
||||
|
||||
const char* PReluPlugin::getPluginType() const
|
||||
{
|
||||
return "PRelu_TRT";
|
||||
}
|
||||
|
||||
const char* PReluPlugin::getPluginVersion() const
|
||||
{
|
||||
return "1";
|
||||
}
|
||||
|
||||
void PReluPlugin::destroy()
|
||||
{
|
||||
delete this;
|
||||
}
|
||||
|
||||
// Clone the plugin
|
||||
IPluginV2IOExt* PReluPlugin::clone() const
|
||||
{
|
||||
PReluPlugin *p = new PReluPlugin(gamma_);
|
||||
p->input_size_ = input_size_;
|
||||
p->setPluginNamespace(mPluginNamespace);
|
||||
return p;
|
||||
}
|
||||
|
||||
__global__ void prelu_kernel(const float *input, float *output, int num_elem, int input_size, int fm_size, const float* gamma) {
|
||||
|
||||
int idx = threadIdx.x + blockDim.x * blockIdx.x;
|
||||
if (idx >= num_elem) return;
|
||||
|
||||
if (input[idx] >= 0.0f) {
|
||||
output[idx] = input[idx];
|
||||
return;
|
||||
}
|
||||
int c = (idx % input_size) / fm_size;
|
||||
output[idx] = input[idx] * gamma[c];
|
||||
}
|
||||
|
||||
void PReluPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) {
|
||||
int block_size = thread_count_;
|
||||
int grid_size = (input_size_ * batchSize + block_size - 1) / block_size;
|
||||
void *dev_gamma;
|
||||
assert(cudaMalloc(&dev_gamma, sizeof(float) * gamma_.size()) == cudaSuccess);
|
||||
assert(cudaMemcpy(dev_gamma, gamma_.data(), sizeof(float) * gamma_.size(), cudaMemcpyHostToDevice) == cudaSuccess);
|
||||
prelu_kernel<<<grid_size, block_size>>>(inputs[0], output, input_size_ * batchSize, input_size_, input_size_ / gamma_.size(), (const float*)dev_gamma);
|
||||
assert(cudaFree(dev_gamma) == cudaSuccess);
|
||||
}
|
||||
|
||||
int PReluPlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream)
|
||||
{
|
||||
//assert(batchSize == 1);
|
||||
//GPU
|
||||
//CUDA_CHECK(cudaStreamSynchronize(stream));
|
||||
forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize);
|
||||
return 0;
|
||||
}
|
||||
|
||||
PluginFieldCollection PReluPluginCreator::mFC{};
|
||||
std::vector<PluginField> PReluPluginCreator::mPluginAttributes;
|
||||
|
||||
PReluPluginCreator::PReluPluginCreator()
|
||||
{
|
||||
mPluginAttributes.emplace_back(PluginField("gamma", nullptr, PluginFieldType::kFLOAT32, 1));
|
||||
|
||||
mFC.nbFields = mPluginAttributes.size();
|
||||
mFC.fields = mPluginAttributes.data();
|
||||
}
|
||||
|
||||
const char* PReluPluginCreator::getPluginName() const
|
||||
{
|
||||
return "PRelu_TRT";
|
||||
}
|
||||
|
||||
const char* PReluPluginCreator::getPluginVersion() const
|
||||
{
|
||||
return "1";
|
||||
}
|
||||
|
||||
const PluginFieldCollection* PReluPluginCreator::getFieldNames()
|
||||
{
|
||||
return &mFC;
|
||||
}
|
||||
|
||||
IPluginV2IOExt* PReluPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
|
||||
{
|
||||
std::vector<float> gamma;
|
||||
const PluginField* fields = fc->fields;
|
||||
for (int i = 0; i < fc->nbFields; ++i) {
|
||||
const char* attrName = fields[i].name;
|
||||
if (!strcmp(attrName, "gamma")) {
|
||||
assert(fields[i].type == PluginFieldType::kFLOAT32);
|
||||
int size = fields[i].length;
|
||||
gamma.reserve(size);
|
||||
const auto* w = static_cast<const float*>(fields[i].data);
|
||||
for (int j = 0; j < size; j++)
|
||||
{
|
||||
gamma.push_back(*w);
|
||||
w++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
PReluPlugin* obj = new PReluPlugin(gamma);
|
||||
obj->setPluginNamespace(mNamespace.c_str());
|
||||
return obj;
|
||||
}
|
||||
|
||||
IPluginV2IOExt* PReluPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
|
||||
{
|
||||
// This object will be deleted when the network is destroyed, which will
|
||||
// call PReluPlugin::destroy()
|
||||
PReluPlugin* obj = new PReluPlugin(serialData, serialLength);
|
||||
obj->setPluginNamespace(mNamespace.c_str());
|
||||
return obj;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
107
arcface/prelu.h
Normal file
107
arcface/prelu.h
Normal file
@ -0,0 +1,107 @@
|
||||
#ifndef _PRELU_PLUGIN_H
|
||||
#define _PRELU_PLUGIN_H
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include "NvInfer.h"
|
||||
|
||||
namespace nvinfer1
|
||||
{
|
||||
class PReluPlugin: public IPluginV2IOExt
|
||||
{
|
||||
public:
|
||||
PReluPlugin(const std::vector<float>& gamma);
|
||||
PReluPlugin(const void* data, size_t length);
|
||||
|
||||
~PReluPlugin();
|
||||
|
||||
int getNbOutputs() const override
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
|
||||
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
|
||||
|
||||
int initialize() override;
|
||||
|
||||
virtual void terminate() override {};
|
||||
|
||||
virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0;}
|
||||
|
||||
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override;
|
||||
|
||||
virtual size_t getSerializationSize() const override;
|
||||
|
||||
virtual void serialize(void* buffer) const override;
|
||||
|
||||
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override {
|
||||
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
|
||||
}
|
||||
|
||||
const char* getPluginType() const override;
|
||||
|
||||
const char* getPluginVersion() const override;
|
||||
|
||||
void destroy() override;
|
||||
|
||||
IPluginV2IOExt* clone() const override;
|
||||
|
||||
void setPluginNamespace(const char* pluginNamespace) override;
|
||||
|
||||
const char* getPluginNamespace() const override;
|
||||
|
||||
DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
|
||||
|
||||
bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
|
||||
|
||||
bool canBroadcastInputAcrossBatch(int inputIndex) const override;
|
||||
|
||||
void attachToContext(
|
||||
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override;
|
||||
|
||||
void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override;
|
||||
|
||||
void detachFromContext() override;
|
||||
|
||||
int input_size_;
|
||||
private:
|
||||
void forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize = 1);
|
||||
int thread_count_ = 256;
|
||||
std::vector<float> gamma_;
|
||||
const char* mPluginNamespace;
|
||||
};
|
||||
|
||||
class PReluPluginCreator : public IPluginCreator
|
||||
{
|
||||
public:
|
||||
PReluPluginCreator();
|
||||
|
||||
~PReluPluginCreator() override = default;
|
||||
|
||||
const char* getPluginName() const override;
|
||||
|
||||
const char* getPluginVersion() const override;
|
||||
|
||||
const PluginFieldCollection* getFieldNames() override;
|
||||
|
||||
IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
|
||||
|
||||
IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
|
||||
|
||||
void setPluginNamespace(const char* libNamespace) override
|
||||
{
|
||||
mNamespace = libNamespace;
|
||||
}
|
||||
|
||||
const char* getPluginNamespace() const override
|
||||
{
|
||||
return mNamespace.c_str();
|
||||
}
|
||||
|
||||
private:
|
||||
std::string mNamespace;
|
||||
static PluginFieldCollection mFC;
|
||||
static std::vector<PluginField> mPluginAttributes;
|
||||
};
|
||||
};
|
||||
#endif
|
||||
Loading…
Reference in New Issue
Block a user