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arcface-r100.cpp
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arcface-r100.cpp
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#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <opencv2/opencv.hpp>
#include <dirent.h>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
//#define USE_FP16 // comment out this if want to use FP32
#define DEVICE 0 // GPU id
#define BATCH_SIZE 1 // currently, only support BATCH=1
using namespace nvinfer1;
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 112;
static const int INPUT_W = 112;
static const int OUTPUT_SIZE = 512;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file) {
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float *gamma = (float*)weightMap[lname + "_gamma"].values;
float *beta = (float*)weightMap[lname + "_beta"].values;
float *mean = (float*)weightMap[lname + "_moving_mean"].values;
float *var = (float*)weightMap[lname + "_moving_var"].values;
int len = weightMap[lname + "_moving_var"].count;
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{DataType::kFLOAT, scval, len};
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{DataType::kFLOAT, shval, len};
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{DataType::kFLOAT, pval, len};
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
ILayer* addPRelu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname) {
float *gamma = (float*)weightMap[lname + "_gamma"].values;
int len = weightMap[lname + "_gamma"].count;
float *scval_1 = reinterpret_cast<float*>(malloc(sizeof(float) * len));
float *scval_2 = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval_1[i] = -1.0;
scval_2[i] = -gamma[i];
}
Weights scale_1{ DataType::kFLOAT, scval_1, len };
Weights scale_2{ DataType::kFLOAT, scval_2, len };
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = 0.0;
}
Weights shift{ DataType::kFLOAT, shval, len };
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{ DataType::kFLOAT, pval, len };
auto relu1 = network->addActivation(input, ActivationType::kRELU);
assert(relu1);
IScaleLayer* scale1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale_1, power);
assert(scale1);
auto relu2 = network->addActivation(*scale1->getOutput(0), ActivationType::kRELU);
assert(relu2);
IScaleLayer* scale2 = network->addScale(*relu2->getOutput(0), ScaleMode::kCHANNEL, shift, scale_2, power);
assert(scale2);
IElementWiseLayer* ew1 = network->addElementWise(*relu1->getOutput(0), *scale2->getOutput(0), ElementWiseOperation::kSUM);
assert(ew1);
return ew1;
}
ILayer* resUnit(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int num_filters, int s, bool dim_match, std::string lname) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
auto bn1 = addBatchNorm2d(network, weightMap, input, lname + "_bn1", 2e-5);
IConvolutionLayer* conv1 = network->addConvolutionNd(*bn1->getOutput(0), num_filters, DimsHW{3, 3}, weightMap[lname + "_conv1_weight"], emptywts);
assert(conv1);
conv1->setPaddingNd(DimsHW{1, 1});
auto bn2 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "_bn2", 2e-5);
auto act1 = addPRelu(network, weightMap, *bn2->getOutput(0), lname + "_relu1");
IConvolutionLayer* conv2 = network->addConvolutionNd(*act1->getOutput(0), num_filters, DimsHW{3, 3}, weightMap[lname + "_conv2_weight"], emptywts);
assert(conv2);
conv2->setStrideNd(DimsHW{s, s});
conv2->setPaddingNd(DimsHW{1, 1});
auto bn3 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "_bn3", 2e-5);
IElementWiseLayer* ew1;
if (dim_match) {
ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
} else {
IConvolutionLayer* conv1sc = network->addConvolutionNd(input, num_filters, DimsHW{1, 1}, weightMap[lname + "_conv1sc_weight"], emptywts);
assert(conv1sc);
conv1sc->setStrideNd(DimsHW{s, s});
auto bn1sc = addBatchNorm2d(network, weightMap, *conv1sc->getOutput(0), lname + "_sc", 2e-5);
ew1 = network->addElementWise(*bn1sc->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
}
assert(ew1);
return ew1;
}
// 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 of shape {3, INPUT_H, INPUT_W} 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("../arcface-r100.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv0 = network->addConvolutionNd(*data, 64, DimsHW{3, 3}, weightMap["conv0_weight"], emptywts);
assert(conv0);
conv0->setPaddingNd(DimsHW{1, 1});
auto bn0 = addBatchNorm2d(network, weightMap, *conv0->getOutput(0), "bn0", 2e-5);
auto relu0 = addPRelu(network, weightMap, *bn0->getOutput(0), "relu0");
auto s1u1 = resUnit(network, weightMap, *relu0->getOutput(0), 64, 2, false, "stage1_unit1");
auto s1u2 = resUnit(network, weightMap, *s1u1->getOutput(0), 64, 1, true, "stage1_unit2");
auto s1u3 = resUnit(network, weightMap, *s1u2->getOutput(0), 64, 1, true, "stage1_unit3");
auto s2u1 = resUnit(network, weightMap, *s1u3->getOutput(0), 128, 2, false, "stage2_unit1");
auto s2u2 = resUnit(network, weightMap, *s2u1->getOutput(0), 128, 1, true, "stage2_unit2");
auto s2u3 = resUnit(network, weightMap, *s2u2->getOutput(0), 128, 1, true, "stage2_unit3");
auto s2u4 = resUnit(network, weightMap, *s2u3->getOutput(0), 128, 1, true, "stage2_unit4");
auto s2u5 = resUnit(network, weightMap, *s2u4->getOutput(0), 128, 1, true, "stage2_unit5");
auto s2u6 = resUnit(network, weightMap, *s2u5->getOutput(0), 128, 1, true, "stage2_unit6");
auto s2u7 = resUnit(network, weightMap, *s2u6->getOutput(0), 128, 1, true, "stage2_unit7");
auto s2u8 = resUnit(network, weightMap, *s2u7->getOutput(0), 128, 1, true, "stage2_unit8");
auto s2u9 = resUnit(network, weightMap, *s2u8->getOutput(0), 128, 1, true, "stage2_unit9");
auto s2u10 = resUnit(network, weightMap, *s2u9->getOutput(0), 128, 1, true, "stage2_unit10");
auto s2u11 = resUnit(network, weightMap, *s2u10->getOutput(0), 128, 1, true, "stage2_unit11");
auto s2u12 = resUnit(network, weightMap, *s2u11->getOutput(0), 128, 1, true, "stage2_unit12");
auto s2u13 = resUnit(network, weightMap, *s2u12->getOutput(0), 128, 1, true, "stage2_unit13");
auto s3u1 = resUnit(network, weightMap, *s2u13->getOutput(0), 256, 2, false, "stage3_unit1");
auto s3u2 = resUnit(network, weightMap, *s3u1->getOutput(0), 256, 1, true, "stage3_unit2");
auto s3u3 = resUnit(network, weightMap, *s3u2->getOutput(0), 256, 1, true, "stage3_unit3");
auto s3u4 = resUnit(network, weightMap, *s3u3->getOutput(0), 256, 1, true, "stage3_unit4");
auto s3u5 = resUnit(network, weightMap, *s3u4->getOutput(0), 256, 1, true, "stage3_unit5");
auto s3u6 = resUnit(network, weightMap, *s3u5->getOutput(0), 256, 1, true, "stage3_unit6");
auto s3u7 = resUnit(network, weightMap, *s3u6->getOutput(0), 256, 1, true, "stage3_unit7");
auto s3u8 = resUnit(network, weightMap, *s3u7->getOutput(0), 256, 1, true, "stage3_unit8");
auto s3u9 = resUnit(network, weightMap, *s3u8->getOutput(0), 256, 1, true, "stage3_unit9");
auto s3u10 = resUnit(network, weightMap, *s3u9->getOutput(0), 256, 1, true, "stage3_unit10");
auto s3u11 = resUnit(network, weightMap, *s3u10->getOutput(0), 256, 1, true, "stage3_unit11");
auto s3u12 = resUnit(network, weightMap, *s3u11->getOutput(0), 256, 1, true, "stage3_unit12");
auto s3u13 = resUnit(network, weightMap, *s3u12->getOutput(0), 256, 1, true, "stage3_unit13");
auto s3u14 = resUnit(network, weightMap, *s3u13->getOutput(0), 256, 1, true, "stage3_unit14");
auto s3u15 = resUnit(network, weightMap, *s3u14->getOutput(0), 256, 1, true, "stage3_unit15");
auto s3u16 = resUnit(network, weightMap, *s3u15->getOutput(0), 256, 1, true, "stage3_unit16");
auto s3u17 = resUnit(network, weightMap, *s3u16->getOutput(0), 256, 1, true, "stage3_unit17");
auto s3u18 = resUnit(network, weightMap, *s3u17->getOutput(0), 256, 1, true, "stage3_unit18");
auto s3u19 = resUnit(network, weightMap, *s3u18->getOutput(0), 256, 1, true, "stage3_unit19");
auto s3u20 = resUnit(network, weightMap, *s3u19->getOutput(0), 256, 1, true, "stage3_unit20");
auto s3u21 = resUnit(network, weightMap, *s3u20->getOutput(0), 256, 1, true, "stage3_unit21");
auto s3u22 = resUnit(network, weightMap, *s3u21->getOutput(0), 256, 1, true, "stage3_unit22");
auto s3u23 = resUnit(network, weightMap, *s3u22->getOutput(0), 256, 1, true, "stage3_unit23");
auto s3u24 = resUnit(network, weightMap, *s3u23->getOutput(0), 256, 1, true, "stage3_unit24");
auto s3u25 = resUnit(network, weightMap, *s3u24->getOutput(0), 256, 1, true, "stage3_unit25");
auto s3u26 = resUnit(network, weightMap, *s3u25->getOutput(0), 256, 1, true, "stage3_unit26");
auto s3u27 = resUnit(network, weightMap, *s3u26->getOutput(0), 256, 1, true, "stage3_unit27");
auto s3u28 = resUnit(network, weightMap, *s3u27->getOutput(0), 256, 1, true, "stage3_unit28");
auto s3u29 = resUnit(network, weightMap, *s3u28->getOutput(0), 256, 1, true, "stage3_unit29");
auto s3u30 = resUnit(network, weightMap, *s3u29->getOutput(0), 256, 1, true, "stage3_unit30");
auto s4u1 = resUnit(network, weightMap, *s3u30->getOutput(0), 512, 2, false, "stage4_unit1");
auto s4u2 = resUnit(network, weightMap, *s4u1->getOutput(0), 512, 1, true, "stage4_unit2");
auto s4u3 = resUnit(network, weightMap, *s4u2->getOutput(0), 512, 1, true, "stage4_unit3");
auto bn1 = addBatchNorm2d(network, weightMap, *s4u3->getOutput(0), "bn1", 2e-5);
IFullyConnectedLayer* fc1 = network->addFullyConnected(*bn1->getOutput(0), 512, weightMap["pre_fc1_weight"], weightMap["pre_fc1_bias"]);
assert(fc1);
auto bn2 = addBatchNorm2d(network, weightMap, *fc1->getOutput(0), "fc1", 2e-5);
bn2->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*bn2->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
#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));
}
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(256, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("arcface-r100.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-r100.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-r100 -s // serialize model to plan file" << std::endl;
std::cerr << "./arcface-r100 -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)[2] - 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)[0] - 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)[2] - 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)[0] - 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;
}