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b95b0a4e26
duan8/retinaface/retina_r50.cpp
wang-xinyu b95b0a4e26 retinaface migrated to trt7
2020-06-04 12:31:37 +08:00

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#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <opencv2/opencv.hpp>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "decode.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
// stuff we know about the network and the input/output blobs
static const int INPUT_H = decodeplugin::INPUT_H; // H, W must be able to be divided by 32.
static const int INPUT_W = decodeplugin::INPUT_W;;
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;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
using namespace nvinfer1;
static Logger gLogger;
REGISTER_TENSORRT_PLUGIN(DecodePluginCreator);
cv::Mat preprocess_img(cv::Mat& img) {
int w, h, x, y;
float r_w = INPUT_W / (img.cols*1.0);
float r_h = INPUT_H / (img.rows*1.0);
if (r_h > r_w) {
w = INPUT_W;
h = r_w * img.rows;
x = 0;
y = (INPUT_H - h) / 2;
} else {
w = r_h* img.cols;
h = INPUT_H;
x = (INPUT_W - w) / 2;
y = 0;
}
cv::Mat re(h, w, CV_8UC3);
cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
return out;
}
cv::Rect get_rect_adapt_landmark(cv::Mat& img, float bbox[4], float lmk[10]) {
int l, r, t, b;
float r_w = INPUT_W / (img.cols * 1.0);
float r_h = INPUT_H / (img.rows * 1.0);
if (r_h > r_w) {
l = bbox[0] / r_w;
r = bbox[2] / r_w;
t = (bbox[1] - (INPUT_H - r_w * img.rows) / 2) / r_w;
b = (bbox[3] - (INPUT_H - r_w * img.rows) / 2) / r_w;
for (int i = 0; i < 10; i += 2) {
lmk[i] /= r_w;
lmk[i + 1] = (lmk[i + 1] - (INPUT_H - r_w * img.rows) / 2) / r_w;
}
} else {
l = (bbox[0] - (INPUT_W - r_h * img.cols) / 2) / r_h;
r = (bbox[2] - (INPUT_W - r_h * img.cols) / 2) / r_h;
t = bbox[1] / r_h;
b = bbox[3] / r_h;
for (int i = 0; i < 10; i += 2) {
lmk[i] = (lmk[i] - (INPUT_W - r_h * img.cols) / 2) / r_h;
lmk[i + 1] /= r_h;
}
}
return cv::Rect(l, t, r-l, b-t);
}
float iou(float lbox[4], float rbox[4]) {
float interBox[] = {
std::max(lbox[0], rbox[0]), //left
std::min(lbox[2], rbox[2]), //right
std::max(lbox[1], rbox[1]), //top
std::min(lbox[3], rbox[3]), //bottom
};
if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
return 0.0f;
float interBoxS = (interBox[1] - interBox[0]) * (interBox[3] - interBox[2]);
return interBoxS / ((lbox[2] - lbox[0]) * (lbox[3] - lbox[1]) + (rbox[2] - rbox[0]) * (rbox[3] - rbox[1]) -interBoxS + 0.000001f);
}
bool cmp(decodeplugin::Detection& a, decodeplugin::Detection& b) {
return a.class_confidence > b.class_confidence;
}
void nms(std::vector<decodeplugin::Detection>& res, float *output, float nms_thresh = 0.4) {
std::vector<decodeplugin::Detection> dets;
for (int i = 0; i < output[0]; i++) {
if (output[15 * i + 1 + 4] <= 0.1) continue;
decodeplugin::Detection det;
memcpy(&det, &output[15 * i + 1], sizeof(decodeplugin::Detection));
dets.push_back(det);
}
std::sort(dets.begin(), dets.end(), cmp);
if (dets.size() > 5000) dets.erase(dets.begin() + 5000, dets.end());
for (size_t m = 0; m < dets.size(); ++m) {
auto& item = dets[m];
res.push_back(item);
//std::cout << item.class_confidence << " bbox " << item.bbox[0] << ", " << item.bbox[1] << ", " << item.bbox[2] << ", " << item.bbox[3] << std::endl;
for (size_t n = m + 1; n < dets.size(); ++n) {
if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
dets.erase(dets.begin()+n);
--n;
}
}
}
}
// Load weights from files
// 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;
}
Weights getWeights(std::map<std::string, Weights>& weightMap, std::string key) {
if (weightMap.count(key) != 1) {
std::cerr << key << " not existed in weight map, fatal error!!!" << std::endl;
exit(-1);
}
return weightMap[key];
}
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float *gamma = (float*)weightMap[lname + ".weight"].values;
float *beta = (float*)weightMap[lname + ".bias"].values;
float *mean = (float*)weightMap[lname + ".running_mean"].values;
float *var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_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;
}
IActivationLayer* bottleneck(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + "conv1.weight"], emptywts);
assert(conv1);
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + "conv2.weight"], emptywts);
assert(conv2);
conv2->setStrideNd(DimsHW{stride, stride});
conv2->setPaddingNd(DimsHW{1, 1});
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);
IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
assert(relu2);
IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + "conv3.weight"], emptywts);
assert(conv3);
IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "bn3", 1e-5);
IElementWiseLayer* ew1;
if (stride != 1 || inch != outch * 4) {
IConvolutionLayer* conv4 = network->addConvolutionNd(input, outch * 4, DimsHW{1, 1}, weightMap[lname + "downsample.0.weight"], emptywts);
assert(conv4);
conv4->setStrideNd(DimsHW{stride, stride});
IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + "downsample.1", 1e-5);
ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
} else {
ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
}
IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
assert(relu3);
return relu3;
}
ILayer* conv_bn_relu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int kernelsize, int stride, int padding, bool userelu, std::string lname) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{kernelsize, kernelsize}, getWeights(weightMap, lname + ".0.weight"), emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{stride, stride});
conv1->setPaddingNd(DimsHW{padding, padding});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
if (!userelu) return bn1;
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
return relu1;
}
IActivationLayer* ssh(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname) {
auto conv3x3 = conv_bn_relu(network, weightMap, input, 256 / 2, 3, 1, 1, false, lname + ".conv3X3");
auto conv5x5_1 = conv_bn_relu(network, weightMap, input, 256 / 4, 3, 1, 1, true, lname + ".conv5X5_1");
auto conv5x5 = conv_bn_relu(network, weightMap, *conv5x5_1->getOutput(0), 256 / 4, 3, 1, 1, false, lname + ".conv5X5_2");
auto conv7x7 = conv_bn_relu(network, weightMap, *conv5x5_1->getOutput(0), 256 / 4, 3, 1, 1, true, lname + ".conv7X7_2");
conv7x7 = conv_bn_relu(network, weightMap, *conv7x7->getOutput(0), 256 / 4, 3, 1, 1, false, lname + ".conv7x7_3");
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 resnet50 ---------------
IConvolutionLayer* conv1 = network->addConvolutionNd(*data, 64, DimsHW{7, 7}, weightMap["body.conv1.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{2, 2});
conv1->setPaddingNd(DimsHW{3, 3});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "body.bn1", 1e-5);
// Add activation layer using the ReLU algorithm.
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
// Add max pooling layer with stride of 2x2 and kernel size of 2x2.
IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
assert(pool1);
pool1->setStrideNd(DimsHW{2, 2});
pool1->setPaddingNd(DimsHW{1, 1});
IActivationLayer* x = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "body.layer1.0.");
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "body.layer1.1.");
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "body.layer1.2.");
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 128, 2, "body.layer2.0.");
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.1.");
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.2.");
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.3.");
IActivationLayer* layer2 = x;
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 256, 2, "body.layer3.0.");
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.1.");
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.2.");
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.3.");
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.4.");
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.5.");
IActivationLayer* layer3 = x;
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 512, 2, "body.layer4.0.");
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "body.layer4.1.");
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "body.layer4.2.");
IActivationLayer* layer4 = x;
// ------------- FPN ---------------
auto output1 = conv_bn_relu(network, weightMap, *layer2->getOutput(0), 256, 1, 1, 0, true, "fpn.output1");
auto output2 = conv_bn_relu(network, weightMap, *layer3->getOutput(0), 256, 1, 1, 0, true, "fpn.output2");
auto output3 = conv_bn_relu(network, weightMap, *layer4->getOutput(0), 256, 1, 1, 0, true, "fpn.output3");
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 256 * 2 * 2));
for (int i = 0; i < 256 * 2 * 2; i++) {
deval[i] = 1.0;
}
Weights deconvwts{DataType::kFLOAT, deval, 256 * 2 * 2};
IDeconvolutionLayer* up3 = network->addDeconvolutionNd(*output3->getOutput(0), 256, DimsHW{2, 2}, deconvwts, emptywts);
assert(up3);
up3->setStrideNd(DimsHW{2, 2});
up3->setNbGroups(256);
weightMap["up3"] = deconvwts;
output2 = network->addElementWise(*output2->getOutput(0), *up3->getOutput(0), ElementWiseOperation::kSUM);
output2 = conv_bn_relu(network, weightMap, *output2->getOutput(0), 256, 3, 1, 1, true, "fpn.merge2");
IDeconvolutionLayer* up2 = network->addDeconvolutionNd(*output2->getOutput(0), 256, DimsHW{2, 2}, deconvwts, emptywts);
assert(up2);
up2->setStrideNd(DimsHW{2, 2});
up2->setNbGroups(256);
output1 = network->addElementWise(*output1->getOutput(0), *up2->getOutput(0), ElementWiseOperation::kSUM);
output1 = conv_bn_relu(network, weightMap, *output1->getOutput(0), 256, 3, 1, 1, true, "fpn.merge1");
// ------------- SSH ---------------
auto ssh1 = ssh(network, weightMap, *output1->getOutput(0), "ssh1");
auto ssh2 = ssh(network, weightMap, *output2->getOutput(0), "ssh2");
auto ssh3 = ssh(network, weightMap, *output3->getOutput(0), "ssh3");
// ------------- 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_r50 -s // serialize model to plan file" << std::endl;
std::cerr << "./retina_r50 -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(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("retina_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 1;
} else if (std::string(argv[1]) == "-d") {
std::ifstream file("retina_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 {
return -1;
}
// prepare input data ---------------------------
static float data[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 (int i = 0; i < INPUT_H * INPUT_W; i++) {
data[i] = pr_img.at<cv::Vec3b>(i)[0] - 104.0;
data[i + INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[1] - 117.0;
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[OUTPUT_SIZE];
std::vector<decodeplugin::Detection> res;
for (int i = 0; i < 20; i++) {
res.clear();
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, 1);
nms(res, prob);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
}
std::cout << "detected before nms -> " << prob[0] << std::endl;
std::cout << "after nms -> " << res.size() << std::endl;
for (size_t j = 0; j < res.size(); j++) {
if (res[j].class_confidence < 0.1) continue;
cv::Rect r = get_rect_adapt_landmark(img, res[j].bbox, res[j].landmark);
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
//cv::putText(img, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);
for (int k = 0; k < 10; k += 2) {
cv::circle(img, cv::Point(res[j].landmark[k], res[j].landmark[k + 1]), 1, cv::Scalar(255 * (k > 2), 255 * (k > 0 && k < 8), 255 * (k < 6)), 4);
}
}
cv::imwrite("result.jpg", img);
// 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;
}
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