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yolo_standard_libray/tensorrtx-master/retinaface/retina_mnet.cpp
haotian 7ac785ddb3 first commit
2025-03-07 11:35:40 +08:00

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#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include "cuda_runtime_api.h"
#include "logging.h"
#include "common.hpp"
#include "calibrator.h"
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define BATCH_SIZE 1
#define CONF_THRESH 0.75
#define IOU_THRESH 0.4
// 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";
static Logger gLogger;
ILayer* conv_bn(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1, float leaky = 0.1) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{1, 1});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
lr->setAlpha(leaky);
assert(lr);
return lr;
}
ILayer* conv_bn_no_relu(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{1, 1});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
return bn1;
}
ILayer* conv_bn1X1(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup, int s = 1, float leaky = 0.1) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, oup, DimsHW{1, 1}, getWeights(weightMap, lname + ".0.weight"), emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{0, 0});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
lr->setAlpha(leaky);
assert(lr);
return lr;
}
ILayer* conv_dw(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int inp, int oup, int s = 1, float leaky = 0.1) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, inp, DimsHW{3, 3}, getWeights(weightMap, lname + ".0.weight"), emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{1, 1});
conv1->setNbGroups(inp);
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".1", 1e-5);
auto lr1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
lr1->setAlpha(leaky);
assert(lr1);
IConvolutionLayer* conv2 = network->addConvolutionNd(*lr1->getOutput(0), oup, DimsHW{1, 1}, getWeights(weightMap, lname + ".3.weight"), emptywts);
assert(conv2);
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".4", 1e-5);
auto lr2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU);
lr2->setAlpha(leaky);
assert(lr2);
return lr2;
}
IActivationLayer* ssh(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, int oup) {
auto conv3x3 = conv_bn_no_relu(network, weightMap, input, lname + ".conv3X3", oup / 2);
auto conv5x5_1 = conv_bn(network, weightMap, input, lname + ".conv5X5_1", oup / 4);
auto conv5x5 = conv_bn_no_relu(network, weightMap, *conv5x5_1->getOutput(0), lname + ".conv5X5_2", oup / 4);
auto conv7x7 = conv_bn(network, weightMap, *conv5x5_1->getOutput(0), lname + ".conv7X7_2", oup / 4);
conv7x7 = conv_bn_no_relu(network, weightMap, *conv7x7->getOutput(0), lname + ".conv7x7_3", oup / 4);
ITensor* inputTensors[] = {conv3x3->getOutput(0), conv5x5->getOutput(0), conv7x7->getOutput(0)};
auto cat = network->addConcatenation(inputTensors, 3);
IActivationLayer* relu1 = network->addActivation(*cat->getOutput(0), ActivationType::kRELU);
assert(relu1);
return relu1;
}
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights("../retinaface.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};
// ------------- backbone mobilenet0.25 ---------------
// stage 1
auto x = conv_bn(network, weightMap, *data, "body.stage1.0", 8, 2);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.1", 8, 16);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.2", 16, 32, 2);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.3", 32, 32);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.4", 32, 64, 2);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage1.5", 64, 64);
auto stage1 = x;
// stage 2
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.0", 64, 128, 2);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.1", 128, 128);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.2", 128, 128);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.3", 128, 128);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.4", 128, 128);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage2.5", 128, 128);
auto stage2 = x;
// stage 3
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage3.0", 128, 256, 2);
x = conv_dw(network, weightMap, *x->getOutput(0), "body.stage3.1", 256, 256);
auto stage3 = x;
//Dims d1 = stage1->getOutput(0)->getDimensions();
//std::cout << d1.d[0] << " " << d1.d[1] << " " << d1.d[2] << std::endl;
// ------------- FPN ---------------
auto output1 = conv_bn1X1(network, weightMap, *stage1->getOutput(0), "fpn.output1", 64);
auto output2 = conv_bn1X1(network, weightMap, *stage2->getOutput(0), "fpn.output2", 64);
auto output3 = conv_bn1X1(network, weightMap, *stage3->getOutput(0), "fpn.output3", 64);
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 64 * 2 * 2));
for (int i = 0; i < 64 * 2 * 2; i++) {
deval[i] = 1.0;
}
Weights deconvwts{DataType::kFLOAT, deval, 64 * 2 * 2};
IDeconvolutionLayer* up3 = network->addDeconvolutionNd(*output3->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts);
assert(up3);
up3->setStrideNd(DimsHW{2, 2});
up3->setNbGroups(64);
weightMap["up3"] = deconvwts;
output2 = network->addElementWise(*output2->getOutput(0), *up3->getOutput(0), ElementWiseOperation::kSUM);
output2 = conv_bn(network, weightMap, *output2->getOutput(0), "fpn.merge2", 64);
IDeconvolutionLayer* up2 = network->addDeconvolutionNd(*output2->getOutput(0), 64, DimsHW{2, 2}, deconvwts, emptywts);
assert(up2);
up2->setStrideNd(DimsHW{2, 2});
up2->setNbGroups(64);
output1 = network->addElementWise(*output1->getOutput(0), *up2->getOutput(0), ElementWiseOperation::kSUM);
output1 = conv_bn(network, weightMap, *output1->getOutput(0), "fpn.merge1", 64);
// ------------- SSH ---------------
auto ssh1 = ssh(network, weightMap, *output1->getOutput(0), "ssh1", 64);
auto ssh2 = ssh(network, weightMap, *output2->getOutput(0), "ssh2", 64);
auto ssh3 = ssh(network, weightMap, *output3->getOutput(0), "ssh3", 64);
//// ------------- Head ---------------
auto bbox_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.0.conv1x1.weight"], weightMap["BboxHead.0.conv1x1.bias"]);
auto bbox_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.1.conv1x1.weight"], weightMap["BboxHead.1.conv1x1.bias"]);
auto bbox_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 4, DimsHW{1, 1}, weightMap["BboxHead.2.conv1x1.weight"], weightMap["BboxHead.2.conv1x1.bias"]);
auto cls_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.0.conv1x1.weight"], weightMap["ClassHead.0.conv1x1.bias"]);
auto cls_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.1.conv1x1.weight"], weightMap["ClassHead.1.conv1x1.bias"]);
auto cls_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 2, DimsHW{1, 1}, weightMap["ClassHead.2.conv1x1.weight"], weightMap["ClassHead.2.conv1x1.bias"]);
auto lmk_head1 = network->addConvolutionNd(*ssh1->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.0.conv1x1.weight"], weightMap["LandmarkHead.0.conv1x1.bias"]);
auto lmk_head2 = network->addConvolutionNd(*ssh2->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.1.conv1x1.weight"], weightMap["LandmarkHead.1.conv1x1.bias"]);
auto lmk_head3 = network->addConvolutionNd(*ssh3->getOutput(0), 2 * 10, DimsHW{1, 1}, weightMap["LandmarkHead.2.conv1x1.weight"], weightMap["LandmarkHead.2.conv1x1.bias"]);
//// ------------- Decode bbox, conf, landmark ---------------
ITensor* inputTensors1[] = {bbox_head1->getOutput(0), cls_head1->getOutput(0), lmk_head1->getOutput(0)};
auto cat1 = network->addConcatenation(inputTensors1, 3);
ITensor* inputTensors2[] = {bbox_head2->getOutput(0), cls_head2->getOutput(0), lmk_head2->getOutput(0)};
auto cat2 = network->addConcatenation(inputTensors2, 3);
ITensor* inputTensors3[] = {bbox_head3->getOutput(0), cls_head3->getOutput(0), lmk_head3->getOutput(0)};
auto cat3 = network->addConcatenation(inputTensors3, 3);
auto creator = getPluginRegistry()->getPluginCreator("Decode_TRT", "1");
PluginFieldCollection pfc;
IPluginV2 *pluginObj = creator->createPlugin("decode", &pfc);
ITensor* inputTensors[] = {cat1->getOutput(0), cat2->getOutput(0), cat3->getOutput(0)};
auto decodelayer = network->addPluginV2(inputTensors, 3, *pluginObj);
assert(decodelayer);
decodelayer->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*decodelayer->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(1 << 20);
#if defined(USE_FP16)
config->setFlag(BuilderFlag::kFP16);
#elif defined(USE_INT8)
std::cout << "Your platform support int8: " << builder->platformHasFastInt8() << std::endl;
assert(builder->platformHasFastInt8());
config->setFlag(BuilderFlag::kINT8);
Int8EntropyCalibrator2 *calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./widerface_calib/", "mnet_int8calib.table", INPUT_BLOB_NAME);
config->setInt8Calibrator(calibrator);
#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();
config->destroy();
builder->destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void* buffers[2];
// In order to bind the buffers, we need to know the names of the input and output tensors.
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
int main(int argc, char** argv) {
if (argc != 2) {
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./retina_mnet -s // serialize model to plan file" << std::endl;
std::cerr << "./retina_mnet -d // deserialize plan file and run inference" << std::endl;
return -1;
}
cudaSetDevice(DEVICE);
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (std::string(argv[1]) == "-s") {
IHostMemory* modelStream{nullptr};
APIToModel(BATCH_SIZE, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("retina_mnet.engine", std::ios::binary);
if (!p) {
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 1;
} else if (std::string(argv[1]) == "-d") {
std::ifstream file("retina_mnet.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
} else {
return -1;
}
// prepare input data ---------------------------
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
// data[i] = 1.0;
cv::Mat img = cv::imread("worlds-largest-selfie.jpg");
cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H);
//cv::imwrite("preprocessed.jpg", pr_img);
// For multi-batch, I feed the same image multiple times.
// If you want to process different images in a batch, you need adapt it.
for (int b = 0; b < BATCH_SIZE; b++) {
float *p_data = &data[b * 3 * INPUT_H * INPUT_W];
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
p_data[i] = pr_img.at<cv::Vec3b>(i)[0] - 104.0;
p_data[i + INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[1] - 117.0;
p_data[i + 2 * INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[2] - 123.0;
}
}
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
//ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
// Run inference
static float prob[BATCH_SIZE * OUTPUT_SIZE];
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, BATCH_SIZE);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::microseconds>(end - start).count() << "us" << std::endl;
for (int b = 0; b < BATCH_SIZE; b++) {
std::vector<decodeplugin::Detection> res;
nms(res, &prob[b * OUTPUT_SIZE], IOU_THRESH);
std::cout << "number of detections -> " << prob[b * OUTPUT_SIZE] << std::endl;
std::cout << " -> " << prob[b * OUTPUT_SIZE + 10] << std::endl;
std::cout << "after nms -> " << res.size() << std::endl;
cv::Mat tmp = img.clone();
for (size_t j = 0; j < res.size(); j++) {
if (res[j].class_confidence < CONF_THRESH) continue;
cv::Rect r = get_rect_adapt_landmark(tmp, INPUT_W, INPUT_H, res[j].bbox, res[j].landmark);
cv::rectangle(tmp, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
//cv::putText(tmp, std::to_string((int)(res[j].class_confidence * 100)) + "%", cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 1);
for (int k = 0; k < 10; k += 2) {
cv::circle(tmp, cv::Point(res[j].landmark[k], res[j].landmark[k + 1]), 1, cv::Scalar(255 * (k > 2), 255 * (k > 0 && k < 8), 255 * (k < 6)), 4);
}
}
cv::imwrite(std::to_string(b) + "_result.jpg", tmp);
}
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
// Print histogram of the output distribution
//std::cout << "\nOutput:\n\n";
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
//{
// std::cout << prob[i] << ", ";
// if (i % 10 == 0) std::cout << i / 10 << std::endl;
//}
//std::cout << std::endl;
return 0;
}
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