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yolov5_cls.cpp
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yolov5_cls.cpp
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#include "cuda_utils.h"
#include "logging.h"
#include "utils.h"
#include "model.h"
#include "config.h"
#include "calibrator.h"
#include <iostream>
#include <chrono>
#include <cmath>
#include <numeric>
#include <opencv2/opencv.hpp>
using namespace nvinfer1;
static Logger gLogger;
const static int kOutputSize = kClsNumClass;
void batch_preprocess(std::vector<cv::Mat>& imgs, float* output) {
for (size_t b = 0; b < imgs.size(); b++) {
cv::Mat img;
// cv::resize(imgs[b], img, cv::Size(kClsInputW, kClsInputH));
img = preprocess_img(imgs[b], kClsInputW, kClsInputH);
int i = 0;
for (int row = 0; row < img.rows; ++row) {
uchar* uc_pixel = img.data + row * img.step;
for (int col = 0; col < img.cols; ++col) {
output[b * 3 * img.rows * img.cols + i] = ((float)uc_pixel[2] / 255.0 - 0.485) / 0.229; // R - 0.485
output[b * 3 * img.rows * img.cols + i + img.rows * img.cols] = ((float)uc_pixel[1] / 255.0 - 0.456) / 0.224;
output[b * 3 * img.rows * img.cols + i + 2 * img.rows * img.cols] = ((float)uc_pixel[0] / 255.0 - 0.406) / 0.225;
uc_pixel += 3;
++i;
}
}
}
}
std::vector<float> softmax(float *prob, int n) {
std::vector<float> res;
float sum = 0.0f;
float t;
for (int i = 0; i < n; i++) {
t = expf(prob[i]);
res.push_back(t);
sum += t;
}
for (int i = 0; i < n; i++) {
res[i] /= sum;
}
return res;
}
std::vector<int> topk(const std::vector<float>& vec, int k) {
std::vector<int> topk_index;
std::vector<size_t> vec_index(vec.size());
std::iota(vec_index.begin(), vec_index.end(), 0);
std::sort(vec_index.begin(), vec_index.end(), [&vec](size_t index_1, size_t index_2) { return vec[index_1] > vec[index_2]; });
int k_num = std::min<int>(vec.size(), k);
for (int i = 0; i < k_num; ++i) {
topk_index.push_back(vec_index[i]);
}
return topk_index;
}
std::vector<std::string> read_classes(std::string file_name) {
std::vector<std::string> classes;
std::ifstream ifs(file_name, std::ios::in);
if (!ifs.is_open()) {
std::cerr << file_name << " is not found, pls refer to README and download it." << std::endl;
assert(0);
}
std::string s;
while (std::getline(ifs, s)) {
classes.push_back(s);
}
ifs.close();
return classes;
}
bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, float& gd, float& gw, std::string& img_dir) {
if (argc < 4) return false;
if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
wts = std::string(argv[2]);
engine = std::string(argv[3]);
auto net = std::string(argv[4]);
if (net[0] == 'n') {
gd = 0.33;
gw = 0.25;
} else if (net[0] == 's') {
gd = 0.33;
gw = 0.50;
} else if (net[0] == 'm') {
gd = 0.67;
gw = 0.75;
} else if (net[0] == 'l') {
gd = 1.0;
gw = 1.0;
} else if (net[0] == 'x') {
gd = 1.33;
gw = 1.25;
} else if (net[0] == 'c' && argc == 7) {
gd = atof(argv[5]);
gw = atof(argv[6]);
} else {
return false;
}
} else if (std::string(argv[1]) == "-d" && argc == 4) {
engine = std::string(argv[2]);
img_dir = std::string(argv[3]);
} else {
return false;
}
return true;
}
void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer, float** cpu_input_buffer, float** cpu_output_buffer) {
assert(engine->getNbBindings() == 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(kInputTensorName);
const int outputIndex = engine->getBindingIndex(kOutputTensorName);
assert(inputIndex == 0);
assert(outputIndex == 1);
// Create GPU buffers on device
CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kClsInputH * kClsInputW * sizeof(float)));
CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));
*cpu_input_buffer = new float[kBatchSize * 3 * kClsInputH * kClsInputW];
*cpu_output_buffer = new float[kBatchSize * kOutputSize];
}
void infer(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * kClsInputH * kClsInputW * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
}
void serialize_engine(unsigned int max_batchsize, float& gd, float& gw, std::string& wts_name, std::string& engine_name) {
// 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 = nullptr;
engine = build_cls_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
assert(engine != nullptr);
// Serialize the engine
IHostMemory* serialized_engine = engine->serialize();
assert(serialized_engine != nullptr);
// Save engine to file
std::ofstream p(engine_name, std::ios::binary);
if (!p) {
std::cerr << "Could not open plan output file" << std::endl;
assert(false);
}
p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());
// Close everything down
engine->destroy();
config->destroy();
serialized_engine->destroy();
builder->destroy();
}
void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine, IExecutionContext** context) {
std::ifstream file(engine_name, std::ios::binary);
if (!file.good()) {
std::cerr << "read " << engine_name << " error!" << std::endl;
assert(false);
}
size_t size = 0;
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
char* serialized_engine = new char[size];
assert(serialized_engine);
file.read(serialized_engine, size);
file.close();
*runtime = createInferRuntime(gLogger);
assert(*runtime);
*engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
assert(*engine);
*context = (*engine)->createExecutionContext();
assert(*context);
delete[] serialized_engine;
}
int main(int argc, char** argv) {
cudaSetDevice(kGpuId);
std::string wts_name = "";
std::string engine_name = "";
float gd = 0.0f, gw = 0.0f;
std::string img_dir;
if (!parse_args(argc, argv, wts_name, engine_name, gd, gw, img_dir)) {
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./yolov5_cls -s [.wts] [.engine] [n/s/m/l/x or c gd gw] // serialize model to plan file" << std::endl;
std::cerr << "./yolov5_cls -d [.engine] ../images // deserialize plan file and run inference" << std::endl;
return -1;
}
// Create a model using the API directly and serialize it to a file
if (!wts_name.empty()) {
serialize_engine(kBatchSize, gd, gw, wts_name, engine_name);
return 0;
}
// Deserialize the engine from file
IRuntime* runtime = nullptr;
ICudaEngine* engine = nullptr;
IExecutionContext* context = nullptr;
deserialize_engine(engine_name, &runtime, &engine, &context);
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
// Prepare cpu and gpu buffers
float* gpu_buffers[2];
float* cpu_input_buffer = nullptr;
float* cpu_output_buffer = nullptr;
prepare_buffers(engine, &gpu_buffers[0], &gpu_buffers[1], &cpu_input_buffer, &cpu_output_buffer);
// Read images from directory
std::vector<std::string> file_names;
if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
std::cerr << "read_files_in_dir failed." << std::endl;
return -1;
}
// Read imagenet labels
auto classes = read_classes("imagenet_classes.txt");
// batch predict
for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
// Get a batch of images
std::vector<cv::Mat> img_batch;
std::vector<std::string> img_name_batch;
for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
img_batch.push_back(img);
img_name_batch.push_back(file_names[j]);
}
// Preprocess
batch_preprocess(img_batch, cpu_input_buffer);
// Run inference
auto start = std::chrono::system_clock::now();
infer(*context, stream, (void**)gpu_buffers, cpu_input_buffer, cpu_output_buffer, kBatchSize);
auto end = std::chrono::system_clock::now();
std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
// Postprocess and get top-k result
for (size_t b = 0; b < img_name_batch.size(); b++) {
float* p = &cpu_output_buffer[b * kOutputSize];
auto res = softmax(p, kOutputSize);
auto topk_idx = topk(res, 3);
std::cout << img_name_batch[b] << std::endl;
for (auto idx: topk_idx) {
std::cout << " " << classes[idx] << " " << res[idx] << std::endl;
}
}
}
// Release stream and buffers
cudaStreamDestroy(stream);
CUDA_CHECK(cudaFree(gpu_buffers[0]));
CUDA_CHECK(cudaFree(gpu_buffers[1]));
delete[] cpu_input_buffer;
delete[] cpu_output_buffer;
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}