Files
dasha_f 6e1a22ba8b Добавлены пропсы конвейера и стереодвижки, задействованные в прогоне
assets/conveyors (274 МБ) - ленты и угловая секция NVIDIA, на которые ссылается сцена
относительным путём. Раньше исключались как перекачиваемые, но без них сцена не
композится из коробки.

cv/ - код стереодвижков, которые вызывает control_test, без весов:
* defom-stereo - рабочий бейзлайн (DEFOM vitl, вход 480, iters 24)
* crestereo - второй движок, точнее по габаритам (MAE 23.5 против 32.8 мм)
* fast-foundationstereo - проверялся, в бейзлайн не вошёл
* circular_section.py - показатель кругового сечения, перенесён в measure_plane.py:
  выравнивает облако по СОБСТВЕННЫМ главным осям и режет на пяти высотах вдоль каждой.
  Три самодельные версии (мировые оси, одно сечение) давали хуже; результаты проверки
  на эталонной геометрии - в circular_section_results.json

Веса по-прежнему не в репозитории - источники в MODELS.md. Наборы кадров прежних
прогонов (cv/flow_*, 1.26 ГБ) исключены: это выход, а не исходники.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 13:12:07 +00:00

313 lines
10 KiB
C++

#include "ffs_depth_tensorrt.hpp"
#include "ffs_depth_single_tensorrt.hpp"
#include <cuda_runtime.h>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <algorithm>
#include <chrono>
#include <cmath>
#include <cstdint>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <numeric>
#include <sstream>
#include <stdexcept>
#include <string>
#include <vector>
namespace {
void checkCuda(cudaError_t status, const char* what) {
if (status != cudaSuccess) {
throw std::runtime_error(std::string(what) + ": " + cudaGetErrorString(status));
}
}
struct CudaBuffer {
void* ptr = nullptr;
~CudaBuffer() { if (ptr) cudaFree(ptr); }
void allocate(size_t bytes) { checkCuda(cudaMalloc(&ptr, bytes), "cudaMalloc"); }
template <typename T> T* as() { return static_cast<T*>(ptr); }
};
struct Intrinsics {
float k[9] = {};
float baseline = 0.0f;
};
struct Args {
std::string engine_dir;
std::string left_path;
std::string right_path;
std::string intrinsic_path;
std::string mode = "auto";
int warmup = 10;
int runs = 30;
bool include_depth = false;
};
struct Stats {
double mean = 0.0;
double min = 0.0;
double p50 = 0.0;
double p90 = 0.0;
double max = 0.0;
double stddev = 0.0;
};
void printUsage(const char* prog) {
std::cerr
<< "Usage: " << prog
<< " <engine_dir> <left_image> <right_image> <intrinsic_file>"
<< " [--mode auto|two|single] [--warmup N] [--runs N] [--include-depth]\n";
}
Args parseArgs(int argc, char** argv) {
if (argc < 5) {
printUsage(argv[0]);
throw std::runtime_error("missing required arguments");
}
Args args;
args.engine_dir = argv[1];
args.left_path = argv[2];
args.right_path = argv[3];
args.intrinsic_path = argv[4];
for (int i = 5; i < argc; ++i) {
const std::string key = argv[i];
auto requireValue = [&](const char* name) -> std::string {
if (i + 1 >= argc) {
throw std::runtime_error(std::string("missing value for ") + name);
}
return argv[++i];
};
if (key == "--mode") {
args.mode = requireValue("--mode");
} else if (key == "--warmup") {
args.warmup = std::stoi(requireValue("--warmup"));
} else if (key == "--runs") {
args.runs = std::stoi(requireValue("--runs"));
} else if (key == "--include-depth") {
args.include_depth = true;
} else {
throw std::runtime_error("unknown argument: " + key);
}
}
if (args.mode != "auto" && args.mode != "two" && args.mode != "single") {
throw std::runtime_error("--mode must be auto, two, or single");
}
if (args.warmup < 0 || args.runs <= 0) {
throw std::runtime_error("--warmup must be >= 0 and --runs must be > 0");
}
return args;
}
Intrinsics loadIntrinsics(const std::string& path) {
std::ifstream in(path);
if (!in) throw std::runtime_error("cannot open intrinsic file: " + path);
std::string k_line;
std::string baseline_line;
if (!std::getline(in, k_line) || !std::getline(in, baseline_line)) {
throw std::runtime_error("intrinsic file must contain K on line 1 and baseline on line 2");
}
Intrinsics intr;
std::istringstream ks(k_line);
for (float& v : intr.k) {
if (!(ks >> v)) throw std::runtime_error("K line must contain 9 floats");
}
std::istringstream bs(baseline_line);
if (!(bs >> intr.baseline)) throw std::runtime_error("baseline line must contain one float");
return intr;
}
Stats summarize(std::vector<float> values) {
if (values.empty()) throw std::runtime_error("cannot summarize empty timing vector");
std::sort(values.begin(), values.end());
const double sum = std::accumulate(values.begin(), values.end(), 0.0);
const double mean = sum / static_cast<double>(values.size());
double var = 0.0;
for (float v : values) {
const double d = static_cast<double>(v) - mean;
var += d * d;
}
var /= static_cast<double>(values.size());
auto percentile = [&](double p) {
const size_t idx = static_cast<size_t>(
std::llround((values.size() - 1) * p / 100.0));
return static_cast<double>(values[std::min(idx, values.size() - 1)]);
};
Stats stats;
stats.mean = mean;
stats.min = values.front();
stats.p50 = percentile(50.0);
stats.p90 = percentile(90.0);
stats.max = values.back();
stats.stddev = std::sqrt(var);
return stats;
}
void printStats(const char* label, const Stats& s) {
std::cout
<< label
<< " mean_ms=" << s.mean
<< " p50_ms=" << s.p50
<< " p90_ms=" << s.p90
<< " min_ms=" << s.min
<< " max_ms=" << s.max
<< " std_ms=" << s.stddev
<< "\n";
}
template <typename Runner>
std::vector<float> profileRunner(
Runner& runner,
uint8_t* d_left,
uint8_t* d_right,
int height,
int width,
float* d_disp,
float* d_depth,
float fx,
float baseline,
int warmup,
int runs,
bool include_depth,
std::vector<float>& host_ms)
{
cudaEvent_t start = nullptr;
cudaEvent_t stop = nullptr;
checkCuda(cudaEventCreate(&start), "cudaEventCreate start");
checkCuda(cudaEventCreate(&stop), "cudaEventCreate stop");
std::vector<float> gpu_ms;
gpu_ms.reserve(static_cast<size_t>(runs));
host_ms.clear();
host_ms.reserve(static_cast<size_t>(runs));
for (int i = 0; i < warmup + runs; ++i) {
const auto host_start = std::chrono::steady_clock::now();
checkCuda(cudaEventRecord(start, runner.stream()), "cudaEventRecord start");
runner.infer(d_left, d_right, height, width, d_disp);
if (include_depth) {
runner.dispToDepth(d_disp, height, width, fx, baseline, d_depth);
}
checkCuda(cudaEventRecord(stop, runner.stream()), "cudaEventRecord stop");
checkCuda(cudaEventSynchronize(stop), "cudaEventSynchronize stop");
const auto host_stop = std::chrono::steady_clock::now();
if (i >= warmup) {
float elapsed = 0.0f;
checkCuda(cudaEventElapsedTime(&elapsed, start, stop), "cudaEventElapsedTime");
gpu_ms.push_back(elapsed);
host_ms.push_back(static_cast<float>(
std::chrono::duration<double, std::milli>(host_stop - host_start).count()));
}
}
cudaEventDestroy(stop);
cudaEventDestroy(start);
return gpu_ms;
}
template <typename Runner>
void runProfile(const Args& args,
Runner& runner,
uint8_t* d_left,
uint8_t* d_right,
int height,
int width,
float* d_disp,
float* d_depth,
const Intrinsics& intr,
const char* mode_name) {
std::vector<float> host_ms;
const std::vector<float> gpu_ms = profileRunner(
runner,
d_left,
d_right,
height,
width,
d_disp,
d_depth,
intr.k[0],
intr.baseline,
args.warmup,
args.runs,
args.include_depth,
host_ms);
std::cout << "mode=" << mode_name << "\n";
std::cout << "image=" << width << "x" << height << "\n";
std::cout << "model=" << runner.modelWidth() << "x" << runner.modelHeight() << "\n";
std::cout << "warmup=" << args.warmup << " runs=" << args.runs << "\n";
std::cout << "timed_region=" << (args.include_depth ? "infer+dispToDepth" : "infer") << "\n";
printStats("gpu", summarize(gpu_ms));
printStats("host", summarize(host_ms));
}
} // namespace
int main(int argc, char** argv) {
try {
const Args args = parseArgs(argc, argv);
const Intrinsics intr = loadIntrinsics(args.intrinsic_path);
cv::Mat left = cv::imread(args.left_path, cv::IMREAD_COLOR);
cv::Mat right = cv::imread(args.right_path, cv::IMREAD_COLOR);
if (left.empty()) throw std::runtime_error("cannot read left image: " + args.left_path);
if (right.empty()) throw std::runtime_error("cannot read right image: " + args.right_path);
if (left.size() != right.size()) throw std::runtime_error("left/right size mismatch");
if (!left.isContinuous()) left = left.clone();
if (!right.isContinuous()) right = right.clone();
const int height = left.rows;
const int width = left.cols;
const size_t image_bytes = static_cast<size_t>(height) * width * 3;
const size_t map_bytes = static_cast<size_t>(height) * width * sizeof(float);
CudaBuffer d_left;
CudaBuffer d_right;
CudaBuffer d_disp;
CudaBuffer d_depth;
d_left.allocate(image_bytes);
d_right.allocate(image_bytes);
d_disp.allocate(map_bytes);
d_depth.allocate(map_bytes);
checkCuda(cudaMemcpy(d_left.ptr, left.data, image_bytes, cudaMemcpyHostToDevice), "copy left");
checkCuda(cudaMemcpy(d_right.ptr, right.data, image_bytes, cudaMemcpyHostToDevice), "copy right");
std::string mode = args.mode;
if (mode == "auto") {
mode = std::filesystem::exists(
std::filesystem::path(args.engine_dir) / "fast_foundationstereo.engine")
? "single"
: "two";
}
if (mode == "single") {
ffs_depth::FFSSingleEngineInference runner(args.engine_dir);
runProfile(args, runner, d_left.as<uint8_t>(), d_right.as<uint8_t>(),
height, width, d_disp.as<float>(), d_depth.as<float>(),
intr, "single");
} else {
ffs_depth::FFSDepthInference runner(args.engine_dir);
runProfile(args, runner, d_left.as<uint8_t>(), d_right.as<uint8_t>(),
height, width, d_disp.as<float>(), d_depth.as<float>(),
intr, "two");
}
} catch (const std::exception& e) {
std::cerr << "ERROR: " << e.what() << "\n";
return 1;
}
return 0;
}