gallery/deeptagger/deeptagger.cpp

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#include <getopt.h>
#include <Magick++.h>
#include <onnxruntime_cxx_api.h>
#ifdef __APPLE__
#include <coreml_provider_factory.h>
#endif
#include <algorithm>
#include <condition_variable>
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#include <filesystem>
#include <fstream>
#include <iostream>
#include <mutex>
#include <queue>
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#include <regex>
#include <set>
#include <stdexcept>
#include <string>
#include <thread>
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#include <tuple>
#include <cstdio>
#include <cstdint>
#include <climits>
static struct {
bool cpu = false;
int debug = 0;
long batch = 1;
float threshold = 0.1;
// Execution provider name → Key → Value
std::map<std::string, std::map<std::string, std::string>> options;
} g;
// --- Configuration -----------------------------------------------------------
// Arguably, input normalization could be incorporated into models instead.
struct Config {
std::string name;
enum class Shape {NHWC, NCHW} shape = Shape::NHWC;
enum class Channels {RGB, BGR} channels = Channels::RGB;
bool normalize = false;
enum class Pad {WHITE, EDGE, STRETCH} pad = Pad::WHITE;
int size = -1;
bool sigmoid = false;
std::vector<std::string> tags;
};
static void
read_tags(const std::string &path, std::vector<std::string> &tags)
{
std::ifstream f(path);
f.exceptions(std::ifstream::badbit);
if (!f)
throw std::runtime_error("cannot read tags");
std::string line;
while (std::getline(f, line)) {
if (!line.empty() && line.back() == '\r')
line.erase(line.size() - 1);
tags.push_back(line);
}
}
static void
read_field(Config &config, std::string key, std::string value)
{
if (key == "name") {
config.name = value;
} else if (key == "shape") {
if (value == "nhwc") config.shape = Config::Shape::NHWC;
else if (value == "nchw") config.shape = Config::Shape::NCHW;
else throw std::invalid_argument("bad value for: " + key);
} else if (key == "channels") {
if (value == "rgb") config.channels = Config::Channels::RGB;
else if (value == "bgr") config.channels = Config::Channels::BGR;
else throw std::invalid_argument("bad value for: " + key);
} else if (key == "normalize") {
if (value == "true") config.normalize = true;
else if (value == "false") config.normalize = false;
else throw std::invalid_argument("bad value for: " + key);
} else if (key == "pad") {
if (value == "white") config.pad = Config::Pad::WHITE;
else if (value == "edge") config.pad = Config::Pad::EDGE;
else if (value == "stretch") config.pad = Config::Pad::STRETCH;
else throw std::invalid_argument("bad value for: " + key);
} else if (key == "size") {
config.size = std::stoi(value);
} else if (key == "interpret") {
if (value == "false") config.sigmoid = false;
else if (value == "sigmoid") config.sigmoid = true;
else throw std::invalid_argument("bad value for: " + key);
} else {
throw std::invalid_argument("unsupported config key: " + key);
}
}
static void
read_config(Config &config, const char *path)
{
std::ifstream f(path);
f.exceptions(std::ifstream::badbit);
if (!f)
throw std::runtime_error("cannot read configuration");
std::regex re(R"(^\s*([^#=]+?)\s*=\s*([^#]*?)\s*(?:#|$))",
std::regex::optimize);
std::smatch m;
std::string line;
while (std::getline(f, line)) {
if (std::regex_match(line, m, re))
read_field(config, m[1].str(), m[2].str());
}
read_tags(
std::filesystem::path(path).replace_extension("tags"), config.tags);
}
// --- Data preparation --------------------------------------------------------
static float *
image_to_nhwc(float *data, Magick::Image &image, Config::Channels channels)
{
unsigned int width = image.columns();
unsigned int height = image.rows();
auto pixels = image.getConstPixels(0, 0, width, height);
switch (channels) {
case Config::Channels::RGB:
for (unsigned int y = 0; y < height; y++) {
for (unsigned int x = 0; x < width; x++) {
auto pixel = *pixels++;
*data++ = ScaleQuantumToChar(pixel.red);
*data++ = ScaleQuantumToChar(pixel.green);
*data++ = ScaleQuantumToChar(pixel.blue);
}
}
break;
case Config::Channels::BGR:
for (unsigned int y = 0; y < height; y++) {
for (unsigned int x = 0; x < width; x++) {
auto pixel = *pixels++;
*data++ = ScaleQuantumToChar(pixel.blue);
*data++ = ScaleQuantumToChar(pixel.green);
*data++ = ScaleQuantumToChar(pixel.red);
}
}
}
return data;
}
static float *
image_to_nchw(float *data, Magick::Image &image, Config::Channels channels)
{
unsigned int width = image.columns();
unsigned int height = image.rows();
auto pixels = image.getConstPixels(0, 0, width, height), pp = pixels;
switch (channels) {
case Config::Channels::RGB:
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).red);
pp = pixels;
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).green);
pp = pixels;
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).blue);
break;
case Config::Channels::BGR:
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).blue);
pp = pixels;
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).green);
pp = pixels;
for (unsigned int y = 0; y < height; y++)
for (unsigned int x = 0; x < width; x++)
*data++ = ScaleQuantumToChar((*pp++).red);
}
return data;
}
static Magick::Image
load(const std::string filename,
const Config &config, int64_t width, int64_t height)
{
Magick::Image image;
try {
image.read(filename);
} catch (const Magick::Warning &warning) {
if (g.debug)
fprintf(stderr, "%s: %s\n", filename.c_str(), warning.what());
}
image.autoOrient();
Magick::Geometry adjusted(width, height);
switch (config.pad) {
case Config::Pad::EDGE:
case Config::Pad::WHITE:
adjusted.greater(true);
break;
case Config::Pad::STRETCH:
adjusted.aspect(false);
}
image.resize(adjusted, Magick::LanczosFilter);
// The GraphicsMagick API doesn't offer any good options.
if (config.pad == Config::Pad::EDGE) {
MagickLib::SetImageVirtualPixelMethod(
image.image(), MagickLib::EdgeVirtualPixelMethod);
auto x = (int64_t(image.columns()) - width) / 2;
auto y = (int64_t(image.rows()) - height) / 2;
auto source = image.getConstPixels(x, y, width, height);
std::vector<MagickLib::PixelPacket>
pixels(source, source + width * height);
Magick::Image edged(Magick::Geometry(width, height), "black");
edged.classType(Magick::DirectClass);
auto target = edged.setPixels(0, 0, width, height);
memcpy(target, pixels.data(), pixels.size() * sizeof pixels[0]);
edged.syncPixels();
image = edged;
}
// Center it in a square patch of white, removing any transparency.
// image.extent() could probably be used to do the same thing.
Magick::Image white(Magick::Geometry(width, height), "white");
auto x = (white.columns() - image.columns()) / 2;
auto y = (white.rows() - image.rows()) / 2;
white.composite(image, x, y, Magick::OverCompositeOp);
white.fileName(filename);
if (g.debug > 2)
white.display();
return white;
}
// --- Inference ---------------------------------------------------------------
static void
run(std::vector<Magick::Image> &images, const Config &config,
Ort::Session &session, std::vector<int64_t> shape)
{
auto batch = shape[0] = images.size();
Ort::AllocatorWithDefaultOptions allocator;
auto tensor = Ort::Value::CreateTensor<float>(
allocator, shape.data(), shape.size());
auto input_len = tensor.GetTensorTypeAndShapeInfo().GetElementCount();
auto input_data = tensor.GetTensorMutableData<float>(), pi = input_data;
for (int64_t i = 0; i < batch; i++) {
switch (config.shape) {
case Config::Shape::NCHW:
pi = image_to_nchw(pi, images.at(i), config.channels);
break;
case Config::Shape::NHWC:
pi = image_to_nhwc(pi, images.at(i), config.channels);
}
}
if (config.normalize) {
pi = input_data;
for (size_t i = 0; i < input_len; i++)
*pi++ /= 255.0;
}
std::string input_name =
session.GetInputNameAllocated(0, allocator).get();
std::string output_name =
session.GetOutputNameAllocated(0, allocator).get();
std::vector<const char *> input_names = {input_name.c_str()};
std::vector<const char *> output_names = {output_name.c_str()};
auto outputs = session.Run(Ort::RunOptions{},
input_names.data(), &tensor, input_names.size(),
output_names.data(), output_names.size());
if (outputs.size() != 1 || !outputs[0].IsTensor()) {
fprintf(stderr, "Wrong output\n");
return;
}
auto output_len = outputs[0].GetTensorTypeAndShapeInfo().GetElementCount();
auto output_data = outputs.front().GetTensorData<float>(), po = output_data;
if (output_len != batch * config.tags.size()) {
fprintf(stderr, "Tags don't match the output\n");
return;
}
for (size_t i = 0; i < batch; i++) {
for (size_t t = 0; t < config.tags.size(); t++) {
float value = *po++;
if (config.sigmoid)
value = 1 / (1 + std::exp(-value));
if (value > g.threshold) {
printf("%s\t%.2f\t%s\n", images.at(i).fileName().c_str(),
value, config.tags.at(t).c_str());
}
}
}
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
static void
parse_options(const std::string &options)
{
auto semicolon = options.find(";");
auto name = options.substr(0, semicolon);
auto sequence = options.substr(semicolon);
std::map<std::string, std::string> kv;
std::regex re(R"(;*([^;=]+)=([^;=]+))", std::regex::optimize);
std::sregex_iterator it(sequence.begin(), sequence.end(), re), end;
for (; it != end; ++it)
kv[it->str(1)] = it->str(2);
g.options.insert_or_assign(name, std::move(kv));
}
static std::tuple<std::vector<const char *>, std::vector<const char *>>
unpack_options(const std::string &provider)
{
std::vector<const char *> keys, values;
if (g.options.count(provider)) {
for (const auto &kv : g.options.at(provider)) {
keys.push_back(kv.first.c_str());
values.push_back(kv.second.c_str());
}
}
return {keys, values};
}
static void
add_providers(Ort::SessionOptions &options)
{
auto api = Ort::GetApi();
auto v_providers = Ort::GetAvailableProviders();
std::set<std::string> providers(v_providers.begin(), v_providers.end());
if (g.debug) {
printf("Providers:");
for (const auto &it : providers)
printf(" %s", it.c_str());
printf("\n");
}
// There is a string-based AppendExecutionProvider() method,
// but it cannot be used with all providers.
// TODO: Make it possible to disable providers.
// TODO: Providers will deserve some performance tuning.
if (g.cpu)
return;
#ifdef __APPLE__
if (providers.count("CoreMLExecutionProvider")) {
try {
Ort::ThrowOnError(
OrtSessionOptionsAppendExecutionProvider_CoreML(options, 0));
} catch (const std::exception &e) {
fprintf(stderr, "CoreML unavailable: %s\n", e.what());
}
}
#endif
#if TENSORRT
// TensorRT should be the more performant execution provider, however:
// - it is difficult to set up (needs logging in to download),
// - with WD v1.4 ONNX models, one gets "Your ONNX model has been generated
// with INT64 weights, while TensorRT does not natively support INT64.
// Attempting to cast down to INT32." and that's not nice.
if (providers.count("TensorrtExecutionProvider")) {
OrtTensorRTProviderOptionsV2* tensorrt_options = nullptr;
Ort::ThrowOnError(api.CreateTensorRTProviderOptions(&tensorrt_options));
auto [keys, values] = unpack_options("TensorrtExecutionProvider");
if (!keys.empty()) {
Ort::ThrowOnError(api.UpdateTensorRTProviderOptions(
tensorrt_options, keys.data(), values.data(), keys.size()));
}
try {
options.AppendExecutionProvider_TensorRT_V2(*tensorrt_options);
} catch (const std::exception &e) {
fprintf(stderr, "TensorRT unavailable: %s\n", e.what());
}
api.ReleaseTensorRTProviderOptions(tensorrt_options);
}
#endif
// See CUDA-ExecutionProvider.html for documentation.
if (providers.count("CUDAExecutionProvider")) {
OrtCUDAProviderOptionsV2* cuda_options = nullptr;
Ort::ThrowOnError(api.CreateCUDAProviderOptions(&cuda_options));
auto [keys, values] = unpack_options("CUDAExecutionProvider");
if (!keys.empty()) {
Ort::ThrowOnError(api.UpdateCUDAProviderOptions(
cuda_options, keys.data(), values.data(), keys.size()));
}
try {
options.AppendExecutionProvider_CUDA_V2(*cuda_options);
} catch (const std::exception &e) {
fprintf(stderr, "CUDA unavailable: %s\n", e.what());
}
api.ReleaseCUDAProviderOptions(cuda_options);
}
if (providers.count("ROCMExecutionProvider")) {
OrtROCMProviderOptions rocm_options = {};
auto [keys, values] = unpack_options("ROCMExecutionProvider");
if (!keys.empty()) {
Ort::ThrowOnError(api.UpdateROCMProviderOptions(
&rocm_options, keys.data(), values.data(), keys.size()));
}
try {
options.AppendExecutionProvider_ROCM(rocm_options);
} catch (const std::exception &e) {
fprintf(stderr, "ROCM unavailable: %s\n", e.what());
}
}
// The CPU provider is the default fallback, if everything else fails.
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
struct Thumbnailing {
std::mutex input_mutex;
std::condition_variable input_cv;
std::queue<std::string> input; // All input paths
int work = 0; // Number of images requested
std::mutex output_mutex;
std::condition_variable output_cv;
std::vector<Magick::Image> output; // Processed images
int done = 0; // Finished worker threads
};
static void
thumbnail(const Config &config, int64_t width, int64_t height,
Thumbnailing &ctx)
{
while (true) {
std::unique_lock<std::mutex> input_lock(ctx.input_mutex);
ctx.input_cv.wait(input_lock,
[&]{ return ctx.input.empty() || ctx.work; });
if (ctx.input.empty())
break;
auto path = ctx.input.front();
ctx.input.pop();
ctx.work--;
input_lock.unlock();
Magick::Image image;
try {
image = load(path, config, width, height);
if (height != image.rows() || width != image.columns())
throw std::runtime_error("tensor mismatch");
std::unique_lock<std::mutex> output_lock(ctx.output_mutex);
ctx.output.push_back(image);
output_lock.unlock();
ctx.output_cv.notify_all();
} catch (const std::exception &e) {
fprintf(stderr, "%s: %s\n", path.c_str(), e.what());
std::unique_lock<std::mutex> input_lock(ctx.input_mutex);
ctx.work++;
input_lock.unlock();
ctx.input_cv.notify_all();
}
}
std::unique_lock<std::mutex> output_lock(ctx.output_mutex);
ctx.done++;
output_lock.unlock();
ctx.output_cv.notify_all();
}
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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static std::string
print_shape(const Ort::ConstTensorTypeAndShapeInfo &info)
{
std::vector<const char *> names(info.GetDimensionsCount());
info.GetSymbolicDimensions(names.data(), names.size());
auto shape = info.GetShape();
std::string result;
for (size_t i = 0; i < shape.size(); i++) {
if (shape[i] < 0)
result.append(names.at(i));
else
result.append(std::to_string(shape[i]));
result.append(" x ");
}
if (!result.empty())
result.erase(result.size() - 3);
return result;
}
static void
print_shapes(const Ort::Session &session)
{
Ort::AllocatorWithDefaultOptions allocator;
for (size_t i = 0; i < session.GetInputCount(); i++) {
std::string name = session.GetInputNameAllocated(i, allocator).get();
auto info = session.GetInputTypeInfo(i);
auto shape = print_shape(info.GetTensorTypeAndShapeInfo());
printf("Input: %s: %s\n", name.c_str(), shape.c_str());
}
for (size_t i = 0; i < session.GetOutputCount(); i++) {
std::string name = session.GetOutputNameAllocated(i, allocator).get();
auto info = session.GetOutputTypeInfo(i);
auto shape = print_shape(info.GetTensorTypeAndShapeInfo());
printf("Output: %s: %s\n", name.c_str(), shape.c_str());
}
}
static void
infer(Ort::Env &env, const char *path, const std::vector<std::string> &images)
{
Config config;
read_config(config, path);
Ort::SessionOptions session_options;
add_providers(session_options);
Ort::Session session = Ort::Session(env,
std::filesystem::path(path).replace_extension("onnx").c_str(),
session_options);
if (g.debug)
print_shapes(session);
if (session.GetInputCount() != 1 || session.GetOutputCount() != 1) {
fprintf(stderr, "Invalid input or output shape\n");
exit(EXIT_FAILURE);
}
auto input_info = session.GetInputTypeInfo(0);
auto shape = input_info.GetTensorTypeAndShapeInfo().GetShape();
if (shape.size() != 4) {
fprintf(stderr, "Incompatible input tensor format\n");
exit(EXIT_FAILURE);
}
if (shape.at(0) > 1) {
fprintf(stderr, "Fixed batching not supported\n");
exit(EXIT_FAILURE);
}
if (shape.at(0) >= 0 && g.batch > 1) {
fprintf(stderr, "Requested batching for a non-batching model\n");
exit(EXIT_FAILURE);
}
int64_t *height = {}, *width = {}, *channels = {};
switch (config.shape) {
case Config::Shape::NCHW:
channels = &shape[1];
height = &shape[2];
width = &shape[3];
break;
case Config::Shape::NHWC:
height = &shape[1];
width = &shape[2];
channels = &shape[3];
break;
}
// Variable dimensions don't combine well with batches.
if (*height < 0)
*height = config.size;
if (*width < 0)
*width = config.size;
if (*channels != 3 || *height < 1 || *width < 1) {
fprintf(stderr, "Incompatible input tensor format\n");
return;
}
// By only parallelizing image loads here during batching,
// they never compete for CPU time with inference.
Thumbnailing ctx;
for (const auto &path : images)
ctx.input.push(path);
for (auto i = g.batch; i--; )
std::thread(thumbnail, std::ref(config), *width, *height,
std::ref(ctx)).detach();
while (true) {
std::unique_lock<std::mutex> input_lock(ctx.input_mutex);
ctx.work = g.batch;
input_lock.unlock();
ctx.input_cv.notify_all();
std::unique_lock<std::mutex> output_lock(ctx.output_mutex);
ctx.output_cv.wait(output_lock,
[&]{ return ctx.output.size() == g.batch || ctx.done == g.batch; });
// It would be possible to add dummy entries to the batch,
// so that the model doesn't need to be rebuilt.
if (!ctx.output.empty()) {
run(ctx.output, config, session, shape);
ctx.output.clear();
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}
if (ctx.done == g.batch)
break;
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}
}
int
main(int argc, char *argv[])
{
auto invocation_name = argv[0];
auto print_usage = [=] {
fprintf(stderr,
"Usage: %s [-b BATCH] [--cpu] [-d] [-o EP;KEY=VALUE...] "
"[-t THRESHOLD] MODEL { --pipe | [IMAGE...] }\n", invocation_name);
};
static option opts[] = {
{"batch", required_argument, 0, 'b'},
{"cpu", no_argument, 0, 'c'},
{"debug", no_argument, 0, 'd'},
{"help", no_argument, 0, 'h'},
{"options", required_argument, 0, 'o'},
{"pipe", no_argument, 0, 'p'},
{"threshold", required_argument, 0, 't'},
{nullptr, 0, 0, 0},
};
bool pipe = false;
while (1) {
int option_index = 0;
auto c = getopt_long(argc, const_cast<char *const *>(argv),
"b:cdho:pt:", opts, &option_index);
if (c == -1)
break;
char *end = nullptr;
switch (c) {
case 'b':
errno = 0, g.batch = strtol(optarg, &end, 10);
if (errno || *end || g.batch < 1 || g.batch > SHRT_MAX) {
fprintf(stderr, "Batch size must be a positive number\n");
exit(EXIT_FAILURE);
}
break;
case 'c':
g.cpu = true;
break;
case 'd':
g.debug++;
break;
case 'h':
print_usage();
return 0;
case 'o':
parse_options(optarg);
break;
case 'p':
pipe = true;
break;
case 't':
errno = 0, g.threshold = strtod(optarg, &end);
if (errno || *end || !std::isfinite(g.threshold) ||
g.threshold < 0 || g.threshold > 1) {
fprintf(stderr, "Threshold must be a number within 0..1\n");
exit(EXIT_FAILURE);
}
break;
default:
print_usage();
return 1;
}
}
argv += optind;
argc -= optind;
// TODO: There's actually no need to slurp all the lines up front.
std::vector<std::string> paths;
if (pipe) {
if (argc != 1) {
print_usage();
return 1;
}
std::string line;
while (std::getline(std::cin, line))
paths.push_back(line);
} else {
if (argc < 1) {
print_usage();
return 1;
}
paths.assign(argv + 1, argv + argc);
}
// Load batched images in parallel (the first is for GM, the other for IM).
if (g.batch > 1) {
auto value = std::to_string(
std::max(std::thread::hardware_concurrency() / g.batch, 1L));
setenv("OMP_NUM_THREADS", value.c_str(), true);
setenv("MAGICK_THREAD_LIMIT", value.c_str(), true);
}
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// XXX: GraphicsMagick initializes signal handlers here,
// one needs to use MagickLib::InitializeMagickEx()
// with MAGICK_OPT_NO_SIGNAL_HANDER to prevent that.
//
// ImageMagick conveniently has the opposite default.
Magick::InitializeMagick(nullptr);
OrtLoggingLevel logging = g.debug > 1
? ORT_LOGGING_LEVEL_VERBOSE
: ORT_LOGGING_LEVEL_WARNING;
// Creating an environment before initializing providers in order to avoid:
// "Attempt to use DefaultLogger but none has been registered."
Ort::Env env(logging, invocation_name);
infer(env, argv[0], paths);
return 0;
}