Add a deep tagger in C++
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LICENSE
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Copyright (c) 2023, Přemysl Eric Janouch <p@janouch.name>
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Copyright (c) 2023 - 2024, Přemysl Eric Janouch <p@janouch.name>
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Permission to use, copy, modify, and/or distribute this software for any
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purpose with or without fee is hereby granted.
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# Ubuntu 20.04 LTS
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cmake_minimum_required (VERSION 3.16)
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project (deeptagger VERSION 0.0.1 LANGUAGES CXX)
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# Hint: set ONNXRuntime_ROOT to a directory with a pre-built GitHub release.
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# (Useful for development, otherwise you may need to adjust the rpath.)
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set (CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}")
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find_package (ONNXRuntime REQUIRED)
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find_package (PkgConfig REQUIRED)
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pkg_check_modules (GM REQUIRED GraphicsMagick++)
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add_executable (deeptagger deeptagger.cpp)
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target_compile_features (deeptagger PRIVATE cxx_std_17)
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target_include_directories (deeptagger PRIVATE
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${GM_INCLUDE_DIRS} ${ONNXRuntime_INCLUDE_DIRS})
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target_link_directories (deeptagger PRIVATE
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${GM_LIBRARY_DIRS})
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target_link_libraries (deeptagger PRIVATE
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${GM_LIBRARIES} ${ONNXRuntime_LIBRARIES})
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# Public Domain
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find_path (ONNXRuntime_INCLUDE_DIRS onnxruntime_c_api.h
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PATH_SUFFIXES onnxruntime)
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find_library (ONNXRuntime_LIBRARIES NAMES onnxruntime)
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include (FindPackageHandleStandardArgs)
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FIND_PACKAGE_HANDLE_STANDARD_ARGS (ONNXRuntime DEFAULT_MSG
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ONNXRuntime_INCLUDE_DIRS ONNXRuntime_LIBRARIES)
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mark_as_advanced (ONNXRuntime_LIBRARIES ONNXRuntime_INCLUDE_DIRS)
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deeptagger
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==========
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This is an automatic image tagger/classifier written in C++,
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without using any Python, and primarily targets various anime models.
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Unfortunately, you will still need Python and some luck to prepare the models,
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achieved by running download.sh. You will need about 20 gigabytes of space.
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Very little effort is made to make this work on non-Unix systems.
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Getting this to work
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--------------------
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To build the evaluator, install a C++ compiler, CMake, and development packages
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of GraphicsMagick and ONNX Runtime.
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Prebuilt ONNX Runtime can be most conveniently downloaded from
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https://github.com/microsoft/onnxruntime/releases[GitHub releases].
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Remember to install CUDA packages, such as _nvidia-cudnn_ on Debian,
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if you plan on using the GPU-enabled options.
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$ cmake -DONNXRuntime_ROOT=/path/to/onnxruntime -B build
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$ cmake --build build
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$ ./download.sh
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$ build/deeptagger models/deepdanbooru-v3-20211112-sgd-e28.model image.jpg
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#!/bin/sh -e
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if [ $# -lt 2 ] || ! [ -x "$1" ]
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then
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echo "Usage: $0 DEEPTAGGER FILE..."
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echo "Run this after using download.sh, from the same directory."
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exit 1
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fi
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runner=$1
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shift
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log=bench.out
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: >$log
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run() {
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opts=$1 batch=$2 model=$3
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shift 3
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for i in $(seq 1 3)
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do
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start=$(date +%s)
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"$runner" $opts -b "$batch" -t 0.75 "$model" "$@" >/dev/null || :
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end=$(date +%s)
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printf '%s\t%s\t%s\t%s\t%s\n' \
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"$name" "$model" "$opts" "$batch" "$((end - start))" | tee -a $log
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done
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}
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for model in models/*.model
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do
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name=$(sed -n 's/^name=//p' "$model")
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run "" 1 "$model" "$@"
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run "" 4 "$model" "$@"
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run "" 16 "$model" "$@"
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run --cpu 1 "$model" "$@"
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run --cpu 4 "$model" "$@"
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run --cpu 16 "$model" "$@"
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done
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#include <getopt.h>
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#include <Magick++.h>
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#include <onnxruntime_cxx_api.h>
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#ifdef __APPLE__
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#include <coreml_provider_factory.h>
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#endif
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#include <algorithm>
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#include <filesystem>
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#include <fstream>
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#include <iostream>
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#include <regex>
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#include <set>
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#include <stdexcept>
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#include <string>
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#include <tuple>
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#include <cstdio>
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#include <cstdint>
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#include <climits>
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static struct {
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bool cpu = false;
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int debug = 0;
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long batch = 1;
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float threshold = 0.1;
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// Execution provider name → Key → Value
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std::map<std::string, std::map<std::string, std::string>> options;
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} g;
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// --- Configuration -----------------------------------------------------------
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// Arguably, input normalization could be incorporated into models instead.
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struct Config {
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std::string name;
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enum class Shape {NHWC, NCHW} shape = Shape::NHWC;
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enum class Channels {RGB, BGR} channels = Channels::RGB;
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bool normalize = false;
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enum class Pad {WHITE, EDGE, STRETCH} pad = Pad::WHITE;
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int size = -1;
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bool sigmoid = false;
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std::vector<std::string> tags;
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};
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static void
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read_tags(const std::string &path, std::vector<std::string> &tags)
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{
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std::ifstream f(path);
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f.exceptions(std::ifstream::badbit);
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if (!f)
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throw std::runtime_error("cannot read tags");
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std::string line;
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while (std::getline(f, line)) {
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if (!line.empty() && line.back() == '\r')
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line.erase(line.size() - 1);
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tags.push_back(line);
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}
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}
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static void
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read_field(Config &config, std::string key, std::string value)
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{
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if (key == "name") {
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config.name = value;
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} else if (key == "shape") {
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if (value == "nhwc") config.shape = Config::Shape::NHWC;
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else if (value == "nchw") config.shape = Config::Shape::NCHW;
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else throw std::invalid_argument("bad value for: " + key);
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} else if (key == "channels") {
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if (value == "rgb") config.channels = Config::Channels::RGB;
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else if (value == "bgr") config.channels = Config::Channels::BGR;
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else throw std::invalid_argument("bad value for: " + key);
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} else if (key == "normalize") {
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if (value == "true") config.normalize = true;
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else if (value == "false") config.normalize = false;
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else throw std::invalid_argument("bad value for: " + key);
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} else if (key == "pad") {
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if (value == "white") config.pad = Config::Pad::WHITE;
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else if (value == "edge") config.pad = Config::Pad::EDGE;
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else if (value == "stretch") config.pad = Config::Pad::STRETCH;
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else throw std::invalid_argument("bad value for: " + key);
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} else if (key == "size") {
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config.size = std::stoi(value);
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} else if (key == "interpret") {
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if (value == "false") config.sigmoid = false;
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else if (value == "sigmoid") config.sigmoid = true;
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else throw std::invalid_argument("bad value for: " + key);
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} else {
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throw std::invalid_argument("unsupported config key: " + key);
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}
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}
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static void
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read_config(Config &config, const char *path)
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{
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std::ifstream f(path);
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f.exceptions(std::ifstream::badbit);
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if (!f)
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throw std::runtime_error("cannot read configuration");
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std::regex re(R"(^\s*([^#=]+?)\s*=\s*([^#]*?)\s*(?:#|$))",
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std::regex::optimize);
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std::smatch m;
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std::string line;
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while (std::getline(f, line)) {
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if (std::regex_match(line, m, re))
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read_field(config, m[1].str(), m[2].str());
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}
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read_tags(
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std::filesystem::path(path).replace_extension("tags"), config.tags);
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}
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// --- Data preparation --------------------------------------------------------
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static float *
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image_to_nhwc(float *data, Magick::Image &image, Config::Channels channels)
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{
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unsigned int width = image.columns();
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unsigned int height = image.rows();
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auto pixels = image.getConstPixels(0, 0, width, height);
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switch (channels) {
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case Config::Channels::RGB:
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for (unsigned int y = 0; y < height; y++) {
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for (unsigned int x = 0; x < width; x++) {
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auto pixel = *pixels++;
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*data++ = ScaleQuantumToChar(pixel.red);
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*data++ = ScaleQuantumToChar(pixel.green);
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*data++ = ScaleQuantumToChar(pixel.blue);
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}
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}
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break;
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case Config::Channels::BGR:
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for (unsigned int y = 0; y < height; y++) {
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for (unsigned int x = 0; x < width; x++) {
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auto pixel = *pixels++;
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*data++ = ScaleQuantumToChar(pixel.blue);
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*data++ = ScaleQuantumToChar(pixel.green);
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*data++ = ScaleQuantumToChar(pixel.red);
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}
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}
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}
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return data;
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}
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static float *
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image_to_nchw(float *data, Magick::Image &image, Config::Channels channels)
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{
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unsigned int width = image.columns();
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unsigned int height = image.rows();
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auto pixels = image.getConstPixels(0, 0, width, height), pp = pixels;
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switch (channels) {
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case Config::Channels::RGB:
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).red);
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pp = pixels;
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).green);
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pp = pixels;
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).blue);
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break;
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case Config::Channels::BGR:
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).blue);
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pp = pixels;
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).green);
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pp = pixels;
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for (unsigned int y = 0; y < height; y++)
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for (unsigned int x = 0; x < width; x++)
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*data++ = ScaleQuantumToChar((*pp++).red);
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}
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return data;
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}
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static Magick::Image
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load(const std::string filename,
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const Config &config, int64_t width, int64_t height)
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{
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Magick::Image image;
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try {
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image.read(filename);
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} catch (const Magick::Warning &warning) {
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if (g.debug)
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fprintf(stderr, "%s: %s\n", filename.c_str(), warning.what());
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}
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image.autoOrient();
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Magick::Geometry adjusted(width, height);
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switch (config.pad) {
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case Config::Pad::EDGE:
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case Config::Pad::WHITE:
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adjusted.greater(true);
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break;
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case Config::Pad::STRETCH:
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adjusted.aspect(false);
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}
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image.resize(adjusted, Magick::LanczosFilter);
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// The GraphicsMagick API doesn't offer any good options.
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if (config.pad == Config::Pad::EDGE) {
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MagickLib::SetImageVirtualPixelMethod(
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image.image(), MagickLib::EdgeVirtualPixelMethod);
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auto x = (int64_t(image.columns()) - width) / 2;
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auto y = (int64_t(image.rows()) - height) / 2;
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auto source = image.getConstPixels(x, y, width, height);
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std::vector<MagickLib::PixelPacket>
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pixels(source, source + width * height);
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Magick::Image edged(Magick::Geometry(width, height), "black");
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edged.classType(Magick::DirectClass);
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auto target = edged.setPixels(0, 0, width, height);
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memcpy(target, pixels.data(), pixels.size() * sizeof pixels[0]);
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edged.syncPixels();
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image = edged;
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}
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// Center it in a square patch of white, removing any transparency.
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// image.extent() could probably be used to do the same thing.
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Magick::Image white(Magick::Geometry(width, height), "white");
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auto x = (white.columns() - image.columns()) / 2;
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auto y = (white.rows() - image.rows()) / 2;
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white.composite(image, x, y, Magick::OverCompositeOp);
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white.fileName(filename);
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if (g.debug > 2)
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white.display();
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return white;
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}
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// --- Inference ---------------------------------------------------------------
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static void
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run(std::vector<Magick::Image> &images, const Config &config,
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Ort::Session &session, std::vector<int64_t> shape)
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{
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auto batch = shape[0] = images.size();
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Ort::AllocatorWithDefaultOptions allocator;
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auto tensor = Ort::Value::CreateTensor<float>(
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allocator, shape.data(), shape.size());
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auto input_len = tensor.GetTensorTypeAndShapeInfo().GetElementCount();
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auto input_data = tensor.GetTensorMutableData<float>(), pi = input_data;
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for (int64_t i = 0; i < batch; i++) {
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switch (config.shape) {
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case Config::Shape::NCHW:
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pi = image_to_nchw(pi, images.at(i), config.channels);
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break;
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case Config::Shape::NHWC:
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pi = image_to_nhwc(pi, images.at(i), config.channels);
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}
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}
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if (config.normalize) {
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pi = input_data;
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for (size_t i = 0; i < input_len; i++)
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*pi++ /= 255.0;
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}
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std::string input_name =
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session.GetInputNameAllocated(0, allocator).get();
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std::string output_name =
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session.GetOutputNameAllocated(0, allocator).get();
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std::vector<const char *> input_names = {input_name.c_str()};
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std::vector<const char *> output_names = {output_name.c_str()};
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auto outputs = session.Run(Ort::RunOptions{},
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input_names.data(), &tensor, input_names.size(),
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output_names.data(), output_names.size());
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if (outputs.size() != 1 || !outputs[0].IsTensor()) {
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fprintf(stderr, "Wrong output\n");
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return;
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}
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auto output_len = outputs[0].GetTensorTypeAndShapeInfo().GetElementCount();
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auto output_data = outputs.front().GetTensorData<float>(), po = output_data;
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if (output_len != batch * config.tags.size()) {
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fprintf(stderr, "Tags don't match the output\n");
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return;
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}
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for (size_t i = 0; i < batch; i++) {
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for (size_t t = 0; t < config.tags.size(); t++) {
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float value = *po++;
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if (config.sigmoid)
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value = 1 / (1 + std::exp(-value));
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if (value > g.threshold) {
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printf("%s\t%.2f\t%s\n", images.at(i).fileName().c_str(),
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value, config.tags.at(t).c_str());
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}
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}
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}
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}
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// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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static void
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parse_options(const std::string &options)
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{
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auto semicolon = options.find(";");
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auto name = options.substr(0, semicolon);
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auto sequence = options.substr(semicolon);
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std::map<std::string, std::string> kv;
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std::regex re(R"(;*([^;=]+)=([^;=]+))", std::regex::optimize);
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std::sregex_iterator it(sequence.begin(), sequence.end(), re), end;
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for (; it != end; ++it)
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kv[it->str(1)] = it->str(2);
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g.options.insert_or_assign(name, std::move(kv));
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}
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static std::tuple<std::vector<const char *>, std::vector<const char *>>
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unpack_options(const std::string &provider)
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{
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std::vector<const char *> keys, values;
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if (g.options.count(provider)) {
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for (const auto &kv : g.options.at(provider)) {
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keys.push_back(kv.first.c_str());
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values.push_back(kv.second.c_str());
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}
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}
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return {keys, values};
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}
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static void
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add_providers(Ort::SessionOptions &options)
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{
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auto api = Ort::GetApi();
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auto v_providers = Ort::GetAvailableProviders();
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std::set<std::string> providers(v_providers.begin(), v_providers.end());
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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.
|
||||
}
|
||||
|
||||
// - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
// TODO: Image loading is heavily parallelizable. In theory.
|
||||
std::vector<Magick::Image> batch;
|
||||
for (const auto &filename : images) {
|
||||
Magick::Image image;
|
||||
try {
|
||||
image = load(filename, config, *width, *height);
|
||||
} catch (const std::exception &e) {
|
||||
fprintf(stderr, "%s: %s\n", filename.c_str(), e.what());
|
||||
continue;
|
||||
}
|
||||
|
||||
if (*height != image.rows() || *width != image.columns()) {
|
||||
fprintf(stderr, "%s: %s\n", filename.c_str(), "tensor mismatch");
|
||||
continue;
|
||||
}
|
||||
|
||||
batch.push_back(image);
|
||||
if (batch.size() == g.batch) {
|
||||
run(batch, config, session, shape);
|
||||
batch.clear();
|
||||
}
|
||||
}
|
||||
if (!batch.empty())
|
||||
run(batch, config, session, shape);
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
// 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.
|
||||
//
|
||||
// Once processing images in parallel, consider presetting
|
||||
// OMP_NUM_THREADS=1 (GM) and/or MAGICK_THREAD_LIMIT=1 (IM).
|
||||
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;
|
||||
}
|
||||
|
|
@ -0,0 +1,161 @@
|
|||
#!/bin/sh -e
|
||||
# Requirements: Python ~ 3.11, curl, unzip, git-lfs, awk
|
||||
#
|
||||
# This script downloads a bunch of models into the models/ directory,
|
||||
# after any necessary transformations to run them using the deeptagger binary.
|
||||
#
|
||||
# Once it succeeds, feel free to remove everything but *.{model,tags,onnx}
|
||||
git lfs install
|
||||
mkdir -p models
|
||||
cd models
|
||||
|
||||
# Create a virtual environment for model conversion.
|
||||
#
|
||||
# If any of the Python stuff fails,
|
||||
# retry from within a Conda environment with a different version of Python.
|
||||
export VIRTUAL_ENV=$(pwd)/venv
|
||||
export TF_ENABLE_ONEDNN_OPTS=0
|
||||
if ! [ -f "$VIRTUAL_ENV/ready" ]
|
||||
then
|
||||
python3 -m venv "$VIRTUAL_ENV"
|
||||
#"$VIRTUAL_ENV/bin/pip3" install tensorflow[and-cuda]
|
||||
"$VIRTUAL_ENV/bin/pip3" install tf2onnx 'deepdanbooru[tensorflow]'
|
||||
touch "$VIRTUAL_ENV/ready"
|
||||
fi
|
||||
|
||||
status() {
|
||||
echo "$(tput bold)-- $*$(tput sgr0)"
|
||||
}
|
||||
|
||||
# Using the deepdanbooru package makes it possible to use other models
|
||||
# trained with the project.
|
||||
deepdanbooru() {
|
||||
local name=$1 url=$2
|
||||
status "$name"
|
||||
|
||||
local basename=$(basename "$url")
|
||||
if ! [ -e "$basename" ]
|
||||
then curl -LO "$url"
|
||||
fi
|
||||
|
||||
local modelname=${basename%%.*}
|
||||
if ! [ -d "$modelname" ]
|
||||
then unzip -d "$modelname" "$basename"
|
||||
fi
|
||||
|
||||
if ! [ -e "$modelname.tags" ]
|
||||
then ln "$modelname/tags.txt" "$modelname.tags"
|
||||
fi
|
||||
|
||||
if ! [ -d "$modelname.saved" ]
|
||||
then "$VIRTUAL_ENV/bin/python3" - "$modelname" "$modelname.saved" <<-'END'
|
||||
import sys
|
||||
import deepdanbooru.project as ddp
|
||||
model = ddp.load_model_from_project(
|
||||
project_path=sys.argv[1], compile_model=False)
|
||||
model.export(sys.argv[2])
|
||||
END
|
||||
fi
|
||||
|
||||
if ! [ -e "$modelname.onnx" ]
|
||||
then "$VIRTUAL_ENV/bin/python3" -m tf2onnx.convert \
|
||||
--saved-model "$modelname.saved" --output "$modelname.onnx"
|
||||
fi
|
||||
|
||||
cat > "$modelname.model" <<-END
|
||||
name=$name
|
||||
shape=nhwc
|
||||
channels=rgb
|
||||
normalize=true
|
||||
pad=edge
|
||||
END
|
||||
}
|
||||
|
||||
# ONNX preconversions don't have a symbolic first dimension, thus doing our own.
|
||||
wd14() {
|
||||
local name=$1 repository=$2
|
||||
status "$name"
|
||||
|
||||
local modelname=$(basename "$repository")
|
||||
if ! [ -d "$modelname" ]
|
||||
then git clone "https://huggingface.co/$repository"
|
||||
fi
|
||||
|
||||
# Though link the original export as well.
|
||||
if ! [ -e "$modelname.onnx" ]
|
||||
then ln "$modelname/model.onnx" "$modelname.onnx"
|
||||
fi
|
||||
|
||||
if ! [ -e "$modelname.tags" ]
|
||||
then awk -F, 'NR > 1 { print $2 }' "$modelname/selected_tags.csv" \
|
||||
> "$modelname.tags"
|
||||
fi
|
||||
|
||||
cat > "$modelname.model" <<-END
|
||||
name=$name
|
||||
shape=nhwc
|
||||
channels=bgr
|
||||
normalize=false
|
||||
pad=white
|
||||
END
|
||||
|
||||
if ! [ -e "batch-$modelname.onnx" ]
|
||||
then "$VIRTUAL_ENV/bin/python3" -m tf2onnx.convert \
|
||||
--saved-model "$modelname" --output "batch-$modelname.onnx"
|
||||
fi
|
||||
|
||||
if ! [ -e "batch-$modelname.tags" ]
|
||||
then ln "$modelname.tags" "batch-$modelname.tags"
|
||||
fi
|
||||
|
||||
if ! [ -e "batch-$modelname.model" ]
|
||||
then ln "$modelname.model" "batch-$modelname.model"
|
||||
fi
|
||||
}
|
||||
|
||||
# These models are an undocumented mess, thus using ONNX preconversions.
|
||||
mldanbooru() {
|
||||
local name=$1 basename=$2
|
||||
status "$name"
|
||||
|
||||
if ! [ -d ml-danbooru-onnx ]
|
||||
then git clone https://huggingface.co/deepghs/ml-danbooru-onnx
|
||||
fi
|
||||
|
||||
local modelname=${basename%%.*}
|
||||
if ! [ -e "$basename" ]
|
||||
then ln "ml-danbooru-onnx/$basename"
|
||||
fi
|
||||
|
||||
if ! [ -e "$modelname.tags" ]
|
||||
then awk -F, 'NR > 1 { print $1 }' ml-danbooru-onnx/tags.csv \
|
||||
> "$modelname.tags"
|
||||
fi
|
||||
|
||||
cat > "$modelname.model" <<-END
|
||||
name=$name
|
||||
shape=nchw
|
||||
channels=rgb
|
||||
normalize=true
|
||||
pad=stretch
|
||||
size=640
|
||||
interpret=sigmoid
|
||||
END
|
||||
}
|
||||
|
||||
status "Downloading models, beware that git-lfs doesn't indicate progress"
|
||||
|
||||
deepdanbooru DeepDanbooru \
|
||||
'https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip'
|
||||
|
||||
#wd14 'WD v1.4 ViT v1' 'SmilingWolf/wd-v1-4-vit-tagger'
|
||||
wd14 'WD v1.4 ViT v2' 'SmilingWolf/wd-v1-4-vit-tagger-v2'
|
||||
#wd14 'WD v1.4 ConvNeXT v1' 'SmilingWolf/wd-v1-4-convnext-tagger'
|
||||
wd14 'WD v1.4 ConvNeXT v2' 'SmilingWolf/wd-v1-4-convnext-tagger-v2'
|
||||
wd14 'WD v1.4 ConvNeXTV2 v2' 'SmilingWolf/wd-v1-4-convnextv2-tagger-v2'
|
||||
wd14 'WD v1.4 SwinV2 v2' 'SmilingWolf/wd-v1-4-swinv2-tagger-v2'
|
||||
wd14 'WD v1.4 MOAT v2' 'SmilingWolf/wd-v1-4-moat-tagger-v2'
|
||||
|
||||
# As suggested by author https://github.com/IrisRainbowNeko/ML-Danbooru-webui
|
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mldanbooru 'ML-Danbooru Caformer dec-5-97527' 'ml_caformer_m36_dec-5-97527.onnx'
|
||||
mldanbooru 'ML-Danbooru TResNet-D 6-30000' 'TResnet-D-FLq_ema_6-30000.onnx'
|
||||
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Reference in New Issue