522 lines
26 KiB
C++
522 lines
26 KiB
C++
// Copyright (C) 2018 Intel Corporation
|
|
//
|
|
// SPDX-License-Identifier: Apache-2.0
|
|
//
|
|
|
|
/**
|
|
* @brief The entry point for Inference Engine validation application
|
|
* @file validation_app/main.cpp
|
|
*/
|
|
#include <gflags/gflags.h>
|
|
#include <algorithm>
|
|
#include <functional>
|
|
#include <iostream>
|
|
#include <map>
|
|
#include <fstream>
|
|
#include <random>
|
|
#include <string>
|
|
#include <tuple>
|
|
#include <vector>
|
|
#include <limits>
|
|
#include <iomanip>
|
|
#include <memory>
|
|
|
|
#include <ext_list.hpp>
|
|
|
|
#include <samples/common.hpp>
|
|
#include <samples/slog.hpp>
|
|
|
|
#include "user_exception.hpp"
|
|
#include "calibrator_processors.h"
|
|
#include "SSDObjectDetectionProcessor.hpp"
|
|
#include "YOLOObjectDetectionProcessor.hpp"
|
|
#include "network_serializer.h"
|
|
#include "ie_icnn_network_stats.hpp"
|
|
#include "details/caseless.hpp"
|
|
|
|
using namespace std;
|
|
using namespace InferenceEngine;
|
|
using namespace InferenceEngine::details;
|
|
|
|
using InferenceEngine::details::InferenceEngineException;
|
|
|
|
#define DEFAULT_PATH_P "./lib"
|
|
|
|
/// @brief Message for help argument
|
|
static const char help_message[] = "Print a help message";
|
|
/// @brief Message for images argument
|
|
static const char image_message[] = "Required. Path to a directory with validation images. For Classification models, the directory must contain"
|
|
" folders named as labels with images inside or a .txt file with"
|
|
" a list of images. For Object Detection models, the dataset must be in"
|
|
" VOC format.";
|
|
/// @brief Message for plugin_path argument
|
|
static const char plugin_path_message[] = "Path to a plugin folder";
|
|
/// @brief message for model argument
|
|
static const char model_message[] = "Required. Path to an .xml file with a trained model, including model name and "
|
|
"extension.";
|
|
/// @brief Message for plugin argument
|
|
static const char plugin_message[] = "Plugin name. For example, CPU. If this parameter is passed, "
|
|
"the sample looks for a specified plugin only.";
|
|
/// @brief Message for assigning cnn calculation to device
|
|
static const char target_device_message[] = "Target device to infer on: CPU (default), GPU, FPGA, or MYRIAD."
|
|
" The application looks for a suitable plugin for the specified device.";
|
|
/// @brief Message for label argument
|
|
static const char label_message[] = "Path to a file with labels for a model";
|
|
/// @brief M`essage for batch argumenttype
|
|
static const char batch_message[] = "Batch size value. If not specified, the batch size value is taken from IR";
|
|
/// @brief Message for dump argument
|
|
static const char dump_message[] = "Dump file names and inference results to a .csv file";
|
|
/// @brief Message for network type
|
|
static const char type_message[] = "Type of an inferred network (\"C\" by default)";
|
|
/// @brief Message for pp-type
|
|
static const char preprocessing_type[] = "Preprocessing type. Options: \"None\", \"Resize\", \"ResizeCrop\"";
|
|
/// @brief Message for pp-crop-size
|
|
static const char preprocessing_size[] = "Preprocessing size (used with ppType=\"ResizeCrop\")";
|
|
static const char preprocessing_width[] = "Preprocessing width (overrides -ppSize, used with ppType=\"ResizeCrop\")";
|
|
static const char preprocessing_height[] = "Preprocessing height (overrides -ppSize, used with ppType=\"ResizeCrop\")";
|
|
|
|
static const char obj_detection_annotations_message[] = "Required for Object Detection models. Path to a directory"
|
|
" containing an .xml file with annotations for images.";
|
|
|
|
static const char obj_detection_classes_message[] = "Required for Object Detection models. Path to a file with"
|
|
" a list of classes";
|
|
|
|
static const char obj_detection_subdir_message[] = "Directory between the path to images (specified with -i) and image name (specified in the"
|
|
" .xml file). For VOC2007 dataset, use JPEGImages.";
|
|
|
|
static const char obj_detection_kind_message[] = "Type of an Object Detection model. Options: SSD";
|
|
|
|
/// @brief Message for GPU custom kernels desc
|
|
static const char custom_cldnn_message[] = "Required for GPU custom kernels. "
|
|
"Absolute path to an .xml file with the kernel descriptions.";
|
|
|
|
/// @brief Message for user library argument
|
|
static const char custom_cpu_library_message[] = "Required for CPU custom layers. "
|
|
"Absolute path to a shared library with the kernel implementations.";
|
|
|
|
static const char zero_background_message[] = "\"Zero is a background\" flag. Some networks are trained with a modified"
|
|
" dataset where the class IDs "
|
|
" are enumerated from 1, but 0 is an undefined \"background\" class"
|
|
" (which is never detected)";
|
|
|
|
/// @brief Network type options and their descriptions
|
|
static const char* types_descriptions[][2] = {
|
|
{ "C", "calibrate Classification network and write the calibrated network to IR" },
|
|
// { "SS", "semantic segmentation" }, // Not supported yet
|
|
{ "OD", "calibrate Object Detection network and write the calibrated network to IR" },
|
|
{ "RawC", "collect only statistics for Classification network and write statistics to IR. With this option, a model is not calibrated. For calibration "
|
|
"and statisctics collection, use \"-t C\" instead." },
|
|
{ "RawOD", "collect only statistics for Object Detection network and write statistics to IR. With this option, a model is not calibrated. For calibration "
|
|
"and statisctics collection, use \"-t OD\" instead" },
|
|
{ nullptr, nullptr }
|
|
};
|
|
|
|
static const char accuracy_threshold_message[] = "Threshold for a maximum accuracy drop of quantized model."
|
|
" Must be an integer number (percents)"
|
|
" without a percent sign. Default value is 1, which stands for accepted"
|
|
" accuracy drop in 1%";
|
|
static const char number_of_pictures_message[] = "Number of pictures from the whole validation set to"
|
|
"create the calibration dataset. Default value is 0, which stands for"
|
|
"the whole provided dataset";
|
|
static const char output_model_name[] = "Output name for calibrated model. Default is <original_model_name>_i8.xml|bin";
|
|
|
|
/// @brief Define flag for showing help message <br>
|
|
DEFINE_bool(h, false, help_message);
|
|
/// @brief Define parameter for a path to images <br>
|
|
/// It is a required parameter
|
|
DEFINE_string(i, "", image_message);
|
|
/// @brief Define parameter for a path to model file <br>
|
|
/// It is a required parameter
|
|
DEFINE_string(m, "", model_message);
|
|
/// @brief Define parameter for a plugin name <br>
|
|
/// It is a required parameter
|
|
DEFINE_string(p, "", plugin_message);
|
|
/// @brief Define parameter for a path to a file with labels <br>
|
|
/// Default is empty
|
|
DEFINE_string(OCl, "", label_message);
|
|
/// @brief Define parameter for a path to plugins <br>
|
|
/// Default is ./lib
|
|
DEFINE_string(pp, DEFAULT_PATH_P, plugin_path_message);
|
|
/// @brief Define paraneter for a target device to infer on <br>
|
|
DEFINE_string(d, "CPU", target_device_message);
|
|
/// @brief Define parameter for batch size <br>
|
|
/// Default is 0 (which means that batch size is not specified)
|
|
DEFINE_int32(b, 0, batch_message);
|
|
/// @brief Define flag to dump results to a file <br>
|
|
DEFINE_bool(dump, false, dump_message);
|
|
/// @brief Define parameter for a network type
|
|
DEFINE_string(t, "C", type_message);
|
|
|
|
/// @brief Define parameter for preprocessing type
|
|
DEFINE_string(ppType, "", preprocessing_type);
|
|
|
|
/// @brief Define parameter for preprocessing size
|
|
DEFINE_int32(ppSize, 0, preprocessing_size);
|
|
DEFINE_int32(ppWidth, 0, preprocessing_width);
|
|
DEFINE_int32(ppHeight, 0, preprocessing_height);
|
|
|
|
DEFINE_bool(Czb, false, zero_background_message);
|
|
|
|
DEFINE_string(ODa, "", obj_detection_annotations_message);
|
|
|
|
DEFINE_string(ODc, "", obj_detection_classes_message);
|
|
|
|
DEFINE_string(ODsubdir, "", obj_detection_subdir_message);
|
|
|
|
/// @brief Define parameter for a type of Object Detection network
|
|
DEFINE_string(ODkind, "SSD", obj_detection_kind_message);
|
|
|
|
/// @brief Define parameter for GPU kernels path <br>
|
|
/// Default is ./lib
|
|
DEFINE_string(c, "", custom_cldnn_message);
|
|
|
|
/// @brief Define parameter for a path to CPU library with user layers <br>
|
|
/// It is an optional parameter
|
|
DEFINE_string(l, "", custom_cpu_library_message);
|
|
|
|
/// @brief Define parameter for accuracy drop threshold
|
|
DEFINE_double(threshold, 1.0f, accuracy_threshold_message);
|
|
|
|
DEFINE_int32(subset, 0, number_of_pictures_message);
|
|
|
|
DEFINE_string(output, "", output_model_name);
|
|
|
|
/**
|
|
* @brief This function shows a help message
|
|
*/
|
|
static void showUsage() {
|
|
std::cout << std::endl;
|
|
std::cout << "Usage: calibration_tool [OPTION]" << std::endl << std::endl;
|
|
std::cout << "Available options:" << std::endl;
|
|
std::cout << std::endl;
|
|
std::cout << " -h " << help_message << std::endl;
|
|
std::cout << " -t <type> " << type_message << std::endl;
|
|
for (int i = 0; types_descriptions[i][0] != nullptr; i++) {
|
|
std::cout << " -t \"" << types_descriptions[i][0] << "\" to " << types_descriptions[i][1] << std::endl;
|
|
}
|
|
std::cout << " -i <path> " << image_message << std::endl;
|
|
std::cout << " -m <path> " << model_message << std::endl;
|
|
std::cout << " -l <absolute_path> " << custom_cpu_library_message << std::endl;
|
|
std::cout << " -c <absolute_path> " << custom_cldnn_message << std::endl;
|
|
std::cout << " -d <device> " << target_device_message << std::endl;
|
|
std::cout << " -b N " << batch_message << std::endl;
|
|
std::cout << " -ppType <type> " << preprocessing_type << std::endl;
|
|
std::cout << " -ppSize N " << preprocessing_size << std::endl;
|
|
std::cout << " -ppWidth W " << preprocessing_width << std::endl;
|
|
std::cout << " -ppHeight H " << preprocessing_height << std::endl;
|
|
std::cout << " --dump " << dump_message << std::endl;
|
|
std::cout << " -subset " << number_of_pictures_message << std::endl;
|
|
std::cout << " -output <output_IR> " << output_model_name << std::endl;
|
|
std::cout << " -threshold " << accuracy_threshold_message << std::endl;
|
|
|
|
std::cout << std::endl;
|
|
std::cout << " Classification-specific options:" << std::endl;
|
|
std::cout << " -Czb true " << zero_background_message << std::endl;
|
|
|
|
std::cout << std::endl;
|
|
std::cout << " Object detection-specific options:" << std::endl;
|
|
std::cout << " -ODkind <kind> " << obj_detection_kind_message << std::endl;
|
|
std::cout << " -ODa <path> " << obj_detection_annotations_message << std::endl;
|
|
std::cout << " -ODc <file> " << obj_detection_classes_message << std::endl;
|
|
std::cout << " -ODsubdir <name> " << obj_detection_subdir_message << std::endl << std::endl;
|
|
}
|
|
|
|
enum NetworkType {
|
|
Undefined = -1,
|
|
Classification,
|
|
ObjDetection,
|
|
RawC,
|
|
RawOD
|
|
};
|
|
|
|
std::string strtolower(const std::string& s) {
|
|
std::string res = s;
|
|
std::transform(res.begin(), res.end(), res.begin(), ::tolower);
|
|
return res;
|
|
}
|
|
|
|
void SaveCalibratedIR(const std::string &originalName,
|
|
const std::string &outModelName,
|
|
const std::map<std::string, bool>& layersToInt8,
|
|
const InferenceEngine::NetworkStatsMap& statMap) {
|
|
slog::info << "Layers profile for Int8 quantization\n";
|
|
CNNNetReader networkReader;
|
|
networkReader.ReadNetwork(originalName);
|
|
if (!networkReader.isParseSuccess())THROW_IE_EXCEPTION << "cannot load a failed Model";
|
|
|
|
/** Extract model name and load weights **/
|
|
std::string binFileName = fileNameNoExt(originalName)+ ".bin";
|
|
networkReader.ReadWeights(binFileName.c_str());
|
|
|
|
auto network = networkReader.getNetwork();
|
|
for (auto &&layer : network) {
|
|
if (CaselessEq<std::string>()(layer->type, "convolution")) {
|
|
auto it = layersToInt8.find(layer->name);
|
|
if (it != layersToInt8.end() && it->second == false) {
|
|
layer->params["quantization_level"] = "FP32";
|
|
std::cout << layer->name << ": " << "FP32" << std::endl;
|
|
} else {
|
|
layer->params["quantization_level"] = "I8";
|
|
std::cout << layer->name << ": " << "I8" << std::endl;
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
ICNNNetworkStats* pstats = nullptr;
|
|
StatusCode s = ((ICNNNetwork&)networkReader.getNetwork()).getStats(&pstats, nullptr);
|
|
if (s == StatusCode::OK && pstats) {
|
|
pstats->setNodesStats(statMap);
|
|
}
|
|
|
|
slog::info << "Write calibrated network to " << outModelName << ".(xml|bin) IR file\n";
|
|
CNNNetworkSerializer serializer;
|
|
serializer.Serialize(outModelName + ".xml", outModelName + ".bin", networkReader.getNetwork());
|
|
}
|
|
|
|
/**
|
|
* @brief The main function of inference engine sample application
|
|
* @param argc - The number of arguments
|
|
* @param argv - Arguments
|
|
* @return 0 if all good
|
|
*/
|
|
int main(int argc, char *argv[]) {
|
|
try {
|
|
slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << slog::endl;
|
|
|
|
// ---------------------------Parsing and validating input arguments--------------------------------------
|
|
slog::info << "Parsing input parameters" << slog::endl;
|
|
|
|
bool noOptions = argc == 1;
|
|
|
|
gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
|
|
if (FLAGS_h || noOptions) {
|
|
showUsage();
|
|
return 1;
|
|
}
|
|
|
|
UserExceptions ee;
|
|
|
|
NetworkType netType = Undefined;
|
|
// Checking the network type
|
|
if (std::string(FLAGS_t) == "C") {
|
|
netType = Classification;
|
|
} else if (std::string(FLAGS_t) == "OD") {
|
|
netType = ObjDetection;
|
|
} else if (std::string(FLAGS_t) == "RawC") {
|
|
netType = RawC;
|
|
} else if (std::string(FLAGS_t) == "RawOD") {
|
|
netType = RawOD;
|
|
} else {
|
|
ee << UserException(5, "Unknown network type specified (invalid -t option)");
|
|
}
|
|
|
|
// Checking required options
|
|
if (FLAGS_m.empty()) ee << UserException(3, "Model file is not specified (missing -m option)");
|
|
if (FLAGS_i.empty()) ee << UserException(4, "Images list is not specified (missing -i option)");
|
|
if (FLAGS_d.empty()) ee << UserException(5, "Target device is not specified (missing -d option)");
|
|
if (FLAGS_b < 0) ee << UserException(6, "Batch must be positive (invalid -b option value)");
|
|
|
|
if (netType == ObjDetection) {
|
|
// Checking required OD-specific options
|
|
if (FLAGS_ODa.empty()) ee << UserException(11, "Annotations folder is not specified for object detection (missing -a option)");
|
|
if (FLAGS_ODc.empty()) ee << UserException(12, "Classes file is not specified (missing -c option)");
|
|
if (FLAGS_b > 0) ee << UserException(13, "Batch option other than 0 is not supported for Object Detection networks");
|
|
}
|
|
|
|
if (!ee.empty()) throw ee;
|
|
// -----------------------------------------------------------------------------------------------------
|
|
|
|
// ---------------------Loading plugin for Inference Engine------------------------------------------------
|
|
slog::info << "Loading plugin" << slog::endl;
|
|
/** Loading the library with extensions if provided**/
|
|
InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64", "" }).getPluginByDevice(FLAGS_d);
|
|
|
|
/** Loading default extensions **/
|
|
if (FLAGS_d.find("CPU") != std::string::npos) {
|
|
/**
|
|
* cpu_extensions library is compiled from "extension" folder containing
|
|
* custom CPU plugin layer implementations. These layers are not supported
|
|
* by CPU, but they can be useful for inferring custom topologies.
|
|
**/
|
|
plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
|
|
}
|
|
|
|
if (!FLAGS_l.empty()) {
|
|
// CPU extensions are loaded as a shared library and passed as a pointer to base extension
|
|
IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l);
|
|
plugin.AddExtension(extension_ptr);
|
|
slog::info << "CPU Extension loaded: " << FLAGS_l << slog::endl;
|
|
}
|
|
if (!FLAGS_c.empty()) {
|
|
// GPU extensions are loaded from an .xml description and OpenCL kernel files
|
|
plugin.SetConfig({{PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c}});
|
|
slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl;
|
|
}
|
|
|
|
printPluginVersion(plugin, std::cout);
|
|
|
|
CsvDumper dumper(FLAGS_dump);
|
|
|
|
std::shared_ptr<Processor> processor;
|
|
|
|
PreprocessingOptions preprocessingOptions;
|
|
if (strtolower(FLAGS_ppType.c_str()) == "none") {
|
|
preprocessingOptions = PreprocessingOptions(false, ResizeCropPolicy::DoNothing);
|
|
} else if (strtolower(FLAGS_ppType) == "resizecrop") {
|
|
size_t ppWidth = FLAGS_ppSize;
|
|
size_t ppHeight = FLAGS_ppSize;
|
|
|
|
if (FLAGS_ppWidth > 0) ppWidth = FLAGS_ppSize;
|
|
if (FLAGS_ppHeight > 0) ppHeight = FLAGS_ppSize;
|
|
|
|
if (FLAGS_ppSize > 0 || (FLAGS_ppWidth > 0 && FLAGS_ppHeight > 0)) {
|
|
preprocessingOptions = PreprocessingOptions(false, ResizeCropPolicy::ResizeThenCrop, ppWidth, ppHeight);
|
|
} else {
|
|
THROW_USER_EXCEPTION(2) << "Size must be specified for preprocessing type " << FLAGS_ppType;
|
|
}
|
|
} else if (strtolower(FLAGS_ppType) == "resize" || FLAGS_ppType.empty()) {
|
|
preprocessingOptions = PreprocessingOptions(false, ResizeCropPolicy::Resize);
|
|
} else {
|
|
THROW_USER_EXCEPTION(2) << "Unknown preprocessing type: " << FLAGS_ppType;
|
|
}
|
|
|
|
if (netType == Classification || netType == RawC) {
|
|
processor = std::shared_ptr<Processor>(
|
|
new ClassificationCalibrator(FLAGS_subset, FLAGS_m, FLAGS_d, FLAGS_i, FLAGS_b,
|
|
plugin, dumper, FLAGS_l, preprocessingOptions, FLAGS_Czb));
|
|
} else if (netType == ObjDetection || netType == RawOD) {
|
|
if (FLAGS_ODkind == "SSD") {
|
|
processor = std::shared_ptr<Processor>(
|
|
new SSDObjectDetectionCalibrator(FLAGS_subset, FLAGS_m, FLAGS_d, FLAGS_i, FLAGS_ODsubdir, FLAGS_b,
|
|
0.5, plugin, dumper, FLAGS_ODa, FLAGS_ODc));
|
|
/* } else if (FLAGS_ODkind == "YOLO") {
|
|
processor = std::shared_ptr<Processor>(
|
|
new YOLOObjectDetectionProcessor(FLAGS_m, FLAGS_d, FLAGS_i, FLAGS_ODsubdir, FLAGS_b,
|
|
0.5, plugin, dumper, FLAGS_ODa, FLAGS_ODc));
|
|
*/
|
|
}
|
|
} else {
|
|
THROW_USER_EXCEPTION(2) << "Unknown network type specified" << FLAGS_ppType;
|
|
}
|
|
if (!processor.get()) {
|
|
THROW_USER_EXCEPTION(2) << "Processor pointer is invalid" << FLAGS_ppType;
|
|
}
|
|
|
|
Int8Calibrator* calibrator = dynamic_cast<Int8Calibrator*>(processor.get());
|
|
|
|
if (netType != RawC && netType != RawOD) {
|
|
slog::info << "Collecting accuracy metric in FP32 mode to get a baseline, collecting activation statistics" << slog::endl;
|
|
} else {
|
|
slog::info << "Collecting activation statistics" << slog::endl;
|
|
}
|
|
calibrator->collectFP32Statistic();
|
|
shared_ptr<Processor::InferenceMetrics> pIMFP32 = processor->Process();
|
|
const CalibrationMetrics* mFP32 = dynamic_cast<const CalibrationMetrics*>(pIMFP32.get());
|
|
std:: cout << " FP32 Accuracy: " << OUTPUT_FLOATING(100.0 * mFP32->AccuracyResult) << "% " << std::endl;
|
|
|
|
InferenceEngine::NetworkStatsMap statMap;
|
|
std::map<std::string, bool> layersToInt8;
|
|
bool bAccuracy = false;
|
|
|
|
if (netType != RawC && netType != RawOD) {
|
|
slog::info << "Verification of network accuracy if all possible layers converted to INT8" << slog::endl;
|
|
float bestThreshold = 100.f;
|
|
float maximalAccuracy = 0.f;
|
|
for (float threshold = 100.0f; threshold > 95.0f; threshold -= 0.5) {
|
|
std::cout << "Validate int8 accuracy, threshold for activation statistics = " << threshold << std::endl;
|
|
InferenceEngine::NetworkStatsMap tmpStatMap = calibrator->getStatistic(threshold);
|
|
calibrator->validateInt8Config(tmpStatMap, {});
|
|
shared_ptr<Processor::InferenceMetrics> pIM_I8 = processor->Process();
|
|
const CalibrationMetrics *mI8 = dynamic_cast<const CalibrationMetrics *>(pIM_I8.get());
|
|
if (maximalAccuracy < mI8->AccuracyResult) {
|
|
maximalAccuracy = mI8->AccuracyResult;
|
|
bestThreshold = threshold;
|
|
}
|
|
std::cout << " Accuracy is " << OUTPUT_FLOATING(100.0 * mI8->AccuracyResult) << "%" << std::endl;
|
|
}
|
|
|
|
statMap = calibrator->getStatistic(bestThreshold);
|
|
|
|
if ((mFP32->AccuracyResult - maximalAccuracy) > (FLAGS_threshold / 100)) {
|
|
slog::info << "Accuracy of all layers conversion does not correspond to the required threshold\n";
|
|
cout << "FP32 Accuracy: " << OUTPUT_FLOATING(100.0 * mFP32->AccuracyResult) << "% vs " <<
|
|
"all Int8 layers Accuracy: " << OUTPUT_FLOATING(100.0 * maximalAccuracy) << "%, " <<
|
|
"threshold for activation statistics: " << bestThreshold << "%" << std::endl;
|
|
slog::info << "Collecting intermediate per-layer accuracy drop" << slog::endl;
|
|
// getting statistic on accuracy drop by layers
|
|
calibrator->collectByLayerStatistic(statMap);
|
|
processor->Process();
|
|
// starting to reduce number of layers being converted to Int8
|
|
std::map<std::string, float> layersAccuracyDrop = calibrator->layersAccuracyDrop();
|
|
|
|
std::map<float, std::string> orderedLayersAccuracyDrop;
|
|
for (auto d : layersAccuracyDrop) {
|
|
orderedLayersAccuracyDrop[d.second] = d.first;
|
|
layersToInt8[d.first] = true;
|
|
}
|
|
std::map<float, std::string>::const_reverse_iterator it = orderedLayersAccuracyDrop.crbegin();
|
|
|
|
shared_ptr<Processor::InferenceMetrics> pIM_I8;
|
|
const CalibrationMetrics *mI8;
|
|
while (it != orderedLayersAccuracyDrop.crend() && bAccuracy == false) {
|
|
slog::info << "Returning of '" << it->second << "' to FP32 precision, start validation\n";
|
|
layersToInt8[it->second] = false;
|
|
calibrator->validateInt8Config(statMap, layersToInt8);
|
|
pIM_I8 = processor->Process();
|
|
mI8 = dynamic_cast<const CalibrationMetrics *>(pIM_I8.get());
|
|
maximalAccuracy = mI8->AccuracyResult;
|
|
if ((mFP32->AccuracyResult - maximalAccuracy) > (FLAGS_threshold / 100)) {
|
|
cout << "FP32 Accuracy: " << OUTPUT_FLOATING(100.0 * mFP32->AccuracyResult) << "% vs " <<
|
|
"current Int8 configuration Accuracy: " << OUTPUT_FLOATING(100.0 * maximalAccuracy) << "%" << std::endl;
|
|
} else {
|
|
bAccuracy = true;
|
|
}
|
|
it++;
|
|
}
|
|
} else {
|
|
bAccuracy = true;
|
|
}
|
|
|
|
if (bAccuracy) {
|
|
slog::info << "Achieved required accuracy drop satisfying threshold\n";
|
|
cout << "FP32 accuracy: " << OUTPUT_FLOATING(100.0 * mFP32->AccuracyResult) << "% vs " <<
|
|
"current Int8 configuration accuracy: " << OUTPUT_FLOATING(100.0 * maximalAccuracy) << "% " <<
|
|
"with threshold for activation statistic: " << bestThreshold << "%" << std::endl;
|
|
std::string outModelName = FLAGS_output.empty() ? fileNameNoExt(FLAGS_m) + "_i8" : fileNameNoExt(FLAGS_output);
|
|
SaveCalibratedIR(FLAGS_m, outModelName, layersToInt8, statMap);
|
|
} else {
|
|
slog::info << "Required threshold of accuracy drop cannot be achieved with any int8 quantization\n";
|
|
}
|
|
} else {
|
|
std::cout << "Collected activation statistics, writing maximum values to IR" << std::endl;
|
|
statMap = calibrator->getStatistic(100.0f);
|
|
std::string outModelName = FLAGS_output.empty() ? fileNameNoExt(FLAGS_m) + "_i8" : fileNameNoExt(FLAGS_output);
|
|
SaveCalibratedIR(FLAGS_m, outModelName, layersToInt8, statMap);
|
|
}
|
|
|
|
if (dumper.dumpEnabled()) {
|
|
slog::info << "Dump file generated: " << dumper.getFilename() << slog::endl;
|
|
}
|
|
} catch (const InferenceEngineException& ex) {
|
|
slog::err << "Inference problem: \n" << ex.what() << slog::endl;
|
|
return 1;
|
|
} catch (const UserException& ex) {
|
|
slog::err << "Input problem: \n" << ex.what() << slog::endl;
|
|
showUsage();
|
|
return ex.exitCode();
|
|
} catch (const UserExceptions& ex) {
|
|
if (ex.list().size() == 1) {
|
|
slog::err << "Input problem: " << ex.what() << slog::endl;
|
|
showUsage();
|
|
return ex.list().begin()->exitCode();
|
|
} else {
|
|
const char* s = ex.what();
|
|
slog::err << "Input problems: \n" << ex.what() << slog::endl;
|
|
showUsage();
|
|
return ex.list().begin()->exitCode();
|
|
}
|
|
}
|
|
return 0;
|
|
}
|