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// Copyright (C) 2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <memory>
#include <string>
#include <vector>
#include <algorithm>
#include <utility>
#include <format_reader_ptr.h>
#include <samples/slog.hpp>
#include "inputs_filling.hpp"
using namespace InferenceEngine;
#ifdef USE_OPENCV
static const std::vector<std::string> supported_image_extensions = { "bmp", "dib",
"jpeg", "jpg", "jpe",
"jp2",
"png",
"pbm", "pgm", "ppm",
"sr", "ras",
"tiff", "tif" };
#else
static const std::vector<std::string> supported_image_extensions = { "bmp" };
#endif
static const std::vector<std::string> supported_binary_extensions = { "bin" };
std::vector<std::string> filterFilesByExtensions(const std::vector<std::string>& filePaths,
const std::vector<std::string>& extensions) {
std::vector<std::string> filtered;
auto getExtension = [](const std::string &name) {
auto extensionPosition = name.rfind('.', name.size());
return extensionPosition == std::string::npos ? "" : name.substr(extensionPosition + 1, name.size() - 1);
};
for (auto& filePath : filePaths) {
auto extension = getExtension(filePath);
std::transform(extension.begin(), extension.end(), extension.begin(), ::tolower);
if (std::find(extensions.begin(), extensions.end(), extension) != extensions.end()) {
filtered.push_back(filePath);
}
}
return filtered;
}
void fillBlobImage(Blob::Ptr& inputBlob,
const std::vector<std::string>& filePaths,
const size_t& batchSize,
const InputInfo& info,
const size_t& requestId,
const size_t& inputId,
const size_t& inputSize) {
auto inputBlobData = inputBlob->buffer().as<uint8_t*>();
const TensorDesc& inputBlobDesc = inputBlob->getTensorDesc();
/** Collect images data ptrs **/
std::vector<std::shared_ptr<uint8_t>> vreader;
vreader.reserve(batchSize);
for (size_t i = 0ULL, inputIndex = requestId*batchSize*inputSize + inputId; i < batchSize; i++, inputIndex += inputSize) {
inputIndex %= filePaths.size();
slog::info << "Prepare image " << filePaths[inputIndex] << slog::endl;
FormatReader::ReaderPtr reader(filePaths[inputIndex].c_str());
if (reader.get() == nullptr) {
slog::warn << "Image " << filePaths[inputIndex] << " cannot be read!" << slog::endl << slog::endl;
continue;
}
/** Getting image data **/
TensorDesc desc = info.getTensorDesc();
std::shared_ptr<uint8_t> imageData(reader->getData(getTensorWidth(desc), getTensorHeight(desc)));
if (imageData) {
vreader.push_back(imageData);
}
}
/** Fill input tensor with images. First b channel, then g and r channels **/
const size_t numChannels = getTensorChannels(inputBlobDesc);
const size_t imageSize = getTensorWidth(inputBlobDesc) * getTensorHeight(inputBlobDesc);
/** Iterate over all input images **/
for (size_t imageId = 0; imageId < vreader.size(); ++imageId) {
/** Iterate over all pixel in image (b,g,r) **/
for (size_t pid = 0; pid < imageSize; pid++) {
/** Iterate over all channels **/
for (size_t ch = 0; ch < numChannels; ++ch) {
/** [images stride + channels stride + pixel id ] all in bytes **/
inputBlobData[imageId * imageSize * numChannels + ch * imageSize + pid] = vreader.at(imageId).get()[pid*numChannels + ch];
}
}
}
}
template<typename T>
void fillBlobBinary(Blob::Ptr& inputBlob,
const std::vector<std::string>& filePaths,
const size_t& batchSize,
const size_t& requestId,
const size_t& inputId,
const size_t& inputSize) {
auto inputBlobData = inputBlob->buffer().as<T*>();
for (size_t i = 0ULL, inputIndex = requestId*batchSize*inputSize + inputId; i < batchSize; i++, inputIndex += inputSize) {
inputIndex %= filePaths.size();
slog::info << "Prepare binary file " << filePaths[inputIndex] << slog::endl;
std::ifstream binaryFile(filePaths[inputIndex], std::ios_base::binary | std::ios_base::ate);
if (!binaryFile) {
THROW_IE_EXCEPTION << "Cannot open " << filePaths[inputIndex];
}
auto fileSize = static_cast<std::size_t>(binaryFile.tellg());
binaryFile.seekg(0, std::ios_base::beg);
if (!binaryFile.good()) {
THROW_IE_EXCEPTION << "Can not read " << filePaths[inputIndex];
}
auto inputSize = inputBlob->size()*sizeof(T)/batchSize;
if (fileSize != inputSize) {
THROW_IE_EXCEPTION << "File " << filePaths[inputIndex] << " contains " << std::to_string(fileSize) << " bytes "
"but the network expects " << std::to_string(inputSize);
}
binaryFile.read(reinterpret_cast<char *>(&inputBlobData[i*inputSize]), inputSize);
}
}
template<typename T>
void fillBlobRandom(Blob::Ptr& inputBlob) {
auto inputBlobData = inputBlob->buffer().as<T*>();
for (size_t i = 0; i < inputBlob->size(); i++) {
inputBlobData[i] = (T) rand() / RAND_MAX * 10;
}
}
template<typename T>
void fillBlobImInfo(Blob::Ptr& inputBlob,
const size_t& batchSize,
std::pair<size_t, size_t> image_size) {
auto inputBlobData = inputBlob->buffer().as<T*>();
for (size_t b = 0; b < batchSize; b++) {
size_t iminfoSize = inputBlob->size()/batchSize;
for (size_t i = 0; i < iminfoSize; i++) {
size_t index = b*iminfoSize + i;
if (0 == i)
inputBlobData[index] = static_cast<T>(image_size.first);
else if (1 == i)
inputBlobData[index] = static_cast<T>(image_size.second);
else
inputBlobData[index] = 1;
}
}
}
void fillBlobs(const std::vector<std::string>& inputFiles,
const size_t& batchSize,
const InferenceEngine::InputsDataMap& info,
std::vector<InferReqWrap::Ptr> requests) {
std::vector<std::pair<size_t, size_t>> input_image_sizes;
for (const InputsDataMap::value_type& item : info) {
if (isImage(item.second)) {
input_image_sizes.push_back(std::make_pair(getTensorWidth(item.second->getTensorDesc()),
getTensorHeight(item.second->getTensorDesc())));
}
slog::info << "Network input '" << item.first << "' precision " << item.second->getTensorDesc().getPrecision()
<< ", dimensions (" << item.second->getTensorDesc().getLayout() << "): ";
for (const auto& i : item.second->getTensorDesc().getDims()) {
slog::info << i << " ";
}
slog::info << slog::endl;
}
size_t imageInputCount = input_image_sizes.size();
size_t binaryInputCount = info.size() - imageInputCount;
std::vector<std::string> binaryFiles;
std::vector<std::string> imageFiles;
if (inputFiles.empty()) {
slog::warn << "No input files were given: all inputs will be filled with random values!" << slog::endl;
} else {
binaryFiles = filterFilesByExtensions(inputFiles, supported_binary_extensions);
std::sort(std::begin(binaryFiles), std::end(binaryFiles));
auto binaryToBeUsed = binaryInputCount*batchSize*requests.size();
if (binaryToBeUsed > 0 && binaryFiles.empty()) {
std::stringstream ss;
for (auto& ext : supported_binary_extensions) {
if (!ss.str().empty()) {
ss << ", ";
}
ss << ext;
}
slog::warn << "No supported binary inputs found! Please check your file extensions: " << ss.str() << slog::endl;
} else if (binaryToBeUsed > binaryFiles.size()) {
slog::warn << "Some binary input files will be duplicated: " << binaryToBeUsed <<
" files are required but only " << binaryFiles.size() << " are provided" << slog::endl;
} else if (binaryToBeUsed < binaryFiles.size()) {
slog::warn << "Some binary input files will be ignored: only " << binaryToBeUsed <<
" are required from " << binaryFiles.size() << slog::endl;
}
imageFiles = filterFilesByExtensions(inputFiles, supported_image_extensions);
std::sort(std::begin(imageFiles), std::end(imageFiles));
auto imagesToBeUsed = imageInputCount*batchSize*requests.size();
if (imagesToBeUsed > 0 && imageFiles.empty()) {
std::stringstream ss;
for (auto& ext : supported_image_extensions) {
if (!ss.str().empty()) {
ss << ", ";
}
ss << ext;
}
slog::warn << "No supported image inputs found! Please check your file extensions: " << ss.str() << slog::endl;
} else if (imagesToBeUsed > imageFiles.size()) {
slog::warn << "Some image input files will be duplicated: " << imagesToBeUsed <<
" files are required but only " << imageFiles.size() << " are provided" << slog::endl;
} else if (imagesToBeUsed < imageFiles.size()) {
slog::warn << "Some image input files will be ignored: only " << imagesToBeUsed <<
" are required from " << imageFiles.size() << slog::endl;
}
}
for (size_t requestId = 0; requestId < requests.size(); requestId++) {
slog::info << "Infer Request " << requestId << " filling" << slog::endl;
size_t imageInputId = 0;
size_t binaryInputId = 0;
for (const InputsDataMap::value_type& item : info) {
Blob::Ptr inputBlob = requests.at(requestId)->getBlob(item.first);
if (isImage(inputBlob)) {
if (!imageFiles.empty()) {
// Fill with Images
fillBlobImage(inputBlob, imageFiles, batchSize, *item.second, requestId, imageInputId++, imageInputCount);
continue;
}
} else {
if (!binaryFiles.empty()) {
// Fill with binary files
if (item.second->getPrecision() == InferenceEngine::Precision::FP32) {
fillBlobBinary<float>(inputBlob, binaryFiles, batchSize, requestId, binaryInputId++, binaryInputCount);
} else if (item.second->getPrecision() == InferenceEngine::Precision::FP16) {
fillBlobBinary<short>(inputBlob, binaryFiles, batchSize, requestId, binaryInputId++, binaryInputCount);
} else if (item.second->getPrecision() == InferenceEngine::Precision::I32) {
fillBlobBinary<int32_t>(inputBlob, binaryFiles, batchSize, requestId, binaryInputId++, binaryInputCount);
} else if (item.second->getPrecision() == InferenceEngine::Precision::U8) {
fillBlobBinary<uint8_t>(inputBlob, binaryFiles, batchSize, requestId, binaryInputId++, binaryInputCount);
} else {
THROW_IE_EXCEPTION << "Input precision is not supported for " << item.first;
}
continue;
}
if (isImageInfo(inputBlob)) {
// Fill with image information
if (input_image_sizes.size() != 1)
THROW_IE_EXCEPTION << "Input " << item.first << " cannot be filled: please provide input binary files!";
auto image_size = input_image_sizes.at(0);
slog::info << "Fill input '" << item.first << "' with image size " << image_size.first << "x"
<< image_size.second << slog::endl;
if (item.second->getPrecision() == InferenceEngine::Precision::FP32) {
fillBlobImInfo<float>(inputBlob, batchSize, image_size);
} else if (item.second->getPrecision() == InferenceEngine::Precision::FP16) {
fillBlobImInfo<short>(inputBlob, batchSize, image_size);
} else if (item.second->getPrecision() == InferenceEngine::Precision::I32) {
fillBlobImInfo<int32_t>(inputBlob, batchSize, image_size);
} else {
THROW_IE_EXCEPTION << "Input precision is not supported for image info!";
}
continue;
}
}
// Fill random
slog::info << "Fill input '" << item.first << "' with random values ("
<< std::string((isImage(inputBlob) ? "image" : "some binary data"))
<< " is expected)" << slog::endl;
if (item.second->getPrecision() == InferenceEngine::Precision::FP32) {
fillBlobRandom<float>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::FP16) {
fillBlobRandom<short>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::I32) {
fillBlobRandom<int32_t>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::U8) {
fillBlobRandom<uint8_t>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::I8) {
fillBlobRandom<int8_t>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::U16) {
fillBlobRandom<uint16_t>(inputBlob);
} else if (item.second->getPrecision() == InferenceEngine::Precision::I16) {
fillBlobRandom<int16_t>(inputBlob);
} else {
THROW_IE_EXCEPTION << "Input precision is not supported for " << item.first;
}
}
}
}
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