diff --git a/inference-engine/tools/CMakeLists.txt b/inference-engine/tools/CMakeLists.txt index 22fa6e3e6c2..9112658ce5e 100644 --- a/inference-engine/tools/CMakeLists.txt +++ b/inference-engine/tools/CMakeLists.txt @@ -2,5 +2,4 @@ # SPDX-License-Identifier: Apache-2.0 # -add_subdirectory(vpu) add_subdirectory(compile_tool) diff --git a/inference-engine/tools/vpu/CMakeLists.txt b/inference-engine/tools/vpu/CMakeLists.txt deleted file mode 100644 index fa0357111b2..00000000000 --- a/inference-engine/tools/vpu/CMakeLists.txt +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (C) 2018-2021 Intel Corporation -# SPDX-License-Identifier: Apache-2.0 -# - -if(ENABLE_MYRIAD) - add_subdirectory(vpu_compile) - add_subdirectory(vpu_perfcheck) -endif() diff --git a/inference-engine/tools/vpu/vpu_compile/CMakeLists.txt b/inference-engine/tools/vpu/vpu_compile/CMakeLists.txt deleted file mode 100644 index c590bc0e5b0..00000000000 --- a/inference-engine/tools/vpu/vpu_compile/CMakeLists.txt +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (C) 2018-2021 Intel Corporation -# SPDX-License-Identifier: Apache-2.0 -# - -set(TARGET_NAME myriad_compile) - -file(GLOB SRCS - ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp -) - -add_executable(${TARGET_NAME} ${SRCS}) - -if (CMAKE_COMPILER_IS_GNUCXX) - target_compile_options(${TARGET_NAME} PRIVATE -Wall) -endif() - -target_link_libraries(${TARGET_NAME} PRIVATE - inference_engine - vpu_graph_transformer - gflags - ie_samples_utils -) - -add_dependencies(${TARGET_NAME} myriadPlugin) - -set_target_properties(${TARGET_NAME} PROPERTIES - COMPILE_PDB_NAME ${TARGET_NAME} - FOLDER tools -) - -add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}) - -# install - -ie_cpack_add_component(myriad_dev DEPENDS myriad) - -install(TARGETS ${TARGET_NAME} - RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} - COMPONENT myriad_dev) diff --git a/inference-engine/tools/vpu/vpu_compile/README.md b/inference-engine/tools/vpu/vpu_compile/README.md deleted file mode 100644 index 465ac888724..00000000000 --- a/inference-engine/tools/vpu/vpu_compile/README.md +++ /dev/null @@ -1,82 +0,0 @@ -# myriad_compile tool {#openvino_inference_engine_tools_vpu_vpu_compile_README} - -This topic demonstrates how to run the `myriad_compile` tool application, which intended to dump blob for `vpu` plugins of Inference Engine by configuration options. - -## How It Works - -Upon the start-up, the tool application reads command line parameters and loads a network to the Inference Engine plugin. -Then application exports blob and writes it to the output file. - -## Running - -Running the application with the -h option yields the following usage message: - -```sh -./myriad_compile -h -Inference Engine: - API version ............ - Build .................. - -myriad_compile [OPTIONS] -[OPTIONS]: - -h Optional. Print a usage message. - -m Required. Path to xml model. - -pp Optional. Path to a plugin folder. - -o Optional. Path to the output file. Default value: ".blob". - -c Optional. Path to the configuration file. Default value: "config". - -ip Optional. Specifies precision for all input layers of network. Supported values: FP32, FP16, U8. Default value: FP16. - -op Optional. Specifies precision for all output layers of network. Supported values: FP32, FP16, U8. Default value: FP16. - -iop "" Optional. Specifies precision for input/output layers by name. - By default all inputs and outputs have FP16 precision. - Available precisions: FP32, FP16, U8. - Example: -iop "input:FP16, output:FP16". - Notice that quotes are required. - Overwrites precision from ip and op options for specified layers. - -VPU_NUMBER_OF_SHAVES Optional. Specifies number of shaves. Should be set with "VPU_NUMBER_OF_CMX_SLICES". Overwrites value from config. - -VPU_NUMBER_OF_CMX_SLICES Optional. Specifies number of CMX slices. Should be set with "VPU_NUMBER_OF_SHAVES". Overwrites value from config. - -VPU_TILING_CMX_LIMIT_KB Optional. Specifies CMX limit for data tiling in kB. Value should be equal or greater than -1, where -1 means default value of limit. Overwrites value from config. -``` - -Running the application with the empty list of options yields an error message. - -You can use the following command to dump blob using a trained Faster R-CNN network: - -```sh -./myriad_compile -m /model_name.xml -``` - -## Import and Export functionality - -#### Export - -You can save a blob file from your application. -To do this, you should call the `Export()` method on the `ExecutableNetwork` object. -`Export()` has the following argument: -* Name of output blob [IN] - -Example: - -```sh -InferenceEngine::Core core; -InferenceEngine::ExecutableNetwork executableNetwork = core.LoadNetwork(network); -executableNetwork.Export("model_name.blob"); -``` - -#### Import - -You can upload blob with network into your application. -To do this, you should call the `ImportNetwork()` method on the `Core` object. -`ImportNetwork()` has the following arguments: -* Path to blob [IN] -* Config options [IN] -And returns `ExecutableNetwork` object - -Example: - -```sh -std::string modelFilename ("model_name.blob"); -InferenceEngine::Core core; -InferenceEngine::ExecutableNetwork importedNetwork = core.ImportNetwork(modelFilename); -``` - -> **NOTE**: Models should be first converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer). diff --git a/inference-engine/tools/vpu/vpu_compile/main.cpp b/inference-engine/tools/vpu/vpu_compile/main.cpp deleted file mode 100644 index 01a7177e069..00000000000 --- a/inference-engine/tools/vpu/vpu_compile/main.cpp +++ /dev/null @@ -1,271 +0,0 @@ -// Copyright (C) 2018-2021 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -#include -#include -#include -#include -#include -#include -#include -#include - -#include - -#include "inference_engine.hpp" -#include -#include "samples/common.hpp" -#include - -#include "vpu_tools_common.hpp" - -static constexpr char help_message[] = "Optional. Print a usage message."; -static constexpr char model_message[] = "Required. Path to xml model."; -static constexpr char plugin_path_message[] = "Optional. Path to a plugin folder."; -static constexpr char output_message[] = "Optional. Path to the output file. Default value: \".blob\"."; -static constexpr char config_message[] = "Optional. Path to the configuration file. Default value: \"config\"."; -static constexpr char number_of_shaves_message[] = "Optional. Specifies number of shaves." - " Should be set with \"VPU_NUMBER_OF_CMX_SLICES\"." - " Overwrites value from config."; -static constexpr char number_of_cmx_slices_message[] = "Optional. Specifies number of CMX slices." - " Should be set with \"VPU_NUMBER_OF_SHAVES\"." - " Overwrites value from config."; -static constexpr char tiling_cmx_limit_message[] = "Optional. Specifies CMX limit for data tiling." - " Value should be equal or greater than -1." - " Overwrites value from config."; -static constexpr char inputs_precision_message[] = "Optional. Specifies precision for all input layers of network." - " Supported values: FP32, FP16, U8. Default value: FP16."; -static constexpr char outputs_precision_message[] = "Optional. Specifies precision for all output layers of network." - " Supported values: FP32, FP16, U8. Default value: FP16."; -static constexpr char iop_message[] = "Optional. Specifies precision for input/output layers by name.\n" -" By default all inputs and outputs have FP16 precision.\n" -" Available precisions: FP32, FP16, U8.\n" -" Example: -iop \"input:FP16, output:FP16\".\n" -" Notice that quotes are required.\n" -" Overwrites precision from ip and op options for specified layers."; - -DEFINE_bool(h, false, help_message); -DEFINE_string(m, "", model_message); -DEFINE_string(pp, "", plugin_path_message); -DEFINE_string(o, "", output_message); -DEFINE_string(c, "config", config_message); -DEFINE_string(ip, "", inputs_precision_message); -DEFINE_string(op, "", outputs_precision_message); -DEFINE_string(iop, "", iop_message); -DEFINE_string(VPU_NUMBER_OF_SHAVES, "", number_of_shaves_message); -DEFINE_string(VPU_NUMBER_OF_CMX_SLICES, "", number_of_cmx_slices_message); -DEFINE_string(VPU_TILING_CMX_LIMIT_KB, "", tiling_cmx_limit_message); - -static void showUsage() { - std::cout << std::endl; - std::cout << "myriad_compile [OPTIONS]" << std::endl; - std::cout << "[OPTIONS]:" << std::endl; - std::cout << " -h " << help_message << std::endl; - std::cout << " -m " << model_message << std::endl; - std::cout << " -pp " << plugin_path_message << std::endl; - std::cout << " -o " << output_message << std::endl; - std::cout << " -c " << config_message << std::endl; - std::cout << " -ip " << inputs_precision_message << std::endl; - std::cout << " -op " << outputs_precision_message << std::endl; - std::cout << " -iop \"\" " << iop_message << std::endl; - std::cout << " -VPU_NUMBER_OF_SHAVES " << number_of_shaves_message << std::endl; - std::cout << " -VPU_NUMBER_OF_CMX_SLICES " << number_of_cmx_slices_message << std::endl; - std::cout << " -VPU_TILING_CMX_LIMIT_KB " << tiling_cmx_limit_message << std::endl; - std::cout << std::endl; -} - -static bool parseCommandLine(int *argc, char ***argv) { - gflags::ParseCommandLineNonHelpFlags(argc, argv, true); - - if (FLAGS_h) { - showUsage(); - return false; - } - - if (FLAGS_m.empty()) { - throw std::invalid_argument("Path to model xml file is required"); - } - - if (1 < *argc) { - std::stringstream message; - message << "Unknown arguments: "; - for (auto arg = 1; arg < *argc; arg++) { - message << argv[arg]; - if (arg < *argc) { - message << " "; - } - } - throw std::invalid_argument(message.str()); - } - - return true; -} - -static std::map configure(const std::string &configFile, const std::string &xmlFileName) { - auto config = parseConfig(configFile); - - IE_SUPPRESS_DEPRECATED_START - config[VPU_MYRIAD_CONFIG_KEY(PLATFORM)] = "VPU_MYRIAD_2480"; - IE_SUPPRESS_DEPRECATED_END - - if (!FLAGS_VPU_NUMBER_OF_SHAVES.empty()) { - config[InferenceEngine::MYRIAD_NUMBER_OF_SHAVES] = FLAGS_VPU_NUMBER_OF_SHAVES; - } - - if (!FLAGS_VPU_NUMBER_OF_CMX_SLICES.empty()) { - config[InferenceEngine::MYRIAD_NUMBER_OF_CMX_SLICES] = FLAGS_VPU_NUMBER_OF_CMX_SLICES; - } - - if (!FLAGS_VPU_TILING_CMX_LIMIT_KB.empty()) { - config[InferenceEngine::MYRIAD_TILING_CMX_LIMIT_KB] = FLAGS_VPU_TILING_CMX_LIMIT_KB; - } - - return config; -} - -static std::map parsePrecisions(const std::string &iop) { - std::string user_input = iop; - user_input.erase(std::remove_if(user_input.begin(), user_input.end(), ::isspace), user_input.end()); - - std::vector inputs; - vpu::splitStringList(user_input, inputs, ','); - - std::map precisions; - for (auto &&input : inputs) { - std::vector precision; - vpu::splitStringList(input, precision, ':'); - if (precision.size() != 2) { - throw std::invalid_argument("Invalid precision " + input + ". Expected layer_name : precision_value"); - } - - precisions[precision[0]] = precision[1]; - } - - return precisions; -} - -using supported_precisions_t = std::unordered_map; - -static InferenceEngine::Precision getPrecision(const std::string &value, - const supported_precisions_t &supported_precisions, - const std::string& error_report = std::string()) { - std::string upper_value = value; - std::transform(value.begin(), value.end(), upper_value.begin(), ::toupper); - auto precision = supported_precisions.find(upper_value); - if (precision == supported_precisions.end()) { - std::string report = error_report.empty() ? ("") : (" " + error_report); - throw std::logic_error("\"" + value + "\"" + " is not a valid precision" + report); - } - - return precision->second; -} - -static InferenceEngine::Precision getInputPrecision(const std::string &value) { - static const supported_precisions_t supported_precisions = { - { "FP32", InferenceEngine::Precision::FP32 }, - { "FP16", InferenceEngine::Precision::FP16 }, - { "U8", InferenceEngine::Precision::U8 } - }; - return getPrecision(value, supported_precisions, "for input layer"); -} - -static InferenceEngine::Precision getOutputPrecision(const std::string &value) { - static const supported_precisions_t supported_precisions = { - { "FP32", InferenceEngine::Precision::FP32 }, - { "FP16", InferenceEngine::Precision::FP16 } - }; - return getPrecision(value, supported_precisions, "for output layer"); -} - -void setPrecisions(const InferenceEngine::CNNNetwork &network, const std::string &iop) { - auto user_precisions_map = parsePrecisions(iop); - auto inputs = network.getInputsInfo(); - auto outputs = network.getOutputsInfo(); - - for (auto &&item : user_precisions_map) { - std::string layer_name = item.first; - std::string user_precision = item.second; - - auto input = inputs.find(layer_name); - auto output = outputs.find(layer_name); - - if (input != inputs.end()) { - const auto input_precision = input->second->getPrecision(); - if ((isFloat(input_precision) && isFloat(getInputPrecision(user_precision))) || - (isFP16(input_precision) && isU8(getInputPrecision(user_precision)))) { - input->second->setPrecision(getInputPrecision(user_precision)); - } - } else if (output != outputs.end()) { - auto output_precision = output->second->getPrecision(); - if (isFloat(output_precision) && isFloat(getOutputPrecision(user_precision))) { - output->second->setPrecision(getOutputPrecision(user_precision)); - } - } else { - throw std::logic_error(layer_name + " is not an input neither output"); - } - } -} - -static void processPrecisions(InferenceEngine::CNNNetwork &network, - const std::string &inputs_precision, const std::string &outputs_precision, - const std::string &iop) { - setPrecisions(network); - - if (!inputs_precision.empty()) { - auto precision = getInputPrecision(inputs_precision); - for (auto &&layer : network.getInputsInfo()) { - const auto layerPrecision = layer.second->getPrecision(); - if ((isFloat(layerPrecision) && isFloat(precision)) || - (isFP16(layerPrecision) && isU8(precision))) { - layer.second->setPrecision(precision); - } - } - } - - if (!outputs_precision.empty()) { - auto precision = getOutputPrecision(outputs_precision); - for (auto &&layer : network.getOutputsInfo()) { - const auto layerPrecision = layer.second->getPrecision(); - if (isFloat(layerPrecision) && isFloat(precision)) { - layer.second->setPrecision(precision); - } - } - } - - if (!iop.empty()) { - setPrecisions(network, iop); - } -} - -int main(int argc, char *argv[]) { - try { - std::cout << "Inference Engine: " << InferenceEngine::GetInferenceEngineVersion() << std::endl; - - if (!parseCommandLine(&argc, &argv)) { - return EXIT_SUCCESS; - } - - auto network = readNetwork(FLAGS_m); - - processPrecisions(network, FLAGS_ip, FLAGS_op, FLAGS_iop); - - InferenceEngine::Core ie; - auto executableNetwork = ie.LoadNetwork(network, "MYRIAD", configure(FLAGS_c, FLAGS_m)); - - std::string outputName = FLAGS_o; - if (outputName.empty()) { - outputName = fileNameNoExt(FLAGS_m) + ".blob"; - } - executableNetwork.Export(outputName); - } catch (const std::exception &error) { - std::cerr << error.what() << std::endl; - return EXIT_FAILURE; - } catch (...) { - std::cerr << "Unknown/internal exception happened." << std::endl; - return EXIT_FAILURE; - } - - std::cout << "Done" << std::endl; - return EXIT_SUCCESS; -} diff --git a/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.cpp b/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.cpp deleted file mode 100644 index e3071a557be..00000000000 --- a/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.cpp +++ /dev/null @@ -1,299 +0,0 @@ -// Copyright (C) 2018-2021 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -/* on windows min and max already defined that makes using numeric_limits impossible */ -#ifdef _WIN32 -# define NOMINMAX -#endif - -#include - -#include -#include -#include -#include -#include -#include -#include - -#include "vpu_tools_common.hpp" -#include - -#include -#include - -#include "precision_utils.h" - -InferenceEngine::CNNNetwork readNetwork(const std::string &xmlFileName) { - return InferenceEngine::Core().ReadNetwork(xmlFileName); -} - -bool isFP16(InferenceEngine::Precision precision) { - return precision == InferenceEngine::Precision::FP16; -} - -bool isFP32(InferenceEngine::Precision precision) { - return precision == InferenceEngine::Precision::FP32; -} - -bool isU8(InferenceEngine::Precision precision) { - return precision == InferenceEngine::Precision::U8; -} - -bool isFloat(InferenceEngine::Precision precision) { - return isFP16(precision) || isFP32(precision); -} - -void setPrecisions(const InferenceEngine::CNNNetwork &network) { - for (auto &&layer : network.getInputsInfo()) { - if (isFP32(layer.second->getPrecision())) { - layer.second->setPrecision(InferenceEngine::Precision::FP16); - } - } - - for (auto &&layer : network.getOutputsInfo()) { - if (isFP32(layer.second->getPrecision())) { - layer.second->setPrecision(InferenceEngine::Precision::FP16); - } - } -} - -BitMap::BitMap(const std::string &filename) { - BmpHeader header; - BmpInfoHeader infoHeader; - - std::ifstream input(filename, std::ios::binary); - if (!input) { - return; - } - - input.read(reinterpret_cast(&header.type), 2); - - if (header.type != 'M'*256+'B') { - std::cerr << "[BMP] file is not bmp type\n"; - return; - } - - input.read(reinterpret_cast(&header.size), 4); - input.read(reinterpret_cast(&header.reserved), 4); - input.read(reinterpret_cast(&header.offset), 4); - - input.read(reinterpret_cast(&infoHeader), sizeof(BmpInfoHeader)); - - bool rowsReversed = infoHeader.height < 0; - _width = static_cast(infoHeader.width); - _height = static_cast(std::abs(infoHeader.height)); - - if (infoHeader.bits != 24) { - std::cerr << "[BMP] 24bpp only supported. But input has:" << infoHeader.bits << "\n"; - return; - } - - if (infoHeader.compression != 0) { - std::cerr << "[BMP] compression not supported\n"; - } - - auto padSize = _width & 3; - char pad[3]; - size_t size = _width * _height * 3; - - _data.reset(new unsigned char[size], std::default_delete()); - - input.seekg(header.offset, std::ios::beg); - - // reading by rows in invert vertically - for (uint32_t i = 0; i < _height; i++) { - uint32_t storeAt = rowsReversed ? i : (uint32_t)_height - 1 - i; - input.read(reinterpret_cast(_data.get()) + _width * 3 * storeAt, _width * 3); - input.read(pad, padSize); - } -} - -void loadImage(const std::string &imageFilename, InferenceEngine::Blob::Ptr &blob) { - InferenceEngine::TensorDesc tensDesc = blob->getTensorDesc(); - if (tensDesc.getPrecision() != InferenceEngine::Precision::FP16) { - throw std::invalid_argument("Input must have FP16 precision"); - } - - BitMap reader(imageFilename); - - const auto dims = tensDesc.getDims(); - auto numBlobChannels = dims[1]; - size_t batch = dims[0]; - size_t w = dims[3]; - size_t h = dims[2]; - size_t img_w = reader.width(); - size_t img_h = reader.height(); - - size_t numImageChannels = reader.size() / (reader.width() * reader.height()); - if (numBlobChannels != numImageChannels && numBlobChannels != 1) { - throw std::invalid_argument("Input channels mismatch: image channels " + std::to_string(numImageChannels) + - ", network channels " + std::to_string(numBlobChannels) + - ", expecting count of image channels are equal to count if network channels" - "or count of network channels are equal to 1"); - } - - int16_t *blobDataPtr = std::dynamic_pointer_cast>(blob)->data(); - auto nPixels = w * h; - unsigned char *RGB8 = reader.getData().get(); - float xscale = 1.0f * img_w / w; - float yscale = 1.0f * img_h / h; - - for (std::size_t n = 0; n != batch; n++) { - for (std::size_t i = 0; i < h; ++i) { - int y = static_cast(std::floor((i + 0.5f) * yscale)); - for (std::size_t j = 0; j < w; ++j) { - int x = static_cast(std::floor((j + 0.5f) * xscale)); - for (std::size_t k = 0; k < numBlobChannels; k++) { - float src = 1.0f * RGB8[(y * img_w + x) * numImageChannels + k]; - if (tensDesc.getLayout() == InferenceEngine::NHWC) { - blobDataPtr[n * h * w * numBlobChannels + (i * w + j) * numBlobChannels + k] = - InferenceEngine::PrecisionUtils::f32tof16(src); - } else { - blobDataPtr[n * h * w * numBlobChannels + (i * w + j) + k * nPixels] = - InferenceEngine::PrecisionUtils::f32tof16(src); - } - } - } - } - } -} - -void printPerformanceCounts(const std::map& perfMap, const std::string report) { - std::vector> perfVec(perfMap.begin(), - perfMap.end()); - std::sort(perfVec.begin(), perfVec.end(), - [=](const std::pair &pair1, - const std::pair &pair2) -> bool { - return pair1.second.execution_index < pair2.second.execution_index; - }); - - size_t maxLayerName = 0u, maxExecType = 0u; - for (auto &&entry : perfVec) { - maxLayerName = std::max(maxLayerName, entry.first.length()); - maxExecType = std::max(maxExecType, std::strlen(entry.second.exec_type)); - } - - size_t indexWidth = 7, nameWidth = maxLayerName + 5, typeWidth = maxExecType + 5, timeWidth = 10; - size_t totalWidth = indexWidth + nameWidth + typeWidth + timeWidth; - - std::cout << std::endl << "Detailed " << report << " Profile" << std::endl; - for (size_t i = 0; i < totalWidth; i++) - std::cout << "="; - std::cout << std::endl; - std::cout << std::setw(static_cast(indexWidth)) << std::left << "Index" - << std::setw(static_cast(nameWidth)) << std::left << "Name" - << std::setw(static_cast(typeWidth)) << std::left << "Type" - << std::setw(static_cast(timeWidth)) << std::right << "Time (ms)" - << std::endl; - - for (size_t i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; - - long long totalTime = 0; - for (const auto& p : perfVec) { - const auto& stageName = p.first; - const auto& info = p.second; - if (info.status == InferenceEngine::InferenceEngineProfileInfo::EXECUTED) { - std::cout << std::setw(static_cast(indexWidth)) << std::left << info.execution_index - << std::setw(static_cast(nameWidth)) << std::left << stageName - << std::setw(static_cast(typeWidth)) << std::left << info.exec_type - << std::setw(static_cast(timeWidth)) << std::right << info.realTime_uSec / 1000.0 - << std::endl; - - totalTime += info.realTime_uSec; - } - } - - for (std::size_t i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; - std::cout << std::setw(static_cast(totalWidth / 2)) << std::right << "Total inference time:" - << std::setw(static_cast(totalWidth / 2 + 1)) << std::right << totalTime / 1000.0 - << std::endl; - for (std::size_t i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; -} - -std::vector extractFilesByExtension(const std::string& directory, const std::string& extension) { - return extractFilesByExtension(directory, extension, std::numeric_limits::max()); -} - -std::vector extractFilesByExtension(const std::string& directory, const std::string& extension, - std::size_t max_size) { - if (max_size == 0) { - return {}; - } - - std::vector files; - - DIR* dir = opendir(directory.c_str()); - if (!dir) { - throw std::invalid_argument("Can not open " + directory); - } - - auto getExtension = [](const std::string& name) { - auto extensionPosition = name.rfind('.', name.size()); - return extensionPosition == std::string::npos ? "" : name.substr(extensionPosition + 1, name.size() - 1); - }; - - dirent* ent = nullptr; - while ((ent = readdir(dir)) && files.size() < max_size) { - std::string file_name = ent->d_name; - if (getExtension(file_name) != extension) { - continue; - } - - std::string full_file_name = directory + "/" + file_name; - - struct stat st = {}; - if (stat(full_file_name.c_str(), &st) != 0) { - continue; - } - - bool is_directory = (st.st_mode & S_IFDIR) != 0; - if (is_directory) { - continue; - } - - files.emplace_back(full_file_name); - } - - closedir(dir); - - return files; -} - -void loadBinaryTensor(const std::string &binaryFileName, InferenceEngine::Blob::Ptr& blob) { - InferenceEngine::TensorDesc tensDesc = blob->getTensorDesc(); - if (tensDesc.getPrecision() != InferenceEngine::Precision::FP16) { - throw std::invalid_argument("Input must have FP16 precision"); - } - - std::ifstream binaryFile(binaryFileName, std::ios_base::binary | std::ios_base::ate); - if (!binaryFile) { - throw std::invalid_argument("Can not open \"" + binaryFileName + "\""); - } - - auto fileSize = static_cast(binaryFile.tellg()); - binaryFile.seekg(0, std::ios_base::beg); - if (!binaryFile.good()) { - throw std::invalid_argument("Can not read \"" + binaryFileName + "\""); - } - - auto expected_size = blob->byteSize(); - if (fileSize != expected_size) { - throw std::invalid_argument("File \"" + binaryFileName + "\" contains " + std::to_string(fileSize) + " bytes " - "but network expects " + std::to_string(expected_size)); - } - /* try to read 32 bits data */ - std::int16_t *blobDataPtr = std::dynamic_pointer_cast>(blob)->data(); - for (std::size_t i = 0; i < blob->size(); i++) { - float tmp = 0.f; - binaryFile.read(reinterpret_cast(&tmp), sizeof(float)); - blobDataPtr[i] = InferenceEngine::PrecisionUtils::f32tof16(tmp); - } -} diff --git a/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.hpp b/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.hpp deleted file mode 100644 index 3ce092ef12a..00000000000 --- a/inference-engine/tools/vpu/vpu_compile/vpu_tools_common.hpp +++ /dev/null @@ -1,71 +0,0 @@ -// Copyright (C) 2018-2021 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -#pragma once - -#include -#include -#include -#include - -#include "inference_engine.hpp" - -InferenceEngine::CNNNetwork readNetwork(const std::string &xmlFileName); - -bool isFP16(InferenceEngine::Precision precision); -bool isFP32(InferenceEngine::Precision precision); -bool isU8(InferenceEngine::Precision precision); -bool isFloat(InferenceEngine::Precision precision); -/* Set FP32 to FP16, all others without changes */ -void setPrecisions(const InferenceEngine::CNNNetwork &network); - -class BitMap { -private: - typedef struct { - unsigned short type; /* Magic identifier */ - unsigned int size; /* File size in bytes */ - unsigned int reserved; - unsigned int offset; /* Offset to image data, bytes */ - } BmpHeader; - - typedef struct { - unsigned int size; /* Header size in bytes */ - int width, height; /* Width and height of image */ - unsigned short planes; /* Number of colour planes */ - unsigned short bits; /* Bits per pixel */ - unsigned int compression; /* Compression type */ - unsigned int imagesize; /* Image size in bytes */ - int xresolution, yresolution; /* Pixels per meter */ - unsigned int ncolours; /* Number of colours */ - unsigned int importantcolours; /* Important colours */ - } BmpInfoHeader; - -public: - explicit BitMap(const std::string &filename); - - ~BitMap() = default; - - size_t _height = 0; - size_t _width = 0; - std::shared_ptr _data; - -public: - size_t size() const { return _width * _height * 3; } - size_t width() const { return _width; } - size_t height() const { return _height; } - - std::shared_ptr getData() { - return _data; - } -}; - -void loadImage(const std::string &imageFilename, InferenceEngine::Blob::Ptr &blob); - -void printPerformanceCounts(const std::map& perfMap, const std::string report = "per_layer"); - -std::vector extractFilesByExtension(const std::string& directory, const std::string& extension); -std::vector extractFilesByExtension(const std::string& directory, const std::string& extension, - std::size_t max_size); - -void loadBinaryTensor(const std::string &binaryFileName, InferenceEngine::Blob::Ptr& blob); diff --git a/inference-engine/tools/vpu/vpu_perfcheck/CMakeLists.txt b/inference-engine/tools/vpu/vpu_perfcheck/CMakeLists.txt deleted file mode 100644 index 89b7cabd253..00000000000 --- a/inference-engine/tools/vpu/vpu_perfcheck/CMakeLists.txt +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (C) 2018-2021 Intel Corporation -# SPDX-License-Identifier: Apache-2.0 -# - -function(add_perfcheck_target TARGET_NAME PLUGIN_NAME) - file(GLOB SOURCES *.cpp) - - add_executable(${TARGET_NAME} ${SOURCES}) - - # TODO: enable some day and fix all warnings -# if(CMAKE_COMPILER_IS_GNUCXX) -# target_compile_options(${TARGET_NAME} -# PRIVATE -# "-Wall") -# endif() - - target_include_directories(${TARGET_NAME} - SYSTEM PRIVATE - $) - - target_link_libraries(${TARGET_NAME} - PRIVATE - inference_engine - inference_engine_plugin_api - format_reader - Threads::Threads - ie_samples_utils) - - add_dependencies(${TARGET_NAME} - ${PLUGIN_NAME} ${ARGN}) - - set_target_properties(${TARGET_NAME} PROPERTIES - COMPILE_PDB_NAME ${TARGET_NAME} - FOLDER tools) - - add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}) -endfunction() - -if(ENABLE_MYRIAD) - add_perfcheck_target(myriad_perfcheck myriadPlugin) - - ie_cpack_add_component(myriad_tools DEPENDS myriad) - - install(TARGETS myriad_perfcheck - RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} - COMPONENT myriad_tools) -endif() diff --git a/inference-engine/tools/vpu/vpu_perfcheck/main.cpp b/inference-engine/tools/vpu/vpu_perfcheck/main.cpp deleted file mode 100644 index 1a59be99943..00000000000 --- a/inference-engine/tools/vpu/vpu_perfcheck/main.cpp +++ /dev/null @@ -1,749 +0,0 @@ -// Copyright (C) 2018-2021 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -#if defined(_WIN32) -#define NOMINMAX -#endif -#if (defined(_WIN32) || defined(_WIN64)) -#define WIN32_LEAN_AND_MEAN -#else -#include -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include - -#include -#include -#include - -#include - -static char* m_exename = nullptr; - -#if defined(_WIN32) || defined(__APPLE__) || defined(ANDROID) -typedef std::chrono::time_point time_point; -#else -typedef std::chrono::time_point time_point; -#endif -typedef std::chrono::high_resolution_clock Time; -typedef std::chrono::duration> ms; -typedef std::chrono::duration fsec; - -#define TIMEDIFF(start, end) ((std::chrono::duration_cast((end) - (start))).count()) - -class BitMap { -private: - struct BmpHeader { - unsigned short type = 0u; /* Magic identifier */ - unsigned int size = 0u; /* File size in bytes */ - unsigned int reserved = 0u; - unsigned int offset = 0u; /* Offset to image data, bytes */ - }; - - struct BmpInfoHeader { - unsigned int size = 0u; /* Header size in bytes */ - int width = 0, height = 0; /* Width and height of image */ - unsigned short planes = 0u; /* Number of colour planes */ - unsigned short bits = 0u; /* Bits per pixel */ - unsigned int compression = 0u; /* Compression type */ - unsigned int imagesize = 0u; /* Image size in bytes */ - int xresolution = 0, yresolution = 0; /* Pixels per meter */ - unsigned int ncolours = 0u; /* Number of colours */ - unsigned int importantcolours = 0u; /* Important colours */ - }; - -public: - explicit BitMap(const std::string &filename) { - BmpHeader header; - BmpInfoHeader infoHeader; - - std::ifstream input(filename, std::ios::binary); - if (!input) { - return; - } - - input.read(reinterpret_cast(&header.type), 2); - - if (header.type != 'M'*256+'B') { - std::cerr << "[BMP] file is not bmp type\n"; - return; - } - - input.read(reinterpret_cast(&header.size), 4); - input.read(reinterpret_cast(&header.reserved), 4); - input.read(reinterpret_cast(&header.offset), 4); - - input.read(reinterpret_cast(&infoHeader), sizeof(BmpInfoHeader)); - - bool rowsReversed = infoHeader.height < 0; - _width = infoHeader.width; - _height = abs(infoHeader.height); - - if (infoHeader.bits != 24) { - std::cerr << "[BMP] 24bpp only supported. But input has:" << infoHeader.bits << "\n"; - return; - } - - if (infoHeader.compression != 0) { - std::cerr << "[BMP] compression not supported\n"; - } - - int padSize = _width & 3; - char pad[3]; - size_t size = _width * _height * 3; - - _data.reset(new unsigned char[size], std::default_delete()); - - input.seekg(header.offset, std::ios::beg); - - // reading by rows in invert vertically - for (uint32_t i = 0; i < _height; i++) { - uint32_t storeAt = rowsReversed ? i : (uint32_t)_height - 1 - i; - input.read(reinterpret_cast(_data.get()) + _width * 3 * storeAt, _width * 3); - input.read(pad, padSize); - } - } - - ~BitMap() = default; - - size_t _height = 0; - size_t _width = 0; - std::shared_ptr _data; - -public: - size_t size() const { return _width * _height * 3; } - size_t width() const { return _width; } - size_t height() const { return _height; } - - std::shared_ptr getData() { - return _data; - } -}; - - -static bool loadImage(const std::string &imageFilename, InferenceEngine::Blob::Ptr &blob); -static bool loadVideo(const std::vector &imagesFolder, InferenceEngine::Blob::Ptr &blob); -static bool loadBinaryTensor(const std::string &binaryFilename, InferenceEngine::Blob::Ptr &blob); - - -static void setConfig(std::map& config, - const std::string& file_config_cl) { - config[CONFIG_KEY(LOG_LEVEL)] = CONFIG_VALUE(LOG_WARNING); - config[InferenceEngine::MYRIAD_ENABLE_RECEIVING_TENSOR_TIME] = CONFIG_VALUE(YES); - config[InferenceEngine::MYRIAD_CUSTOM_LAYERS] = file_config_cl; -} - -static void printPerformanceCounts(const std::map& perfMap) { - std::vector> perfVec(perfMap.begin(), - perfMap.end()); - std::sort(perfVec.begin(), perfVec.end(), - [=](const std::pair &pair1, - const std::pair &pair2) -> bool { - return pair1.second.execution_index < pair2.second.execution_index; - }); - - size_t maxLayerName = 0u, maxExecType = 0u; - for (auto & it : perfVec) { - maxLayerName = std::max(maxLayerName, it.first.length()); - maxExecType = std::max(maxExecType, std::strlen(it.second.exec_type)); - } - - size_t indexWidth = 7, nameWidth = maxLayerName + 5, typeWidth = maxExecType + 5, timeWidth = 10; - size_t totalWidth = indexWidth + nameWidth + typeWidth + timeWidth; - - std::cout << std::endl << "Detailed Per Stage Profile" << std::endl; - for (size_t i = 0; i < totalWidth; i++) - std::cout << "="; - std::cout << std::endl; - std::cout << std::setw(indexWidth) << std::left << "Index" - << std::setw(nameWidth) << std::left << "Name" - << std::setw(typeWidth) << std::left << "Type" - << std::setw(timeWidth) << std::right << "Time (ms)" - << std::endl; - for (size_t i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; - - long long totalTime = 0; - for (const auto& p : perfVec) { - const auto& stageName = p.first; - const auto& info = p.second; - if (info.status == InferenceEngine::InferenceEngineProfileInfo::EXECUTED) { - std::cout << std::setw(indexWidth) << std::left << info.execution_index - << std::setw(nameWidth) << std::left << stageName - << std::setw(typeWidth) << std::left << info.exec_type - << std::setw(timeWidth) << std::right << info.realTime_uSec / 1000.0 - << std::endl; - - totalTime += info.realTime_uSec; - } - } - - for (int i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; - std::cout << std::setw(totalWidth / 2) << std::right << "Total inference time:" - << std::setw(totalWidth / 2 + 1) << std::right << totalTime / 1000.0 - << std::endl; - for (int i = 0; i < totalWidth; i++) - std::cout << "-"; - std::cout << std::endl; -} - -static std::string getAppRealName(const char* name) { - std::string filename(name); - size_t splitpos = filename.find_last_of('\\'); - if (std::string::npos == splitpos) { - splitpos = filename.find_last_of('/'); - if (std::string::npos == splitpos) { - return filename; - } - } - return filename.substr(splitpos + 1); -} - -static void print_usage() { - std::cout << "Usage:" << std::endl << getAppRealName(m_exename) << " [number of iterations >= 1000]" - << " [batch >= 1, default=1] [num_networks, default=1] [config_file_custom_layer, default='']" << std::endl; -} - -static void getBMPFiles(std::vector &out, const std::string &directory) { - const std::string ext = ".bmp"; - DIR *dir; - dirent *ent; - dir = opendir(directory.c_str()); - if (!dir) - return; - while ((ent = readdir(dir)) != nullptr) { - const std::string file_name = ent->d_name; - const std::string full_file_name = directory + "/" + file_name; - if ((file_name.length() >= ext.length()) - && (0 == file_name.compare(file_name.length() - ext.length(), ext.length(), ext))) { - // proceed - } else { - continue; - } - struct stat st; - if (stat(full_file_name.c_str(), &st) == -1) - continue; - const bool is_directory = (st.st_mode & S_IFDIR) != 0; - if (is_directory) - continue; - out.push_back(full_file_name); - } - closedir(dir); -} - -static void getBINFiles(std::vector &out, const std::string &directory) { - const std::string ext = ".bin"; - DIR *dir; - dirent *ent; - dir = opendir(directory.c_str()); - if (!dir) - return; - while ((ent = readdir(dir)) != nullptr) { - const std::string file_name = ent->d_name; - const std::string full_file_name = directory + "/" + file_name; - if ((file_name.length() >= ext.length()) - && (0 == file_name.compare(file_name.length() - ext.length(), ext.length(), ext))) { - // proceed - } else { - continue; - } - struct stat st; - if (stat(full_file_name.c_str(), &st) == -1) - continue; - const bool is_directory = (st.st_mode & S_IFDIR) != 0; - if (is_directory) - continue; - out.push_back(full_file_name); - } - closedir(dir); -} - -int num_requests = 4; - -#define MIN_ITER 1000 - -#define USE_CALLBACK - -int niter; -std::atomic iterations_to_run; -std::mutex done_mutex; -std::condition_variable alldone; -int reallydone = 0; - -std::vector iter_start; -std::vector iter_end; -std::vector iter_time; - -const int profile = 0; -std::map perfMap; - -int process(const std::string& modelFileName, const std::string& inputsDir, - std::string& file_config_cl, int nBatch, int num_networks) { - InferenceEngine::Core ie; - niter /= nBatch; - num_requests = num_requests * num_networks; - - // add some more requests. they'll be excluded on performance measurement - niter += 2 * 2 * num_requests; - -#if !(defined(_WIN32) || defined(_WIN64)) - if (pthread_setname_np( -#ifndef __APPLE__ - pthread_self(), -#endif - "MainThread") != 0) { - perror("Setting name for main thread failed"); - } -#endif - -#ifdef USE_KMB_PLUGIN - std::string deivceName = "KMB"; -#else - std::string deviceName = "MYRIAD"; -#endif - const auto pluginVersion = ie.GetVersions(deviceName); - std::cout << "InferenceEngine: " << std::endl; - std::cout << pluginVersion << std::endl << std::endl; - - std::ifstream file(file_config_cl); - if (!file.is_open()) { - file_config_cl.clear(); - } - - std::vector pictures; - getBMPFiles(pictures, inputsDir); - int numPictures = pictures.size(); - - std::vector binaries; - getBINFiles(binaries, inputsDir); - int numBinaries = binaries.size(); - - if (pictures.empty() && binaries.empty()) { - std::cout << inputsDir << " directory doesn't contain input files" << std::endl; - return 1; - } - - InferenceEngine::CNNNetwork cnnNetwork = ie.ReadNetwork(modelFileName); - - if (nBatch != 1) { - std::cout << "Setting batch to : "<< nBatch << "\n"; - cnnNetwork.setBatchSize(nBatch); - } - - InferenceEngine::InputsDataMap networkInputs; - networkInputs = cnnNetwork.getInputsInfo(); - InferenceEngine::OutputsDataMap networkOutputs; - networkOutputs = cnnNetwork.getOutputsInfo(); - - for (auto &input : networkInputs) { - const auto inputPrecision = input.second->getPrecision(); - if (inputPrecision == InferenceEngine::Precision::FP32 || - inputPrecision == InferenceEngine::Precision::U8) { - input.second->setPrecision(InferenceEngine::Precision::FP16); - } - } - - for (auto &output : networkOutputs) { - const auto outputPrecision = output.second->getPrecision(); - if (outputPrecision == InferenceEngine::Precision::FP32) { - output.second->setPrecision(InferenceEngine::Precision::FP16); - } - } - - std::vector exeNetwork(num_networks); - std::map networkConfig; - setConfig(networkConfig, file_config_cl); - - for (int n = 0; n < num_networks; ++n) { - if (num_networks > 1) - printf("Load network %d...\n", n); - else - printf("Load network... \n"); - fflush(stdout); - exeNetwork[n] = ie.LoadNetwork(cnnNetwork, deviceName, networkConfig); - } - - std::vector request(num_requests); - iter_start.resize(niter); - iter_end.resize(niter); - iter_time.resize(niter); - - iterations_to_run = niter - num_requests; - - for (int r = 0, idxPic = 0; r < num_requests; ++r) { - int n = r % num_networks; - request[r] = exeNetwork[n].CreateInferRequest(); - - for (auto &input : networkInputs) { - auto inputBlob = request[r].GetBlob(input.first); - const auto& dims = inputBlob->getTensorDesc().getDims(); - auto layout = inputBlob->getTensorDesc().getLayout(); - - // number of channels is 3 for Image, dims order is always NCHW - const bool isImage = ((layout == InferenceEngine::NHWC || layout == InferenceEngine::NCHW) && dims[1] == 3); - const bool isVideo = (inputBlob->getTensorDesc().getDims().size() == 5); - if (isImage && (numPictures > 0)) { - if (!loadImage(pictures[(idxPic++) % numPictures], inputBlob)) - return 1; - } else if (isVideo && (numPictures > 0)) { - if (!loadVideo(pictures, inputBlob)) - return 1; - } else if (numBinaries > 0) { - if (!loadBinaryTensor(binaries[(idxPic++) % numBinaries], inputBlob)) - return 1; - } else { - std::cout << inputsDir << " directory doesn't contain correct input files" << std::endl; - return 1; - } - } - - request[r].SetCompletionCallback>( - [](InferenceEngine::InferRequest request, InferenceEngine::StatusCode code) -> void { - if (code != InferenceEngine::OK) { - std::cout << "Infer failed: " << code << std::endl; - exit(1); - } - - int iter = --iterations_to_run; - int reqIdx = (niter - iter - 1) - num_requests; - - iter_end[reqIdx] = Time::now(); - - if (profile && (reqIdx == niter / 2)) { - perfMap = request.GetPerformanceCounts(); - } - - if (iter >= 0) { - iter_start[reqIdx + (num_requests)] = Time::now(); - request.StartAsync(); - } - - iter_time[reqIdx] = TIMEDIFF(iter_start[reqIdx], iter_end[reqIdx]); - // printf("request#%d %fms\n", reqIdx, iter_time[reqIdx]); - - if (iter == -num_requests) { - reallydone = 1; - alldone.notify_all(); - } - }); - } - - printf("Inference started. Running %d iterations...\n", niter - 2 * 2 * num_requests); - fflush(stdout); - for (int r = 0; r < num_requests; ++r) { - iter_start[r] = Time::now(); - request[r].StartAsync(); - } - - { - std::unique_lock lock(done_mutex); - alldone.wait(lock, [&](){return reallydone;}); - } - - // check 10 time intervals to get min/max fps values - const int fps_checks = 10; - // exclude (2 * num_requests) first and last iterations - int num_exclude = 2 * num_requests; - time_point cstart = iter_end[num_exclude - 1]; - time_point cend = iter_end[niter - num_exclude - 1]; - - double totalTime = (std::chrono::duration_cast(cend - cstart)).count(); - std::cout << std::endl << "Total time: " << (totalTime) << " ms" << std::endl; - - std::cout << "Average fps on " << (niter - 2 * num_exclude) << " iterations" - << (nBatch == 1 ? ": " : (" of " + std::to_string(nBatch) + " frames: ")) - << static_cast(niter - 2 * num_exclude) * 1000.0 * nBatch / (totalTime) << " fps" << std::endl; - - double check_time = totalTime / fps_checks; - - double min_fps = 100000; - double max_fps = -100000; - int citer = num_exclude; - for (int f = 0; f < fps_checks; ++f) { - int fiter = 0; - auto fend = (f < fps_checks - 1) ? cstart + std::chrono::microseconds((unsigned int)(check_time * 1000)) : cend; - while ((citer + fiter < niter - num_exclude) && iter_end[citer + fiter] <= fend) { - fiter++; - } - - double ffps = 1000 * fiter * nBatch / (check_time); - min_fps = std::min(min_fps, ffps); - max_fps = std::max(max_fps, ffps); - citer += fiter; - cstart = fend; - } - - std::cout << "Min fps: " << min_fps << std::endl; - std::cout << "Max fps: " << max_fps << std::endl; - - if (profile) { - printPerformanceCounts(perfMap); - } - - return 0; -} - -int main(int argc, char *argv[]) { - niter = MIN_ITER; - int num_networks = 1; - int nBatch = 1; - std::string file_config_cl; - - m_exename = argv[0]; - - if (argc < 3) { - print_usage(); - return 0; - } - - auto parse = [](const std::string& src) { - try { - return std::stol(src, nullptr, 0); - } catch (const std::invalid_argument& exception) { - std::cout << "Cannot perform conversion for " << src << ": " << exception.what() << std::endl; - print_usage(); - std::abort(); - } catch (const std::out_of_range& exception) { - std::cout << src << " is out of range: " << exception.what() << std::endl; - print_usage(); - std::abort(); - } catch (...) { - std::cout << "Unexpected exception" << std::endl; - print_usage(); - std::abort(); - } - }; - - if (argc > 3) { - niter = static_cast(parse(argv[3])); - } - - if (argc > 4) { - nBatch = static_cast(parse(argv[4])); - } - - if (argc > 5) { - num_networks = static_cast(parse(argv[5])); - } - - if (argc > 6) { - file_config_cl = std::string(argv[6]); - } - - if (niter < MIN_ITER) { - print_usage(); - return 0; - } - - if (num_networks < 1 || num_networks > 16) { - print_usage(); - return 0; - } - - if (nBatch < 1) { - print_usage(); - return 0; - } - - try { - std::string modelFileName(argv[1]); - std::string inputsDir(argv[2]); - return process(modelFileName, inputsDir, file_config_cl, nBatch, num_networks); - } - catch (const std::exception& ex) { - std::cout << ex.what(); - } - - return -1; -} - -static bool loadImage(const std::string &imageFilename, InferenceEngine::Blob::Ptr &blob) { - InferenceEngine::TensorDesc tensDesc = blob->getTensorDesc(); - const InferenceEngine::Layout layout = tensDesc.getLayout(); - if (tensDesc.getPrecision() != InferenceEngine::Precision::FP16) { - std::cout << "loadImage error: Input must have FP16 precision" << std::endl; - return false; - } - - if (layout != InferenceEngine::NHWC && layout != InferenceEngine::NCHW) { - std::cout << "loadImage error: Input must have NCHW or NHWC layout" << std::endl; - return false; - } - - BitMap reader(imageFilename); - - const auto dims = tensDesc.getDims(); - - const size_t N = dims[0]; - const size_t C = dims[1]; - const size_t H = dims[2]; - const size_t W = dims[3]; - - const size_t img_w = reader.width(); - const size_t img_h = reader.height(); - - const auto strides = tensDesc.getBlockingDesc().getStrides(); - const auto strideN = strides[0]; - const auto strideC = layout == InferenceEngine::NHWC ? strides[3] : strides[1]; - const auto strideH = layout == InferenceEngine::NHWC ? strides[1] : strides[2]; - const auto strideW = layout == InferenceEngine::NHWC ? strides[2] : strides[3]; - - const size_t numImageChannels = reader.size() / (reader.width() * reader.height()); - if (C != numImageChannels && C != 1) { - std::cout << "loadImage error: Input channels mismatch: image channels " << numImageChannels << ", " - << "network channels " << C << ", expecting count of image channels are equal " - << "to count if network channels or count of network channels are equal to 1" << std::endl; - return false; - } - - int16_t* blobDataPtr = std::dynamic_pointer_cast>(blob)->data(); - const unsigned char* RGB8 = reader.getData().get(); - const float xScale = 1.0f * img_w / W; - const float yScale = 1.0f * img_h / H; - - for (int n = 0; n != N; n++) { - for (int h = 0; h < H; ++h) { - int y = static_cast(std::floor((h + 0.5f) * yScale)); - for (int w = 0; w < W; ++w) { - int x = static_cast(std::floor((w + 0.5f) * xScale)); - for (int c = 0; c < C; c++) { - blobDataPtr[n * strideN + c * strideC + h * strideH + w * strideW] = - InferenceEngine::PrecisionUtils::f32tof16(1.0 * RGB8[(y * img_w + x) * numImageChannels + c]); - } - } - } - } - - return true; -} - -static bool loadVideo(const std::vector &imagesFolder, InferenceEngine::Blob::Ptr &blob) { - InferenceEngine::TensorDesc tensDesc = blob->getTensorDesc(); - const InferenceEngine::Layout layout = tensDesc.getLayout(); - - if (tensDesc.getPrecision() != InferenceEngine::Precision::FP16) { - std::cout << "loadVideo error: Input must have FP16 precision" << std::endl; - return false; - } - if (layout != InferenceEngine::NDHWC && layout != InferenceEngine::NCDHW) { - std::cout << "loadVideo error: Input must have NCDHW or NDHWC layout" << std::endl; - return false; - } - - const auto dims = tensDesc.getDims(); - const size_t N = dims[0]; - const size_t C = dims[1]; - const size_t D = dims[2]; - const size_t H = dims[3]; - const size_t W = dims[4]; - - const auto numUsedImages = std::min(D, imagesFolder.size()); - const auto strides = tensDesc.getBlockingDesc().getStrides(); - const auto strideN = strides[0]; - const auto strideC = layout == InferenceEngine::NDHWC ? strides[4] : strides[1]; - const auto strideD = layout == InferenceEngine::NDHWC ? strides[1] : strides[2]; - const auto strideH = layout == InferenceEngine::NDHWC ? strides[2] : strides[3]; - const auto strideW = layout == InferenceEngine::NDHWC ? strides[3] : strides[4]; - - auto d = 0; - int16_t* blobDataPtr = std::dynamic_pointer_cast>(blob)->data(); - for ( ; d < numUsedImages; d++) { - BitMap reader(imagesFolder[d]); - const size_t img_w = reader.width(); - const size_t img_h = reader.height(); - const size_t numImageChannels = reader.size() / (reader.width() * reader.height()); - - if (C != numImageChannels && C != 1) { - std::cout << "loadVideo error: Input channels mismatch: image channels " << numImageChannels << ", " - << "network channels " << C << ", expecting count of image channels are equal " - << "to count if network channels or count of network channels are equal to 1" << std::endl; - return false; - } - - const unsigned char* RGB8 = reader.getData().get(); - const float xScale = 1.0f * img_w / W; - const float yScale = 1.0f * img_h / H; - - for (int n = 0; n != N; n++) { - for (int h = 0; h < H; ++h) { - int y = static_cast(std::floor((h + 0.5f) * yScale)); - for (int w = 0; w < W; ++w) { - int x = static_cast(std::floor((w + 0.5f) * xScale)); - for (int c = 0; c < C; c++) { - blobDataPtr[n * strideN + c * strideC + d * strideD + h * strideH + w * strideW] = - InferenceEngine::PrecisionUtils::f32tof16(1.0 * RGB8[(y * img_w + x) * numImageChannels + c]); - } - } - } - } - } - - for (; d < D; d++) - for (auto n = 0; n != N; n++) - for (auto c = 0; c < C; c++) - for (auto k = 0; k < strideD; k++) { - blobDataPtr[n * strideN + c * strideC + (d) * strideD + k] = - blobDataPtr[n * strideN + c * strideC + (d - 1) * strideD + k]; - } - - return true; -} - -bool loadBinaryTensor(const std::string &binaryFilename, InferenceEngine::Blob::Ptr &blob) { - InferenceEngine::TensorDesc tensDesc = blob->getTensorDesc(); - if (tensDesc.getPrecision() != InferenceEngine::Precision::FP16) { - std::cout << "loadBinaryTensor error: Input must have FP16 precision" << std::endl; - return false; - } - - std::ifstream binaryFile(binaryFilename, std::ios_base::binary | std::ios_base::ate); - - if (!binaryFile) { - std::cout << "loadBinaryTensor error: While opening a file an error is encountered" << std::endl; - return false; - } - - int fileSize = binaryFile.tellg(); - binaryFile.seekg(0, std::ios_base::beg); - size_t count = blob->size(); - if (fileSize != count * sizeof(float)) { - std::cout << "loadBinaryTensor error: File contains insufficient items" << std::endl; - return false; - } - - if (binaryFile.good()) { - int16_t *blobDataPtr = std::dynamic_pointer_cast>(blob)->data(); - for (size_t i = 0; i < count; i++) { - float tmp = 0.f; - binaryFile.read(reinterpret_cast(&tmp), sizeof(float)); - blobDataPtr[i] = InferenceEngine::PrecisionUtils::f32tof16(tmp); - } - } else { - std::cout << "loadBinaryTensor error: While reading a file an error is encountered" << std::endl; - return false; - } - return true; -} diff --git a/scripts/deployment_manager/configs/darwin.json b/scripts/deployment_manager/configs/darwin.json index 215f2716a1d..f3581e31cba 100644 --- a/scripts/deployment_manager/configs/darwin.json +++ b/scripts/deployment_manager/configs/darwin.json @@ -46,8 +46,6 @@ "dependencies" : ["ie_core"], "files": [ "deployment_tools/inference_engine/lib/intel64/libmyriadPlugin.so", - "deployment_tools/inference_engine/lib/intel64/myriad_compile", - "deployment_tools/inference_engine/lib/intel64/myriad_perfcheck", "deployment_tools/inference_engine/lib/intel64/libinference_engine_legacy.dylib", "deployment_tools/inference_engine/lib/intel64/usb-ma2x8x.mvcmd", "deployment_tools/inference_engine/lib/intel64/pcie-ma2x8x.mvcmd" diff --git a/scripts/deployment_manager/configs/linux.json b/scripts/deployment_manager/configs/linux.json index 7a3d745a78c..fd4600abb8e 100644 --- a/scripts/deployment_manager/configs/linux.json +++ b/scripts/deployment_manager/configs/linux.json @@ -66,8 +66,6 @@ "deployment_tools/inference_engine/lib/intel64/usb-ma2x8x.mvcmd", "deployment_tools/inference_engine/lib/intel64/pcie-ma2x8x.mvcmd", "deployment_tools/inference_engine/lib/intel64/libmyriadPlugin.so", - "deployment_tools/inference_engine/lib/intel64/myriad_compile", - "deployment_tools/inference_engine/lib/intel64/myriad_perfcheck", "deployment_tools/inference_engine/lib/intel64/vpu_custom_kernels", "deployment_tools/inference_engine/lib/intel64/libinference_engine_legacy.so", "install_dependencies/install_NCS_udev_rules.sh" diff --git a/scripts/deployment_manager/configs/windows.json b/scripts/deployment_manager/configs/windows.json index d47bcff3b6a..ba95c29e244 100644 --- a/scripts/deployment_manager/configs/windows.json +++ b/scripts/deployment_manager/configs/windows.json @@ -61,9 +61,7 @@ "deployment_tools/inference_engine/bin/intel64/Release/usb-ma2x8x.mvcmd", "deployment_tools/inference_engine/bin/intel64/Release/pcie-ma2x8x.elf", "deployment_tools/inference_engine/bin/intel64/Release/myriadPlugin.dll", - "deployment_tools/inference_engine/bin/intel64/Release/inference_engine_legacy.dll", - "deployment_tools/inference_engine/bin/intel64/Release/myriad_compile.exe", - "deployment_tools/inference_engine/bin/intel64/Release/myriad_perfcheck.exe" + "deployment_tools/inference_engine/bin/intel64/Release/inference_engine_legacy.dll" ] }, "gna": {