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Add fill_inputs step in timetests (#2413)

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Vitaliy Urusovskij 2020-09-25 14:08:03 +03:00 committed by GitHub
parent 1c97b13e9a
commit 0bd966ad08
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2 changed files with 165 additions and 1 deletions
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154
tests/time_tests/src/timetests/common.h Normal file
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@ -0,0 +1,154 @@
// Copyright (C) 2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
using namespace InferenceEngine;
/**
* @brief Determine if InferenceEngine blob means image or not
*/
template<typename T>
static bool isImage(const T &blob) {
auto descriptor = blob->getTensorDesc();
if (descriptor.getLayout() != InferenceEngine::NCHW) {
return false;
}
auto channels = descriptor.getDims()[1];
return channels == 3;
}
/**
* @brief Determine if InferenceEngine blob means image information or not
*/
template<typename T>
static bool isImageInfo(const T &blob) {
auto descriptor = blob->getTensorDesc();
if (descriptor.getLayout() != InferenceEngine::NC) {
return false;
}
auto channels = descriptor.getDims()[1];
return (channels >= 2);
}
/**
* @brief Return height and width from provided InferenceEngine tensor description
*/
inline std::pair<size_t, size_t> getTensorHeightWidth(const InferenceEngine::TensorDesc& desc) {
const auto& layout = desc.getLayout();
const auto& dims = desc.getDims();
const auto& size = dims.size();
if ((size >= 2) &&
(layout == InferenceEngine::Layout::NCHW ||
layout == InferenceEngine::Layout::NHWC ||
layout == InferenceEngine::Layout::NCDHW ||
layout == InferenceEngine::Layout::NDHWC ||
layout == InferenceEngine::Layout::OIHW ||
layout == InferenceEngine::Layout::GOIHW ||
layout == InferenceEngine::Layout::OIDHW ||
layout == InferenceEngine::Layout::GOIDHW ||
layout == InferenceEngine::Layout::CHW ||
layout == InferenceEngine::Layout::HW)) {
// Regardless of layout, dimensions are stored in fixed order
return std::make_pair(dims.back(), dims.at(size - 2));
} else {
THROW_IE_EXCEPTION << "Tensor does not have height and width dimensions";
}
}
/**
* @brief Fill InferenceEngine blob with random values
*/
template<typename T>
void fillBlobRandom(Blob::Ptr& inputBlob) {
MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
// locked memory holder should be alive all time while access to its buffer happens
auto minputHolder = minput->wmap();
auto inputBlobData = minputHolder.as<T *>();
for (size_t i = 0; i < inputBlob->size(); i++) {
auto rand_max = RAND_MAX;
inputBlobData[i] = (T) rand() / static_cast<T>(rand_max) * 10;
}
}
/**
* @brief Fill InferenceEngine blob with image information
*/
template<typename T>
void fillBlobImInfo(Blob::Ptr& inputBlob,
const size_t& batchSize,
std::pair<size_t, size_t> image_size) {
MemoryBlob::Ptr minput = as<MemoryBlob>(inputBlob);
// locked memory holder should be alive all time while access to its buffer happens
auto minputHolder = minput->wmap();
auto inputBlobData = minputHolder.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;
}
}
}
/**
* @brief Fill InferRequest blobs with random values or image information
*/
void fillBlobs(InferenceEngine::InferRequest inferRequest,
const InferenceEngine::ConstInputsDataMap& inputsInfo,
const size_t& batchSize) {
std::vector<std::pair<size_t, size_t>> input_image_sizes;
for (const ConstInputsDataMap::value_type& item : inputsInfo) {
if (isImage(item.second))
input_image_sizes.push_back(getTensorHeightWidth(item.second->getTensorDesc()));
}
for (const ConstInputsDataMap::value_type& item : inputsInfo) {
Blob::Ptr inputBlob = inferRequest.GetBlob(item.first);
if (isImageInfo(inputBlob) && (input_image_sizes.size() == 1)) {
// Fill image information
auto image_size = input_image_sizes.at(0);
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
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;
}
}
}

12
tests/time_tests/src/timetests/timetest_infer.cpp
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@ -5,10 +5,12 @@
#include <inference_engine.hpp>
#include <iostream>
#include "common.h"
#include "timetests_helper/timer.h"
#include "timetests_helper/utils.h"
using namespace InferenceEngine;
/**
* @brief Function that contain executable pipeline which will be called from
* main(). The function should not throw any exceptions and responsible for
@ -17,6 +19,7 @@ using namespace InferenceEngine;
int runPipeline(const std::string &model, const std::string &device) {
auto pipeline = [](const std::string &model, const std::string &device) {
Core ie;
CNNNetwork cnnNetwork;
ExecutableNetwork exeNetwork;
InferRequest inferRequest;
@ -33,7 +36,6 @@ int runPipeline(const std::string &model, const std::string &device) {
exeNetwork = ie.ImportNetwork(model, device);
}
else {
CNNNetwork cnnNetwork;
{
SCOPED_TIMER(read_network);
cnnNetwork = ie.ReadNetwork(model);
@ -50,6 +52,14 @@ int runPipeline(const std::string &model, const std::string &device) {
{
SCOPED_TIMER(first_inference);
inferRequest = exeNetwork.CreateInferRequest();
{
SCOPED_TIMER(fill_inputs)
auto batchSize = cnnNetwork.getBatchSize();
batchSize = batchSize != 0 ? batchSize : 1;
const InferenceEngine::ConstInputsDataMap inputsInfo(exeNetwork.GetInputsInfo());
fillBlobs(inferRequest, inputsInfo, batchSize);
}
inferRequest.Infer();
}
};