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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "../InferenceTestImage.hpp"
#include <armnn/TypesUtils.hpp>
#include <armnnUtils/TContainer.hpp>
#include <armnnUtils/Permute.hpp>
#include <algorithm>
#include <fstream>
#include <iterator>
#include <string>
// Parameters used in normalizing images
struct NormalizationParameters
{
float scale{ 1.0 };
std::array<float, 3> mean{ { 0.0, 0.0, 0.0 } };
std::array<float, 3> stddev{ { 1.0, 1.0, 1.0 } };
};
enum class SupportedFrontend
{
TFLite = 0,
};
/** Get normalization parameters.
* Note that different flavours of models and different model data types have different normalization methods.
* This tool currently only supports TF and TFLite models
*
* @param[in] modelFormat One of the supported frontends
* @param[in] outputType Output type of the image tensor, also the type of the intended model
*/
NormalizationParameters GetNormalizationParameters(const SupportedFrontend& modelFormat,
const armnn::DataType& outputType)
{
NormalizationParameters normParams;
// Explicitly set default parameters
normParams.scale = 1.0;
normParams.mean = { 0.0, 0.0, 0.0 };
normParams.stddev = { 1.0, 1.0, 1.0 };
switch (modelFormat)
{
case SupportedFrontend::TFLite:
default:
switch (outputType)
{
case armnn::DataType::Float32:
normParams.scale = 127.5;
normParams.mean = { 1.0, 1.0, 1.0 };
break;
case armnn::DataType::Signed32:
normParams.mean = { 128.0, 128.0, 128.0 };
break;
case armnn::DataType::QAsymmU8:
break;
case armnn::DataType::QAsymmS8:
normParams.mean = { 128.0, 128.0, 128.0 };
break;
default:
break;
}
break;
}
return normParams;
}
/** Prepare raw image tensor data by loading the image from imagePath and preprocessing it.
*
* @param[in] imagePath Path to the image file
* @param[in] newWidth The new width of the output image tensor
* @param[in] newHeight The new height of the output image tensor
* @param[in] normParams Normalization parameters for the normalization of the image
* @param[in] batchSize Batch size
* @param[in] outputLayout Data layout of the output image tensor
*/
template <typename ElemType>
std::vector<ElemType> PrepareImageTensor(const std::string& imagePath,
unsigned int newWidth,
unsigned int newHeight,
const NormalizationParameters& normParams,
unsigned int batchSize = 1,
const armnn::DataLayout& outputLayout = armnn::DataLayout::NHWC);
// Prepare float32 image tensor
template <>
std::vector<float> PrepareImageTensor<float>(const std::string& imagePath,
unsigned int newWidth,
unsigned int newHeight,
const NormalizationParameters& normParams,
unsigned int batchSize,
const armnn::DataLayout& outputLayout)
{
// Generate image tensor
std::vector<float> imageData;
InferenceTestImage testImage(imagePath.c_str());
if (newWidth == 0)
{
newWidth = testImage.GetWidth();
}
if (newHeight == 0)
{
newHeight = testImage.GetHeight();
}
// Resize the image to new width and height or keep at original dimensions if the new width and height are specified
// as 0 Centre/Normalise the image.
imageData = testImage.Resize(newWidth, newHeight, CHECK_LOCATION(),
InferenceTestImage::ResizingMethods::BilinearAndNormalized, normParams.mean,
normParams.stddev, normParams.scale);
if (outputLayout == armnn::DataLayout::NCHW)
{
// Convert to NCHW format
const armnn::PermutationVector NHWCToArmNN = { 0, 2, 3, 1 };
armnn::TensorShape dstShape({ batchSize, 3, newHeight, newWidth });
std::vector<float> tempImage(imageData.size());
armnnUtils::Permute(dstShape, NHWCToArmNN, imageData.data(), tempImage.data(), sizeof(float));
imageData.swap(tempImage);
}
return imageData;
}
// Prepare int32 image tensor
template <>
std::vector<int> PrepareImageTensor<int>(const std::string& imagePath,
unsigned int newWidth,
unsigned int newHeight,
const NormalizationParameters& normParams,
unsigned int batchSize,
const armnn::DataLayout& outputLayout)
{
// Get float32 image tensor
std::vector<float> imageDataFloat =
PrepareImageTensor<float>(imagePath, newWidth, newHeight, normParams, batchSize, outputLayout);
// Convert to int32 image tensor with static cast
std::vector<int> imageDataInt;
imageDataInt.reserve(imageDataFloat.size());
std::transform(imageDataFloat.begin(), imageDataFloat.end(), std::back_inserter(imageDataInt),
[](float val) { return static_cast<int>(val); });
return imageDataInt;
}
// Prepare qasymmu8 image tensor
template <>
std::vector<uint8_t> PrepareImageTensor<uint8_t>(const std::string& imagePath,
unsigned int newWidth,
unsigned int newHeight,
const NormalizationParameters& normParams,
unsigned int batchSize,
const armnn::DataLayout& outputLayout)
{
// Get float32 image tensor
std::vector<float> imageDataFloat =
PrepareImageTensor<float>(imagePath, newWidth, newHeight, normParams, batchSize, outputLayout);
std::vector<uint8_t> imageDataQasymm8;
imageDataQasymm8.reserve(imageDataFloat.size());
// Convert to uint8 image tensor with static cast
std::transform(imageDataFloat.begin(), imageDataFloat.end(), std::back_inserter(imageDataQasymm8),
[](float val) { return static_cast<uint8_t>(val); });
return imageDataQasymm8;
}
// Prepare qasymms8 image tensor
template <>
std::vector<int8_t> PrepareImageTensor<int8_t>(const std::string& imagePath,
unsigned int newWidth,
unsigned int newHeight,
const NormalizationParameters& normParams,
unsigned int batchSize,
const armnn::DataLayout& outputLayout)
{
// Get float32 image tensor
std::vector<float> imageDataFloat =
PrepareImageTensor<float>(imagePath, newWidth, newHeight, normParams, batchSize, outputLayout);
std::vector<int8_t> imageDataQasymms8;
imageDataQasymms8.reserve(imageDataFloat.size());
// Convert to uint8 image tensor with static cast
std::transform(imageDataFloat.begin(), imageDataFloat.end(), std::back_inserter(imageDataQasymms8),
[](float val) { return static_cast<uint8_t>(val); });
return imageDataQasymms8;
}
/** Write image tensor to ofstream
*
* @param[in] imageData Image tensor data
* @param[in] imageTensorFile Output filestream (ofstream) to which the image tensor data is written
*/
template <typename ElemType>
void WriteImageTensorImpl(const std::vector<ElemType>& imageData, std::ofstream& imageTensorFile)
{
std::copy(imageData.begin(), imageData.end(), std::ostream_iterator<ElemType>(imageTensorFile, " "));
}
// For uint8_t image tensor, cast it to int before writing it to prevent writing data as characters instead of
// numerical values
template <>
void WriteImageTensorImpl<uint8_t>(const std::vector<uint8_t>& imageData, std::ofstream& imageTensorFile)
{
std::copy(imageData.begin(), imageData.end(), std::ostream_iterator<int>(imageTensorFile, " "));
}
// For int8_t image tensor, cast it to int before writing it to prevent writing data as characters instead of
// numerical values
template <>
void WriteImageTensorImpl<int8_t>(const std::vector<int8_t>& imageData, std::ofstream& imageTensorFile)
{
std::copy(imageData.begin(), imageData.end(), std::ostream_iterator<int>(imageTensorFile, " "));
}
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