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// Copyright (C) 2018 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include "inference_engine.hpp"
#include "mkldnn_dims.h"
#include <vector>
#include <limits>
namespace MKLDNNPlugin {
class MeanImage {
public:
MeanImage();
public:
void Load(const MKLDNNDims& inputDims, InferenceEngine::InputInfo::Ptr inputInfo);
void Subtract(const MKLDNNDims &inputDims, float *input);
template<typename T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
void Subtract(const MKLDNNDims &inputDims, T *input) {
IE_ASSERT(input != nullptr);
if (inputDims.ndims() != 4) {
THROW_IE_EXCEPTION << "Expecting input as 4 dimension blob with format NxCxHxW.";
}
int MB = inputDims[0];
int srcSize = inputDims.size() / MB;
if (meanBuffer && meanBuffer->size()) {
const float * meanBufferValues = meanBuffer->readOnly();
# pragma omp parallel for collapse(2) schedule(static)
for (int mb = 0; mb < MB; mb++) {
for (int i = 0; i < srcSize; i++) {
int buf = input[srcSize * mb + i];
buf -= meanBufferValues[i];
if (buf < std::numeric_limits<T>::min()) buf = std::numeric_limits<T>::min();
if (buf > std::numeric_limits<T>::max()) buf = std::numeric_limits<T>::max();
input[srcSize * mb + i] = buf;
}
}
} else if (!meanValues.empty()) {
int C = inputDims[1];
srcSize /= inputDims[1];
# pragma omp parallel for collapse(3) schedule(static)
for (int mb = 0; mb < MB; mb++) {
for (int c = 0; c < C; c++) {
for (int i = 0; i < srcSize; i++) {
int buf = input[srcSize * mb * C + c * srcSize + i];
buf -= meanValues[c];
if (buf < std::numeric_limits<T>::min()) buf = std::numeric_limits<T>::min();
if (buf > std::numeric_limits<T>::max()) buf = std::numeric_limits<T>::max();
input[srcSize * mb * C + c * srcSize + i] = buf;
}
}
}
}
}
private:
std::vector<float> meanValues;
InferenceEngine::TBlob<float>::Ptr meanBuffer;
};
} // namespace MKLDNNPlugin
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