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// Copyright (C) 2018 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
//
#include "mkldnn_lrn_node.h"
#include "desc_iterator.hpp"
#include <ie_layers.h>
#include <string>
#include <mkldnn_extension_utils.h>
using namespace mkldnn;
using namespace MKLDNNPlugin;
using namespace InferenceEngine;
MKLDNNLrnNode::MKLDNNLrnNode(const InferenceEngine::CNNLayerPtr& layer, const mkldnn::engine& eng) : MKLDNNNode(layer, eng) {}
void MKLDNNLrnNode::getSupportedDescriptors() {
if (!descs.empty())
return;
InferenceEngine::Precision precision = getCnnLayer()->insData[0].lock()->getPrecision();
if (precision != InferenceEngine::Precision::FP32)
precision = InferenceEngine::Precision::FP32;
auto inputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(precision);
auto * lrnLayer = dynamic_cast<NormLayer*>(getCnnLayer().get());
if (lrnLayer == nullptr)
THROW_IE_EXCEPTION << "Cannot convert lrn layer.";
if (getParentEdges().size() != 1)
THROW_IE_EXCEPTION << "Incorrect number of input edges.";
if (getChildEdges().empty())
THROW_IE_EXCEPTION << "Incorrect number of output edges.";
isAcrossMaps = lrnLayer->_isAcrossMaps;
alpha = lrnLayer->_alpha;
beta = lrnLayer->_beta;
size = lrnLayer->_size;
k = lrnLayer->_k;
auto parentDims = getParentEdgeAt(0)->getDims();
for (auto format : getAvailableFormatsForDims(parentDims)) {
MKLDNNMemoryDesc in_candidate(parentDims, inputDataType, format);
createDescriptor({in_candidate}, {});
}
}
void MKLDNNLrnNode::createPrimitive() {
if (prim)
return;
auto prim_desc = createPrimitiveDescriptor<lrn_forward::primitive_desc, lrn_forward::desc>();
prim.reset(new lrn_forward(prim_desc, getParentEdgeAt(0)->getMemory().GetPrimitive(),
getChildEdgeAt(0)->getMemory().GetPrimitive()));
}
bool MKLDNNLrnNode::created() const {
return getType() == Lrn;
}
void MKLDNNLrnNode::initOptimalPrimitiveDescriptor() {
auto config = getSelectedPrimitiveDescriptor()->getConfig();
if (isInitConfig(config))
return;
if (config.inConfs.size() != 1 || config.outConfs.size() != 1 ||
(!isUninitTensorDesc(config.inConfs[0].desc) &&
!isUninitTensorDesc(config.outConfs[0].desc) && config.inConfs[0].desc != config.outConfs[0].desc))
THROW_IE_EXCEPTION << "Layer " << getName() << " has incorrect selected config!";
if (!isUninitTensorDesc(config.inConfs[0].desc)) {
config.outConfs[0].desc = config.inConfs[0].desc;
} else if (!isUninitTensorDesc(config.outConfs[0].desc)) {
config.inConfs[0].desc = config.outConfs[0].desc;
} else {
config.outConfs[0].desc = config.inConfs[0].desc = getConfiguredInputDesc(config, 0);
}
initDescriptor(config);
}
void MKLDNNLrnNode::createDescriptor(const std::vector<InferenceEngine::TensorDesc> &inputDesc,
const std::vector<InferenceEngine::TensorDesc> &outputDesc) {
algorithm alg = (isAcrossMaps) ? lrn_across_channels : lrn_within_channel;
MKLDNNMemoryDesc in_candidate(inputDesc[0]);
MKLDNNDescriptor desc(std::shared_ptr<lrn_forward::desc>(
new lrn_forward::desc(prop_kind::forward_scoring, alg, in_candidate, size, alpha, beta, k)));
descs.push_back(desc);
}
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