inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp

   1 // Copyright (C) 2018-2019 Intel Corporation
   2 // SPDX-License-Identifier: Apache-2.0
   3 //
   4
   5 #include "mkldnn_lrn_node.h"
   6 #include "desc_iterator.hpp"
   7 #include <ie_layers.h>
   8 #include <string>
   9 #include <mkldnn_extension_utils.h>
  10
  11 using namespace mkldnn;
  12 using namespace MKLDNNPlugin;
  13 using namespace InferenceEngine;
  14
  15 MKLDNNLrnNode::MKLDNNLrnNode(const InferenceEngine::CNNLayerPtr& layer, const mkldnn::engine& eng) : MKLDNNNode(layer, eng) {}
  16
  17 void MKLDNNLrnNode::getSupportedDescriptors() {
  18     if (!descs.empty())
  19         return;
  20     InferenceEngine::Precision precision = getCnnLayer()->insData[0].lock()->getPrecision();
  21     if (precision != InferenceEngine::Precision::FP32)
  22         precision = InferenceEngine::Precision::FP32;
  23     auto inputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(precision);
  24     auto * lrnLayer = dynamic_cast<NormLayer*>(getCnnLayer().get());
  25
  26     if (lrnLayer == nullptr)
  27         THROW_IE_EXCEPTION << "Cannot convert lrn layer.";
  28
  29     if (getParentEdges().size() != 1)
  30         THROW_IE_EXCEPTION << "Incorrect number of input edges for layer " << getName();
  31     if (getChildEdges().empty())
  32         THROW_IE_EXCEPTION << "Incorrect number of output edges for layer " << getName();
  33
  34     isAcrossMaps = lrnLayer->_isAcrossMaps;
  35     alpha = lrnLayer->_alpha;
  36     beta = lrnLayer->_beta;
  37     size = lrnLayer->_size;
  38     k = lrnLayer->_k;
  39
  40     auto parentDims = getParentEdgeAt(0)->getDims();
  41
  42     for (auto format : getAvailableFormatsForDims(parentDims)) {
  43         MKLDNNMemoryDesc in_candidate(parentDims, inputDataType, format);
  44         createDescriptor({in_candidate}, {});
  45     }
  46 }
  47
  48 void MKLDNNLrnNode::createPrimitive() {
  49     if (prim)
  50         return;
  51
  52     auto prim_desc = createPrimitiveDescriptor<lrn_forward::primitive_desc, lrn_forward::desc>();
  53
  54     prim.reset(new lrn_forward(prim_desc, getParentEdgeAt(0)->getMemory().GetPrimitive(),
  55                                getChildEdgeAt(0)->getMemory().GetPrimitive()));
  56 }
  57
  58 bool MKLDNNLrnNode::created() const {
  59     return getType() == Lrn;
  60 }
  61
  62 void MKLDNNLrnNode::initOptimalPrimitiveDescriptor() {
  63     auto config = getSelectedPrimitiveDescriptor()->getConfig();
  64     if (isInitConfig(config))
  65         return;
  66
  67     if (config.inConfs.size() != 1 || config.outConfs.size() != 1 ||
  68             (!isUninitTensorDesc(config.inConfs[0].desc) &&
  69                     !isUninitTensorDesc(config.outConfs[0].desc) && config.inConfs[0].desc != config.outConfs[0].desc))
  70         THROW_IE_EXCEPTION << "Layer " << getName() << " has incorrect selected config!";
  71
  72     if (!isUninitTensorDesc(config.inConfs[0].desc)) {
  73         config.outConfs[0].desc = config.inConfs[0].desc;
  74     } else if (!isUninitTensorDesc(config.outConfs[0].desc)) {
  75         config.inConfs[0].desc = config.outConfs[0].desc;
  76     } else {
  77         config.outConfs[0].desc = config.inConfs[0].desc = getConfiguredInputDesc(config, 0);
  78     }
  79
  80     initDescriptor(config);
  81 }
  82
  83 void MKLDNNLrnNode::createDescriptor(const std::vector<InferenceEngine::TensorDesc> &inputDesc,
  84                                      const std::vector<InferenceEngine::TensorDesc> &outputDesc) {
  85     algorithm alg = (isAcrossMaps) ? lrn_across_channels : lrn_within_channel;
  86     MKLDNNMemoryDesc in_candidate(inputDesc[0]);
  87     MKLDNNDescriptor desc(std::shared_ptr<lrn_forward::desc>(
  88             new lrn_forward::desc(prop_kind::forward_scoring, alg, in_candidate, size, alpha, beta, k)));
  89     descs.push_back(desc);
  90 }