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Fixed compilation with clang-10 + xcode (#521)

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This commit is contained in:
Ilya Lavrenov 2020-05-26 17:17:36 +03:00 committed by GitHub
parent 4943a954c7
commit bb039adef8
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GPG Key ID: 4AEE18F83AFDEB23
43 changed files with 80 additions and 147 deletions
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9
CMakeLists.txt
View File

@ -9,8 +9,13 @@ cmake_policy(SET CMP0054 NEW)
# See https://blog.kitware.com/cmake-3-13-0-available-for-download/
if (APPLE)
# due to https://cmake.org/cmake/help/v3.12/policy/CMP0068.html
cmake_minimum_required(VERSION 3.9 FATAL_ERROR)
if(CMAKE_GENERATOR STREQUAL "Xcode")
# due to https://gitlab.kitware.com/cmake/cmake/issues/14254
cmake_minimum_required(VERSION 3.12.0 FATAL_ERROR)
else()
# due to https://cmake.org/cmake/help/v3.12/policy/CMP0068.html
cmake_minimum_required(VERSION 3.9 FATAL_ERROR)
endif()
else()
cmake_minimum_required(VERSION 3.7.2 FATAL_ERROR)
endif()

12
cmake/developer_package.cmake
View File

@ -172,8 +172,8 @@ endif()
set(CMAKE_DEBUG_POSTFIX ${IE_DEBUG_POSTFIX})
set(CMAKE_RELEASE_POSTFIX ${IE_RELEASE_POSTFIX})
if (WIN32)
# Support CMake multiconfiguration for Visual Studio build
if (WIN32 OR CMAKE_GENERATOR STREQUAL "Xcode")
# Support CMake multiconfiguration for Visual Studio or Xcode build
set(IE_BUILD_POSTFIX $<$<CONFIG:Debug>:${IE_DEBUG_POSTFIX}>$<$<CONFIG:Release>:${IE_RELEASE_POSTFIX}>)
else ()
if (${CMAKE_BUILD_TYPE} STREQUAL "Debug" )
@ -187,10 +187,6 @@ message(STATUS "CMAKE_BUILD_TYPE: ${CMAKE_BUILD_TYPE}")
add_definitions(-DIE_BUILD_POSTFIX=\"${IE_BUILD_POSTFIX}\")
if(NOT UNIX)
if (WIN32)
# set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /MT")
# set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /MTd")
endif()
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${OUTPUT_ROOT}/${BIN_FOLDER})
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${OUTPUT_ROOT}/${BIN_FOLDER})
set(CMAKE_COMPILE_PDB_OUTPUT_DIRECTORY ${OUTPUT_ROOT}/${BIN_FOLDER})
@ -205,6 +201,10 @@ else()
endif()
if(APPLE)
# WA for Xcode generator + object libraries issue:
# https://gitlab.kitware.com/cmake/cmake/issues/20260
# http://cmake.3232098.n2.nabble.com/XCODE-DEPEND-HELPER-make-Deletes-Targets-Before-and-While-They-re-Built-td7598277.html
set(CMAKE_XCODE_GENERATE_TOP_LEVEL_PROJECT_ONLY ON)
set(CMAKE_MACOSX_RPATH ON)
endif()

2
inference-engine/src/inference_engine/generic_ie.cpp
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@ -65,7 +65,7 @@ ngraph::op::GenericIE::GenericIE(const ngraph::NodeVector& inputs,
ngraph::op::GenericIE::GenericIE(const ngraph::OutputVector& inputs,
const std::map<std::string, InferenceEngine::Parameter>& params,
const std::string type, const std::vector<PortIE>& outputs)
: Op(inputs), params(params), type(type), outputs(outputs), initialized(0) {
: Op(inputs), params(params), outputs(outputs), type(type), initialized(0) {
constructor_validate_and_infer_types();
}

6
inference-engine/src/inference_engine/ie_layouts.cpp
View File

@ -10,17 +10,17 @@
using namespace InferenceEngine;
TensorDesc::TensorDesc(const Precision& precision, SizeVector dims, Layout layout)
: blockingDesc(dims, layout), precision(precision) {
: precision(precision), blockingDesc(dims, layout) {
this->dims = dims;
this->layout = layout;
}
TensorDesc::TensorDesc(const Precision& precision, Layout layout): blockingDesc(), precision(precision) {
TensorDesc::TensorDesc(const Precision& precision, Layout layout): precision(precision), blockingDesc() {
this->layout = layout;
}
TensorDesc::TensorDesc(const Precision& precision, SizeVector dims, const BlockingDesc& blockDesc)
: dims(dims), blockingDesc(blockDesc), precision(precision) {
: dims(dims), precision(precision), blockingDesc(blockDesc) {
if (dims.size() == 0 || blockingDesc.getBlockDims().size() == 0) {
layout = Layout::SCALAR;
return;

9
inference-engine/src/legacy_api/CMakeLists.txt
View File

@ -38,7 +38,14 @@ target_include_directories(${TARGET_NAME}_obj PRIVATE ${PUBLIC_HEADERS_DIR} ${CM
# Create shared library
file(TOUCH ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp)
add_library(${TARGET_NAME} SHARED
# according to https://cmake.org/cmake/help/latest/command/add_library.html#id4
# Some native build systems (such as Xcode) may not like targets that have only
# object files, so consider adding at least one real source file to any target that
# references $<TARGET_OBJECTS:objlib>.
${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp
$<TARGET_OBJECTS:${TARGET_NAME}_obj>
$<TARGET_OBJECTS:inference_engine_common_obj>)
@ -46,7 +53,7 @@ set_ie_threading_interface_for(${TARGET_NAME})
target_link_libraries(${TARGET_NAME} PRIVATE ${NGRAPH_LIBRARIES} inference_engine_transformations pugixml)
add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME} EXCLUDE_PATTERNS ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp)
# export targets

1
inference-engine/src/legacy_api/src/cnn_network_int8_normalizer.cpp
View File

@ -935,7 +935,6 @@ void CNNNetworkInt8Normalizer::QuantizeConvolutionOrFullyConnected(CNNLayer::Ptr
if (weights) {
const float* weight = static_cast<const float*>(weights->buffer());
WeightableLayer* pConv = dynamic_cast<WeightableLayer*>(target_layer.get());
ConvolutionLayer* pConv1 = dynamic_cast<ConvolutionLayer*>(target_layer.get());
if (pConv1 != nullptr && pConv1->_group == 0) {

6
inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp
View File

@ -447,12 +447,6 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
}
CNNLayerPtr InferenceEngine::details::CNNLayerCreator::create() {
auto one_from = [](const std::string& desc, const std::vector<std::string>& descs) -> bool {
for (const auto& d : descs) {
if (details::CaselessEq<std::string>()(d, desc)) return true;
}
return false;
};
LayerParams attrs = {node->get_friendly_name(), node->description(),
details::convertPrecision(node->get_output_element_type(0))};
if (creators.find(node->description()) != creators.end())

4
inference-engine/src/legacy_api/src/graph_transformer.cpp
View File

@ -67,7 +67,7 @@ static std::vector<DataPtr> get_outputs(details::CNNNetworkImpl* _network) {
}
ConstTransformer::ConstTransformer(details::CNNNetworkImpl* _network)
: inputs(get_inputs(_network)), outputs(get_outputs(_network)), network(_network) {
: network(_network), inputs(get_inputs(_network)), outputs(get_outputs(_network)) {
if (!_network)
THROW_IE_EXCEPTION << "[ERROR]: Failed to init ConstTransformer with null pointer of network";
}
@ -86,7 +86,7 @@ ConstTransformer::ConstTransformer(ICNNNetwork* _network) {
}
ConstTransformer::ConstTransformer(std::vector<DataPtr> &_inputs, std::vector<DataPtr> &_outputs)
: inputs(_inputs), outputs(_outputs), network(nullptr) {
: network(nullptr), inputs(_inputs), outputs(_outputs) {
if (inputs.empty() || outputs.empty())
THROW_IE_EXCEPTION << "[ERROR]: Failed to init ConstTransformer with empty list of inputs or outputs";
}

2
inference-engine/src/legacy_api/src/ie_cnn_layer_builder_ngraph.h
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@ -28,6 +28,7 @@ class INodeConverter {
public:
virtual CNNLayer::Ptr createLayer(const std::shared_ptr<ngraph::Node>& layer) const = 0;
virtual bool canCreate(const std::shared_ptr<ngraph::Node>& node) const = 0;
virtual ~INodeConverter() = default;
};
template <class T>
@ -55,6 +56,7 @@ template <class NGT>
class NodeConverter : public INodeConverter {
public:
NodeConverter() = default;
~NodeConverter() override = default;
CNNLayer::Ptr createLayer(const std::shared_ptr<ngraph::Node>& layer) const override;

2
inference-engine/src/legacy_api/src/ie_deprecated.cpp
View File

@ -46,7 +46,7 @@ void CNNNetwork::AddExtension(InferenceEngine::IShapeInferExtensionPtr extension
}
CNNLayer::CNNLayer(const LayerParams& prms)
: name(prms.name), type(prms.type), precision(prms.precision), userValue({0}), node(nullptr) {}
: node(nullptr), name(prms.name), type(prms.type), precision(prms.precision), userValue({0}) {}
CNNLayer::CNNLayer(const CNNLayer& other)
: node(other.node), name(other.name), type(other.type), precision(other.precision),

4
inference-engine/src/legacy_api/src/ie_layer_validators.cpp
View File

@ -19,6 +19,10 @@
#include "ie_layers.h"
#include "xml_parse_utils.h"
#ifdef __clang__
#pragma clang diagnostic ignored "-Wunused-variable"
#endif
namespace InferenceEngine {
using namespace details;

2
inference-engine/src/legacy_api/src/ie_layers_internal.cpp
View File

@ -124,7 +124,7 @@ Paddings getPaddingsImpl(const CNNLayer& layer) {
int getNumIteration(const TensorIterator& tensorIterator) {
using PortMap = TensorIterator::PortMap;
const auto isIterable = [](const PortMap& rule) { return rule.axis != -1; };
const auto getNumIterations = [&tensorIterator](const PortMap& rule, const DataPtr& iterableData) -> int {
const auto getNumIterations = [](const PortMap& rule, const DataPtr& iterableData) -> int {
if (iterableData == nullptr) {
THROW_IE_EXCEPTION << ": Iteration over an invalid data object (null pointer dereference)";
}

27
inference-engine/src/legacy_api/src/net_pass.cpp
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@ -304,19 +304,6 @@ void RemoveLayer(CNNLayerPtr& layer) {
/**** Converter Passes ************************************/
/************************************************************/
static RNNSequenceLayer::CellType cell_type_from_name(std::string& layer_type) {
RNNSequenceLayer::CellType res;
if (layer_type == "LSTMCell")
res = RNNSequenceLayer::LSTM;
else if (layer_type == "GRUCell")
res = RNNSequenceLayer::GRU;
else if (layer_type == "RNNCell")
res = RNNSequenceLayer::RNN;
else
THROW_IE_EXCEPTION << "Unknown Cell type (" << layer_type << "). Expected LSTMCell|GRUCell|RNNCell";
return res;
}
static std::string cell_name(RNNSequenceLayer::CellType type) {
std::string res;
switch (type) {
@ -777,20 +764,6 @@ static void _link_with_clip(CNNLayerPtr src, CNNLayerPtr dst, const float clip_v
_link(clip, dst, 0, dst_port);
}
}
static Blob::Ptr make_partial_copy(Blob::Ptr src, size_t off, size_t size) {
auto res = make_plain_blob(src->getTensorDesc().getPrecision(), {size});
res->allocate();
size_t elem_size = src->getTensorDesc().getPrecision().size();
auto src_ptr = src->buffer().as<uint8_t*>();
auto dst_ptr = res->buffer().as<uint8_t*>();
ie_memcpy(dst_ptr, res->byteSize(), src_ptr + off * elem_size, size * elem_size);
return res;
}
static Blob::Ptr wrap_as_tensor(Blob::Ptr src, SizeVector dims) {
auto res = make_blob_with_precision(
TensorDesc {src->getTensorDesc().getPrecision(), dims, TensorDesc::getLayoutByDims(dims)}, src->buffer());

1
inference-engine/src/legacy_api/src/shape_infer/built-in/ie_unsqueeze_shape_infer.hpp
View File

@ -43,7 +43,6 @@ public:
SizeVector outShape;
if (idx_dims.size() > 1) THROW_IE_EXCEPTION << " Index vector should be 1 dimension";
size_t max = data_dims.size();
switch (inBlobs[UNSQUEEZE_INDEXES]->getTensorDesc().getPrecision()) {
case Precision::FP32: {
procIndices<float>(inBlobs, UNSQUEEZE_INDEXES, data_dims, outShape, idx_dims);

1
inference-engine/src/legacy_api/src/shape_infer/const_infer/ie_range_const_infer.hpp
View File

@ -78,7 +78,6 @@ public:
<< "and only FP32 and I32 are supported!";
}
StatusCode retcode = OK;
switch (outData[0]->getTensorDesc().getPrecision()) {
case Precision::FP32: {
range((inData[RANGE_START]->cbuffer().as<float*>() +

4
inference-engine/src/legacy_api/src/shape_infer/const_infer/ie_strided_slice_const_infer.hpp
View File

@ -316,7 +316,7 @@ private:
size_t work_amount_dst = dstStrides[0] * dst_dims[0] / dst_dims[dims_size_1];
parallel_nt(0, [&](const int ithr, const int nthr) {
size_t i, start = 0, end = 0;
size_t start = 0, end = 0;
SizeVector counters(dims_size_1, 0);
splitter(work_amount_dst, nthr, ithr, start, end);
int src_idx = begin_dms[dims_size_1];
@ -352,7 +352,7 @@ private:
size_t work_amount_dst = dstStrides[0] * dst_dims[0];
parallel_nt(0, [&](const int ithr, const int nthr) {
size_t i, start = 0, end = 0;
size_t start = 0, end = 0;
SizeVector counters(dims_size, 0);
splitter(work_amount_dst, nthr, ithr, start, end);
int src_idx = 0;

2
inference-engine/src/mkldnn_plugin/mean_image.cpp
View File

@ -24,8 +24,6 @@ void MeanImage::Load(const MKLDNNDims& inputDims, InputInfo::Ptr inputInfo) {
THROW_IE_EXCEPTION << "channels mismatch between mean and input";
}
ResponseDesc resp;
switch (pp.getMeanVariant()) {
case MEAN_VALUE: {
// mean image common value per channel (1x1xC)

1
inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.cpp
View File

@ -52,7 +52,6 @@ std::shared_ptr<ICNNNetwork> dump_graph_as_ie_net(const MKLDNNGraph &graph) {
for (int i = 0; i < ch_edges.size(); i++) {
auto edge = node->getChildEdgeAt(i);
int out_port = edge->getInputNum();
int in_port = edge->getOutputNum();
auto ch_node = edge->getChild();
auto ch = node2layer[ch_node];

1
inference-engine/src/mkldnn_plugin/mkldnn_memory.cpp
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@ -53,7 +53,6 @@ void MKLDNNMemory::Create(memory::dims dims, memory::data_type data_type, memory
void MKLDNNMemory::Create(const mkldnn::memory::desc& desc, const void *data, bool pads_zeroing) {
auto primitive_desc = memory::primitive_desc(desc, eng);
uint8_t itemSize = MKLDNNExtensionUtils::sizeOfDataType(mkldnn::memory::data_type(desc.data.data_type));
if (data == nullptr) {
prim.reset(new memory(primitive_desc));

8
inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
View File

@ -139,9 +139,9 @@ void MKLDNNNode::AddNode(const std::string& name, CreatorByLayerFunction factory
MKLDNNNode::MKLDNNNode(const InferenceEngine::CNNLayerPtr& layer, const mkldnn::engine& eng,
MKLDNNWeightsSharing::Ptr &w_cache)
: cnnLayer(layer), name(layer->name), typeStr(layer->type), type(TypeFromName(layer->type)), engine(eng),
selectedPrimitiveDescriptorIndex(-1), permanent(false), temporary(false), constant(ConstantType::Unknown),
profilingTask(name), weightCache(w_cache) {
: selectedPrimitiveDescriptorIndex(-1), permanent(false), temporary(false), constant(ConstantType::Unknown),
weightCache(w_cache), cnnLayer(layer), engine(eng), name(layer->name), typeStr(layer->type),
type(TypeFromName(layer->type)), profilingTask(name) {
if (!layer->outData.empty()) {
for (const auto& outData : layer->outData) {
outDims.emplace_back(outData->getDims());
@ -1128,4 +1128,4 @@ Layout MKLDNNNode::getWeightsLayoutByDims(SizeVector dims, bool isGrouped) {
void MKLDNNNode::appendPostOps(mkldnn::post_ops& ops) {
THROW_IE_EXCEPTION << "Fusing of " << this->getType() << " operation is not implemented";
}
}

7
inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
View File

@ -24,9 +24,9 @@ using namespace MKLDNNPlugin;
using namespace InferenceEngine;
MKLDNNConvolutionNode::MKLDNNConvolutionNode(const InferenceEngine::CNNLayerPtr& layer, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
: MKLDNNNode(layer, eng, cache), withBiases(false), withSum(false), dw_conv_iw(0), dw_conv_ih(0),
dw_conv_oc(0), dw_conv_in_dt(memory::data_type::data_undef), isDW(false), isMerged(false), withActivation(false),
isGrouped(false), baseInputsNumber(1), eltwisePrecision(Precision::FP32), withDWConv(false), groupNum(1lu) {
: MKLDNNNode(layer, eng, cache), withBiases(false), withSum(false), withDWConv(false), isDW(false), isMerged(false),
isGrouped(false), dw_conv_oc(0), dw_conv_ih(0), dw_conv_iw(0), dw_conv_in_dt(memory::data_type::data_undef),
groupNum(1lu), baseInputsNumber(1), eltwisePrecision(Precision::FP32) {
internalBlobDesc.emplace_back([&](primitive_desc_iterator &primitive_desc_it, size_t idx) -> MKLDNNMemoryDesc {
return MKLDNNMemoryDesc(primitive_desc_it.weights_primitive_desc(0).desc());
});
@ -736,7 +736,6 @@ void MKLDNNConvolutionNode::addZeroPoints(mkldnn::primitive_attr& attr) const {
}
void MKLDNNConvolutionNode::addScaleToPrimitiveAttr(mkldnn::primitive_attr attr) const {
bool scaled = false;
if (wScale != nullptr) {
float* wScaleData = static_cast<float*>(wScale->buffer());

1
inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.h
View File

@ -61,7 +61,6 @@ private:
void addZeroPoints(mkldnn::primitive_attr& attr) const;
bool withBiases;
bool withActivation;
bool withSum;
bool withDWConv;
bool isDW;

4
inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
View File

@ -177,10 +177,6 @@ void MKLDNNDeconvolutionNode::createDescriptor(const std::vector<InferenceEngine
const std::vector<InferenceEngine::TensorDesc> &outputDesc) {
MKLDNNMemoryDesc in_candidate(inputDesc[0]);
MKLDNNMemoryDesc out_candidate(outputDesc[0]);
auto in_fmt = in_candidate.getFormat();
auto out_fmt = out_candidate.getFormat();
int O_IND = withGroups ? 1 : 0;
int I_IND = withGroups ? 2 : 1;
// grouping and autoblicking is not compatible
if ((withGroups && !isDW) && (in_candidate.blocksExtended() || out_candidate.blocksExtended()))

1
inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
View File

@ -325,7 +325,6 @@ const std::vector<impl_desc_type>& MKLDNNFullyConnectedNode::getPrimitivesPriori
std::shared_ptr<mkldnn::primitive_attr> MKLDNNFullyConnectedNode::initPrimitiveAttr() {
auto attr = std::make_shared<mkldnn::primitive_attr>(mkldnn::primitive_attr());
bool scaled = false;
if (wScale != nullptr) {
float* wScaleData = static_cast<float*>(wScale->buffer());

1
inference-engine/src/mkldnn_plugin/nodes/mkldnn_mvn_node.cpp
View File

@ -737,7 +737,6 @@ void MKLDNNMVNNode::mvn_pln(const float* src_data, float* dst_data, const SizeVe
blk_size = 4;
}
size_t dims_size = dims.size();
size_t N = 0; size_t C = 0; size_t D = 0; size_t H = 0; size_t W = 0;
std::tie(N, C, D, H, W) = get5dShapes(dims);

1
inference-engine/src/mkldnn_plugin/nodes/mkldnn_reshape_node.cpp
View File

@ -36,7 +36,6 @@ void MKLDNNReshapeNode::initSupportedPrimitiveDescriptors() {
if (inputDataType != outputDataType)
inputDataType = outputDataType;
auto& inDims = getParentEdgeAt(0)->getDims();
auto& outDims = getChildEdgeAt(0)->getDims();
memory::format outFormat = MKLDNNMemory::GetPlainFormat(outDims);
InferenceEngine::LayerConfig config;

6
inference-engine/src/mkldnn_plugin/nodes/mkldnn_tensoriterator_node.cpp
View File

@ -62,7 +62,6 @@ public:
auto full_dims = full_blob->GetDims();
auto part_dims = part_blob->GetDims();
bool simple_copy = port_map.axis == -1;
if (port_map.axis == -1) {
// simple copy mode. No iteration through this tensor
reorders.emplace_back(from->GetPrimitive(), to->GetPrimitive());
@ -132,13 +131,8 @@ class BackEdgePortHelper : public PortMapHelper {
public:
BackEdgePortHelper(const MKLDNNMemoryPtr &from, const MKLDNNMemoryPtr &to, const mkldnn::engine& eng, int n_iter) {
auto mem_desc = from->GetDescriptor();
mem_holder.emplace_back(mkldnn::memory::primitive_desc(mem_desc, eng));
auto &temp_mem = mem_holder.back();
reorders.emplace_back(from->GetPrimitive(), to->GetPrimitive());
iter_count = n_iter;
}

9
inference-engine/src/mkldnn_plugin/nodes/roifeatureextractor_onnx.cpp
View File

@ -394,15 +394,6 @@ private:
int pooled_width_ = 0;
std::vector<int> pyramid_scales_;
int sampling_ratio_ = 0;
int channels = 0;
int height = 0;
int width = 0;
int nn = 0;
int nc = 0;
int nh = 0;
int nw = 0;
};
REG_FACTORY_FOR(ExperimentalDetectronROIFeatureExtractorImpl, ExperimentalDetectronROIFeatureExtractor);

2
inference-engine/src/plugin_api/ie_metric_helpers.hpp
View File

@ -52,7 +52,7 @@ struct MetricType;
#define IE_SET_METRIC_RETURN(name, ...) \
typename ::InferenceEngine::Metrics::MetricType<::InferenceEngine::Metrics::name>::type _##name##_value = \
__VA_ARGS__; \
return std::move(_##name##_value)
return _##name##_value
/**
* @def IE_SET_METRIC(name, ...)

10
inference-engine/src/preprocessing/CMakeLists.txt
View File

@ -152,7 +152,14 @@ set_ie_threading_interface_for(${TARGET_NAME}_obj)
# Create shared library file from object library
file(TOUCH ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp)
add_library(${TARGET_NAME} SHARED
# according to https://cmake.org/cmake/help/latest/command/add_library.html#id4
# Some native build systems (such as Xcode) may not like targets that have only
# object files, so consider adding at least one real source file to any target that
# references $<TARGET_OBJECTS:objlib>.
${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp
$<TARGET_OBJECTS:${TARGET_NAME}_obj>)
set_ie_threading_interface_for(${TARGET_NAME})
@ -165,6 +172,9 @@ if(WIN32)
set_target_properties(${TARGET_NAME} PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME})
endif()
add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}
EXCLUDE_PATTERNS ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp)
# Static library used for unit tests which are always built
add_library(${TARGET_NAME}_s STATIC

2
inference-engine/src/transformations/include/ngraph_ops/deconvolution_ie.hpp
View File

@ -48,8 +48,6 @@ public:
std::shared_ptr<Node> copy_with_new_args(const NodeVector& new_args) const override;
std::shared_ptr<Node> copy(const OutputVector & new_args) const;
/// \return The data batch shape.
const PartialShape get_output_shape() { return m_output_shape; }
void set_output_shape(const Shape& output_shape) { m_output_shape = output_shape; }

2
inference-engine/src/transformations/include/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp
View File

@ -22,7 +22,7 @@ class INFERENCE_ENGINE_API_CLASS(ConvertOneHotToOneHotIE);
class ngraph::pass::ConvertOneHotToOneHotIE: public ngraph::pass::GraphRewrite {
public:
ConvertOneHotToOneHotIE() : is_f16(false), GraphRewrite() {
ConvertOneHotToOneHotIE() : GraphRewrite(), is_f16(false) {
convert_one_hot();
}

6
inference-engine/src/transformations/include/transformations/utils/annotations.hpp
View File

@ -21,12 +21,12 @@ public:
explicit EltwiseAttrs(std::shared_ptr<EltwiseAttrs> & attrs):
m_has_constant_input(attrs->has_constant_input()),
m_consumers_count(attrs->get_consumers_count()),
m_const_input_id(attrs->get_const_input_id()) {}
m_const_input_id(attrs->get_const_input_id()),
m_consumers_count(attrs->get_consumers_count()) {}
EltwiseAttrs(size_t constant_input_id, size_t consumers_count):
m_const_input_id(constant_input_id),
m_has_constant_input(true),
m_const_input_id(constant_input_id),
m_consumers_count(consumers_count) {}
bool has_constant_input() {

37
inference-engine/src/transformations/src/ngraph_ops/deconvolution_ie.cpp
View File

@ -2,12 +2,12 @@
// SPDX-License-Identifier: Apache-2.0
//
#include "ngraph_ops/deconvolution_ie.hpp"
#include <algorithm>
#include <memory>
#include <vector>
#include "ngraph_ops/deconvolution_ie.hpp"
#include "ngraph/util.hpp"
#include "ngraph/validation_util.hpp"
@ -31,8 +31,8 @@ op::DeconvolutionIE::DeconvolutionIE(const Output<Node>& data,
, m_pads_begin(pads_begin)
, m_pads_end(pads_end)
, m_auto_pad(auto_pad)
, m_group(group)
, m_output_shape(output_shape) {
, m_output_shape(output_shape)
, m_group(group) {
constructor_validate_and_infer_types();
}
@ -52,8 +52,8 @@ op::DeconvolutionIE::DeconvolutionIE(const Output<Node>& data,
, m_pads_begin(pads_begin)
, m_pads_end(pads_end)
, m_auto_pad(auto_pad)
, m_group(group)
, m_output_shape(output_shape) {
, m_output_shape(output_shape)
, m_group(group) {
constructor_validate_and_infer_types();
}
@ -85,28 +85,3 @@ shared_ptr<Node> op::DeconvolutionIE::copy_with_new_args(const NodeVector& new_a
m_auto_pad);
}
}
shared_ptr<Node> op::DeconvolutionIE::copy(const OutputVector& new_args) const {
if (new_args.size() == 2) {
return make_shared<DeconvolutionIE>(new_args.at(0),
new_args.at(1),
m_strides,
m_pads_begin,
m_pads_end,
m_dilations,
m_output_shape,
m_group,
m_auto_pad);
} else {
return make_shared<DeconvolutionIE>(new_args.at(0),
new_args.at(1),
new_args.at(2),
m_strides,
m_pads_begin,
m_pads_end,
m_dilations,
m_output_shape,
m_group,
m_auto_pad);
}
}

3
inference-engine/src/transformations/src/ngraph_ops/fully_connected.cpp
View File

@ -17,8 +17,7 @@ using namespace ngraph;
constexpr NodeTypeInfo op::FullyConnected::type_info;
op::FullyConnected::FullyConnected(const Output<Node>& A, const Output<Node>& B, const Output<Node>& C, const Shape & output_shape)
: m_output_shape(output_shape),
Op({A, B, C}) {
: Op({A, B, C}), m_output_shape(output_shape) {
constructor_validate_and_infer_types();
}

2
inference-engine/src/transformations/src/ngraph_ops/nms_ie.cpp
View File

@ -20,7 +20,7 @@ op::NonMaxSuppressionIE::NonMaxSuppressionIE(const Output<Node> &boxes,
int center_point_box,
bool sort_result_descending)
: Op({boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold}),
m_output_shape{output_shape}, m_center_point_box{center_point_box}, m_sort_result_descending{sort_result_descending} {
m_center_point_box{center_point_box}, m_sort_result_descending{sort_result_descending}, m_output_shape{output_shape} {
constructor_validate_and_infer_types();
}

2
inference-engine/src/transformations/src/ngraph_ops/onehot_ie.cpp
View File

@ -12,7 +12,7 @@ using namespace ngraph;
constexpr NodeTypeInfo op::OneHotIE::type_info;
op::OneHotIE::OneHotIE(const Output<ngraph::Node>& input, int axis, int depth, float on_value, float off_value, element::Type type)
: Op({input}), m_axis(axis), m_depth(depth), m_on_value(on_value), m_off_value(off_value), m_type(type) {
: Op({input}), m_type(type), m_axis(axis), m_depth(depth), m_off_value(off_value), m_on_value(on_value) {
constructor_validate_and_infer_types();
}

2
inference-engine/src/transformations/src/ngraph_ops/power.cpp
View File

@ -15,7 +15,7 @@ using namespace ngraph;
constexpr NodeTypeInfo op::PowerIE::type_info;
op::PowerIE::PowerIE(const Output<ngraph::Node>& data_batch, const float power, const float scale, const float shift)
: Op({data_batch}), power(power), scale(scale), shift(shift) {
: Op({data_batch}), scale(scale), power(power), shift(shift) {
constructor_validate_and_infer_types();
}

4
inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.cpp
View File

@ -57,11 +57,11 @@ void ngraph::pass::ConvertLRNToLRNIE::convert_lrn() {
region);
lrn_ie->set_friendly_name(lrn->get_friendly_name());
ngraph:copy_runtime_info(lrn, lrn_ie);
ngraph::copy_runtime_info(lrn, lrn_ie);
ngraph::replace_node(lrn, lrn_ie);
return true;
};
auto m = std::make_shared<ngraph::pattern::Matcher>(lrn, "ConvertLRNToLRNIE");
this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE);
}
}

4
inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
View File

@ -23,7 +23,7 @@ void ngraph::pass::ConvertMatMulToFCorGemm::convert_matmul() {
auto input_1 = std::make_shared<pattern::op::Label>(element::f32, Shape {1, 1});
auto matmul = std::make_shared<ngraph::opset1::MatMul>(input_0, input_1);
ngraph::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) {
auto matmul = std::dynamic_pointer_cast<ngraph::opset1::MatMul>(m.get_match_root());
if (!matmul) {
return false;
@ -200,4 +200,4 @@ void ngraph::pass::ConvertMatMulToFCorGemm::convert_matmul() {
auto m = std::make_shared<ngraph::pattern::Matcher>(matmul, "ConvertMatMulToFCorGemm");
this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE);
}
}

2
inference-engine/tests/ie_test_utils/common_test_utils/common_layers_params.cpp
View File

@ -17,7 +17,6 @@ void getConvOutShape(const std::vector<size_t> &inShape,
outShape.resize(inShape.size(), 1lu);
outShape[0] = inShape[0];
outShape[1] = params.out_c;
size_t in_size = inShape.size();
for (int i = 0; i < params.kernel.size() && i + 2 < outShape.size(); i++) {
outShape[i + 2] =
(inShape[i + 2] + params.pads_begin[i] + params.pads_end[i] -
@ -58,7 +57,6 @@ void getPoolOutShape(const std::vector<size_t> &inShape,
outShape.resize(inShape.size(), 1lu);
outShape[0] = inShape[0];
outShape[1] = inShape[1];
size_t in_size = inShape.size();
for (int i = 0; i < params.kernel.size() && i + 2 < outShape.size(); i++) {
outShape[i + 2] =
(inShape[i + 2] + params.pads_begin[i] + params.pads_end[i] - params.kernel[i]) / params.stride[i] +

14
inference-engine/tests/ie_test_utils/common_test_utils/xml_net_builder/ir_net.hpp
View File

@ -75,9 +75,9 @@ protected:
private:
Port(std::weak_ptr<Layer> parent, size_t id, const std::vector<size_t> &shape,
std::map<std::string, std::string> common_attributes = {})
: m_parent(std::move(parent)),
m_id(id),
: m_id(id),
m_shape(shape),
m_parent(std::move(parent)),
m_common_attributes(std::move(common_attributes)) {}
};
@ -235,9 +235,9 @@ private:
std::weak_ptr<IRNet> m_parent;
Layer(std::weak_ptr<IRNet> parent, size_t id, std::map<std::string, std::string> common_attributes = {})
: m_parent(std::move(parent)),
m_id(id),
m_common_attributes(std::move(common_attributes)) {
: m_id(id),
m_common_attributes(std::move(common_attributes)),
m_parent(std::move(parent)) {
}
};
@ -303,7 +303,7 @@ public:
// convert to string
std::stringstream ss;
doc.print(ss, " ");
return std::move(ss.str());
return ss.str();
}
protected:
@ -482,4 +482,4 @@ private:
std::shared_ptr<IRNet> m_ir_net;
};
} // namespace CommonTestUtils
} // namespace CommonTestUtils

1
inference-engine/tests/ie_test_utils/unit_test_utils/CMakeLists.txt
View File

@ -7,7 +7,6 @@ set(TARGET_NAME unitTestUtils)
list(APPEND EXPORT_DEPENDENCIES
commonTestUtils_s
inference_engine_s
inference_engine_preproc_s
inference_engine_lp_transformations
inference_engine_ir_readers
gmock)