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Fix warnings spotted by cl compiler (#4793)

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* Fix warnings spotted by cl compiler

* Use inline instead of anonymous namespace

* Review comments
This commit is contained in:
Maksim Derbasov 2021-03-17 13:08:42 +03:00 committed by GitHub
parent cc718f5602
commit 2a19fc2d8a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
29 changed files with 60 additions and 63 deletions
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2
docs/template_extension/cpu_kernel.cpp
View File

@ -99,7 +99,7 @@ InferenceEngine::StatusCode OpImplementation::init(InferenceEngine::LayerConfig
config.inConfs[0].desc.getPrecision() != InferenceEngine::Precision::FP32) {
THROW_IE_EXCEPTION << "Operation supports only FP32 precisions!";
}
} catch (InferenceEngine::details::InferenceEngineException& ex) {
} catch (InferenceEngine::details::InferenceEngineException&) {
if (resp) {
strncpy(resp->msg, error.c_str(), sizeof(resp->msg) - 1);
resp->msg[sizeof(resp->msg)-1] = 0;

2
docs/template_extension/fft_kernel.cpp
View File

@ -65,7 +65,7 @@ InferenceEngine::StatusCode FFTImpl::init(InferenceEngine::LayerConfig &config,
config.inConfs[0].desc.getPrecision() != InferenceEngine::Precision::FP32) {
THROW_IE_EXCEPTION << "Operation supports only FP32 precisions!";
}
} catch (InferenceEngine::details::InferenceEngineException& ex) {
} catch (InferenceEngine::details::InferenceEngineException&) {
if (resp) {
strncpy(resp->msg, error.c_str(), sizeof(resp->msg) - 1);
resp->msg[sizeof(resp->msg)-1] = 0;

2
inference-engine/ie_bridges/c/src/ie_c_api.cpp
View File

@ -524,7 +524,7 @@ IEStatusCode ie_core_get_available_devices(const ie_core_t *core, ie_available_d
avai_devices->devices = dev_ptrs.release();
} catch (const IE::details::InferenceEngineException& e) {
return e.hasStatus() ? status_map[e.getStatus()] : IEStatusCode::UNEXPECTED;
} catch (const std::exception& e) {
} catch (const std::exception&) {
return IEStatusCode::UNEXPECTED;
} catch (...) {
return IEStatusCode::UNEXPECTED;

6
inference-engine/samples/hello_query_device/main.cpp
View File

@ -27,7 +27,9 @@ std::ostream & operator << (std::ostream & stream, const std::vector<T> & v) {
}
void printParameterValue(const Parameter & value) {
if (value.is<bool>()) {
if (value.empty()) {
std::cout << "EMPTY VALUE" << std::endl;
} else if (value.is<bool>()) {
std::cout << std::boolalpha << value.as<bool>() << std::noboolalpha << std::endl;
} else if (value.is<int>()) {
std::cout << value.as<int>() << std::endl;
@ -112,7 +114,7 @@ int main(int argc, char *argv[]) {
std::cout << std::endl;
}
} catch (const std::exception & ex) {
std::cerr << ex.what() << std::endl;
std::cerr << std::endl << "Exception occurred: " << ex.what() << std::endl << std::flush;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;

2
inference-engine/src/cldnn_engine/cldnn_program.cpp
View File

@ -215,7 +215,7 @@ bool Program::IsOpSupported(const InferenceEngine::CNNNetwork& network, const st
CreateSingleLayerPrimitive(topology, op);
CleanupBuild();
DisableQueryMode();
} catch (std::exception& ex) {
} catch (std::exception&) {
// Exception means that an operation or some of it's parameters are not supported
CleanupBuild();
return false;

6
inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp
View File

@ -1267,19 +1267,19 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_
<< GNAPluginNS::memory::MemoryOffset(component[i].op.pwl.ptr_segments, ptr_dnn_memory_) << "\n";
if (compute_precision_ == kDnnInt) {
out_file << "<slope> ";
for (int segment = 0; segment < num_segments; segment++) {
for (uint32_t segment = 0; segment < num_segments; segment++) {
out_file << "0x" << std::setfill('0') << std::setw(4) << std::hex
<< ptr_segment[segment].slope << " ";
}
out_file << "\n";
out_file << "<intercept> ";
for (int segment = 0; segment < component[i].op.pwl.num_segments; segment++) {
for (uint32_t segment = 0; segment < component[i].op.pwl.num_segments; segment++) {
out_file << "0x" << std::setfill('0') << std::setw(4) << std::hex
<< ptr_segment[segment].yBase << " ";
}
out_file << "\n";
out_file << "<offset> ";
for (int segment = 0; segment < component[i].op.pwl.num_segments; segment++) {
for (uint32_t segment = 0; segment < component[i].op.pwl.num_segments; segment++) {
out_file << "0x" << std::setfill('0') << std::setw(8) << std::hex
<< ptr_segment[segment].xBase << " ";
}

8
inference-engine/src/gna_plugin/backend/dnn_types.h
View File

@ -257,7 +257,7 @@ static std::string OvGnaModeToString(OvGnaMode mode) {
}
#endif
typedef struct {
struct intel_dnn_component_t {
#if GNA_LIB_VER == 2
std::vector < OvGnaTensor > tensors;
#endif
@ -287,9 +287,9 @@ typedef struct {
float output_scale_factor;
float input_scale_factor;
const char * original_layer_name = nullptr;
} intel_dnn_component_t;
};
typedef struct {
struct intel_score_error_t {
uint32_t num_scores;
uint32_t num_errors;
float threshold;
@ -301,4 +301,4 @@ typedef struct {
float max_rel_error;
float sum_rel_error;
float sum_squared_rel_error;
} intel_score_error_t;
};

4
inference-engine/src/gna_plugin/frontend/scale_factor_calc.hpp
View File

@ -72,7 +72,7 @@ static float selectBestOutputScaleFactors(float inScale, std::vector<float> outS
for (size_t j = 0; j < slopes.size(); ++j) {
auto s = gna_slope(slopes[j], inScale, outScale);
auto slope = static_cast<uint32_t>(s.slope * s.slope_scale);
if (slope < std::numeric_limits<int16_t>::min() && slope > std::numeric_limits<int16_t>::max()) {
if (slope < static_cast<uint32_t>(std::numeric_limits<int16_t>::min()) && slope > static_cast<uint32_t>(std::numeric_limits<int16_t>::max())) {
sd += std::numeric_limits<int8_t>::max();
continue;
}
@ -119,7 +119,7 @@ static float selectBestWeightsScaleFactors(float inScale, float outScale, std::v
for (size_t j = 0; j < slopes.size(); ++j) {
auto s = gna_slope(slopes[j], inScale * weightScale, outScale);
auto slope = static_cast<uint32_t>(s.slope * s.slope_scale);
if (slope < std::numeric_limits<int16_t>::min() && slope > std::numeric_limits<int16_t>::max()) {
if (slope < static_cast<uint32_t>(std::numeric_limits<int16_t>::min()) && slope > static_cast<uint32_t>(std::numeric_limits<int16_t>::max())) {
sd += std::numeric_limits<int8_t>::max();
continue;
}

4
inference-engine/src/gna_plugin/gna2_model_helper.cpp
View File

@ -156,7 +156,7 @@ Gna2Shape* create_shape2D_parameter(uint32_t x, uint32_t y) {
void freeGna2Operation(Gna2Operation& operation) {
if (operation.Operands != nullptr) {
for (auto i = 0; i < operation.NumberOfOperands; i++) {
for (uint32_t i = 0; i < operation.NumberOfOperands; i++) {
if (operation.Operands[i] != nullptr) {
gnaUserFree(const_cast<Gna2Tensor*>(operation.Operands[i]));
operation.Operands[i] = nullptr;
@ -167,7 +167,7 @@ void freeGna2Operation(Gna2Operation& operation) {
operation.NumberOfOperands = 0;
}
if (operation.Parameters != nullptr) {
for (auto i = 0; i < operation.NumberOfParameters; i++) {
for (uint32_t i = 0; i < operation.NumberOfParameters; i++) {
if (operation.Parameters[i] != nullptr) {
gnaUserFree(operation.Parameters[i]);
operation.Parameters[i] = nullptr;

4
inference-engine/src/gna_plugin/gna_api_wrapper.hpp
View File

@ -51,7 +51,7 @@ class CPPWrapper<Gna2Model> {
THROW_GNA_EXCEPTION << "out of memory in while allocating "<< n << " GNA layers";
}
obj.NumberOfOperations = n;
for (int i = 0; i < obj.NumberOfOperations; i++) {
for (uint32_t i = 0; i < obj.NumberOfOperations; i++) {
obj.Operations[i].Type = Gna2OperationTypeNone;
obj.Operations[i].Operands = nullptr;
obj.Operations[i].NumberOfOperands = 0;
@ -61,7 +61,7 @@ class CPPWrapper<Gna2Model> {
}
~CPPWrapper() {
if (obj.Operations != nullptr) {
for (int i = 0; i < obj.NumberOfOperations; i++) {
for (uint32_t i = 0; i < obj.NumberOfOperations; i++) {
freeGna2Operation(obj.Operations[i]);
}
gnaUserFree(obj.Operations);

6
inference-engine/src/gna_plugin/gna_graph_compiler.cpp
View File

@ -1380,7 +1380,7 @@ void GNAGraphCompiler::AffinePrimitive(InferenceEngine::CNNLayerPtr layer, bool
64);
} else {
gnamem->readonly().push_initializer(ptr_weights, weightable._weights->byteSize(), [=](void* data, size_t size) {
for (int k = 0; k < (isDiag ? 1 : num_rows_out); k++) {
for (uint32_t k = 0; k < (isDiag ? 1 : num_rows_out); k++) {
auto rowOffset = k * transposedRows * transposedCols * weightable.precision.size();
auto cbuffer = weightable._weights->cbuffer().as<const uint8_t*>() + rowOffset;
auto u8Data = reinterpret_cast<uint8_t*>(data) + rowOffset;
@ -1409,7 +1409,7 @@ void GNAGraphCompiler::AffinePrimitive(InferenceEngine::CNNLayerPtr layer, bool
auto paddedWeightsSize = paddedWeights * weightable.precision.size();
gnamem->readonly().push_initializer(ptr_weights, paddedWeightsSize, [=](void* data, size_t size) {
for (int i = 0; i < (isDiag ? 1 : num_rows_out); i++) {
for (uint32_t i = 0; i < (isDiag ? 1 : num_rows_out); i++) {
ie_memcpy(data, size,
weightable._weights->cbuffer().as<const uint8_t*>() + num_rows_in * i * weightable.precision.size(),
num_rows_in * weightable.precision.size());
@ -1663,7 +1663,7 @@ void GNAGraphCompiler::AffineFilterPrimitive(InferenceEngine::CNNLayerPtr layer)
gnamem->readonly().push_initializer(ptr_weights, paddedWeightsSize, [=](void* data, size_t size) {
size_t offset = 0;
for (int i = 0; i < num_rows_out && size >= offset; i++) {
for (uint32_t i = 0; i < num_rows_out && size >= offset; i++) {
ie_memcpy(reinterpret_cast<uint8_t*>(data) + offset, size - offset,
filterLayer->_weights->cbuffer().as<const uint8_t*>() + num_rows_in * i * filterLayer->precision.size(),
num_rows_in* filterLayer->precision.size());

4
inference-engine/src/gna_plugin/gna_model_serial.cpp
View File

@ -837,7 +837,7 @@ void GNAModelSerial::ImportInputs(std::istream &is,
InferenceEngine::InputsDataMap& dataMap) {
dataMap.clear();
for (auto inputIndex = 0; inputIndex < modelHeader.nInputs; inputIndex++) {
for (uint32_t inputIndex = 0; inputIndex < modelHeader.nInputs; inputIndex++) {
const std::string& name = (modelHeader.version.major == 2 && modelHeader.version.minor >= 3)
? inputNames.at(inputIndex) : std::string("input" + std::to_string(inputIndex));
HeaderLatest::RuntimeEndPoint input;
@ -866,7 +866,7 @@ void GNAModelSerial::ImportOutputs(std::istream &is,
dataMap.clear();
desc.resize(modelHeader.nOutputs);
for (auto outputIndex = 0; outputIndex < modelHeader.nOutputs; outputIndex++) {
for (uint32_t outputIndex = 0; outputIndex < modelHeader.nOutputs; outputIndex++) {
const std::string& name = (modelHeader.version.major == 2 && modelHeader.version.minor >= 3)
? outputNames.at(outputIndex) : std::string("output" + std::to_string(outputIndex));
HeaderLatest::RuntimeEndPoint output;

8
inference-engine/src/gna_plugin/gna_plugin.cpp
View File

@ -129,7 +129,7 @@ void GNAPlugin::copyInputData(T *dst,
T *ptr_dst_vec = reinterpret_cast<T *>(dst) + i * num_vector_stride;
const U *ptr_src_vec = reinterpret_cast<const U *>(src) + i * num_vector_elements;
std::memset(ptr_dst_vec, 0, num_vector_stride * sizeof(T));
for (int j=0; j < num_vector_elements; j++) {
for (uint32_t j=0; j < num_vector_elements; j++) {
ptr_dst_vec[j] = GNAPluginNS::ConvertFloatToInt16(ptr_src_vec[j] * scaleFactor);
}
}
@ -1271,9 +1271,9 @@ GnaWaitStatus GNAPlugin::WaitFor(uint32_t request_idx, int64_t millisTimeout) {
for (auto && outputBlobIt : request) {
auto & outputBlob = outputBlobIt.second;
auto & outputDesc = outputsDesc[output_idx];
if (!outputBlob->getTensorDesc().getLayout() == Layout::C && !outputBlob->getTensorDesc().getLayout() == Layout::NC &&
!outputBlob->getTensorDesc().getLayout() == Layout::CN && !outputBlob->getTensorDesc().getLayout() == Layout::NCHW &&
!outputBlob->getTensorDesc().getLayout() == Layout::CHW) {
if (outputBlob->getTensorDesc().getLayout() != Layout::C && outputBlob->getTensorDesc().getLayout() != Layout::NC &&
outputBlob->getTensorDesc().getLayout() != Layout::CN && outputBlob->getTensorDesc().getLayout() != Layout::NCHW &&
outputBlob->getTensorDesc().getLayout() != Layout::CHW) {
THROW_GNA_EXCEPTION << "Expected output blob to have Layout::C, Layout::NC, Layout::CN, Layout::NCHW or Layout::CHW. But was "
<< outputBlob->getTensorDesc().getLayout();
}

2
inference-engine/src/gna_plugin/gna_plugin_config.hpp
View File

@ -41,7 +41,7 @@ struct Config {
#endif
inputScaleFactors = r.inputScaleFactors;
gnaFlags = r.gnaFlags;
std::lock_guard<std::mutex>(r.mtx4keyConfigMap);
std::lock_guard<std::mutex> lock(r.mtx4keyConfigMap);
keyConfigMap = r.keyConfigMap;
}
void UpdateFromMap(const std::map<std::string, std::string>& configMap);

4
inference-engine/src/gna_plugin/gna_slope_scale.h
View File

@ -6,10 +6,10 @@
#include <cstdint>
typedef struct {
struct pwl_gna_slope_scale_t {
double slope {};
uint64_t slope_scale = 0;
uint32_t slope_scale_index {};
} pwl_gna_slope_scale_t;
};
pwl_gna_slope_scale_t gna_slope(const double slope, const double in_scale, const double out_scale);

2
inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
View File

@ -975,7 +975,7 @@ void FlattenTrivialConcatPass::run() {
auto axis = concatLayer->_axis;
bool skip_layer = false;
for (int i = 0; i < axis; i++) {
for (unsigned int i = 0; i < axis; i++) {
if (concatLayer->insData[0].lock()->getDims()[i] != 1) skip_layer = true;
}
if (skip_layer) continue;

4
inference-engine/src/inference_engine/ie_core.cpp
View File

@ -93,7 +93,7 @@ template <typename F>
void allowNotImplemented(F && f) {
try {
f();
} catch (const NotImplemented & ex) { }
} catch (const NotImplemented&) { }
}
} // namespace
@ -993,7 +993,7 @@ std::vector<std::string> Core::GetAvailableDevices() const {
try {
Parameter p = GetMetric(deviceName, propertyName);
devicesIDs = p.as<std::vector<std::string>>();
} catch (details::InferenceEngineException& e) {
} catch (details::InferenceEngineException&) {
// plugin is not created by e.g. invalid env
} catch (const std::exception& ex) {
THROW_IE_EXCEPTION << "An exception is thrown while trying to create the " << deviceName

2
inference-engine/src/multi_device/multi_device_exec_network.cpp
View File

@ -168,7 +168,7 @@ RemoteContext::Ptr MultiDeviceExecutableNetwork::GetContext() const {
const auto& n = _networksPerDevice.at(device.deviceName);
try {
return n.GetContext();
} catch (const NotImplemented& ex) {
} catch (const NotImplemented&) {
}
}
THROW_IE_EXCEPTION_WITH_STATUS(NOT_IMPLEMENTED) << "None of the devices in the MULTI has an associated remote context."

2
inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
View File

@ -276,7 +276,7 @@ protected:
INFERENCE_PLUGIN_API(void) CreatePluginEngine(::std::shared_ptr<::InferenceEngine::IInferencePlugin>& plugin) { \
try { \
plugin = ::std::make_shared<PluginType>(__VA_ARGS__); \
} catch (const InferenceEngine::details::InferenceEngineException& e) { \
} catch (const InferenceEngine::details::InferenceEngineException&) { \
throw; \
} catch (const std::exception& ex) { \
THROW_IE_EXCEPTION << ex.what(); \

2
inference-engine/tests/functional/inference_engine/cnn_network/convert_ngraph_to_cnn_network_tests.cpp
View File

@ -73,7 +73,7 @@ TEST(ConvertFunctionToCNNNetworkTests, ConvertConvolutionNetwork) {
InferenceEngine::CNNNetwork nGraphImpl(f);
try {
auto net = std::make_shared<InferenceEngine::details::CNNNetworkImpl>(nGraphImpl);
} catch (InferenceEngine::details::InferenceEngineException &err) {
} catch (InferenceEngine::details::InferenceEngineException&) {
FAIL();
}
}

2
inference-engine/tests/functional/inference_engine/core_threading_tests.cpp
View File

@ -109,7 +109,7 @@ TEST_F(CoreThreadingTests, RegisterPlugins) {
runParallel([&] () {
std::string fileName, deviceName;
std:tie(fileName, deviceName) = getPluginXml();
std::tie(fileName, deviceName) = getPluginXml();
ie.RegisterPlugins(fileName);
ie.GetVersions(deviceName);
ASSERT_EQ(0, std::remove(fileName.c_str()));

10
inference-engine/tests/functional/plugin/myriad/ngraph/operations/dynamic_shape_resolver.cpp
View File

@ -42,7 +42,7 @@ protected:
TEST_P(DynamicShapeResolverTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::DynamicShapeResolver> dynamicShapeResolver;
ASSERT_NO_THROW(dynamicShapeResolver = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(ngraph::NodeVector{dynamicShapeResolver}, ngraph::ParameterVector{data, dims}));
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(ngraph::NodeVector{dynamicShapeResolver}, ngraph::ParameterVector{data, dims}));
}
std::set<ngraph::element::Type_t> allNGraphTypes() {
@ -117,7 +117,7 @@ protected:
using DynamicShapeResolverNegativeTestsDataType = DynamicShapeResolverNegativeTests;
TEST_P(DynamicShapeResolverNegativeTestsDataType, ThrowsOnInvalidDimsType) {
ASSERT_THROW(std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
ASSERT_THROW(auto dynamicShapeResolver = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
}
INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDataType, testing::Combine(
@ -128,7 +128,7 @@ INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDataType,
using DynamicShapeResolverNegativeTestsDimsType = DynamicShapeResolverNegativeTests;
TEST_P(DynamicShapeResolverNegativeTestsDimsType, ThrowsOnInvalidDimsType) {
ASSERT_THROW(std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
ASSERT_THROW(auto dynamicShapeResolver = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
}
std::set<ngraph::element::Type_t> allNGraphNotIntegralTypes() {
@ -148,7 +148,7 @@ INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDimsType,
using DynamicShapeResolverNegativeTestsDataShape = DynamicShapeResolverNegativeTests;
TEST_P(DynamicShapeResolverNegativeTestsDataShape, ThrowsOnInvalidDimsType) {
ASSERT_THROW(std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
ASSERT_THROW(auto dynamicShapeResolver = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
}
INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDataShape, testing::Combine(
@ -163,7 +163,7 @@ INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDataShape
using DynamicShapeResolverNegativeTestsDimsShape = DynamicShapeResolverNegativeTests;
TEST_P(DynamicShapeResolverNegativeTestsDimsShape, ThrowsOnInvalidDimsType) {
ASSERT_THROW(std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
ASSERT_THROW(auto dynamicShapeResolver = std::make_shared<ngraph::vpu::op::DynamicShapeResolver>(data, dims), ngraph::ngraph_error);
}
INSTANTIATE_TEST_CASE_P(smoke_NGraph, DynamicShapeResolverNegativeTestsDimsShape, testing::Combine(

2
inference-engine/tests/functional/plugin/myriad/ngraph/operations/out_shape_of_reshape.cpp
View File

@ -94,7 +94,7 @@ TEST_P(OutShapeOfReshapeTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::OutShapeOfReshape> op;
ASSERT_NO_THROW(op = std::make_shared<ngraph::vpu::op::OutShapeOfReshape>(
m_inDataShapeParam, m_outShapeDescriptorParam, true));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(
ngraph::OutputVector{op->output(0)},
ngraph::ParameterVector{m_inDataShapeParam, m_outShapeDescriptorParam}));
}

6
inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_broadcast.cpp
View File

@ -146,7 +146,7 @@ TEST_P(StaticShapeBroadcastNumpyTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::StaticShapeBroadcast> op;
ASSERT_NO_THROW(op = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(
m_tensor, shapeOf));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(
ngraph::OutputVector{op->output(0)},
ngraph::ParameterVector{m_tensor, m_tensorWithTargetShape}));
ASSERT_EQ(m_tensorWithTargetShape->get_shape(), op->output(0).get_shape());
@ -162,7 +162,7 @@ TEST_P(StaticShapeBroadcastExplicitTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::StaticShapeBroadcast> op;
ASSERT_NO_THROW(op = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(
m_tensor, shapeOf, m_axesMapping));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(
ngraph::OutputVector{op->output(0)},
ngraph::ParameterVector{m_tensor, m_tensorWithTargetShape}));
ASSERT_EQ(m_tensorWithTargetShape->get_shape(), op->get_output_shape(0));
@ -178,7 +178,7 @@ TEST_P(StaticShapeBroadcastBidirectionalTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::StaticShapeBroadcast> op;
ASSERT_NO_THROW(op = std::make_shared<ngraph::vpu::op::StaticShapeBroadcast>(
m_tensor, shapeOf, ngraph::op::BroadcastType::BIDIRECTIONAL));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(
ngraph::OutputVector{op->output(0)},
ngraph::ParameterVector{m_tensor, m_tensorWithTargetShape}));
ASSERT_EQ(m_tensorWithOutput->get_shape(), op->output(0).get_shape());

2
inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_nonzero.cpp
View File

@ -84,7 +84,7 @@ std::vector<ngraph::element::Type> outputTypes {
TEST_P(StaticShapeNonZeroTests, CanValidateAndInferTypes) {
std::shared_ptr<ngraph::vpu::op::StaticShapeNonZero> op;
ASSERT_NO_THROW(op = std::make_shared<ngraph::vpu::op::StaticShapeNonZero>(m_param, m_outputType));
ASSERT_NO_THROW(std::make_shared<ngraph::Function>(
ASSERT_NO_THROW(auto fun = std::make_shared<ngraph::Function>(
ngraph::OutputVector{op->output(0), op->output(1)},
ngraph::ParameterVector{m_param}));
}

6
inference-engine/tests/functional/plugin/shared/include/behavior/infer_request_cancellation.hpp
View File

@ -46,7 +46,7 @@ TEST_P(CancellationTests, canCancelAsyncRequest) {
ASSERT_NO_THROW(req.Cancel());
try {
req.Wait(InferenceEngine::IInferRequest::WaitMode::RESULT_READY);
} catch (const InferenceEngine::InferCancelled& ex) {
} catch (const InferenceEngine::InferCancelled&) {
SUCCEED();
}
}
@ -65,7 +65,7 @@ TEST_P(CancellationTests, canResetAfterCancelAsyncRequest) {
ASSERT_NO_THROW(req.Cancel());
try {
req.Wait(InferenceEngine::IInferRequest::WaitMode::RESULT_READY);
} catch (const InferenceEngine::InferCancelled& ex) {
} catch (const InferenceEngine::InferCancelled&) {
SUCCEED();
}
@ -107,7 +107,7 @@ TEST_P(CancellationTests, canCancelInferRequest) {
ASSERT_NO_THROW(req.Cancel());
try {
infer.get();
} catch (const InferenceEngine::InferCancelled& ex) {
} catch (const InferenceEngine::InferCancelled&) {
SUCCEED();
}
}

2
inference-engine/tests/functional/plugin/shared/src/execution_graph_tests/exec_graph_serialization.cpp
View File

@ -255,7 +255,7 @@ void ExecGraphSerializationTest::SetUp() {
const std::string BIN_EXT = ".bin";
std::string test_name = ::testing::UnitTest::GetInstance()->current_test_info()->name();
std::remove(test_name.begin(), test_name.end(), '/');
test_name.erase(std::remove(test_name.begin(), test_name.end(), '/'), test_name.end());
test_name += getTimestamp();
m_out_xml_path = test_name + XML_EXT;

9
inference-engine/tests/ie_test_utils/common_test_utils/test_assertions.hpp
View File

@ -12,19 +12,14 @@
#include <ie_preprocess.hpp>
#include <ie_icnn_network.hpp>
namespace {
bool strContains(const std::string & str, const std::string & substr) {
inline bool strContains(const std::string & str, const std::string & substr) {
return str.find(substr) != std::string::npos;
}
bool strDoesnotContain(const std::string & str, const std::string & substr) {
(void)strDoesnotContain; // to overcome unused warning
inline bool strDoesnotContain(const std::string & str, const std::string & substr) {
return !strContains(str, substr);
}
} // namespace
#define ASSERT_STR_CONTAINS(str, substr) \
ASSERT_PRED2(&strContains, str, substr)

8
inference-engine/tools/vpu/vpu_perfcheck/main.cpp
View File

@ -51,14 +51,14 @@ typedef std::chrono::duration<float> fsec;
class BitMap {
private:
typedef struct {
struct BmpHeader {
unsigned short type = 0u; /* Magic identifier */
unsigned int size = 0u; /* File size in bytes */
unsigned int reserved = 0u;
unsigned int offset = 0u; /* Offset to image data, bytes */
} BmpHeader;
};
typedef struct {
struct BmpInfoHeader {
unsigned int size = 0u; /* Header size in bytes */
int width = 0, height = 0; /* Width and height of image */
unsigned short planes = 0u; /* Number of colour planes */
@ -68,7 +68,7 @@ private:
int xresolution = 0, yresolution = 0; /* Pixels per meter */
unsigned int ncolours = 0u; /* Number of colours */
unsigned int importantcolours = 0u; /* Important colours */
} BmpInfoHeader;
};
public:
explicit BitMap(const std::string &filename) {