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[IE][VPU]: Reduce tests execution time (#2378)

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* [IE][VPU]: Reduce tests execution time

* [IE TESTS] Remove 'ConfigurePlugin()' from 'memory_LSTMCell.hpp'

* [IE VPU TESTS] Myriad conv layer tests was changed

Co-authored-by: Maksim Doronin <maksim.doronin@intel.com>
Co-authored-by: kora6 <kora6@github.com>
This commit is contained in:
Aleksandr Korolev
2020-09-25 14:24:12 +03:00
committed by GitHub
parent 0bd966ad08
commit eda9498b79
9 changed files with 134 additions and 111 deletions
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1
inference-engine/tests/functional/plugin/gna/import_export_network.cpp
View File

@@ -49,7 +49,6 @@ class ImportNetworkTest : public testing::WithParamInterface<exportImportNetwork
void Run() override {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
LoadNetwork();
Infer();
executableNetwork.Export("exported_model.blob");

4
inference-engine/tests/functional/plugin/shared/src/behavior/stress_tests.cpp
View File

@@ -26,14 +26,12 @@ void MultipleAllocations::SetUp() {
TEST_P(MultipleAllocations, InferWorksCorrectAfterAllocations) {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
InferenceEngine::CNNNetwork cnnNet(function);
auto ie = PluginCache::get().ie();
std::cout << "Load the network " << m_allocationsCount << " times..." << std::flush;
for (int i = 0; i < m_allocationsCount; ++i) {
ie->LoadNetwork(cnnNet, targetDevice);
ie->LoadNetwork(cnnNet, targetDevice, configuration);
}
std::cout << "\nCheck inference.\n";

1
inference-engine/tests/functional/plugin/shared/src/subgraph_tests/basic_lstm.cpp
View File

@@ -103,7 +103,6 @@ void Basic_LSTM_S::SetUp() {
void Basic_LSTM_S::Run() {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
LoadNetwork();
Infer();

1
inference-engine/tests/functional/plugin/shared/src/subgraph_tests/memory_LSTMCell.cpp
View File

@@ -197,7 +197,6 @@ namespace SubgraphTestsDefinitions {
void MemoryLSTMCellTest::Run() {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
LoadNetwork();
auto states = executableNetwork.QueryState();
for (auto& state : states) {

1
inference-engine/tests/functional/plugin/shared/src/subgraph_tests/reshape_permute_conv_permute_reshape_act.cpp
View File

@@ -87,7 +87,6 @@ namespace LayerTestsDefinitions {
void ConvReshapeAct::Run() {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
LoadNetwork();
inferRequest = executableNetwork.CreateInferRequest();

15
inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp
View File

@@ -16,18 +16,11 @@ LayerTestsCommon::LayerTestsCommon() : threshold(1e-2f) {
void LayerTestsCommon::Run() {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
ConfigurePlugin();
LoadNetwork();
Infer();
Validate();
}
LayerTestsCommon::~LayerTestsCommon() {
if (!configuration.empty()) {
PluginCache::get().reset();
}
}
InferenceEngine::Blob::Ptr LayerTestsCommon::GenerateInput(const InferenceEngine::InputInfo &info) const {
return FuncTestUtils::createAndFillBlob(info.getTensorDesc());
}
@@ -83,12 +76,6 @@ void LayerTestsCommon::Compare(const InferenceEngine::Blob::Ptr &expected, const
}
}
void LayerTestsCommon::ConfigurePlugin() {
if (!configuration.empty()) {
core->SetConfig(configuration, targetDevice);
}
}
void LayerTestsCommon::ConfigureNetwork() const {
for (const auto &in : cnnNetwork.getInputsInfo()) {
if (inLayout != InferenceEngine::Layout::ANY) {
@@ -112,7 +99,7 @@ void LayerTestsCommon::ConfigureNetwork() const {
void LayerTestsCommon::LoadNetwork() {
cnnNetwork = InferenceEngine::CNNNetwork{function};
ConfigureNetwork();
executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice);
executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
}
void LayerTestsCommon::Infer() {

3
inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp
View File

@@ -63,7 +63,6 @@ public:
protected:
LayerTestsCommon();
~LayerTestsCommon() override;
template<class T>
void Compare(const T *expected, const T *actual, std::size_t size, T threshold) {
@@ -92,8 +91,6 @@ protected:
return core;
}
void ConfigurePlugin();
void ConfigureNetwork() const;
void LoadNetwork();

95
inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp
View File

@@ -8,9 +8,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_chw_dilation, myriadLayerConvolution_smoke,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 37, 43, 43)
, MAKE_STRUCT(tensor_test_params, 1, 37, 19, 19))
, ::testing::Values<kernel>(
MAKE_STRUCT(param_size, 3, 3)
)
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
, ::testing::Values<stride>(MAKE_STRUCT(param_size, 1, 1)
, MAKE_STRUCT(param_size, 2, 2)
, MAKE_STRUCT(param_size, 3, 3)
@@ -251,7 +249,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_3X3X3_ConstInput_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTensorFlow_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTensorFlow,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 3, 1024, 1024))
, ::testing::Values<DimsOutput>(MAKE_STRUCT(tensor_test_params, 1, 3, 512, 512))
@@ -264,7 +262,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTe
)
);
INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 16, 28, 28))
, ::testing::Values<DimsOutput>(MAKE_STRUCT(tensor_test_params, 1, 64, 14, 14))
@@ -277,7 +275,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow_
)
);
INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 3, 224, 224),
MAKE_STRUCT(tensor_test_params, 1, 32, 224, 224)
@@ -292,7 +290,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow_
)
);
INSTANTIATE_TEST_CASE_P(test_3x3_SSD_dilation, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_3x3_SSD_dilation, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 512, 19, 19))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -317,7 +315,7 @@ INSTANTIATE_TEST_CASE_P(test_TF_Resnet_50, myriadLayers_IR3_ConvTests_smoke,
)
);
INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 24, 20, 20))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -332,7 +330,7 @@ INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 5, 5))
@@ -348,7 +346,7 @@ INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 7, 7))
@@ -365,7 +363,7 @@ INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution_smoke,
);
INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 10, 13, 13))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 1, 1))
@@ -379,7 +377,7 @@ INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 512, 13, 13))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -392,7 +390,7 @@ INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLa
)
);
INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 4608, 13, 13))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 1, 1))
@@ -405,7 +403,7 @@ INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLa
)
);
INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77)
, MAKE_STRUCT(tensor_test_params, 1, 32, 112, 96))
@@ -424,7 +422,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_group_large_input, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_group_large_input, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 32, 192, 336))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -451,12 +449,27 @@ INSTANTIATE_TEST_CASE_P(accuracy_any_group, myriadLayerConvolution_smoke,
, ::testing::Values<out_channels>(32)
, ::testing::Values<group>(2, 4, 16)
, ::testing::Values<dilation_factor>(MAKE_STRUCT(param_size, 1, 2), MAKE_STRUCT(param_size, 2, 3), MAKE_STRUCT(param_size, 3, 4)),
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor,
vpu::LayoutPreference::ChannelMajor)
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMajor)
)
);
INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_any_group, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
, ::testing::Values<stride>(MAKE_STRUCT(param_size, 1, 1)
, MAKE_STRUCT(param_size, 2, 2))
, ::testing::Values<pad>(MAKE_STRUCT(param_size, 1, 1)
, MAKE_STRUCT(param_size, 0, 0)
, MAKE_STRUCT(param_size, 2, 2))
, ::testing::Values<out_channels>(32)
, ::testing::Values<group>(2, 4, 16)
, ::testing::Values<dilation_factor>(MAKE_STRUCT(param_size, 1, 2), MAKE_STRUCT(param_size, 2, 3), MAKE_STRUCT(param_size, 3, 4)),
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor)
)
);
INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 24, 80, 80)
, MAKE_STRUCT(tensor_test_params, 1, 36, 80, 80))
@@ -471,7 +484,7 @@ INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolut
)
);
INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 24, 80, 80))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -484,7 +497,7 @@ INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution_smoke
)
);
INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 16, 64, 64)
, MAKE_STRUCT(tensor_test_params, 1, 32, 1, 1))
@@ -501,7 +514,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 8, 16, 16)
, MAKE_STRUCT(tensor_test_params, 1, 8, 59, 73))
@@ -517,7 +530,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_1x3, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_1x3, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 8, 59, 73))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 1, 3), MAKE_STRUCT(param_size, 3, 1))
@@ -544,12 +557,28 @@ INSTANTIATE_TEST_CASE_P(accuracy_5x5, myriadLayerConvolution_smoke,
, ::testing::Values<out_channels>(16, 32)
, ::testing::Values<group>(1)
, ::testing::Values<dilation_factor>(MAKE_STRUCT(param_size, 1, 2)),
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor,
vpu::LayoutPreference::ChannelMajor)
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMajor)
)
);
INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_5x5, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 16, 32, 32)
/*, MAKE_STRUCT(tensor_test_params, 1, 8, 511, 399) failed*/)
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 5, 5))
, ::testing::Values<stride>(MAKE_STRUCT(param_size, 1, 1)
, MAKE_STRUCT(param_size, 2, 2))
, ::testing::Values<pad>(MAKE_STRUCT(param_size, 0, 0)
/*, MAKE_STRUCT(param_size, 1, 1) failed*/
, MAKE_STRUCT(param_size, 2, 2))
, ::testing::Values<out_channels>(16, 32)
, ::testing::Values<group>(1)
, ::testing::Values<dilation_factor>(MAKE_STRUCT(param_size, 1, 2)),
::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor)
)
);
INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 8, 32, 32)
/*, MAKE_STRUCT(tensor_test_params, 1, 8, 511, 399) failed*/)
@@ -566,7 +595,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 3, 720, 1280))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -579,7 +608,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution_smoke
)
);
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 24, 357, 637))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -593,7 +622,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution_smoke
);
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 16, 359, 639))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -606,7 +635,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution_smoke
)
);
INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 24, 355, 635))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 1, 1))
@@ -619,7 +648,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution_smoke,
)
);
INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 128, 38, 38))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -631,7 +660,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution_smoke,
, ::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor)
)
);
INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 256, 2, 3))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -643,7 +672,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution_smoke,
, ::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor)
)
);
INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 256, 2, 2))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))
@@ -655,7 +684,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution_smoke,
, ::testing::Values<layoutPreference>(vpu::LayoutPreference::ChannelMinor)
)
);
INSTANTIATE_TEST_CASE_P(accuracy_small_input_3, myriadLayerConvolution_smoke,
INSTANTIATE_TEST_CASE_P(accuracy_small_input_3, myriadLayerConvolution,
::testing::Combine(
::testing::Values<DimsInput>(MAKE_STRUCT(tensor_test_params, 1, 256, 1, 1))
, ::testing::Values<kernel>(MAKE_STRUCT(param_size, 3, 3))

124
inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.hpp
View File

@@ -21,75 +21,91 @@ PRETTY_PARAM(group, int);
PRETTY_PARAM(dilation_factor, param_size);
PRETTY_PARAM(layoutPreference, vpu::LayoutPreference);
typedef myriadLayerTestBaseWithParam<tuple<DimsInput, kernel, stride, pad
, out_channels, group, dilation_factor, layoutPreference >> myriadLayerConvolution_smoke;
class myriadLayerConvolutionBase : public myriadLayerTestBaseWithParam<
tuple<DimsInput, kernel, stride, pad, out_channels, group, dilation_factor, layoutPreference>>
{
public:
void testConvolution() {
tensor_test_params input_dims = get<0>(GetParam());
param_size kernel = get<1>(GetParam());
param_size stride = get<2>(GetParam());
param_size pad = get<3>(GetParam());
size_t out_channels = get<4>(GetParam());
size_t group = get<5>(GetParam());
param_size dilation_factor = get<6>(GetParam());
vpu::LayoutPreference layoutPreference = get<7>(GetParam());
typedef myriadLayerTestBaseWithParam<tuple<DimsInput, DimsOutput, kernel, stride, pad
, group, dilation_factor, layoutPreference >> myriadLayerConvolutionTensorFlow_smoke;
size_t out_w = (input_dims.w + 2 * pad.x - dilation_factor.x * (kernel.x - 1) - 1 + stride.x) / stride.x;
size_t out_h = (input_dims.h + 2 * pad.y - dilation_factor.y * (kernel.y - 1) - 1 + stride.y) / stride.y;
TEST_P(myriadLayerConvolution_smoke, Convolution) {
tensor_test_params input_dims = get<0>(GetParam());
param_size kernel = get<1>(GetParam());
param_size stride = get<2>(GetParam());
param_size pad = get<3>(GetParam());
size_t out_channels = get<4>(GetParam());
size_t group = get<5>(GetParam());
param_size dilation_factor = get<6>(GetParam());
vpu::LayoutPreference layoutPreference = get<7>(GetParam());
tensor_test_params output_dims = {1, out_channels, out_h, out_w};
size_t out_w = (input_dims.w + 2 * pad.x - dilation_factor.x * (kernel.x - 1) - 1 + stride.x) / stride.x;
size_t out_h = (input_dims.h + 2 * pad.y - dilation_factor.y * (kernel.y - 1) - 1 + stride.y) / stride.y;
SetInputTensor(input_dims);
SetOutputTensor(output_dims);
tensor_test_params output_dims = {1, out_channels, out_h, out_w};
size_t num_weights = kernel.x * kernel.y * (input_dims.c / group) * output_dims.c;
size_t num_bias = output_dims.c;
SetInputTensor(input_dims);
SetOutputTensor(output_dims);
InferenceEngine::TBlob<uint8_t>::Ptr weights_ptr =
InferenceEngine::TBlob<uint8_t>::Ptr(GenWeights(num_weights + num_bias));
ie_fp16* weights = weights_ptr->data().as<ie_fp16*>();
ie_fp16* bias = weights + num_weights;
size_t num_weights = kernel.x * kernel.y * (input_dims.c / group) * output_dims.c;
size_t num_bias = output_dims.c;
std::map<std::string, std::string> layer_params = {
{"kernel-x", std::to_string(kernel.x)}
, {"kernel-y", std::to_string(kernel.y)}
, {"stride-x", std::to_string(stride.x)}
, {"stride-y", std::to_string(stride.y)}
, {"pad-x", std::to_string(pad.x)}
, {"pad-y", std::to_string(pad.y)}
, {"output", std::to_string(out_channels)}
, {"group", std::to_string(group)}
, {"dilation-x", std::to_string(dilation_factor.x)}
, {"dilation-y", std::to_string(dilation_factor.y)}
};
ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("Convolution")
.params(layer_params)
.weights(num_weights)
.biases(num_bias),
{},
weights_ptr));
SetFirstInputToRange(-0.9f, 0.9f);
InferenceEngine::TBlob<uint8_t>::Ptr weights_ptr =
InferenceEngine::TBlob<uint8_t>::Ptr(GenWeights(num_weights + num_bias));
ie_fp16* weights = weights_ptr->data().as<ie_fp16*>();
ie_fp16* bias = weights + num_weights;
ASSERT_TRUE(Infer());
auto inputBlob = _inputMap.begin()->second;
auto outputBlob = _outputMap.begin()->second;
std::map<std::string, std::string> layer_params = {
{"kernel-x", std::to_string(kernel.x)}
, {"kernel-y", std::to_string(kernel.y)}
, {"stride-x", std::to_string(stride.x)}
, {"stride-y", std::to_string(stride.y)}
, {"pad-x", std::to_string(pad.x)}
, {"pad-y", std::to_string(pad.y)}
, {"output", std::to_string(out_channels)}
, {"group", std::to_string(group)}
, {"dilation-x", std::to_string(dilation_factor.x)}
, {"dilation-y", std::to_string(dilation_factor.y)}
};
ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("Convolution")
.params(layer_params)
.weights(num_weights)
.biases(num_bias),
{},
weights_ptr));
SetFirstInputToRange(-0.9f, 0.9f);
ref_convolution(inputBlob, _refBlob, weights, bias, kernel, stride, pad, group, dilation_factor);
ASSERT_TRUE(Infer());
auto inputBlob = _inputMap.begin()->second;
auto outputBlob = _outputMap.begin()->second;
float maxerr = 0;
ref_convolution(inputBlob, _refBlob, weights, bias, kernel, stride, pad, group, dilation_factor);
if (group == 1)
maxerr = 0.00055 * input_dims.c * kernel.x * kernel.y;
else // TODO: currently dephConv is slightly less accurate
maxerr = 0.00066 * (input_dims.c / group) * kernel.x * kernel.y;
float maxerr = 0;
CompareCommonAbsolute(outputBlob, _refBlob, maxerr);
}
};
if (group == 1)
maxerr = 0.00055 * input_dims.c * kernel.x * kernel.y;
else // TODO: currently dephConv is slightly less accurate
maxerr = 0.00066 * (input_dims.c / group) * kernel.x * kernel.y;
class myriadLayerConvolution : public myriadLayerConvolutionBase {};
CompareCommonAbsolute(outputBlob, _refBlob, maxerr);
TEST_P(myriadLayerConvolution, Convolution) {
testConvolution();
}
TEST_P(myriadLayerConvolutionTensorFlow_smoke, Convolution) {
class myriadLayerConvolution_smoke : public myriadLayerConvolutionBase {};
TEST_P(myriadLayerConvolution_smoke, Convolution) {
testConvolution();
}
class myriadLayerConvolutionTensorFlow :
public myriadLayerTestBaseWithParam<tuple<DimsInput, DimsOutput, kernel, stride,
pad, group, dilation_factor, layoutPreference >> {};
TEST_P(myriadLayerConvolutionTensorFlow, Convolution) {
tensor_test_params input_dims = get<0>(GetParam());
tensor_test_params output_dims = get<1>(GetParam());
param_size kernel = get<2>(GetParam());