TEST_P(ModelTransformationsTest, LPT) {}
+static void checkLayerInputPrecision(const ICNNNetwork& network, const std::string& layerName, Precision expectedPrecision, int inputIndex = -1) {
+ CNNLayerPtr layer = getLayer(network, layerName);
+ if (layer == nullptr) {
+ THROW_IE_EXCEPTION << "layer '" << layerName << "' was not found";
+ }
+ for (size_t index = 0ul; index < layer->insData.size(); ++index) {
+ if ((inputIndex != -1) && (index != inputIndex)) {
+ continue;
+ }
+
+ const DataWeakPtr weakData = layer->insData[index];
+ ASSERT_EQ(expectedPrecision, weakData.lock()->getPrecision()) << " unexpected precision " << weakData.lock()->getPrecision() << " for layer " << layerName;
+ }
+}
+
ModelParams getModelParams(const std::string modelName) {
std::map<std::string, ModelParams> modelParams = {
{
for (const std::pair<std::string, std::string> item : fakeQuantizeAndConcolutionItems) {
TestsCommonFunc::checkLayerOuputPrecision(*usedNetwork, item.first, Precision::U8);
if (!item.second.empty()) {
- TestsCommonFunc::checkLayerInputPrecision(*usedNetwork, item.second, Precision::U8, 0);
+ checkLayerInputPrecision(*usedNetwork, item.second, Precision::U8, 0);
}
}
}
protected:
+ static void fill_data_bin(float *data, size_t size) {
+ for (size_t i = 0; i < size; i++) {
+ data[i] = sinf((float)i) > 0.f ? 1.f : -1.f;
+ }
+ }
+
+ static void fill_data_bin_packed(int8_t *data, size_t size) {
+ int nbits = 8;
+ for (size_t i = 0; i < div_up(size, nbits); i++) {
+ data[i] = static_cast<int8_t>(i % 255);
+ }
+ }
+
size_t calculateOutDim(size_t in_dim, size_t kernel, size_t stride, size_t pad_begin) {
return (in_dim + 2lu * pad_begin - kernel) / stride + 1lu;
}
}
}
+static void relative_compare(
+ const float* res,
+ const float* ref,
+ size_t size,
+ float max_diff = 0.01f,
+ const std::string assertDetails = "",
+ float zero_diff = 1e-7f) {
+ for (size_t i = 0lu; i < size; i++) {
+ if (std::isnan(res[i]) && std::isnan(ref[i])) {
+ continue;
+ }
+
+ if ((ref[i] == 0.f) || (res[i] == 0.f)) {
+ const float diff = fabs(res[i] - ref[i]);
+ ASSERT_TRUE(diff < zero_diff) <<
+ "\nAbsolute comparison of values ref: " << ref[i] << " and res: " << res[i] <<
+ ", diff: " << diff <<
+ ", index: " << i << "\n" << assertDetails;
+ } else {
+ const float diff = fabs((res[i] - ref[i]) / (std::max)(ref[i], res[i]));
+ ASSERT_LT(diff, max_diff) <<
+ "\nRelative comparison of values ref: " << ref[i] << " and res: " << res[i] <<
+ ", diff: " << diff <<
+ ", max_diff: " << max_diff <<
+ ", index: " << i << "\n" << assertDetails;
+ }
+ }
+}
+
void SingleLayerTransformationsTest::SetUp() {
try {
const SingleLayerTransformationsTestParams p = ::testing::WithParamInterface<SingleLayerTransformationsTestParams>::GetParam();
#endif
#define REPLACE_WITH_NUM(SRC, PATTERN, NUM) REPLACE_WITH_STR(SRC, PATTERN, to_string_c_locale(NUM))
+
#define REPLACE_WITH_NUM_VECTOR(SRC, PATTERN, NUMS) \
{ std::string result; \
if (NUMS.size() > 0u) { \
} \
} \
REPLACE_WITH_STR(SRC, PATTERN, result); }
+
#define REPLACE_WITH_NUM_VECTOR_REVERSE(SRC, PATTERN, NUMS) \
{ std::string result; \
auto nums_size = NUMS.size(); \
} \
} \
REPLACE_WITH_STR(SRC, PATTERN, result); }
+
#define REMOVE_LINE(SRC, PATTERN) REPLACE_WITH_STR(SRC, PATTERN, "")
#define PRETTY_PARAM(name, type) \
*os << #name ": " << ::testing::PrintToString((name::param_type)(param)); \
}
-struct MapStrStr {
- std::map<std::string, std::string> data{};
-
- explicit MapStrStr(std::map<std::string, std::string> _data) : data(std::move(_data)) {}
-
- MapStrStr() = default;
-};
-
template<int Version = 3>
inline InferenceEngine::CNNNetwork
buildSingleLayerNetworkCommon(const std::string &layerType,
InferenceEngine::ExecutorManager::getInstance()->clear();
}
-IE_SUPPRESS_DEPRECATED_START
-
-class BaseTestCreator {
-protected:
- std::string _type;
-public:
- explicit BaseTestCreator(const std::string& type) : _type(type) {}
- virtual ~BaseTestCreator() = default;
-
- virtual InferenceEngine::CNNLayerPtr create(const std::string& type) = 0;
-
- virtual bool shouldCreate(const std::string& type) = 0;
-};
-
-template<class LT>
-class LayerTestCreator : public BaseTestCreator {
-public:
- explicit LayerTestCreator(const std::string& type) : BaseTestCreator(type) {}
-
- InferenceEngine::CNNLayerPtr create(const std::string& type) override {
- InferenceEngine::LayerParams params;
- params.type = type;
- return std::make_shared<LT>(params);
+/**
+ * @brief Copies a 8-bit RGB image to the blob.
+ *
+ * Throws an exception in case of dimensions or input size mismatch
+ *
+ * @tparam data_t Type of the target blob
+ * @param RGB8 8-bit RGB image
+ * @param RGB8_size Size of the image
+ * @param blob Target blob to write image to
+ */
+template <typename data_t>
+void copyFromRGB8(uint8_t* RGB8, size_t RGB8_size, InferenceEngine::TBlob<data_t>* blob) {
+ InferenceEngine::SizeVector dims = blob->getTensorDesc().getDims();
+ if (4 != dims.size())
+ THROW_IE_EXCEPTION << "Cannot write data to input blob! Blob has incorrect dimensions size " << dims.size();
+ size_t num_channels = dims[1]; // because RGB
+ size_t num_images = dims[0];
+ size_t w = dims[3];
+ size_t h = dims[2];
+ size_t nPixels = w * h;
+
+ if (RGB8_size != w * h * num_channels * num_images)
+ THROW_IE_EXCEPTION << "input pixels mismatch, expecting " << w * h * num_channels * num_images
+ << " bytes, got: " << RGB8_size;
+
+ std::vector<data_t*> dataArray;
+ for (unsigned int n = 0; n < num_images; n++) {
+ for (unsigned int i = 0; i < num_channels; i++) {
+ if (!n && !i && dataArray.empty()) {
+ dataArray.push_back(blob->data());
+ } else {
+ dataArray.push_back(dataArray.at(n * num_channels + i - 1) + nPixels);
+ }
+ }
}
-
- bool shouldCreate(const std::string& type) override {
- return type == _type;
+ for (size_t n = 0; n < num_images; n++) {
+ size_t n_num_channels = n * num_channels;
+ size_t n_num_channels_nPixels = n_num_channels * nPixels;
+ for (size_t i = 0; i < nPixels; i++) {
+ size_t i_num_channels = i * num_channels + n_num_channels_nPixels;
+ for (size_t j = 0; j < num_channels; j++) {
+ dataArray.at(n_num_channels + j)[i] = RGB8[i_num_channels + j];
+ }
+ }
}
-};
-
-static std::vector<std::shared_ptr<BaseTestCreator>>& getCreators() {
- // there should be unique_ptr but it cant be used with initializer lists
- static std::vector<std::shared_ptr<BaseTestCreator> > creators = {
- std::make_shared<LayerTestCreator<InferenceEngine::PowerLayer>>("Power"),
- std::make_shared<LayerTestCreator<InferenceEngine::ConvolutionLayer>>("Convolution"),
- std::make_shared<LayerTestCreator<InferenceEngine::DeconvolutionLayer>>("Deconvolution"),
- std::make_shared<LayerTestCreator<InferenceEngine::PoolingLayer>>("Pooling"),
- std::make_shared<LayerTestCreator<InferenceEngine::FullyConnectedLayer>>("InnerProduct"),
- std::make_shared<LayerTestCreator<InferenceEngine::FullyConnectedLayer>>("FullyConnected"),
- std::make_shared<LayerTestCreator<InferenceEngine::NormLayer>>("LRN"),
- std::make_shared<LayerTestCreator<InferenceEngine::NormLayer>>("Norm"),
- std::make_shared<LayerTestCreator<InferenceEngine::SoftMaxLayer>>("Softmax"),
- std::make_shared<LayerTestCreator<InferenceEngine::SoftMaxLayer>>("LogSoftMax"),
- std::make_shared<LayerTestCreator<InferenceEngine::GRNLayer>>("GRN"),
- std::make_shared<LayerTestCreator<InferenceEngine::MVNLayer>>("MVN"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReLULayer>>("ReLU"),
- std::make_shared<LayerTestCreator<InferenceEngine::ClampLayer>>("Clamp"),
- std::make_shared<LayerTestCreator<InferenceEngine::SplitLayer>>("Split"),
- std::make_shared<LayerTestCreator<InferenceEngine::SplitLayer>>("Slice"),
- std::make_shared<LayerTestCreator<InferenceEngine::ConcatLayer>>("Concat"),
- std::make_shared<LayerTestCreator<InferenceEngine::EltwiseLayer>>("Eltwise"),
- std::make_shared<LayerTestCreator<InferenceEngine::ScaleShiftLayer>>("ScaleShift"),
- std::make_shared<LayerTestCreator<InferenceEngine::PReLULayer>>("PReLU"),
- std::make_shared<LayerTestCreator<InferenceEngine::CropLayer>>("Crop"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReshapeLayer>>("Reshape"),
- std::make_shared<LayerTestCreator<InferenceEngine::TileLayer>>("Tile"),
- std::make_shared<LayerTestCreator<InferenceEngine::BatchNormalizationLayer>>("BatchNormalization"),
- std::make_shared<LayerTestCreator<InferenceEngine::GemmLayer>>("Gemm"),
- std::make_shared<LayerTestCreator<InferenceEngine::PadLayer>>("Pad"),
- std::make_shared<LayerTestCreator<InferenceEngine::GatherLayer>>("Gather"),
- std::make_shared<LayerTestCreator<InferenceEngine::StridedSliceLayer>>("StridedSlice"),
- std::make_shared<LayerTestCreator<InferenceEngine::ShuffleChannelsLayer>>("ShuffleChannels"),
- std::make_shared<LayerTestCreator<InferenceEngine::DepthToSpaceLayer>>("DepthToSpace"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReverseSequenceLayer>>("ReverseSequence"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Abs"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Acos"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Acosh"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Asin"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Asinh"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Atan"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Atanh"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Ceil"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Cos"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Cosh"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Erf"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Floor"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("HardSigmoid"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Log"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Exp"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Reciprocal"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Selu"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sign"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sin"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sinh"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Softplus"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Softsign"),
- std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Tan"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceAnd"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceL1"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceL2"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceLogSum"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceLogSumExp"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMax"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMean"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMin"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceOr"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceProd"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceSum"),
- std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceSumSquare"),
- std::make_shared<LayerTestCreator<InferenceEngine::TopKLayer>>("TopK"),
- std::make_shared<LayerTestCreator<InferenceEngine::NonMaxSuppressionLayer>>("NonMaxSuppression"),
- std::make_shared<LayerTestCreator<InferenceEngine::ScatterUpdateLayer>>("ScatterUpdate"),
- std::make_shared<LayerTestCreator<InferenceEngine::ScatterElementsUpdateLayer>>("ScatterElementsUpdate")
- };
- return creators;
}
-InferenceEngine::CNNLayer::Ptr TestsCommon::createLayer(const std::string& type) {
- for (auto& creator : getCreators()) {
- if (!creator->shouldCreate(type))
- continue;
- return creator->create(type);
- }
- static LayerTestCreator<InferenceEngine::GenericLayer> genericCreator("");
- return genericCreator.create(type);
+/**
+ * @brief Splits the RGB channels to either I16 Blob or float blob.
+ *
+ * The image buffer is assumed to be packed with no support for strides.
+ *
+ * @param imgBufRGB8 Packed 24bit RGB image (3 bytes per pixel: R-G-B)
+ * @param lengthbytesSize Size in bytes of the RGB image. It is equal to amount of pixels times 3 (number of channels)
+ * @param input Blob to contain the split image (to 3 channels)
+ */
+void ConvertImageToInput(unsigned char* imgBufRGB8, size_t lengthbytesSize, InferenceEngine::Blob& input) {
+ InferenceEngine::TBlob<float>* float_input = dynamic_cast<InferenceEngine::TBlob<float>*>(&input);
+ if (float_input != nullptr)
+ copyFromRGB8(imgBufRGB8, lengthbytesSize, float_input);
+
+ InferenceEngine::TBlob<short>* short_input = dynamic_cast<InferenceEngine::TBlob<short>*>(&input);
+ if (short_input != nullptr)
+ copyFromRGB8(imgBufRGB8, lengthbytesSize, short_input);
+
+ InferenceEngine::TBlob<uint8_t>* byte_input = dynamic_cast<InferenceEngine::TBlob<uint8_t>*>(&input);
+ if (byte_input != nullptr)
+ copyFromRGB8(imgBufRGB8, lengthbytesSize, byte_input);
}
-IE_SUPPRESS_DEPRECATED_END
}
class TestsCommon : public ::testing::Test {
-public:
- IE_SUPPRESS_DEPRECATED_START
-
- static InferenceEngine::CNNLayer::Ptr createLayer(const std::string &type);
-
- IE_SUPPRESS_DEPRECATED_END
-
protected:
void SetUp() override;
+ static std::string make_so_name(const std::string & input) {
+ return CommonTestUtils::pre + input + IE_BUILD_POSTFIX + CommonTestUtils::ext;
+ }
+
void TearDown() override;
public:
return make_plugin_name("mock_engine");
}
- static std::string make_so_name(const std::string & input) {
- return CommonTestUtils::pre + input + IE_BUILD_POSTFIX + CommonTestUtils::ext;
- }
-
static std::string make_plugin_name(const std::string & input) {
return make_so_name(input);
}
}
}
- static void fill_data_non_zero(int32_t *data, size_t size, int n) {
- for (size_t i = 0; i < size; i++) {
- data[i] = n*i%254+1;
- }
- }
-
- static void fill_data_bin(float *data, size_t size) {
- for (size_t i = 0; i < size; i++) {
- data[i] = sinf((float)i) > 0.f ? 1.f : -1.f;
- }
- }
-
- static void fill_data_bin_packed(int8_t *data, size_t size) {
- int nbits = 8;
- for (size_t i = 0; i < div_up(size, nbits); i++) {
- data[i] = static_cast<int8_t>(i % 255);
- }
- }
-
static void fill_data_dbgval(float *data, size_t size, float alpha = 1.0f) {
for (size_t i = 0; i < size; i++) {
data[i] = i * alpha;
}
}
- static void relative_compare(
- const float* res,
- const float* ref,
- size_t size,
- float max_diff = 0.01f,
- const std::string assertDetails = "",
- float zero_diff = 1e-7f) {
- for (size_t i = 0lu; i < size; i++) {
- if (std::isnan(res[i]) && std::isnan(ref[i])) {
- continue;
- }
-
- if ((ref[i] == 0.f) || (res[i] == 0.f)) {
- const float diff = fabs(res[i] - ref[i]);
- ASSERT_TRUE(diff < zero_diff) <<
- "\nAbsolute comparison of values ref: " << ref[i] << " and res: " << res[i] <<
- ", diff: " << diff <<
- ", index: " << i << "\n" << assertDetails;
- } else {
- const float diff = fabs((res[i] - ref[i]) / (std::max)(ref[i], res[i]));
- ASSERT_LT(diff, max_diff) <<
- "\nRelative comparison of values ref: " << ref[i] << " and res: " << res[i] <<
- ", diff: " << diff <<
- ", max_diff: " << max_diff <<
- ", index: " << i << "\n" << assertDetails;
- }
- }
- }
-
void replace(std::string& str, const std::string& from, const std::string& to) {
std::string::size_type pos = 0;
# error Unsupported architecture
#endif
-
-template <typename T,typename S>
-std::shared_ptr<InferenceEngine::TBlob<T>> to_tblob(const std::shared_ptr<S> &obj)
-{
- return std::dynamic_pointer_cast<InferenceEngine::TBlob<T>>(obj);
-}
-
inline InferenceEngine::InputInfo::Ptr getFirstInput(InferenceEngine::ICNNNetwork *pNet)
{
InferenceEngine::InputsDataMap inputs;
}
/**
- * @brief Copies a 8-bit RGB image to the blob.
- *
- * Throws an exception in case of dimensions or input size mismatch
- *
- * @tparam data_t Type of the target blob
- * @param RGB8 8-bit RGB image
- * @param RGB8_size Size of the image
- * @param blob Target blob to write image to
- */
-template <typename data_t>
-void copyFromRGB8(uint8_t* RGB8, size_t RGB8_size, InferenceEngine::TBlob<data_t>* blob) {
- InferenceEngine::SizeVector dims = blob->getTensorDesc().getDims();
- if (4 != dims.size())
- THROW_IE_EXCEPTION << "Cannot write data to input blob! Blob has incorrect dimensions size " << dims.size();
- size_t num_channels = dims[1]; // because RGB
- size_t num_images = dims[0];
- size_t w = dims[3];
- size_t h = dims[2];
- size_t nPixels = w * h;
-
- if (RGB8_size != w * h * num_channels * num_images)
- THROW_IE_EXCEPTION << "input pixels mismatch, expecting " << w * h * num_channels * num_images
- << " bytes, got: " << RGB8_size;
-
- std::vector<data_t*> dataArray;
- for (unsigned int n = 0; n < num_images; n++) {
- for (unsigned int i = 0; i < num_channels; i++) {
- if (!n && !i && dataArray.empty()) {
- dataArray.push_back(blob->data());
- } else {
- dataArray.push_back(dataArray.at(n * num_channels + i - 1) + nPixels);
- }
- }
- }
- for (size_t n = 0; n < num_images; n++) {
- size_t n_num_channels = n * num_channels;
- size_t n_num_channels_nPixels = n_num_channels * nPixels;
- for (size_t i = 0; i < nPixels; i++) {
- size_t i_num_channels = i * num_channels + n_num_channels_nPixels;
- for (size_t j = 0; j < num_channels; j++) {
- dataArray.at(n_num_channels + j)[i] = RGB8[i_num_channels + j];
- }
- }
- }
-}
-
-/**
* @brief Splits the RGB channels to either I16 Blob or float blob.
*
* The image buffer is assumed to be packed with no support for strides.
* @param lengthbytesSize Size in bytes of the RGB image. It is equal to amount of pixels times 3 (number of channels)
* @param input Blob to contain the split image (to 3 channels)
*/
-inline void ConvertImageToInput(unsigned char* imgBufRGB8, size_t lengthbytesSize, InferenceEngine::Blob& input) {
- InferenceEngine::TBlob<float>* float_input = dynamic_cast<InferenceEngine::TBlob<float>*>(&input);
- if (float_input != nullptr)
- copyFromRGB8(imgBufRGB8, lengthbytesSize, float_input);
-
- InferenceEngine::TBlob<short>* short_input = dynamic_cast<InferenceEngine::TBlob<short>*>(&input);
- if (short_input != nullptr)
- copyFromRGB8(imgBufRGB8, lengthbytesSize, short_input);
-
- InferenceEngine::TBlob<uint8_t>* byte_input = dynamic_cast<InferenceEngine::TBlob<uint8_t>*>(&input);
- if (byte_input != nullptr)
- copyFromRGB8(imgBufRGB8, lengthbytesSize, byte_input);
-}
\ No newline at end of file
+void ConvertImageToInput(unsigned char* imgBufRGB8, size_t lengthbytesSize, InferenceEngine::Blob& input);
IE_SUPPRESS_DEPRECATED_START
class TestsCommonFunc {
-public:
-
- InferenceEngine::Blob::Ptr readInput(std::string path, int batch = 1);
-
static CNNLayerPtr getLayer(const ICNNNetwork& network, const std::string& layerName) {
std::vector<CNNLayerPtr> layers = InferenceEngine::details::CNNNetSortTopologically(network);
for (CNNLayerPtr layer : layers) {
return nullptr;
}
+public:
+
+ InferenceEngine::Blob::Ptr readInput(std::string path, int batch = 1);
static void checkLayerOuputPrecision(
const ICNNNetwork& network,
}
}
- static void checkLayerInputPrecision(const ICNNNetwork& network, const std::string& layerName, Precision expectedPrecision, int inputIndex = -1) {
- CNNLayerPtr layer = getLayer(network, layerName);
- if (layer == nullptr) {
- THROW_IE_EXCEPTION << "layer '" << layerName << "' was not found";
- }
- for (size_t index = 0ul; index < layer->insData.size(); ++index) {
- if ((inputIndex != -1) && (index != inputIndex)) {
- continue;
- }
-
- const DataWeakPtr weakData = layer->insData[index];
- ASSERT_EQ(expectedPrecision, weakData.lock()->getPrecision()) << " unexpected precision " << weakData.lock()->getPrecision() << " for layer " << layerName;
- }
- }
-
static void checkLayerOuputPrecision(const ICNNNetwork& network, const std::string& layerName, std::vector<Precision> expectedPrecisions) {
CNNLayerPtr layer = getLayer(network, layerName);
if (layer == nullptr) {
}
}
- static bool hasBlobEqualsValues(Blob& blob) {
- const float* buffer = blob.buffer().as<float*>();
- for (int i = 0; i < (blob.size() - 1); ++i) {
- if (buffer[i] != buffer[i + 1]) {
- return false;
- }
- }
- return true;
- }
-
- static bool checkScalesAndShifts(const CNNLayer& scaleShift, const bool equals) {
- const Blob::Ptr scalesBlob = InferenceEngine::details::CNNNetworkHelper::getBlob(std::make_shared<CNNLayer>(scaleShift), "weights");
- if (equals != hasBlobEqualsValues(*scalesBlob)) {
- return false;
- }
-
- const Blob::Ptr shiftsBlob = InferenceEngine::details::CNNNetworkHelper::getBlob(std::make_shared<CNNLayer>(scaleShift), "biases");
- if (equals != hasBlobEqualsValues(*shiftsBlob)) {
- return false;
- }
-
- return true;
- }
-
bool compareTop(
InferenceEngine::Blob& blob,
std::vector<std::pair<int, float>> &ref_top,
--- /dev/null
+// Copyright (C) 2018-2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "layer_builder.h"
+
+class BaseTestCreator {
+protected:
+ std::string _type;
+public:
+ explicit BaseTestCreator(const std::string& type) : _type(type) {}
+ virtual ~BaseTestCreator() = default;
+
+ virtual InferenceEngine::CNNLayerPtr create(const std::string& type) = 0;
+
+ virtual bool shouldCreate(const std::string& type) = 0;
+};
+
+template<class LT>
+class LayerTestCreator : public BaseTestCreator {
+public:
+ explicit LayerTestCreator(const std::string& type) : BaseTestCreator(type) {}
+
+ InferenceEngine::CNNLayerPtr create(const std::string& type) override {
+ InferenceEngine::LayerParams params;
+ params.type = type;
+ return std::make_shared<LT>(params);
+ }
+
+ bool shouldCreate(const std::string& type) override {
+ return type == _type;
+ }
+};
+
+static std::vector<std::shared_ptr<BaseTestCreator>>& getCreators() {
+ // there should be unique_ptr but it cant be used with initializer lists
+ static std::vector<std::shared_ptr<BaseTestCreator> > creators = {
+ std::make_shared<LayerTestCreator<InferenceEngine::PowerLayer>>("Power"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ConvolutionLayer>>("Convolution"),
+ std::make_shared<LayerTestCreator<InferenceEngine::DeconvolutionLayer>>("Deconvolution"),
+ std::make_shared<LayerTestCreator<InferenceEngine::PoolingLayer>>("Pooling"),
+ std::make_shared<LayerTestCreator<InferenceEngine::FullyConnectedLayer>>("InnerProduct"),
+ std::make_shared<LayerTestCreator<InferenceEngine::FullyConnectedLayer>>("FullyConnected"),
+ std::make_shared<LayerTestCreator<InferenceEngine::NormLayer>>("LRN"),
+ std::make_shared<LayerTestCreator<InferenceEngine::NormLayer>>("Norm"),
+ std::make_shared<LayerTestCreator<InferenceEngine::SoftMaxLayer>>("Softmax"),
+ std::make_shared<LayerTestCreator<InferenceEngine::SoftMaxLayer>>("LogSoftMax"),
+ std::make_shared<LayerTestCreator<InferenceEngine::GRNLayer>>("GRN"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MVNLayer>>("MVN"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReLULayer>>("ReLU"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ClampLayer>>("Clamp"),
+ std::make_shared<LayerTestCreator<InferenceEngine::SplitLayer>>("Split"),
+ std::make_shared<LayerTestCreator<InferenceEngine::SplitLayer>>("Slice"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ConcatLayer>>("Concat"),
+ std::make_shared<LayerTestCreator<InferenceEngine::EltwiseLayer>>("Eltwise"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ScaleShiftLayer>>("ScaleShift"),
+ std::make_shared<LayerTestCreator<InferenceEngine::PReLULayer>>("PReLU"),
+ std::make_shared<LayerTestCreator<InferenceEngine::CropLayer>>("Crop"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReshapeLayer>>("Reshape"),
+ std::make_shared<LayerTestCreator<InferenceEngine::TileLayer>>("Tile"),
+ std::make_shared<LayerTestCreator<InferenceEngine::BatchNormalizationLayer>>("BatchNormalization"),
+ std::make_shared<LayerTestCreator<InferenceEngine::GemmLayer>>("Gemm"),
+ std::make_shared<LayerTestCreator<InferenceEngine::PadLayer>>("Pad"),
+ std::make_shared<LayerTestCreator<InferenceEngine::GatherLayer>>("Gather"),
+ std::make_shared<LayerTestCreator<InferenceEngine::StridedSliceLayer>>("StridedSlice"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ShuffleChannelsLayer>>("ShuffleChannels"),
+ std::make_shared<LayerTestCreator<InferenceEngine::DepthToSpaceLayer>>("DepthToSpace"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReverseSequenceLayer>>("ReverseSequence"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Abs"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Acos"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Acosh"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Asin"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Asinh"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Atan"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Atanh"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Ceil"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Cos"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Cosh"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Erf"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Floor"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("HardSigmoid"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Log"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Exp"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Reciprocal"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Selu"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sign"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sin"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Sinh"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Softplus"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Softsign"),
+ std::make_shared<LayerTestCreator<InferenceEngine::MathLayer>>("Tan"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceAnd"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceL1"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceL2"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceLogSum"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceLogSumExp"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMax"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMean"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceMin"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceOr"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceProd"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceSum"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ReduceLayer>>("ReduceSumSquare"),
+ std::make_shared<LayerTestCreator<InferenceEngine::TopKLayer>>("TopK"),
+ std::make_shared<LayerTestCreator<InferenceEngine::NonMaxSuppressionLayer>>("NonMaxSuppression"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ScatterUpdateLayer>>("ScatterUpdate"),
+ std::make_shared<LayerTestCreator<InferenceEngine::ScatterElementsUpdateLayer>>("ScatterElementsUpdate")
+ };
+ return creators;
+}
+
+InferenceEngine::CNNLayer::Ptr CNNLayerValidationTests::createLayer(const std::string& type) {
+ for (auto& creator : getCreators()) {
+ if (!creator->shouldCreate(type))
+ continue;
+ return creator->create(type);
+ }
+ static LayerTestCreator<InferenceEngine::GenericLayer> genericCreator("");
+ return genericCreator.create(type);
+}
class CNNLayerValidationTests : public testing::TestWithParam<std::string>{
public:
+ static InferenceEngine::CNNLayer::Ptr createLayer(const std::string &type);
+
void SetUp() override {
auto params = GetParam();
type = params;
}
std::shared_ptr<LayerBuilder>& createConcreteLayer(const std::string& type) {
- layer = std::make_shared<LayerBuilder>(TestsCommon::createLayer(type));
+ layer = std::make_shared<LayerBuilder>(createLayer(type));
return layer;
}
projects / platform / upstream / dldt.git / commitdiff