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Removed runtime::Tensor alias (#22429) 

* Removed AllocatorImpl, runtime::Tensor alias

* Update OMZ

* Updated submodule
This commit is contained in:
Ilya Lavrenov 2024-01-30 15:34:21 +04:00 committed by GitHub
parent cb679a73c9
commit b0e507c6c3
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
35 changed files with 116 additions and 127 deletions
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8
src/core/include/openvino/runtime/allocator.hpp
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@ -14,9 +14,9 @@
#include "openvino/core/any.hpp"
#include "openvino/core/core_visibility.hpp"
#include "openvino/core/deprecated.hpp"
namespace ov {
class Tensor;
/**
@ -158,10 +158,4 @@ public:
explicit operator bool() const noexcept;
};
OPENVINO_SUPPRESS_DEPRECATED_START
namespace runtime {
using ov::Allocator;
} // namespace runtime
OPENVINO_SUPPRESS_DEPRECATED_END
} // namespace ov

5
src/core/include/openvino/runtime/tensor.hpp
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@ -259,9 +259,4 @@ public:
*/
using TensorVector = std::vector<Tensor>;
namespace runtime {
using ov::Tensor;
using ov::TensorVector;
} // namespace runtime
} // namespace ov

2
src/core/src/preprocess/preprocess_impls.hpp
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@ -218,7 +218,7 @@ public:
m_shape_set = true;
}
void set_from(const ov::runtime::Tensor& runtime_tensor) {
void set_from(const ov::Tensor& runtime_tensor) {
set_shape(runtime_tensor.get_shape());
set_element_type(runtime_tensor.get_element_type());
}

2
src/plugins/intel_cpu/tests/functional/single_layer_tests/gather.cpp
View File

@ -128,7 +128,7 @@ protected:
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
ov::test::utils::InputGenerateData in_data;
if (funcInput.get_node()->get_friendly_name() == "data") {

2
src/plugins/intel_cpu/tests/functional/single_layer_tests/grid_sample.cpp
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@ -136,7 +136,7 @@ protected:
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
ov::test::utils::InputGenerateData in_data;
if (funcInput.get_node()->get_friendly_name() == "data") {

2
src/plugins/intel_cpu/tests/functional/single_layer_tests/non_max_suppression.cpp
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@ -96,7 +96,7 @@ public:
auto node = funcInputs[2].get_node_shared_ptr();
auto it = inputs.find(node);
if (it == inputs.end()) return;
auto tensor = ov::runtime::Tensor(node->get_element_type(), targetInputStaticShapes[2], &maxOutBoxesPerClass);
auto tensor = ov::Tensor(node->get_element_type(), targetInputStaticShapes[2], &maxOutBoxesPerClass);
inputs[node] = tensor;
}

6
src/plugins/intel_cpu/tests/functional/single_layer_tests/rdft.cpp
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@ -152,21 +152,21 @@ protected:
const auto& funcInputs = function->inputs();
auto funcInput = funcInputs.begin();
inputs.clear();
runtime::Tensor data_tensor = test::utils::create_and_fill_tensor_normal_distribution(funcInput->get_element_type(),
ov::Tensor data_tensor = test::utils::create_and_fill_tensor_normal_distribution(funcInput->get_element_type(),
targetInputStaticShapes[0], 0, 1, 0);
inputs.insert({funcInput->get_node_shared_ptr(), data_tensor});
funcInput++;
if (!constAxes && funcInput != funcInputs.end()) {
ASSERT_TRUE(inputIdx < axes.size());
auto tensor = ov::runtime::Tensor{funcInput->get_element_type(), Shape{axes[inputIdx].size()}};
auto tensor = ov::Tensor{funcInput->get_element_type(), Shape{axes[inputIdx].size()}};
std::memcpy(tensor.data(), axes[inputIdx].data(), axes[inputIdx].size() * sizeof(axes[0][0]));
inputs.insert({funcInput->get_node_shared_ptr(), tensor});
funcInput++;
}
if (!constSignalSizes && funcInput != funcInputs.end()) {
ASSERT_TRUE(inputIdx < signalSizes.size());
auto tensor = ov::runtime::Tensor{funcInput->get_element_type(), Shape{signalSizes[inputIdx].size()}};
auto tensor = ov::Tensor{funcInput->get_element_type(), Shape{signalSizes[inputIdx].size()}};
std::memcpy(tensor.data(), signalSizes[inputIdx].data(), signalSizes[inputIdx].size() * sizeof(signalSizes[0][0]));
inputs.insert({funcInput->get_node_shared_ptr(), tensor});
}

4
src/plugins/intel_cpu/tests/functional/single_layer_tests/shape_ops.cpp
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@ -83,11 +83,11 @@ protected:
const auto& funcInputs = function->inputs();
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
if (i == 1) {
#define RESHAPE_TEST_CASE(INT_TYPE) \
case ov::element::Type_t::INT_TYPE: { \
tensor = ov::runtime::Tensor{ov::element::INT_TYPE, targetInputStaticShapes[i]}; \
tensor = ov::Tensor{ov::element::INT_TYPE, targetInputStaticShapes[i]}; \
auto inputData = tensor.data<ov::element_type_traits<ov::element::INT_TYPE>::value_type>(); \
ASSERT_TRUE(idx < data.size()); \
for (size_t j = 0lu; j < data[idx].size(); ++j) { \

2
src/plugins/intel_cpu/tests/functional/single_layer_tests/unique.cpp
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@ -120,7 +120,7 @@ protected:
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
if (funcInput.get_node()->get_friendly_name() == "data") {
int32_t range = std::accumulate(targetInputStaticShapes[0].begin(), targetInputStaticShapes[0].end(), 1, std::multiplies<size_t>());

4
src/plugins/intel_cpu/tests/functional/subgraph_tests/src/reshape_inplace.cpp
View File

@ -57,9 +57,9 @@ protected:
const auto& funcInputs = function->inputs();
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
if (i == 1) {
tensor = ov::runtime::Tensor{ov::element::i32, targetInputStaticShapes[i]};
tensor = ov::Tensor{ov::element::i32, targetInputStaticShapes[i]};
auto inputData = tensor.data<ov::element_type_traits<ov::element::i32>::value_type>();
const std::vector<unsigned> data = {38, 38, 15, 4};
for (size_t j = 0lu; j < data.size(); ++j) {

2
src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/grid_sample.cpp
View File

@ -99,7 +99,7 @@ protected:
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
ov::runtime::Tensor tensor;
ov::Tensor tensor;
ov::test::utils::InputGenerateData in_data;
if (funcInput.get_node()->get_friendly_name() == "data") {

2
src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/non_max_suppression.cpp
View File

@ -103,7 +103,7 @@ public:
auto node = funcInputs[2].get_node_shared_ptr();
auto it = inputs.find(node);
if (it == inputs.end()) return;
auto tensor = ov::runtime::Tensor(node->get_element_type(), targetInputStaticShapes[2], &maxOutBoxesPerClass);
auto tensor = ov::Tensor(node->get_element_type(), targetInputStaticShapes[2], &maxOutBoxesPerClass);
inputs[node] = tensor;
}

2
src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/dynamic_smoke_test_with_empty_tensor.cpp
View File

@ -54,7 +54,7 @@ protected:
const auto& funcInputs = function->inputs();
for (size_t i = 0; i < funcInputs.size(); ++i) {
auto node = funcInputs[i].get_node_shared_ptr();
auto tensor = ov::runtime::Tensor(node->get_element_type(), targetInputStaticShapes[i]);
auto tensor = ov::Tensor(node->get_element_type(), targetInputStaticShapes[i]);
if (i == 0) {
// All zero inputs for non_zero op
auto tensor_ptr = static_cast<int32_t*>(tensor.data());

16
src/plugins/template/backend/executable.hpp
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@ -19,16 +19,16 @@ public:
Executable();
virtual ~Executable();
/// \param outputs vector of runtime::Tensor used as outputs
/// \param inputs vector of runtime::Tensor used as inputs
/// \param outputs vector of Tensor used as outputs
/// \param inputs vector of Tensor used as inputs
/// \param collect_performance Enable per operation performance statistic
/// \returns true if iteration is successful, false otherwise
virtual bool call(std::vector<ov::Tensor>& outputs,
const std::vector<ov::Tensor>& inputs,
bool collect_performance = false) = 0;
/// \param outputs vector of runtime::Tensor used as outputs
/// \param inputs vector of runtime::Tensor used as inputs
/// \param outputs vector of Tensor used as outputs
/// \param inputs vector of Tensor used as inputs
/// \param context Evaluation context
/// \param collect_performance Enable per operation performance statistic
/// \returns true if iteration is successful, false otherwise
@ -41,14 +41,14 @@ public:
virtual void cancel() = 0;
/// \brief Executes a single iteration of a Function.
/// \param outputs vector of runtime::Tensor used as outputs
/// \param inputs vector of runtime::Tensor used as inputs
/// \param outputs vector of Tensor used as outputs
/// \param inputs vector of Tensor used as inputs
/// \returns true if iteration is successful, false otherwise
bool call_with_validate(std::vector<ov::Tensor>& outputs, const std::vector<ov::Tensor>& inputs);
/// \brief Validates a Function.
/// \param outputs vector of runtime::Tensor used as outputs
/// \param inputs vector of runtime::Tensor used as inputs
/// \param outputs vector of Tensor used as outputs
/// \param inputs vector of Tensor used as inputs
void validate(const std::vector<ov::Tensor>& outputs, const std::vector<ov::Tensor>& inputs);
/// \brief Query the input Parameters

8
src/plugins/template/tests/functional/op_reference/interpolate.cpp
View File

@ -50,8 +50,8 @@ struct InterpolateV1Params {
Shape outShape;
element::Type inType;
element::Type outType;
runtime::Tensor inData;
runtime::Tensor outData;
ov::Tensor inData;
ov::Tensor outData;
std::shared_ptr<op::v0::Constant> outShapeInput;
op::v0::Interpolate::Attributes attrs;
};
@ -82,8 +82,8 @@ struct InterpolateV4Params {
Shape outShape;
element::Type inType;
element::Type outType;
runtime::Tensor inData;
runtime::Tensor outData;
ov::Tensor inData;
ov::Tensor outData;
std::vector<size_t> outShapeInput;
element::Type outShapeInputType;
std::vector<float> scales;

8
src/plugins/template/tests/functional/op_reference/memory.cpp
View File

@ -36,8 +36,8 @@ struct ReadValueAssignParams {
Shape m_output_shape;
element::Type m_input_type;
element::Type m_output_type;
runtime::Tensor m_input_data;
runtime::Tensor m_expected_data;
ov::Tensor m_input_data;
ov::Tensor m_expected_data;
std::string m_variable_id;
};
@ -245,8 +245,8 @@ struct MemoryTestParams {
ov::Shape m_output_shape;
ov::element::Type m_input_type;
ov::element::Type m_output_type;
ov::runtime::Tensor m_input_data;
ov::runtime::Tensor m_expected_data;
ov::Tensor m_input_data;
ov::Tensor m_expected_data;
std::vector<std::string> m_variable_id;
size_t m_count_runs;
size_t m_reset_on_run;

4
src/plugins/template/tests/functional/op_reference/region_yolo.cpp
View File

@ -67,8 +67,8 @@ struct RegionYoloParams {
ov::PartialShape inputShape;
ov::element::Type inType;
ov::element::Type outType;
ov::runtime::Tensor inputData;
ov::runtime::Tensor refData;
ov::Tensor inputData;
ov::Tensor refData;
std::string testcaseName;
};

2
src/tests/functional/plugin/conformance/test_runner/op_conformance_runner/src/read_ir/read_ir.cpp
View File

@ -312,7 +312,7 @@ std::vector<ov::Tensor> ReadIRTest::calculate_refs() {
size_t pos = 0;
for (const auto& output : functionRefs->outputs()) {
auto out_tensor = ov::runtime::Tensor(output.get_element_type(), output.get_shape(), &ref_buffer[pos]);
auto out_tensor = ov::Tensor(output.get_element_type(), output.get_shape(), &ref_buffer[pos]);
pos += out_tensor.get_byte_size();
}
}

2
src/tests/functional/plugin/shared/include/behavior/ov_infer_request/infer_request_dynamic.hpp
View File

@ -41,7 +41,7 @@ public:
protected:
void SetUp() override;
bool checkOutput(const ov::runtime::Tensor& in, const ov::runtime::Tensor& actual);
bool checkOutput(const ov::Tensor& in, const ov::Tensor& actual);
std::shared_ptr<ov::Core> ie = utils::PluginCache::get().core();
std::shared_ptr<Model> function;

2
src/tests/functional/plugin/shared/include/behavior/ov_infer_request/iteration_chaining.hpp
View File

@ -39,7 +39,7 @@ struct OVIterationChaining : public OVInferRequestTests {
private:
static std::shared_ptr<ov::Model> getIterativeFunction();
bool checkOutput(const ov::runtime::Tensor& in, const ov::runtime::Tensor& actual);
bool checkOutput(const ov::Tensor& in, const ov::Tensor& actual);
};
} // namespace behavior

16
src/tests/functional/plugin/shared/src/behavior/ov_infer_request/infer_request_dynamic.cpp
View File

@ -44,7 +44,7 @@ void OVInferRequestDynamicTests::SetUp() {
APIBaseTest::SetUp();
}
bool OVInferRequestDynamicTests::checkOutput(const ov::runtime::Tensor& in, const ov::runtime::Tensor& actual) {
bool OVInferRequestDynamicTests::checkOutput(const ov::Tensor& in, const ov::Tensor& actual) {
bool result = true;
auto net = ie->compile_model(function, ov::test::utils::DEVICE_TEMPLATE);
ov::InferRequest req;
@ -96,7 +96,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetwork) {
ov::test::utils::InputGenerateData in_data;
in_data.start_from = -50;
in_data.range = 100;
ov::runtime::Tensor inTensor = ov::test::utils::create_and_fill_tensor(element::f32, shape, in_data);
ov::Tensor inTensor = ov::test::utils::create_and_fill_tensor(element::f32, shape, in_data);
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
OV_ASSERT_NO_THROW(req.set_tensor("input_tensor", inTensor));
OV_ASSERT_NO_THROW(req.infer());
@ -115,7 +115,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkSetUnexpectedOutputTensorB
auto execNet = ie->compile_model(function, target_device, configuration);
// Create InferRequest
ov::InferRequest req;
ov::runtime::Tensor tensor, otensor;
ov::Tensor tensor, otensor;
const std::string outputname = function->outputs().back().get_any_name();
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
ov::test::utils::InputGenerateData in_data;
@ -144,7 +144,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkSetOutputTensorPreAllocate
auto execNet = ie->compile_model(function, target_device, configuration);
// Create InferRequest
ov::InferRequest req;
ov::runtime::Tensor tensor;
ov::Tensor tensor;
const std::string outputname = function->outputs().back().get_any_name();
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
ov::test::utils::InputGenerateData in_data;
@ -153,7 +153,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkSetOutputTensorPreAllocate
tensor = ov::test::utils::create_and_fill_tensor(element::f32, refShape, in_data);
OV_ASSERT_NO_THROW(req.set_tensor("input_tensor", tensor));
float ptr[5000];
ov::runtime::Tensor otensor(element::f32, refOutShape, ptr);
ov::Tensor otensor(element::f32, refOutShape, ptr);
OV_ASSERT_NO_THROW(req.set_tensor(outputname, otensor));
OV_ASSERT_NO_THROW(req.infer());
ASSERT_EQ(req.get_tensor(outputname).data<float>(), ptr);
@ -172,7 +172,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkSetOutputShapeBeforeInfer)
auto execNet = ie->compile_model(function, target_device, configuration);
// Create InferRequest
ov::InferRequest req;
ov::runtime::Tensor tensor, otensor;
ov::Tensor tensor, otensor;
const std::string outputname = function->outputs().back().get_any_name();
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
ov::test::utils::InputGenerateData in_data;
@ -199,7 +199,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkGetOutputThenSetOutputTens
auto execNet = ie->compile_model(function, target_device, configuration);
// Create InferRequest
ov::InferRequest req;
ov::runtime::Tensor tensor;
ov::Tensor tensor;
const std::string outputname = function->outputs().back().get_any_name();
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
ov::test::utils::InputGenerateData in_data;
@ -213,7 +213,7 @@ TEST_P(OVInferRequestDynamicTests, InferDynamicNetworkGetOutputThenSetOutputTens
ASSERT_TRUE(checkOutput(req.get_tensor("input_tensor"), req.get_tensor(outputname)));
// then, set output tensor
float ptr[5000];
ov::runtime::Tensor otensor(element::f32, refOutShape, ptr);
ov::Tensor otensor(element::f32, refOutShape, ptr);
OV_ASSERT_NO_THROW(req.set_tensor(outputname, otensor));
OV_ASSERT_NO_THROW(req.infer());
ASSERT_EQ(req.get_tensor(outputname).data<float>(), ptr);

4
src/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
View File

@ -235,7 +235,7 @@ TEST_P(OVInferRequestIOTensorTest, InferStaticNetworkSetChangedInputTensorThrow)
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
OV_ASSERT_NO_THROW(req.infer());
// Get input_tensor
ov::runtime::Tensor tensor;
ov::Tensor tensor;
OV_ASSERT_NO_THROW(tensor = req.get_tensor(function->inputs().back().get_any_name()));
// Set shape
OV_ASSERT_NO_THROW(tensor.set_shape(shape2));
@ -258,7 +258,7 @@ TEST_P(OVInferRequestIOTensorTest, InferStaticNetworkSetChangedOutputTensorThrow
OV_ASSERT_NO_THROW(req = execNet.create_infer_request());
OV_ASSERT_NO_THROW(req.infer());
// Get output_tensor
ov::runtime::Tensor tensor;
ov::Tensor tensor;
OV_ASSERT_NO_THROW(tensor = req.get_tensor(function->outputs().back().get_any_name()););
// Set shape
OV_ASSERT_NO_THROW(tensor.set_shape(shape2));

2
src/tests/functional/plugin/shared/src/behavior/ov_infer_request/iteration_chaining.cpp
View File

@ -78,7 +78,7 @@ void OVIterationChaining::TearDown() {
OVInferRequestTests::TearDown();
}
bool OVIterationChaining::checkOutput(const ov::runtime::Tensor& in, const ov::runtime::Tensor& actual) {
bool OVIterationChaining::checkOutput(const ov::Tensor& in, const ov::Tensor& actual) {
bool result = true;
auto net = core->compile_model(function, ov::test::utils::DEVICE_TEMPLATE);
ov::InferRequest req;

4
src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/compare_results.hpp
View File

@ -13,8 +13,8 @@ namespace utils {
using CompareMap = std::map<ov::NodeTypeInfo, std::function<void(
const std::shared_ptr<ov::Node> &node,
size_t port,
const ov::runtime::Tensor &expected,
const ov::runtime::Tensor &actual,
const ov::Tensor &expected,
const ov::Tensor &actual,
double absThreshold,
double relThreshold)>>;

2
src/tests/functional/shared_test_classes/include/shared_test_classes/base/utils/generate_inputs.hpp
View File

@ -17,7 +17,7 @@ void reset_const_ranges();
std::vector<uint8_t> color_test_image(size_t height, size_t width, int b_step, ov::preprocess::ColorFormat format);
using InputsMap = std::map<ov::NodeTypeInfo, std::function<ov::runtime::Tensor(
using InputsMap = std::map<ov::NodeTypeInfo, std::function<ov::Tensor(
const std::shared_ptr<ov::Node>& node,
size_t port,
const ov::element::Type& elemType,

12
src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/stateful_model.hpp
View File

@ -113,7 +113,7 @@ public:
inputs.clear();
const auto& funcInputs = function->inputs();
const auto& funcInput = funcInputs.front();
auto tensor = ov::runtime::Tensor{ov::element::f32, funcInput.get_shape()};
auto tensor = ov::Tensor{ov::element::f32, funcInput.get_shape()};
auto inputData = tensor.data<ov::element_type_traits<ov::element::f32>::value_type>();
inputData[0] = input_vals[i];
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
@ -222,7 +222,7 @@ public:
inputs.clear();
const auto& funcInputs = function->inputs();
const auto& funcInput = funcInputs.front();
auto tensor = ov::runtime::Tensor{testPrc, funcInput.get_shape()};
auto tensor = ov::Tensor{testPrc, funcInput.get_shape()};
auto inputData = tensor.data<ov::element_type_traits<testPrc>::value_type>();
inputData[0] = input_vals[i];
inputs.insert({funcInput.get_node_shared_ptr(), tensor});
@ -336,7 +336,7 @@ public:
auto states = inferRequest.query_state();
ASSERT_FALSE(states.empty());
auto init_tensor = ov::runtime::Tensor{testPrc, ov::Shape{1, 1}};
auto init_tensor = ov::Tensor{testPrc, ov::Shape{1, 1}};
auto init_data = init_tensor.data<ov::element_type_traits<testPrc>::value_type>();
init_data[0] = vec_state[0];
states.front().set_state(init_tensor);
@ -348,7 +348,7 @@ public:
auto &input_shape = shapes.front();
const auto& funcInputs = function->inputs();
const auto& funcInput = funcInputs.front();
auto tensor = ov::runtime::Tensor{testPrc, input_shape};
auto tensor = ov::Tensor{testPrc, input_shape};
auto input_data = tensor.data<ov::element_type_traits<testPrc>::value_type>();
for (size_t i = 0; i < input_shape.front(); ++i) {
input_data[i] = input_vals[i];
@ -507,7 +507,7 @@ public:
auto states = inferRequest.query_state();
ASSERT_FALSE(states.empty());
auto init_tensor = ov::runtime::Tensor{testPrc, ov::Shape{1, 1}};
auto init_tensor = ov::Tensor{testPrc, ov::Shape{1, 1}};
auto init_data = init_tensor.data<ov::element_type_traits<testPrc>::value_type>();
init_data[0] = vec_state[0];
states.front().set_state(init_tensor);
@ -519,7 +519,7 @@ public:
auto &input_shape = shapes.front();
const auto& funcInputs = function->inputs();
for (auto&& funcInput : funcInputs) {
auto tensor = ov::runtime::Tensor{testPrc, input_shape};
auto tensor = ov::Tensor{testPrc, input_shape};
auto input_data = tensor.data<ov::element_type_traits<testPrc>::value_type>();
for (size_t i = 0; i < input_shape[1]; ++i) {
input_data[i] = input_vals[i];

4
src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_prior_grid_generator.cpp
View File

@ -74,7 +74,7 @@ void ExperimentalDetectronPriorGridGeneratorLayerTest::SetUp() {
namespace {
template<typename T>
ov::runtime::Tensor generateTensorByShape(const Shape &shape) {
ov::Tensor generateTensorByShape(const Shape &shape) {
return ov::test::utils::create_tensor<T>(
ov::element::from<T>(),
shape,
@ -98,7 +98,7 @@ void ExperimentalDetectronPriorGridGeneratorLayerTest::generate_inputs(const std
inputs.insert({funcInputs[i].get_node_shared_ptr(), inputTensors.second[i]});
}
for (auto j = i; j < funcInputs.size(); ++j) {
ov::runtime::Tensor inputTensor = (netPrecision == element::f16)
ov::Tensor inputTensor = (netPrecision == element::f16)
? generateTensorByShape<ov::float16>(targetInputStaticShapes[j])
: generateTensorByShape<float>(
targetInputStaticShapes[j]);

4
src/tests/functional/shared_test_classes/src/single_op/activation.cpp
View File

@ -111,7 +111,7 @@ void ActivationLayerTest::generate_inputs(const std::vector<ov::Shape>& targetIn
const auto& funcInputs = function->inputs();
auto funcInput = funcInputs.begin();
inputs.clear();
runtime::Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
targetInputStaticShapes[0],
data_range,
data_start_from,
@ -132,7 +132,7 @@ void ActivationParamLayerTest::generate_inputs(const std::vector<ov::Shape>& tar
const auto& funcInputs = function->inputs();
for (size_t i = 0; i < funcInputs.size(); ++i) {
const auto& funcInput = funcInputs[i];
runtime::Tensor data_tensor;
Tensor data_tensor;
const std::string& name = funcInput.get_node()->get_friendly_name();
if (0 == name.compare("negativeSlope")) {
ov::test::utils::InputGenerateData in_data;

2
src/tests/functional/shared_test_classes/src/single_op/dft.cpp
View File

@ -52,7 +52,7 @@ void DFTLayerTest::generate_inputs(const std::vector<ov::Shape>& targetInputStat
}
inputs.clear();
runtime::Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
targetInputStaticShapes[0],
inGenData.range, inGenData.start_from, inGenData.resolution, inGenData.seed);
inputs.insert({funcInput->get_node_shared_ptr(), data_tensor});

2
src/tests/functional/shared_test_classes/src/single_op/rdft.cpp
View File

@ -51,7 +51,7 @@ void RDFTLayerTest::generate_inputs(const std::vector<ov::Shape>& targetInputSta
}
inputs.clear();
runtime::Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
Tensor data_tensor = ov::test::utils::create_and_fill_tensor_act_dft(funcInput->get_element_type(),
targetInputStaticShapes[0],
inGenData.range, inGenData.start_from, inGenData.resolution, inGenData.seed);
inputs.insert({funcInput->get_node_shared_ptr(), data_tensor});

2
src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
View File

@ -170,7 +170,7 @@ void fill_psroi(ov::Tensor& tensor,
int spatialBinsY,
const std::string& mode);
void fill_data_roi(ov::runtime::Tensor& tensor,
void fill_data_roi(ov::Tensor& tensor,
const uint32_t range,
const int height,
const int width,

62
src/tests/test_utils/common_test_utils/include/common_test_utils/ov_tensor_utils.hpp
View File

@ -36,46 +36,46 @@ ov::Tensor create_and_fill_tensor(const ov::element::Type element_type,
const int seed = 1);
template <class T>
static ov::runtime::Tensor create_tensor(const ov::element::Type& element_type,
const ov::Shape& shape,
const std::vector<T>& values,
const size_t size = 0) {
static ov::Tensor create_tensor(const ov::element::Type& element_type,
const ov::Shape& shape,
const std::vector<T>& values,
const size_t size = 0) {
const size_t real_size = size ? size : values.size() * sizeof(T) / element_type.size();
ov::runtime::Tensor tensor{element_type, shape};
ov::Tensor tensor{element_type, shape};
std::memcpy(tensor.data(), values.data(), std::min(real_size * element_type.size(), sizeof(T) * values.size()));
return tensor;
}
ov::runtime::Tensor create_and_fill_tensor_act_dft(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range = 10,
const double_t start_from = 0,
const int32_t resolution = 1,
const int seed = 1);
ov::Tensor create_and_fill_tensor_act_dft(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range = 10,
const double_t start_from = 0,
const int32_t resolution = 1,
const int seed = 1);
ov::runtime::Tensor create_and_fill_tensor_unique_sequence(const ov::element::Type element_type,
const ov::Shape& shape,
const int32_t start_from = 0,
const int32_t resolution = 1,
const int seed = 1);
ov::Tensor create_and_fill_tensor_unique_sequence(const ov::element::Type element_type,
const ov::Shape& shape,
const int32_t start_from = 0,
const int32_t resolution = 1,
const int seed = 1);
ov::runtime::Tensor create_and_fill_tensor_normal_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float mean,
const float stddev,
const int seed = 1);
ov::Tensor create_and_fill_tensor_normal_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float mean,
const float stddev,
const int seed = 1);
ov::runtime::Tensor create_and_fill_tensor_consistently(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range,
const int32_t start_from,
const int32_t resolution);
ov::Tensor create_and_fill_tensor_consistently(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range,
const int32_t start_from,
const int32_t resolution);
ov::runtime::Tensor create_and_fill_tensor_real_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float min,
const float max,
const int seed);
ov::Tensor create_and_fill_tensor_real_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float min,
const float max,
const int seed);
void compare(const ov::Tensor& expected,
const ov::Tensor& actual,

4
src/tests/test_utils/common_test_utils/src/data_utils.cpp
View File

@ -338,7 +338,7 @@ void fill_psroi(ov::Tensor& tensor,
}
template <ov::element::Type_t type>
inline void fill_data_roi_impl(ov::runtime::Tensor& tensor,
inline void fill_data_roi_impl(ov::Tensor& tensor,
const uint32_t range,
const int height,
const int width,
@ -384,7 +384,7 @@ inline void fill_data_roi_impl(ov::runtime::Tensor& tensor,
}
}
void fill_data_roi(ov::runtime::Tensor& tensor,
void fill_data_roi(ov::Tensor& tensor,
const uint32_t range,
const int height,
const int width,

36
src/tests/test_utils/common_test_utils/src/ov_tensor_utils.cpp
View File

@ -174,12 +174,12 @@ ov::Tensor create_and_fill_tensor_unique_sequence(const ov::element::Type elemen
return tensor;
}
ov::runtime::Tensor create_and_fill_tensor_normal_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float mean,
const float stddev,
const int seed) {
auto tensor = ov::runtime::Tensor{element_type, shape};
ov::Tensor create_and_fill_tensor_normal_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float mean,
const float stddev,
const int seed) {
auto tensor = ov::Tensor{element_type, shape};
#define CASE(X) \
case X: \
fill_data_ptr_normal_random_float(tensor.data<element_type_traits<X>::value_type>(), \
@ -218,12 +218,12 @@ ov::runtime::Tensor create_and_fill_tensor_normal_distribution(const ov::element
return tensor;
}
ov::runtime::Tensor create_and_fill_tensor_real_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float min,
const float max,
const int seed) {
auto tensor = ov::runtime::Tensor{element_type, shape};
ov::Tensor create_and_fill_tensor_real_distribution(const ov::element::Type element_type,
const ov::Shape& shape,
const float min,
const float max,
const int seed) {
auto tensor = ov::Tensor{element_type, shape};
#define CASE(X) \
case X: \
fill_data_ptr_real_random_float(tensor.data<element_type_traits<X>::value_type>(), \
@ -258,12 +258,12 @@ ov::runtime::Tensor create_and_fill_tensor_real_distribution(const ov::element::
return tensor;
}
ov::runtime::Tensor create_and_fill_tensor_consistently(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range,
const int32_t start_from,
const int32_t resolution) {
auto tensor = ov::runtime::Tensor{element_type, shape};
ov::Tensor create_and_fill_tensor_consistently(const ov::element::Type element_type,
const ov::Shape& shape,
const uint32_t range,
const int32_t start_from,
const int32_t resolution) {
auto tensor = ov::Tensor{element_type, shape};
#define CASE(X) \
case X: \
fill_data_ptr_consistently(tensor.data<element_type_traits<X>::value_type>(), \

2
thirdparty/open_model_zoo vendored

@ -1 +1 @@
Subproject commit e8fb4cd86a516ce5765290e9665f8afe87b79b2e
Subproject commit 126fac1894c806190a0e0a9737b5fc692d7f4baa