[PyOV] OVDict class - new return value from inference (#16370)

src/bindings/python/requirements.txt

@@ -1 +1,2 @@
 numpy>=1.16.6
+singledispatchmethod; python_version<'3.8'

src/bindings/python/requirements_test.txt

@@ -40,3 +40,4 @@ types-pkg_resources
 wheel>=0.38.1
 protobuf~=3.18.1
 numpy>=1.16.6,<=1.23.4
+singledispatchmethod; python_version<'3.8'

src/bindings/python/src/openvino/runtime/ie_api.py

@@ -2,7 +2,6 @@
 # Copyright (C) 2018-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from functools import singledispatch
 from typing import Any, Iterable, Union, Dict, Optional
 from pathlib import Path
 
@@ -16,6 +15,7 @@ from openvino._pyopenvino import ConstOutput
 from openvino._pyopenvino import Tensor
 
 from openvino.runtime.utils.data_helpers import (
+    OVDict,
     _InferRequestWrapper,
     _data_dispatch,
     tensor_from_file,
@@ -25,7 +25,7 @@ from openvino.runtime.utils.data_helpers import (
 class InferRequest(_InferRequestWrapper):
     """InferRequest class represents infer request which can be run in asynchronous or synchronous manners."""
 
-    def infer(self, inputs: Any = None, shared_memory: bool = False) -> dict:
+    def infer(self, inputs: Any = None, shared_memory: bool = False) -> OVDict:
         """Infers specified input(s) in synchronous mode.
 
         Blocks all methods of InferRequest while request is running.
@@ -68,14 +68,14 @@ class InferRequest(_InferRequestWrapper):
 
                               Default value: False
         :type shared_memory: bool, optional
-        :return: Dictionary of results from output tensors with ports as keys.
+        :return: Dictionary of results from output tensors with port/int/str keys.
-        :rtype: Dict[openvino.runtime.ConstOutput, numpy.ndarray]
+        :rtype: OVDict
         """
-        return super().infer(_data_dispatch(
+        return OVDict(super().infer(_data_dispatch(
             self,
             inputs,
             is_shared=shared_memory,
-        ))
+        )))
 
     def start_async(
         self,
@@ -138,6 +138,15 @@ class InferRequest(_InferRequestWrapper):
             userdata,
         )
 
+    @property
+    def results(self) -> OVDict:
+        """Gets all outputs tensors of this InferRequest.
+
+        :return: Dictionary of results from output tensors with ports as keys.
+        :rtype: Dict[openvino.runtime.ConstOutput, numpy.array]
+        """
+        return OVDict(super().results)
+
 
 class CompiledModel(CompiledModelBase):
     """CompiledModel class.
@@ -161,7 +170,7 @@ class CompiledModel(CompiledModelBase):
         """
         return InferRequest(super().create_infer_request())
 
-    def infer_new_request(self, inputs: Union[dict, list, tuple, Tensor, np.ndarray] = None) -> dict:
+    def infer_new_request(self, inputs: Union[dict, list, tuple, Tensor, np.ndarray] = None) -> OVDict:
         """Infers specified input(s) in synchronous mode.
 
         Blocks all methods of CompiledModel while request is running.
@@ -187,8 +196,8 @@ class CompiledModel(CompiledModelBase):
 
         :param inputs: Data to be set on input tensors.
         :type inputs: Union[Dict[keys, values], List[values], Tuple[values], Tensor, numpy.ndarray], optional
-        :return: Dictionary of results from output tensors with ports as keys.
+        :return: Dictionary of results from output tensors with port/int/str keys.
-        :rtype: Dict[openvino.runtime.ConstOutput, numpy.array]
+        :rtype: OVDict
         """
         # It returns wrapped python InferReqeust and then call upon
         # overloaded functions of InferRequest class
@@ -196,7 +205,7 @@ class CompiledModel(CompiledModelBase):
 
     def __call__(self,
                  inputs: Union[dict, list, tuple, Tensor, np.ndarray] = None,
-                 shared_memory: bool = True) -> dict:
+                 shared_memory: bool = True) -> OVDict:
         """Callable infer wrapper for CompiledModel.
 
         Infers specified input(s) in synchronous mode.
@@ -248,8 +257,8 @@ class CompiledModel(CompiledModelBase):
                               Default value: True
         :type shared_memory: bool, optional
 
-        :return: Dictionary of results from output tensors with ports as keys.
+        :return: Dictionary of results from output tensors with port/int/str as keys.
-        :rtype: Dict[openvino.runtime.ConstOutput, numpy.ndarray]
+        :rtype: OVDict
         """
         if self._infer_request is None:
             self._infer_request = self.create_infer_request()

src/bindings/python/src/openvino/runtime/utils/data_helpers/__init__.py

@@ -5,3 +5,4 @@
 from openvino.runtime.utils.data_helpers.data_dispatcher import _data_dispatch
 from openvino.runtime.utils.data_helpers.wrappers import tensor_from_file
 from openvino.runtime.utils.data_helpers.wrappers import _InferRequestWrapper
+from openvino.runtime.utils.data_helpers.wrappers import OVDict

src/bindings/python/src/openvino/runtime/utils/data_helpers/wrappers.py

@@ -4,7 +4,17 @@
 
 import numpy as np
 
-from openvino._pyopenvino import Tensor
+# TODO: remove this WA and refactor OVDict when Python3.8
+# becomes minimal supported version.
+try:
+    from functools import singledispatchmethod
+except ImportError:
+    from singledispatchmethod import singledispatchmethod  # type: ignore[no-redef]
+
+from collections.abc import Mapping
+from typing import Union, Dict, List, Iterator, KeysView, ItemsView, ValuesView
+
+from openvino._pyopenvino import Tensor, ConstOutput
 from openvino._pyopenvino import InferRequest as InferRequestBase
 
 
@@ -20,3 +30,109 @@ class _InferRequestWrapper(InferRequestBase):
         # Private memeber to store newly created shared memory data
         self._inputs_data = None
         super().__init__(other)
+
+
+class OVDict(Mapping):
+    """Custom OpenVINO dictionary with inference results.
+
+    This class is a dict-like object. It provides possibility to
+    address data tensors with three key types:
+
+    * `openvino.runtime.ConstOutput` - port of the output
+    * `int` - index of the output
+    * `str` - names of the output
+
+    This class follows `frozenset`/`tuple` concept of immutability.
+    It is prohibited to assign new items or edit them.
+
+    To revert to the previous behavior use `to_dict` method which
+    return shallow copy of underlaying dictionary.
+    Note: It removes addressing feature! New dictionary keeps
+          only `ConstOutput` keys.
+
+    If a tuple returns value is needed, use `to_tuple` method which
+    converts values to the tuple.
+
+    :Example:
+
+    .. code-block:: python
+
+        # Reverts to the previous behavior of the native dict
+        result = request.infer(inputs).to_dict()
+        # or alternatively:
+        result = dict(request.infer(inputs))
+
+    .. code-block:: python
+
+        # To dispatch outputs of multi-ouput inference:
+        out1, out2, out3, _ = request.infer(inputs).values()
+        # or alternatively:
+        out1, out2, out3, _ = request.infer(inputs).to_tuple()
+    """
+    def __init__(self, _dict: Dict[ConstOutput, np.ndarray]) -> None:
+        self._dict = _dict
+
+    def __iter__(self) -> Iterator:
+        return self._dict.__iter__()
+
+    def __len__(self) -> int:
+        return len(self._dict)
+
+    def __repr__(self) -> str:
+        return self._dict.__repr__()
+
+    def __get_key(self, index: int) -> ConstOutput:
+        return list(self._dict.keys())[index]
+
+    @singledispatchmethod
+    def __getitem_impl(self, key: Union[ConstOutput, int, str]) -> np.ndarray:
+        raise TypeError("Unknown key type!")
+
+    @__getitem_impl.register
+    def _(self, key: ConstOutput) -> np.ndarray:
+        return self._dict[key]
+
+    @__getitem_impl.register
+    def _(self, key: int) -> np.ndarray:
+        try:
+            return self._dict[self.__get_key(key)]
+        except IndexError:
+            raise KeyError(key)
+
+    @__getitem_impl.register
+    def _(self, key: str) -> np.ndarray:
+        try:
+            return self._dict[self.__get_key(self.names().index(key))]
+        except ValueError:
+            raise KeyError(key)
+
+    def __getitem__(self, key: Union[ConstOutput, int, str]) -> np.ndarray:
+        return self.__getitem_impl(key)
+
+    def keys(self) -> KeysView[ConstOutput]:
+        return self._dict.keys()
+
+    def values(self) -> ValuesView[np.ndarray]:
+        return self._dict.values()
+
+    def items(self) -> ItemsView[ConstOutput, np.ndarray]:
+        return self._dict.items()
+
+    def names(self) -> List[str]:
+        """Return a name of every output key.
+
+        Throws RuntimeError if any of ConstOutput keys has no name.
+        """
+        return [key.get_any_name() for key in self._dict.keys()]
+
+    def to_dict(self) -> Dict[ConstOutput, np.ndarray]:
+        """Return underlaying native dictionary.
+
+        Function performs shallow copy, thus any modifications to
+        returned values may affect this class as well.
+        """
+        return self._dict
+
+    def to_tuple(self) -> tuple:
+        """Convert values of this dictionary to a tuple."""
+        return tuple(self._dict.values())

src/bindings/python/src/pyopenvino/core/common.cpp

@@ -53,6 +53,27 @@ const std::map<std::string, ov::element::Type>& dtype_to_ov_type() {
     return dtype_to_ov_type_mapping;
 }
 
+namespace containers {
+const TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs) {
+    TensorIndexMap result_map;
+    for (auto&& input : inputs) {
+        int idx;
+        if (py::isinstance<py::int_>(input.first)) {
+            idx = input.first.cast<int>();
+        } else {
+            throw py::type_error("incompatible function arguments!");
+        }
+        if (py::isinstance<ov::Tensor>(input.second)) {
+            auto tensor = Common::cast_to_tensor(input.second);
+            result_map[idx] = tensor;
+        } else {
+            throw ov::Exception("Unable to cast tensor " + std::to_string(idx) + "!");
+        }
+    }
+    return result_map;
+}
+};  // namespace containers
+
 namespace array_helpers {
 
 bool is_contiguous(const py::array& array) {
@@ -110,6 +131,67 @@ py::array as_contiguous(py::array& array, ov::element::Type type) {
     }
 }
 
+py::array array_from_tensor(ov::Tensor&& t) {
+    switch (t.get_element_type()) {
+    case ov::element::Type_t::f32: {
+        return py::array_t<float>(t.get_shape(), t.data<float>());
+        break;
+    }
+    case ov::element::Type_t::f64: {
+        return py::array_t<double>(t.get_shape(), t.data<double>());
+        break;
+    }
+    case ov::element::Type_t::bf16: {
+        return py::array(py::dtype("float16"), t.get_shape(), t.data<ov::bfloat16>());
+        break;
+    }
+    case ov::element::Type_t::f16: {
+        return py::array(py::dtype("float16"), t.get_shape(), t.data<ov::float16>());
+        break;
+    }
+    case ov::element::Type_t::i8: {
+        return py::array_t<int8_t>(t.get_shape(), t.data<int8_t>());
+        break;
+    }
+    case ov::element::Type_t::i16: {
+        return py::array_t<int16_t>(t.get_shape(), t.data<int16_t>());
+        break;
+    }
+    case ov::element::Type_t::i32: {
+        return py::array_t<int32_t>(t.get_shape(), t.data<int32_t>());
+        break;
+    }
+    case ov::element::Type_t::i64: {
+        return py::array_t<int64_t>(t.get_shape(), t.data<int64_t>());
+        break;
+    }
+    case ov::element::Type_t::u8: {
+        return py::array_t<uint8_t>(t.get_shape(), t.data<uint8_t>());
+        break;
+    }
+    case ov::element::Type_t::u16: {
+        return py::array_t<uint16_t>(t.get_shape(), t.data<uint16_t>());
+        break;
+    }
+    case ov::element::Type_t::u32: {
+        return py::array_t<uint32_t>(t.get_shape(), t.data<uint32_t>());
+        break;
+    }
+    case ov::element::Type_t::u64: {
+        return py::array_t<uint64_t>(t.get_shape(), t.data<uint64_t>());
+        break;
+    }
+    case ov::element::Type_t::boolean: {
+        return py::array_t<bool>(t.get_shape(), t.data<bool>());
+        break;
+    }
+    default: {
+        throw ov::Exception("Numpy array cannot be created from given OV Tensor!");
+        break;
+    }
+    }
+}
+
 };  // namespace array_helpers
 
 template <>
@@ -226,38 +308,6 @@ const ov::Tensor& cast_to_tensor(const py::handle& tensor) {
     return tensor.cast<const ov::Tensor&>();
 }
 
-const Containers::TensorNameMap cast_to_tensor_name_map(const py::dict& inputs) {
-    Containers::TensorNameMap result_map;
-    for (auto&& input : inputs) {
-        std::string name;
-        if (py::isinstance<py::str>(input.first)) {
-            name = input.first.cast<std::string>();
-        } else {
-            throw py::type_error("incompatible function arguments!");
-        }
-        OPENVINO_ASSERT(py::isinstance<ov::Tensor>(input.second), "Unable to cast tensor ", name, "!");
-        auto tensor = Common::cast_to_tensor(input.second);
-        result_map[name] = tensor;
-    }
-    return result_map;
-}
-
-const Containers::TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs) {
-    Containers::TensorIndexMap result_map;
-    for (auto&& input : inputs) {
-        int idx;
-        if (py::isinstance<py::int_>(input.first)) {
-            idx = input.first.cast<int>();
-        } else {
-            throw py::type_error("incompatible function arguments!");
-        }
-        OPENVINO_ASSERT(py::isinstance<ov::Tensor>(input.second), "Unable to cast tensor ", idx, "!");
-        auto tensor = Common::cast_to_tensor(input.second);
-        result_map[idx] = tensor;
-    }
-    return result_map;
-}
-
 void set_request_tensors(ov::InferRequest& request, const py::dict& inputs) {
     if (!inputs.empty()) {
         for (auto&& input : inputs) {
@@ -293,67 +343,10 @@ uint32_t get_optimal_number_of_requests(const ov::CompiledModel& actual) {
     }
 }
 
-py::dict outputs_to_dict(const std::vector<ov::Output<const ov::Node>>& outputs, ov::InferRequest& request) {
+py::dict outputs_to_dict(InferRequestWrapper& request) {
     py::dict res;
-    for (const auto& out : outputs) {
+    for (const auto& out : request.m_outputs) {
-        ov::Tensor t{request.get_tensor(out)};
+        res[py::cast(out)] = array_helpers::array_from_tensor(request.m_request.get_tensor(out));
-        switch (t.get_element_type()) {
-        case ov::element::Type_t::i8: {
-            res[py::cast(out)] = py::array_t<int8_t>(t.get_shape(), t.data<int8_t>());
-            break;
-        }
-        case ov::element::Type_t::i16: {
-            res[py::cast(out)] = py::array_t<int16_t>(t.get_shape(), t.data<int16_t>());
-            break;
-        }
-        case ov::element::Type_t::i32: {
-            res[py::cast(out)] = py::array_t<int32_t>(t.get_shape(), t.data<int32_t>());
-            break;
-        }
-        case ov::element::Type_t::i64: {
-            res[py::cast(out)] = py::array_t<int64_t>(t.get_shape(), t.data<int64_t>());
-            break;
-        }
-        case ov::element::Type_t::u8: {
-            res[py::cast(out)] = py::array_t<uint8_t>(t.get_shape(), t.data<uint8_t>());
-            break;
-        }
-        case ov::element::Type_t::u16: {
-            res[py::cast(out)] = py::array_t<uint16_t>(t.get_shape(), t.data<uint16_t>());
-            break;
-        }
-        case ov::element::Type_t::u32: {
-            res[py::cast(out)] = py::array_t<uint32_t>(t.get_shape(), t.data<uint32_t>());
-            break;
-        }
-        case ov::element::Type_t::u64: {
-            res[py::cast(out)] = py::array_t<uint64_t>(t.get_shape(), t.data<uint64_t>());
-            break;
-        }
-        case ov::element::Type_t::bf16: {
-            res[py::cast(out)] = py::array(py::dtype("float16"), t.get_shape(), t.data<ov::bfloat16>());
-            break;
-        }
-        case ov::element::Type_t::f16: {
-            res[py::cast(out)] = py::array(py::dtype("float16"), t.get_shape(), t.data<ov::float16>());
-            break;
-        }
-        case ov::element::Type_t::f32: {
-            res[py::cast(out)] = py::array_t<float>(t.get_shape(), t.data<float>());
-            break;
-        }
-        case ov::element::Type_t::f64: {
-            res[py::cast(out)] = py::array_t<double>(t.get_shape(), t.data<double>());
-            break;
-        }
-        case ov::element::Type_t::boolean: {
-            res[py::cast(out)] = py::array_t<bool>(t.get_shape(), t.data<bool>());
-            break;
-        }
-        default: {
-            break;
-        }
-        }
     }
     return res;
 }

src/bindings/python/src/pyopenvino/core/common.hpp

@@ -20,14 +20,20 @@
 #include "openvino/runtime/infer_request.hpp"
 #include "openvino/runtime/tensor.hpp"
 #include "openvino/pass/serialize.hpp"
-#include "pyopenvino/core/containers.hpp"
 #include "pyopenvino/graph/any.hpp"
 #include "pyopenvino/graph/ops/constant.hpp"
+#include "pyopenvino/core/infer_request.hpp"
 
 namespace py = pybind11;
 
 namespace Common {
 
+namespace containers {
+    using TensorIndexMap = std::map<size_t, ov::Tensor>;
+
+    const TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs);
+}; // namespace containers
+
 namespace values {
 
 // Minimum amount of bits for common numpy types. Used to perform checks against OV types.
@@ -52,6 +58,8 @@ std::vector<size_t> get_strides(const py::array& array);
 
 py::array as_contiguous(py::array& array, ov::element::Type type);
 
+py::array array_from_tensor(ov::Tensor&& t);
+
 }; // namespace array_helpers
 
 template <typename T>
@@ -80,15 +88,11 @@ ov::PartialShape partial_shape_from_list(const py::list& shape);
 
 const ov::Tensor& cast_to_tensor(const py::handle& tensor);
 
-const Containers::TensorNameMap cast_to_tensor_name_map(const py::dict& inputs);
-
-const Containers::TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs);
-
 void set_request_tensors(ov::InferRequest& request, const py::dict& inputs);
 
 uint32_t get_optimal_number_of_requests(const ov::CompiledModel& actual);
 
-py::dict outputs_to_dict(const std::vector<ov::Output<const ov::Node>>& outputs, ov::InferRequest& request);
+py::dict outputs_to_dict(InferRequestWrapper& request);
 
 ov::pass::Serialize::Version convert_to_version(const std::string& version);
 

src/bindings/python/src/pyopenvino/core/compiled_model.cpp

@@ -9,13 +9,9 @@
 
 #include "common.hpp"
 #include "pyopenvino/core/compiled_model.hpp"
-#include "pyopenvino/core/containers.hpp"
 #include "pyopenvino/core/infer_request.hpp"
 #include "pyopenvino/utils/utils.hpp"
 
-PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
-PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
-
 namespace py = pybind11;
 
 void regclass_CompiledModel(py::module m) {

src/bindings/python/src/pyopenvino/core/containers.cpp

@@ -1,23 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/containers.hpp"
-
-#include <pybind11/stl_bind.h>
-
-PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
-PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
-
-namespace py = pybind11;
-
-namespace Containers {
-
-void regclass_TensorIndexMap(py::module m) {
-    py::bind_map<TensorIndexMap>(m, "TensorIndexMap");
-}
-
-void regclass_TensorNameMap(py::module m) {
-    py::bind_map<TensorNameMap>(m, "TensorNameMap");
-}
-}  // namespace Containers

src/bindings/python/src/pyopenvino/core/containers.hpp

@@ -1,23 +0,0 @@
-// Copyright (C) 2018-2023 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <string>
-#include <map>
-#include <vector>
-
-#include <pybind11/pybind11.h>
-
-#include <openvino/runtime/tensor.hpp>
-
-namespace py = pybind11;
-
-namespace Containers {
-    using TensorIndexMap = std::map<size_t, ov::Tensor>;
-    using TensorNameMap = std::map<std::string, ov::Tensor>;
-
-    void regclass_TensorIndexMap(py::module m);
-    void regclass_TensorNameMap(py::module m);
-}

src/bindings/python/src/pyopenvino/core/infer_request.cpp

@@ -11,12 +11,8 @@
 #include <string>
 
 #include "pyopenvino/core/common.hpp"
-#include "pyopenvino/core/containers.hpp"
 #include "pyopenvino/utils/utils.hpp"
 
-PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
-PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
-
 namespace py = pybind11;
 
 inline py::dict run_sync_infer(InferRequestWrapper& self) {
@@ -26,7 +22,7 @@ inline py::dict run_sync_infer(InferRequestWrapper& self) {
         self.m_request.infer();
         *self.m_end_time = Time::now();
     }
-    return Common::outputs_to_dict(self.m_outputs, self.m_request);
+    return Common::outputs_to_dict(self);
 }
 
 void regclass_InferRequest(py::module m) {
@@ -103,7 +99,7 @@ void regclass_InferRequest(py::module m) {
     cls.def(
         "set_output_tensors",
         [](InferRequestWrapper& self, const py::dict& outputs) {
-            auto outputs_map = Common::cast_to_tensor_index_map(outputs);
+            auto outputs_map = Common::containers::cast_to_tensor_index_map(outputs);
             for (auto&& output : outputs_map) {
                 self.m_request.set_output_tensor(output.first, output.second);
             }
@@ -120,7 +116,7 @@ void regclass_InferRequest(py::module m) {
     cls.def(
         "set_input_tensors",
         [](InferRequestWrapper& self, const py::dict& inputs) {
-            auto inputs_map = Common::cast_to_tensor_index_map(inputs);
+            auto inputs_map = Common::containers::cast_to_tensor_index_map(inputs);
             for (auto&& input : inputs_map) {
                 self.m_request.set_input_tensor(input.first, input.second);
             }
@@ -719,7 +715,7 @@ void regclass_InferRequest(py::module m) {
     cls.def_property_readonly(
         "results",
         [](InferRequestWrapper& self) {
-            return Common::outputs_to_dict(self.m_outputs, self.m_request);
+            return Common::outputs_to_dict(self);
         },
         R"(
             Gets all outputs tensors of this InferRequest.

src/bindings/python/src/pyopenvino/pyopenvino.cpp

@@ -24,7 +24,6 @@
 #endif
 #include "pyopenvino/core/async_infer_queue.hpp"
 #include "pyopenvino/core/compiled_model.hpp"
-#include "pyopenvino/core/containers.hpp"
 #include "pyopenvino/core/core.hpp"
 #include "pyopenvino/core/extension.hpp"
 #include "pyopenvino/core/infer_request.hpp"
@@ -210,9 +209,6 @@ PYBIND11_MODULE(_pyopenvino, m) {
 
     regclass_Core(m);
     regclass_Tensor(m);
-    // Registering specific types of containers
-    Containers::regclass_TensorIndexMap(m);
-    Containers::regclass_TensorNameMap(m);
 
     regclass_CompiledModel(m);
     regclass_InferRequest(m);

src/bindings/python/tests/test_runtime/test_ovdict.py

@@ -0,0 +1,249 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from collections.abc import Mapping
+import numpy as np
+import pytest
+
+import openvino.runtime.opset10 as ops
+from openvino.runtime import Core, ConstOutput, CompiledModel, InferRequest, Model
+from openvino.runtime.ie_api import OVDict
+
+
+def _get_ovdict(
+    device,
+    input_shape=None,
+    data_type=np.float32,
+    input_names=None,
+    output_names=None,
+    multi_output=False,
+    direct_infer=False,
+    split_num=5,
+):
+    # Create model
+    # If model is multi-output (multi_output=True), input_shape must match
+    # requirements of split operation.
+    # TODO OpenSource: refactor it to be more generic
+    if input_shape is None:
+        input_shape = [1, 20]
+    if input_names is None:
+        input_names = ["data_0"]
+    if output_names is None:
+        output_names = ["output_0"]
+    if multi_output:
+        assert isinstance(output_names, (list, tuple))
+        assert len(output_names) > 1
+        assert len(output_names) == split_num
+    param = ops.parameter(input_shape, data_type, name=input_names[0])
+    model = Model(
+        ops.split(param, 1, split_num) if multi_output else ops.abs(param), [param],
+    )
+    # Manually name outputs
+    for i in range(len(output_names)):
+        model.output(i).tensor.names = {output_names[i]}
+    # Compile model
+    core = Core()
+    compiled_model = core.compile_model(model, device)
+    # Create test data
+    input_data = np.random.random(input_shape).astype(data_type)
+    # Two ways of infering
+    if direct_infer:
+        result = compiled_model(input_data)
+        assert result is not None
+        return result, compiled_model
+
+    request = compiled_model.create_infer_request()
+    result = request.infer(input_data)
+    assert result is not None
+    return result, request
+
+
+def _check_keys(keys, outs):
+    outs_iter = iter(outs)
+    for key in keys:
+        assert isinstance(key, ConstOutput)
+        assert key == next(outs_iter)
+    return True
+
+
+def _check_values(result):
+    for value in result.values():
+        assert isinstance(value, np.ndarray)
+    return True
+
+
+def _check_items(result, outs, output_names):
+    i = 0
+    for key, value in result.items():
+        assert isinstance(key, ConstOutput)
+        assert isinstance(value, np.ndarray)
+        # Check values
+        assert np.equal(result[outs[i]], result[key]).all()
+        assert np.equal(result[outs[i]], result[i]).all()
+        assert np.equal(result[outs[i]], result[output_names[i]]).all()
+        i += 1
+    return True
+
+
+def _check_dict(result, obj, output_names=None):
+    if output_names is None:
+        output_names = ["output_0"]
+
+    outs = obj.model_outputs if isinstance(obj, InferRequest) else obj.outputs
+    assert len(outs) == len(result)
+    assert len(outs) == len(output_names)
+    # Check for __iter__
+    assert _check_keys(result, outs)
+    # Check for keys function
+    assert _check_keys(result.keys(), outs)
+    assert _check_values(result)
+    assert _check_items(result, outs, output_names)
+    assert result.names() == output_names
+
+    return True
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+def test_ovdict_assign(device, is_direct):
+    result, _ = _get_ovdict(device, multi_output=False, direct_infer=is_direct)
+
+    with pytest.raises(TypeError) as e:
+        result["some_name"] = 99
+    assert "'OVDict' object does not support item assignment" in str(e.value)
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+def test_ovdict_single_output_basic(device, is_direct):
+    result, obj = _get_ovdict(device, multi_output=False, direct_infer=is_direct)
+
+    assert isinstance(result, OVDict)
+    if isinstance(obj, (InferRequest, CompiledModel)):
+        assert _check_dict(result, obj)
+    else:
+        raise TypeError("Unknown `obj` type!")
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+def test_ovdict_single_output_noname(device, is_direct):
+    result, obj = _get_ovdict(
+        device,
+        multi_output=False,
+        direct_infer=is_direct,
+        output_names=[],
+    )
+
+    assert isinstance(result, OVDict)
+
+    outs = obj.model_outputs if isinstance(obj, InferRequest) else obj.outputs
+
+    assert isinstance(result[outs[0]], np.ndarray)
+    assert isinstance(result[0], np.ndarray)
+
+    with pytest.raises(RuntimeError) as e0:
+        _ = result["some_name"]
+    assert "Attempt to get a name for a Tensor without names" in str(e0.value)
+
+    with pytest.raises(RuntimeError) as e1:
+        _ = result.names()
+    assert "Attempt to get a name for a Tensor without names" in str(e1.value)
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+def test_ovdict_single_output_wrongname(device, is_direct):
+    result, obj = _get_ovdict(
+        device,
+        multi_output=False,
+        direct_infer=is_direct,
+        output_names=["output_21"],
+    )
+
+    assert isinstance(result, OVDict)
+
+    outs = obj.model_outputs if isinstance(obj, InferRequest) else obj.outputs
+
+    assert isinstance(result[outs[0]], np.ndarray)
+    assert isinstance(result[0], np.ndarray)
+
+    with pytest.raises(KeyError) as e:
+        _ = result["output_37"]
+    assert "output_37" in str(e.value)
+
+    with pytest.raises(KeyError) as e:
+        _ = result[6]
+    assert "6" in str(e.value)
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+@pytest.mark.parametrize("use_function", [True, False])
+def test_ovdict_single_output_dict(device, is_direct, use_function):
+    result, obj = _get_ovdict(
+        device,
+        multi_output=False,
+        direct_infer=is_direct,
+    )
+
+    assert isinstance(result, OVDict)
+
+    outs = obj.model_outputs if isinstance(obj, InferRequest) else obj.outputs
+    native_dict = result.to_dict() if use_function else dict(result)
+
+    assert issubclass(type(native_dict), dict)
+    assert not isinstance(native_dict, OVDict)
+    assert isinstance(native_dict[outs[0]], np.ndarray)
+
+    with pytest.raises(KeyError) as e:
+        _ = native_dict["output_0"]
+    assert "output_0" in str(e.value)
+
+    with pytest.raises(KeyError) as e:
+        _ = native_dict[0]
+    assert "0" in str(e.value)
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+def test_ovdict_multi_output_basic(device, is_direct):
+    output_names = ["output_0", "output_1", "output_2", "output_3", "output_4"]
+    result, obj = _get_ovdict(
+        device,
+        multi_output=True,
+        direct_infer=is_direct,
+        output_names=output_names,
+    )
+
+    assert isinstance(result, OVDict)
+    if isinstance(obj, (InferRequest, CompiledModel)):
+        assert _check_dict(result, obj, output_names)
+    else:
+        raise TypeError("Unknown `obj` type!")
+
+
+@pytest.mark.parametrize("is_direct", [True, False])
+@pytest.mark.parametrize("use_function", [True, False])
+def test_ovdict_multi_output_tuple0(device, is_direct, use_function):
+    output_names = ["output_0", "output_1"]
+    result, obj = _get_ovdict(
+        device,
+        input_shape=(1, 10),
+        multi_output=True,
+        direct_infer=is_direct,
+        split_num=2,
+        output_names=output_names,
+    )
+
+    out0, out1 = None, None
+    if use_function:
+        assert isinstance(result.to_tuple(), tuple)
+        out0, out1 = result.to_tuple()
+    else:
+        out0, out1 = result.values()
+
+    assert out0 is not None
+    assert out1 is not None
+    assert isinstance(out0, np.ndarray)
+    assert isinstance(out1, np.ndarray)
+
+    outs = obj.model_outputs if isinstance(obj, InferRequest) else obj.outputs
+
+    assert np.equal(result[outs[0]], out0).all()
+    assert np.equal(result[outs[1]], out1).all()