Fixed coverity issues for core componets (#9558)
* Fixed coverity issues for core componets * Fixed some tests
This commit is contained in:
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a49f1b3bc6
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02cabcda3c
@ -48,7 +48,7 @@ private:
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static constexpr size_t INPUT_MEAN = 3;
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static constexpr size_t INPUT_MEAN = 3;
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static constexpr size_t INPUT_VARIANCE = 4;
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static constexpr size_t INPUT_VARIANCE = 4;
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double m_epsilon;
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double m_epsilon{0};
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};
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};
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} // namespace v0
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} // namespace v0
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namespace v5 {
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namespace v5 {
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@ -89,7 +89,7 @@ private:
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static constexpr size_t INPUT_MEAN = 3;
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static constexpr size_t INPUT_MEAN = 3;
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static constexpr size_t INPUT_VARIANCE = 4;
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static constexpr size_t INPUT_VARIANCE = 4;
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double m_epsilon;
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double m_epsilon{0};
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};
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};
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} // namespace v5
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} // namespace v5
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} // namespace op
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} // namespace op
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@ -51,7 +51,7 @@ public:
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private:
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private:
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element::Type m_output_type;
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element::Type m_output_type;
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bool m_with_right_bound;
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bool m_with_right_bound{true};
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};
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};
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} // namespace v3
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} // namespace v3
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} // namespace op
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} // namespace op
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@ -709,7 +709,7 @@ private:
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element::Type m_element_type;
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element::Type m_element_type;
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Shape m_shape{};
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Shape m_shape{};
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std::shared_ptr<ngraph::runtime::AlignedBuffer> m_data;
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std::shared_ptr<ngraph::runtime::AlignedBuffer> m_data;
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bool m_all_elements_bitwise_identical;
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bool m_all_elements_bitwise_identical = false;
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bool m_alloc_buffer_on_visit_attributes = true;
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bool m_alloc_buffer_on_visit_attributes = true;
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};
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};
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} // namespace v0
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} // namespace v0
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@ -31,7 +31,7 @@ public:
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}
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}
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private:
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private:
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bool m_ctc_merge_repeated;
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bool m_ctc_merge_repeated{true};
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};
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};
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} // namespace v0
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} // namespace v0
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} // namespace op
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} // namespace op
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@ -92,7 +92,7 @@ public:
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}
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}
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private:
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private:
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bool m_merge_repeated;
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bool m_merge_repeated{true};
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element::Type m_classes_index_type{element::i32};
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element::Type m_classes_index_type{element::i32};
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element::Type m_sequence_length_type{element::i32};
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element::Type m_sequence_length_type{element::i32};
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};
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};
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@ -166,7 +166,7 @@ public:
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}
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}
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private:
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private:
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bool m_bilinear_interpolation_pad;
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bool m_bilinear_interpolation_pad{false};
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};
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};
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} // namespace v8
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} // namespace v8
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} // namespace op
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} // namespace op
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@ -94,8 +94,8 @@ public:
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}
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}
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private:
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private:
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int64_t m_output_dim;
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int64_t m_output_dim{0};
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float m_spatial_scale;
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float m_spatial_scale{0};
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int64_t m_group_size = 1;
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int64_t m_group_size = 1;
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std::string m_mode = "bilinear_deformable";
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std::string m_mode = "bilinear_deformable";
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int64_t m_spatial_bins_x = 1;
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int64_t m_spatial_bins_x = 1;
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@ -35,7 +35,7 @@ public:
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}
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}
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private:
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private:
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double m_alpha;
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double m_alpha{0};
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};
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};
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} // namespace v0
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} // namespace v0
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} // namespace op
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} // namespace op
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@ -39,7 +39,7 @@ public:
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}
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}
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private:
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private:
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size_t m_max_rois;
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size_t m_max_rois{0};
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template <class T>
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template <class T>
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friend void shape_infer(ExperimentalDetectronTopKROIs* op,
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friend void shape_infer(ExperimentalDetectronTopKROIs* op,
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@ -64,7 +64,7 @@ private:
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Shape m_patch_sizes;
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Shape m_patch_sizes;
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Strides m_patch_movement_strides;
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Strides m_patch_movement_strides;
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Shape m_patch_selection_rates;
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Shape m_patch_selection_rates;
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PadType m_padding;
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PadType m_padding{PadType::EXPLICIT};
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template <class T>
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template <class T>
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friend void shape_infer(const ExtractImagePatches* op,
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friend void shape_infer(const ExtractImagePatches* op,
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const std::vector<T>& input_shapes,
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const std::vector<T>& input_shapes,
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@ -33,7 +33,7 @@ public:
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}
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}
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private:
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private:
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int64_t m_axis;
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int64_t m_axis{0};
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template <class T>
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template <class T>
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void friend shape_infer(const GatherElements* op,
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void friend shape_infer(const GatherElements* op,
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const std::vector<T>& input_shapes,
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const std::vector<T>& input_shapes,
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@ -191,7 +191,7 @@ public:
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}
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}
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private:
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private:
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direction m_direction;
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direction m_direction{direction::FORWARD};
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};
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};
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} // namespace v5
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} // namespace v5
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} // namespace op
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} // namespace op
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@ -74,7 +74,7 @@ public:
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bool has_evaluate() const override;
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bool has_evaluate() const override;
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private:
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private:
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PadMode m_pad_mode;
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PadMode m_pad_mode{PadMode::CONSTANT};
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bool evaluate_pad(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
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bool evaluate_pad(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
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};
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};
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} // namespace v1
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} // namespace v1
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@ -45,7 +45,7 @@ public:
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private:
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private:
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Shape m_output_size{0, 0};
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Shape m_output_size{0, 0};
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float m_spatial_scale;
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float m_spatial_scale{0};
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std::string m_method = "max";
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std::string m_method = "max";
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};
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};
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} // namespace v0
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} // namespace v0
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@ -44,7 +44,7 @@ public:
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}
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}
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private:
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private:
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RoundMode m_mode;
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RoundMode m_mode{RoundMode::HALF_TO_EVEN};
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};
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};
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} // namespace v5
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} // namespace v5
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} // namespace op
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} // namespace op
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@ -160,7 +160,7 @@ void shape_infer_base(const DetectionOutputBase* op,
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if (!num_prior_boxes && box_logits_pshape[1].is_static()) {
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if (!num_prior_boxes && box_logits_pshape[1].is_static()) {
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auto box_logits_pshape_2nd_dim = box_logits_pshape[1].get_length();
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auto box_logits_pshape_2nd_dim = box_logits_pshape[1].get_length();
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NODE_VALIDATION_CHECK(op,
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NODE_VALIDATION_CHECK(op,
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(box_logits_pshape_2nd_dim % (num_loc_classes * 4)) == 0,
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num_loc_classes != 0 && (box_logits_pshape_2nd_dim % (num_loc_classes * 4)) == 0,
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"Box logits' second dimension must be a multiply of num_loc_classes * 4 (",
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"Box logits' second dimension must be a multiply of num_loc_classes * 4 (",
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num_loc_classes * 4,
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num_loc_classes * 4,
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"). Current value is: ",
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"). Current value is: ",
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@ -340,4 +340,4 @@ void shape_infer(const DetectionOutput* op, const std::vector<T>& input_shapes,
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} // namespace v8
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} // namespace v8
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} // namespace op
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} // namespace op
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} // namespace ov
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} // namespace ov
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@ -71,7 +71,7 @@ void shape_infer(const Einsum* op, const std::vector<T>& input_shapes, std::vect
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label_to_shape[label].compatible(T{pshape[label_ind]}),
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label_to_shape[label].compatible(T{pshape[label_ind]}),
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"Different input dimensions indicated by the same labels for Einsum "
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"Different input dimensions indicated by the same labels for Einsum "
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"must be compatible.");
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"must be compatible.");
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T::merge_into(label_to_shape[label], T{pshape[dim_ind]});
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OPENVINO_ASSERT(T::merge_into(label_to_shape[label], T{pshape[dim_ind]}));
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}
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}
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++dim_ind;
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++dim_ind;
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}
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}
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@ -3,6 +3,7 @@
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//
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//
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#pragma once
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#pragma once
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#include <openvino/op/lstm_cell.hpp>
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#include <openvino/op/lstm_cell.hpp>
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#include "utils.hpp"
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#include "utils.hpp"
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namespace ov {
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namespace ov {
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@ -16,7 +17,7 @@ void lstm_shape_infer(const OpsType* op,
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using DimType = typename std::iterator_traits<typename ShapeType::iterator>::value_type;
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using DimType = typename std::iterator_traits<typename ShapeType::iterator>::value_type;
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enum { X, initial_hidden_state, initial_cell_state, W, R, B };
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enum { X, initial_hidden_state, initial_cell_state, W, R, B };
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std::vector<bool> input_rank_static(6, false);
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std::vector<bool> input_rank_static(6, false);
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bool all_rank_dynamic = false;
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bool all_rank_dynamic = true;
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bool all_rank_static = true;
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bool all_rank_static = true;
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// Prepare OutShape
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// Prepare OutShape
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auto& hidden_shape = output_shapes[0];
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auto& hidden_shape = output_shapes[0];
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@ -43,7 +44,7 @@ void lstm_shape_infer(const OpsType* op,
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bool is_hidden_init = false;
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bool is_hidden_init = false;
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// deduce batch/hidden_size
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// deduce batch/hidden_size
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for (size_t i = 0; i < input_shapes.size() && i < 6 ; i++) {
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for (size_t i = 0; i < input_shapes.size() && i < 6; i++) {
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const auto& input = input_shapes[i];
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const auto& input = input_shapes[i];
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if (input_rank_static[i]) {
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if (input_rank_static[i]) {
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// batch could be deduced from x, cell_state or hidden_state
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// batch could be deduced from x, cell_state or hidden_state
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@ -94,7 +95,9 @@ void lstm_shape_infer(const OpsType* op,
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} else {
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} else {
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NODE_VALIDATION_CHECK(
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NODE_VALIDATION_CHECK(
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op,
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op,
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DimType::merge(output_hidden_size, output_hidden_size, input[0].get_length() / gates_count),
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DimType::merge(output_hidden_size,
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output_hidden_size,
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input[0].get_length() / gates_count),
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"Parameter hidden_size not matched for W, R, B, initial_hidden_state and "
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"Parameter hidden_size not matched for W, R, B, initial_hidden_state and "
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"initial_cell_state "
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"initial_cell_state "
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"inputs.");
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"inputs.");
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@ -115,7 +118,9 @@ void lstm_shape_infer(const OpsType* op,
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} else {
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} else {
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NODE_VALIDATION_CHECK(
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NODE_VALIDATION_CHECK(
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op,
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op,
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DimType::merge(output_hidden_size, output_hidden_size, input[0].get_length() / gates_count),
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DimType::merge(output_hidden_size,
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output_hidden_size,
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input[0].get_length() / gates_count),
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"Parameter hidden_size not matched for W, R, B, initial_hidden_state and "
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"Parameter hidden_size not matched for W, R, B, initial_hidden_state and "
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"initial_cell_state "
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"initial_cell_state "
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"inputs.");
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"inputs.");
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@ -140,9 +145,7 @@ void lstm_shape_infer(const OpsType* op,
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// Check peepholes
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// Check peepholes
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if (input_shapes.size() == 7) {
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if (input_shapes.size() == 7) {
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const auto& p_pshape = input_shapes[6];
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const auto& p_pshape = input_shapes[6];
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NODE_VALIDATION_CHECK(op,
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NODE_VALIDATION_CHECK(op, (p_pshape.rank().compatible(1)), "LSTMCell input tensor P shall have dimension 1D.");
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(p_pshape.rank().compatible(1)),
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"LSTMCell input tensor P shall have dimension 1D.");
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}
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}
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// check input size
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// check input size
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auto out_dim = DimType{};
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auto out_dim = DimType{};
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if (class_probs_ps.rank().is_static() && bbox_deltas_ps.rank().is_static()) {
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if (class_probs_ps.rank().is_static() && bbox_deltas_ps.rank().is_static()) {
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DimType::merge(out_dim, class_probs_ps[0], bbox_deltas_ps[0]);
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OPENVINO_ASSERT(DimType::merge(out_dim, class_probs_ps[0], bbox_deltas_ps[0]));
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} else if (class_probs_ps.rank().is_static()) {
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} else if (class_probs_ps.rank().is_static()) {
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out_dim = class_probs_ps[0];
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out_dim = class_probs_ps[0];
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} else if (bbox_deltas_ps.rank().is_static()) {
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} else if (bbox_deltas_ps.rank().is_static()) {
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@ -62,7 +62,7 @@ void shape_infer(const ov::op::v3::ROIAlign* op, const std::vector<T>& input_sha
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// if either of those 2 dimensions is static its value will be used
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// if either of those 2 dimensions is static its value will be used
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// for the first dimension of the output shape - 'NUM_ROIS'
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// for the first dimension of the output shape - 'NUM_ROIS'
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if (rois_ps_rank.is_static() && batch_indices_ps_rank.is_static()) {
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if (rois_ps_rank.is_static() && batch_indices_ps_rank.is_static()) {
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DimType::merge(output_shape[0], batch_indices_ps[0], rois_ps[0]);
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OPENVINO_ASSERT(DimType::merge(output_shape[0], batch_indices_ps[0], rois_ps[0]));
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} else if (rois_ps_rank.is_static()) {
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} else if (rois_ps_rank.is_static()) {
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output_shape[0] = rois_ps[0];
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output_shape[0] = rois_ps[0];
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} else if (batch_indices_ps_rank.is_static()) {
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} else if (batch_indices_ps_rank.is_static()) {
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@ -74,4 +74,4 @@ void shape_infer(const ov::op::v3::ROIAlign* op, const std::vector<T>& input_sha
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} // namespace v3
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} // namespace v3
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} // namespace op
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} // namespace op
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} // namespace ov
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} // namespace ov
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}
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}
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// Load plugins until we found the right one
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// Load plugins until we found the right one
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for (auto& plugin : m_plugins) {
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for (auto& plugin : m_plugins) {
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plugin.load();
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OPENVINO_ASSERT(plugin.load(), "Cannot load frontend ", plugin.get_name_from_file());
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if (plugin.get_creator().m_name == framework) {
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if (plugin.get_creator().m_name == framework) {
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auto fe_obj = std::make_shared<FrontEnd>();
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auto fe_obj = std::make_shared<FrontEnd>();
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fe_obj->m_shared_object = plugin.get_so_pointer();
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fe_obj->m_shared_object = plugin.get_so_pointer();
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