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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| // Copyright (C) 2018-2024 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
|
|
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| #include "openvino/op/bucketize.hpp" | ||
|
|
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| #include "openvino/frontend/pytorch/node_context.hpp" | ||
| #include "openvino/op/add.hpp" | ||
| #include "openvino/op/concat.hpp" | ||
| #include "openvino/op/convert_like.hpp" | ||
| #include "openvino/op/logical_or.hpp" | ||
| #include "openvino/op/multiply.hpp" | ||
| #include "utils.hpp" | ||
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|
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| namespace ov { | ||
| namespace frontend { | ||
| namespace pytorch { | ||
| namespace op { | ||
|
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| using namespace ov::op; | ||
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| OutputVector translate_bucketize(const NodeContext& context) { | ||
| num_inputs_check(context, 2, 5); | ||
| auto input = context.get_input(0); | ||
| auto boundaries = context.get_input(1); | ||
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| element::Type output_type = ov::element::i64; | ||
| if (!context.input_is_none(2) && context.const_input<bool>(2)) { | ||
| output_type = ov::element::i32; | ||
| } | ||
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| bool with_right_bound = true; | ||
| if (!context.input_is_none(3)) { | ||
| with_right_bound = !context.const_input<bool>(3); | ||
| } | ||
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| auto bucketize = | ||
| context.mark_node(std::make_shared<v3::Bucketize>(input, boundaries, output_type, with_right_bound)); | ||
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| if (!context.input_is_none(4)) { | ||
| context.mutate_input(4, bucketize); | ||
| } | ||
|
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| return {bucketize}; | ||
| }; | ||
|
|
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| } // namespace op | ||
| } // namespace pytorch | ||
| } // namespace frontend | ||
| } // namespace ov |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| // Copyright (C) 2018-2024 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
|
|
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| #include "common_op_table.hpp" | ||
| #include "helper_ops/complex_type_mark.hpp" | ||
| #include "openvino/op/add.hpp" | ||
| #include "openvino/op/atan.hpp" | ||
| #include "openvino/op/constant.hpp" | ||
| #include "openvino/op/convert.hpp" | ||
| #include "openvino/op/divide.hpp" | ||
| #include "openvino/op/equal.hpp" | ||
| #include "openvino/op/gather.hpp" | ||
| #include "openvino/op/greater.hpp" | ||
| #include "openvino/op/greater_eq.hpp" | ||
| #include "openvino/op/less.hpp" | ||
| #include "openvino/op/logical_and.hpp" | ||
| #include "openvino/op/multiply.hpp" | ||
| #include "openvino/op/select.hpp" | ||
| #include "openvino/op/subtract.hpp" | ||
|
|
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| using namespace std; | ||
| using namespace ov::op; | ||
|
|
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| namespace ov { | ||
| namespace frontend { | ||
| namespace tensorflow { | ||
| namespace op { | ||
|
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| OutputVector translate_angle_op(const NodeContext& node) { | ||
| default_op_checks(node, 1, {"Angle"}, true); | ||
| auto complex = node.get_input(0); | ||
| auto result_type = node.get_attribute<ov::element::Type>("Tout"); | ||
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| auto complex_type_mark = as_type_ptr<ComplexTypeMark>(complex.get_node_shared_ptr()); | ||
|
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| TENSORFLOW_OP_VALIDATION( | ||
| node, | ||
| complex_type_mark, | ||
| "[TensorFlow Frontend] inconsistent model: Angle operation expects complex type tensor on input"); | ||
|
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| complex = complex_type_mark->input_value(0); | ||
| auto real_index = make_shared<v0::Constant>(element::i32, Shape{}, 0); | ||
| auto imag_index = make_shared<v0::Constant>(element::i32, Shape{}, 1); | ||
| auto gather_axis = make_shared<v0::Constant>(element::i32, Shape{1}, -1); | ||
|
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| auto x = make_shared<v8::Gather>(complex, real_index, gather_axis)->output(0); | ||
| auto y = make_shared<v8::Gather>(complex, imag_index, gather_axis)->output(0); | ||
|
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| // handle the first condition : x>0 | ||
| auto div_y_x = make_shared<v1::Divide>(y, x); | ||
| auto atan = make_shared<v0::Atan>(div_y_x); | ||
| auto const_zero = create_same_type_const_scalar<int32_t>(x, 0); | ||
| auto result = atan->output(0); | ||
|
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| // handle the second condition : x<0 && y>=0 | ||
| auto const_pi = create_same_type_const_scalar<double>(x, std::atan(1.0) * 4); | ||
| auto is_x_negative = make_shared<v1::Less>(x, const_zero); | ||
| auto y_non_negative = make_shared<v1::GreaterEqual>(y, const_zero); | ||
| auto cond1 = make_shared<v1::LogicalAnd>(is_x_negative, y_non_negative); | ||
| auto atan_y_x_plus_pi = make_shared<v1::Add>(atan, const_pi); | ||
| result = make_shared<v1::Select>(cond1, atan_y_x_plus_pi, result); | ||
|
|
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| // handle the third condition : x<0 && y<0 | ||
| auto is_y_negative = make_shared<v1::Less>(y, const_zero); | ||
| auto cond2 = make_shared<v1::LogicalAnd>(is_x_negative, is_y_negative); | ||
| auto atan_y_x_minus_pi = make_shared<v1::Subtract>(atan, const_pi); | ||
| result = make_shared<v1::Select>(cond2, atan_y_x_minus_pi, result); | ||
|
|
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| // handle the fourth condition : x=0 && y>0 | ||
| auto is_x_zero = make_shared<v1::Equal>(x, const_zero); | ||
| auto is_y_positive = make_shared<v1::Greater>(y, const_zero); | ||
| auto cond3 = make_shared<v1::LogicalAnd>(is_x_zero, is_y_positive); | ||
| auto const_two = create_same_type_const_scalar<int32_t>(x, 2); | ||
| auto pi_div_two = make_shared<v1::Divide>(const_pi, const_two); | ||
| result = make_shared<v1::Select>(cond3, pi_div_two, result); | ||
|
|
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| // handle the fifth condition : x=0 && y<0 | ||
| auto cond4 = make_shared<v1::LogicalAnd>(is_x_zero, is_y_negative); | ||
| auto const_minus_two = create_same_type_const_scalar<int32_t>(x, -2); | ||
| auto pi_div_minus_two = make_shared<v1::Divide>(const_pi, const_minus_two); | ||
| result = make_shared<v1::Select>(cond4, pi_div_two, result); | ||
| auto result_changed_type = make_shared<v0::Convert>(result, result_type)->output(0); | ||
|
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| set_node_name(node.get_name(), result_changed_type.get_node_shared_ptr()); | ||
| return {result_changed_type}; | ||
| } | ||
| } // namespace op | ||
| } // namespace tensorflow | ||
| } // namespace frontend | ||
| } // namespace ov |
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