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[TF FE] Support Angle operation for TensorFlow models (openvinotoolkit#23028)
### Details: - *Support Angle operation for TensorFlow models* ### Tickets: - Closes openvinotoolkit#22083 --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
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‎src/frontends/tensorflow/docs/supported_ops.md

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@@ -26,7 +26,7 @@ A "supported operation" is one that TensorFlow Frontend can convert to the OpenV
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| All | YES | |
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| AllCandidateSampler | NO | |
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| AllToAll | NO | |
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| Angle | NO | |
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| Angle | YES | |
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| AnonymousHashTable | NO | |
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| AnonymousIterator | NO | |
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| AnonymousIteratorV2 | NO | |

‎src/frontends/tensorflow/src/op_table.cpp

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@@ -205,6 +205,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
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// Separate translators:
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{"AddN", CreatorFunction(translate_add_n_op)},
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{"AdjustContrastv2", CreatorFunction(translate_adjust_contrast_op)},
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{"Angle", CreatorFunction(translate_angle_op)},
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{"ArgMax", CreatorFunction(translate_arg_max_op)},
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{"ArgMin", CreatorFunction(translate_arg_min_op)},
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{"Assert", CreatorFunction(translate_no_op)},

‎src/frontends/tensorflow_common/include/common_op_table.hpp

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@@ -35,6 +35,7 @@ OP_CONVERTER(translate_addv2_op);
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OP_CONVERTER(translate_add_n_op);
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OP_CONVERTER(translate_approximate_equal_op);
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OP_CONVERTER(translate_adjust_contrast_op);
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OP_CONVERTER(translate_angle_op);
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OP_CONVERTER(translate_arg_max_op);
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OP_CONVERTER(translate_arg_min_op);
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OP_CONVERTER(translate_atan2_op);

‎src/frontends/tensorflow_common/src/op/angle.cpp

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// Copyright (C) 2018-2024 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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#include "common_op_table.hpp"
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#include "helper_ops/complex_type_mark.hpp"
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#include "openvino/op/add.hpp"
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#include "openvino/op/atan.hpp"
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#include "openvino/op/constant.hpp"
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#include "openvino/op/convert.hpp"
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#include "openvino/op/divide.hpp"
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#include "openvino/op/equal.hpp"
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#include "openvino/op/gather.hpp"
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#include "openvino/op/greater.hpp"
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#include "openvino/op/greater_eq.hpp"
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#include "openvino/op/less.hpp"
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#include "openvino/op/logical_and.hpp"
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#include "openvino/op/multiply.hpp"
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#include "openvino/op/select.hpp"
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#include "openvino/op/subtract.hpp"
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using namespace std;
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using namespace ov::op;
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namespace ov {
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namespace frontend {
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namespace tensorflow {
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namespace op {
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OutputVector translate_angle_op(const NodeContext& node) {
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default_op_checks(node, 1, {"Angle"}, true);
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auto complex = node.get_input(0);
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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(
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node,
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complex_type_mark,
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"[TensorFlow Frontend] inconsistent model: Angle operation expects complex type tensor on input");
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complex = complex_type_mark->input_value(0);
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auto real_index = make_shared<v0::Constant>(element::i32, Shape{}, 0);
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auto imag_index = make_shared<v0::Constant>(element::i32, Shape{}, 1);
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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);
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auto y = make_shared<v8::Gather>(complex, imag_index, gather_axis)->output(0);
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// handle the first condition : x>0
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auto div_y_x = make_shared<v1::Divide>(y, x);
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auto atan = make_shared<v0::Atan>(div_y_x);
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auto const_zero = create_same_type_const_scalar<int32_t>(x, 0);
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auto result = atan->output(0);
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// handle the second condition : x<0 && y>=0
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auto const_pi = create_same_type_const_scalar<double>(x, std::atan(1.0) * 4);
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auto is_x_negative = make_shared<v1::Less>(x, const_zero);
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auto y_non_negative = make_shared<v1::GreaterEqual>(y, const_zero);
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auto cond1 = make_shared<v1::LogicalAnd>(is_x_negative, y_non_negative);
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auto atan_y_x_plus_pi = make_shared<v1::Add>(atan, const_pi);
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result = make_shared<v1::Select>(cond1, atan_y_x_plus_pi, result);
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// handle the third condition : x<0 && y<0
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auto is_y_negative = make_shared<v1::Less>(y, const_zero);
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auto cond2 = make_shared<v1::LogicalAnd>(is_x_negative, is_y_negative);
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auto atan_y_x_minus_pi = make_shared<v1::Subtract>(atan, const_pi);
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result = make_shared<v1::Select>(cond2, atan_y_x_minus_pi, result);
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// handle the fourth condition : x=0 && y>0
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auto is_x_zero = make_shared<v1::Equal>(x, const_zero);
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auto is_y_positive = make_shared<v1::Greater>(y, const_zero);
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auto cond3 = make_shared<v1::LogicalAnd>(is_x_zero, is_y_positive);
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auto const_two = create_same_type_const_scalar<int32_t>(x, 2);
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auto pi_div_two = make_shared<v1::Divide>(const_pi, const_two);
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result = make_shared<v1::Select>(cond3, pi_div_two, result);
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// handle the fifth condition : x=0 && y<0
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auto cond4 = make_shared<v1::LogicalAnd>(is_x_zero, is_y_negative);
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auto const_minus_two = create_same_type_const_scalar<int32_t>(x, -2);
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auto pi_div_minus_two = make_shared<v1::Divide>(const_pi, const_minus_two);
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result = make_shared<v1::Select>(cond4, pi_div_two, result);
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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());
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return {result_changed_type};
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}
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} // namespace op
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} // namespace tensorflow
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} // namespace frontend
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} // namespace ov

‎tests/layer_tests/tensorflow_tests/test_tf_angle.py

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# Copyright (C) 2018-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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class TestAngle(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'y:0' in inputs_info
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assert 'x:0' in inputs_info
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y_shape = inputs_info['y:0']
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x_shape = inputs_info['x:0']
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inputs_data = {}
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inputs_data['y:0'] = np.random.rand(*y_shape).astype(self.input_type) - np.random.rand(*y_shape).astype(self.input_type)
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inputs_data['x:0'] = np.random.rand(*x_shape).astype(self.input_type) - np.random.rand(*x_shape).astype(self.input_type)
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return inputs_data
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def create_angle_net(self, input_shape, input_type):
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self.input_type = input_type
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tf.compat.v1.reset_default_graph()
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# Create the graph and model
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with tf.compat.v1.Session() as sess:
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y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
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x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
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complex = tf.raw_ops.Complex(real=x, imag=y)
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tf.raw_ops.Angle(input=complex)
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[1, 2], input_type=np.float32),
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dict(input_shape=[2, 3, 4], input_type=np.float32),
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_angle(self, params, ie_device, precision, ir_version, temp_dir,
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use_legacy_frontend):
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self._test(*self.create_angle_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_legacy_frontend=use_legacy_frontend)

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