[TF FE] Support Angle operation for TensorFlow models

src/frontends/tensorflow/docs/supported_ops.md

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

src/frontends/tensorflow/src/op_table.cpp

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

src/frontends/tensorflow_common/include/common_op_table.hpp

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

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

@@ -0,0 +1,91 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
+#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"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+
+OutputVector translate_angle_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Angle"}, true);
+    auto complex = node.get_input(0);
+    auto result_type = node.get_attribute<ov::element::Type>("Tout");
+
+    auto complex_type_mark = as_type_ptr<ComplexTypeMark>(complex.get_node_shared_ptr());
+
+    TENSORFLOW_OP_VALIDATION(
+        node,
+        complex_type_mark,
+        "[TensorFlow Frontend] inconsistent model: Angle operation expects complex type tensor on input");
+
+    element::Type complex_part_type = complex_type_mark->get_complex_part_type();
+    auto 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);
+
+    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);
+
+    // 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);
+
+    // 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);
+
+    // 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);
+
+    // 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);
+
+    // 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);
+
+    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

tests/layer_tests/tensorflow_tests/test_tf_angle.py

@@ -0,0 +1,47 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+
+class TestAngle(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'y:0' in inputs_info
+        assert 'x:0' in inputs_info
+        y_shape = inputs_info['y:0']
+        x_shape = inputs_info['x:0']
+        inputs_data = {}
+        inputs_data['y:0'] = np.random.rand(*y_shape).astype(self.input_type) - np.random.rand(*y_shape).astype(self.input_type)
+        inputs_data['x:0'] = np.random.rand(*x_shape).astype(self.input_type) - np.random.rand(*x_shape).astype(self.input_type)
+        return inputs_data
+
+    def create_angle_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
+            complex = tf.raw_ops.complex(real = x,imag =y)
+            tf.raw_ops.Angle(input = x)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[1, 2], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_angle(self, params, ie_device, precision, ir_version, temp_dir,
+                               use_new_frontend, use_old_api):
+        self._test(*self.create_angle_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)