[TF FE] Fix TF1 OD SSD PPN model conversion (#20994)

* [TF FE] Fix TF1 SSD PPN model conversion

It contains a case when one Merge node eliminated different conditional flows.

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Add layer test

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

src/frontends/tensorflow/src/transformations/switch_merge_resolve.cpp

@@ -55,12 +55,14 @@ void generate_if_clusters(const shared_ptr<Model>& ov_model,
                 continue;
             }
             auto eliminated_markers = merge_node->get_eliminated_cond_flow_marker();
-            if (eliminated_markers.size() != 1) {
-                continue;
-            }
 
-            auto eliminated_marker = eliminated_markers[0];
-            auto switch_nodes = merge_node->get_switch_nodes_set_by_cond_index(eliminated_marker);
+            // combine all Switch nodes for which conditional flow is resolved
+            // by the current Merge node
+            SetOfSwitchNodes switch_nodes;
+            for (const auto& eliminated_marker : eliminated_markers) {
+                auto curr_switch_nodes = merge_node->get_switch_nodes_set_by_cond_index(eliminated_marker);
+                switch_nodes.insert(curr_switch_nodes.begin(), curr_switch_nodes.end());
+            }
 
             // insert into clusters
             ClusterType combined_cluster = {switch_nodes, {merge_node}};
@@ -121,11 +123,13 @@ void insert_result_before_merge(const shared_ptr<Merge>& merge_node,
                             "[TensorFlow Frontend] internal error: Merge node " + merge_node_name +
                                 " does not have conditional flow marker");
 
-    // get eliminated marker and check that it is the single one
+    // get eliminated marker and check that eliminated marker exists
+    // Merge node may contain several eliminated markers, in this case it means some Switch nodes have different
+    // condition nodes and values generated by this condition nodes are identical
     auto merge_cf_marker = get_cf_marker(merge_node);
-    FRONT_END_GENERAL_CHECK(merge_cf_marker.merge_eliminated_markers.size() == 1,
+    FRONT_END_GENERAL_CHECK(merge_cf_marker.merge_eliminated_markers.size() > 0,
                             "[TensorFlow Frontend] internal error: Merge node " + merge_node_name +
-                                " does not contain the single eliminated marker");
+                                " does not contain any eliminated marker");
     auto eliminated_marker = merge_cf_marker.merge_eliminated_markers.begin()->first;
 
     // check that producer contains the same conditional flow marker

tests/layer_tests/tensorflow_tests/test_tf_SwitchMerge.py

@@ -0,0 +1,65 @@
+# Copyright (C) 2018-2023 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 TestSwitchMerge(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        x_shape = inputs_info['x']
+        inputs_data = {}
+        rng = np.random.default_rng()
+        inputs_data['x'] = rng.integers(-10, 10, x_shape).astype(self.x_type)
+        return inputs_data
+
+    def merge_eliminating_several_cond_flows_net(self, x_shape, x_type, cond_value):
+        self.x_type = x_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(x_type, x_shape, 'x')
+            cond = tf.constant(cond_value, dtype=tf.bool)
+            switch_false, switch_true = tf.raw_ops.Switch(data=x, pred=cond)
+
+            cond2 = tf.constant(cond_value, dtype=tf.bool)
+            switch2_false, switch2_true = tf.raw_ops.Switch(data=cond2, pred=cond2)
+            with tf.control_dependencies([switch2_true]):
+                const_sub = tf.constant(5, dtype=x_type)
+            with tf.control_dependencies([switch2_false]):
+                const_add = tf.constant(2, dtype=x_type)
+
+            add = tf.raw_ops.AddV2(x=switch_false, y=const_add)
+            sub = tf.raw_ops.Sub(x=switch_true, y=const_sub)
+            merge = tf.raw_ops.Merge(inputs=[add, sub])
+            const_main = tf.constant(1, dtype=x_type)
+            tf.raw_ops.AddV2(x=merge[0], y=const_main)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(x_shape=[]),
+        dict(x_shape=[2]),
+        dict(x_shape=[4, 3]),
+    ]
+
+    @pytest.mark.parametrize("cond_value", [
+        True, False
+    ])
+    @pytest.mark.parametrize("x_type", [
+        np.float32, np.int32
+    ])
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_merge_eliminating_several_cond_flows(self, params, cond_value, x_type, ie_device, precision, ir_version,
+                                                  temp_dir,
+                                                  use_new_frontend, use_old_api):
+        self._test(*self.merge_eliminating_several_cond_flows_net(**params, cond_value=cond_value, x_type=x_type),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)