support UniqueWithCounts for TF FE (#21565)

* support UniqueWithCounts for TF FE

* set node name and fix code style

src/frontends/tensorflow/src/op_table.cpp

@@ -327,6 +327,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"Transpose", CreatorFunction(translate_transpose_op)},
         {"TruncateDiv", CreatorFunction(translate_truncate_div_op)},
         {"TruncateMod", CreatorFunction(translate_truncate_mod_op)},
+        {"UniqueWithCounts", CreatorFunction(translate_unique_with_counts_op)},
         {"Unpack", CreatorFunction(translate_unpack_op)},
         {"UnravelIndex", CreatorFunction(translate_unravel_index_op)},
         {"UnsortedSegmentSum", CreatorFunction(translate_unsorted_segment_sum_op)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -160,6 +160,7 @@ OP_CONVERTER_NAMED(translate_top_k_v2_op);
 OP_CONVERTER(translate_transpose_op);
 OP_CONVERTER(translate_truncate_div_op);
 OP_CONVERTER(translate_truncate_mod_op);
+OP_CONVERTER(translate_unique_with_counts_op);
 OP_CONVERTER(translate_unpack_op);
 OP_CONVERTER(translate_unravel_index_op);
 OP_CONVERTER(translate_unsorted_segment_sum_op);

src/frontends/tensorflow_common/src/op/unique_with_counts.cpp

@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/unique.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+
+OutputVector translate_unique_with_counts_op(const NodeContext& node) {
+    default_op_checks(node, 1, {"UniqueWithCounts"});
+
+    // get input 'x' from node and node name
+    auto x = node.get_input(0);
+    auto node_name = node.get_name();
+
+    auto unique = make_shared<v10::Unique>(x, false, ov::element::i32, ov::element::i32);
+    set_node_name(node_name, unique);
+
+    // story 'y', 'idx', and 'count' outputs from Unique in separate variables
+    auto y = unique->output(0);
+    auto idx = unique->output(2);
+    auto count = unique->output(3);
+
+    return {{y}, {idx}, {count}};
+}
+
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_UniqueWithCounts.py

@@ -0,0 +1,47 @@
+# 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 TestUnique(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
+        x_shape = inputs_info['x']
+        inputs_data = {}
+        inputs_data['x'] = np.random.randint(-10, 10, x_shape)
+        return inputs_data
+
+    def create_unique_net(self, x_shape, data_type, out_idx):
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(data_type, x_shape, 'x')
+            unique = tf.raw_ops.UniqueWithCounts(x=x, out_idx=out_idx)
+            tf.identity(unique[0], name='unique_elements')
+            tf.identity(unique[1], name='unique_indices')
+            tf.identity(unique[2], name='unique_counts')
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(x_shape=[1], data_type=tf.float32, out_idx=tf.int32),
+        dict(x_shape=[50], data_type=tf.float32, out_idx=tf.int32),
+        dict(x_shape=[100], data_type=tf.float32, out_idx=tf.int64),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_unique_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                          use_new_frontend, use_old_api):
+        if not use_new_frontend:
+            pytest.skip("Unique operation is not supported via legacy frontend.")
+        self._test(*self.create_unique_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)