[TF FE] Refactor ReverseSequence and add layer test (#15807)

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

src/frontends/tensorflow_common/src/op/reverse_sequence.cpp

@@ -18,10 +18,10 @@ OutputVector translate_reverse_sequence_op(const NodeContext& node) {
     auto seq_lengths = node.get_input(1);
 
     // retrieve attributes
-    auto seq_axis = node.get_attribute<int64_t>("seq_dim");
+    auto seq_dim = node.get_attribute<int64_t>("seq_dim");
-    auto batch_axis = node.get_attribute<int64_t>("batch_dim", 0);
+    auto batch_dim = node.get_attribute<int64_t>("batch_dim", 0);
 
-    auto reverse_sequence = make_shared<ReverseSequence>(input, seq_lengths, batch_axis, seq_axis);
+    auto reverse_sequence = make_shared<ReverseSequence>(input, seq_lengths, batch_dim, seq_dim);
     set_node_name(node.get_name(), reverse_sequence);
     return {reverse_sequence};
 }

tests/layer_tests/tensorflow_tests/test_tf_ReverseSequence.py

@@ -0,0 +1,53 @@
+# 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 TestReverseSequence(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'input' in inputs_info
+        assert 'seq_lengths' in inputs_info
+        input_shape = inputs_info['input']
+        seq_lengths_shape = inputs_info['seq_lengths']
+        inputs_data = {}
+        inputs_data['input'] = np.random.randint(-50, 50, input_shape).astype(self.input_type)
+        inputs_data['seq_lengths'] = np.random.randint(0, self.max_seq_length + 1, seq_lengths_shape).astype(
+            self.seq_lengths_type)
+        return inputs_data
+
+    def create_reverse_sequence_net(self, input_shape, input_type, seq_lengths_type, seq_dim, batch_dim):
+        self.input_type = input_type
+        self.seq_lengths_type = seq_lengths_type
+        assert 0 <= batch_dim and batch_dim < len(input_shape), "Incorrect `batch_dim` in the test case"
+        assert 0 <= seq_dim and seq_dim < len(input_shape), "Incorrect `seq_dim` in the test case"
+        self.max_seq_length = input_shape[seq_dim]
+        batch_size = input_shape[batch_dim]
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            input = tf.compat.v1.placeholder(input_type, input_shape, 'input')
+            seq_lengths = tf.compat.v1.placeholder(seq_lengths_type, [batch_size], 'seq_lengths')
+            tf.raw_ops.ReverseSequence(input=input, seq_lengths=seq_lengths, seq_dim=seq_dim, batch_dim=batch_dim)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[2, 3], input_type=np.int32, seq_lengths_type=np.int64, seq_dim=1, batch_dim=0),
+        dict(input_shape=[3, 6, 4], input_type=np.float32, seq_lengths_type=np.int32, seq_dim=2, batch_dim=0),
+        dict(input_shape=[6, 3, 4, 2], input_type=np.float32, seq_lengths_type=np.int32, seq_dim=0, batch_dim=3),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_reverse_sequence_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                                    use_new_frontend, use_old_api):
+        self._test(*self.create_reverse_sequence_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)