[TF FE] Refactor Unpack and add layer test (#15519)

* [TF FE] Refactor Unpack and add layer test

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

* Update tests/layer_tests/tensorflow_tests/test_tf_Unpack.py

---------

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

src/frontends/tensorflow_common/src/op/unpack.cpp

@@ -14,23 +14,20 @@ namespace tensorflow {
 namespace op {
 
 OutputVector translate_unpack_op(const NodeContext& node) {
-    TENSORFLOW_OP_VALIDATION(node, node.get_input_size() > 0, "Unpack must have at least one input.");
-    auto input = node.get_input(0);
+    default_op_checks(node, 1, {"Unpack", "UNPACK"});
+    auto value = node.get_input(0);
     auto axis = node.get_attribute<int64_t>("axis", 0);
     auto num = node.get_attribute<int64_t>("num");
 
     auto axis_const = make_shared<Constant>(element::i64, Shape{}, axis);
-    auto split = make_shared<Split>(input, axis_const, num);
-    OutputVector res;
-    int idx = 0;
-    for (auto out : split->outputs()) {
-        auto squeezed_res = make_shared<Squeeze>(out, axis_const);
-        squeezed_res->set_friendly_name(node.get_name() + "/squeeze_" + to_string(idx));
-        set_out_name(node.get_name() + ":" + std::to_string(idx), squeezed_res->output(0));
-        ++idx;
-        res.push_back(squeezed_res);
+    auto split = make_shared<Split>(value, axis_const, num);
+    OutputVector unpack_outputs;
+    for (int output_ind = 0; output_ind < num; ++output_ind) {
+        auto unpack_output = make_shared<Squeeze>(split->output(output_ind), axis_const);
+        set_out_name(node.get_name() + ":" + to_string(output_ind), unpack_output);
+        unpack_outputs.push_back(unpack_output);
     }
-    return res;
+    return unpack_outputs;
 }
 }  // namespace op
 }  // namespace tensorflow

tests/layer_tests/tensorflow_tests/test_tf_Unpack.py

@@ -0,0 +1,55 @@
+# 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 TestUnpack(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).astype(self.input_type)
+        return inputs_data
+
+    def create_unpack_net(self, input_shape, num, axis, 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:
+            type_map = {
+                np.float32: tf.float32,
+                np.int32: tf.int32,
+            }
+            assert input_type in type_map, "Test error: need to update type_map"
+            tf_type = type_map[input_type]
+            x = tf.compat.v1.placeholder(tf_type, input_shape, 'x')
+            if axis is not None:
+                unpack = tf.raw_ops.Unpack(value=x, num=num, axis=axis)
+            else:
+                unpack = tf.raw_ops.Unpack(value=x, num=num)
+            for ind in range(num):
+                tf.identity(unpack[ind], name="output_" + str(ind))
+            tf.compat.v1.global_variables_initializer()
+
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[2, 3, 4], num=3, axis=1, input_type=np.float32),
+        dict(input_shape=[3, 4], num=3, axis=None, input_type=np.int32),
+        dict(input_shape=[4, 2, 3], num=2, axis=-2, input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_unpack_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                          use_new_frontend, use_old_api):
+        self._test(*self.create_unpack_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)