[TF FE] feat: implement complex type support for selectv2

src/frontends/tensorflow_common/src/op/select.cpp

@@ -31,7 +31,19 @@ OutputVector translate_select_base_op(const NodeContext& node,
     set_node_name(node.get_name(), select);
     return {select};
 }
-
+bool has_complex_inputs(Output<Node>& x, Output<Node>& y, element::Type& complex_part_type) {
+    auto complex_type_mark_x = as_type_ptr<ComplexTypeMark>(x.get_node_shared_ptr());
+    auto complex_type_mark_y = as_type_ptr<ComplexTypeMark>(y.get_node_shared_ptr());
+    if (complex_type_mark_x) {
+        x = complex_type_mark_x->input_value(0);
+        complex_part_type = complex_type_mark_x->get_complex_part_type();
+    }
+    if (complex_type_mark_y) {
+        y = complex_type_mark_y->input_value(0);
+        complex_part_type = complex_type_mark_y->get_complex_part_type();
+    }
+    return (complex_type_mark_x || complex_type_mark_y);
+}
 OutputVector translate_select_v2_op(const NodeContext& node) {
     // according to the TensorFlow documentation. See in the code:
     // https://github.com/tensorflow/tensorflow/blob/v2.4.1/tensorflow/lite/kernels/select.cc#L188-L211
@@ -40,10 +52,23 @@ OutputVector translate_select_v2_op(const NodeContext& node) {
     // is true or the value of 'y' if false. There are valid condition input sizes:
     // 1. Either the same shape (in which case the select is elementwise), or
     // 2. Broadcastable shapes between 'condition', 'x' and 'y'.
-    default_op_checks(node, 3, {"SelectV2", "SELECT_V2"});
-    // no preparation for inputs are needed
-    // inputs are already NumPy broadcastable
-    return translate_select_base_op(node, node.get_input(0), node.get_input(1), node.get_input(2));
+    default_op_checks(node, 3, {"SelectV2", "SELECT_V2"}, true);
+    auto condition = node.get_input(0);
+    auto x = node.get_input(1);
+    auto y = node.get_input(2);
+
+    element::Type complex_part_type;
+    auto is_complex = has_complex_inputs(x, y, complex_part_type);
+
+    if (is_complex) {
+        auto const_negative_one = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
+        auto new_condition = make_shared<v0::Unsqueeze>(condition, const_negative_one);
+        auto result = translate_select_base_op(node, new_condition, x, y);
+        auto complex_result = make_shared<ComplexTypeMark>(result[0].get_node_shared_ptr(), complex_part_type);
+        return {complex_result->output(0)};
+    } else {
+        return translate_select_base_op(node, condition, x, y);
+    }
 }
 
 OutputVector translate_select_op(const NodeContext& node) {
@@ -59,21 +84,9 @@ OutputVector translate_select_op(const NodeContext& node) {
     auto condition = node.get_input(0);
     auto x = node.get_input(1);
     auto y = node.get_input(2);
-    auto complex_type_mark_x = as_type_ptr<ComplexTypeMark>(x.get_node_shared_ptr());
-    auto complex_type_mark_y = as_type_ptr<ComplexTypeMark>(y.get_node_shared_ptr());
 
-    auto is_complex = (complex_type_mark_x || complex_type_mark_y);
     element::Type complex_part_type;
-
-    if (complex_type_mark_x) {
-        x = complex_type_mark_x->input_value(0);
-        complex_part_type = complex_type_mark_x->get_complex_part_type();
-    }
-
-    if (complex_type_mark_y) {
-        y = complex_type_mark_y->input_value(0);
-        complex_part_type = complex_type_mark_y->get_complex_part_type();
-    }
+    auto is_complex = has_complex_inputs(x, y, complex_part_type);
 
     // compute number of dimensions to unsqueeze the condition
     auto cond_rank = compute_subgraph_scalar_rank(condition, element::i32);
@@ -85,14 +98,13 @@ OutputVector translate_select_op(const NodeContext& node) {
     auto new_subshape = make_shared<v3::Broadcast>(const_one, num_new_axes);
     auto cond_shape = make_shared<v3::ShapeOf>(condition, element::i32);
     // use extra dimensions in the begin to avoid concatenation of empty tensors that is not supported by Concat
-    auto const_1 = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
-    auto new_cond_shape = make_shared<v0::Concat>(OutputVector{const_1, cond_shape, new_subshape}, 0);
+    auto new_cond_shape = make_shared<v0::Concat>(OutputVector{const_one, cond_shape, new_subshape}, 0);
 
     // prepare the condition to have the same rank as operands `x` and `y`
     auto prep_cond = make_shared<v1::Reshape>(condition, new_cond_shape, false)->output(0);
     // squeeze prep_cond by one extra dimension specially added
-    auto const_0 = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
-    prep_cond = make_shared<v0::Squeeze>(prep_cond, const_0);
+    auto const_zero = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
+    prep_cond = make_shared<v0::Squeeze>(prep_cond, const_zero);
 
     auto result = translate_select_base_op(node, prep_cond, x, y);
     if (is_complex) {

tests/layer_tests/tensorflow_tests/test_tf_SelectV2.py

@@ -51,3 +51,52 @@ def test_select_v2_basic(self, params, ie_device, precision, ir_version, temp_di
         self._test(*self.create_select_v2_net(**params),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+
+class TestComplexSelectV2(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        rng = np.random.default_rng()
+        assert 'cond:0' in inputs_info, "Test error: inputs_info must contain `cond`"
+        assert 'x_real:0' in inputs_info, "Test error: inputs_info must contain `x_real`"
+        assert 'x_imag:0' in inputs_info, "Test error: inputs_info must contain `x_imag`"
+        assert 'y_real:0' in inputs_info, "Test error: inputs_info must contain `y_real`"
+        assert 'y_imag:0' in inputs_info, "Test error: inputs_info must contain `y_imag`"
+        cond_shape = inputs_info['cond:0']
+        inputs_data = {}
+        inputs_data['cond:0'] = np.random.randint(0, 2, cond_shape).astype(bool)
+        for part in ['x_real:0', 'x_imag:0', 'y_real:0', 'y_imag:0']:
+            inputs_data[part] = 4 * rng.random(inputs_info[part]).astype(np.float32) - 2
+        return inputs_data
+
+    def create_complex_select_v2_net(self, cond_shape, x_shape, y_shape):
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            cond = tf.compat.v1.placeholder(tf.bool, cond_shape, 'cond')
+            x_real = tf.compat.v1.placeholder(tf.float32, x_shape, 'x_real')
+            x_imag = tf.compat.v1.placeholder(tf.float32, x_shape, 'x_imag')
+            y_real = tf.compat.v1.placeholder(tf.float32, y_shape, 'y_real')
+            y_imag = tf.compat.v1.placeholder(tf.float32, y_shape, 'y_imag')
+            complex_x = tf.raw_ops.Complex(real=x_real, imag=x_imag)
+            complex_y = tf.raw_ops.Complex(real=y_real, imag=y_imag)
+            complex_select = tf.raw_ops.SelectV2(condition=cond, t=complex_x, e=complex_y)
+            tf.raw_ops.Real(input=complex_select)
+            tf.raw_ops.Imag(input=complex_select)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+        return tf_net, None
+
+    test_data_basic = [
+        dict(cond_shape=[3, 1], x_shape=[3, 1], y_shape=[3, 1]),
+        dict(cond_shape=[], x_shape=[2], y_shape=[3, 2]),
+        dict(cond_shape=[4], x_shape=[3, 2, 1], y_shape=[2, 4]),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit
+    @pytest.mark.nightly
+    def test_complex_select_v2(self, params, ie_device, precision, ir_version, temp_dir,
+                                  use_legacy_frontend):
+        self._test(*self.create_complex_select_v2_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)