test_tf_Inv.py

# Copyright (C) 2018-2025 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 TestInv(CommonTFLayerTest):
    def _prepare_input(self, inputs_info):
        assert 'x:0' in inputs_info
        x_shape = inputs_info['x:0']
        inputs_data = {}
        inputs_data['x:0'] = np.random.choice([-5, -4, -3, -2, -1, 1, 2, 3, 4, 5], x_shape).astype(np.float32)

        return inputs_data

    def create_inv_net(self, input_shape, 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:
            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
            tf.raw_ops.Inv(x=x)
            tf.compat.v1.global_variables_initializer()
            tf_net = sess.graph_def

        return tf_net, None

    test_data_basic = [
        dict(input_shape=[], input_type=np.float32),
        dict(input_shape=[10, 20], input_type=np.float32),
        dict(input_shape=[2, 3, 4], input_type=np.float32),   
    ]

    @pytest.mark.parametrize("params", test_data_basic)
    @pytest.mark.precommit
    @pytest.mark.nightly
    def test_inv_basic(self, params, ie_device, precision, ir_version, temp_dir,
                                         use_legacy_frontend):
        self._test(*self.create_inv_net(**params),
                   ie_device, precision, ir_version, temp_dir=temp_dir,
                   use_legacy_frontend=use_legacy_frontend)

class TestComplexInv(CommonTFLayerTest):
    def _prepare_input(self, inputs_info):
        rng = np.random.default_rng()
        assert 'param_real:0' in inputs_info
        assert 'param_imag:0' in inputs_info
        param_real_shape_1 = inputs_info['param_real:0']
        param_imag_shape_1 = inputs_info['param_imag:0']
        inputs_data = {}
        inputs_data['param_real:0'] = 4 * rng.random(param_real_shape_1).astype(np.float32) - 2
        inputs_data['param_imag:0'] = 4 * rng.random(param_imag_shape_1).astype(np.float32) - 2
        return inputs_data

    def create_complex_inv_net(self, input_shape):
        tf.compat.v1.reset_default_graph()
        # Create the graph and model
        with tf.compat.v1.Session() as sess:
            param_real = tf.compat.v1.placeholder(np.float32, input_shape, 'param_real')
            param_imag = tf.compat.v1.placeholder(np.float32, input_shape, 'param_imag')
            complex = tf.raw_ops.Complex(real=param_real, imag=param_imag)
            inv = tf.raw_ops.Inv(x=complex, name="complex_inv")
            real = tf.raw_ops.Real(input=inv)
            img = tf.raw_ops.Imag(input=inv)
            tf.compat.v1.global_variables_initializer()
            tf_net = sess.graph_def

        return tf_net, None

    test_data_basic = [
        dict(input_shape=[]),
        dict(input_shape=[2]),
        dict(input_shape=[1, 3]),
        dict(input_shape=[2, 3, 4]),
        dict(input_shape=[3, 4, 5, 6]),
    ]
    @pytest.mark.parametrize("params", test_data_basic)
    @pytest.mark.precommit
    @pytest.mark.nightly
    def test_complex_inv(self, params, ie_device, precision, ir_version, temp_dir,
                         use_legacy_frontend):
        self._test(
            *self.create_complex_inv_net(**params),
            ie_device, precision, ir_version, temp_dir=temp_dir,
            use_legacy_frontend=use_legacy_frontend)