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test_tf_ConjugateTranspose.py
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# 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
# Testing operation ConjugateTranspose
# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/ConjugateTranspose
class TestComplexConjugateTranspose(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
rng = np.random.default_rng()
assert 'real_part:0' in inputs_info
real_part_shape = inputs_info['real_part:0']
assert 'imag_part:0' in inputs_info
imag_part_shape = inputs_info['imag_part:0']
inputs_data = {}
inputs_data['real_part:0'] = 4 * rng.random(real_part_shape).astype(np.float32) - 2
inputs_data['imag_part:0'] = 4 * rng.random(imag_part_shape).astype(np.float32) - 2
return inputs_data
def create_complex_conjugate_transpose_net(self, input_shape, perm):
"""
TensorFlow net IR net
Placeholder->ConjugateTranspose => Placeholder->Transpose->Conjugate->Transpose
"""
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
real_part = tf.compat.v1.placeholder(np.float32, input_shape, 'real_part')
imag_part = tf.compat.v1.placeholder(np.float32, input_shape, 'imag_part')
complex_input = tf.raw_ops.Complex(real=real_part, imag=imag_part)
conj_tranpose = tf.raw_ops.ConjugateTranspose(x=complex_input, perm=perm, name="Operation")
tf.raw_ops.Real(input=conj_tranpose)
tf.raw_ops.Imag(input=conj_tranpose)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
test_data = [
(dict(input_shape=[1, 2], perm=[1, 0])),
(dict(input_shape=[1, 2, 3], perm=[2, 1, 0])),
(dict(input_shape=[1, 2, 3, 4], perm=[0, 3, 2, 1])),
(dict(input_shape=[1, 2, 3, 4, 5], perm=[0, 2, 1, 3, 4])),
]
@pytest.mark.parametrize("params", test_data)
@pytest.mark.precommit
@pytest.mark.nightly
def test_conjugate_transpose(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_complex_conjugate_transpose_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
class TestConjugateTranspose(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
assert 'input:0' in inputs_info
input_shape = inputs_info['input:0']
inputs_data = {}
inputs_data['input:0'] = np.random.default_rng().random(input_shape).astype(np.float32)
return inputs_data
def create_conjugate_transpose_net(self, input_shape, perm):
"""
TensorFlow net IR net
Placeholder->ConjugateTranspose => Placeholder->Transpose->Conjugate->Transpose
"""
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
input = tf.compat.v1.placeholder(np.float32, input_shape, 'input')
tf.raw_ops.ConjugateTranspose(x=input, perm=perm, name="Operation")
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
test_data = [
(dict(input_shape=[1, 2], perm=[1, 0])),
(dict(input_shape=[1, 2, 3], perm=[2, 1, 0])),
(dict(input_shape=[1, 2, 3, 4], perm=[0, 3, 2, 1])),
(dict(input_shape=[1, 2, 3, 4, 5], perm=[0, 2, 1, 3, 4])),
]
@pytest.mark.parametrize("params", test_data)
@pytest.mark.precommit
@pytest.mark.nightly
def test_conjugate_transpose(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_conjugate_transpose_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)