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test_tf_IdentityN.py
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# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import pytest
import tensorflow as tf
from common.tf_layer_test_class import CommonTFLayerTest
class TestIdentityN(CommonTFLayerTest):
def create_identityn_net(self, value_shape, size_splits_values, axis_value):
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
axis = tf.constant(axis_value, dtype=tf.int32)
size_splits = tf.constant(size_splits_values, dtype=tf.int32)
value = tf.compat.v1.placeholder(tf.float32, value_shape, 'value')
num_split = len(size_splits_values)
# we have to do a trick using Split operation to avoid fusing output tensors into input tensors
# so that Parameter node will contain both input and output tensor names
# 97192: this is a limitation of layer test infrastructure
split = tf.raw_ops.SplitV(value=value, size_splits=size_splits, axis=axis, num_split=num_split)
split_outputs = []
for output_ind in range(num_split):
split_outputs.append(split[output_ind])
identity_n = tf.raw_ops.IdentityN(input=split_outputs)
for output_ind in range(num_split):
tf.identity(identity_n[output_ind], name="Identity_" + str(output_ind))
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
return tf_net, None
test_data_basic = [
dict(value_shape=[2, 3, 9], size_splits_values=[1, 2, 3, -1, 1], axis_value=2),
]
@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit
@pytest.mark.nightly
def test_split_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_identityn_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)