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test_tf_Pack.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
class TestPack(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
inputs_data = {}
for input_name, input_shape in inputs_info.items():
inputs_data[input_name] = np.random.randint(-5, 5, input_shape).astype(self.input_type)
return inputs_data
def create_pack_net(self, input_shape, input_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:
inputs = []
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]
for ind in range(input_num):
inputs.append(tf.compat.v1.placeholder(tf_type, input_shape, 'input' + str(ind)))
if axis is not None:
tf.raw_ops.Pack(values=inputs, axis=axis)
else:
tf.raw_ops.Pack(values=inputs)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
return tf_net, None
test_data_basic = [
dict(input_shape=[2, 4], input_num=2, axis=None, input_type=np.float32),
dict(input_shape=[3, 1, 2], input_num=3, axis=1, input_type=np.int32),
]
@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit
@pytest.mark.nightly
def test_pack_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_pack_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
test_data_negative_axis = [
dict(input_shape=[2, 4], input_num=2, axis=-1, input_type=np.float32),
dict(input_shape=[3, 1, 2], input_num=3, axis=-2, input_type=np.int32),
]
@pytest.mark.parametrize("params", test_data_negative_axis)
@pytest.mark.precommit
@pytest.mark.nightly
def test_pack_negative_axis(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_pack_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
class TestComplexPack(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
inputs_data = {}
for input_name, input_shape in inputs_info.items():
inputs_data[input_name] = np.random.randint(-5, 5, input_shape).astype(np.float32)
return inputs_data
def create_complex_pack_net(self, input_shape, input_num, axis):
tf.compat.v1.reset_default_graph()
with tf.compat.v1.Session() as sess:
inputs_real = []
inputs_imag = []
for ind in range(input_num):
input_real = tf.compat.v1.placeholder(tf.float32, input_shape, 'input' + str(ind) + '_real')
input_imag = tf.compat.v1.placeholder(tf.float32, input_shape, 'input' + str(ind) + '_imag')
inputs_real.append(input_real)
inputs_imag.append(input_imag)
if axis is not None:
tf.raw_ops.Pack(values=inputs_real + inputs_imag, axis=axis)
else:
tf.raw_ops.Pack(values=inputs_real + inputs_imag)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
return tf_net, None
test_data_basic = [
dict(input_shape=[2, 4], input_num=2, axis=None),
dict(input_shape=[3, 1, 2], input_num=3, axis=1),
]
@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit
@pytest.mark.nightly
def test_complex_pack_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_complex_pack_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
test_data_negative_axis = [
dict(input_shape=[2, 4], input_num=2, axis=-1),
dict(input_shape=[3, 1, 2], input_num=3, axis=-2),
]
@pytest.mark.parametrize("params", test_data_negative_axis)
@pytest.mark.precommit
@pytest.mark.nightly
def test_complex_pack_negative_axis(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_complex_pack_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)