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test_tf_Cumsum.py
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# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
from common.tf_layer_test_class import CommonTFLayerTest
# Testing Cumsum operation
# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Cumsum
class TestCumsum(CommonTFLayerTest):
# input_shape - should be an array
# axis - array which points on axis for the operation
# exclusive - enables exclusive Cumsum
# reverse - enables reverse order of Cumsum
def create_cumsum_net(self, input_shape, axis, exclusive, reverse):
import tensorflow as tf
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
tf_input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input')
tf_axis = tf.constant(axis, dtype=tf.int32)
tf.raw_ops.Cumsum(x=tf_input, axis=tf_axis, exclusive=exclusive, reverse=reverse)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
test_data = [
dict(input_shape=[2], axis=-1),
dict(input_shape=[2, 3], axis=0),
dict(input_shape=[2, 3], axis=1),
dict(input_shape=[2, 3], axis=-2),
dict(input_shape=[2, 3, 3, 4], axis=2),
dict(input_shape=[2, 3, 3, 4], axis=-3),
]
@pytest.mark.parametrize("params", test_data)
@pytest.mark.parametrize("exclusive", [False, True, None])
@pytest.mark.parametrize("reverse", [False, True, None])
@pytest.mark.precommit
@pytest.mark.precommit
@pytest.mark.nightly
def test_cumsum_basic(self, params, exclusive, reverse, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_cumsum_net(**params, exclusive=exclusive, reverse=reverse),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
class TestComplexCumsum(CommonTFLayerTest):
# input_shape - should be an array
# axis - array which points on axis for the operation
# exclusive - enables exclusive Cumsum
# reverse - enables reverse order of Cumsum
def _prepare_input(self, inputs_info):
rng = np.random.default_rng()
assert 'x_real:0' in inputs_info
assert 'x_imag:0' in inputs_info
x_shape = inputs_info['x_real:0']
inputs_data = {}
inputs_data['x_real:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2
inputs_data['x_imag:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2
return inputs_data
def create_cumsum_net(self, input_shape, axis, exclusive, reverse):
import tensorflow as tf
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
x_real = tf.compat.v1.placeholder(tf.float32, input_shape, 'x_real')
x_imag = tf.compat.v1.placeholder(tf.float32, input_shape, 'x_imag')
complex_input = tf.complex(x_real, x_imag)
tf_axis = tf.constant(axis, dtype=tf.int32)
result = tf.raw_ops.Cumsum(x=complex_input, axis=tf_axis, exclusive=exclusive, reverse=reverse)
real = tf.raw_ops.Real(input=result)
img = tf.raw_ops.Imag(input=result)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
test_data = [
dict(input_shape=[2], axis=-1),
dict(input_shape=[2, 3], axis=0),
dict(input_shape=[2, 3], axis=1),
dict(input_shape=[2, 3], axis=-2),
dict(input_shape=[2, 3, 3, 4], axis=2),
dict(input_shape=[2, 3, 3, 4], axis=-3),
]
@pytest.mark.parametrize("params", test_data)
@pytest.mark.parametrize("exclusive", [False, True, None])
@pytest.mark.parametrize("reverse", [False, True, None])
@pytest.mark.precommit
@pytest.mark.nightly
def test_cumsum_basic(self, params, exclusive, reverse, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_cumsum_net(**params, exclusive=exclusive, reverse=reverse),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)