680cdacc11
* Added test * working ManagerWrapper * Clean-up in ManagerWrapper * worksave * fixed building error * Finished test of constant folding * remove unused param * Added get_vector function * clean up
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
# ******************************************************************************
|
|
# Copyright 2017-2020 Intel Corporation
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ******************************************************************************
|
|
# flake8: noqa
|
|
import json
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import ngraph as ng
|
|
from ngraph.impl import Function, PartialShape, Shape
|
|
from ngraph.impl.passes import Manager
|
|
from tests.test_ngraph.util import count_ops_of_type
|
|
|
|
|
|
def test_constant_folding():
|
|
node_constant = ng.constant(np.array([[0.0, 0.1, -0.1], [-2.5, 2.5, 3.0]], dtype=np.float32))
|
|
node_ceil = ng.ceiling(node_constant)
|
|
func = Function(node_ceil, [], "TestFunction")
|
|
|
|
assert count_ops_of_type(func, node_ceil) == 1
|
|
assert count_ops_of_type(func, node_constant) == 1
|
|
|
|
pass_manager = Manager()
|
|
pass_manager.register_pass("ConstantFolding")
|
|
pass_manager.run_passes(func)
|
|
|
|
assert count_ops_of_type(func, node_ceil) == 0
|
|
assert count_ops_of_type(func, node_constant) == 1
|
|
|
|
new_const = func.get_results()[0].input(0).get_source_output().get_node()
|
|
|
|
values_out = new_const.get_vector()
|
|
values_expected = [0.0, 1.0, 0.0, -2.0, 3.0, 3.0]
|
|
|
|
assert np.allclose(values_out, values_expected)
|