56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
# ******************************************************************************
|
|
# Copyright 2018-2021 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.
|
|
# ******************************************************************************
|
|
from functools import reduce
|
|
|
|
import numpy as np
|
|
import onnx
|
|
import pytest
|
|
|
|
from tests.test_onnx.utils import run_node
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"onnx_op,numpy_func", [("Sum", np.add), ("Min", np.minimum), ("Max", np.maximum)]
|
|
)
|
|
def test_variadic(onnx_op, numpy_func):
|
|
data = [
|
|
np.array([1, 2, 3], dtype=np.int32),
|
|
np.array([4, 5, 6], dtype=np.int32),
|
|
np.array([7, 8, 9], dtype=np.int32),
|
|
]
|
|
node = onnx.helper.make_node(
|
|
onnx_op, inputs=["data_0", "data_1", "data_2"], outputs=["y"]
|
|
)
|
|
expected_output = reduce(numpy_func, data)
|
|
|
|
ng_results = run_node(node, data)
|
|
assert np.array_equal(ng_results, [expected_output])
|
|
|
|
|
|
def test_mean():
|
|
data = [
|
|
np.array([1, 2, 3], dtype=np.int32),
|
|
np.array([4, 5, 6], dtype=np.int32),
|
|
np.array([7, 8, 9], dtype=np.int32),
|
|
]
|
|
node = onnx.helper.make_node(
|
|
"Mean", inputs=["data_0", "data_1", "data_2"], outputs=["y"]
|
|
)
|
|
expected_output = reduce(np.add, data) / len(data)
|
|
|
|
ng_results = run_node(node, data)
|
|
assert np.array_equal(ng_results, [expected_output])
|