054a7cdf8d
* Enable ngraph python tests * Refactor and unify ngraph with onnx python tests * Revert deprecated test cases * Set ngraph and onnx python tests as a one test suite execution * Change unstrict Xfails to strict ones * Update after review: - add model zoo to onnx tests, - improvements of tests * Revert mounting zoo models dir Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>
145 lines
4.9 KiB
Python
145 lines
4.9 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.
|
|
# ******************************************************************************
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import ngraph as ng
|
|
from tests.test_ngraph.util import run_op_node, run_op_numeric_data
|
|
from tests import xfail_issue_35929, xfail_issue_34323
|
|
|
|
|
|
@xfail_issue_35929
|
|
@pytest.mark.parametrize(
|
|
"ng_api_fn, numpy_fn, range_start, range_end",
|
|
[
|
|
(ng.absolute, np.abs, -1, 1),
|
|
(ng.abs, np.abs, -1, 1),
|
|
(ng.acos, np.arccos, -1, 1),
|
|
(ng.asin, np.arcsin, -1, 1),
|
|
(ng.atan, np.arctan, -100.0, 100.0),
|
|
(ng.ceiling, np.ceil, -100.0, 100.0),
|
|
(ng.ceil, np.ceil, -100.0, 100.0),
|
|
(ng.cos, np.cos, -100.0, 100.0),
|
|
(ng.cosh, np.cosh, -100.0, 100.0),
|
|
(ng.exp, np.exp, -100.0, 100.0),
|
|
(ng.floor, np.floor, -100.0, 100.0),
|
|
(ng.log, np.log, 0, 100.0),
|
|
(ng.relu, lambda x: np.maximum(0, x), -100.0, 100.0),
|
|
(ng.sign, np.sign, -100.0, 100.0),
|
|
(ng.sin, np.sin, -100.0, 100.0),
|
|
(ng.sinh, np.sinh, -100.0, 100.0),
|
|
(ng.sqrt, np.sqrt, 0.0, 100.0),
|
|
(ng.tan, np.tan, -1.0, 1.0),
|
|
(ng.tanh, np.tanh, -100.0, 100.0),
|
|
],
|
|
)
|
|
def test_unary_op_array(ng_api_fn, numpy_fn, range_start, range_end):
|
|
np.random.seed(133391)
|
|
input_data = range_start + np.random.rand(2, 3, 4) * (range_end - range_start)
|
|
expected = numpy_fn(input_data)
|
|
|
|
result = run_op_node([input_data], ng_api_fn)
|
|
assert np.allclose(result, expected, rtol=0.001)
|
|
|
|
result = run_op_numeric_data(input_data, ng_api_fn)
|
|
assert np.allclose(result, expected, rtol=0.001)
|
|
|
|
|
|
@xfail_issue_34323
|
|
@pytest.mark.parametrize(
|
|
"ng_api_fn, numpy_fn, input_data",
|
|
[
|
|
(ng.absolute, np.abs, np.float32(-3)),
|
|
(ng.abs, np.abs, np.float32(-3)),
|
|
(ng.acos, np.arccos, np.float32(-0.5)),
|
|
(ng.asin, np.arcsin, np.float32(-0.5)),
|
|
(ng.atan, np.arctan, np.float32(-0.5)),
|
|
(ng.ceiling, np.ceil, np.float32(1.5)),
|
|
(ng.ceil, np.ceil, np.float32(1.5)),
|
|
(ng.cos, np.cos, np.float32(np.pi / 4.0)),
|
|
(ng.cosh, np.cosh, np.float32(np.pi / 4.0)),
|
|
(ng.exp, np.exp, np.float32(1.5)),
|
|
(ng.floor, np.floor, np.float32(1.5)),
|
|
(ng.log, np.log, np.float32(1.5)),
|
|
(ng.relu, lambda x: np.maximum(0, x), np.float32(-0.125)),
|
|
(ng.sign, np.sign, np.float32(0.0)),
|
|
(ng.sin, np.sin, np.float32(np.pi / 4.0)),
|
|
(ng.sinh, np.sinh, np.float32(0.0)),
|
|
(ng.sqrt, np.sqrt, np.float32(3.5)),
|
|
(ng.tan, np.tan, np.float32(np.pi / 4.0)),
|
|
(ng.tanh, np.tanh, np.float32(0.1234)),
|
|
],
|
|
)
|
|
def test_unary_op_scalar(ng_api_fn, numpy_fn, input_data):
|
|
expected = numpy_fn(input_data)
|
|
|
|
result = run_op_node([input_data], ng_api_fn)
|
|
assert np.allclose(result, expected)
|
|
|
|
result = run_op_numeric_data(input_data, ng_api_fn)
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
@xfail_issue_34323
|
|
@pytest.mark.parametrize(
|
|
"input_data", [(np.array([True, False, True, False])), (np.array(True)), (np.array(False))]
|
|
)
|
|
def test_logical_not(input_data):
|
|
expected = np.logical_not(input_data)
|
|
|
|
result = run_op_node([input_data], ng.logical_not)
|
|
|
|
assert np.allclose(result, expected)
|
|
result = run_op_numeric_data(input_data, ng.logical_not)
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
@xfail_issue_34323
|
|
def test_sigmoid():
|
|
input_data = np.array([-3.14, -1.0, 0.0, 2.71001, 1000.0], dtype=np.float32)
|
|
result = run_op_node([input_data], ng.sigmoid)
|
|
|
|
def sigmoid(x):
|
|
return 1.0 / (1.0 + np.exp(-x))
|
|
|
|
expected = np.array(list(map(sigmoid, input_data)))
|
|
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
@xfail_issue_34323
|
|
def test_softmax():
|
|
axis = 0
|
|
input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
|
|
|
|
result = run_op_node([input_tensor], ng.softmax, axis)
|
|
|
|
expected = [[0.00426978, 0.01160646, 0.03154963], [0.08576079, 0.23312202, 0.6336913]]
|
|
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
@xfail_issue_34323
|
|
def test_erf():
|
|
input_tensor = np.array([-1.0, 0.0, 1.0, 2.5, 3.14, 4.0], dtype=np.float32)
|
|
expected = [-0.842701, 0.0, 0.842701, 0.999593, 0.999991, 1.0]
|
|
|
|
result = run_op_node([input_tensor], ng.erf)
|
|
assert np.allclose(result, expected)
|
|
|
|
result = run_op_numeric_data(input_tensor, ng.erf)
|
|
assert np.allclose(result, expected)
|