138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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import numpy as np
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import pytest
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import ngraph as ng
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from tests.test_ngraph.util import run_op_node, run_op_numeric_data
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@pytest.mark.parametrize(
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"ng_api_fn, numpy_fn, range_start, range_end",
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[
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(ng.absolute, np.abs, -1, 1),
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(ng.abs, np.abs, -1, 1),
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(ng.acos, np.arccos, -1, 1),
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(ng.asin, np.arcsin, -1, 1),
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(ng.atan, np.arctan, -100.0, 100.0),
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(ng.ceiling, np.ceil, -100.0, 100.0),
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(ng.ceil, np.ceil, -100.0, 100.0),
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(ng.cos, np.cos, -100.0, 100.0),
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(ng.cosh, np.cosh, -100.0, 100.0),
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(ng.exp, np.exp, -100.0, 100.0),
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(ng.floor, np.floor, -100.0, 100.0),
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(ng.log, np.log, 0, 100.0),
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(ng.relu, lambda x: np.maximum(0, x), -100.0, 100.0),
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(ng.sign, np.sign, -100.0, 100.0),
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(ng.sin, np.sin, -100.0, 100.0),
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(ng.sinh, np.sinh, -100.0, 100.0),
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(ng.sqrt, np.sqrt, 0.0, 100.0),
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(ng.tan, np.tan, -1.0, 1.0),
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(ng.tanh, np.tanh, -100.0, 100.0),
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],
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)
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def test_unary_op_array(ng_api_fn, numpy_fn, range_start, range_end):
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np.random.seed(133391)
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input_data = range_start + np.random.rand(2, 3, 4) * (range_end - range_start)
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expected = numpy_fn(input_data)
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result = run_op_node([input_data], ng_api_fn)
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assert np.allclose(result, expected, rtol=0.001)
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result = run_op_numeric_data(input_data, ng_api_fn)
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assert np.allclose(result, expected, rtol=0.001)
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@pytest.mark.parametrize(
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"ng_api_fn, numpy_fn, input_data",
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[
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(ng.absolute, np.abs, np.float32(-3)),
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(ng.abs, np.abs, np.float32(-3)),
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(ng.acos, np.arccos, np.float32(-0.5)),
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(ng.asin, np.arcsin, np.float32(-0.5)),
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(ng.atan, np.arctan, np.float32(-0.5)),
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(ng.ceiling, np.ceil, np.float32(1.5)),
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(ng.ceil, np.ceil, np.float32(1.5)),
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(ng.cos, np.cos, np.float32(np.pi / 4.0)),
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(ng.cosh, np.cosh, np.float32(np.pi / 4.0)),
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(ng.exp, np.exp, np.float32(1.5)),
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(ng.floor, np.floor, np.float32(1.5)),
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(ng.log, np.log, np.float32(1.5)),
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(ng.relu, lambda x: np.maximum(0, x), np.float32(-0.125)),
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(ng.sign, np.sign, np.float32(0.0)),
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(ng.sin, np.sin, np.float32(np.pi / 4.0)),
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(ng.sinh, np.sinh, np.float32(0.0)),
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(ng.sqrt, np.sqrt, np.float32(3.5)),
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(ng.tan, np.tan, np.float32(np.pi / 4.0)),
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(ng.tanh, np.tanh, np.float32(0.1234)),
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],
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)
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def test_unary_op_scalar(ng_api_fn, numpy_fn, input_data):
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expected = numpy_fn(input_data)
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result = run_op_node([input_data], ng_api_fn)
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assert np.allclose(result, expected)
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result = run_op_numeric_data(input_data, ng_api_fn)
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assert np.allclose(result, expected)
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@pytest.mark.parametrize(
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"input_data", [(np.array([True, False, True, False])), (np.array(True)), (np.array(False))]
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)
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def test_logical_not(input_data):
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expected = np.logical_not(input_data)
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result = run_op_node([input_data], ng.logical_not)
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assert np.allclose(result, expected)
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result = run_op_numeric_data(input_data, ng.logical_not)
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assert np.allclose(result, expected)
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def test_sigmoid():
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input_data = np.array([-3.14, -1.0, 0.0, 2.71001, 1000.0], dtype=np.float32)
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result = run_op_node([input_data], ng.sigmoid)
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-x))
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expected = np.array(list(map(sigmoid, input_data)))
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assert np.allclose(result, expected)
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def test_softmax():
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axis = 0
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input_tensor = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
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result = run_op_node([input_tensor], ng.softmax, axis)
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expected = [[0.00426978, 0.01160646, 0.03154963], [0.08576079, 0.23312202, 0.6336913]]
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assert np.allclose(result, expected)
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def test_erf():
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input_tensor = np.array([-1.0, 0.0, 1.0, 2.5, 3.14, 4.0], dtype=np.float32)
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expected = [-0.842701, 0.0, 0.842701, 0.999593, 0.999991, 1.0]
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result = run_op_node([input_tensor], ng.erf)
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assert np.allclose(result, expected)
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result = run_op_numeric_data(input_tensor, ng.erf)
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assert np.allclose(result, expected)
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