1# Copyright 2022 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import pytest 17import numpy as np 18 19import mindspore as ms 20import mindspore.nn as nn 21import mindspore.ops as ops 22 23 24class Net(nn.Cell): 25 def construct(self, x, values): 26 output = ops.heaviside(x, values) 27 return output 28 29 30@pytest.mark.level2 31@pytest.mark.platform_x86_cpu 32@pytest.mark.platform_arm_cpu 33@pytest.mark.platform_x86_gpu_training 34@pytest.mark.env_onecard 35@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) 36def test_heaviside_normal(mode): 37 """ 38 Feature: heaviside 39 Description: Verify the result of heaviside 40 Expectation: success 41 """ 42 ms.set_context(mode=mode) 43 net = Net() 44 x = ms.Tensor([-1.5, 0., 2.], ms.float32) 45 values = ms.Tensor([0.5], ms.float32) 46 expect_output = np.array([0., 0.5, 1.], dtype=np.float32) 47 out = net(x, values) 48 assert np.allclose(out.asnumpy(), expect_output) 49