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 numpy as np 17import pytest 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): 26 out = ops.lp_pool1d(x, norm_type=1, kernel_size=3, stride=1) 27 return out 28 29 30@pytest.mark.level1 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_lppool1d_normal(mode): 37 """ 38 Feature: LPPool1d 39 Description: Verify the result of LPPool1d 40 Expectation: success 41 """ 42 ms.set_context(mode=mode) 43 net = Net() 44 x = ms.Tensor(np.arange(2 * 3 * 4).reshape((2, 3, 4)), dtype=ms.float32) 45 y = ms.Tensor(np.arange(3 * 4).reshape((3, 4)), dtype=ms.float32) 46 out = net(x) 47 out2 = net(y) 48 expect_out = np.array([[[3., 6.], 49 [15., 18.], 50 [27., 30.]], 51 [[39., 42.], 52 [51., 54.], 53 [63., 66.]]]) 54 expect_out2 = np.array([[3., 6.], 55 [15., 18.], 56 [27., 30.]]) 57 assert np.allclose(out.asnumpy(), expect_out) 58 assert np.allclose(out2.asnumpy(), expect_out2) 59