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# ============================================================================ 15import numpy as np 16import pytest 17import mindspore as ms 18import mindspore.nn as nn 19import mindspore.ops as ops 20from mindspore import Tensor 21 22 23class Roll(nn.Cell): 24 def construct(self, x): 25 return ops.roll(x, shifts=2, dims=0) 26 27 28@pytest.mark.level1 29@pytest.mark.platform_arm_ascend_training 30@pytest.mark.platform_x86_ascend_training 31@pytest.mark.env_onecard 32@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) 33def test_tensor_roll(mode): 34 """ 35 Feature: tensor.roll 36 Description: Verify the result of roll 37 Expectation: success 38 """ 39 ms.set_context(mode=mode) 40 input_x = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.float32)) 41 net = Roll() 42 output = net(input_x) 43 expect_output = [3., 4., 0., 1., 2.] 44 assert np.allclose(output.asnumpy(), expect_output) 45