1# Copyright 2019 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.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops.operations import _grad_ops as G 23 24 25class NetFlattenGrad(nn.Cell): 26 def __init__(self): 27 super(NetFlattenGrad, self).__init__() 28 self.flattengrad = G.FlattenGrad() 29 self.type = (2, 3) 30 31 def construct(self, x): 32 return self.flattengrad(x, self.type) 33 34 35@pytest.mark.level0 36@pytest.mark.platform_x86_gpu_training 37@pytest.mark.env_onecard 38def test_flatten_grad(): 39 x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]).astype(np.float32)) 40 expect = np.array([[-0.1, 0.3, 3.6], 41 [0.4, 0.5, -3.2]]).astype(np.float32) 42 43 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 44 flattengrad = NetFlattenGrad() 45 output = flattengrad(x) 46 assert (output.asnumpy() == expect).all() 47