1# Copyright 2020 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 17 18import mindspore.context as context 19import mindspore.nn as nn 20from mindspore import Tensor 21from mindspore.common.api import ms_function 22from mindspore.ops.composite import GradOperation 23 24context.set_context(mode=context.GRAPH_MODE, device_target='CPU') 25 26 27class Grad(nn.Cell): 28 def __init__(self, network): 29 super(Grad, self).__init__() 30 self.grad = GradOperation(get_all=True, sens_param=True) 31 self.network = network 32 33 @ms_function 34 def construct(self, input_, output_grad): 35 return self.grad(self.network)(input_, output_grad) 36 37 38@pytest.mark.level0 39@pytest.mark.platform_x86_cpu 40@pytest.mark.env_onecard 41def test_net(): 42 x = np.arange(1 * 1 * 6 * 6).reshape((1, 1, 6, 6)).astype(np.float32) 43 net = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='valid') 44 out = net(Tensor(x)) 45 46 out_shape = out.asnumpy().shape 47 sens = np.arange(int(np.prod(out_shape))).reshape(out_shape).astype(np.float32) 48 backword_net = Grad(net) 49 output = backword_net(Tensor(x), Tensor(sens)) 50 print(len(output)) 51 print(output[0].asnumpy()) 52