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 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.ops import operations as P 21 22 23class Net(nn.Cell): 24 def __init__(self, keep_prob): 25 super(Net, self).__init__() 26 self.drop = P.Dropout(keep_prob) 27 28 def construct(self, x_): 29 return self.drop(x_) 30 31 32@pytest.mark.level0 33@pytest.mark.platform_x86_gpu_training 34@pytest.mark.env_onecard 35def test_dropout(): 36 context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU") 37 x_shape = [4096, 768] 38 x = np.ones(x_shape).astype(np.float32) 39 keep_prob = 0.9 40 dropout = Net(keep_prob) 41 tx = Tensor(x) 42 output, mask = dropout(tx) 43 44 output_np = output.asnumpy() 45 elem_count = x.size 46 nonzero_count = np.count_nonzero(output_np) 47 assert (elem_count * (keep_prob - 0.1)) < nonzero_count < (elem_count * (keep_prob + 0.1)) 48 output_sum = np.sum(output_np) 49 x_sum = np.sum(x) 50 assert abs(output_sum - x_sum)/x_sum < 0.1 51 # check mask 52 mask_np = mask.asnumpy() 53 mask_sum = np.sum(mask_np) 54 assert np.count_nonzero(mask_np) == nonzero_count 55 assert abs(mask_sum - nonzero_count)/nonzero_count < 0.1 56