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mindspore/tests/st/ops/gpu/test_dropout.py

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
class Net(nn.Cell):
def __init__(self, keep_prob):
super(Net, self).__init__()
self.drop = P.Dropout(keep_prob)
def construct(self, x_):
return self.drop(x_)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dropout():
x_shape = [32, 16, 2, 5]
x = np.ones(x_shape).astype(np.float32)
keep_prob = 0.4
dropout = Net(keep_prob)
tx = Tensor(x)
output, mask = dropout(tx)
# check output
output_np = output.asnumpy()
elem_count = x.size
nonzero_count = np.count_nonzero(output_np)
assert (elem_count * (keep_prob - 0.1)) < nonzero_count < (elem_count * (keep_prob + 0.1))
output_sum = np.sum(output_np)
x_sum = np.sum(x)
assert abs(output_sum - x_sum)/x_sum < 0.1
# check mask
mask_np = mask.asnumpy()
mask_sum = np.sum(mask_np)
assert np.count_nonzero(mask_np) == nonzero_count
assert abs(mask_sum - nonzero_count)/nonzero_count < 0.1