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100 lines
3.5 KiB
100 lines
3.5 KiB
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.context as context
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class Net(nn.Cell):
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def __init__(self, keep_prob):
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super(Net, self).__init__()
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self.drop = P.Dropout(keep_prob)
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def construct(self, x_):
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return self.drop(x_)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dropout():
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x_shape = [32, 16, 2, 5]
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x = np.ones(x_shape).astype(np.float32)
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keep_prob = 0.4
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dropout = Net(keep_prob)
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tx = Tensor(x)
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output, mask = dropout(tx)
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# check output
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output_np = output.asnumpy()
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elem_count = x.size
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nonzero_count = np.count_nonzero(output_np)
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assert (elem_count * (keep_prob - 0.1)) < nonzero_count < (elem_count * (keep_prob + 0.1))
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output_sum = np.sum(output_np)
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x_sum = np.sum(x)
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assert abs(output_sum - x_sum)/x_sum < 0.1
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# check mask
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mask_np = mask.asnumpy()
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mask_sum = np.sum(mask_np)
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assert np.count_nonzero(mask_np) == nonzero_count
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assert abs(mask_sum - nonzero_count)/nonzero_count < 0.1
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class DropoutDynamic(nn.Cell):
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def __init__(self, keep_prob):
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super(DropoutDynamic, self).__init__()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.drop = P.Dropout(keep_prob)
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def construct(self, x):
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x = self.test_dynamic(x)
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return self.drop(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dropout_dynamic():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_1 = np.ones([32, 16, 2, 5]).astype(np.float32)
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x_2 = np.ones([32, 16, 2, 5, 6]).astype(np.float32)
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keep_prob = 0.4
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net = DropoutDynamic(keep_prob)
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output_1, mask_1 = net(Tensor(x_1))
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elem_count_1 = x_1.size
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nonzero_count_1 = np.count_nonzero(output_1.asnumpy())
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assert (elem_count_1 * (keep_prob - 0.1)) < nonzero_count_1 < (elem_count_1 * (keep_prob + 0.1))
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output_sum_1 = np.sum(output_1.asnumpy())
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x_sum_1 = np.sum(x_1)
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assert abs(output_sum_1 - x_sum_1)/x_sum_1 < 0.1
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mask_sum_1 = np.sum(mask_1.asnumpy())
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assert np.count_nonzero(mask_1.asnumpy()) == nonzero_count_1
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assert abs(mask_sum_1 - nonzero_count_1)/nonzero_count_1 < 0.1
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output_2, mask_2 = net(Tensor(x_2))
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elem_count_2 = x_2.size
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nonzero_count_2 = np.count_nonzero(output_2.asnumpy())
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assert (elem_count_2 * (keep_prob - 0.1)) < nonzero_count_2 < (elem_count_2 * (keep_prob + 0.1))
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output_sum_2 = np.sum(output_2.asnumpy())
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x_sum_2 = np.sum(x_2)
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assert abs(output_sum_2 - x_sum_2)/x_sum_2 < 0.1
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mask_sum_2 = np.sum(mask_2.asnumpy())
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assert np.count_nonzero(mask_2.asnumpy()) == nonzero_count_2
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assert abs(mask_sum_2 - nonzero_count_2)/nonzero_count_2 < 0.1
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