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81 lines
2.6 KiB
81 lines
2.6 KiB
# Copyright 2019 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.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops.operations import _grad_ops as G
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Net_Pool_Grad(nn.Cell):
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def __init__(self):
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super(Net_Pool_Grad, self).__init__()
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self.maxpool_grad_fun = G.MaxPoolGrad(pad_mode="VALID",
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kernel_size=2,
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strides=2)
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self.x = Parameter(initializer(
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Tensor(np.array([[[
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[0, 1, 2, 3, 4, 5],
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[6, 7, 8, 9, 10, 11],
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[12, 13, 14, 15, 16, 17],
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[18, 19, 20, 21, 22, 23],
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[24, 25, 26, 27, 28, 29],
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[30, 31, 32, 33, 34, 35]
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]]]).astype(np.float32)), [1, 1, 6, 6]), name='x')
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self.a = Parameter(initializer(
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Tensor(np.array([[[
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[3, 3, 3],
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[3, 3, 3],
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[3, 3, 3]
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]]]).astype(np.float32)), [1, 1, 3, 3]), name='a')
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self.d = Parameter(initializer(
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Tensor(np.array([[[
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[7, 9, 11],
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[19, 21, 23],
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[31, 33, 35]
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]]]).astype(np.float32)), [1, 1, 3, 3]), name='d')
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def construct(self):
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return self.maxpool_grad_fun(self.x, self.a, self.d)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_maxpool2d_grad():
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maxpool2d_grad = Net_Pool_Grad()
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output = maxpool2d_grad()
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print(output)
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expect_result = (np.array([[[
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[0, 0, 0, 0, 0, 0],
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[0, 7, 0, 9, 0, 11],
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[0, 0, 0, 0, 0, 0],
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[0, 19, 0, 21, 0, 23],
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[0, 0, 0, 0, 0, 0],
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[0, 31, 0, 33, 0, 35]
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]]]))
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assert (output.asnumpy() == expect_result).all()
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