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- # Copyright 2021 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.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.common.initializer import initializer
- from mindspore.common.parameter import Parameter
- from mindspore.ops.operations import _grad_ops as G
-
- context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
-
-
- class NetReluGrad(nn.Cell):
- def __init__(self):
- super(NetReluGrad, self).__init__()
- self.relu6_grad = G.ReLU6Grad()
- self.x = Parameter(initializer(Tensor(np.array([[[[1, 0, 6],
- [-2, 3, 6],
- [-3, 1, 8]]]]).astype(np.float32)), [1, 1, 3, 3]), name='x')
- self.dy = Parameter(initializer(Tensor(np.array([[[[1, 2, 3],
- [4, 5, 6],
- [7, 8, 9]]]]).astype(np.float32)), [1, 1, 3, 3]), name='dy')
-
- def construct(self):
- return self.relu6_grad(self.dy, self.x)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_cpu
- @pytest.mark.env_onecard
- def test_relu_grad():
- relu_grad = NetReluGrad()
- output = relu_grad()
- expect = np.array([[[[1, 0, 3], [0, 5, 6], [0, 8, 0]]]]).astype(np.float32)
- error = np.ones(shape=[3, 3]) * 1.0e-6
- diff = np.abs(output.asnumpy() - expect)
- assert np.all(diff < error)
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