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- # Copyright 2019 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.ops.operations import _grad_ops as G
-
-
- class NetReluGrad(nn.Cell):
- def __init__(self):
- super(NetReluGrad, self).__init__()
- self.rekuGrad = G.ReluGrad()
-
- def construct(self, x, dy):
- return self.rekuGrad(dy, x)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_relu_grad():
- x = Tensor(np.array([[[[-1, 1, 1],
- [1, -1, 1],
- [1, 1, -1]]]]).astype(np.float32))
- dy = Tensor(np.array([[[[1, 0, 1],
- [0, 1, 0],
- [1, 1, 1]]]]).astype(np.float32))
- expect = np.array([[[[0, 0, 1,], [0, 0, 0,], [1, 1, 0.]]]]).astype(np.float32)
- error = np.ones(shape=[3, 3]) * 1.0e-6
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- relu_grad = NetReluGrad()
- output = relu_grad(x, dy)
- diff = output.asnumpy() - expect
- assert np.all(diff < error)
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