forked from mindspore-Ecosystem/mindspore
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
# Copyright 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.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.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 NetEluGrad(nn.Cell):
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def __init__(self):
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super(NetEluGrad, self).__init__()
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self.elu_grad = G.EluGrad()
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def construct(self, dy, y):
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return self.elu_grad(dy, y)
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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_elu_grad_fp32():
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y = Tensor(np.array([[[[-0.3, 1, 2],
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[1, -0.6, 1],
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[2, 1, -2]]]]).astype(np.float32))
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dy = Tensor(np.array([[[[-11, 2, 4],
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[-1, 1, -1],
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[-4, 4, -4]]]]).astype(np.float32))
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expect = np.array([[[[-7.7, 2, 4],
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[-1, 0.4, -1],
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[-4, 4, 4]]]]).astype(np.float32)
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error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6
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elu_grad = NetEluGrad()
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output = elu_grad(dy, y)
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print(output)
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diff = np.abs(output.asnumpy() - expect)
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double_check = diff / expect
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assert np.all(double_check < error)
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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_elu_grad_fp16():
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y = Tensor(np.array([[0.5, 2, 5.5], [4.5, -2, 0]]).astype(np.float16))
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dy = Tensor(np.array([[2, 1, 1.5], [-0.5, -1, -3]]).astype(np.float16))
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expect = np.array([[2, 1, 1.5], [-0.5, 1, -3]]).astype(np.float16)
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error = np.ones(shape=[2, 3]) * 1.0e-3
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elu_grad = NetEluGrad()
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output = elu_grad(dy, y)
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print(output)
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diff = np.abs(output.asnumpy() - expect)
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double_check = diff / expect
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assert np.all(double_check < error)
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