forked from mindspore-Ecosystem/mindspore
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
# Copyright 2022 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 as ms
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import mindspore.nn as nn
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.pool = nn.PReLU(channel=2, w=-0.25)
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def construct(self, x):
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out = self.pool(x)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_prelu_normal(mode):
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"""
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Feature: PReLU
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Description: Verify the result of PReLU
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Expectation: success
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"""
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ms.set_context(mode=mode)
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x = ms.Tensor([[[0.9192, -0.1487],
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[-0.3999, -0.6840]],
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[[0.4745, -0.6271],
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[-0.6547, -0.5856]],
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[[-0.2572, -0.8412],
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[0.1918, -0.6117]]])
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net = Net()
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out = net(x)
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expect_out = np.array([[[0.9192, 0.037175],
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[0.099975, 0.171]],
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[[0.4745, 0.156775],
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[0.163675, 0.1464]],
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[[0.0643, 0.2103],
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[0.1918, 0.152925]]])
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assert np.allclose(out.asnumpy().astype(np.float16), expect_out.astype(np.float16))
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