forked from mindspore-Ecosystem/mindspore
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
# Copyright 2020-2021 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 import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class NetReLU6(nn.Cell):
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def __init__(self):
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super(NetReLU6, self).__init__()
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self.relu6 = P.ReLU6()
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def construct(self, x):
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return self.relu6(x)
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class NetRelu6Dynamic(nn.Cell):
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def __init__(self):
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super(NetRelu6Dynamic, self).__init__()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.relu6 = P.ReLU6()
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def construct(self, x):
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x = self.test_dynamic(x)
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return self.relu6(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu6():
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x = Tensor(np.array([[[[-1, 1, 10],
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[5.9, 6.1, 6],
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[10, 1, -1]]]]).astype(np.float32))
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expect = np.array([[[[0, 1, 6,],
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[5.9, 6, 6,],
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[6, 1, 0.]]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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relu6 = NetReLU6()
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output = relu6(x)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu6 = NetReLU6()
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output = relu6(x)
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu6_dynamic():
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x1 = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]).astype(np.float32))
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expect1 = np.array([[0, 4, 0,],
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[2, 0, 6,]]).astype(np.float32)
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x2 = Tensor(np.array([[[[-1, 1, 10],
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[5.9, 6.1, 6],
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[10, 1, -1]]]]).astype(np.float32))
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expect2 = np.array([[[[0, 1, 6,],
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[5.9, 6, 6,],
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[6, 1, 0.]]]]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu6 = NetRelu6Dynamic()
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output1 = relu6(x1)
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assert (output1.asnumpy() == expect1).all()
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output2 = relu6(x2)
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assert (output2.asnumpy() == expect2).all()
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