forked from mindspore-Ecosystem/mindspore
166 lines
4.9 KiB
Python
166 lines
4.9 KiB
Python
# Copyright 2020 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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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class SquareNet(nn.Cell):
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def __init__(self):
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super(SquareNet, self).__init__()
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self.square = P.Square()
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def construct(self, x):
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return self.square(x)
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class FloorNet(nn.Cell):
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def __init__(self):
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super(FloorNet, self).__init__()
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self.floor = P.Floor()
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def construct(self, x):
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return self.floor(x)
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class ReciprocalNet(nn.Cell):
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def __init__(self):
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super(ReciprocalNet, self).__init__()
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self.reciprocal = P.Reciprocal()
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def construct(self, x):
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return self.reciprocal(x)
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class RintNet(nn.Cell):
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def __init__(self):
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super(RintNet, self).__init__()
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self.rint = P.Rint()
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def construct(self, x):
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return self.rint(x)
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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_square():
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x = np.array([1, 2, 3]).astype(np.int16)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.int16)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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x = np.array([1, 2, 3]).astype(np.int32)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.int32)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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x = np.array([1, 2, 3]).astype(np.int64)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.int64)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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x = np.array([1, 2, 3]).astype(np.float16)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.float16)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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x = np.array([1, 2, 3]).astype(np.float32)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.float32)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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x = np.array([1, 2, 3]).astype(np.float64)
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net = SquareNet()
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output = net(Tensor(x))
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expect_output = np.array([1, 4, 9]).astype(np.float64)
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print(output)
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assert np.all(output.asnumpy() == expect_output)
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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_floor():
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net = FloorNet()
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x = np.random.randn(3, 4).astype(np.float16)
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x = x * 100
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output = net(Tensor(x))
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expect_output = np.floor(x).astype(np.float16)
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print(output.asnumpy())
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assert np.all(output.asnumpy() == expect_output)
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x = np.random.randn(4, 3).astype(np.float32)
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x = x * 100
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output = net(Tensor(x))
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expect_output = np.floor(x)
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print(output.asnumpy())
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assert np.all(output.asnumpy() == expect_output)
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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_rint():
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net = RintNet()
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5, 6).astype(np.float16) * prop
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output = net(Tensor(x))
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expect_output = np.rint(x).astype(np.float16)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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x = np.random.randn(3, 4, 5, 6).astype(np.float32) * prop
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output = net(Tensor(x))
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expect_output = np.rint(x).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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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_reciprocal():
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net = ReciprocalNet()
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5, 6).astype(np.float16) * prop
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output = net(Tensor(x))
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expect_output = (1. / x).astype(np.float16)
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diff = output.asnumpy() - expect_output
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error = np.ones(shape=expect_output.shape) * 1.0e-5
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assert np.all(np.abs(diff) < error)
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x = np.random.randn(3, 4, 5, 6).astype(np.float32) * prop
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output = net(Tensor(x))
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expect_output = (1. / x).astype(np.float32)
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diff = output.asnumpy() - expect_output
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error = np.ones(shape=expect_output.shape) * 1.0e-5
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assert np.all(np.abs(diff) < error)
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