forked from mindspore-Ecosystem/mindspore
420 lines
14 KiB
Python
420 lines
14 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 RoundNet(nn.Cell):
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def __init__(self):
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super(RoundNet, self).__init__()
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self.round = P.Round()
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def construct(self, x):
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return self.round(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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class IdentityNet(nn.Cell):
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def __init__(self):
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super(IdentityNet, self).__init__()
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self.identity = P.Identity()
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def construct(self, x):
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return self.identity(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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x = np.random.randn(4, 3).astype(np.float64)
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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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x = np.random.randn(3, 4, 5, 6).astype(np.float64) * prop
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output = net(Tensor(x))
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expect_output = np.rint(x).astype(np.float64)
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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_round():
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net = RoundNet()
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x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float16)
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output = net(Tensor(x))
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expect_output = np.round(x).astype(np.float16)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float32)
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output = net(Tensor(x))
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expect_output = np.round(x).astype(np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(np.float64)
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output = net(Tensor(x))
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expect_output = np.round(x).astype(np.float64)
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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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
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@pytest.mark.parametrize('dtype, tol',
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[(np.int32, 1.0e-7), (np.float16, 1.0e-5), (np.float32, 1.0e-5), (np.float64, 1.0e-7)])
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def test_reciprocal(shape, dtype, tol):
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"""
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Feature: ALL To ALL
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Description: test cases for reciprocal
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Expectation: the result match to numpy
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"""
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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(*shape).astype(dtype) * prop
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output = net(Tensor(x))
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expect_output = np.reciprocal(x).astype(dtype)
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diff = output.asnumpy() - expect_output
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error = np.ones(shape=expect_output.shape) * tol
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assert np.all(np.abs(diff) < 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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
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@pytest.mark.parametrize('dtype, tol',
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[(np.int32, 1.0e-7), (np.float16, 1.0e-5), (np.float32, 1.0e-5)])
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def test_inv(shape, dtype, tol):
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"""
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Feature: ALL To ALL
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Description: test cases for inv
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Expectation: the result match to numpy
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"""
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inv = P.Inv()
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(*shape).astype(dtype) * prop
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output = inv(Tensor(x))
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expect_output = np.reciprocal(x).astype(dtype)
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diff = output.asnumpy() - expect_output
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error = np.ones(shape=expect_output.shape) * tol
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assert np.all(np.abs(diff) < 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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
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@pytest.mark.parametrize('dtype', [np.int16, np.uint16])
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def test_invert(shape, dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for invert
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Expectation: the result match to numpy
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"""
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invert = P.Invert()
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prop = 100 if np.random.random() > 0.5 else -100
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input_x = (np.random.randn(*shape) * prop).astype(dtype)
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output = invert(Tensor(input_x))
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expect_output = np.invert(input_x)
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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_identity_pynative():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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net = IdentityNet()
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x = np.random.randn(3, 4, 5, 6).astype(np.float64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.float32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.float16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int8)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.bool)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(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_identity_graph():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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net = IdentityNet()
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x = np.random.randn(3, 4, 5, 6).astype(np.float64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.float32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.float16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int64)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int32)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int16)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.int8)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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x = np.random.randn(3, 4, 5, 6).astype(np.bool)
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input_tensor = Tensor(x)
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output = net(input_tensor)
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np.testing.assert_almost_equal(output.asnumpy(), input_tensor.asnumpy())
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assert id(input_tensor) != id(output)
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