116 lines
4.2 KiB
Python
116 lines
4.2 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.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 import functional as F
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from mindspore.ops.operations import _inner_ops as inner
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class NetInv(nn.Cell):
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def __init__(self):
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super(NetInv, self).__init__()
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self.inv = P.Inv()
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def construct(self, x):
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return self.inv(x)
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class InvDynamicShapeNet(nn.Cell):
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def __init__(self):
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super(InvDynamicShapeNet, self).__init__()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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def construct(self, x):
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x = self.test_dynamic(x)
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return F.inv(x)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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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-4), (np.int64, 1.0e-4), (np.float16, 1.0e-3), (np.float32, 1.0e-4),
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(np.float64, 1.0e-5), (np.complex64, 1.0e-6), (np.complex128, 1.0e-10)])
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def test_inv(mode, 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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context.set_context(mode=mode, device_target="GPU")
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inv = NetInv()
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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 = (1.0 / x).astype(dtype)
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assert np.allclose(output.asnumpy(), expect_output, atol=tol, rtol=tol, equal_nan=True)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_vmap(mode):
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"""
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Feature: test inv vmap feature.
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Description: test inv vmap feature.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([[0.25, 0.4, 0.31, 0.52], [0.5, 0.12, 0.31, 0.58]], dtype=np.float32))
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# Case 1
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output = F.vmap(F.inv, 0, 0)(x)
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expect_output = np.array([[4., 2.5, 3.2258065, 1.923077], [2., 8.333334, 3.2258065, 1.724138]], dtype=np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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# Case 2
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output = F.vmap(F.inv, 1, 0)(x)
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expect_output = np.array([[4., 2.], [2.5, 8.333334], [3.2258065, 3.2258065], [1.923077, 1.724138]],
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dtype=np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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# Case 3
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output = F.vmap(F.inv, 0, 1)(x)
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expect_output = np.array([[4., 2.], [2.5, 8.333334], [3.2258065, 3.2258065], [1.923077, 1.724138]],
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dtype=np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_inv_dynamic_shape(mode):
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"""
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Feature: test inv dynamic_shape feature.
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Description: test inv dynamic_shape feature.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([[0.25, 0.4, 0.31, 0.52],
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[0.5, 0.12, 0.31, 0.58]], dtype=np.float32))
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output = InvDynamicShapeNet()(x)
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expect_output = np.array([[4., 2.5, 3.2258065, 1.923077],
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[2., 8.333334, 3.2258065, 1.724138]], dtype=np.float32)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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