forked from mindspore-Ecosystem/mindspore
104 lines
3.7 KiB
Python
104 lines
3.7 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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from mindspore import Tensor
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import mindspore.nn as nn
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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 InvertDynamicShapeNet(nn.Cell):
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def __init__(self):
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super(InvertDynamicShapeNet, 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.invert(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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@pytest.mark.level0
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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', [np.int16, np.uint16])
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def test_invert(mode, 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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context.set_context(mode=mode, device_target="GPU")
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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_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_invert_vmap(mode):
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"""
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Feature: test invert vmap feature.
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Description: test invert 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([[25, 4, 13, 9], [2, -1, 0, -5]], dtype=np.int16))
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# Case 1
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output = F.vmap(F.invert, 0, 0)(x)
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expect_output = np.array([[-26, -5, -14, -10], [-3, 0, -1, 4]], dtype=np.int16)
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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.invert, 1, 0)(x)
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expect_output = np.array([[-26, -3], [-5, 0], [-14, -1], [-10, 4]], dtype=np.int16)
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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.invert, 0, 1)(x)
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expect_output = np.array([[-26, -3], [-5, 0], [-14, -1], [-10, 4]], dtype=np.int16)
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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_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_invert_dynamic_shape(mode):
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"""
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Feature: test invert dynamic_shape feature.
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Description: test invert 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([[25, 4, 13, 9],
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[2, -1, 0, -5]], dtype=np.int16))
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output = InvertDynamicShapeNet()(x)
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expect_output = np.array([[-26, -5, -14, -10],
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[-3, 0, -1, 4]], dtype=np.int16)
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np.testing.assert_almost_equal(output.asnumpy(), expect_output)
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