forked from huawei/mindspore2022
180 lines
6.5 KiB
Python
180 lines
6.5 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 pytest
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import numpy as np
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from mindspore import context, nn, Tensor
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from mindspore import dtype as mstype
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE)
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single_element_fg = C.MultitypeFuncGraph("single_element_fg")
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@single_element_fg.register("Tensor")
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def single_element_fg_for_tensor(x):
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return P.Square()(x)
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double_elements_fg = C.MultitypeFuncGraph("double_elements_fg")
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@double_elements_fg.register("Tensor", "Tuple")
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def double_elements_fg_for_tensor_tuple(x, y):
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return P.Tile()(x, y)
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@double_elements_fg.register("Tensor", "List")
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def double_elements_fg_for_tensor_list(x, y):
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return x + y[0]
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class HyperMapNet(nn.Cell):
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def __init__(self, fg):
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super(HyperMapNet, self).__init__()
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self.common_map = C.HyperMap()
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self.fg = fg
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def construct(self, nest_tensor_list):
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output = self.common_map(self.fg, *nest_tensor_list)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_single_element_hypermap_with_tensor_input():
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"""
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Feature: HyperMap
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Description: Test whether the HyperMap with single tensor input can run successfully.
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Expectation: success.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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common_map = HyperMapNet(single_element_fg)
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output = common_map((x,))
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expect_output_1 = np.array([1.0, 4.0, 9.0])
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expect_output_2 = np.array([16.0, 25.0, 36.0])
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assert isinstance(output, tuple)
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assert len(output) == 2
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assert isinstance(output[0], Tensor)
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assert isinstance(output[1], Tensor)
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assert np.allclose(output[0].asnumpy(), expect_output_1)
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assert np.allclose(output[1].asnumpy(), expect_output_2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_double_elements_hypermap_tensor_tuple_inputs():
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"""
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Feature: HyperMap
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Description: Test whether the HyperMap with tensor and tuple inputs can run successfully.
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Expectation: success.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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y = ((1, 2), (2, 1))
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common_map = HyperMapNet(double_elements_fg)
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output = common_map((x, y))
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expect_output_1 = np.array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0])
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expect_output_2 = np.array([[4.0, 5.0, 6.0], [4.0, 5.0, 6.0]])
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assert isinstance(output, tuple)
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assert len(output) == 2
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assert isinstance(output[0], Tensor)
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assert isinstance(output[1], Tensor)
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assert np.allclose(output[0].asnumpy(), expect_output_1)
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assert np.allclose(output[1].asnumpy(), expect_output_2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_double_elements_hypermap_tensor_list_inputs():
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"""
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Feature: HyperMap
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Description: Test whether the HyperMap with tensor and list inputs can run successfully.
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Expectation: success.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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y = ([1, 2], [2, 1])
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common_map = HyperMapNet(double_elements_fg)
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output = common_map((x, y))
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expect_output_1 = np.array([2.0, 3.0, 4.0])
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expect_output_2 = np.array([6.0, 7.0, 8.0])
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assert isinstance(output, tuple)
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assert len(output) == 2
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assert isinstance(output[0], Tensor)
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assert isinstance(output[1], Tensor)
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assert np.allclose(output[0].asnumpy(), expect_output_1)
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assert np.allclose(output[1].asnumpy(), expect_output_2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_doubel_elements_hypermap_correct_mix_inputs():
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"""
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Feature: HyperMap
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Description: Test whether the HyperMap with mix correct inputs (Tensor + Tuple and Tensor + List)
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can run successfully.
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Expectation: success.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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y = ((1, 2), [2, 1])
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common_map = HyperMapNet(double_elements_fg)
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output = common_map((x, y))
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expect_output_1 = np.array([1.0, 2.0, 3.0, 1.0, 2.0, 3.0])
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expect_output_2 = np.array([6.0, 7.0, 8.0])
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assert isinstance(output, tuple)
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assert len(output) == 2
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assert isinstance(output[0], Tensor)
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assert isinstance(output[1], Tensor)
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assert np.allclose(output[0].asnumpy(), expect_output_1)
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assert np.allclose(output[1].asnumpy(), expect_output_2)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_double_elements_hypermap_inputs_length_mismatch():
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"""
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Feature: HyperMap
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Description: When the inputs to hypermap have different length, error will be raised.
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Expectation: error.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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y = ((1, 2), (2, 1), (5, 6))
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common_map = HyperMapNet(double_elements_fg)
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with pytest.raises(Exception, match="The length of tuples in HyperMap must be the same"):
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common_map((x, y))
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_double_elements_hypermap_inconsistent_inputs():
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"""
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Feature: HyperMap
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Description: When the inputs to hypermap is inconsistent, error will be raised.
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Expectation: error.
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"""
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x = (Tensor(np.array([1, 2, 3]), mstype.float32), Tensor(np.array([4, 5, 6]), mstype.float32))
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y = [(1, 2), (2, 1)]
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common_map = HyperMapNet(double_elements_fg)
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with pytest.raises(Exception, match="the types of arguments in HyperMap must be consistent"):
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common_map((x, y))
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