forked from mindspore-Ecosystem/mindspore
126 lines
4.3 KiB
Python
126 lines
4.3 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 as ms
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import mindspore.nn as nn
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import mindspore.ops as ops
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class Net(nn.Cell):
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def construct(self, x, y):
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output = ops.inner(x, y)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_inner_normal(mode):
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"""
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Feature: ops.inner
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Description: Verify the result of ops.inner
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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x = ms.Tensor([[[1, 2, 3], [3, 2, 1]], [[4, 5, 6], [4, 5, 6]]], ms.float32)
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y = ms.Tensor([[2, 3, 4], [4, 3, 2]], ms.float32)
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out = net(x, y)
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expect_out = np.array([[[20, 16], [16, 20]], [[47, 43], [47, 43]]], dtype=np.float32)
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assert np.allclose(out.asnumpy(), expect_out)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_inner_with_scalar(mode):
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"""
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Feature: ops.inner
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Description: Verify the result of ops.inner
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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x = ms.Tensor([[[1, 2, 3], [3, 2, 1]], [[4, 5, 6], [4, 5, 6]]], ms.float32)
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y = ms.Tensor(2, ms.float32)
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out = net(x, y)
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expect_out = np.array([[[2, 4, 6], [6, 4, 2]], [[8, 10, 12], [8, 10, 12]]], dtype=np.float32)
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assert np.allclose(out.asnumpy(), expect_out)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_inner_1d(mode):
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"""
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Feature: ops.inner
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Description: Verify the result of ops.inner
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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x = ms.Tensor([1, 2, 3], ms.float32)
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y = ms.Tensor([4, 5, 6], ms.float32)
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out = net(x, y)
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expect_out = np.array(32, dtype=np.float32)
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assert np.allclose(out.asnumpy(), expect_out)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_inner_multidims(mode):
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"""
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Feature: ops.inner
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Description: Verify the result of ops.inner
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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x = ms.Tensor([[0.8173, 1.0874, 1.1784], [0.3279, 0.1234, 2.7894]], dtype=ms.float32)
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y = ms.Tensor([[[-0.4682, -0.7159, 0.1506], [0.4034, -0.3657, 1.0387],
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[0.9892, -0.6684, 0.1774], [0.9482, 1.3261, 0.3917]],
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[[0.4537, 0.7493, 1.1724], [0.2291, 0.5749, -0.2267],
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[-0.7920, 0.3607, -0.3701], [1.3666, -0.5850, -1.7242]]], dtype=ms.float32)
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out = net(x, y)
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expect_out = np.array([[[-0.9836624, 1.1560408, 0.29070318, 2.6785443],
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[2.567154, 0.54524636, -0.6912023, -1.5510042]],
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[[0.17821884, 2.9844973, 0.7367177, 1.5671635],
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[3.5115247, -0.48629245, -1.2475433, -4.433564]]], dtype=np.float32)
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assert np.allclose(out.asnumpy(), expect_out)
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