forked from mindspore-Ecosystem/mindspore
55 lines
1.9 KiB
Python
55 lines
1.9 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.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops.operations import _grad_ops as G
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class CdistGradTEST(nn.Cell):
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def __init__(self, p):
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super(CdistGradTEST, self).__init__()
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self.cdist_grad = G.CdistGrad(p)
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def construct(self, grad, x1, x2, dist):
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return self.cdist_grad(grad, x1, x2, dist)
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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_CdistGradP0_float32():
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"""
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Feature: Cdist cpu kernel
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Description: test the cdist p = 0.0.
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Expectation: the output[0] is same as numpy
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"""
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cdist_grad = CdistGradTEST(3.)
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grad = Tensor(np.array([[[1.0, 1.0], [2.0, 2.0]]]).astype(np.float32))
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x1 = Tensor(np.array([[[1.0, 1.0], [2.0, 2.0]]]).astype(np.float32))
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x2 = Tensor(np.array([[[3.0, 3.0], [3.0, 3.0]]]).astype(np.float32))
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dist = Tensor(np.array([[[3.0, 3.0], [3.0, 3.0]]]).astype(np.float32))
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output = cdist_grad(grad, x1, x2, dist)
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expect = np.array(
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[[[-0.8888889, -0.8888889], [-0.44444445, -0.44444445]]]).astype(np.float32)
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print(output)
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assert (output.asnumpy() == expect).all()
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