forked from mindspore-Ecosystem/mindspore
209 lines
6.5 KiB
Python
209 lines
6.5 KiB
Python
# Copyright 2020 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.context as context
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class GatherNdNet(nn.Cell):
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def __init__(self):
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super(GatherNdNet, self).__init__()
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self.gathernd = P.GatherNd()
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def construct(self, x, indices):
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return self.gathernd(x, indices)
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def gathernd0(nptype):
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x = Tensor(np.arange(3 * 2, dtype=nptype).reshape(3, 2))
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indices = Tensor(np.array([[1, 1], [0, 1]]).astype(np.int32))
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expect = np.array([3, 1]).astype(nptype)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gathernd = GatherNdNet()
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output = gathernd(x, indices)
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assert np.array_equal(output.asnumpy(), expect)
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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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def test_gathernd0_float32():
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gathernd0(np.float32)
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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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def test_gathernd0_float16():
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gathernd0(np.float16)
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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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def test_gathernd0_int32():
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gathernd0(np.int32)
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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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def test_gathernd0_int16():
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gathernd0(np.int16)
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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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def test_gathernd0_uint8():
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gathernd0(np.uint8)
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def gathernd1(nptype):
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x = Tensor(np.arange(2 * 3 * 4 * 5, dtype=nptype).reshape(2, 3, 4, 5))
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indices = Tensor(np.array([[[[[l, k, j, i] for i in [1, 3, 4]] for j in range(4)]
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for k in range(3)] for l in range(2)], dtype='i4'))
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expect = np.array([[[[1., 3., 4.],
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[6., 8., 9.],
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[11., 13., 14.],
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[16., 18., 19.]],
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[[21., 23., 24.],
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[26., 28., 29.],
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[31., 33., 34.],
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[36., 38., 39.]],
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[[41., 43., 44.],
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[46., 48., 49.],
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[51., 53., 54.],
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[56., 58., 59.]]],
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[[[61., 63., 64.],
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[66., 68., 69.],
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[71., 73., 74.],
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[76., 78., 79.]],
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[[81., 83., 84.],
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[86., 88., 89.],
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[91., 93., 94.],
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[96., 98., 99.]],
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[[101., 103., 104.],
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[106., 108., 109.],
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[111., 113., 114.],
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[116., 118., 119.]]]]).astype(nptype)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gather = GatherNdNet()
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output = gather(x, indices)
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assert np.array_equal(output.asnumpy(), expect)
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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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def test_gathernd1_float32():
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gathernd1(np.float32)
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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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def test_gathernd1_float16():
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gathernd1(np.float16)
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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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def test_gathernd1_int32():
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gathernd1(np.int32)
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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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def test_gathernd1_int16():
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gathernd1(np.int16)
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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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def test_gathernd1_uint8():
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gathernd1(np.uint8)
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def gathernd2(nptype):
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x = Tensor(np.array([[4., 5., 4., 1., 5.],
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[4., 9., 5., 6., 4.],
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[9., 8., 4., 3., 6.],
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[0., 4., 2., 2., 8.],
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[1., 8., 6., 2., 8.],
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[8., 1., 9., 7., 3.],
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[7., 9., 2., 5., 7.],
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[9., 8., 6., 8., 5.],
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[3., 7., 2., 7., 4.],
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[4., 2., 8., 2., 9.]]).astype(np.float16))
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indices = Tensor(np.array([[4000], [1], [300000]]).astype(np.int32))
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expect = np.array([[0., 0., 0., 0., 0.],
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[4., 9., 5., 6., 4.],
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[0., 0., 0., 0., 0.]])
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gathernd = GatherNdNet()
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output = gathernd(x, indices)
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assert np.array_equal(output.asnumpy(), expect)
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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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def test_gathernd2_float32():
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gathernd2(np.float32)
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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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def test_gathernd2_float16():
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gathernd2(np.float16)
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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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def test_gathernd2_int32():
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gathernd2(np.int32)
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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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def test_gathernd2_int16():
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gathernd2(np.int16)
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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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def test_gathernd2_uint8():
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gathernd2(np.uint8)
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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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def test_gathernd_bool():
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x = Tensor(np.array([[True, False], [False, False]]).astype(np.bool))
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indices = Tensor(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).astype(np.int32))
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expect = np.array([True, False, False, False]).astype(np.bool)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gathernd = GatherNdNet()
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output = gathernd(x, indices)
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assert np.array_equal(output.asnumpy(), expect)
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