forked from mindspore-Ecosystem/mindspore
83 lines
3.1 KiB
Python
83 lines
3.1 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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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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context.set_context(device_target='CPU')
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class NetOneHot(nn.Cell):
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def __init__(self):
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super(NetOneHot, self).__init__()
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self.on_value = 2.0
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self.off_value = 3.0
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self.depth_1 = 6
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self.one_hot_1 = nn.OneHot(-1, self.depth_1, self.on_value, self.off_value)
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self.depth_2 = 4
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self.one_hot_2 = nn.OneHot(0, self.depth_1, self.on_value, self.off_value)
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self.one_hot_3 = nn.OneHot(0, self.depth_2, self.on_value, self.off_value)
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self.one_hot_4 = nn.OneHot(1, self.depth_1, self.on_value, self.off_value)
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@ms_function
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def construct(self, indices1, indices2, indices3, indices4):
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return (self.one_hot_1(indices1), self.one_hot_2(indices2),
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self.one_hot_3(indices3), self.one_hot_4(indices4))
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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_one_hot():
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one_hot = NetOneHot()
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indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
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indices2 = Tensor(np.array([1, 2, 3]).astype(np.int32))
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indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(np.int32))
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indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
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output = one_hot(indices1, indices2, indices3, indices4)
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expect_0 = np.array([
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[[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]],
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[[3., 3., 3., 3., 2., 3.], [3., 3., 3., 3., 3., 2.]],
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[[3., 3., 2., 3., 3., 3.], [3., 3., 3., 3., 3., 3.]]
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]).astype(np.float32)
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expect_1 = np.array([
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[3., 3., 3.],
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[2., 3., 3.],
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[3., 2., 3.],
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[3., 3., 2.],
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[3., 3., 3.],
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[3., 3., 3.]
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]).astype(np.float32)
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expect_2 = np.array([
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[[2., 3.], [3., 2.]], [[3., 2.], [2., 3.]], [[3., 3.], [3., 3.]],
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[[3., 3.], [3., 3.]]
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]).astype(np.float32)
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expect_3 = np.array([
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[[2., 3.], [3., 2.], [3., 3.], [3., 3.], [3., 3.], [3., 3.]],
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[[3., 3.], [3., 3.], [3., 3.], [3., 3.], [2., 3.], [3., 2.]],
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[[3., 3.], [3., 3.], [2., 3.], [3., 3.], [3., 3.], [3., 3.]]
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]).astype(np.float32)
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assert (output[0].asnumpy() == expect_0).all()
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assert (output[1].asnumpy() == expect_1).all()
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assert (output[2].asnumpy() == expect_2).all()
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assert (output[3].asnumpy() == expect_3).all()
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