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50 lines
1.7 KiB
50 lines
1.7 KiB
# 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.ops import composite as C
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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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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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class Net(nn.Cell):
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def __init__(self, sample, replacement, seed=0):
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super(Net, self).__init__()
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self.sample = sample
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self.replacement = replacement
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self.seed = seed
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def construct(self, x):
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return C.multinomial(x, self.sample, self.replacement, self.seed)
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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_multinomial():
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x0 = Tensor(np.array([0.9, 0.2]).astype(np.float32))
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x1 = Tensor(np.array([[0.9, 0.2], [0.9, 0.2]]).astype(np.float32))
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net0 = Net(1, True, 20)
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net1 = Net(2, True, 20)
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net2 = Net(6, True, 20)
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out0 = net0(x0)
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out1 = net1(x0)
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out2 = net2(x1)
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assert out0.asnumpy().shape == (1,)
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assert out1.asnumpy().shape == (2,)
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assert out2.asnumpy().shape == (2, 6)
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