forked from mindspore-Ecosystem/mindspore
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
# Copyright 2021 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.ops import operations as P
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from mindspore.ops import composite as C
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def maskedselect():
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x = np.array([1, 2, 3, 4]).astype(np.int32)
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mask = np.array([[[0], [1], [0], [1]], [[0], [1], [0], [1]]]).astype(np.bool)
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net = P.MaskedSelect()
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return net(Tensor(x), Tensor(mask))
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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_maskedselect():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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y = maskedselect()
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expect = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]
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assert (y.asnumpy() == expect).all()
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, x, mask, grad):
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gout = self.grad(self.network)(x, mask, grad)
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return gout
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.op = P.MaskedSelect()
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def construct(self, x, mask):
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return self.op(x, mask)
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def masked_select_grad():
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x = np.array([1, 2, 3, 4]).astype(np.int32)
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mask = np.array([[0], [1], [0], [1]]).astype(np.bool)
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dy = np.array([i for i in range(8)]).astype(np.int32)
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grad = Grad(Net())
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return grad(Tensor(x), Tensor(mask), Tensor(dy))[0]
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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_masked_select_grad():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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dx = masked_select_grad()
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expect = [4, 6, 8, 10]
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assert (dx.asnumpy() == expect).all()
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