forked from mindspore-Ecosystem/mindspore
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
# Copyright 2019 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.operations import _grad_ops as G
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class NetFlattenGrad(nn.Cell):
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def __init__(self):
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super(NetFlattenGrad, self).__init__()
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self.flattengrad = G.FlattenGrad()
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self.type = (2, 3)
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def construct(self, x):
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return self.flattengrad(x, self.type)
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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_flatten_grad():
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x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]).astype(np.float32))
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expect = np.array([[-0.1, 0.3, 3.6],
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[0.4, 0.5, -3.2]]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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flattengrad = NetFlattenGrad()
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output = flattengrad(x)
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assert (output.asnumpy() == expect).all()
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