forked from mindspore-Ecosystem/mindspore
54 lines
1.8 KiB
Python
54 lines
1.8 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
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations as ops
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from mindspore import Tensor
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from mindspore.ops.operations import _inner_ops as inner
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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):
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super(Net, self).__init__()
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self.d_shape = ops.TensorShape()
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self.d_broadcastto = inner.DynamicBroadcastTo()
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def construct(self, data, shape):
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shape = self.d_shape(shape)
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return self.d_broadcastto(data, shape)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_float32():
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"""
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Feature: Dynamic BroadcastTo.
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Description: test cases for dynamic_broadcastto.
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Expectation: the result match expected array.
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"""
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data = Tensor(np.array([1, 2, 3]), mindspore.float32)
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shape = Tensor(np.zeros((2, 3)), mindspore.int64)
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expect_data = np.array([[1, 2, 3], [1, 2, 3]]).astype(np.float32)
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net = Net()
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output = net(data, shape)
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print(output.asnumpy())
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assert np.array_equal(output.asnumpy(), expect_data)
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