forked from mindspore-Ecosystem/mindspore
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
# Copyright 2022 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 pytest
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import numpy as np
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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="Ascend")
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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.bidense = nn.BiDense(20, 30, 40)
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@ms_function
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def construct(self, x1, x2):
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return self.bidense(x1, x2)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_net():
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"""
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Feature: Assert BiDense output shape
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Description: test the output.shape == (128, 40).
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Expectation: match the shape.
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"""
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x1 = np.random.randn(128, 20).astype(np.float32)
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x2 = np.random.randn(128, 30).astype(np.float32)
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net = Net()
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output = net(Tensor(x1), Tensor(x2))
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print(output.asnumpy())
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assert output.shape == (128, 40)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_net_nd():
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"""
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Feature: Assert BiDense output shape for n-dimensional input
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Description: test the output.shape == (128, 4, 40).
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Expectation: match the shape.
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"""
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x1 = np.random.randn(128, 4, 20).astype(np.float32)
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x2 = np.random.randn(128, 4, 30).astype(np.float32)
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net = Net()
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output = net(Tensor(x1), Tensor(x2))
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print(output.asnumpy())
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assert output.shape == (128, 4, 40)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_net_1d():
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"""
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Feature: Assert BiDense output shape for 1-dimensional input
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Description: test the output.shape == (40,).
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Expectation: match the shape.
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"""
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x1 = np.random.randn(20).astype(np.float32)
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x2 = np.random.randn(30).astype(np.float32)
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net = Net()
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output = net(Tensor(x1), Tensor(x2))
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print(output.asnumpy())
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assert output.shape == (40,)
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