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63 lines
2.0 KiB
63 lines
2.0 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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""" test Dense """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from ..ut_filter import non_graph_engine
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self,
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input_channels,
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output_channels,
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weight='normal',
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bias='zeros',
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has_bias=True):
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super(Net, self).__init__()
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self.fc = nn.Dense(input_channels,
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output_channels,
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weight,
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bias,
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has_bias)
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def construct(self, input_x):
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return self.fc(input_x)
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@non_graph_engine
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def test_compile():
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weight = Tensor(np.ones([12, 8], np.float32))
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bias = Tensor(np.ones([12], np.float32))
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net = Net(8, 12, weight=weight, bias=bias)
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input_data = Tensor(np.ones([1, 8], np.float32))
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# since simulator currently not support matMul
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output = net(input_data)
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print(output.asnumpy())
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@non_graph_engine
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def test_compile_nobias():
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weight = Tensor(np.ones([12, 8], np.float32))
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net = Net(8, 12, weight=weight, has_bias=False)
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input_data = Tensor(np.ones([1, 8], np.float32))
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# since simulator currently not support matMu
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# enable it when staging function is ready
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output = net(input_data)
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print(output.asnumpy())
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