forked from mindspore-Ecosystem/mindspore
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
# 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 gat model."""
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.common.api import _executor
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from gat import GAT
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context.set_context(mode=context.GRAPH_MODE)
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def test_GAT():
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ft_sizes = 1433
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num_class = 7
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num_nodes = 2708
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hid_units = [8]
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n_heads = [8, 1]
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activation = nn.ELU()
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residual = False
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input_data = Tensor(
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np.array(np.random.rand(1, 2708, 1433), dtype=np.float32))
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biases = Tensor(np.array(np.random.rand(1, 2708, 2708), dtype=np.float32))
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net = GAT(ft_sizes,
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num_class,
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num_nodes,
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hidden_units=hid_units,
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num_heads=n_heads,
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attn_drop=0.6,
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ftr_drop=0.6,
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activation=activation,
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residual=residual)
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_executor.compile(net, input_data, biases)
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