77 lines
2.8 KiB
Python
77 lines
2.8 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 gnn aggregator."""
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import numpy as np
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from aggregator import MeanAggregator, AttentionHead, AttentionAggregator
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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.composite as C
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from mindspore import Tensor
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from mindspore.common.api import _cell_graph_executor
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context.set_context(mode=context.GRAPH_MODE)
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grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
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class MeanAggregatorGrad(nn.Cell):
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"""Backward of MeanAggregator"""
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def __init__(self, network):
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super(MeanAggregatorGrad, self).__init__()
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self.grad_op = grad_all_with_sens
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self.network = network
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def construct(self, x, sens):
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grad_op = self.grad_op(self.network)(x, sens)
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return grad_op
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def test_MeanAggregator():
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"""Compile MeanAggregator forward graph"""
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aggregator = MeanAggregator(32, 64, activation="relu", dropout_ratio=0.5)
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input_data = Tensor(np.array(np.random.rand(32, 3, 32), dtype=np.float32))
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_cell_graph_executor.compile(aggregator, input_data)
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def test_MeanAggregator_grad():
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"""Compile MeanAggregator backward graph"""
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aggregator = MeanAggregator(32, 64, activation="relu", dropout_ratio=0.5)
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input_data = Tensor(np.array(np.random.rand(32, 3, 32), dtype=np.float32))
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sens = Tensor(np.ones([32, 64]).astype(np.float32))
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grad_op = MeanAggregatorGrad(aggregator)
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_cell_graph_executor.compile(grad_op, input_data, sens)
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def test_AttentionHead():
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"""Compile AttentionHead forward graph"""
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head = AttentionHead(1433,
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8,
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in_drop_ratio=0.6,
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coef_drop_ratio=0.6,
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residual=False)
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input_data = Tensor(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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_cell_graph_executor.compile(head, input_data, biases)
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def test_AttentionAggregator():
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input_data = Tensor(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 = AttentionAggregator(1433, 8, 8)
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_cell_graph_executor.compile(net, input_data, biases)
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