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!730 Add base aggregator of gnn and its ut
Merge pull request !730 from yuanhan/master
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# 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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"""Aggregator."""
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore._extends import cell_attr_register
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from mindspore import Tensor, Parameter
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from mindspore.common.initializer import initializer
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from mindspore._checkparam import check_int_positive, check_bool
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from mindspore.nn.layer.activation import get_activation
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class GNNFeatureTransform(nn.Cell):
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r"""
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The GNN featuren transform layer for input.
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Applies linear transformation for the input feature. This layer implements the operation as:
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.. math::
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\text{outputs} = \text{inputs} * \text{kernel} + \text{bias},
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where :math:`\text{activation}` is the activation function passed as the activation
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argument (if passed in),:math:`\text{activation}` is a weight matrix with the same
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data type as the inputs created by the layer, and :math:`\text{bias}` is a bias vector
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with the same data type as the inputs created by the layer (only if has_bias is True).
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Args:
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in_channels (int): The number of channels in the input space.
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out_channels (int): The number of channels in the output space.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
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is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
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same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
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Raises:
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ValueError: If weight_init or bias_init shape is incorrect.
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Inputs:
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- **input_x** (Tensor) - The first tensor to be multiplied. The shape of the tensor is :math:`(*B, N, C)`,
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where :math:`*B` represents the batch size which can be multidimensional, :math:`N` and :math:`C` are the
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size of the last two dimensions. If `transpose_a` is True, its shape should be :math:`(*B, C, N)`.
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Outputs:
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Tensor, the shape of the output tensor is :math:`(*B, N, M)`.
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Examples:
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>>> net = nn.Dense(3, 4)
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>>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
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>>> net(input)
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[[ 2.5246444 2.2738023 0.5711005 -3.9399147 ]
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[ 1.0739875 4.0155234 0.94188046 -5.459526 ]]
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"""
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@cell_attr_register(attrs=['has_bias', 'activation'])
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def __init__(self,
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in_channels,
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out_channels,
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weight_init='normal',
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bias_init='zeros',
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has_bias=True):
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super(GNNFeatureTransform, self).__init__()
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self.in_channels = check_int_positive(in_channels)
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self.out_channels = check_int_positive(out_channels)
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self.has_bias = check_bool(has_bias)
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if isinstance(weight_init, Tensor):
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if weight_init.dim() != 2 or weight_init.shape()[0] != out_channels or \
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weight_init.shape()[1] != in_channels:
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raise ValueError("weight_init shape error")
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self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
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if self.has_bias:
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if isinstance(bias_init, Tensor):
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if bias_init.dim() != 1 or bias_init.shape()[0] != out_channels:
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raise ValueError("bias_init shape error")
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self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
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self.matmul = P.MatMul(transpose_b=True)
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self.bias_add = P.BiasAdd()
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def construct(self, x):
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tensor_shape = F.shape(x)
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input_feature = F.reshape(x, (tensor_shape[0] * tensor_shape[1], tensor_shape[2]))
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output = self.matmul(input_feature, self.weight)
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if self.has_bias:
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output = self.bias_add(output, self.bias)
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output = F.reshape(output, (tensor_shape[0], tensor_shape[1], self.out_channels))
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return output
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def extend_repr(self):
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str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}' \
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.format(self.in_channels, self.out_channels, self.weight, self.has_bias)
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if self.has_bias:
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str_info = str_info + ', bias={}'.format(self.bias)
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return str_info
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class _BaseAggregator(nn.Cell):
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"""
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Base Aggregator of GNN
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Args:
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feature_in_dim (int): Node or edge input feature dim.
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feature_out_dim (int): Node or edge outpout feature dim.
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use_fc (bool): Specifies whether a linear transformation before message is aggregated. Default: True
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
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is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
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same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
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dropout_ratio (float): The keep rate of dropout layer, greater than 0 and less equal than 1. Default: None.
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activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
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Examples:
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>>> class MyAggregator(_BaseAggregator):
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>>> def __init__(self):
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>>> super(MyAggregator, self).__init__(self, feature_in_dim, feature_out_dim)
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>>> self.reduce_mean = P.ReduceSum()
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>>>
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>>> def construct(self, x):
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>>> return self.reduce_mean(x, 1)
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"""
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def __init__(self,
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feature_in_dim,
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feature_out_dim,
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use_fc=True,
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weight_init="normal",
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bias_init="zeros",
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has_bias=True,
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dropout_ratio=None,
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activation=None):
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super(_BaseAggregator, self).__init__()
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self.in_dim = feature_in_dim
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self.out_dim = feature_out_dim
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self.use_fc = use_fc
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if self.use_fc:
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self.weight_init = weight_init
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self.bias_init = bias_init
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self.has_bias = has_bias
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self.fc = GNNFeatureTransform(self.in_dim,
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self.out_dim,
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weight_init=self.weight_init,
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bias_init=self.bias_init,
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has_bias=self.has_bias)
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self.dropout_ratio = dropout_ratio
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if self.dropout_ratio is not None:
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self.dropout = nn.Dropout(keep_prob=self.dropout_ratio)
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self.dropout_flag = self.dropout_ratio is not None
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self.activation = get_activation(activation)
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self.activation_flag = self.activation is not None
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def construct(self, **kward):
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"""Must be overridden by all subclasses."""
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raise NotImplementedError
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class MeanAggregator(_BaseAggregator):
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"""
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Mean Aggregator of GNN
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Args:
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feature_in_dim (int): Node or edge input feature dim.
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feature_out_dim (int): Node or edge outpout feature dim.
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use_fc (bool): Specifies whether a linear transformation before message is aggregated. Default: True
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
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is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
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same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
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dropout_ratio (float): The keep rate of dropout layer, greater than 0 and less equal than 1. Default: None.
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activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
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Examples:
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>>> net = MeanAggregator(32, 64, activation="relu", dropout=0.5)
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>>> input_data = Tensor(np.array(np.random.rand(32, 3, 32), dtypy=np.float32))
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>>> output = net(input_data)
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"""
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def __init__(self,
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feature_in_dim,
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feature_out_dim,
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use_fc=True,
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weight_init="normal",
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bias_init="zeros",
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has_bias=True,
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dropout_ratio=None,
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activation=None):
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super(MeanAggregator, self).__init__(
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feature_in_dim,
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feature_out_dim,
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use_fc=True,
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weight_init="normal",
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bias_init="zeros",
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has_bias=True,
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dropout_ratio=None,
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activation=None)
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self.reduce_mean = P.ReduceMean(keep_dims=False)
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def construct(self, input_feature):
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if self.use_fc:
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input_feature = self.fc(input_feature)
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if self.dropout_flag:
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input_feature = self.dropout(input_feature)
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if self.activation_flag:
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input_feature = self.activation(input_feature)
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output_feature = self.reduce_mean(input_feature, 1)
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return output_feature
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@ -0,0 +1,53 @@
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# 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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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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import mindspore.ops.composite as C
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from aggregator import MeanAggregator
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context.set_context(mode=context.GRAPH_MODE)
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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 = C.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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_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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_executor.compile(grad_op, input_data, sens)
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