mindspore_gl.nn.conv.gmmconv 源代码

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"""GMMConv Layer"""
import math
import mindspore as ms
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell


[文档]class GMMConv(GNNCell): r""" Gaussian mixture model convolutional layer. From the paper `Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs <http://openaccess.thecvf.com/content_cvpr_2017/papers/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf>`_ . .. math:: u_{ij} = f(x_i, x_j), x_j \in \mathcal{N}(i) \\ w_k(u) = \exp\left(-\frac{1}{2}(u-\mu_k)^T \Sigma_k^{-1} (u - \mu_k)\right) \\ h_i^{l+1} = \mathrm{aggregate}\left(\left\{\frac{1}{K} \sum_{k}^{K} w_k(u_{ij}), \forall j\in \mathcal{N}(i)\right\}\right) where :math:`u` represents the pseudo coordinate between the vertex and one of its neighbors, computed using the function :math:`f`, where :math:`\Sigma_k^{-1}` and :math:`\mu_k` are the learnable parameters of the covariance matrix and the mean vector of the Gaussian kernel. Args: in_feat_size (int): Input node feature size. out_feat_size (int): Output node feature size. coord_dim (int): Dimension of pseudo-coordinates. n_kernels (int): Number of kernels. residual (bool, optional): Whether use residual. Default: False. bias (bool, optional): Whether use bias. Default: False. aggregator_type (str, optional): Type of aggregator, should be 'sum'. Default: 'sum'. Inputs: - **x** (Tensor) - The input node features. The shape is :math:`(N, D_{in})` where :math:`N` is the number of nodes, and :math:`D_{in}` should be equal to `in_feat_size` in `Args`. - **pseudo** (Tensor) - Pseudo coordinate tensor. - **g** (Graph) - The input graph. Outputs: - Tensor, output node features with shape of :math:`(N, D_{out})`, where :math:`(D_{out})` should be the same as `out_size` in `Args`. Raises: SyntaxError: when the aggregator type not equals to 'sum'. TypeError: If `in_feat_size` or `out_feat_size` or `coord_dim` or `n_kernels` is not an int. TypeError: If `bias` or `residual` is not a bool. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore as ms >>> from mindspore_gl.nn import GMMConv >>> from mindspore_gl import GraphField >>> n_nodes = 4 >>> n_edges = 7 >>> node_feat_size = 7 >>> src_idx = ms.Tensor([0, 1, 1, 2, 2, 3, 3], ms.int32) >>> dst_idx = ms.Tensor([0, 0, 2, 1, 3, 0, 1], ms.int32) >>> ones = ms.ops.Ones() >>> node_feat = ones((n_nodes, node_feat_size), ms.float32) >>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges) >>> meanconv = GMMConv(in_feat_size=node_feat_size, out_feat_size=2, coord_dim=3, n_kernels=2) >>> pseudo = ones((7, 3), ms.float32) >>> res = meanconv(node_feat, pseudo, *graph_field.get_graph()) >>> print(res.shape) (4, 2) """ def __init__(self, in_feat_size: int, out_feat_size: int, coord_dim: int, n_kernels: int, residual=False, bias=False, aggregator_type="sum"): super().__init__() assert isinstance(in_feat_size, int) and in_feat_size > 0, "in_feat_size must be positive int" assert isinstance(out_feat_size, int) and out_feat_size > 0, "out_feat_size must be positive int" assert isinstance(coord_dim, int) and coord_dim > 0, "coord_dim must be positive int" assert isinstance(n_kernels, int) and n_kernels > 0, "n_kernels must be positive int" assert isinstance(bias, bool), "bias must be bool" assert isinstance(residual, bool), "residual must be bool" if aggregator_type != "sum": raise TypeError("Don't support aggregator type other than sum.") self.mu = ms.Parameter( ms.ops.normal((n_kernels, coord_dim), ms.Tensor([[0. for _ in range(coord_dim)]], ms.float32), ms.Tensor([[0.1 for _ in range(coord_dim)]], ms.float32))) self.inv_sigma = ms.Parameter(ms.ops.Ones()((n_kernels, coord_dim), ms.float32)) gain = math.sqrt(2) self.dense = ms.nn.Dense(in_feat_size, out_feat_size * n_kernels, has_bias=bias, weight_init=XavierUniform(gain)) self.residual = None if residual: self.residual = ms.nn.Dense(in_feat_size, out_feat_size, has_bias=bias, weight_init=XavierUniform(gain)) self.agg_type = aggregator_type self.n_kernels = n_kernels self.out_feat_size = out_feat_size self.coord_dim = coord_dim # pylint: disable=arguments-differ def construct(self, x, pseudo, g: Graph): """ Construct function for GMMConv. """ g.set_vertex_attr({"h": ms.ops.Reshape()(self.dense(x), (-1, self.n_kernels, self.out_feat_size))}) gaussian = -0.5 * ((ms.ops.Reshape()(pseudo, (-1, 1, self.coord_dim)) - ms.ops.Reshape()(self.mu, ( 1, self.n_kernels, self.coord_dim))) ** 2) gaussian = gaussian * (ms.ops.Reshape()(self.inv_sigma, (1, self.n_kernels, self.coord_dim)) ** 2) gaussian = ms.ops.Exp()(ms.ops.ReduceSum(keep_dims=True)(gaussian, -1)) g.set_edge_attr({"g": gaussian}) for v in g.dst_vertex: e = [s.h * e.g for s, e in v.inedges] v.rt = g.sum(e) v.rt = ms.ops.ReduceSum()(v.rt, 1) if self.residual is not None: v.rt = v.rt + self.residual(v.h) return [v.rt for v in g.dst_vertex]