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mindspore_gl.nn.EDGEConv

class mindspore_gl.nn.EDGEConv(in_feat_size: int, out_feat_size: int, batch_norm: bool, bias=True)

EdgeConv layer. From the paper Dynamic Graph CNN for Learning on Point Clouds .

$$h_i^{(l+1)}=\underset{j\in \mathcal{N}(i)}{max}(\mathrm{\Theta }\cdot (h_j^{(l)}-h_i^{(l)})+\mathrm{\Phi }\cdot h_i^{(l)})$$

$\mathcal{N}(i)$ represents the neighbour node of $i$. $\mathrm{\Theta }$ and $\mathrm{\Phi }$ represents linear layers.

Parameters

• in_feat_size (int) – Input node feature size.

• out_feat_size (int) – Output node feature size.

• batch_norm (bool) – Whether use batch norm.

• bias (bool) – Whether use bias. Default: True.

Inputs:

• x (Tensor): The input node features. The shape is $(N,\ast )$ where $N$ is the number of nodes, and $\ast$ could be of any shape.

• g (Graph): The input graph.

Outputs:

• Tensor, output node features. The shape is $(N,out\mathrm{\_}feat\mathrm{\_}size)$.

Raises

• TypeError – If ‘in_feat_size’ is not a positive int.

• TypeError – If ‘out_feat_size’ is not a positive int.

• TypeError – If ‘batch_norm’ is not a bool.

• TypeError – If ‘bias’ is not a bool.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn import EDGEConv
>>> from mindspore_gl import GraphField
>>> n_nodes = 4
>>> n_edges = 8
>>> feat_size = 16
>>> src_idx = ms.Tensor([0, 0, 0, 1, 1, 1, 2, 3], ms.int32)
>>> dst_idx = ms.Tensor([0, 1, 3, 1, 2, 3, 3, 2], ms.int32)
>>> ones = ms.ops.Ones()
>>> nodes_feat = ones((n_nodes, feat_size), ms.float32)
>>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
>>> out_size = 4
>>> conv = EDGEConv(feat_size, out_size, batch_norm=True)
>>> ret = conv(nodes_feat, *graph_field.get_graph())
>>> print(ret.shape)
(4, 4)