wehub-resource-sync
78 lines
2.0 KiB
Python
78 lines
2.0 KiB
Python
"""
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Graph Attention Networks in DGL using SPMV optimization.
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References
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----------
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Paper: https://arxiv.org/abs/1710.10903
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Author's code: https://github.com/PetarV-/GAT
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Pytorch implementation: https://github.com/Diego999/pyGAT
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"""
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import mxnet.gluon.nn as nn
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from dgl.nn.mxnet.conv import GATConv
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class GAT(nn.Block):
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def __init__(
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self,
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g,
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num_layers,
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in_dim,
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num_hidden,
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num_classes,
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heads,
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activation,
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feat_drop,
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attn_drop,
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alpha,
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residual,
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):
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super(GAT, self).__init__()
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self.g = g
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self.num_layers = num_layers
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self.gat_layers = []
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self.activation = activation
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# input projection (no residual)
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self.gat_layers.append(
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GATConv(
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in_dim, num_hidden, heads[0], feat_drop, attn_drop, alpha, False
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)
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)
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# hidden layers
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for l in range(1, num_layers):
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# due to multi-head, the in_dim = num_hidden * num_heads
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self.gat_layers.append(
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GATConv(
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num_hidden * heads[l - 1],
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num_hidden,
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heads[l],
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feat_drop,
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attn_drop,
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alpha,
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residual,
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)
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)
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# output projection
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self.gat_layers.append(
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GATConv(
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num_hidden * heads[-2],
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num_classes,
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heads[-1],
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feat_drop,
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attn_drop,
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alpha,
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residual,
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)
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)
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for i, layer in enumerate(self.gat_layers):
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self.register_child(layer, "gat_layer_{}".format(i))
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def forward(self, inputs):
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h = inputs
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for l in range(self.num_layers):
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h = self.gat_layers[l](self.g, h).flatten()
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h = self.activation(h)
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# output projection
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logits = self.gat_layers[-1](self.g, h).mean(1)
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return logits
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