dmlc--dgl
Quan (Andy) Gan
60 行
2.0 KiB
Python
60 行
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 torch
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import torch.nn as nn
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import dgl.function as fn
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from dgl.nn import GATConv
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class GAT(nn.Module):
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def __init__(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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negative_slope,
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residual):
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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 = nn.ModuleList()
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self.activation = activation
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if num_layers > 1:
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# input projection (no residual)
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self.gat_layers.append(GATConv(
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in_dim, num_hidden, heads[0],
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feat_drop, attn_drop, negative_slope, False, self.activation))
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# hidden layers
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for l in range(1, num_layers-1):
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# due to multi-head, the in_dim = num_hidden * num_heads
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self.gat_layers.append(GATConv(
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num_hidden * heads[l-1], num_hidden, heads[l],
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feat_drop, attn_drop, negative_slope, residual, self.activation))
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# output projection
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self.gat_layers.append(GATConv(
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num_hidden * heads[-2], num_classes, heads[-1],
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feat_drop, attn_drop, negative_slope, residual, None))
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else:
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self.gat_layers.append(GATConv(
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in_dim, num_classes, heads[0],
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feat_drop, attn_drop, negative_slope, residual, None))
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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)
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h = h.flatten(1) if l != self.num_layers - 1 else h.mean(1)
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return h
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