wehub-resource-sync
88 lines
2.3 KiB
Python
88 lines
2.3 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/pdf/2105.14491.pdf
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Author's code: https://github.com/tech-srl/how_attentive_are_gats
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"""
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import torch
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import torch.nn as nn
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from dgl.nn import GATv2Conv
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class GATv2(nn.Module):
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def __init__(
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self,
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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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):
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super(GATv2, self).__init__()
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self.num_layers = num_layers
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self.gatv2_layers = nn.ModuleList()
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self.activation = activation
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# input projection (no residual)
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self.gatv2_layers.append(
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GATv2Conv(
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in_dim,
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num_hidden,
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heads[0],
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feat_drop,
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attn_drop,
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negative_slope,
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False,
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self.activation,
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bias=False,
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share_weights=True,
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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.gatv2_layers.append(
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GATv2Conv(
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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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negative_slope,
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residual,
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self.activation,
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bias=False,
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share_weights=True,
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)
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)
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# output projection
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self.gatv2_layers.append(
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GATv2Conv(
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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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negative_slope,
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residual,
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None,
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bias=False,
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share_weights=True,
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)
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)
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def forward(self, g, inputs):
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h = inputs
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for l in range(self.num_layers):
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h = self.gatv2_layers[l](g, h).flatten(1)
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# output projection
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logits = self.gatv2_layers[-1](g, h).mean(1)
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return logits
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