61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import torch
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import torch.nn as nn
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from torch_geometric.nn import MessagePassing
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from torch.nn.utils import spectral_norm
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class FiniteDifferenceStep(MessagePassing):
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"""
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TODO: add docstring.
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"""
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def __init__(self, edge_ch=5, hidden_dim=16, aggr: str = "add"):
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super().__init__(aggr=aggr)
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self.x_embedding = nn.Sequential(
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spectral_norm(nn.Linear(1, hidden_dim // 2)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
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)
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# self.update_net = nn.Sequential(
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# spectral_norm(nn.Linear(2 * hidden_dim, hidden_dim)),
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# nn.GELU(),
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# spectral_norm(nn.Linear(hidden_dim, hidden_dim)),
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# )
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self.out_net = nn.Sequential(
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spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim // 2, 1)),
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)
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def forward(self, x, edge_index, edge_attr, deg):
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"""
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TODO: add docstring.
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"""
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x_ = self.x_embedding(x)
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out = self.propagate(edge_index, x=x_, edge_attr=edge_attr, deg=deg)
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return self.out_net(x_ + out)
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def message(self, x_i, x_j, edge_attr):
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"""
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TODO: add docstring.
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"""
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return (x_j - x_i) * edge_attr.view(-1, 1)
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def update(self, aggr_out, x):
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"""
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TODO: add docstring.
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"""
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# update_input = torch.cat([x, aggr_out], dim=-1)
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# return self.update_net(update_input)
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return aggr_out
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def aggregate(self, inputs, index, deg):
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"""
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TODO: add docstring.
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"""
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out = super().aggregate(inputs, index)
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deg = deg + 1e-7
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return out / deg.view(-1, 1)
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