103 lines
2.7 KiB
Python
103 lines
2.7 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 tqdm import tqdm
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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__(
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self,
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aggr: str = "add",
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normalize: bool = True,
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root_weight: float = 1.0,
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):
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super().__init__(aggr=aggr)
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self.normalize = normalize
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assert (
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aggr == "add"
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), "Per somme pesate, l'aggregazione deve essere 'add'."
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self.root_weight = float(root_weight)
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def forward(self, x, edge_index, edge_weight, deg):
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"""
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TODO: add docstring.
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"""
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out = self.propagate(edge_index, x=x, edge_weight=edge_weight, deg=deg)
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return out
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def message(self, x_j, edge_weight):
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"""
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TODO: add docstring.
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"""
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return edge_weight.view(-1, 1) * x_j
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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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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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return self.root_weight * aggr_out + (1 - self.root_weight) * x
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class GraphFiniteDifference(nn.Module):
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"""
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TODO: add docstring.
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"""
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def __init__(self, max_iters: int = 1000, threshold: float = 1e-4):
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"""
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TODO: add docstring.
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"""
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super().__init__()
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self.max_iters = max_iters
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self.threshold = threshold
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self.fd_step = FiniteDifferenceStep(
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aggr="add", normalize=True, root_weight=1.0
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)
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@staticmethod
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def _compute_deg(edge_index, edge_weight, num_nodes):
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"""
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TODO: add docstring.
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"""
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deg = torch.zeros(num_nodes, device=edge_index.device)
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deg = deg.scatter_add(0, edge_index[1], edge_weight)
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return deg + 1e-7
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@staticmethod
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def _compute_c_ij(c, edge_index):
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"""
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TODO: add docstring.
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"""
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return (0.5 * (c[edge_index[0]] + c[edge_index[1]])).squeeze()
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def forward(
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self, x, edge_index, edge_weight, c, boundary_mask, boundary_values
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):
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"""
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TODO: add docstring.
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"""
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c_ij = self._compute_c_ij(c, edge_index)
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edge_weight = edge_weight * c_ij
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deg = self._compute_deg(edge_index, edge_weight, x.size(0))
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conv_thres = self.threshold * torch.norm(x)
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for _i in tqdm(range(self.max_iters)):
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out = self.fd_step(x, edge_index, edge_weight, deg)
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out[boundary_mask] = boundary_values.unsqueeze(-1)
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if torch.norm(out - x) < conv_thres:
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break
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x = out
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return out
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