models.py

import numpy as np
import os
import csv
from tqdm import tqdm
import pandas as pd
from typing import Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch_geometric.utils
from torch import Tensor
from torch.nn import Parameter
from torch_sparse import SparseTensor, set_diag

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import (
    Adj,
    NoneType,
    OptPairTensor,
    OptTensor,
    Size,
)
from torch_geometric.utils import add_self_loops, remove_self_loops, softmax

from torch_geometric.nn.models import MLP, GCN, GAT
from torch_geometric.nn.models.basic_gnn import BasicGNN
from torch_geometric.nn.conv import GATConv
from torch_geometric.utils import k_hop_subgraph
import torch_geometric.transforms as T


class GATConv_ad(GATConv):

    def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
                edge_attr: OptTensor = None, size: Size = None,
                return_attention_weights=None):

        H, C = self.heads, self.out_channels

        # We first transform the input node features. If a tuple is passed, we
        # transform source and target node features via separate weights:
        if isinstance(x, Tensor):
            assert x.dim() == 2, "Static graphs not supported in 'GATConv'"
            x_src = x_dst = self.lin_src(x).view(-1, H, C)
        else:  # Tuple of source and target node features:
            x_src, x_dst = x
            assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'"
            x_src = self.lin_src(x_src).view(-1, H, C)
            if x_dst is not None:
                x_dst = self.lin_dst(x_dst).view(-1, H, C)

        x = (x_src, x_dst)

        # Next, we compute node-level attention coefficients, both for source
        # and target nodes (if present):
        alpha_src = (x_src * self.att_src).sum(dim=-1)
        alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1)
        alpha = (alpha_src, alpha_dst)

        if self.add_self_loops:
            if isinstance(edge_index, Tensor):
                # We only want to add self-loops for nodes that appear both as
                # source and target nodes:
                num_nodes = x_src.size(0)
                if x_dst is not None:
                    num_nodes = min(num_nodes, x_dst.size(0))
                num_nodes = min(size) if size is not None else num_nodes
                edge_index, edge_attr = remove_self_loops(
                    edge_index, edge_attr)
                edge_index, edge_attr = add_self_loops(
                    edge_index, edge_attr, fill_value=self.fill_value,
                    num_nodes=num_nodes)
            elif isinstance(edge_index, SparseTensor):
                if self.edge_dim is None:
                    edge_index = set_diag(edge_index)
                else:
                    raise NotImplementedError(
                        "The usage of 'edge_attr' and 'add_self_loops' "
                        "simultaneously is currently not yet supported for "
                        "'edge_index' in a 'SparseTensor' form")

        # edge_updater_type: (alpha: OptPairTensor, edge_attr: OptTensor)
        alpha = self.edge_updater(edge_index, alpha=alpha, edge_attr=edge_attr)

        # propagate_type: (x: OptPairTensor, alpha: Tensor)
        out = self.propagate(edge_index, x=x, alpha=alpha, size=size)

        if self.concat:
            out = out.view(-1, self.heads * self.out_channels)
        else:
            out = out.mean(dim=1)

        if self.bias is not None:
            out = out + self.bias

        # out += (torch.rand(out.shape[0], 1) - 0.5) * out.mean(-1, keepdim=True) * 5e-4

        if isinstance(return_attention_weights, bool):
            if isinstance(edge_index, Tensor):
                return out, (edge_index, alpha)
            elif isinstance(edge_index, SparseTensor):
                return out, edge_index.set_value(alpha, layout='coo')
        else:
            return out

    def edge_update(self, alpha_j: Tensor, alpha_i: OptTensor,
                    edge_attr: OptTensor, index: Tensor, size_i: Optional[int],
                    ptr: OptTensor) -> Tensor:
        # Given edge-level attention coefficients for source and target nodes,
        # we simply need to sum them up to "emulate" concatenation:
        alpha = alpha_j if alpha_i is None else alpha_j + alpha_i

        if edge_attr is not None and self.lin_edge is not None:
            if edge_attr.dim() == 1:
                edge_attr = edge_attr.view(-1, 1)
            edge_attr = self.lin_edge(edge_attr)
            edge_attr = edge_attr.view(-1, self.heads, self.out_channels)
            alpha_edge = (edge_attr * self.att_edge).sum(dim=-1)
            alpha = alpha + alpha_edge

        # alpha = F.leaky_relu(alpha, self.negative_slope)
        alpha = softmax(alpha, index, ptr, size_i)
        alpha += (torch.rand_like(alpha) - 0.5) * 5e-2
        alpha = torch.clamp(alpha, 0, 1)
        self.alpha = alpha
        alpha = F.dropout(alpha, p=self.dropout, training=self.training)
        return alpha

    def message(self, x_j: Tensor, alpha: Tensor) -> Tensor:
        return alpha.unsqueeze(-1) * x_j

    def __repr__(self) -> str:
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, heads={self.heads})')


class GAT_ad(GAT):

    def init_conv(self, in_channels: Union[int, Tuple[int, int]],
                  out_channels: int, **kwargs) -> MessagePassing:
        heads = kwargs.pop('heads', 1)
        concat = kwargs.pop('concat', True)
        return GATConv_ad(in_channels, out_channels, heads=heads, concat=concat, dropout=self.dropout, **kwargs)