baselines.py

"""
Graph-Induced Sum-Product Networks

Files: baselines.py

Authors:  Federico Errica (federico.errica@neclab.eu)
     Mathias Niepert (mathias.niepert@ki.uni-stuttgart.de)

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"""
from typing import Tuple, Optional, List

import torch
from ogb.graphproppred.mol_encoder import AtomEncoder
from pydgn.model.interface import ModelInterface
from torch import Tensor
from torch.nn import Sequential, Linear, BatchNorm1d, ReLU, PReLU
from torch.nn.functional import dropout
from torch_geometric.data import Batch
from torch_geometric.nn import global_add_pool, global_mean_pool, GINConv, \
    SAGEConv
from torch_geometric.nn.inits import reset, uniform


class GIN(ModelInterface):
    def __init__(self, dim_node_features, dim_edge_features, dim_target, readout_class, config):
        super().__init__(dim_node_features, dim_edge_features, dim_target, readout_class, config)

        self.config = config
        self.dropout = config['dropout']
        self.num_layers = config['num_layers']
        self.embeddings_dim = [config['dim_embedding'] for _ in range(self.num_layers)]
        self.first_h = []
        self.nns = []
        self.convs = []
        self.linears = []
        self.atom_encoder = None
        if 'atom_encoder_dim' in config:
            # works with ogbg-molpbca only
            atom_emb_dim = config['atom_encoder_dim']
            self.atom_encoder = AtomEncoder(emb_dim=atom_emb_dim)
            dim_node_features = atom_emb_dim

        train_eps = config['train_eps']
        if config['global_aggregation'] == 'sum':
            self.pooling = global_add_pool
        elif config['global_aggregation'] == 'mean':
            self.pooling = global_mean_pool

        for layer, out_emb_dim in enumerate(self.embeddings_dim):

            if layer == 0:
                self.first_h = Sequential(Linear(dim_node_features, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),
                                    Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU())
                self.linears.append(Linear(out_emb_dim, dim_target))
            else:
                input_emb_dim = self.embeddings_dim[layer-1]
                self.nns.append(Sequential(Linear(input_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),
                                      Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU()))
                self.convs.append(GINConv(self.nns[-1], train_eps=train_eps))  # Eq. 4.2

                self.linears.append(Linear(out_emb_dim, dim_target))

        self.nns = torch.nn.ModuleList(self.nns)
        self.convs = torch.nn.ModuleList(self.convs)
        self.linears = torch.nn.ModuleList(self.linears)  # has got one more for initial input


    def forward(self, data: Batch) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[object]]]:
        x, edge_index, batch = data.x, data.edge_index, data.batch

        if self.atom_encoder is not None:
            x = self.atom_encoder(x)
        else:
            x = x.float()

        out = 0

        for layer in range(self.num_layers):
            if layer == 0:
                x = self.first_h(x)
                out += dropout(self.pooling(self.linears[layer](x), batch), p=self.dropout)
            else:
                # Layer l ("convolution" layer)
                x = self.convs[layer-1](x, edge_index)
                out += dropout(self.linears[layer](self.pooling(x, batch)), p=self.dropout, training=self.training)

        return out, x


class GAE_Adj(ModelInterface):
    """
    Original GAE that reconstructs the adjacency matrix to produce latent node representations
    """
    def __init__(self, dim_node_features, dim_edge_features, dim_target, readout_class, config):
        super().__init__(dim_node_features, dim_edge_features, dim_target, readout_class, config)

        self.config = config
        self.num_layers = config['num_layers']
        self.embeddings_dim = [config['dim_embedding'] for _ in range(self.num_layers)]
        self.first_h = []
        self.nns = []
        self.convs = []
        self.concat_out_across_layers = config['concat_out_across_layers']
        train_eps = config['train_eps']
        self.atom_encoder = None
        if 'atom_encoder_dim' in config:
            # works with ogbg-molpbca only
            atom_emb_dim = config['atom_encoder_dim']
            self.atom_encoder = AtomEncoder(emb_dim=atom_emb_dim)
            dim_node_features = atom_emb_dim

        for layer, out_emb_dim in enumerate(self.embeddings_dim):

            if layer == 0:
                self.first_h = Sequential(Linear(dim_node_features, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),
                                    Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU())
            else:
                input_emb_dim = self.embeddings_dim[layer-1]
                self.nns.append(Sequential(Linear(input_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),
                                      Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU()))
                self.convs.append(GINConv(self.nns[-1], train_eps=train_eps))  # Eq. 4.2

        self.nns = torch.nn.ModuleList(self.nns)
        self.convs = torch.nn.ModuleList(self.convs)


    def forward(self, data: Batch) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[object]]]:

        # extract data
        x, edge_index, edge_attr, batch = (
            data.x,
            data.edge_index,
            data.edge_attr,
            data.batch,
        )

        node_embeddings = []

        if self.atom_encoder is not None:
            x = self.atom_encoder(x)
        else:
            x = x.float()

        for layer in range(self.num_layers):
            if layer == 0:
                h = self.first_h(x.float())
                if self.concat_out_across_layers:
                    node_embeddings.append(h)
            else:
                # Layer l ("convolution" layer)
                h = self.convs[layer-1](h, edge_index)
                if self.concat_out_across_layers:
                    node_embeddings.append(h)

        if not self.concat_out_across_layers:
            node_embeddings = h
        else:
            node_embeddings = torch.cat(node_embeddings, dim=1)

        return (
            None,
            node_embeddings,
            [data.edge_index, data.neg_edge_index],
        )


class DGI(ModelInterface):
    """
    Original DGI. Code taken and adapted from PyG
    """
    def __init__(self, dim_node_features, dim_edge_features, dim_target, readout_class, config):
        super().__init__(dim_node_features, dim_edge_features, dim_target, readout_class, config)

        self.config = config
        self.num_layers = config['num_layers']
        self.embeddings_dim = [config['dim_embedding'] for _ in range(self.num_layers)]
        self.convs = []
        self.activations = []
        self.atom_encoder = None
        if 'atom_encoder_dim' in config:
            # works with ogbg-molpbca only
            atom_emb_dim = config['atom_encoder_dim']
            self.atom_encoder = AtomEncoder(emb_dim=atom_emb_dim)
            dim_node_features = atom_emb_dim

        # this is the encoder
        for layer, out_emb_dim in enumerate(self.embeddings_dim):

            if layer == 0:
                self.convs.append(SAGEConv(dim_node_features,
                                           out_emb_dim))
            else:
                input_emb_dim = self.embeddings_dim[layer-1]
                self.convs.append(SAGEConv(input_emb_dim,
                                           out_emb_dim))
            self.activations.append(PReLU())

        self.convs = torch.nn.ModuleList(self.convs)
        self.activations = torch.nn.ModuleList(self.activations)

        self.summary = lambda z, batch: torch.sigmoid(global_mean_pool(z,
                                                               batch))
        self.corruption = None  # implemented as transform in PyDGN

        self.reset_parameters()

    def reset_parameters(self):
        reset(self.convs)
        reset(self.summary)


    def forward(self, data: Batch) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[object]]]:

        # extract data
        x, x_corrupted, edge_index, edge_attr, batch = (
            data.x,
            data.x_corrupted,
            data.edge_index,
            data.edge_attr,
            data.batch,
        )

        if self.atom_encoder is not None:
            x = self.atom_encoder(x)
            x_corrupted = self.atom_encoder(x_corrupted)
        else:
            x = x.float()
            x_corrupted = x_corrupted.float()

        """Returns the latent space for the input arguments, their
        corruptions and their summary representation."""

        # Create positive Z
        for layer in range(self.num_layers):
            # Layer l ("convolution" layer)

            if layer == 0:
                h_pos = x

            h_pos = self.convs[layer](h_pos, edge_index)
            h_pos = self.activations[layer](h_pos)

        pos_z = h_pos
        summary = self.summary(pos_z, batch)
        summary_node_broadcast = summary[batch]

        # Create negative Z
        for layer in range(self.num_layers):
            # Layer l ("convolution" layer)

            if layer == 0:
                h_neg = x_corrupted

            h_neg = self.convs[layer](h_neg, edge_index)
            h_neg = self.activations[layer](h_neg)

        neg_z = h_neg

        return (
            None,
            pos_z,
            [pos_z, neg_z, summary_node_broadcast],
        )