AutoGL/examples/glfogb.py

import os
os.environ["AUTOGL_BACKEND"] = "pyg"

from torch_geometric.data import DataLoader, Data
import torch.optim as optim
from tqdm import tqdm
from ogb.graphproppred import Evaluator
import random
import torch
import numpy as np
from autogl.datasets import build_dataset_from_name
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from autogl import backend


if backend.DependentBackend.is_dgl():
    feat = 'feat'
    label = 'label'
else:
    feat = 'x'
    label = 'y'

cls_criterion = torch.nn.BCEWithLogitsLoss()
reg_criterion = torch.nn.MSELoss()

# model
from torch_geometric.nn import MessagePassing, global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
import torch.nn.functional as F
from torch_geometric.nn.inits import uniform

from torch_scatter import scatter_mean

from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder
from torch_geometric.utils import degree

### GIN convolution along the graph structure
class GINConv(MessagePassing):
    def __init__(self, emb_dim):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GINConv, self).__init__(aggr = "add")

        self.mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, emb_dim))
        self.eps = torch.nn.Parameter(torch.Tensor([0]))

        self.bond_encoder = BondEncoder(emb_dim = emb_dim)

    def forward(self, x, edge_index, edge_attr):
        edge_embedding = self.bond_encoder(edge_attr)
        out = self.mlp((1 + self.eps) *x + self.propagate(edge_index, x=x, edge_attr=edge_embedding))

        return out

    def message(self, x_j, edge_attr):
        return F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out

### GCN convolution along the graph structure
class GCNConv(MessagePassing):
    def __init__(self, emb_dim):
        super(GCNConv, self).__init__(aggr='add')

        self.linear = torch.nn.Linear(emb_dim, emb_dim)
        self.root_emb = torch.nn.Embedding(1, emb_dim)
        self.bond_encoder = BondEncoder(emb_dim = emb_dim)

    def forward(self, x, edge_index, edge_attr):
        x = self.linear(x)
        edge_embedding = self.bond_encoder(edge_attr)

        row, col = edge_index

        #edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device)
        deg = degree(row, x.size(0), dtype = x.dtype) + 1
        deg_inv_sqrt = deg.pow(-0.5)
        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

        return self.propagate(edge_index, x=x, edge_attr = edge_embedding, norm=norm) + F.relu(x + self.root_emb.weight) * 1./deg.view(-1,1)

    def message(self, x_j, edge_attr, norm):
        return norm.view(-1, 1) * F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out


### GNN to generate node embedding
class GNN_node(torch.nn.Module):
    """
    Output:
        node representations
    """
    def __init__(self, num_layer, emb_dim, drop_ratio = 0.5, JK = "last", residual = False, gnn_type = 'gin'):
        '''
            emb_dim (int): node embedding dimensionality
            num_layer (int): number of GNN message passing layers

        '''

        super(GNN_node, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        ### add residual connection or not
        self.residual = residual

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        self.atom_encoder = AtomEncoder(emb_dim)

        ###List of GNNs
        self.convs = torch.nn.ModuleList()
        self.batch_norms = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError('Undefined GNN type called {}'.format(gnn_type))

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

    def forward(self, batched_data):
        x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch

        ### computing input node embedding

        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):

            h = self.convs[layer](h_list[layer], edge_index, edge_attr)
            h = self.batch_norms[layer](h)

            if layer == self.num_layer - 1:
                #remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training = self.training)
            else:
                h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)

            if self.residual:
                h += h_list[layer]

            h_list.append(h)

        ### Different implementations of Jk-concat
        if self.JK == "last":
            node_representation = h_list[-1]
        elif self.JK == "sum":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation


### Virtual GNN to generate node embedding
class GNN_node_Virtualnode(torch.nn.Module):
    """
    Output:
        node representations
    """
    def __init__(self, num_layer, emb_dim, drop_ratio = 0.5, JK = "last", residual = False, gnn_type = 'gin'):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GNN_node_Virtualnode, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        ### add residual connection or not
        self.residual = residual

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        self.atom_encoder = AtomEncoder(emb_dim)

        ### set the initial virtual node embedding to 0.
        self.virtualnode_embedding = torch.nn.Embedding(1, emb_dim)
        torch.nn.init.constant_(self.virtualnode_embedding.weight.data, 0)

        ### List of GNNs
        self.convs = torch.nn.ModuleList()
        ### batch norms applied to node embeddings
        self.batch_norms = torch.nn.ModuleList()

        ### List of MLPs to transform virtual node at every layer
        self.mlp_virtualnode_list = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError('Undefined GNN type called {}'.format(gnn_type))

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

        for layer in range(num_layer - 1):
            self.mlp_virtualnode_list.append(torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), \
                                                    torch.nn.Linear(2*emb_dim, emb_dim), torch.nn.BatchNorm1d(emb_dim), torch.nn.ReLU()))


    def forward(self, batched_data):

        x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch

        ### virtual node embeddings for graphs
        virtualnode_embedding = self.virtualnode_embedding(torch.zeros(batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device))

        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):
            ### add message from virtual nodes to graph nodes
            h_list[layer] = h_list[layer] + virtualnode_embedding[batch]

            ### Message passing among graph nodes
            h = self.convs[layer](h_list[layer], edge_index, edge_attr)

            h = self.batch_norms[layer](h)
            if layer == self.num_layer - 1:
                #remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training = self.training)
            else:
                h = F.dropout(F.relu(h), self.drop_ratio, training = self.training)

            if self.residual:
                h = h + h_list[layer]

            h_list.append(h)

            ### update the virtual nodes
            if layer < self.num_layer - 1:
                ### add message from graph nodes to virtual nodes
                virtualnode_embedding_temp = global_add_pool(h_list[layer], batch) + virtualnode_embedding
                ### transform virtual nodes using MLP

                if self.residual:
                    virtualnode_embedding = virtualnode_embedding + F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training)
                else:
                    virtualnode_embedding = F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, training = self.training)

        ### Different implementations of Jk-concat
        if self.JK == "last":
            node_representation = h_list[-1]
        elif self.JK == "sum":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation

class GNN(torch.nn.Module):

    def __init__(self, num_tasks, num_layer = 5, emb_dim = 300,
                    gnn_type = 'gin', virtual_node = False, residual = False, drop_ratio = 0.5, JK = "last", graph_pooling = "mean"):
        '''
            num_tasks (int): number of labels to be predicted
            virtual_node (bool): whether to add virtual node or not
        '''

        super(GNN, self).__init__()

        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        self.emb_dim = emb_dim
        self.num_tasks = num_tasks
        self.graph_pooling = graph_pooling

        if self.num_layer < 2:
            raise ValueError("Number of GNN layers must be greater than 1.")

        ### GNN to generate node embeddings
        if virtual_node:
            self.gnn_node = GNN_node_Virtualnode(num_layer, emb_dim, JK = JK, drop_ratio = drop_ratio, residual = residual, gnn_type = gnn_type)
        else:
            self.gnn_node = GNN_node(num_layer, emb_dim, JK = JK, drop_ratio = drop_ratio, residual = residual, gnn_type = gnn_type)


        ### Pooling function to generate whole-graph embeddings
        if self.graph_pooling == "sum":
            self.pool = global_add_pool
        elif self.graph_pooling == "mean":
            self.pool = global_mean_pool
        elif self.graph_pooling == "max":
            self.pool = global_max_pool
        elif self.graph_pooling == "attention":
            self.pool = GlobalAttention(gate_nn = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, 1)))
        elif self.graph_pooling == "set2set":
            self.pool = Set2Set(emb_dim, processing_steps = 2)
        else:
            raise ValueError("Invalid graph pooling type.")

        if graph_pooling == "set2set":
            self.graph_pred_linear = torch.nn.Linear(2*self.emb_dim, self.num_tasks)
        else:
            self.graph_pred_linear = torch.nn.Linear(self.emb_dim, self.num_tasks)

    def forward(self, batched_data):
        h_node = self.gnn_node(batched_data)

        h_graph = self.pool(h_node, batched_data.batch)

        return self.graph_pred_linear(h_graph)


def train(model, device, loader, optimizer, task_type):
    model.train()

    for step, batch in enumerate(tqdm(loader, desc="Iteration")):
        batch = batch.to(device)

        if batch.x.shape[0] == 1 or batch.batch[-1] == 0:
            pass
        else:
            pred = model(batch)
            optimizer.zero_grad()
            is_labeled = batch.y == batch.y
            loss = cls_criterion(pred.to(torch.float32)[is_labeled], batch.y.to(torch.float32)[is_labeled])
            loss.backward()
            optimizer.step()

def eval(model, device, loader, evaluator):
    model.eval()
    y_true = []
    y_pred = []

    for step, batch in enumerate(tqdm(loader, desc="Iteration")):
        batch = batch.to(device)

        if batch.x.shape[0] == 1:
            pass
        else:
            with torch.no_grad():
                pred = model(batch)

            y_true.append(batch.y.view(pred.shape).detach().cpu())
            y_pred.append(pred.detach().cpu())

    y_true = torch.cat(y_true, dim = 0).numpy()
    y_pred = torch.cat(y_pred, dim = 0).numpy()

    input_dict = {"y_true": y_true, "y_pred": y_pred}

    return evaluator.eval(input_dict)

def trans(dataset):
    ret = []
    for i in range(len(dataset)):
        x = dataset[i].nodes.data[feat]
        y = dataset[i].data[label].view(-1, 1)
        edge_index = dataset[i].edges.connections
        edge_attr = dataset[i].edges.data['edge_feat']
        data = Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr)
        ret.append(data)
    return ret

if __name__ == "__main__":
    parser = ArgumentParser(
        "auto graph classification", formatter_class=ArgumentDefaultsHelpFormatter
    )
    parser.add_argument(
        "--dataset",
        default="ogbg-molhiv",
        type=str,
        help="graph classification dataset",
        choices=["mutag", "imdb-b", "imdb-m", "proteins", "collab", "ogbg-molbace"],
    )
    parser.add_argument("--device", type=int, default=0, help="device to run on, -1 means cpu")
    parser.add_argument("--seed", type=int, default=0, help="random seed")

    args = parser.parse_args()

    if args.device == -1:
        args.device = "cpu"

    if torch.cuda.is_available() and args.device != "cpu":
        torch.cuda.set_device(args.device)
    seed = args.seed
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

    dataset = build_dataset_from_name(args.dataset)
    model = GNN(num_tasks=1, gnn_type = 'gcn').to(args.device)
    evaluator = Evaluator(args.dataset)

    train_dataset = trans(dataset.train_split)
    val_dataset = trans(dataset.val_split)
    test_dataset = trans(dataset.test_split)

    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True,
                              num_workers=0)
    valid_loader = DataLoader(val_dataset, batch_size=32, shuffle=False,
                              num_workers=0)
    test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False,
                             num_workers=0)

    optimizer = optim.Adam(model.parameters(), lr=0.001)

    valid_curve = []
    test_curve = []
    train_curve = []
    for epoch in range(1, 100 + 1):
        print("=====Epoch {}".format(epoch))
        print('Training...')
        train(model, args.device, train_loader, optimizer, 'binary classification')

        print('Evaluating...')
        train_perf = eval(model, args.device, train_loader, evaluator)
        valid_perf = eval(model, args.device, valid_loader, evaluator)
        test_perf = eval(model, args.device, test_loader, evaluator)

        print({'Train': train_perf, 'Validation': valid_perf, 'Test': test_perf})

        train_curve.append(train_perf['rocauc'])
        valid_curve.append(valid_perf['rocauc'])
        test_curve.append(test_perf['rocauc'])

    best_val_epoch = np.argmax(np.array(valid_curve))
    best_train = max(train_curve)

    print('Finished training!')
    print('Best validation score: {}'.format(valid_curve[best_val_epoch]))
    print('Test score: {}'.format(test_curve[best_val_epoch]))