def get_dataset(name, sparse=True, dirname=None):
if dirname is None:
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data',
name)
else:
path = osp.join(dirname, name)
if name == "QM9":
dataset = QM9(path)
elif name == "QM7b":
dataset = QM7b(path)
else:
dataset = TUDataset(path, name)
dataset.data.edge_attr = None
if dataset.data.x is None:
max_degree = 0
degs = []
for data in dataset:
degs += [degree(data.edge_index[0], dtype=torch.long)]
max_degree = max(max_degree, degs[-1].max().item())
if max_degree < 1000:
dataset.transform = T.OneHotDegree(max_degree)
else:
deg = torch.cat(degs, dim=0).to(torch.float)
mean, std = deg.mean().item(), deg.std().item()
dataset.transform = NormalizedDegree(mean, std)
num_nodes = max_num_nodes = 0
for data in dataset:
num_nodes += data.num_nodes
max_num_nodes = max(data.num_nodes, max_num_nodes)
# Filter out a few really large graphs in order to apply DiffPool.
if name == 'REDDIT-BINARY':
num_nodes = min(int(num_nodes / len(dataset) * 1.5), max_num_nodes)
else:
num_nodes = min(int(num_nodes / len(dataset) * 5), max_num_nodes)
# num_nodes = max_num_nodes
indices = []
for i, data in enumerate(dataset):
if data.num_nodes <= num_nodes:
indices.append(i)
dataset = dataset[torch.tensor(indices)]
if not sparse:
if dataset.transform is None:
dataset.transform = T.ToDense(num_nodes)
else:
dataset.transform = T.Compose(
[dataset.transform, T.ToDense(num_nodes)])
return dataset
def load_pyg(name, dataset_dir):
"""
Load PyG dataset objects. (More PyG datasets will be supported)
Args:
name (string): dataset name
dataset_dir (string): data directory
Returns: PyG dataset object
"""
dataset_dir = '{}/{}'.format(dataset_dir, name)
if name in ['Cora', 'CiteSeer', 'PubMed']:
dataset = Planetoid(dataset_dir, name)
elif name[:3] == 'TU_':
# TU_IMDB doesn't have node features
if name[3:] == 'IMDB':
name = 'IMDB-MULTI'
dataset = TUDataset(dataset_dir, name, transform=T.Constant())
else:
dataset = TUDataset(dataset_dir, name[3:])
elif name == 'Karate':
dataset = KarateClub()
elif 'Coauthor' in name:
if 'CS' in name:
dataset = Coauthor(dataset_dir, name='CS')
else:
dataset = Coauthor(dataset_dir, name='Physics')
elif 'Amazon' in name:
if 'Computers' in name:
dataset = Amazon(dataset_dir, name='Computers')
else:
dataset = Amazon(dataset_dir, name='Photo')
elif name == 'MNIST':
dataset = MNISTSuperpixels(dataset_dir)
elif name == 'PPI':
dataset = PPI(dataset_dir)
elif name == 'QM7b':
dataset = QM7b(dataset_dir)
else:
raise ValueError('{} not support'.format(name))
return dataset
def load_dataset_example(format, name, dataset_dir):
dataset_dir = '{}/{}'.format(dataset_dir, name)
if format == 'PyG':
if name == 'QM7b':
dataset_raw = QM7b(dataset_dir)
return dataset_raw
def load_dataset_example(format, name, dataset_dir):
dataset_dir = f'{dataset_dir}/{name}'
if format == 'PyG':
if name == 'QM7b':
dataset_raw = QM7b(dataset_dir)
return dataset_raw
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.datasets import TUDataset, QM7b
from torch_geometric.data import DataLoader
from torch_geometric.nn import GINConv, global_add_pool
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'QM7b')
dataset = QM7b(path).shuffle()
test_dataset = dataset[:len(dataset) // 10]
train_dataset = dataset[len(dataset) // 10:]
test_loader = DataLoader(test_dataset, batch_size=128)
train_loader = DataLoader(train_dataset, batch_size=128)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
num_features = dataset.num_features
dim = 32
nn1 = Sequential(Linear(num_features, dim), ReLU(), Linear(dim, dim))
self.conv1 = GINConv(nn1)
self.bn1 = torch.nn.BatchNorm1d(dim)
nn2 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
self.conv2 = GINConv(nn2)
self.bn2 = torch.nn.BatchNorm1d(dim)