import sys
import time
import os
import os.path as osp
import requests
import shutil
import tqdm
import pickle
import numpy as np
import torch
from cogdl.data import Data, Dataset, download_url
from . import register_dataset
[docs]def untar(path, fname, deleteTar=True):
"""
Unpacks the given archive file to the same directory, then (by default)
deletes the archive file.
"""
print('unpacking ' + fname)
fullpath = os.path.join(path, fname)
shutil.unpack_archive(fullpath, path)
if deleteTar:
os.remove(fullpath)
[docs]class GTNDataset(Dataset):
r"""The network datasets "ACM", "DBLP" and "IMDB" from the
`"Graph Transformer Networks"
<https://arxiv.org/abs/1911.06455>`_ paper.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"gtn-acm"`,
:obj:`"gtn-dblp"`, :obj:`"gtn-imdb"`).
"""
def __init__(self, root, name):
self.name = name
self.url = f'https://github.com/cenyk1230/gtn-data/blob/master/{name}.zip?raw=true'
super(GTNDataset, self).__init__(root)
self.data = torch.load(self.processed_paths[0])
self.num_classes = torch.max(self.data.train_target).item() + 1
self.num_edge = len(self.data.adj)
self.num_nodes = self.data.x.shape[0]
@property
[docs] def raw_file_names(self):
names = ["edges.pkl", "labels.pkl", "node_features.pkl"]
return names
@property
[docs] def processed_file_names(self):
return ["data.pt"]
[docs] def read_gtn_data(self, folder):
edges = pickle.load(open(osp.join(folder, 'edges.pkl'), 'rb'))
labels = pickle.load(open(osp.join(folder, 'labels.pkl'), 'rb'))
node_features = pickle.load(open(osp.join(folder, 'node_features.pkl'), 'rb'))
data = Data()
data.x = torch.from_numpy(node_features).type(torch.FloatTensor)
num_nodes = edges[0].shape[0]
node_type = np.zeros((num_nodes), dtype=int)
assert len(edges)==4
assert len(edges[0].nonzero())==2
node_type[edges[0].nonzero()[0]] = 0
node_type[edges[0].nonzero()[1]] = 1
node_type[edges[1].nonzero()[0]] = 1
node_type[edges[1].nonzero()[1]] = 0
node_type[edges[2].nonzero()[0]] = 0
node_type[edges[2].nonzero()[1]] = 2
node_type[edges[3].nonzero()[0]] = 2
node_type[edges[3].nonzero()[1]] = 0
print(node_type)
data.pos = torch.from_numpy(node_type)
edge_list = []
for i, edge in enumerate(edges):
edge_tmp = torch.from_numpy(np.vstack((edge.nonzero()[0], edge.nonzero()[1]))).type(torch.LongTensor)
edge_list.append(edge_tmp)
data.edge_index = torch.cat(edge_list, 1)
A = []
for i,edge in enumerate(edges):
edge_tmp = torch.from_numpy(np.vstack((edge.nonzero()[0], edge.nonzero()[1]))).type(torch.LongTensor)
value_tmp = torch.ones(edge_tmp.shape[1]).type(torch.FloatTensor)
A.append((edge_tmp,value_tmp))
edge_tmp = torch.stack((torch.arange(0,num_nodes),torch.arange(0,num_nodes))).type(torch.LongTensor)
value_tmp = torch.ones(num_nodes).type(torch.FloatTensor)
A.append((edge_tmp,value_tmp))
data.adj = A
data.train_node = torch.from_numpy(np.array(labels[0])[:,0]).type(torch.LongTensor)
data.train_target = torch.from_numpy(np.array(labels[0])[:,1]).type(torch.LongTensor)
data.valid_node = torch.from_numpy(np.array(labels[1])[:,0]).type(torch.LongTensor)
data.valid_target = torch.from_numpy(np.array(labels[1])[:,1]).type(torch.LongTensor)
data.test_node = torch.from_numpy(np.array(labels[2])[:,0]).type(torch.LongTensor)
data.test_target = torch.from_numpy(np.array(labels[2])[:,1]).type(torch.LongTensor)
y = np.zeros((num_nodes), dtype=int)
x_index = torch.cat((data.train_node, data.valid_node, data.test_node))
y_index = torch.cat((data.train_target, data.valid_target, data.test_target))
y[x_index.numpy()] = y_index.numpy()
data.y = torch.from_numpy(y)
self.data = data
[docs] def get(self, idx):
assert idx == 0
return self.data
[docs] def apply_to_device(self, device):
self.data.x = self.data.x.to(device)
self.data.train_node = self.data.train_node.to(device)
self.data.valid_node = self.data.valid_node.to(device)
self.data.test_node = self.data.test_node.to(device)
self.data.train_target = self.data.train_target.to(device)
self.data.valid_target = self.data.valid_target.to(device)
self.data.test_target = self.data.test_target.to(device)
new_adj = []
for (t1, t2) in self.data.adj:
new_adj.append((t1.to(device), t2.to(device)))
self.data.adj = new_adj
[docs] def download(self):
download_url(self.url, self.raw_dir, name=self.name + '.zip')
untar(self.raw_dir, self.name + '.zip')
[docs] def process(self):
self.read_gtn_data(self.raw_dir)
torch.save(self.data, self.processed_paths[0])
[docs] def __repr__(self):
return "{}()".format(self.name)
[docs]@register_dataset("gtn-acm")
class ACM_GTNDataset(GTNDataset):
def __init__(self):
dataset = "gtn-acm"
path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
super(ACM_GTNDataset, self).__init__(path, dataset)
[docs]@register_dataset("gtn-dblp")
class DBLP_GTNDataset(GTNDataset):
def __init__(self):
dataset = "gtn-dblp"
path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
super(DBLP_GTNDataset, self).__init__(path, dataset)
[docs]@register_dataset("gtn-imdb")
class IMDB_GTNDataset(GTNDataset):
def __init__(self):
dataset = "gtn-imdb"
path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
super(IMDB_GTNDataset, self).__init__(path, dataset)