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dmlc--dgl
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0.7.x
T
Xiagkun Hu c37076dfe6 [Model] Gated Graph Neural Network for bAbI tasks (#880) 
* rrn model and sudoku

* add README

* refine the code, add doc strings

* add sudoku solver

* add example for sudoku_solver

* ggnn example

* Rewrite README file

* fix typos
2019-10-21 16:07:32 +08:00

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12 KiB
Python
原始文件 永久链接 Blame 文件历史

"""
Data utils for processing bAbI datasets
"""
import os
from torch.utils.data import DataLoader
import dgl
import torch
import string
from dgl.data.utils import download, get_download_dir, _get_dgl_url, extract_archive
def get_babi_dataloaders(batch_size, train_size=50, task_id=4, q_type=0):
_download_babi_data()
node_dict = dict(zip(list(string.ascii_uppercase), range(len(string.ascii_uppercase))))
if task_id == 4:
edge_dict = {'n': 0, 's': 1, 'w': 2, 'e': 3}
reverse_edge = {}
return _ns_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, reverse_edge, '04')
elif task_id == 15:
edge_dict = {'is': 0, 'has_fear': 1}
reverse_edge = {}
return _ns_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, reverse_edge, '15')
elif task_id == 16:
edge_dict = {'is': 0, 'has_color': 1}
reverse_edge = {0: 0}
return _ns_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, reverse_edge, '16')
elif task_id == 18:
edge_dict = {'>': 0, '<': 1}
label_dict = {'false': 0, 'true': 1}
reverse_edge = {0: 1, 1: 0}
return _gc_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, label_dict, reverse_edge, '18')
elif task_id == 19:
edge_dict = {'n': 0, 's': 1, 'w': 2, 'e': 3, '<end>': 4}
reverse_edge = {0: 1, 1: 0, 2: 3, 3: 2}
max_seq_length = 2
return _path_finding_dataloader(train_size, batch_size, node_dict, edge_dict, reverse_edge, '19', max_seq_length)
def _ns_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, reverse_edge, path):
def _collate_fn(batch):
graphs = []
labels = []
for d in batch:
edges = d['edges']
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.DGLGraph()
g.add_nodes(len(node_ids))
g.ndata['node_id'] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
# convert label to node index
label = d['eval'][2]
label_idx = nid2idx[label]
labels.append(label_idx)
edge_types = []
for s, e, t in edges:
g.add_edge(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edge(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata['type'] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros(len(node_ids), dtype=torch.long)
annotation[nid2idx[d['eval'][0]]] = 1
g.ndata['annotation'] = annotation.unsqueeze(-1)
graphs.append(g)
batch_graph = dgl.batch(graphs)
labels = torch.tensor(labels, dtype=torch.long)
return batch_graph, labels
def _get_dataloader(data, shuffle):
return DataLoader(dataset=data, batch_size=batch_size, shuffle=shuffle, collate_fn=_collate_fn)
train_set, dev_set, test_sets = _convert_ns_dataset(train_size, node_dict, edge_dict, path, q_type)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_ns_dataset(train_size, node_dict, edge_dict, path, q_type):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, 'r') as f:
for i, line in enumerate(f.readlines()):
line = line.strip().split()
if line[0] == '1' and len(d) > 0:
d = dict()
if line[1] == 'eval':
# (src, edge, label)
d['eval'] = (node_dict[line[2]], edge_dict[line[3]], node_dict[line[4]])
if d['eval'][1] == q_type:
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if 'edges' not in d:
d['edges'] = []
d['edges'].append((node_dict[line[1]], edge_dict[line[2]], node_dict[line[3]]))
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, 'babi_data', path, 'data.txt')
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1): 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _gc_dataloader(train_size, q_type, batch_size, node_dict, edge_dict, label_dict, reverse_edge, path):
def _collate_fn(batch):
graphs = []
labels = []
for d in batch:
edges = d['edges']
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.DGLGraph()
g.add_nodes(len(node_ids))
g.ndata['node_id'] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
labels.append(d['eval'][-1])
edge_types = []
for s, e, t in edges:
g.add_edge(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edge(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata['type'] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros([len(node_ids), 2], dtype=torch.long)
annotation[nid2idx[d['eval'][0]]][0] = 1
annotation[nid2idx[d['eval'][2]]][1] = 1
g.ndata['annotation'] = annotation
graphs.append(g)
batch_graph = dgl.batch(graphs)
labels = torch.tensor(labels, dtype=torch.long)
return batch_graph, labels
def _get_dataloader(data, shuffle):
return DataLoader(dataset=data, batch_size=batch_size, shuffle=shuffle, collate_fn=_collate_fn)
train_set, dev_set, test_sets = _convert_gc_dataset(train_size, node_dict, edge_dict, label_dict, path, q_type)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_gc_dataset(train_size, node_dict, edge_dict, label_dict, path, q_type):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, 'r') as f:
for i, line in enumerate(f.readlines()):
line = line.strip().split()
if line[0] == '1' and len(d) > 0:
d = dict()
if line[1] == 'eval':
# (src, edge, label)
if 'eval' not in d:
d['eval'] = (node_dict[line[2]], edge_dict[line[3]], node_dict[line[4]], label_dict[line[5]])
if d['eval'][1] == q_type:
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if 'edges' not in d:
d['edges'] = []
d['edges'].append((node_dict[line[1]], edge_dict[line[2]], node_dict[line[3]]))
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, 'babi_data', path, 'data.txt')
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1): 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _path_finding_dataloader(train_size, batch_size, node_dict, edge_dict, reverse_edge, path, max_seq_length):
def _collate_fn(batch):
graphs = []
ground_truths = []
seq_lengths = []
for d in batch:
edges = d['edges']
node_ids = []
for s, e, t in edges:
if s not in node_ids:
node_ids.append(s)
if t not in node_ids:
node_ids.append(t)
g = dgl.DGLGraph()
g.add_nodes(len(node_ids))
g.ndata['node_id'] = torch.tensor(node_ids, dtype=torch.long)
nid2idx = dict(zip(node_ids, list(range(len(node_ids)))))
truth = d['seq_out'] + [edge_dict['<end>']] * (max_seq_length - len(d['seq_out']))
seq_len = len(d['seq_out'])
ground_truths.append(truth)
seq_lengths.append(seq_len)
edge_types = []
for s, e, t in edges:
g.add_edge(nid2idx[s], nid2idx[t])
edge_types.append(e)
if e in reverse_edge:
g.add_edge(nid2idx[t], nid2idx[s])
edge_types.append(reverse_edge[e])
g.edata['type'] = torch.tensor(edge_types, dtype=torch.long)
annotation = torch.zeros([len(node_ids), 2], dtype=torch.long)
annotation[nid2idx[d['eval'][0]]][0] = 1
annotation[nid2idx[d['eval'][1]]][1] = 1
g.ndata['annotation'] = annotation
graphs.append(g)
batch_graph = dgl.batch(graphs)
ground_truths = torch.tensor(ground_truths, dtype=torch.long)
seq_lengths = torch.tensor(seq_lengths, dtype=torch.long)
return batch_graph, ground_truths, seq_lengths
def _get_dataloader(data, shuffle):
return DataLoader(dataset=data, batch_size=batch_size, shuffle=shuffle, collate_fn=_collate_fn)
train_set, dev_set, test_sets = _convert_path_finding(train_size, node_dict, edge_dict, path)
train_dataloader = _get_dataloader(train_set, True)
dev_dataloader = _get_dataloader(dev_set, False)
test_dataloaders = []
for d in test_sets:
dl = _get_dataloader(d, False)
test_dataloaders.append(dl)
return train_dataloader, dev_dataloader, test_dataloaders
def _convert_path_finding(train_size, node_dict, edge_dict, path):
total_num = 11000
def convert(file):
dataset = []
d = dict()
with open(file, 'r') as f:
for line in f.readlines():
line = line.strip().split()
if line[0] == '1' and len(d) > 0:
d = dict()
if line[1] == 'eval':
# (src, edge, label)
d['eval'] = (node_dict[line[3]], node_dict[line[4]])
d['seq_out'] = []
seq_out = line[5].split(',')
for e in seq_out:
d['seq_out'].append(edge_dict[e])
dataset.append(d)
if len(dataset) >= total_num:
break
else:
if 'edges' not in d:
d['edges'] = []
d['edges'].append((node_dict[line[1]], edge_dict[line[2]], node_dict[line[3]]))
return dataset
download_dir = get_download_dir()
filename = os.path.join(download_dir, 'babi_data', path, 'data.txt')
data = convert(filename)
assert len(data) == total_num
train_set = data[:train_size]
dev_set = data[950:1000]
test_sets = []
for i in range(10):
test = data[1000 * (i + 1): 1000 * (i + 2)]
test_sets.append(test)
return train_set, dev_set, test_sets
def _download_babi_data():
download_dir = get_download_dir()
zip_file_path = os.path.join(download_dir, 'babi_data.zip')
data_url = _get_dgl_url('models/ggnn_babi_data.zip')
download(data_url, path=zip_file_path)
extract_dir = os.path.join(download_dir, 'babi_data')
if not os.path.exists(extract_dir):
extract_archive(zip_file_path, extract_dir)
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