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import logging
import os
import os.path as osp
import pickle
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
from torch_geometric.utils import remove_self_loops
class GNNBenchmarkDataset(InMemoryDataset):
r"""A variety of artificially and semi-artificially generated graph
datasets from the `"Benchmarking Graph Neural Networks"
<https://arxiv.org/abs/2003.00982>`_ paper.
.. note::
The ZINC dataset is provided via
:class:`torch_geometric.datasets.ZINC`.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (one of :obj:`"PATTERN"`,
:obj:`"CLUSTER"`, :obj:`"MNIST"`, :obj:`"CIFAR10"`,
:obj:`"TSP"`, :obj:`"CSL"`)
split (str, optional): If :obj:`"train"`, loads the training dataset.
If :obj:`"val"`, loads the validation dataset.
If :obj:`"test"`, loads the test dataset.
(default: :obj:`"train"`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 20 10 10 10 10 10
:header-rows: 1
* - Name
- #graphs
- #nodes
- #edges
- #features
- #classes
* - PATTERN
- 14,000
- ~118.9
- ~6,098.9
- 3
- 2
* - CLUSTER
- 12,000
- ~117.2
- ~4,303.9
- 7
- 6
* - MNIST
- 70,000
- ~70.6
- ~564.5
- 3
- 10
* - CIFAR10
- 60,000
- ~117.6
- ~941.2
- 5
- 10
* - TSP
- 12,000
- ~275.4
- ~6,885.0
- 2
- 2
* - CSL
- 150
- ~41.0
- ~164.0
- 0
- 10
"""
names = ['PATTERN', 'CLUSTER', 'MNIST', 'CIFAR10', 'TSP', 'CSL']
root_url = 'https://data.pyg.org/datasets/benchmarking-gnns'
urls = {
'PATTERN': f'{root_url}/PATTERN_v2.zip',
'CLUSTER': f'{root_url}/CLUSTER_v2.zip',
'MNIST': f'{root_url}/MNIST_v2.zip',
'CIFAR10': f'{root_url}/CIFAR10_v2.zip',
'TSP': f'{root_url}/TSP_v2.zip',
'CSL': 'https://www.dropbox.com/s/rnbkp5ubgk82ocu/CSL.zip?dl=1',
}
def __init__(
self,
root: str,
name: str,
split: str = "train",
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name
assert self.name in self.names
if self.name == 'CSL' and split != 'train':
split = 'train'
logging.warning(
"Dataset 'CSL' does not provide a standardized splitting. "
"Instead, it is recommended to perform 5-fold cross "
"validation with stratifed sampling")
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
if split == 'train':
path = self.processed_paths[0]
elif split == 'val':
path = self.processed_paths[1]
elif split == 'test':
path = self.processed_paths[2]
else:
raise ValueError(f"Split '{split}' found, but expected either "
f"'train', 'val', or 'test'")
self.load(path)
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> List[str]:
if self.name == 'CSL':
return [
'graphs_Kary_Deterministic_Graphs.pkl',
'y_Kary_Deterministic_Graphs.pt'
]
else:
name = self.urls[self.name].split('/')[-1][:-4]
return [f'{name}.pt']
@property
def processed_file_names(self) -> List[str]:
if self.name == 'CSL':
return ['data.pt']
else:
return ['train_data.pt', 'val_data.pt', 'test_data.pt']
def download(self) -> None:
path = download_url(self.urls[self.name], self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
if self.name == 'CSL':
data_list = self.process_CSL()
self.save(data_list, self.processed_paths[0])
else:
inputs = fs.torch_load(self.raw_paths[0])
for i in range(len(inputs)):
data_list = [Data(**data_dict) for data_dict in inputs[i]]
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
self.save(data_list, self.processed_paths[i])
def process_CSL(self) -> List[Data]:
with open(self.raw_paths[0], 'rb') as f:
adjs = pickle.load(f)
ys = fs.torch_load(self.raw_paths[1]).tolist()
data_list = []
for adj, y in zip(adjs, ys):
row, col = torch.from_numpy(adj.row), torch.from_numpy(adj.col)
edge_index = torch.stack([row, col], dim=0).to(torch.long)
edge_index, _ = remove_self_loops(edge_index)
data = Data(edge_index=edge_index, y=y, num_nodes=adj.shape[0])
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
return data_list
def __repr__(self) -> str:
return f'{self.name}({len(self)})'
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