import collections
import copy
import os.path as osp
from itertools import repeat, product
import torch.utils.data
from cogdl.utils import makedirs
from cogdl.utils import accuracy, cross_entropy_loss
def to_list(x):
if not isinstance(x, collections.Iterable) or isinstance(x, str):
x = [x]
return x
def files_exist(files):
return all([osp.exists(f) for f in files])
[docs]class Dataset(torch.utils.data.Dataset):
r"""Dataset base class for creating graph datasets.
See `here <https://rusty1s.github.io/pycogdl/build/html/notes/
create_dataset.html>`__ for the accompanying tutorial.
Args:
root (string): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`cogdl.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:`cogdl.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:`cogdl.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
"""
[docs] @staticmethod
def add_args(parser):
"""Add dataset-specific arguments to the parser."""
pass
@property
def raw_file_names(self):
r"""The name of the files to find in the :obj:`self.raw_dir` folder in
order to skip the download."""
raise NotImplementedError
@property
def processed_file_names(self):
r"""The name of the files to find in the :obj:`self.processed_dir`
folder in order to skip the processing."""
raise NotImplementedError
[docs] def download(self):
r"""Downloads the dataset to the :obj:`self.raw_dir` folder."""
raise NotImplementedError
[docs] def process(self):
r"""Processes the dataset to the :obj:`self.processed_dir` folder."""
raise NotImplementedError
def __len__(self):
r"""The number of examples in the dataset."""
raise NotImplementedError
[docs] def get(self, idx):
r"""Gets the data object at index :obj:`idx`."""
raise NotImplementedError
def __init__(self, root, transform=None, pre_transform=None, pre_filter=None):
super(Dataset, self).__init__()
self.root = osp.expanduser(osp.normpath(root))
self.raw_dir = osp.join(self.root, "raw")
self.processed_dir = osp.join(self.root, "processed")
self.transform = transform
self.pre_transform = pre_transform
self.pre_filter = pre_filter
self._download()
self._process()
@property
def num_features(self):
r"""Returns the number of features per node in the graph."""
return self[0].num_features
@property
def raw_paths(self):
r"""The filepaths to find in order to skip the download."""
files = to_list(self.raw_file_names)
return [osp.join(self.raw_dir, f) for f in files]
@property
def processed_paths(self):
r"""The filepaths to find in the :obj:`self.processed_dir`
folder in order to skip the processing."""
files = to_list(self.processed_file_names)
return [osp.join(self.processed_dir, f) for f in files]
def _download(self):
if files_exist(self.raw_paths): # pragma: no cover
return
makedirs(self.raw_dir)
self.download()
def _process(self):
if files_exist(self.processed_paths): # pragma: no cover
return
print("Processing...")
makedirs(self.processed_dir)
self.process()
print("Done!")
[docs] def get_evaluator(self):
return accuracy
[docs] def get_loss_fn(self):
return cross_entropy_loss
def __getitem__(self, idx): # pragma: no cover
r"""Gets the data object at index :obj:`idx` and transforms it (in case
a :obj:`self.transform` is given)."""
data = self.get(idx)
data = data if self.transform is None else self.transform(data)
return data
@property
def num_classes(self):
r"""The number of classes in the dataset."""
y = self.data.y
return y.max().item() + 1 if y.dim() == 1 else y.size(1)
def __repr__(self): # pragma: no cover
return "{}({})".format(self.__class__.__name__, len(self))
[docs]class MultiGraphDataset(Dataset):
def __init__(self, root=None, transform=None, pre_transform=None, pre_filter=None):
super(MultiGraphDataset, self).__init__(root, transform, pre_transform, pre_filter)
self.data, self.slices = None, None
@property
def num_classes(self):
r"""The number of classes in the dataset."""
y = self.data.y
return y.max().item() + 1 if y.dim() == 1 else y.size(1)
[docs] def len(self):
for item in self.slices.values():
return len(item) - 1
return 0
def _get(self, idx):
data = self.data.__class__()
if hasattr(self.data, "__num_nodes__"):
data.num_nodes = self.data.__num_nodes__[idx]
for key in self.data.keys:
item, slices = self.data[key], self.slices[key]
start, end = slices[idx].item(), slices[idx + 1].item()
if torch.is_tensor(item):
s = list(repeat(slice(None), item.dim()))
s[self.data.__cat_dim__(key, item)] = slice(start, end)
elif start + 1 == end:
s = slices[start]
else:
s = slice(start, end)
data[key] = item[s]
return data
[docs] def get(self, idx):
if isinstance(idx, int) or (len(idx) == 0):
return self._get(idx)
elif len(idx) > 1:
data_list = [self._get(i) for i in idx]
data, slices = self.from_data_list(data_list)
dataset = copy.copy(self)
dataset.data = data
dataset.slices = slices
return dataset
[docs] @staticmethod
def from_data_list(data_list):
r""" Borrowed from PyG"""
keys = data_list[0].keys
data = data_list[0].__class__()
for key in keys:
data[key] = []
slices = {key: [0] for key in keys}
for item, key in product(data_list, keys):
data[key].append(item[key])
if torch.is_tensor(item[key]):
s = slices[key][-1] + item[key].size(item.__cat_dim__(key, item[key]))
else:
s = slices[key][-1] + 1
slices[key].append(s)
if hasattr(data_list[0], "__num_nodes__"):
data.__num_nodes__ = []
for item in data_list:
data.__num_nodes__.append(item.num_nodes)
for key in keys:
item = data_list[0][key]
if torch.is_tensor(item):
data[key] = torch.cat(data[key], dim=data.__cat_dim__(key, item))
elif isinstance(item, int) or isinstance(item, float):
data[key] = torch.tensor(data[key])
slices[key] = torch.tensor(slices[key], dtype=torch.long)
return data, slices
def __len__(self):
for item in self.slices.values():
return len(item) - 1
return 0