1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
|
from typing import Iterator, List, Optional
import torch
from torch_geometric.data import Dataset
class DynamicBatchSampler(torch.utils.data.sampler.Sampler):
r"""Dynamically adds samples to a mini-batch up to a maximum size (either
based on number of nodes or number of edges). When data samples have a
wide range in sizes, specifying a mini-batch size in terms of number of
samples is not ideal and can cause CUDA OOM errors.
Within the :class:`DynamicBatchSampler`, the number of steps per epoch is
ambiguous, depending on the order of the samples. By default the
:meth:`__len__` will be undefined. This is fine for most cases but
progress bars will be infinite. Alternatively, :obj:`num_steps` can be
supplied to cap the number of mini-batches produced by the sampler.
.. code-block:: python
from torch_geometric.loader import DataLoader, DynamicBatchSampler
sampler = DynamicBatchSampler(dataset, max_num=10000, mode="node")
loader = DataLoader(dataset, batch_sampler=sampler, ...)
Args:
dataset (Dataset): Dataset to sample from.
max_num (int): Size of mini-batch to aim for in number of nodes or
edges.
mode (str, optional): :obj:`"node"` or :obj:`"edge"` to measure
batch size. (default: :obj:`"node"`)
shuffle (bool, optional): If set to :obj:`True`, will have the data
reshuffled at every epoch. (default: :obj:`False`)
skip_too_big (bool, optional): If set to :obj:`True`, skip samples
which cannot fit in a batch by itself. (default: :obj:`False`)
num_steps (int, optional): The number of mini-batches to draw for a
single epoch. If set to :obj:`None`, will iterate through all the
underlying examples, but :meth:`__len__` will be :obj:`None` since
it is ambiguous. (default: :obj:`None`)
"""
def __init__(
self,
dataset: Dataset,
max_num: int,
mode: str = 'node',
shuffle: bool = False,
skip_too_big: bool = False,
num_steps: Optional[int] = None,
):
if max_num <= 0:
raise ValueError(f"`max_num` should be a positive integer value "
f"(got {max_num})")
if mode not in ['node', 'edge']:
raise ValueError(f"`mode` choice should be either "
f"'node' or 'edge' (got '{mode}')")
self.dataset = dataset
self.max_num = max_num
self.mode = mode
self.shuffle = shuffle
self.skip_too_big = skip_too_big
self.num_steps = num_steps
self.max_steps = num_steps or len(dataset)
def __iter__(self) -> Iterator[List[int]]:
if self.shuffle:
indices = torch.randperm(len(self.dataset)).tolist()
else:
indices = range(len(self.dataset))
samples: List[int] = []
current_num: int = 0
num_steps: int = 0
num_processed: int = 0
while (num_processed < len(self.dataset)
and num_steps < self.max_steps):
for i in indices[num_processed:]:
data = self.dataset[i]
num = data.num_nodes if self.mode == 'node' else data.num_edges
if current_num + num > self.max_num:
if current_num == 0:
if self.skip_too_big:
continue
else: # Mini-batch filled:
break
samples.append(i)
num_processed += 1
current_num += num
yield samples
samples: List[int] = []
current_num = 0
num_steps += 1
def __len__(self) -> int:
if self.num_steps is None:
raise ValueError(f"The length of '{self.__class__.__name__}' is "
f"undefined since the number of steps per epoch "
f"is ambiguous. Either specify `num_steps` or "
f"use a static batch sampler.")
return self.num_steps
|
