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
|
"""Base PyTorch Dataset class."""
from typing import Any, Callable, Dict, Sequence, Tuple, Union
import torch
from torch import Tensor
from torch.utils.data import Dataset
class BaseDataset(Dataset):
"""
Base Dataset class that processes data and targets through optional transfroms.
Args:
data (Union[Sequence, Tensor]): Torch tensors, numpy arrays, or PIL images.
targets (Union[Sequence, Tensor]): Torch tensors or numpy arrays.
tranform (Callable): Function that takes a datum and applies transforms.
target_transform (Callable): Fucntion that takes a target and applies
target transforms.
"""
def __init__(
self,
data: Union[Sequence, Tensor],
targets: Union[Sequence, Tensor],
transform: Callable = None,
target_transform: Callable = None,
) -> None:
if len(data) != len(targets):
raise ValueError("Data and targets must be of equal length.")
self.data = data
self.targets = targets
self.transform = transform
self.target_transform = target_transform
def __len__(self) -> int:
"""Return the length of the dataset."""
return len(self.data)
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""Return a datum and its target, after processing by transforms.
Args:
index (int): Index of a datum in the dataset.
Returns:
Tuple[Any, Any]: Datum and target pair.
"""
datum, target = self.data[index], self.targets[index]
if self.transform is not None:
datum = self.transform(datum)
if self.target_transform is not None:
target = self.target_transform(target)
return datum, target
def convert_strings_to_labels(
strings: Sequence[str], mapping: Dict[str, int], length: int
) -> Tensor:
"""
Convert a sequence of N strings to (N, length) ndarray, with each string wrapped with <s> and </s> tokens,
and padded wiht the <p> token.
"""
labels = torch.ones((len(strings), length), dtype=torch.long) * mapping["<p>"]
for i, string in enumerate(strings):
tokens = list(string)
tokens = ["<s>", *tokens, "</s>"]
for j, token in enumerate(tokens):
labels[i, j] = mapping[token]
return labels
def split_dataset(
dataset: BaseDataset, fraction: float, seed: int
) -> Tuple[BaseDataset, BaseDataset]:
"""Split dataset into two parts with fraction * size and (1 - fraction) * size."""
if fraction >= 1.0:
raise ValueError("Fraction cannot be larger greater or equal to 1.0.")
split_1 = int(fraction * len(dataset))
split_2 = len(dataset) - split_1
return torch.utils.data.random_split(
dataset, [split_1, split_2], generator=torch.Generator().manual_seed(seed)
)
|
