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"""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
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