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"""Base PyTorch Dataset class."""
from typing import Callable, Dict, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
from torch.utils.data import Dataset
from text_recognizer.data.transforms.load_transform import load_transform_from_file
class BaseDataset(Dataset):
r"""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: Union[Optional[Callable], str],
target_transform: Union[Optional[Callable], str],
) -> None:
super().__init__()
self.data = data
self.targets = targets
self.transform = transform
self.target_transform = target_transform
def __attrs_pre_init__(self) -> None:
"""Pre init constructor."""
super().__init__()
def __attrs_post_init__(self) -> None:
"""Post init constructor."""
if len(self.data) != len(self.targets):
raise ValueError("Data and targets must be of equal length.")
self.transform = self._load_transform(self.transform)
self.target_transform = self._load_transform(self.target_transform)
@staticmethod
def _load_transform(
transform: Union[Optional[Callable], str]
) -> Optional[Callable]:
if isinstance(transform, str):
return load_transform_from_file(transform)
return transform
def __len__(self) -> int:
"""Return the length of the dataset."""
return len(self.data)
def __getitem__(
self, index: int
) -> Tuple[Union[Tensor, Tuple[Tensor, Tensor]], Tensor]:
"""Return a datum and its target, after processing by transforms.
Args:
index (int): Index of a datum in the dataset.
Returns:
Tuple[Union[Tensor, Tuple[Tensor, Tensor]], Tensor]: 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:
r"""Convert a sequence of N strings to (N, length) ndarray.
Add each string with <s> and </s> tokens, and padded wiht the <p> token.
Args:
strings (Sequence[str]): Sequence of strings.
mapping (Dict[str, int]): Mapping of characters and digits to integers.
length (int): Max lenght of all strings.
Returns:
Tensor: Target with emnist mapping indices.
"""
labels = torch.ones((len(strings), length), dtype=torch.long) * mapping["<p>"]
for i, string in enumerate(strings):
tokens = list(string)
tokens = ["<s>", *tokens, "<e>"]
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)
)
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