File size: 3,845 Bytes
8c4821f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | from abc import ABC, abstractmethod
from itertools import groupby
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch.nn.utils.rnn import pad_sequence
class CharsetAdapter:
def __init__(self, target_charset) -> None:
super().__init__()
self.charset = target_charset
self.lowercase_only = target_charset == target_charset.lower()
self.uppercase_only = target_charset == target_charset.upper()
def __call__(self, label):
if self.lowercase_only:
label = label.lower()
elif self.uppercase_only:
label = label.upper()
return label
class BaseTokenizer(ABC):
def __init__(self, charset: str, specials_first: tuple = (), specials_last: tuple = ()) -> None:
self._itos = specials_first + tuple(charset + "[UNK]") + specials_last
self._stoi = {s: i for i, s in enumerate(self._itos)}
def __len__(self):
return len(self._itos)
def _tok2ids(self, tokens: str) -> List[int]:
return [self._stoi[s] for s in tokens]
def _ids2tok(self, token_ids: List[int], join: bool = True) -> str:
tokens = [self._itos[i] for i in token_ids]
return "".join(tokens) if join else tokens
@abstractmethod
def encode(self, labels: List[str], device: Optional[torch.device] = None) -> Tensor:
raise NotImplementedError
@abstractmethod
def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
raise NotImplementedError
def decode(self, token_dists: Tensor, raw: bool = False) -> Tuple[List[str], List[Tensor]]:
batch_tokens = []
batch_probs = []
for dist in token_dists:
probs, ids = dist.max(-1)
if not raw:
probs, ids = self._filter(probs, ids)
tokens = self._ids2tok(ids, not raw)
batch_tokens.append(tokens)
batch_probs.append(probs)
return batch_tokens, batch_probs
class Tokenizer(BaseTokenizer):
BOS = "[B]"
EOS = "[E]"
PAD = "[P]"
def __init__(self, charset: str) -> None:
specials_first = (self.EOS,)
specials_last = (self.BOS, self.PAD)
super().__init__(charset, specials_first, specials_last)
self.eos_id, self.bos_id, self.pad_id = [
self._stoi[s] for s in specials_first + specials_last
]
def encode(self, labels: List[str], device: Optional[torch.device] = None) -> Tensor:
batch = [
torch.as_tensor(
[self.bos_id] + self._tok2ids(y) + [self.eos_id],
dtype=torch.long,
device=device,
)
for y in labels
]
return pad_sequence(batch, batch_first=True, padding_value=self.pad_id)
def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
ids = ids.tolist()
try:
eos_idx = ids.index(self.eos_id)
except ValueError:
eos_idx = len(ids)
ids = ids[:eos_idx]
probs = probs[:eos_idx + 1]
return probs, ids
class CTCTokenizer(BaseTokenizer):
BLANK = "[B]"
def __init__(self, charset: str) -> None:
super().__init__(charset, specials_first=(self.BLANK,))
self.blank_id = self._stoi[self.BLANK]
def encode(self, labels: List[str], device: Optional[torch.device] = None) -> Tensor:
batch = [
torch.as_tensor(self._tok2ids(y), dtype=torch.long, device=device)
for y in labels
]
return pad_sequence(batch, batch_first=True, padding_value=self.blank_id)
def _filter(self, probs: Tensor, ids: Tensor) -> Tuple[Tensor, List[int]]:
ids = list(zip(*groupby(ids.tolist())))[0]
ids = [x for x in ids if x != self.blank_id]
return probs, ids |