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"""Lightning model for base Transformers."""
from typing import Callable, Optional, Sequence, Tuple, Type
from text_recognizer.models.greedy_decoder import GreedyDecoder
import torch
from omegaconf import DictConfig
from torch import nn, Tensor
from torchmetrics import CharErrorRate, WordErrorRate
from text_recognizer.data.tokenizer import Tokenizer
from text_recognizer.models.base import LitBase
class LitTransformer(LitBase):
"""A PyTorch Lightning model for transformer networks."""
def __init__(
self,
network: Type[nn.Module],
loss_fn: Type[nn.Module],
optimizer_config: DictConfig,
tokenizer: Tokenizer,
decoder: Callable = GreedyDecoder,
lr_scheduler_config: Optional[DictConfig] = None,
max_output_len: int = 682,
) -> None:
super().__init__(
network,
loss_fn,
optimizer_config,
lr_scheduler_config,
tokenizer,
)
self.max_output_len = max_output_len
self.val_cer = CharErrorRate()
self.test_cer = CharErrorRate()
self.val_wer = WordErrorRate()
self.test_wer = WordErrorRate()
self.decoder = decoder
def forward(self, data: Tensor) -> Tensor:
"""Autoregressive forward pass."""
return self.predict(data)
def teacher_forward(self, data: Tensor, targets: Tensor) -> Tensor:
"""Non-autoregressive forward pass."""
return self.network(data, targets)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self.teacher_forward(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
self.log("train/loss", loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
logits = self.teacher_forward(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
preds = self.predict(data)
pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
self.val_acc(preds, targets)
self.val_cer(pred_text, target_text)
self.val_wer(pred_text, target_text)
self.log("val/loss", loss, on_step=False, on_epoch=True)
self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("val/wer", self.val_wer, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, targets = batch
logits = self.teacher_forward(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
preds = self(data)
pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
self.test_acc(preds, targets)
self.test_cer(pred_text, target_text)
self.test_wer(pred_text, target_text)
self.log("test/loss", loss, on_step=False, on_epoch=True)
self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)
def _to_tokens(
self,
indecies: Tensor,
) -> Sequence[str]:
return [self.tokenizer.decode(i) for i in indecies]
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor:
return self.decoder(x)
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