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"""Lightning model for transformer networks."""
from typing import Callable, Optional, Tuple, Type
import torch
from omegaconf import DictConfig
from torch import Tensor, nn
from torchmetrics import CharErrorRate, WordErrorRate
from text_recognizer.data.tokenizer import Tokenizer
from text_recognizer.decoder.greedy_decoder import GreedyDecoder
from .base import LitBase
class LitTransformer(LitBase):
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."""
logits = self.network(data, targets) # [B, N, C]
return logits.permute(0, 2, 1) # [B, C, N]
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Training step."""
data, targets = batch
logits = self.teacher_forward(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
self.log("train/loss", loss, prog_bar=True)
outputs = {"loss": loss}
if self.is_logged_batch():
preds, gts = self.tokenizer.decode_logits(
logits
), self.tokenizer.batch_decode(targets)
outputs.update({"predictions": preds, "ground_truths": gts})
return outputs
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Validation step."""
data, targets = batch
preds = self(data)
preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
targets
)
self.val_cer(preds, gts)
self.val_wer(preds, gts)
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)
outputs = {}
self.add_on_first_batch(
{"predictions": preds, "ground_truths": gts}, outputs, batch_idx
)
return outputs
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Test step."""
data, targets = batch
preds = self(data)
preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
targets
)
self.test_cer(preds, gts)
self.test_wer(preds, gts)
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)
outputs = {}
self.add_on_first_batch(
{"predictions": preds, "ground_truths": gts}, outputs, batch_idx
)
return outputs
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor:
return self.decoder(x)
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