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"""PyTorch Lightning model for base Transformers."""
from typing import Dict, List, Optional, Union, Tuple
from omegaconf import OmegaConf
import pytorch_lightning as pl
import torch
from torch import nn
from torch import Tensor
import torch.nn.functional as F
import wandb
from text_recognizer.data.emnist import emnist_mapping
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.base import LitBaseModel
class LitTransformerModel(LitBaseModel):
"""A PyTorch Lightning model for transformer networks."""
def __init__(
self,
network: Type[nn, Module],
optimizer: Union[OmegaConf, Dict],
lr_scheduler: Union[OmegaConf, Dict],
criterion: Union[OmegaConf, Dict],
monitor: str = "val_loss",
mapping: Optional[List[str]] = None,
) -> None:
super().__init__(network, optimizer, lr_scheduler, criterion, monitor)
self.mapping, ignore_tokens = self.configure_mapping(mapping)
self.val_cer = CharacterErrorRate(ignore_tokens)
self.test_cer = CharacterErrorRate(ignore_tokens)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network.predict(data)
@staticmethod
def configure_mapping(mapping: Optional[List[str]]) -> Tuple[List[str], List[int]]:
"""Configure mapping."""
# TODO: Fix me!!!
mapping, inverse_mapping, _ = emnist_mapping()
start_index = inverse_mapping["<s>"]
end_index = inverse_mapping["<e>"]
pad_index = inverse_mapping["<p>"]
ignore_tokens = [start_index, end_index, pad_index]
# TODO: add case for sentence pieces
return mapping, ignore_tokens
def _log_prediction(self, data: Tensor, pred: Tensor) -> None:
"""Logs prediction on image with wandb."""
pred_str = "".join(
self.mapping[i] for i in pred[0].tolist() if i != 3
) # pad index is 3
try:
self.logger.experiment.log(
{"val_pred_examples": [wandb.Image(data[0], caption=pred_str)]}
)
except AttributeError:
pass
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self.network(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.network(data, targets[:-1])
loss = self.loss_fn(logits, targets[1:])
self.log("val_loss", loss, prog_bar=True)
pred = self.network.predict(data)
self._log_prediction(data, pred)
self.val_cer(pred, targets)
self.log("val_cer", self.val_cer, 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
pred = self.network.predict(data)
self._log_prediction(data, pred)
self.test_cer(pred, targets)
self.log("test_cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
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