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"""PyTorch Lightning model for base Transformers."""
from typing import Dict, List, Optional, Union, Tuple, Type
import attr
import hydra
from omegaconf import DictConfig
from torch import nn, Tensor
from text_recognizer.data.emnist import emnist_mapping
from text_recognizer.data.mappings import AbstractMapping
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.base import BaseLitModel
@attr.s(auto_attribs=True)
class TransformerLitModel(BaseLitModel):
"""A PyTorch Lightning model for transformer networks."""
mapping_config: DictConfig = attr.ib(converter=DictConfig)
def __attrs_post_init__(self) -> None:
self.mapping, ignore_tokens = self._configure_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() -> Tuple[Type[AbstractMapping], List[int]]:
"""Configure mapping."""
# TODO: Fix me!!!
# Load config with hydra
mapping, inverse_mapping, _ = emnist_mapping(["\n"])
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 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.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.test_cer(pred, targets)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
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