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"""Lightning model for base Transformers."""
from typing import Optional, Tuple, Type
from omegaconf import DictConfig
import torch
from torch import nn, Tensor
from text_recognizer.data.mappings import EmnistMapping
from text_recognizer.models.base import LitBase
from text_recognizer.models.metrics import CharacterErrorRate
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,
lr_scheduler_config: Optional[DictConfig],
mapping: EmnistMapping,
max_output_len: int = 451,
start_token: str = "<s>",
end_token: str = "<e>",
pad_token: str = "<p>",
) -> None:
super().__init__(
network, loss_fn, optimizer_config, lr_scheduler_config, mapping
)
self.max_output_len = max_output_len
self.start_token = start_token
self.end_token = end_token
self.pad_token = pad_token
self.start_index = int(self.mapping.get_index(self.start_token))
self.end_index = int(self.mapping.get_index(self.end_token))
self.pad_index = int(self.mapping.get_index(self.pad_token))
self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
self.val_cer = CharacterErrorRate(self.ignore_indices)
self.test_cer = CharacterErrorRate(self.ignore_indices)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.predict(data)
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
preds = self.predict(data)
self.val_acc(preds, targets)
self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
self.val_cer(preds, 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
# Compute the text prediction.
pred = self(data)
self.test_cer(pred, targets)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.test_acc(pred, targets)
self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor:
"""Predicts text in image.
Args:
x (Tensor): Image(s) to extract text from.
Shapes:
- x: :math: `(B, H, W)`
- output: :math: `(B, S)`
Returns:
Tensor: A tensor of token indices of the predictions from the model.
"""
bsz = x.shape[0]
# Encode image(s) to latent vectors.
z = self.network.encode(x)
# Create a placeholder matrix for storing outputs from the network
output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
output[:, 0] = self.start_index
for Sy in range(1, self.max_output_len):
context = output[:, :Sy] # (B, Sy)
logits = self.network.decode(z, context) # (B, C, Sy)
tokens = torch.argmax(logits, dim=1) # (B, Sy)
output[:, Sy : Sy + 1] = tokens[:, -1:]
# Early stopping of prediction loop if token is end or padding token.
if (
(output[:, Sy - 1] == self.end_index)
| (output[:, Sy - 1] == self.pad_index)
).all():
break
# Set all tokens after end token to pad token.
for Sy in range(1, self.max_output_len):
idx = (output[:, Sy - 1] == self.end_index) | (
output[:, Sy - 1] == self.pad_index
)
output[idx, Sy] = self.pad_index
return output
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