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"""PyTorch Lightning model for base Transformers."""
from typing import Tuple
import attr
import torch
from torch import Tensor
from text_recognizer.models.transformer import TransformerLitModel
@attr.s(auto_attribs=True, eq=False)
class VqTransformerLitModel(TransformerLitModel):
"""A PyTorch Lightning model for transformer networks."""
alpha: float = attr.ib(default=1.0)
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, commitment_loss = self.network(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:]) + self.alpha * commitment_loss
self.log("train/loss", loss)
self.log("train/commitment_loss", commitment_loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
# Compute the loss.
logits, commitment_loss = self.network(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:]) + self.alpha * commitment_loss
self.log("val/loss", loss, prog_bar=True)
self.log("val/commitment_loss", commitment_loss)
# Get the token prediction.
# pred = self(data)
# self.val_cer(pred, targets)
# self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
# self.test_acc(pred, targets)
# self.log("val/acc", self.test_acc, on_step=False, on_epoch=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)
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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