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"""Lightning Conformer model."""
import itertools
from typing import Optional, Tuple, Type
from omegaconf import DictConfig
import torch
from torch import nn, Tensor
from text_recognizer.data.mappings import AbstractMapping
from text_recognizer.models.base import LitBase
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.util import first_element
class LitConformer(LitBase):
"""A PyTorch Lightning model for transformer networks."""
def __init__(
self,
network: Type[nn.Module],
loss_fn: Type[nn.Module],
optimizer_configs: DictConfig,
lr_scheduler_configs: Optional[DictConfig],
mapping: Type[AbstractMapping],
max_output_len: int = 451,
start_token: str = "<s>",
end_token: str = "<e>",
pad_token: str = "<p>",
blank_token: str = "<b>",
) -> None:
super().__init__(
network, loss_fn, optimizer_configs, lr_scheduler_configs, mapping
)
self.max_output_len = max_output_len
self.start_token = start_token
self.end_token = end_token
self.pad_token = pad_token
self.blank_token = blank_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.blank_index = int(self.mapping.get_index(self.blank_token))
self.ignore_indices = set(
[self.start_index, self.end_index, self.pad_index, self.blank_index]
)
self.val_cer = CharacterErrorRate(self.ignore_indices)
self.test_cer = CharacterErrorRate(self.ignore_indices)
@torch.no_grad()
def predict(self, x: Tensor) -> str:
"""Predicts a sequence of characters."""
logits = self(x)
logprobs = torch.log_softmax(logits, dim=1)
pred = self.decode(logprobs, self.max_output_len)[0]
return "".join([self.mapping[i] for i in pred if i not in self.ignore_indices])
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self(data)
logprobs = torch.log_softmax(logits, dim=1)
B, _, S = logprobs.shape
input_length = torch.ones(B).types_as(logprobs).int() * S
target_length = first_element(targets, self.pad_index).types_as(targets)
loss = self.loss_fn(
logprobs.permute(2, 0, 1), targets, input_length, target_length
)
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(data)
logprobs = torch.log_softmax(logits, dim=1)
B, _, S = logprobs.shape
input_length = torch.ones(B).types_as(logprobs).int() * S
target_length = first_element(targets, self.pad_index).types_as(targets)
loss = self.loss_fn(
logprobs.permute(2, 0, 1), targets, input_length, target_length
)
self.log("val/loss", loss)
preds = self.decode(logprobs, targets.shape[1])
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
logits = self(data)
logprobs = torch.log_softmax(logits, dim=1)
preds = self.decode(logprobs, targets.shape[1])
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 decode(self, logprobs: Tensor, max_length: int) -> Tensor:
"""Greedly decodes a log prob sequence.
Args:
logprobs (Tensor): Log probabilities.
max_length (int): Max length of a sequence.
Shapes:
- x: :math: `(B, C, Y)`
- output: :math: `(B, S)`
Returns:
Tensor: A predicted sequence of characters.
"""
B = logprobs.shape[0]
argmax = logprobs.argmax(1)
decoded = torch.ones((B, max_length)).types_as(logprobs).int() * self.pad_index
for i in range(B):
seq = [
b
for b, _ in itertools.groupby(argmax[i].tolist())
if b != self.blank_index
][:max_length]
for j, c in enumerate(seq):
decoded[i, j] = c
return decoded
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