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"""Lightning model for transformer networks."""
from typing import Callable, Optional, Tuple, Type
from text_recognizer.network.mammut import MaMMUT
import torch
from einops import rearrange
from omegaconf import DictConfig
from torch import einsum, nn, Tensor
from torchmetrics import CharErrorRate, WordErrorRate
import torch.nn.functional as F
from text_recognizer.decoder.greedy_decoder import GreedyDecoder
from text_recognizer.data.tokenizer import Tokenizer
from .base import LitBase
class LitMaMMUT(LitBase):
def __init__(
self,
network: MaMMUT,
loss_fn: Type[nn.Module],
optimizer_config: DictConfig,
tokenizer: Tokenizer,
decoder: Callable = GreedyDecoder,
lr_scheduler_config: Optional[DictConfig] = None,
max_output_len: int = 682,
caption_loss_weight=1.0,
contrastive_loss_weight=1.0,
) -> None:
super().__init__(
network,
loss_fn,
optimizer_config,
lr_scheduler_config,
tokenizer,
)
self.max_output_len = max_output_len
self.val_cer = CharErrorRate()
self.test_cer = CharErrorRate()
self.val_wer = WordErrorRate()
self.test_wer = WordErrorRate()
self.decoder = decoder
self.contrastive_loss = F.cross_entropy
self.caption_loss_weight = caption_loss_weight
self.contrastive_loss_weight = contrastive_loss_weight
self.temperature = nn.Parameter(Tensor([1.0]))
def forward(self, data: Tensor) -> Tensor:
"""Autoregressive forward pass."""
return self.predict(data)
def to_caption_loss(self, logits: Tensor, text: Tensor) -> Tensor:
caption_loss = self.loss_fn(logits, text[:, 1:])
return self.caption_loss_weight * caption_loss
def to_contrastive_loss(
self, image_embeddings: Tensor, text_embeddings: Tensor
) -> Tensor:
b, device = image_embeddings.shape[0], image_embeddings.device
image_latents, text_latents = self.network.to_latents(
image_embeddings, text_embeddings
)
sim = einsum("i d, j d -> i j", text_latents, image_latents)
sim = sim * self.temperature.exp()
contrastive_labels = torch.arange(b, device=device)
contrastive_loss = (
F.cross_entropy(sim, contrastive_labels)
+ F.cross_entropy(sim.t(), contrastive_labels)
) / 2
return self.contrastive_loss_weight * contrastive_loss
def teacher_forward(self, images: Tensor, text: Tensor) -> Tuple[Tensor, Tensor]:
"""Non-autoregressive forward pass."""
text_embeddings = self.network.to_text_cls_features(text[:, :-1])
image_embeddings, image_features = self.network.to_image_features(images)
logits = self.network.decode(text[:, :-1], image_features)
logits = rearrange(logits, "b n c -> b c n")
caption_loss = self.to_caption_loss(logits, text)
contrastive_loss = self.to_contrastive_loss(image_embeddings, text_embeddings)
self.log("train/caption_loss", caption_loss)
self.log("train/contrastive_loss", contrastive_loss)
return logits, caption_loss + contrastive_loss
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Training step."""
data, targets = batch
logits, loss = self.teacher_forward(data, targets)
self.log("train/loss", loss, prog_bar=True)
outputs = {"loss": loss}
if self.is_logged_batch():
preds, gts = self.tokenizer.decode_logits(
logits
), self.tokenizer.batch_decode(targets)
outputs.update({"predictions": preds, "ground_truths": gts})
return outputs
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Validation step."""
data, targets = batch
preds = self(data)
preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
targets
)
self.val_cer(preds, gts)
self.val_wer(preds, gts)
self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("val/wer", self.val_wer, on_step=False, on_epoch=True, prog_bar=True)
outputs = {}
self.add_on_first_batch(
{"predictions": preds, "ground_truths": gts}, outputs, batch_idx
)
return outputs
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Test step."""
data, targets = batch
preds = self(data)
preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
targets
)
self.test_cer(preds, gts)
self.test_wer(preds, gts)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)
outputs = {}
self.add_on_first_batch(
{"predictions": preds, "ground_truths": gts}, outputs, batch_idx
)
return outputs
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor:
return self.decoder(x)
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