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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2023-09-02 01:52:44 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2023-09-02 01:52:44 +0200
commit579f3edc3e20ddbe8207ee0c4189a270b2dfedc1 (patch)
treedb84fd73e62ee4705088953ab55f871c86603dca /text_recognizer
parent09f9eab02ef40b1ca26e4693ad77f1f2df79a945 (diff)
Refactor lit models
Diffstat (limited to 'text_recognizer')
-rw-r--r--text_recognizer/model/base.py10
-rw-r--r--text_recognizer/model/transformer.py59
2 files changed, 49 insertions, 20 deletions
diff --git a/text_recognizer/model/base.py b/text_recognizer/model/base.py
index 1cff796..adcb8da 100644
--- a/text_recognizer/model/base.py
+++ b/text_recognizer/model/base.py
@@ -94,3 +94,13 @@ class LitBase(L.LightningModule):
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
pass
+
+ def is_logged_batch(self) -> bool:
+ if self.trainer is None:
+ return False
+ else:
+ return self.trainer._logger_connector.should_update_logs
+
+ def add_on_first_batch(self, metrics: dict, output: dict, batch_idx: int) -> None:
+ if batch_idx == 0:
+ output.update(metrics)
diff --git a/text_recognizer/model/transformer.py b/text_recognizer/model/transformer.py
index 23b2a3a..ae6947c 100644
--- a/text_recognizer/model/transformer.py
+++ b/text_recognizer/model/transformer.py
@@ -1,12 +1,12 @@
"""Lightning model for transformer networks."""
-from typing import Callable, Optional, Sequence, Tuple, Type
-from text_recognizer.model.greedy_decoder import GreedyDecoder
+from typing import Callable, Optional, Tuple, Type
import torch
from omegaconf import DictConfig
from torch import nn, Tensor
from torchmetrics import CharErrorRate, WordErrorRate
+from .greedy_decoder import GreedyDecoder
from text_recognizer.data.tokenizer import Tokenizer
from text_recognizer.model.base import LitBase
@@ -42,47 +42,66 @@ class LitTransformer(LitBase):
def teacher_forward(self, data: Tensor, targets: Tensor) -> Tensor:
"""Non-autoregressive forward pass."""
- return self.network(data, targets)
+ logits = self.network(data, targets) # [B, N, C]
+ return logits.permute(0, 2, 1) # [B, C, N]
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self.teacher_forward(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
+
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 loss
- def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+ def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
"""Validation step."""
data, targets = batch
preds = self(data)
- pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
+ preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
+ targets
+ )
+
+ self.val_cer(preds, gts)
+ self.val_wer(preds, gts)
- self.val_acc(preds, targets)
- self.val_cer(pred_text, target_text)
- self.val_wer(pred_text, target_text)
- self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
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)
- def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+ 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)
- pred_text, target_text = self._to_tokens(preds), self._to_tokens(targets)
+ preds, gts = self.tokenizer.batch_decode(preds), self.tokenizer.batch_decode(
+ targets
+ )
+
+ self.test_cer(preds, gts)
+ self.test_wer(preds, gts)
- self.test_acc(preds, targets)
- self.test_cer(pred_text, target_text)
- self.test_wer(pred_text, target_text)
- self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
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)
- def _to_tokens(
- self,
- indices: Tensor,
- ) -> Sequence[str]:
- return [self.tokenizer.decode(i) for i in indices]
+ 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: