Update lit models

3 files changed, 4 insertions, 11 deletions

diff --git a/text_recognizer/model/base.py b/text_recognizer/model/base.py
index adcb8da..9a751bf 100644
--- a/text_recognizer/model/base.py
+++ b/text_recognizer/model/base.py
@@ -7,7 +7,6 @@ from loguru import logger as log
 from omegaconf import DictConfig
 import pytorch_lightning as L
 from torch import nn, Tensor
-from torchmetrics import Accuracy
 
 from text_recognizer.data.tokenizer import Tokenizer
 
@@ -24,17 +23,11 @@ class LitBase(L.LightningModule):
         tokenizer: Tokenizer,
     ) -> None:
         super().__init__()
-
         self.network = network
         self.loss_fn = loss_fn
         self.optimizer_config = optimizer_config
         self.lr_scheduler_config = lr_scheduler_config
         self.tokenizer = tokenizer
-        ignore_index = int(self.tokenizer.get_value("<p>"))
-        # Placeholders
-        self.train_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
-        self.val_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
-        self.test_acc = Accuracy(mdmc_reduce="samplewise", ignore_index=ignore_index)
 
     def optimizer_zero_grad(
         self,
diff --git a/text_recognizer/model/greedy_decoder.py b/text_recognizer/model/greedy_decoder.py
index 2c4c16e..8d55a02 100644
--- a/text_recognizer/model/greedy_decoder.py
+++ b/text_recognizer/model/greedy_decoder.py
@@ -34,7 +34,7 @@ class GreedyDecoder:
         for i in range(1, self.max_output_len):
             tokens = indecies[:, :i]  # (B, Sy)
             logits = self.network.decode(tokens, img_features)  # [ B, N, C ]
-            indecies_ = torch.argmax(logits, dim=2)  # [ B, N ]
+            indecies_ = logits.argmax(dim=2)  # [ B, N ]
             indecies[:, i] = indecies_[:, -1]
 
             # Early stopping of prediction loop if token is end or padding token.
diff --git a/text_recognizer/model/transformer.py b/text_recognizer/model/transformer.py
index ae6947c..598d995 100644
--- a/text_recognizer/model/transformer.py
+++ b/text_recognizer/model/transformer.py
@@ -8,7 +8,7 @@ from torchmetrics import CharErrorRate, WordErrorRate
 
 from .greedy_decoder import GreedyDecoder
 from text_recognizer.data.tokenizer import Tokenizer
-from text_recognizer.model.base import LitBase
+from .base import LitBase
 
 
 class LitTransformer(LitBase):
@@ -45,7 +45,7 @@ class LitTransformer(LitBase):
         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:
+    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
         """Training step."""
         data, targets = batch
         logits = self.teacher_forward(data, targets[:, :-1])
@@ -61,7 +61,7 @@ class LitTransformer(LitBase):
             ), self.tokenizer.batch_decode(targets)
             outputs.update({"predictions": preds, "ground_truths": gts})
 
-        return loss
+        return outputs
 
     def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> dict:
         """Validation step."""