Rename mapping to tokenizer

2 files changed, 37 insertions, 38 deletions

diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index bb4e695..f8f4b40 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -9,7 +9,7 @@ from pytorch_lightning import LightningModule
 from torch import nn, Tensor
 from torchmetrics import Accuracy
 
-from text_recognizer.data.mappings import EmnistMapping
+from text_recognizer.data.tokenizer import Tokenizer
 
 
 class LitBase(LightningModule):
@@ -21,8 +21,7 @@ class LitBase(LightningModule):
         loss_fn: Type[nn.Module],
         optimizer_config: DictConfig,
         lr_scheduler_config: Optional[DictConfig],
-        mapping: EmnistMapping,
-        ignore_index: Optional[int] = None,
+        tokenizer: Tokenizer,
     ) -> None:
         super().__init__()
 
@@ -30,8 +29,8 @@ class LitBase(LightningModule):
         self.loss_fn = loss_fn
         self.optimizer_config = optimizer_config
         self.lr_scheduler_config = lr_scheduler_config
-        self.mapping = mapping
-
+        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)
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index 2c74b7e..752f3eb 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,11 +1,12 @@
 """Lightning model for base Transformers."""
+from collections.abc import Sequence
 from typing import Optional, Tuple, Type
 
 import torch
 from omegaconf import DictConfig
 from torch import nn, Tensor
 
-from text_recognizer.data.mappings import EmnistMapping
+from text_recognizer.data.tokenizer import Tokenizer
 from text_recognizer.models.base import LitBase
 from text_recognizer.models.metrics.cer import CharacterErrorRate
 from text_recognizer.models.metrics.wer import WordErrorRate
@@ -19,33 +20,23 @@ class LitTransformer(LitBase):
         network: Type[nn.Module],
         loss_fn: Type[nn.Module],
         optimizer_config: DictConfig,
-        mapping: EmnistMapping,
+        tokenizer: Tokenizer,
         lr_scheduler_config: Optional[DictConfig] = None,
         max_output_len: int = 682,
-        start_token: str = "<s>",
-        end_token: str = "<e>",
-        pad_token: str = "<p>",
     ) -> None:
-        self.max_output_len = max_output_len
-        self.start_token = start_token
-        self.end_token = end_token
-        self.pad_token = pad_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.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
-        self.val_cer = CharacterErrorRate(self.ignore_indices)
-        self.test_cer = CharacterErrorRate(self.ignore_indices)
-        self.val_wer = WordErrorRate(self.ignore_indices)
-        self.test_wer = WordErrorRate(self.ignore_indices)
         super().__init__(
             network,
             loss_fn,
             optimizer_config,
             lr_scheduler_config,
-            mapping,
-            self.pad_index,
+            tokenizer,
         )
+        self.max_output_len = max_output_len
+        self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
+        self.val_cer = CharacterErrorRate(self.ignore_indices)
+        self.test_cer = CharacterErrorRate(self.ignore_indices)
+        self.val_wer = WordErrorRate(self.ignore_indices)
+        self.test_wer = WordErrorRate(self.ignore_indices)
 
     def forward(self, data: Tensor) -> Tensor:
         """Forward pass with the transformer network."""
@@ -63,11 +54,12 @@ class LitTransformer(LitBase):
         """Validation step."""
         data, targets = batch
         preds = self.predict(data)
-        self.val_acc(preds, targets)
+        pred_text, target_text = self.get_text(preds, targets)
+        self.val_acc(pred_text, target_text)
         self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
-        self.val_cer(preds, targets)
+        self.val_cer(pred_text, target_text)
         self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.val_wer(preds, targets)
+        self.val_wer(pred_text, target_text)
         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:
@@ -75,14 +67,22 @@ class LitTransformer(LitBase):
         data, targets = batch
 
         # Compute the text prediction.
-        pred = self(data)
-        self.test_acc(pred, targets)
+        preds = self(data)
+        pred_text, target_text = self.get_text(preds, targets)
+        self.test_acc(pred_text, target_text)
         self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
-        self.test_cer(pred, targets)
+        self.test_cer(pred_text, target_text)
         self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
-        self.test_wer(pred, targets)
+        self.test_wer(pred_text, target_text)
         self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)
 
+    def get_text(
+        self, preds: Tensor, targets: Tensor
+    ) -> Tuple[Sequence[str], Sequence[str]]:
+        pred_text = [self.tokenizer.decode(p) for p in preds]
+        target_text = [self.tokenizer.decode(t) for t in targets]
+        return pred_text, target_text
+
     @torch.no_grad()
     def predict(self, x: Tensor) -> Tensor:
         """Predicts text in image.
@@ -97,6 +97,9 @@ class LitTransformer(LitBase):
         Returns:
             Tensor: A tensor of token indices of the predictions from the model.
         """
+        start_index = self.tokenizer.start_index
+        end_index = self.tokenizer.start_index
+        pad_index = self.tokenizer.start_index
         bsz = x.shape[0]
 
         # Encode image(s) to latent vectors.
@@ -104,7 +107,7 @@ class LitTransformer(LitBase):
 
         # 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
+        output[:, 0] = start_index
 
         for Sy in range(1, self.max_output_len):
             context = output[:, :Sy]  # (B, Sy)
@@ -114,16 +117,13 @@ class LitTransformer(LitBase):
 
             # 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)
+                (output[:, Sy - 1] == end_index) | (output[:, Sy - 1] == 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
+            idx = (output[:, Sy - 1] == end_index) | (output[:, Sy - 1] == pad_index)
+            output[idx, Sy] = pad_index
 
         return output