Configs, refactor with attrs, fix attr bug in iam

1 files changed, 6 insertions, 20 deletions

diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index 8c9fe8a..f5cb491 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,13 +1,11 @@
 """PyTorch Lightning model for base Transformers."""
-from typing import Dict, List, Optional, Union, Tuple, Type
+from typing import Dict, List, Optional, Sequence, Union, Tuple, Type
 
 import attr
 import hydra
 from omegaconf import DictConfig
 from torch import nn, Tensor
 
-from text_recognizer.data.emnist import emnist_mapping
-from text_recognizer.data.mappings import AbstractMapping
 from text_recognizer.models.metrics import CharacterErrorRate
 from text_recognizer.models.base import BaseLitModel
 
@@ -16,30 +14,18 @@ from text_recognizer.models.base import BaseLitModel
 class TransformerLitModel(BaseLitModel):
     """A PyTorch Lightning model for transformer networks."""
 
-    mapping_config: DictConfig = attr.ib(converter=DictConfig)
+    ignore_tokens: Sequence[str] = attr.ib(default=("<s>", "<e>", "<p>",))
+    val_cer: CharacterErrorRate = attr.ib(init=False)
+    test_cer: CharacterErrorRate = attr.ib(init=False)
 
     def __attrs_post_init__(self) -> None:
-        self.mapping, ignore_tokens = self._configure_mapping()
-        self.val_cer = CharacterErrorRate(ignore_tokens)
-        self.test_cer = CharacterErrorRate(ignore_tokens)
+        self.val_cer = CharacterErrorRate(self.ignore_tokens)
+        self.test_cer = CharacterErrorRate(self.ignore_tokens)
 
     def forward(self, data: Tensor) -> Tensor:
         """Forward pass with the transformer network."""
         return self.network.predict(data)
 
-    @staticmethod
-    def _configure_mapping() -> Tuple[Type[AbstractMapping], List[int]]:
-        """Configure mapping."""
-        # TODO: Fix me!!!
-        # Load config with hydra
-        mapping, inverse_mapping, _ = emnist_mapping(["\n"])
-        start_index = inverse_mapping["<s>"]
-        end_index = inverse_mapping["<e>"]
-        pad_index = inverse_mapping["<p>"]
-        ignore_tokens = [start_index, end_index, pad_index]
-        # TODO: add case for sentence pieces
-        return mapping, ignore_tokens
-
     def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
         """Training step."""
         data, targets = batch