Working on mapping

1 files changed, 6 insertions, 17 deletions

diff --git a/text_recognizer/networks/image_transformer.py b/text_recognizer/networks/image_transformer.py
index 9ed67a4..daededa 100644
--- a/text_recognizer/networks/image_transformer.py
+++ b/text_recognizer/networks/image_transformer.py
@@ -10,16 +10,15 @@ TODO: Local attention for lower layer in attention.
 """
 import importlib
 import math
-from typing import Dict, List, Union, Sequence, Tuple, Type
+from typing import Dict, Optional, Union, Sequence, Type
 
 from einops import rearrange
 from omegaconf import DictConfig, OmegaConf
 import torch
 from torch import nn
 from torch import Tensor
-import torchvision
 
-from text_recognizer.data.emnist import emnist_mapping
+from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
 from text_recognizer.networks.transformer import (
     Decoder,
     DecoderLayer,
@@ -28,6 +27,8 @@ from text_recognizer.networks.transformer import (
     target_padding_mask,
 )
 
+NUM_WORD_PIECES = 1000
+
 
 class ImageTransformer(nn.Module):
     def __init__(
@@ -35,7 +36,7 @@ class ImageTransformer(nn.Module):
         input_shape: Sequence[int],
         output_shape: Sequence[int],
         encoder: Union[DictConfig, Dict],
-        mapping: str,
+        vocab_size: Optional[int] = None,
         num_decoder_layers: int = 4,
         hidden_dim: int = 256,
         num_heads: int = 4,
@@ -43,14 +44,9 @@ class ImageTransformer(nn.Module):
         dropout_rate: float = 0.1,
         transformer_activation: str = "glu",
     ) -> None:
-        # Configure mapping
-        mapping, inverse_mapping = self._configure_mapping(mapping)
-        self.vocab_size = len(mapping)
+        self.vocab_size = NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
         self.hidden_dim = hidden_dim
         self.max_output_length = output_shape[0]
-        self.start_index = inverse_mapping["<s>"]
-        self.end_index = inverse_mapping["<e>"]
-        self.pad_index = inverse_mapping["<p>"]
 
         # Image backbone
         self.encoder = self._configure_encoder(encoder)
@@ -107,13 +103,6 @@ class ImageTransformer(nn.Module):
         encoder_class = getattr(network_module, encoder.type)
         return encoder_class(**encoder.args)
 
-    def _configure_mapping(self, mapping: str) -> Tuple[List[str], Dict[str, int]]:
-        """Configures mapping."""
-        # TODO: Fix me!!!
-        if mapping == "emnist":
-            mapping, inverse_mapping, _ = emnist_mapping()
-        return mapping, inverse_mapping
-
     def encode(self, image: Tensor) -> Tensor:
         """Extracts image features with backbone.