From 0ab820d3595e4f17d4f1f3c310e186692f65cc67 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 11 Apr 2021 21:48:34 +0200
Subject: Working on mapping

---
 text_recognizer/data/mapping.py               |  8 ++++++++
 text_recognizer/models/base.py                |  1 +
 text_recognizer/models/transformer.py         |  5 ++---
 text_recognizer/networks/image_transformer.py | 23 ++++++-----------------
 4 files changed, 17 insertions(+), 20 deletions(-)
 create mode 100644 text_recognizer/data/mapping.py

(limited to 'text_recognizer')

diff --git a/text_recognizer/data/mapping.py b/text_recognizer/data/mapping.py
new file mode 100644
index 0000000..f0edf3f
--- /dev/null
+++ b/text_recognizer/data/mapping.py
@@ -0,0 +1,8 @@
+"""Mapping to and from word pieces."""
+from pathlib import Path
+
+
+class WordPieces:
+
+    def __init__(self) -> None:
+        pass
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 0928e6c..c6d5d73 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -60,6 +60,7 @@ class LitBaseModel(pl.LightningModule):
         scheduler["scheduler"] = getattr(
             torch.optim.lr_scheduler, self._lr_scheduler.type
             )(optimizer, **args)
+
         return scheduler
 
     def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index b23685b..7dc1352 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,5 +1,5 @@
 """PyTorch Lightning model for base Transformers."""
-from typing import Dict, List, Optional, Union, Tuple
+from typing import Dict, List, Optional, Union, Tuple, Type
 
 from omegaconf import DictConfig, OmegaConf
 import pytorch_lightning as pl
@@ -19,7 +19,7 @@ class LitTransformerModel(LitBaseModel):
 
     def __init__(
         self,
-        network: Type[nn, Module],
+        network: Type[nn.Module],
         optimizer: Union[DictConfig, Dict],
         lr_scheduler: Union[DictConfig, Dict],
         criterion: Union[DictConfig, Dict],
@@ -27,7 +27,6 @@ class LitTransformerModel(LitBaseModel):
         mapping: Optional[List[str]] = None,
     ) -> None:
         super().__init__(network, optimizer, lr_scheduler, criterion, monitor)
-
         self.mapping, ignore_tokens = self.configure_mapping(mapping)
         self.val_cer = CharacterErrorRate(ignore_tokens)
         self.test_cer = CharacterErrorRate(ignore_tokens)
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.
 
-- 
cgit v1.2.3-70-g09d2