Configs, refactor with attrs, fix attr bug in iam

2 files changed, 31 insertions, 14 deletions

diff --git a/text_recognizer/networks/base.py b/text_recognizer/networks/base.py
new file mode 100644
index 0000000..07b6a32
--- /dev/null
+++ b/text_recognizer/networks/base.py
@@ -0,0 +1,18 @@
+"""Base network with required methods."""
+from abc import abstractmethod
+
+import attr
+from torch import nn, Tensor
+
+
+@attr.s
+class BaseNetwork(nn.Module):
+    """Base network."""
+
+    def __attrs_pre_init__(self) -> None:
+        super().__init__()
+
+    @abstractmethod
+    def predict(self, x: Tensor) -> Tensor:
+        """Return token indices for predictions."""
+        ...
diff --git a/text_recognizer/networks/cnn_tranformer.py b/text_recognizer/networks/conv_transformer.py
index ce7ec43..4acdc36 100644
--- a/text_recognizer/networks/cnn_tranformer.py
+++ b/text_recognizer/networks/conv_transformer.py
@@ -7,6 +7,7 @@ import torch
 from torch import nn, Tensor
 
 from text_recognizer.data.mappings import AbstractMapping
+from text_recognizer.networks.base import BaseNetwork
 from text_recognizer.networks.encoders.efficientnet import EfficientNet
 from text_recognizer.networks.transformer.layers import Decoder
 from text_recognizer.networks.transformer.positional_encodings import (
@@ -15,39 +16,37 @@ from text_recognizer.networks.transformer.positional_encodings import (
 )
 
 
-@attr.s
-class Reader(nn.Module):
-    def __attrs_pre_init__(self) -> None:
-        super().__init__()
-
+@attr.s(auto_attribs=True)
+class ConvTransformer(BaseNetwork):
     # Parameters and placeholders,
     input_dims: Tuple[int, int, int] = attr.ib()
     hidden_dim: int = attr.ib()
     dropout_rate: float = attr.ib()
     max_output_len: int = attr.ib()
     num_classes: int = attr.ib()
-    padding_idx: int = attr.ib()
     start_token: str = attr.ib()
-    start_index: int = attr.ib(init=False)
+    start_index: Tensor = attr.ib(init=False)
     end_token: str = attr.ib()
-    end_index: int = attr.ib(init=False)
+    end_index: Tensor = attr.ib(init=False)
     pad_token: str = attr.ib()
-    pad_index: int = attr.ib(init=False)
+    pad_index: Tensor = attr.ib(init=False)
 
     # Modules.
     encoder: EfficientNet = attr.ib()
     decoder: Decoder = attr.ib()
+    mapping: Type[AbstractMapping] = attr.ib()
+
     latent_encoder: nn.Sequential = attr.ib(init=False)
     token_embedding: nn.Embedding = attr.ib(init=False)
     token_pos_encoder: PositionalEncoding = attr.ib(init=False)
     head: nn.Linear = attr.ib(init=False)
-    mapping: Type[AbstractMapping] = attr.ib(init=False)
 
     def __attrs_post_init__(self) -> None:
         """Post init configuration."""
-        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.start_index = self.mapping.get_index(self.start_token)
+        self.end_index = self.mapping.get_index(self.end_token)
+        self.pad_index = self.mapping.get_index(self.pad_token)
+
         # Latent projector for down sampling number of filters and 2d
         # positional encoding.
         self.latent_encoder = nn.Sequential(
@@ -130,7 +129,7 @@ class Reader(nn.Module):
         Returns:
             Tensor: Sequence of word piece embeddings.
         """
-        context_mask = context != self.padding_idx
+        context_mask = context != self.pad_index
         context = self.token_embedding(context) * math.sqrt(self.hidden_dim)
         context = self.token_pos_encoder(context)
         out = self.decoder(x=context, context=z, mask=context_mask)