1 files changed, 62 insertions, 0 deletions
diff --git a/text_recognizer/networks/text_decoder.py b/text_recognizer/networks/text_decoder.py
new file mode 100644
index 0000000..c054b41
--- /dev/null
+++ b/text_recognizer/networks/text_decoder.py
@@ -0,0 +1,62 @@
+"""Text decoder."""
+from typing import Type
+
+import torch
+from torch import Tensor, nn
+
+from text_recognizer.networks.transformer.decoder import Decoder
+
+
+class TextDecoder(nn.Module):
+    """Decoder transformer network."""
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_classes: int,
+        pad_index: Tensor,
+        decoder: Decoder,
+        token_pos_embedding: Type[nn.Module],
+    ) -> None:
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.num_classes = num_classes
+        self.pad_index = pad_index
+        self.decoder = decoder
+        self.token_embedding = nn.Embedding(
+            num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
+        )
+        self.token_pos_embedding = token_pos_embedding
+        self.to_logits = nn.Linear(
+            in_features=self.hidden_dim, out_features=self.num_classes
+        )
+
+    def forward(self, tokens: Tensor, img_features: Tensor) -> Tensor:
+        """Decodes latent images embedding into word pieces.
+
+        Args:
+            tokens (Tensor): Token indecies.
+            img_features (Tensor): Latent images embedding.
+
+        Shapes:
+            - tokens: :math: `(B, Sy)`
+            - img_features: :math: `(B, Sx, D)`
+            - logits: :math: `(B, Sy, C)`
+
+            where Sy is the length of the output, C is the number of classes
+            and D is the hidden dimension.
+
+        Returns:
+            Tensor: Sequence of logits.
+        """
+        tokens = tokens.long()
+        mask = tokens != self.pad_index
+        tokens = self.token_embedding(tokens)
+        tokens = tokens + self.token_pos_embedding(tokens)
+        tokens = self.decoder(x=tokens, context=img_features, mask=mask)
+        logits = (
+            tokens @ torch.transpose(self.token_embedding.weight.to(tokens.dtype), 0, 1)
+        ).float()
+        logits = self.to_logits(tokens)  # [B, Sy, C]
+        logits = logits.permute(0, 2, 1)  # [B, C, Sy]
+        return logits