Add base network

1 files changed, 103 insertions, 0 deletions

diff --git a/text_recognizer/networks/base.py b/text_recognizer/networks/base.py
new file mode 100644
index 0000000..f6f1831
--- /dev/null
+++ b/text_recognizer/networks/base.py
@@ -0,0 +1,103 @@
+"""Base network module."""
+import math
+from typing import Optional, Tuple, Type
+
+from loguru import logger as log
+from torch import nn, Tensor
+
+from text_recognizer.networks.transformer.layers import Decoder
+
+
+class BaseTransformer(nn.Module):
+    def __init__(
+        self,
+        input_dims: Tuple[int, int, int],
+        hidden_dim: int,
+        num_classes: int,
+        pad_index: Tensor,
+        encoder: Type[nn.Module],
+        decoder: Decoder,
+        token_pos_embedding: Optional[Type[nn.Module]] = None,
+    ) -> None:
+        super().__init__()
+        self.input_dims = input_dims
+        self.hidden_dim = hidden_dim
+        self.num_classes = num_classes
+        self.pad_index = pad_index
+        self.encoder = encoder
+        self.decoder = decoder
+
+        # Token embedding.
+        self.token_embedding = nn.Embedding(
+            num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
+        )
+
+        # Positional encoding for decoder tokens.
+        if not self.decoder.has_pos_emb:
+            self.token_pos_embedding = token_pos_embedding
+        else:
+            self.token_pos_embedding = None
+            log.debug("Decoder already have a positional embedding.")
+
+        self.norm = nn.LayerNorm(self.hidden_dim)
+
+        # Output layer
+        self.to_logits = nn.Linear(
+            in_features=self.hidden_dim, out_features=self.num_classes
+        )
+
+    def encode(self, x: Tensor) -> Tensor:
+        """Encodes images with encoder."""
+        return self.encoder(x)
+
+    def decode(self, src: Tensor, trg: Tensor) -> Tensor:
+        """Decodes latent images embedding into word pieces.
+
+        Args:
+            src (Tensor): Latent images embedding.
+            trg (Tensor): Word embeddings.
+
+        Shapes:
+            - z: :math: `(B, Sx, E)`
+            - context: :math: `(B, Sy)`
+            - out: :math: `(B, Sy, T)`
+
+            where Sy is the length of the output and T is the number of tokens.
+
+        Returns:
+            Tensor: Sequence of word piece embeddings.
+        """
+        trg = trg.long()
+        trg_mask = trg != self.pad_index
+        trg = self.token_embedding(trg) * math.sqrt(self.hidden_dim)
+        trg = (
+            self.token_pos_embedding(trg)
+            if self.token_pos_embedding is not None
+            else trg
+        )
+        out = self.decoder(x=trg, context=src, input_mask=trg_mask)
+        out = self.norm(out)
+        logits = self.to_logits(out)  # [B, Sy, T]
+        logits = logits.permute(0, 2, 1)  # [B, T, Sy]
+        return logits
+
+    def forward(self, x: Tensor, context: Tensor) -> Tensor:
+        """Encodes images into word piece logtis.
+
+        Args:
+            x (Tensor): Input image(s).
+            context (Tensor): Target word embeddings.
+
+        Shapes:
+            - x: :math: `(B, C, H, W)`
+            - context: :math: `(B, Sy, T)`
+
+            where B is the batch size, C is the number of input channels, H is
+            the image height and W is the image width.
+
+        Returns:
+            Tensor: Sequence of logits.
+        """
+        z = self.encode(x)
+        logits = self.decode(z, context)
+        return logits