Remove abstract lightning module

1 files changed, 0 insertions, 102 deletions

diff --git a/text_recognizer/networks/base.py b/text_recognizer/networks/base.py
deleted file mode 100644
index 29c3bbc..0000000
--- a/text_recognizer/networks/base.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""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.decoder import Decoder
-
-
-class BaseTransformer(nn.Module):
-    """Base transformer network."""
-
-    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.")
-
-        # 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)
-        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