Remove default transformer

1 files changed, 0 insertions, 62 deletions

diff --git a/text_recognizer/networks/transformer/transformer.py b/text_recognizer/networks/transformer/transformer.py
deleted file mode 100644
index 31088b4..0000000
--- a/text_recognizer/networks/transformer/transformer.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Transformer wrapper."""
-from typing import Any, Optional, Type
-
-from torch import nn, Tensor
-
-from .layers import AttentionLayers
-from text_recognizer.networks.transformer.positional_encodings import (
-    AbsolutePositionalEmbedding,
-)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        num_tokens: int,
-        max_seq_len: int,
-        attn_layers: Type[AttentionLayers],
-        emb_dim: Optional[int] = None,
-        emb_dropout: float = 0.0,
-        use_pos_emb: bool = True,
-    ) -> None:
-        super().__init__()
-        dim = attn_layers.dim
-        self.attn_layers = attn_layers
-        emb_dim = emb_dim if emb_dim is not None else dim
-        self.max_seq_len = max_seq_len
-
-        self.token_emb = nn.Embedding(num_tokens, emb_dim)
-        self.emb_dropout = nn.Dropout(emb_dropout)
-        self.pos_emb = (
-            AbsolutePositionalEmbedding(emb_dim, max_seq_len)
-            if (use_pos_emb and not self.attn_layers.has_pos_emb)
-            else None
-        )
-
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
-        self.norm = nn.LayerNorm(dim)
-
-        self._init_weights()
-
-        self.logits = nn.Linear(dim, num_tokens)
-
-    def _init_weights(self) -> None:
-        nn.init.normal_(self.token_emb.weight, std=0.02)
-
-    def forward(
-        self,
-        x: Tensor,
-        mask: Optional[Tensor] = None,
-        return_embeddings: bool = False,
-        **kwargs: Any
-    ) -> Tensor:
-        b, n, device = *x.shape, x.device
-        x = self.token_emb(x)
-        if self.pos_emb is not None:
-            x += self.pos_emb(x)
-        x = self.emb_dropout(x)
-
-        x = self.project_emb(x)
-        x = self.attn_layers(x, mask=mask, **kwargs)
-        out = self.logits(x) if not return_embeddings else x
-        return out