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-rw-r--r--text_recognizer/networks/transformer/transformer.py62
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