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"""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
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