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|
"""Transformer decoder module."""
from typing import Optional
from torch import Tensor, nn
from text_recognizer.network.transformer.attention import Attention
from text_recognizer.network.transformer.ff import FeedForward
class Decoder(nn.Module):
def __init__(
self,
dim: int,
inner_dim: int,
heads: int,
dim_head: int,
depth: int,
dropout_rate: float = 0.0,
) -> None:
super().__init__()
self.norm = nn.LayerNorm(dim)
self.layers = nn.ModuleList(
[
nn.ModuleList(
[
Attention(
dim,
heads,
True,
dim_head,
dropout_rate,
),
FeedForward(dim, inner_dim, dropout_rate),
Attention(
dim,
heads,
False,
dim_head,
dropout_rate,
),
]
)
for _ in range(depth)
]
)
def forward(
self,
x: Tensor,
context: Tensor,
mask: Optional[Tensor] = None,
) -> Tensor:
"""Applies decoder block on input signals."""
for self_attn, ff, cross_attn in self.layers:
x = x + self_attn(x, mask=mask)
x = x + ff(x)
x = x + cross_attn(x, context=context)
return self.norm(x)
|
