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"""Transformer attention layer."""
from copy import deepcopy
from typing import Any, Optional, Tuple, Type
import attr
from torch import nn, Tensor
from text_recognizer.networks.transformer.attention import Attention
from text_recognizer.networks.transformer.local_attention import LocalAttention
from text_recognizer.networks.transformer.mlp import FeedForward
from text_recognizer.networks.transformer.residual import Residual
@attr.s(eq=False)
class AttentionLayers(nn.Module):
"""Standard transfomer layer."""
def __attrs_pre_init__(self) -> None:
"""Pre init constructor."""
super().__init__()
depth: int = attr.ib()
self_attn: Attention = attr.ib()
norm: Type[nn.Module] = attr.ib()
ff: FeedForward = attr.ib()
cross_attn: Optional[Attention] = attr.ib(default=None)
local_self_attn: Optional[LocalAttention] = attr.ib(default=None)
pre_norm: bool = attr.ib(default=True)
local_depth: Optional[int] = attr.ib(default=None)
has_pos_emb: bool = attr.ib(default=False)
layer_types: Tuple[str, ...] = attr.ib(init=False)
layers: nn.ModuleList = attr.ib(init=False)
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
if self.local_self_attn is not None:
if self.local_depth is None:
ValueError("Local depth has to be specified")
self.layer_types = self._get_layer_types() * self.depth
self.layers = self._build_network()
def _get_layer_types(self) -> Tuple:
"""Get layer specification."""
if self.cross_attn is not None:
return "a", "c", "f"
return "a", "f"
def _self_attn_block(self, i: int) -> Type[nn.Module]:
if self.local_depth is not None and i < self.local_depth:
return deepcopy(self.local_self_attn)
return deepcopy(self.self_attn)
def _delete(self) -> None:
del self.self_attn
del self.local_self_attn
del self.ff
del self.norm
del self.cross_attn
def _build_network(self) -> nn.ModuleList:
"""Configures transformer network."""
layers = nn.ModuleList([])
self_attn_depth = 0
for layer_type in self.layer_types:
if layer_type == "a":
layer = self._self_attn_block(self_attn_depth)
self_attn_depth += 1
elif layer_type == "c":
layer = deepcopy(self.cross_attn)
elif layer_type == "f":
layer = deepcopy(self.ff)
layers.append(nn.ModuleList([deepcopy(self.norm), layer, Residual()]))
self._delete()
return layers
def forward(
self,
x: Tensor,
context: Optional[Tensor] = None,
mask: Optional[Tensor] = None,
context_mask: Optional[Tensor] = None,
) -> Tensor:
"""Forward pass."""
for i, (layer_type, (norm, block, residual_fn)) in enumerate(
zip(self.layer_types, self.layers)
):
is_last = i == len(self.layers) - 1
residual = x
if self.pre_norm:
x = norm(x)
if layer_type == "a":
out, _ = block(x=x, mask=mask)
elif layer_type == "c":
out, _ = block(x, context=context, mask=mask, context_mask=context_mask)
elif layer_type == "f":
out = block(x)
x = residual_fn(out, residual)
if not self.pre_norm and not is_last:
x = norm(x)
return x
class Decoder(AttentionLayers):
"""Decoder module."""
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
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