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"""Residual block."""
import attr
from torch import nn
from torch import Tensor
from text_recognizer.networks.vqvae.norm import Normalize
@attr.s(eq=False)
class Residual(nn.Module):
in_channels: int = attr.ib()
out_channels: int = attr.ib()
dropout_rate: float = attr.ib(default=0.0)
use_norm: bool = attr.ib(default=False)
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
super().__init__()
self.block = self._build_res_block()
if self.in_channels != self.out_channels:
self.conv_shortcut = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=3, stride=1, padding=1)
else:
self.conv_shortcut = None
def _build_res_block(self) -> nn.Sequential:
"""Build residual block."""
block = []
if self.use_norm:
block.append(Normalize(num_channels=self.in_channels))
block += [
nn.Mish(),
nn.Conv2d(
self.in_channels,
self.out_channels,
kernel_size=3,
padding=1,
bias=False,
),
]
if self.dropout_rate:
block += [nn.Dropout(p=self.dropout_rate)]
if self.use_norm:
block.append(Normalize(num_channels=self.out_channels))
block += [
nn.Mish(),
nn.Conv2d(self.out_channels, self.out_channels, kernel_size=1, bias=False),
]
return nn.Sequential(*block)
def forward(self, x: Tensor) -> Tensor:
"""Apply the residual forward pass."""
residual = self.conv_shortcut(x) if self.conv_shortcut is not None else x
return residual + self.block(x)
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