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"""Residual block."""
import attr
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.vqvae.norm import Normalize
@attr.s(eq=False)
class Residual(nn.Module):
in_channels: int = attr.ib()
residual_channels: int = attr.ib()
use_norm: bool = attr.ib(default=False)
activation: str = attr.ib(default="relu")
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
super().__init__()
self.block = self._build_res_block()
def _build_res_block(self) -> nn.Sequential:
"""Build residual block."""
block = []
activation_fn = activation_function(activation=self.activation)
if self.use_norm:
block.append(Normalize(num_channels=self.in_channels))
block += [
activation_fn,
nn.Conv2d(
self.in_channels,
self.residual_channels,
kernel_size=3,
padding=1,
bias=False,
),
]
if self.use_norm:
block.append(Normalize(num_channels=self.residual_channels))
block += [
activation_fn,
nn.Conv2d(
self.residual_channels, self.in_channels, kernel_size=1, bias=False
),
]
return nn.Sequential(*block)
def forward(self, x: Tensor) -> Tensor:
"""Apply the residual forward pass."""
return x + self.block(x)
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