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"""CNN decoder for the VQ-VAE."""
import attr
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.vqvae.residual import Residual
@attr.s(eq=False)
class Decoder(nn.Module):
"""A CNN encoder network."""
in_channels: int = attr.ib()
embedding_dim: int = attr.ib()
out_channels: int = attr.ib()
res_channels: int = attr.ib()
num_residual_layers: int = attr.ib()
activation: str = attr.ib()
decoder: nn.Sequential = attr.ib(init=False)
def __attrs_pre_init__(self) -> None:
super().__init__()
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
self.decoder = self._build_decompression_block()
def _build_decompression_block(self,) -> nn.Sequential:
activation_fn = activation_function(self.activation)
blocks = [
nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.embedding_dim,
kernel_size=3,
padding=1,
)
]
for _ in range(self.num_residual_layers):
blocks.append(
Residual(in_channels=self.embedding_dim, out_channels=self.res_channels)
)
blocks.append(activation_fn)
blocks += [
nn.ConvTranspose2d(
in_channels=self.embedding_dim,
out_channels=self.embedding_dim // 2,
kernel_size=4,
stride=2,
padding=1,
),
activation_fn,
nn.ConvTranspose2d(
in_channels=self.embedding_dim // 2,
out_channels=self.out_channels,
kernel_size=4,
stride=2,
padding=1,
),
]
return nn.Sequential(*blocks)
def forward(self, z_q: Tensor) -> Tensor:
"""Reconstruct input from given codes."""
return self.decoder(z_q)
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