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"""CNN decoder for the VQ-VAE."""
from typing import Sequence
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.vqvae.norm import Normalize
from text_recognizer.networks.vqvae.residual import Residual
class Decoder(nn.Module):
"""A CNN encoder network."""
def __init__(
self,
out_channels: int,
hidden_dim: int,
channels_multipliers: Sequence[int],
dropout_rate: float,
activation: str = "mish",
) -> None:
super().__init__()
self.out_channels = out_channels
self.hidden_dim = hidden_dim
self.channels_multipliers = tuple(channels_multipliers)
self.activation = activation
self.dropout_rate = dropout_rate
self.decoder = self._build_decompression_block()
def _build_decompression_block(self,) -> nn.Sequential:
in_channels = self.hidden_dim * self.channels_multipliers[0]
decoder = []
for _ in range(2):
decoder += [
Residual(
in_channels=in_channels,
out_channels=in_channels,
dropout_rate=self.dropout_rate,
use_norm=False,
),
]
activation_fn = activation_function(self.activation)
out_channels_multipliers = self.channels_multipliers + (1,)
num_blocks = len(self.channels_multipliers)
for i in range(num_blocks):
in_channels = self.hidden_dim * self.channels_multipliers[i]
out_channels = self.hidden_dim * out_channels_multipliers[i + 1]
decoder += [
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=4,
stride=2,
padding=1,
),
activation_fn,
]
decoder += [
Normalize(num_channels=self.hidden_dim * out_channels_multipliers[-1]),
nn.Mish(),
nn.Conv2d(
in_channels=self.hidden_dim * out_channels_multipliers[-1],
out_channels=self.out_channels,
kernel_size=3,
stride=1,
padding=1,
),
]
return nn.Sequential(*decoder)
def forward(self, z_q: Tensor) -> Tensor:
"""Reconstruct input from given codes."""
return self.decoder(z_q)
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