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"""PixelCNN encoder and decoder.
Same as in VQGAN among other. Hopefully, better reconstructions...
TODO: Add num of residual layers.
"""
from typing import Sequence
from torch import nn
from torch import Tensor
from text_recognizer.networks.vqvae.attention import Attention
from text_recognizer.networks.vqvae.norm import Normalize
from text_recognizer.networks.vqvae.residual import Residual
from text_recognizer.networks.vqvae.resize import Downsample, Upsample
class Encoder(nn.Module):
"""PixelCNN encoder."""
def __init__(
self,
in_channels: int,
hidden_dim: int,
channels_multipliers: Sequence[int],
dropout_rate: float,
) -> None:
super().__init__()
self.in_channels = in_channels
self.dropout_rate = dropout_rate
self.hidden_dim = hidden_dim
self.channels_multipliers = tuple(channels_multipliers)
self.encoder = self._build_encoder()
def _build_encoder(self) -> nn.Sequential:
"""Builds encoder."""
encoder = [
nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.hidden_dim,
kernel_size=3,
stride=1,
padding=1,
),
]
num_blocks = len(self.channels_multipliers)
in_channels_multipliers = (1,) + self.channels_multipliers
for i in range(num_blocks):
in_channels = self.hidden_dim * in_channels_multipliers[i]
out_channels = self.hidden_dim * self.channels_multipliers[i]
encoder += [
Residual(
in_channels=in_channels,
out_channels=out_channels,
dropout_rate=self.dropout_rate,
use_norm=True,
),
]
if i == num_blocks - 1:
encoder.append(Attention(in_channels=out_channels))
encoder.append(Downsample())
for _ in range(2):
encoder += [
Residual(
in_channels=self.hidden_dim * self.channels_multipliers[-1],
out_channels=self.hidden_dim * self.channels_multipliers[-1],
dropout_rate=self.dropout_rate,
use_norm=True,
),
Attention(in_channels=self.hidden_dim * self.channels_multipliers[-1]),
]
encoder += [
Normalize(num_channels=self.hidden_dim * self.channels_multipliers[-1]),
nn.Mish(),
nn.Conv2d(
in_channels=self.hidden_dim * self.channels_multipliers[-1],
out_channels=self.hidden_dim * self.channels_multipliers[-1],
kernel_size=3,
stride=1,
padding=1,
),
]
return nn.Sequential(*encoder)
def forward(self, x: Tensor) -> Tensor:
"""Encodes input to a latent representation."""
return self.encoder(x)
class Decoder(nn.Module):
"""PixelCNN decoder."""
def __init__(
self,
hidden_dim: int,
channels_multipliers: Sequence[int],
out_channels: int,
dropout_rate: float,
) -> None:
super().__init__()
self.hidden_dim = hidden_dim
self.out_channels = out_channels
self.channels_multipliers = tuple(channels_multipliers)
self.dropout_rate = dropout_rate
self.decoder = self._build_decoder()
def _build_decoder(self) -> nn.Sequential:
"""Builds decoder."""
in_channels = self.hidden_dim * self.channels_multipliers[0]
decoder = [
Residual(
in_channels=in_channels,
out_channels=in_channels,
dropout_rate=self.dropout_rate,
use_norm=True,
),
Attention(in_channels=in_channels),
Residual(
in_channels=in_channels,
out_channels=in_channels,
dropout_rate=self.dropout_rate,
use_norm=True,
),
]
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.append(
Residual(
in_channels=in_channels,
out_channels=out_channels,
dropout_rate=self.dropout_rate,
use_norm=True,
)
)
if i == 0:
decoder.append(Attention(in_channels=out_channels))
decoder.append(Upsample())
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, x: Tensor) -> Tensor:
"""Decodes latent vector."""
return self.decoder(x)
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