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"""CNN decoder for the VQ-VAE."""
from typing import List, Optional, Tuple, Type
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.vqvae.encoder import _ResidualBlock
class Decoder(nn.Module):
"""A CNN encoder network."""
def __init__(
self,
channels: List[int],
kernel_sizes: List[int],
strides: List[int],
num_residual_layers: int,
embedding_dim: int,
upsampling: Optional[List[List[int]]] = None,
activation: str = "leaky_relu",
dropout_rate: float = 0.0,
) -> None:
super().__init__()
if dropout_rate:
if activation == "selu":
dropout = nn.AlphaDropout(p=dropout_rate)
else:
dropout = nn.Dropout(p=dropout_rate)
else:
dropout = None
self.upsampling = upsampling
self.res_block = nn.ModuleList([])
self.upsampling_block = nn.ModuleList([])
self.embedding_dim = embedding_dim
activation = activation_function(activation)
# Configure encoder.
self.decoder = self._build_decoder(
channels,
kernel_sizes,
strides,
num_residual_layers,
activation,
dropout,
)
def _build_decompression_block(
self,
in_channels: int,
channels: int,
kernel_sizes: List[int],
strides: List[int],
activation: Type[nn.Module],
dropout: Optional[Type[nn.Module]],
) -> nn.ModuleList:
modules = nn.ModuleList([])
configuration = zip(channels, kernel_sizes, strides)
for i, (out_channels, kernel_size, stride) in enumerate(configuration):
modules.append(
nn.Sequential(
nn.ConvTranspose2d(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=1,
),
activation,
)
)
if self.upsampling and i < len(self.upsampling):
modules.append(
nn.Upsample(size=self.upsampling[i]),
)
if dropout is not None:
modules.append(dropout)
in_channels = out_channels
modules.extend(
nn.Sequential(
nn.ConvTranspose2d(
in_channels, 1, kernel_size=kernel_size, stride=stride, padding=1
),
nn.Tanh(),
)
)
return modules
def _build_decoder(
self,
channels: int,
kernel_sizes: List[int],
strides: List[int],
num_residual_layers: int,
activation: Type[nn.Module],
dropout: Optional[Type[nn.Module]],
) -> nn.Sequential:
self.res_block.append(
nn.Conv2d(
self.embedding_dim,
channels[0],
kernel_size=1,
stride=1,
)
)
# Bottleneck module.
self.res_block.extend(
nn.ModuleList(
[
_ResidualBlock(channels[0], channels[0], dropout)
for i in range(num_residual_layers)
]
)
)
# Decompression module
self.upsampling_block.extend(
self._build_decompression_block(
channels[0], channels[1:], kernel_sizes, strides, activation, dropout
)
)
self.res_block = nn.Sequential(*self.res_block)
self.upsampling_block = nn.Sequential(*self.upsampling_block)
return nn.Sequential(self.res_block, self.upsampling_block)
def forward(self, z_q: Tensor) -> Tensor:
"""Reconstruct input from given codes."""
x_reconstruction = self.decoder(z_q)
return x_reconstruction
|
