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"""CNN encoder 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.vector_quantizer import VectorQuantizer
class _ResidualBlock(nn.Module):
def __init__(
self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
) -> None:
super().__init__()
self.block = [
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
]
if dropout is not None:
self.block.append(dropout)
self.block = nn.Sequential(*self.block)
def forward(self, x: Tensor) -> Tensor:
"""Apply the residual forward pass."""
return x + self.block(x)
class Encoder(nn.Module):
"""A CNN encoder network."""
def __init__(
self,
in_channels: int,
channels: List[int],
kernel_sizes: List[int],
strides: List[int],
num_residual_layers: int,
embedding_dim: int,
num_embeddings: int,
beta: float = 0.25,
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.embedding_dim = embedding_dim
self.num_embeddings = num_embeddings
self.beta = beta
activation = activation_function(activation)
# Configure encoder.
self.encoder = self._build_encoder(
in_channels,
channels,
kernel_sizes,
strides,
num_residual_layers,
activation,
dropout,
)
# Configure Vector Quantizer.
self.vector_quantizer = VectorQuantizer(
self.num_embeddings, self.embedding_dim, self.beta
)
def _build_compression_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 out_channels, kernel_size, stride in configuration:
modules.append(
nn.Sequential(
nn.Conv2d(
in_channels, out_channels, kernel_size, stride=stride, padding=1
),
activation,
)
)
if dropout is not None:
modules.append(dropout)
in_channels = out_channels
return modules
def _build_encoder(
self,
in_channels: int,
channels: int,
kernel_sizes: List[int],
strides: List[int],
num_residual_layers: int,
activation: Type[nn.Module],
dropout: Optional[Type[nn.Module]],
) -> nn.Sequential:
encoder = nn.ModuleList([])
# compression module
encoder.extend(
self._build_compression_block(
in_channels, channels, kernel_sizes, strides, activation, dropout
)
)
# Bottleneck module.
encoder.extend(
nn.ModuleList(
[
_ResidualBlock(channels[-1], channels[-1], dropout)
for i in range(num_residual_layers)
]
)
)
encoder.append(
nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
)
return nn.Sequential(*encoder)
def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes input into a discrete representation."""
z_e = self.encoder(x)
z_q, vq_loss = self.vector_quantizer(z_e)
return z_q, vq_loss
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