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"""The VQ-VAE."""
from typing import Any, Dict, List, Optional, Tuple
from torch import nn
from torch import Tensor
from text_recognizer.networks.vqvae import Decoder, Encoder
class VQVAE(nn.Module):
"""Vector Quantized Variational AutoEncoder."""
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,
upsampling: Optional[List[List[int]]] = None,
beta: float = 0.25,
activation: str = "leaky_relu",
dropout_rate: float = 0.0,
*args: Any,
**kwargs: Dict,
) -> None:
super().__init__()
# configure encoder.
self.encoder = Encoder(
in_channels,
channels,
kernel_sizes,
strides,
num_residual_layers,
embedding_dim,
num_embeddings,
beta,
activation,
dropout_rate,
)
# Configure decoder.
channels.reverse()
kernel_sizes.reverse()
strides.reverse()
self.decoder = Decoder(
channels,
kernel_sizes,
strides,
num_residual_layers,
embedding_dim,
upsampling,
activation,
dropout_rate,
)
def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes input to a latent code."""
return self.encoder(x)
def decode(self, z_q: Tensor) -> Tensor:
"""Reconstructs input from latent codes."""
return self.decoder(z_q)
def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Compresses and decompresses input."""
if len(x.shape) < 4:
x = x[(None,) * (4 - len(x.shape))]
z_q, vq_loss = self.encode(x)
x_reconstruction = self.decode(z_q)
return x_reconstruction, vq_loss
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