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"""CNN encoder for the VQ-VAE."""
from typing import List, Tuple
from torch import nn
from torch import Tensor
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.vqvae.residual import Residual
class Encoder(nn.Module):
"""A CNN encoder network."""
def __init__(
self,
in_channels: int,
hidden_dim: int,
channels_multipliers: List[int],
dropout_rate: float,
activation: str = "mish",
) -> None:
super().__init__()
self.in_channels = in_channels
self.hidden_dim = hidden_dim
self.channels_multipliers = tuple(channels_multipliers)
self.activation = activation
self.dropout_rate = dropout_rate
self.encoder = self._build_compression_block()
def _build_compression_block(self) -> nn.Sequential:
"""Builds encoder network."""
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)
channels_multipliers = (1,) + self.channels_multipliers
activation_fn = activation_function(self.activation)
for i in range(num_blocks):
in_channels = self.hidden_dim * channels_multipliers[i]
out_channels = self.hidden_dim * channels_multipliers[i + 1]
encoder += [
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=4,
stride=2,
padding=1,
),
activation_fn,
]
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=False,
)
]
return nn.Sequential(*encoder)
def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes input into a discrete representation."""
return self.encoder(x)
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