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"""Vector quantized encoder, transformer decoder."""
import math
from typing import Tuple
from torch import nn, Tensor
from text_recognizer.networks.encoders.efficientnet import EfficientNet
from text_recognizer.networks.conv_transformer import ConvTransformer
from text_recognizer.networks.transformer.layers import Decoder
from text_recognizer.networks.transformer.positional_encodings import (
PositionalEncoding,
PositionalEncoding2D,
)
class VqTransformer(ConvTransformer):
"""Convolutional encoder and transformer decoder network."""
def __init__(
self,
input_dims: Tuple[int, int, int],
hidden_dim: int,
dropout_rate: float,
num_classes: int,
pad_index: Tensor,
encoder: EfficientNet,
decoder: Decoder,
) -> None:
# TODO: Load pretrained vqvae encoder.
super().__init__(
input_dims=input_dims,
hidden_dim=hidden_dim,
dropout_rate=dropout_rate,
num_classes=num_classes,
pad_index=pad_index,
encoder=encoder,
decoder=decoder,
)
# Latent projector for down sampling number of filters and 2d
# positional encoding.
self.latent_encoder = nn.Sequential(
nn.Conv2d(
in_channels=self.encoder.out_channels,
out_channels=self.hidden_dim,
kernel_size=1,
),
PositionalEncoding2D(
hidden_dim=self.hidden_dim,
max_h=self.input_dims[1],
max_w=self.input_dims[2],
),
nn.Flatten(start_dim=2),
)
def encode(self, x: Tensor) -> Tensor:
"""Encodes an image into a latent feature vector.
Args:
x (Tensor): Image tensor.
Shape:
- x: :math: `(B, C, H, W)`
- z: :math: `(B, Sx, E)`
where Sx is the length of the flattened feature maps projected from
the encoder. E latent dimension for each pixel in the projected
feature maps.
Returns:
Tensor: A Latent embedding of the image.
"""
z = self.encoder(x)
z = self.latent_encoder(z)
# Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
z = z.permute(0, 2, 1)
return z
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