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"""Vector quantized encoder, transformer decoder."""
from typing import Optional, Tuple, Type
from torch import nn, Tensor
from text_recognizer.networks.conv_transformer import ConvTransformer
from text_recognizer.networks.quantizer.quantizer import VectorQuantizer
from text_recognizer.networks.transformer.layers import Decoder
class VqTransformer(ConvTransformer):
"""Convolutional encoder and transformer decoder network."""
def __init__(
self,
input_dims: Tuple[int, int, int],
hidden_dim: int,
num_classes: int,
pad_index: Tensor,
encoder: nn.Module,
decoder: Decoder,
pixel_pos_embedding: Type[nn.Module],
quantizer: VectorQuantizer,
token_pos_embedding: Optional[Type[nn.Module]] = None,
) -> None:
super().__init__(
input_dims=input_dims,
hidden_dim=hidden_dim,
num_classes=num_classes,
pad_index=pad_index,
encoder=encoder,
decoder=decoder,
pixel_pos_embedding=pixel_pos_embedding,
token_pos_embedding=token_pos_embedding,
)
self.quantizer = quantizer
def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
"""Encodes an image into a discrete (VQ) latent representation.
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.conv(z)
z, _, commitment_loss = self.quantizer(z)
z = self.pixel_pos_embedding(z)
z = z.flatten(start_dim=2)
# Permute tensor from [B, E, Ho * Wo] to [B, Sx, E]
z = z.permute(0, 2, 1)
return z, commitment_loss
def forward(self, x: Tensor, context: Tensor) -> Tensor:
"""Encodes images into word piece logtis.
Args:
x (Tensor): Input image(s).
context (Tensor): Target word embeddings.
Shapes:
- x: :math: `(B, C, H, W)`
- context: :math: `(B, Sy, T)`
where B is the batch size, C is the number of input channels, H is
the image height and W is the image width.
Returns:
Tensor: Sequence of logits.
"""
z, commitment_loss = self.encode(x)
logits = self.decode(z, context)
return logits, commitment_loss
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