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"""Vector quantized encoder, transformer decoder."""
from pathlib import Path
from typing import Tuple, Optional
import torch
from torch import Tensor
from text_recognizer.networks.vqvae.vqvae import VQVAE
from text_recognizer.networks.conv_transformer import ConvTransformer
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],
encoder_dim: int,
hidden_dim: int,
dropout_rate: float,
num_classes: int,
pad_index: Tensor,
encoder: VQVAE,
decoder: Decoder,
no_grad: bool,
pretrained_encoder_path: Optional[str] = None,
) -> None:
super().__init__(
input_dims=input_dims,
encoder_dim=encoder_dim,
hidden_dim=hidden_dim,
dropout_rate=dropout_rate,
num_classes=num_classes,
pad_index=pad_index,
encoder=encoder,
decoder=decoder,
)
# For typing
self.encoder: VQVAE
self.no_grad = no_grad
if pretrained_encoder_path is not None:
self.pretrained_encoder_path = (
Path(__file__).resolve().parents[2] / pretrained_encoder_path
)
self._setup_encoder()
else:
self.pretrained_encoder_path = None
def _load_pretrained_encoder(self) -> None:
self.encoder.load_state_dict(
torch.load(self.pretrained_encoder_path)["state_dict"]["network"]
)
def _setup_encoder(self) -> None:
"""Remove unecessary layers."""
self._load_pretrained_encoder()
del self.encoder.decoder
# del self.encoder.post_codebook_conv
def _encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
z_e = self.encoder.encode(x)
z_q, commitment_loss = self.encoder.quantize(z_e)
return z_q, commitment_loss
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.
"""
if self.no_grad:
with torch.no_grad():
z_q, commitment_loss = self._encode(x)
else:
z_q, commitment_loss = self._encode(x)
z = self.latent_encoder(z_q)
# 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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