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"""Vision transformer for character recognition."""
import math
from typing import Tuple, Type
from loguru import logger as log
from torch import nn, Tensor
from text_recognizer.networks.transformer.layers import Decoder
class ConvTransformer(nn.Module):
"""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: nn.Module,
decoder: Decoder,
pixel_pos_embedding: Type[nn.Module],
token_pos_embedding: Type[nn.Module],
) -> None:
super().__init__()
self.input_dims = input_dims
self.encoder_dim = encoder_dim
self.hidden_dim = hidden_dim
self.dropout_rate = dropout_rate
self.num_classes = num_classes
self.pad_index = pad_index
self.encoder = encoder
self.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_dim,
out_channels=self.hidden_dim,
kernel_size=1,
),
pixel_pos_embedding,
nn.Flatten(start_dim=2),
)
# Token embedding.
self.token_embedding = nn.Embedding(
num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
)
# Positional encoding for decoder tokens.
if not decoder.has_pos_emb:
self.token_pos_embedding = token_pos_embedding
else:
self.token_pos_embedding = None
log.debug("Decoder already have positional embedding.")
# Head
self.head = nn.Linear(
in_features=self.hidden_dim, out_features=self.num_classes
)
# Initalize weights for encoder.
self.init_weights()
def init_weights(self) -> None:
"""Initalize weights for decoder network and head."""
bound = 0.1
self.token_embedding.weight.data.uniform_(-bound, bound)
self.head.bias.data.zero_()
self.head.weight.data.uniform_(-bound, bound)
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
def decode(self, src: Tensor, trg: Tensor) -> Tensor:
"""Decodes latent images embedding into word pieces.
Args:
src (Tensor): Latent images embedding.
trg (Tensor): Word embeddings.
Shapes:
- z: :math: `(B, Sx, E)`
- context: :math: `(B, Sy)`
- out: :math: `(B, Sy, T)`
where Sy is the length of the output and T is the number of tokens.
Returns:
Tensor: Sequence of word piece embeddings.
"""
trg = trg.long()
trg_mask = trg != self.pad_index
trg = self.token_embedding(trg) * math.sqrt(self.hidden_dim)
trg = (
self.token_pos_embedding(trg)
if self.token_pos_embedding is not None
else trg
)
out = self.decoder(x=trg, context=src, mask=trg_mask)
logits = self.head(out) # [B, Sy, T]
logits = logits.permute(0, 2, 1) # [B, T, Sy]
return logits
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 = self.encode(x)
logits = self.decode(z, context)
return logits
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