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"""Base network module."""
from typing import Optional, Tuple, Type
import torch
from torch import Tensor, nn
from text_recognizer.networks.transformer.decoder import Decoder
from text_recognizer.networks.transformer.embeddings.axial import (
AxialPositionalEmbeddingImage,
)
class ConvTransformer(nn.Module):
"""Base transformer network."""
def __init__(
self,
input_dims: Tuple[int, int, int],
hidden_dim: int,
num_classes: int,
pad_index: Tensor,
encoder: Type[nn.Module],
decoder: Decoder,
pixel_embedding: AxialPositionalEmbeddingImage,
token_pos_embedding: Type[nn.Module],
) -> None:
super().__init__()
self.input_dims = input_dims
self.hidden_dim = hidden_dim
self.num_classes = num_classes
self.pad_index = pad_index
self.encoder = encoder
self.decoder = decoder
# Token embedding.
self.token_embedding = nn.Embedding(
num_embeddings=self.num_classes, embedding_dim=self.hidden_dim
)
# Positional encoding for decoder tokens.
self.token_pos_embedding = token_pos_embedding
self.pixel_embedding = pixel_embedding
# Latent projector for down sampling number of filters and 2d
# positional encoding.
self.conv = nn.Conv2d(
in_channels=self.encoder.out_channels,
out_channels=self.hidden_dim,
kernel_size=1,
)
# Output layer
self.to_logits = 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 to_logits."""
nn.init.kaiming_normal_(self.token_embedding.weight)
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.conv(z)
z = z + self.pixel_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
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, D)`
- context: :math: `(B, Sy)`
- out: :math: `(B, Sy, C)`
where Sy is the length of the output and C is the number of classes.
Returns:
Tensor: Sequence of word piece embeddings.
"""
trg = trg.long()
trg_mask = trg != self.pad_index
trg = self.token_embedding(trg)
trg = trg + self.token_pos_embedding(trg)
out = self.decoder(x=trg, context=src, input_mask=trg_mask)
logits = (
out @ torch.transpose(self.token_embedding.weight.to(trg.dtype), 0, 1)
).float()
logits = self.to_logits(out) # [B, Sy, C]
logits = logits.permute(0, 2, 1) # [B, C, 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, D, H, W)`
- context: :math: `(B, Sy, C)`
where B is the batch size, D is the number of input channels, H is
the image height, W is the image width, and C is the number of classes.
Returns:
Tensor: Sequence of logits.
"""
z = self.encode(x)
logits = self.decode(z, context)
return logits
|
