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"""A VQ-Transformer for image to text recognition."""
from typing import Dict, Optional, Tuple
from einops import rearrange, repeat
import torch
from torch import nn
from torch import Tensor
from text_recognizer.networks.transformer import PositionalEncoding, Transformer
from text_recognizer.networks.util import activation_function
from text_recognizer.networks.util import configure_backbone
from text_recognizer.networks.vqvae.encoder import _ResidualBlock
class VQTransformer(nn.Module):
"""VQ+Transfomer for image to character sequence prediction."""
def __init__(
self,
num_encoder_layers: int,
num_decoder_layers: int,
hidden_dim: int,
vocab_size: int,
num_heads: int,
adaptive_pool_dim: Tuple,
expansion_dim: int,
dropout_rate: float,
trg_pad_index: int,
max_len: int,
backbone: str,
backbone_args: Optional[Dict] = None,
activation: str = "gelu",
) -> None:
super().__init__()
# Configure vector quantized backbone.
self.backbone = configure_backbone(backbone, backbone_args)
self.conv = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2),
nn.ReLU(inplace=True),
)
# Configure embeddings for Transformer network.
self.trg_pad_index = trg_pad_index
self.vocab_size = vocab_size
self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
nn.init.normal_(self.character_embedding.weight, std=0.02)
self.adaptive_pool = (
nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
)
self.transformer = Transformer(
num_encoder_layers,
num_decoder_layers,
hidden_dim,
num_heads,
expansion_dim,
dropout_rate,
activation,
)
self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
def _create_trg_mask(self, trg: Tensor) -> Tensor:
# Move this outside the transformer.
trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
trg_len = trg.shape[1]
trg_sub_mask = torch.tril(
torch.ones((trg_len, trg_len), device=trg.device)
).bool()
trg_mask = trg_pad_mask & trg_sub_mask
return trg_mask
def encoder(self, src: Tensor) -> Tensor:
"""Forward pass with the encoder of the transformer."""
return self.transformer.encoder(src)
def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
"""Forward pass with the decoder of the transformer + classification head."""
return self.head(
self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
)
def extract_image_features(self, src: Tensor) -> Tuple[Tensor, Tensor]:
"""Extracts image features with a backbone neural network.
It seem like the winning idea was to swap channels and width dimension and collapse
the height dimension. The transformer is learning like a baby with this implementation!!! :D
Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
Args:
src (Tensor): Input tensor.
Returns:
Tensor: The input src to the transformer and the vq loss.
"""
# If batch dimension is missing, it needs to be added.
if len(src.shape) < 4:
src = src[(None,) * (4 - len(src.shape))]
src, vq_loss = self.backbone.encode(src)
# src = self.backbone.decoder.res_block(src)
src = self.conv(src)
if self.adaptive_pool is not None:
src = rearrange(src, "b c h w -> b w c h")
src = self.adaptive_pool(src)
src = src.squeeze(3)
else:
src = rearrange(src, "b c h w -> b (w h) c")
b, t, _ = src.shape
src += self.src_position_embedding[:, :t]
return src, vq_loss
def target_embedding(self, trg: Tensor) -> Tensor:
"""Encodes target tensor with embedding and postion.
Args:
trg (Tensor): Target tensor.
Returns:
Tensor: Encoded target tensor.
"""
trg = self.character_embedding(trg.long())
trg = self.trg_position_encoding(trg)
return trg
def decode_image_features(
self, image_features: Tensor, trg: Optional[Tensor] = None
) -> Tensor:
"""Takes images features from the backbone and decodes them with the transformer."""
trg_mask = self._create_trg_mask(trg)
trg = self.target_embedding(trg)
out = self.transformer(image_features, trg, trg_mask=trg_mask)
logits = self.head(out)
return logits
def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
"""Forward pass with CNN transfomer."""
image_features, vq_loss = self.extract_image_features(x)
logits = self.decode_image_features(image_features, trg)
return logits, vq_loss
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