1 files changed, 150 insertions, 0 deletions
diff --git a/src/text_recognizer/networks/vq_transformer.py b/src/text_recognizer/networks/vq_transformer.py
new file mode 100644
index 0000000..c673d96
--- /dev/null
+++ b/src/text_recognizer/networks/vq_transformer.py
@@ -0,0 +1,150 @@
+"""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