Add Vq transfomer model

2 files changed, 154 insertions, 12 deletions

diff --git a/text_recognizer/models/vq_transformer.py b/text_recognizer/models/vq_transformer.py
new file mode 100644
index 0000000..71ca2ef
--- /dev/null
+++ b/text_recognizer/models/vq_transformer.py
@@ -0,0 +1,99 @@
+"""PyTorch Lightning model for base Transformers."""
+from typing import Tuple, Type, Set
+
+import attr
+import torch
+from torch import Tensor
+
+from text_recognizer.models.metrics import CharacterErrorRate
+from text_recognizer.models.transformer import TransformerLitModel
+
+
+@attr.s(auto_attribs=True, eq=False)
+class VqTransformerLitModel(TransformerLitModel):
+    """A PyTorch Lightning model for transformer networks."""
+
+    def forward(self, data: Tensor) -> Tensor:
+        """Forward pass with the transformer network."""
+        return self.predict(data)
+
+    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
+        """Training step."""
+        data, targets = batch
+        logits, commitment_loss = self.network(data, targets[:-1])
+        loss = self.loss_fn(logits, targets[1:]) + commitment_loss
+        self.log("train/loss", loss)
+        self.log("train/commitment_loss", commitment_loss)
+        return loss
+
+    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Validation step."""
+        data, targets = batch
+
+        # Compute the loss.
+        logits, commitment_loss = self.network(data, targets[:-1])
+        loss = self.loss_fn(logits, targets[1:]) + commitment_loss
+        self.log("val/loss", loss, prog_bar=True)
+        self.log("val/commitment_loss", commitment_loss)
+
+        # Get the token prediction.
+        pred = self(data)
+        self.val_cer(pred, targets)
+        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
+        self.test_acc(pred, targets)
+        self.log("val/acc", self.test_acc, on_step=False, on_epoch=True)
+
+    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Test step."""
+        data, targets = batch
+
+        # Compute the text prediction.
+        pred = self(data)
+        self.test_cer(pred, targets)
+        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
+        self.test_acc(pred, targets)
+        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
+
+    def predict(self, x: Tensor) -> Tensor:
+        """Predicts text in image.
+        
+        Args:
+            x (Tensor): Image(s) to extract text from.
+
+        Shapes:
+            - x: :math: `(B, H, W)`
+            - output: :math: `(B, S)`
+
+        Returns:
+            Tensor: A tensor of token indices of the predictions from the model.
+        """
+        bsz = x.shape[0]
+
+        # Encode image(s) to latent vectors.
+        z, _ = self.network.encode(x)
+
+        # Create a placeholder matrix for storing outputs from the network
+        output = torch.ones((bsz, self.max_output_len), dtype=torch.long).to(x.device)
+        output[:, 0] = self.start_index
+
+        for Sy in range(1, self.max_output_len):
+            context = output[:, :Sy]  # (B, Sy)
+            logits = self.network.decode(z, context)  # (B, Sy, C)
+            tokens = torch.argmax(logits, dim=-1)  # (B, Sy)
+            output[:, Sy : Sy + 1] = tokens[:, -1:]
+
+            # Early stopping of prediction loop if token is end or padding token.
+            if (
+                (output[:, Sy - 1] == self.end_index)
+                | (output[:, Sy - 1] == self.pad_index)
+            ).all():
+                break
+
+        # Set all tokens after end token to pad token.
+        for Sy in range(1, self.max_output_len):
+            idx = (output[:, Sy - 1] == self.end_index) | (
+                output[:, Sy - 1] == self.pad_index
+            )
+            output[idx, Sy] = self.pad_index
+
+        return output
diff --git a/text_recognizer/networks/vq_transformer.py b/text_recognizer/networks/vq_transformer.py
index 0433863..69f68fd 100644
--- a/text_recognizer/networks/vq_transformer.py
+++ b/text_recognizer/networks/vq_transformer.py
@@ -1,5 +1,6 @@
 """Vector quantized encoder, transformer decoder."""
-from typing import Tuple
+from pathlib import Path
+from typing import Tuple, Optional
 
 import torch
 from torch import Tensor
@@ -22,7 +23,8 @@ class VqTransformer(ConvTransformer):
         pad_index: Tensor,
         encoder: VQVAE,
         decoder: Decoder,
-        pretrained_encoder_path: str,
+        no_grad: bool,
+        pretrained_encoder_path: Optional[str] = None,
     ) -> None:
         super().__init__(
             input_dims=input_dims,
@@ -34,18 +36,36 @@ class VqTransformer(ConvTransformer):
             encoder=encoder,
             decoder=decoder,
         )
-        self.pretrained_encoder_path = pretrained_encoder_path
-
         # For typing
         self.encoder: VQVAE
 
-    def setup_encoder(self) -> None:
+        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."""
-        # TODO: load pretrained vqvae
+        self._load_pretrained_encoder()
         del self.encoder.decoder
-        del self.encoder.post_codebook_conv
+        # 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) -> Tensor:
+    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
         """Encodes an image into a discrete (VQ) latent representation.
 
         Args:
@@ -62,12 +82,35 @@ class VqTransformer(ConvTransformer):
         Returns:
             Tensor: A Latent embedding of the image.
         """
-        with torch.no_grad():
-            z_e = self.encoder.encode(x)
-            z_q, _ = self.encoder.quantize(z_e)
+        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
+        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