Add Vq transfomer model

1 files changed, 55 insertions, 12 deletions

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