Many updates, cool stuff on the way.

4 files changed, 92 insertions, 4 deletions

diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index eb5dbce..7647d7e 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -5,6 +5,7 @@ from .crnn_model import CRNNModel
 from .ctc_transformer_model import CTCTransformerModel
 from .segmentation_model import SegmentationModel
 from .transformer_model import TransformerModel
+from .vqvae_model import VQVAEModel
 
 __all__ = [
     "CharacterModel",
@@ -13,4 +14,5 @@ __all__ = [
     "Model",
     "SegmentationModel",
     "TransformerModel",
+    "VQVAEModel",
 ]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index f2cd4b8..70f4cdb 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -332,7 +332,7 @@ class Model(ABC):
     def summary(
         self,
         input_shape: Optional[Union[List, Tuple]] = None,
-        depth: int = 4,
+        depth: int = 3,
         device: Optional[str] = None,
     ) -> None:
         """Prints a summary of the network architecture."""
diff --git a/src/text_recognizer/models/transformer_model.py b/src/text_recognizer/models/transformer_model.py
index 12e497f..3f63053 100644
--- a/src/text_recognizer/models/transformer_model.py
+++ b/src/text_recognizer/models/transformer_model.py
@@ -6,9 +6,9 @@ import torch
 from torch import nn
 from torch import Tensor
 from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
 
 from text_recognizer.datasets import EmnistMapper
+import text_recognizer.datasets.transforms as transforms
 from text_recognizer.models.base import Model
 from text_recognizer.networks import greedy_decoder
 
@@ -60,13 +60,19 @@ class TransformerModel(Model):
                 eos_token=self.eos_token,
                 lower=self.lower,
             )
-        self.tensor_transform = ToTensor()
-
+        self.tensor_transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize(mean=[0.912], std=[0.168])]
+        )
         self.softmax = nn.Softmax(dim=2)
 
     @torch.no_grad()
     def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
         src = self.network.extract_image_features(image)
+
+        # Added for vqvae transformer.
+        if isinstance(src, Tuple):
+            src = src[0]
+
         memory = self.network.encoder(src)
 
         confidence_of_predictions = []
diff --git a/src/text_recognizer/models/vqvae_model.py b/src/text_recognizer/models/vqvae_model.py
new file mode 100644
index 0000000..70f6f1f
--- /dev/null
+++ b/src/text_recognizer/models/vqvae_model.py
@@ -0,0 +1,80 @@
+"""Defines the VQVAEModel class."""
+from typing import Callable, Dict, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+
+
+class VQVAEModel(Model):
+    """Model for reconstructing images from codebook."""
+
+    def __init__(
+        self,
+        network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
+        network_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
+        metrics: Optional[Dict] = None,
+        criterion: Optional[Callable] = None,
+        criterion_args: Optional[Dict] = None,
+        optimizer: Optional[Callable] = None,
+        optimizer_args: Optional[Dict] = None,
+        lr_scheduler: Optional[Callable] = None,
+        lr_scheduler_args: Optional[Dict] = None,
+        swa_args: Optional[Dict] = None,
+        device: Optional[str] = None,
+    ) -> None:
+        """Initializes the CharacterModel."""
+
+        super().__init__(
+            network_fn,
+            dataset,
+            network_args,
+            dataset_args,
+            metrics,
+            criterion,
+            criterion_args,
+            optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
+            swa_args,
+            device,
+        )
+        self.pad_token = dataset_args["args"]["pad_token"]
+        if self._mapper is None:
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
+        self.tensor_transform = ToTensor()
+        self.softmax = nn.Softmax(dim=0)
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, torch.Tensor]) -> torch.Tensor:
+        """Reconstruction of image.
+
+        Args:
+            image (Union[np.ndarray, torch.Tensor]): An image containing a character.
+
+        Returns:
+            Tuple[str, float]: The predicted character and the confidence in the prediction.
+
+        """
+        self.eval()
+
+        if image.dtype == np.uint8:
+            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
+            image = self.tensor_transform(image)
+        if image.dtype == torch.uint8:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+        image_reconstructed, _ = self.forward(image)
+
+        return image_reconstructed