Inital commit for refactoring to lightning

1 files changed, 0 insertions, 80 deletions

diff --git a/src/text_recognizer/models/vqvae_model.py b/src/text_recognizer/models/vqvae_model.py
deleted file mode 100644
index 70f6f1f..0000000
--- a/src/text_recognizer/models/vqvae_model.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""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