1 files changed, 0 insertions, 111 deletions
diff --git a/src/text_recognizer/models/transformer_encoder_model.py b/src/text_recognizer/models/transformer_encoder_model.py
deleted file mode 100644
index e35e298..0000000
--- a/src/text_recognizer/models/transformer_encoder_model.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""Defines the CNN-Transformer class."""
-from typing import Callable, Dict, List, Optional, Tuple, Type, Union
-
-import numpy as np
-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
-from text_recognizer.models.base import Model
-
-
-class TransformerEncoderModel(Model):
-    """A class for only using the encoder part in the sequence modelling."""
-
-    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:
-        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.init_token = dataset_args["args"]["init_token"]
-        self.pad_token = dataset_args["args"]["pad_token"]
-        self.eos_token = dataset_args["args"]["eos_token"]
-        if network_args is not None:
-            self.max_len = network_args["max_len"]
-        else:
-            self.max_len = 128
-
-        if self._mapper is None:
-            self._mapper = EmnistMapper(
-                # init_token=self.init_token,
-                pad_token=self.pad_token,
-                eos_token=self.eos_token,
-            )
-        self.tensor_transform = ToTensor()
-
-        self.softmax = nn.Softmax(dim=2)
-
-    @torch.no_grad()
-    def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
-        logits = self.network(image)
-        # Convert logits to probabilities.
-        probs = self.softmax(logits).squeeze(0)
-
-        confidence, pred_tokens = probs.max(1)
-        pred_tokens = pred_tokens
-
-        eos_index = torch.nonzero(
-            pred_tokens == self._mapper(self.eos_token), as_tuple=False,
-        )
-
-        eos_index = eos_index[0].item() if eos_index.nelement() else -1
-
-        predicted_characters = "".join(
-            [self.mapper(x) for x in pred_tokens[:eos_index].tolist()]
-        )
-
-        confidence = np.min(confidence.tolist())
-
-        return predicted_characters, confidence
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
-        """Predict on a single input."""
-        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)
-
-        # Rescale image between 0 and 1.
-        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)
-
-        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
-            image
-        )
-
-        return predicted_characters, confidence_of_prediction