Inital commit for refactoring to lightning

1 files changed, 0 insertions, 58 deletions

diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
deleted file mode 100644
index af9b700..0000000
--- a/src/text_recognizer/networks/ctc.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Decodes the CTC output."""
-from typing import Callable, List, Optional, Tuple
-
-from einops import rearrange
-import torch
-from torch import Tensor
-
-from text_recognizer.datasets.util import EmnistMapper
-
-
-def greedy_decoder(
-    predictions: Tensor,
-    targets: Optional[Tensor] = None,
-    target_lengths: Optional[Tensor] = None,
-    character_mapper: Optional[Callable] = None,
-    blank_label: int = 79,
-    collapse_repeated: bool = True,
-) -> Tuple[List[str], List[str]]:
-    """Greedy CTC decoder.
-
-    Args:
-        predictions (Tensor): Tenor of network predictions, shape [time, batch, classes].
-        targets (Optional[Tensor]): Target tensor, shape is [batch, targets]. Defaults to None.
-        target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None.
-        character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters.  Defaults
-            to None.
-        blank_label (int): The blank character to be ignored. Defaults to 80.
-        collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True.
-
-    Returns:
-        Tuple[List[str], List[str]]: Tuple of decoded predictions and decoded targets.
-
-    """
-
-    if character_mapper is None:
-        character_mapper = EmnistMapper(pad_token="_")  # noqa: S106
-
-    predictions = rearrange(torch.argmax(predictions, dim=2), "t b -> b t")
-    decoded_predictions = []
-    decoded_targets = []
-    for i, prediction in enumerate(predictions):
-        decoded_prediction = []
-        decoded_target = []
-        if targets is not None and target_lengths is not None:
-            for target_index in targets[i][: target_lengths[i]]:
-                if target_index == blank_label:
-                    continue
-                decoded_target.append(character_mapper(int(target_index)))
-            decoded_targets.append(decoded_target)
-        for j, index in enumerate(prediction):
-            if index != blank_label:
-                if collapse_repeated and j != 0 and index == prediction[j - 1]:
-                    continue
-                decoded_prediction.append(index.item())
-        decoded_predictions.append(
-            [character_mapper(int(pred_index)) for pred_index in decoded_prediction]
-        )
-    return decoded_predictions, decoded_targets