From 7e8e54e84c63171e748bbf09516fd517e6821ace Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Sat, 20 Mar 2021 18:09:06 +0100 Subject: Inital commit for refactoring to lightning --- src/text_recognizer/networks/ctc.py | 58 ------------------------------------- 1 file changed, 58 deletions(-) delete mode 100644 src/text_recognizer/networks/ctc.py (limited to 'src/text_recognizer/networks/ctc.py') 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 -- cgit v1.2.3-70-g09d2