1 files changed, 0 insertions, 123 deletions
diff --git a/text_recognizer/networks/metrics.py b/text_recognizer/networks/metrics.py
deleted file mode 100644
index 2605731..0000000
--- a/text_recognizer/networks/metrics.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""Utility functions for models."""
-from typing import Optional
-
-from einops import rearrange
-import Levenshtein as Lev
-import torch
-from torch import Tensor
-
-from text_recognizer.networks import greedy_decoder
-
-
-def accuracy(outputs: Tensor, labels: Tensor, pad_index: int = 53) -> float:
-    """Computes the accuracy.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        labels (Tensor): Ground truth labels.
-        pad_index (int): Padding index.
-
-    Returns:
-        float: The accuracy for the batch.
-
-    """
-
-    _, predicted = torch.max(outputs, dim=-1)
-
-    # Mask out the pad tokens
-    mask = labels != pad_index
-
-    predicted *= mask
-    labels *= mask
-
-    acc = (predicted == labels).sum().float() / labels.shape[0]
-    acc = acc.item()
-    return acc
-
-
-def cer(
-    outputs: Tensor,
-    targets: Tensor,
-    batch_size: Optional[int] = None,
-    blank_label: Optional[int] = int,
-) -> float:
-    """Computes the character error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-        batch_size (Optional[int]): Batch size if target and output has been flattend.
-        blank_label (Optional[int]): The blank character to be ignored. Defaults to 79.
-
-    Returns:
-        float: The cer for the batch.
-
-    """
-    if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None:
-        targets = rearrange(targets, "(b t) -> b t", b=batch_size)
-        outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size)
-
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths, blank_label=blank_label,
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-        prediction, target = (
-            prediction.replace(" ", ""),
-            target.replace(" ", ""),
-        )
-        lev_dist += Lev.distance(prediction, target)
-    return lev_dist / len(decoded_predictions)
-
-
-def wer(
-    outputs: Tensor,
-    targets: Tensor,
-    batch_size: Optional[int] = None,
-    blank_label: Optional[int] = int,
-) -> float:
-    """Computes the Word error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-        batch_size (optional[int]): Batch size if target and output has been flattend.
-        blank_label (Optional[int]): The blank character to be ignored. Defaults to 79.
-
-    Returns:
-        float: The wer for the batch.
-
-    """
-    if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None:
-        targets = rearrange(targets, "(b t) -> b t", b=batch_size)
-        outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size)
-
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths, blank_label=blank_label,
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-
-        b = set(prediction.split() + target.split())
-        word2char = dict(zip(b, range(len(b))))
-
-        w1 = [chr(word2char[w]) for w in prediction.split()]
-        w2 = [chr(word2char[w]) for w in target.split()]
-
-        lev_dist += Lev.distance("".join(w1), "".join(w2))
-
-    return lev_dist / len(decoded_predictions)