Many updates, cool stuff on the way.

1 files changed, 29 insertions, 4 deletions

diff --git a/src/text_recognizer/networks/metrics.py b/src/text_recognizer/networks/metrics.py
index ffad792..2605731 100644
--- a/src/text_recognizer/networks/metrics.py
+++ b/src/text_recognizer/networks/metrics.py
@@ -1,4 +1,7 @@
 """Utility functions for models."""
+from typing import Optional
+
+from einops import rearrange
 import Levenshtein as Lev
 import torch
 from torch import Tensor
@@ -32,22 +35,33 @@ def accuracy(outputs: Tensor, labels: Tensor, pad_index: int = 53) -> float:
     return acc
 
 
-def cer(outputs: Tensor, targets: Tensor) -> float:
+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
+        outputs, targets, target_lengths, blank_label=blank_label,
     )
 
     lev_dist = 0
@@ -63,22 +77,33 @@ def cer(outputs: Tensor, targets: Tensor) -> float:
     return lev_dist / len(decoded_predictions)
 
 
-def wer(outputs: Tensor, targets: Tensor) -> float:
+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
+        outputs, targets, target_lengths, blank_label=blank_label,
     )
 
     lev_dist = 0