IAM datasets implemented.

1 files changed, 75 insertions, 5 deletions

diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index ac8d68e..6a26216 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -1,19 +1,89 @@
 """Utility functions for models."""
-
+import Levenshtein as Lev
 import torch
+from torch import Tensor
+
+from text_recognizer.networks import greedy_decoder
 
 
-def accuracy(outputs: torch.Tensor, labels: torch.Tensor) -> float:
+def accuracy(outputs: Tensor, labels: Tensor) -> float:
     """Computes the accuracy.
 
     Args:
-        outputs (torch.Tensor): The output from the network.
-        labels (torch.Tensor): Ground truth labels.
+        outputs (Tensor): The output from the network.
+        labels (Tensor): Ground truth labels.
 
     Returns:
         float: The accuracy for the batch.
 
     """
     _, predicted = torch.max(outputs.data, dim=1)
-    acc = (predicted == labels).sum().item() / labels.shape[0]
+    acc = (predicted == labels).sum().float() / labels.shape[0]
+    acc = acc.item()
     return acc
+
+
+def cer(outputs: Tensor, targets: Tensor) -> float:
+    """Computes the character error rate.
+
+    Args:
+        outputs (Tensor): The output from the network.
+        targets (Tensor): Ground truth labels.
+
+    Returns:
+        float: The cer for the batch.
+
+    """
+    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
+    )
+
+    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) -> float:
+    """Computes the Word error rate.
+
+    Args:
+        outputs (Tensor): The output from the network.
+        targets (Tensor): Ground truth labels.
+
+    Returns:
+        float: The wer for the batch.
+
+    """
+    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
+    )
+
+    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)