Working cnn transformer.

1 files changed, 18 insertions, 3 deletions

diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index 42c3c6e..af9adb5 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -6,7 +6,23 @@ from torch import Tensor
 from text_recognizer.networks import greedy_decoder
 
 
-def accuracy(outputs: Tensor, labels: Tensor) -> float:
+def accuracy_ignore_pad(
+    output: Tensor,
+    target: Tensor,
+    pad_index: int = 79,
+    eos_index: int = 81,
+    seq_len: int = 97,
+) -> float:
+    """Sets all predictions after eos to pad."""
+    start_indices = torch.nonzero(target == eos_index, as_tuple=False).squeeze(1)
+    end_indices = torch.arange(seq_len, target.shape[0] + 1, seq_len)
+    for start, stop in zip(start_indices, end_indices):
+        output[start + 1 : stop] = pad_index
+
+    return accuracy(output, target)
+
+
+def accuracy(outputs: Tensor, labels: Tensor,) -> float:
     """Computes the accuracy.
 
     Args:
@@ -17,10 +33,9 @@ def accuracy(outputs: Tensor, labels: Tensor) -> float:
         float: The accuracy for the batch.
 
     """
-    # eos_index = torch.nonzero(labels == eos, as_tuple=False)
-    # eos_index = eos_index[0].item() if eos_index.nelement() else -1
 
     _, predicted = torch.max(outputs, dim=-1)
+
     acc = (predicted == labels).sum().float() / labels.shape[0]
     acc = acc.item()
     return acc