From ecf13ba9fc048ad81e8e21f0f9d68eb132605c39 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 25 Oct 2021 22:32:17 +0200
Subject: Add ctc loss

---
 text_recognizer/criterions/ctc.py | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 text_recognizer/criterions/ctc.py

diff --git a/text_recognizer/criterions/ctc.py b/text_recognizer/criterions/ctc.py
new file mode 100644
index 0000000..42a0b25
--- /dev/null
+++ b/text_recognizer/criterions/ctc.py
@@ -0,0 +1,38 @@
+"""CTC loss."""
+import torch
+from torch import LongTensor, nn, Tensor
+import torch.nn.functional as F
+
+
+class CTCLoss(nn.Module):
+    """CTC loss."""
+
+    def __init__(self, blank: int) -> None:
+        super().__init__()
+        self.blank = blank
+
+    def forward(self, outputs: Tensor, targets: Tensor) -> Tensor:
+        """Computes the CTC loss."""
+        device = outputs.device
+
+        log_probs = F.log_softmax(outputs, dim=2).permute(1, 0, 2)
+        output_lengths = LongTensor([outputs.shape[1]] * outputs.shape[0]).to(device)
+
+        targets_ = LongTensor([]).to(device)
+        target_lengths = LongTensor([]).to(device)
+        for target in targets:
+            # Remove padding
+            target = target[target != self.blank].to(device)
+            targets_ = torch.cat((targets_, target))
+            target_lengths = torch.cat(
+                (target_lengths, torch.LongTensor([len(target)]).to(device)), dim=0
+            )
+
+        return F.ctc_loss(
+            log_probs,
+            targets,
+            output_lengths,
+            target_lengths,
+            blank=self.blank,
+            zero_infinity=True,
+        )
-- 
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