Refactor with attr, working on cnn+transformer network

2 files changed, 43 insertions, 0 deletions

diff --git a/text_recognizer/criterions/__init__.py b/text_recognizer/criterions/__init__.py
new file mode 100644
index 0000000..5b0a7ab
--- /dev/null
+++ b/text_recognizer/criterions/__init__.py
@@ -0,0 +1 @@
+"""Module with custom loss functions."""
diff --git a/text_recognizer/criterions/label_smoothing_loss.py b/text_recognizer/criterions/label_smoothing_loss.py
new file mode 100644
index 0000000..40a7609
--- /dev/null
+++ b/text_recognizer/criterions/label_smoothing_loss.py
@@ -0,0 +1,42 @@
+"""Implementations of custom loss functions."""
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+class LabelSmoothingLoss(nn.Module):
+    """Label smoothing cross entropy loss."""
+
+    def __init__(
+        self, label_smoothing: float, vocab_size: int, ignore_index: int = -100
+    ) -> None:
+        assert 0.0 < label_smoothing <= 1.0
+        self.ignore_index = ignore_index
+        super().__init__()
+
+        smoothing_value = label_smoothing / (vocab_size - 2)
+        one_hot = torch.full((vocab_size,), smoothing_value)
+        one_hot[self.ignore_index] = 0
+        self.register_buffer("one_hot", one_hot.unsqueeze(0))
+
+        self.confidence = 1.0 - label_smoothing
+
+    def forward(self, output: Tensor, targets: Tensor) -> Tensor:
+        """Computes the loss.
+
+        Args:
+            output (Tensor): Predictions from the network.
+            targets (Tensor): Ground truth.
+
+        Shapes:
+            outpus: Batch size x num classes
+            targets: Batch size
+
+        Returns:
+            Tensor: Label smoothing loss.
+        """
+        model_prob = self.one_hot.repeat(targets.size(0), 1)
+        model_prob.scatter_(1, targets.unsqueeze(1), self.confidence)
+        model_prob.masked_fill_((targets == self.ignore_index).unsqueeze(1), 0)
+        return F.kl_div(output, model_prob, reduction="sum")