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-rw-r--r--text_recognizer/criterions/label_smoothing.py42
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diff --git a/text_recognizer/criterions/label_smoothing.py b/text_recognizer/criterions/label_smoothing.py
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+++ b/text_recognizer/criterions/label_smoothing.py
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+"""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")