Remove label smoothing loss

1 files changed, 0 insertions, 50 deletions

diff --git a/text_recognizer/criterion/label_smoothing.py b/text_recognizer/criterion/label_smoothing.py
deleted file mode 100644
index 5b3a47e..0000000
--- a/text_recognizer/criterion/label_smoothing.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Implementations of custom loss functions."""
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class LabelSmoothingLoss(nn.Module):
-    r"""Loss functions for making networks less over confident.
-
-    It is used to calibrate the network so that the predicted probabilities
-    reflect the accuracy. The function is given by:
-
-        L = (1 - \alpha) * y_hot + \alpha / K
-
-    This means that some of the probability mass is transferred to the incorrect
-    labels, thus not forcing the network try to put all probability mass into
-    one label, and this works as a regulizer.
-    """
-
-    def __init__(
-        self, ignore_index: int = -100, smoothing: float = 0.0, dim: int = -1
-    ) -> None:
-        super().__init__()
-        if not 0.0 < smoothing < 1.0:
-            raise ValueError("Smoothing must be between 0.0 and 1.0")
-        self.ignore_index = ignore_index
-        self.confidence = 1.0 - smoothing
-        self.smoothing = smoothing
-        self.dim = dim
-
-    def forward(self, output: Tensor, target: Tensor) -> Tensor:
-        """Computes the loss.
-
-        Args:
-            output (Tensor): outputictions from the network.
-            target (Tensor): Ground truth.
-
-        Shapes:
-            TBC
-
-        Returns:
-            (Tensor): Label smoothing loss.
-        """
-        output = output.log_softmax(dim=self.dim)
-        with torch.no_grad():
-            true_dist = torch.zeros_like(output)
-            true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
-            true_dist.masked_fill_((target == 4).unsqueeze(1), 0)
-            true_dist += self.smoothing / output.size(self.dim)
-        return torch.mean(torch.sum(-true_dist * output, dim=self.dim))