tranformer layer done

2 files changed, 42 insertions, 39 deletions

diff --git a/text_recognizer/networks/loss/label_smoothing_loss.py b/text_recognizer/networks/loss/label_smoothing_loss.py
new file mode 100644
index 0000000..40a7609
--- /dev/null
+++ b/text_recognizer/networks/loss/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")
diff --git a/text_recognizer/networks/loss/loss.py b/text_recognizer/networks/loss/loss.py
deleted file mode 100644
index d12dc9c..0000000
--- a/text_recognizer/networks/loss/loss.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Implementations of custom loss functions."""
-import torch
-from torch import nn
-from torch import Tensor
-
-__all__ = ["LabelSmoothingCrossEntropy"]
-
-
-class LabelSmoothingCrossEntropy(nn.Module):
-    """Label smoothing loss function."""
-
-    def __init__(
-        self,
-        classes: int,
-        smoothing: float = 0.0,
-        ignore_index: int = None,
-        dim: int = -1,
-    ) -> None:
-        super().__init__()
-        self.confidence = 1.0 - smoothing
-        self.smoothing = smoothing
-        self.ignore_index = ignore_index
-        self.cls = classes
-        self.dim = dim
-
-    def forward(self, pred: Tensor, target: Tensor) -> Tensor:
-        """Calculates the loss."""
-        pred = pred.log_softmax(dim=self.dim)
-        with torch.no_grad():
-            # true_dist = pred.data.clone()
-            true_dist = torch.zeros_like(pred)
-            true_dist.fill_(self.smoothing / (self.cls - 1))
-            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
-            if self.ignore_index is not None:
-                true_dist[:, self.ignore_index] = 0
-                mask = torch.nonzero(target == self.ignore_index, as_tuple=False)
-                if mask.dim() > 0:
-                    true_dist.index_fill_(0, mask.squeeze(), 0.0)
-        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))