From 34098ccbbbf6379c0bd29a987440b8479c743746 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Thu, 29 Jul 2021 23:59:52 +0200
Subject: Configs, refactor with attrs, fix attr bug in iam

---
 text_recognizer/criterions/label_smoothing.py      | 42 ++++++++++++++++++++++
 text_recognizer/criterions/label_smoothing_loss.py | 42 ----------------------
 2 files changed, 42 insertions(+), 42 deletions(-)
 create mode 100644 text_recognizer/criterions/label_smoothing.py
 delete mode 100644 text_recognizer/criterions/label_smoothing_loss.py

(limited to 'text_recognizer/criterions')

diff --git a/text_recognizer/criterions/label_smoothing.py b/text_recognizer/criterions/label_smoothing.py
new file mode 100644
index 0000000..40a7609
--- /dev/null
+++ b/text_recognizer/criterions/label_smoothing.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/criterions/label_smoothing_loss.py b/text_recognizer/criterions/label_smoothing_loss.py
deleted file mode 100644
index 40a7609..0000000
--- a/text_recognizer/criterions/label_smoothing_loss.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""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")
-- 
cgit v1.2.3-70-g09d2