1 files changed, 0 insertions, 69 deletions
diff --git a/src/text_recognizer/networks/loss/loss.py b/src/text_recognizer/networks/loss/loss.py
deleted file mode 100644
index cf9fa0d..0000000
--- a/src/text_recognizer/networks/loss/loss.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Implementations of custom loss functions."""
-from pytorch_metric_learning import distances, losses, miners, reducers
-import torch
-from torch import nn
-from torch import Tensor
-from torch.autograd import Variable
-import torch.nn.functional as F
-
-__all__ = ["EmbeddingLoss", "LabelSmoothingCrossEntropy"]
-
-
-class EmbeddingLoss:
-    """Metric loss for training encoders to produce information-rich latent embeddings."""
-
-    def __init__(self, margin: float = 0.2, type_of_triplets: str = "semihard") -> None:
-        self.distance = distances.CosineSimilarity()
-        self.reducer = reducers.ThresholdReducer(low=0)
-        self.loss_fn = losses.TripletMarginLoss(
-            margin=margin, distance=self.distance, reducer=self.reducer
-        )
-        self.miner = miners.MultiSimilarityMiner(epsilon=margin, distance=self.distance)
-
-    def __call__(self, embeddings: Tensor, labels: Tensor) -> Tensor:
-        """Computes the metric loss for the embeddings based on their labels.
-
-        Args:
-            embeddings (Tensor): The laten vectors encoded by the network.
-            labels (Tensor): Labels of the embeddings.
-
-        Returns:
-            Tensor: The metric loss for the embeddings.
-
-        """
-        hard_pairs = self.miner(embeddings, labels)
-        loss = self.loss_fn(embeddings, labels, hard_pairs)
-        return loss
-
-
-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))