Add new training loop with PyTorch Lightning, remove stale files

1 files changed, 1 insertions, 31 deletions

diff --git a/text_recognizer/networks/loss/loss.py b/text_recognizer/networks/loss/loss.py
index cf9fa0d..d12dc9c 100644
--- a/text_recognizer/networks/loss/loss.py
+++ b/text_recognizer/networks/loss/loss.py
@@ -1,39 +1,9 @@
 """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
+__all__ = ["LabelSmoothingCrossEntropy"]
 
 
 class LabelSmoothingCrossEntropy(nn.Module):